Climatic Change

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Prepared by David D. Houghton


























WMO-No. 926 Secretariat of the World Meteorological Organization Geneva – Switzerland


WMO TECHNICAL PUBLICATIONS rela ting to ed uca uca tio n a nd tra ining W M O

N o o. .

1 1 4 — G uid e to q ua ual lifica cat tio ns a and nd tra ining o f m eteo ro lo g ica cal l p er ers so nne nnel l em p lo yed in the p ro visio n o f m eteo ro lo g ica cal l serv ervi ices fo r interna tio na la ir na vig a tio n. 2 nd ed itio n, 1 9 7 4 . (Frenc nch– h–S p a nish) 2 5 8 — G uid eline nes s fo r the ed uca tio n a and nd tra ining o f p ers erso nne nnel lin m eteo ro lo g y and a nd o p er era a tio na lhyd ro lo g y. 4 th ed e d itio n, 2 0 0 1 , in p rep a ra tio n. (Eng lish) 266 — C olum m peend enIId—M ium et oeo f lect ec no tragini ng Fr C ench la ss I cal l p er ers so nne nnel l. Vo Vol lum e I—Ea —E a rth scien ence; ce; 19 1 9 7 0 . (En Eng g lish); Vo Vol ro ltour gey;no 1t9es8 4fo.r(Eng En lish– en ch) )V m eteo ro lo g ica 3 6 4 — C om pe pendi ndium of m eteo eor rolog y for us use e b y C lass Iand C lass IIm eteor eo rolog ical pe per rsonnel onn el. Vo lum e I, Pa rt 1 —D yna yn a m ic m e teo ro lo g y. (Fren Fren ch– ch – S p a nish) nish), Pa rt 2 —Phy —P hysi sica l m e teo ro lo g y. (Fren Fren ch– ch – S p a nish) nish), Pa rt 3 —S yno yno p tic m eteo ro lo g y. (Eng lish– Frenc nch) h), Vo lum e II, Pa rt 1 —G en ene era l hy hyd d ro lo g y. (Eng lish), Pa rt 2 —A erona eron a utica l m eteo ro lo g y. (Eng lish– Fr Fre enc nch– h–S p a ni nish) sh), Par Pa rt 3 —M a rine m eteo ro lo g y. (Eng lish– Fr Fre enc nch– h–S p a nish) nish), Par Pa rt 4 —Tro —Tro p ica l m eteo ro lo g y. (Eng lish), Par Pa rt 5 —H yd ro m eteo ro lo g y. (En Eng g lish), Pa Par rt 6 —A ir chem ch em istry a nd a ir p o llutio n m eteo ro lo g y. (Eng lish–Fren French ch– – S p a nish) 1 8 2 — Internationa lm eteor eo rolog ical vocabul voca bular ary y. Se Second cond edi ed ition, 19 1 9 9 2 . (E/ F/ R/ S) 3 8 5 — Internationa lgloss ossar ary y of hydrolog y. Published jointly b by y W M O and U N ESC O ; 2 nd edition, 1 9 9 2 4 0 7 — Interna erna tio na l clo ud a tla s. Vol Vo lum e I—M a nua l o n the o b servat ervatio n of o f clo uds ud s a nd o ther m eteo rs. Rep Re p rinted in 1 9 9 5 . Vo Vol lum e II(p la tes), 1 9 8 7 . 5 5 1 — Lec ect tur ure e no tes fo r tra ining C la ss IIa nd C la ss IIIa g ricultura lm eteo ro lo g ica lp erson ersonne nel l. 1 9 8 0 ed itio n. (S p a nish) 5 9 3 — Lec ect tur ure e no tes fo r tra ining C la ss IV a g ricultura lm eteo ro lo g ica lp er erson sonne nel l. 1 19 9 8 2 ed itio n. (En Eng g lish– Fren ench– ch–S p a nish) 6 2 2 — C o m p end en d ium o f lect ec ture no not tes on o n m eteo ro lo g ica cal linstrum en ent ts fo r tra ining C la ss IIIa nd C la ss IV m eteo ro lo g ica cal lp er ers so nne nnel l. 1 9 8 6 e d itio n. Vo lum e I., Pa rt 1 —M e teo ro lo g ica l instrum e nts, Pa rt 2 —M ete o ro lo g ica l instrum en ent ts m a inte na nc nce e w o rksho p s, ca lib ra tio n la b o ra to ries a and nd ro utines. Vo lum e II, Pa rt3 —B a sic e el lectro nics fo r the m eteo ro lo g ist. (Eng lish) 6 4 9 — El N iño p heno he nom m en eno o n a nd fluctua uat tio ns of clim a te—L e— Lectures pr p resented a t the thirty-sixth sessio n of o f the W M O Exe Execut cutive C o uncil(1 9 8 4 ), 1 9 8 6 . (En Eng g lish) 6 5 9 — M ari arine cloud a lbum . 1 9 8 7 e edi dition. (English) 6 6 9 — W o rkbo o k on n num um er eri ica cal l w ea the her r p ro d uctio n fo r the tro p ics fo r the tra ining o f C la ss I a nd C la ss II—m eteo ro lo g ica cal l p erson sonne nel l. 1 9 8 6 ed itio n. (En Eng g lish– S p a nish) 7 0 1 — M esom eteo ro lo g y a and nd sho hor rt-ra ng e fo reca sting lecture no n o tes and a nd stud uden ent ts’w o rkbo o k fo r tra ining C la ss I a nd C la ss II— m eteo ro lo g ica lp er erson sonne nel l. Vol Vo lum es Ia nd II. (Eng En g lish, 1 9 9 0 ; Russia n, 19 19 8 8 ) 7 1 2 — M esosca esoscal le fo reca sting a nd its a p p lica cat tio ns—Lect ns—Lectures present presented a t the fo rtieth sessi sessio n o of f the W M O Exe Execut cutive C o uncil (1 9 8 8 ). 1 9 8 9 . (E/ F/R) F/ R) 7 2 6 — C o m p end ium o f lectur ure e no n o tes in cl clim a to lo g y fo r C la ss III a nd C la ss IV p erso nn nne el. Pa rt I—Lect —Lec tur ure en no o tes; Pa rtII—S tud ent’ s w o rkbo kb o o k; Pa Par rtIII—N o tes fo r instruct ucto rs. 1 9 9 2 ed itio n. 7 3 8 — M eteo ro lo g ica cal l a nd hyd ro lo g ica l risk assessm assessm ent en t a nd d isaster red uctio n—L n— Lec ect tures pr p resent esen ted a t the fo rty-firstsessi sessio n o f the W M O Execu Execut tive C ouncil(1 9 8 9 ). 1 9 9 1 . (E/ R) 7 7 0 — M ethod ho d s o f inter erp p reting num er eri ica cal l w ea the her r p red ictio n o out utp ut fo r a er ero o na naut utica cal l m eteo ro lo g y TN -N o . 1 9 5 (2 nd ed itio n). 1999. 7 7 1 — Sp ecia l to p ics o n clim a te—L e— Lectures present presented a t the fo rty-seco nd sess essi io n of o f the W M O Execut Execu tive C ounci ou ncil (1 9 9 0 ). 1 9 9 3 . (E/ R) 7 9 5 — Scientific lectures pr present esented a tthe Eleventh W o rld M eteor eo rolog ical ca lC o ngr ng ress (1 9 9 1 ). 1 9 9 3 7 9 8 — C lim a te cha nge ng e issues—Lectures presented a t the fo rty-fourt ourth sess session of the W M O Executive C ounci ou ncil (1 9 9 2 ). 1 9 9 4 . (Eng lish sh) ) 8 0 5 — Lectures pr present esented a tthe fort orty-fifth sess session of the W M O Executive C o uncil(1 9 9 3 ). 1 9 9 4 . (E/ F) 8 2 2 — Lectures presented a tthe fort orty-sixth sess session of the W M O Executive C ounci oun cil(1 9 9 4 ). 1 9 9 5 . (E/ F) 8 4 5 — Lectures p resented a tthe Tw elfth W orl orld M eteor eo rolog ical ca lC ong on g ress (1 9 9 5 ). 1 9 9 7 . (Eng lish) 8 6 6 — S cient en tific lectures p pr resented a tthe fo rty-eig hth sessi sessio n o of f the W M O Exe Execut cutive C o uncil(1 9 9 6 ). 1 9 9 7 . (En Eng g lish) 9 1 0 — Lectures pr present esented a tthe fort orty-ninth sess session of the W M O Executive C ounci oun cil(1 9 9 7 ). 2 0 0 0 (Eng lish) 9 1 1 — Lectures pr present esented a tthe fiftieth sessio n o f the W M O Executive C ounci oun cil(1 9 9 8 ), 2 0 0 0 . (Eng lish) 9 1 6 — Foreca ecas sting in the 2 1 stC entury. 2 0 0 0 . (Eng lish)




Prepared by David D. Houghton

WMO-No. 926 Secretariat of the World Meteorological Organization Geneva – Switzerland 2002


© 2002, World Meteorological Organization ISBN No. 92-63-10926-5


 The de designations ignations employe mployed and and the pre pressentation ntation of mate materia riall in i n this publica publication tion do not imply theWorld expression of any opinion whatsoever on the the Secretariat of the Meteorological Organization concerning the part legal of status of any country, territory, city or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries.



FOREWORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ACKNOWLEDGEMENTS

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UNDER UNDERS STANDING THE CLIM ATE SYSTEM YSTEM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 3 3 3 3 4 5 5 6 6 7 7 8 10 10 10 14 17 17 19 21 21 21 21 21 22 22 22 22 22 23 23 24 24 24 25 25 25


1.1 Definit Definitions ions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 .1.1 Climate Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 .1.2 Climate Climate sys yste tem m . .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1.1.3 .1.3 Climate Climate chang hange e . .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1.2 Ge Gene nera rall overv overvie iew w . .. .. .. . .. .. .. .. . .. .. .. .. .. . .. .. .. .. . .. . 1.3 Radiation diation pro proccesses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 .3.1 Introd Introduc ucto tory ry comme omments nts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 .3.2 Radiativ diative e ene nerg rgy y bud budg get . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 .3.2.1 .1 Sola olarr rra adiatio diation n . .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1.3.2 .3.2.2 .2 Terre rrestr tria iall radia radiatio tion n . .. .. .. .. .. .. .. .. .. .. .. .. 1.3.2 .3.2.3 .3 The ‘g ‘gre ree enhous nhouse e effe ffecct’ . . . . . . . . . . . . . . . . . . . . . . 1.3.2 .3.2.4 .4 Role of ra radiatio diation n in the ov ove era rall ll ene energ rgy y ba bala lanc nce e . . .. 1.4 Chara Characcteris teristic ticss of clima climate te sys yste tem m com compo pone nents nts . . . . . . . . . . . . . . . . . 1.4.1 .4.1 Introdu Introducctory tory com comme ments nts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 .4.2 Atmos Atmosphe phere re . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.3 .4.3 Ocea Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.4 .4.4 Land Land ssur urfa face ce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.5 .4.5 Cryos Cryosphere phere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.6 .4.6 Biosphe iosphere re . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 1.5 Fee Feedb dba acks cks in the climate climate sys yste tem m . .. .. . .. .. .. .. . .. .. .. .. .. . .. 1.5. 1.5.1 1 Radia Radiation tion ene energ rgy y tra trans nsfe ferr . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 .5.1.1 .1 Tempe mperatu rature re fe fee edb dba ack . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 .5.1.2 .2 Albe Albedo do fe fee edb dba ack . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5. 1.5.2 2 He Hea at ene energ rgy y tra trans nsfe ferr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3 .5.3 Biosphe iosphere re intera interacctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Globa Global natu nature re of the the clima climate sys yste tem m . . .. .. .. .. .. . .. .. .. .. . .. . 1.6.1 .6.1 Introd Introduc uctio tion n . .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1.6.2 .6.2 Ozone Ozone hole in the str tra ato tossphere phere . . . . . . . . . . . . . . . . . . . . . . . 1.6.3 .6.3 El Niño — Sou outhe thern rn Osc Oscillatio illation n (ENS (ENSO) O) . . . . . . . . . . . . . . . . 1.6.4 .6.4 Monsoo Monsoon n . .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . 1.6.5 .6.5 Volca Volcanoes noes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 1.7 Reg Regiona ionall nature nature of the climate climate sys yste tem m . . . . . .. . .. . . .. . .. . .. . . .. 1.7.1 .7.1 Introd Introduc uctio tion n . .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1.7.2 .7.2 Ge Geog ogra raphy phy of clima climate te . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.3 .7.3 Loca Local varia variations tions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.3 .7.3.1 .1 Rainfa infall ll . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.3 .7.3.2 .2 Tempe mperatu rature re . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CHAPTE CHAPT ER 2



 . . . . . . . . . . . . . . . . . . 2.1 Introduc Introduction tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Basic forc forcing ing mec mecha hanis nisms ms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 .2.1 Exte xternal rnal forc forcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 .2.1.1 .1 Astr Astronom onomica icall e effe ffeccts . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 .2.1.1 .1.1 .1 Varia Variatio tions ns in sola solarr rra adiatio diation n emis emisssion . . 2.2.1. 2.2 .1.1.2 1.2 Diurnal and and annual annual cyc cycles les of solar solar

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ra radia diatio tion n input inp . . . para . . rame . .mete . .ters . rs . . of . .. .. .. 2.2.1. 2.2 .1.1.3 1.3 Variations Va riations in ut orbital pa the Eart rth h . . .. .. .. .. .. . .. .. .. .. . .. 2.2.1 .2.1.1 .1.4 .4 Me Mete teor orss . . . . . . . . . . . . . . . . . . . . . . . . .

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2.2.1 .2.1.2 .2

Ge Geolog ologica icall e effe ffec cts . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 .2.1.2 .2.1 .1 Tecto tonic nics s . .. .. .. .. .. .. .. .. .. .. .. . 2.2.1 .2.1.2 .2.2 .2 Vo Volc lca anoes . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 .2.2.. Intera Interac ction o off c clima limate te syste ystem m com compo pone nents nts . . . . . . . . . . . . . . 2.2.2 .2.2.1 .1 Ocean e effe ffect cts s. .. .. .. .. . .. .. .. .. . .. .. .. .. .. . 2.2.2 .2.2.2 .2 Cryos Cryosphere phere effec ffects . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 .2.2.3 .3 Bios iosphe phere re inte intera rac ctions . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 .2.2.4 .4 Interna Internall a atm tmos osphe pheric ric proc proce esses . . . . . . . . . . . . . . . . Obs Obse erv rve ed c clima limate te varia variability bility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. 2.3. 3.1 1 Surfa urface ce te tempe mperatur rature e . . . . . .. . .. . . .. . .. . .. . . .. . .. . .. . . 2.3.2 .3.2 Pr Pre ecipita ipitation tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. 2.3. 3.3 3 Severe vere we wea athe therr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 .3.4 Ocean co condit nditions ions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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HUMAN IMPA IMPACTS CTS ON THE CLIMATE CLIMATE SYS YSTE TEM M  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introd Introduc uction tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. 3.2 2 Atmo Atmos spheric pheric g gre ree enhous nhouse e gas enhance nhanceme ment nt . . . . . . . . . . . . . . . . . . . . 3. 3.2. 2.1 1 Nat Natura urall g gre ree enhous nhouse e gas cons constitue tituents nts . . . . . . . . . . . . . . . . . . . 3. 3.2. 2.2 2 New New g gre ree enhous nhouse e gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. 3.3 3 Atmo Atmos spheric pheric aeros rosol ol enhanc enhance eme ment nt . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 .3.1 Ty Type pes s of a ae eros rosols ols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 3.3 .2 Radiat Radiative ive impa impacts cts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 .3.2.1 .1 Direc Direct imp impa acts cts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 .3.2 .2.2 .2 Indir Indire ect im impa pac cts . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. 3.4 4 Chang Change e of ra radia diative tive effect ffects s of c clouds louds . . . . . . . . . . . . . . . . . . . . . . . . . 3. 3.5 5 Chang Change e of ra radia diative tive prope propertie rties s of the land surfa urface ce . . . . . . . . . . . . . . . 3.6 Sum umma mary ry of hu huma man n imp impa acts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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MODELLIN G CLIM ATE CHANGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introd Introduc uction tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Basics for mode modelling lling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 .2.1 Gov Gove erning phys physical ical e equ qua ations . . . . . . . . . . . . . . . . . . . . . . . . 4. 4.2. 2.2 2 Pa Para rame mete teriza rization tion of phy phys sica icall proc proce esses . . . . . . . . . . . . . . . . . . 4.2.3 .2.3 Mat Mathe hema matic tics s . .. .. . .. .. .. .. . .. .. .. .. . .. .. .. .. .. . .. 4.2.4 .2.4 Comp Comput ute ers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. 4.3 3 Curre Current nt c climat limate e mod mode els and the their ir pe perform rforma ance . . . . . . . . . . . . . . . . . 4.3.1 .3.1 Intr Introd oduc uctio tion n . .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 4.3.2 .3.2 Ove Overa rall ll clima climate te mo mode dell e eva valua luation tion . . . . . . . . . . . . . . . . . . . . . 4.3 .3.2 .2.1 .1 Cur Curre rent nt c clima limate te cond onditio itions ns . . . . . . . . . . . . . . . . . . . 4.3.2 .3.2.2 .2 Pa Pas st c clima limate te cond onditions itions . . . . . . . . . . . . . . . . . . . . . 4.3.3 .3.3 Eva valua luation tion o off c clima limate te mode modell co comp mpone onents nts . . . . . . . . . . . . . . . 4.3.3 .3.3.1 .1 Atmos Atmosphe pheric ric com ompo pone nent nt . . . . . . . . . . . . . . . . . . . . 4.3 .3.3 .3.2 .2 Oce Ocean c com ompo pone nent nt . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 .3.3.3 .3 LandLand-s surfa urfac ce com ompo pone nent nt . . . . . . . . . . . . . . . . . . . . . 4.3.3 .3.3.4 .4 Cryos Cryosphere phere com ompo pone nent nt (s (se ea-ic -ice e mode models ls)) . . . . . . . . . 4.3.4 4.3 .4 Sens nsiti itivity vity of climate mode models ls to mod mode el formula f ormulation, tion, boundary boundary co conditions nditions and pa para rame mete teriza rization tion . . . . . . . . . . . . . . . . . . . . . . 4.3.5 .3.5 Upd Upda ate fro from m 200 2001 1 IPCC Repo Report rt . . . . . . . . . . . . . . . . . . . . . . .

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CL CLIMA IMATE TE PREDIC DICTIO TION N  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introd Introduc uction tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Pr Pre edic dicta tability bility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Short hort-t -te erm c clima limate te for fore ecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Med Medium ium-r -ra ange nge clima limate te for fore ecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 5.5 5 Long Long-ra -rang nge e clima climate te predict diction ion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Pr Pre edic dicta tability bility for re reg gional ional climate climate . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 .6.1 Glob Globa al clima climate te mo mode dels ls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.2 .6.2 Sta tatis tistic tica al downs downsc caling te tech chnique nique . . . . . . . . . . . . . . . . . . . . . 5.6.3 .6.3 Regiona ionall clima climate te mo mode dels ls . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Upd Upda ate highlig highlights hts on c clima limate te mode modelling lling fro from m tthe he 2001 IPCC Repo Report rt

69 69 69 71 73 74 75 75 75 76 76


OBS OBSER ERVA VATIONS TIONS FOR LONG-TE LONG-TERM RM CLIMATE MONI TORING TORING  . . . . . . . . . . . . . . . . . . . . 6.1 Introd Introduc uction tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Ke Key yp princ rinciple iples s for lo long ng-t -te erm c clima limate te monitor monitoring ing . . . . . . . . . . . . . . . .

78 78 78





6. 6.3 3

6. 6.4 4



Sta tatus tus of se select lecte ed observations rvations critica criticall for clima climate te chang change e......... 6.3 .3.1 .1 Obs Obse erv rva ations for basic fo forc rcing ing fa fac ctors tors . . . . . . . . . . . . . . . . . . 6.3 .3.1 .1.1 .1 Sola olarr radia radiation tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 .3.1 .1.2 .2 Gre Gree enhou nhous se gases . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 .3.1.2 .2.1 .1 Carb Carbon on d diox ioxide ide . . . . . . . . . . . . . . . . . . . 6.3 .3.1 .1..2.2 Oz Ozon one e . .. .. .. .. .. .. .. .. .. .. .. .. . 6.3.1 .3.1.2 .2.3 .3 Wa Wate terr va vapo pour ur . . . . . . . . . . . . . . . . . . . . . 6.3.1 .3.1.3 .3 Ae Aero ros sols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. 6.3. 3.2 2 Obse Observa rvations tions for fee feedb dba ack cks s from clima climate te syste ystem m comp compone onents nts 6.3.2 .3.2.1 .1 Clouds Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 .3.2.2 .2 Oce Oceans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2. 6.3 .2.3 3 Surfa urface ce hydrolog hydrology y . .. .. .. .. .. .. .. .. .. .. .. .. . 6.3 .3.2 .2.4 .4 Sur urfa fac ce la land nd c cov ove er . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 .3.2.5 .5 Cryos Cryosph phe ere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 .3.3 .3 Obs Obse erv rva ations for clima climate te re res spo pons nse es . . . . . . . . . . . . . . . . . . . . 6.3 .3.3 .3.1 .1 Sur urfa fac ce te temp mpe era ratu ture re . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 .3.3.2 .2 Pre Precipit ipita ation tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stra trate teg gies ies for imp improv roving ing long-te long-term c clima limate te monitoring monitoring . . . . . . . . . . . 6.4.1 6.4 .1 Dat Data a recov recove ery a and nd re reca cali libra bration tion (‘rehabilitation ’) . . . . . . . . . . 6.4 .4.2 .2 Reanalys nalysis is . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. 6.4. 4.3 3 Increa Increasing tthe he numb numbe er of m me easureme ments nts . . . . . . . . . . . . . . . . 6.4 .4.4 .4 New New m me easur ure eme ment nt s sys yste tems ms . . . . . . . . . . . . . . . . . . . . . . . . . .

MODELLING, DETE DETECTION, CTION, AND ATTR ATTRIBUTION IBUTION OF RECENT RECENT AND FUTUR FUTURE E CLIMATE CHANGE 7.1 Introd Introduc uction tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. 7.2 2 M odel res results for clima climate te cha chang nge e . .. .. . .. .. .. .. . .. .. .. .. .. . .. 7. 7.2. 2.1 1 Recent c climat limate e cha chang nge e . . .. .. . .. .. .. .. .. . .. .. .. .. . .. . 7.2 .2.2 .2 Futur uture e clima limate te cha hang nge e . .. .. .. . .. .. .. .. . .. .. .. .. .. . .. 7.2.2 .2.2.1 .1 Mea Mean c cond onditio itions ns . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 .2.2.2 .2 Va Varia riab bility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 .2.2 .2.3 .3 Cha Chang nge es in e ext xtre reme me events vents . . . . . . . . . . . . . . . . . . . 7.2 .2.2 .2..3.1 Wind . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 .2.2.3 .3.2 .2 Temp mpe era ratu ture re . . . . . . . . . . . . . . . . . . . . . . 7.2.2 .2.2.3 .3.3 .3 Pr Pre ecipit ipita ation tion . . . . . . . . . . . . . . . . . . . . . 7.2.3 7.2 .3 Reduc Reducing ing model model unce uncerta rtainti intie es and and i mproving mproving cli clima mate te c chang hange e estimate timates s . . .. . .. . . .. . .. . .. . . .. . .. . .. . . .. . .. . .. . . . 7. 7.3 3 De Dete tect ction ion and and attrib attribution ution for for c ca ause uses of rre ecent c clima limate te chang change e . .. .. 7.3.1 .3.1 Intr Introd oduc uctio tion n . .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 7.3.2 7.3 .2 Rece Recent nt prog progre res ss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79 79 79 80 80 80 80 81 81 81 81 82 82 82 83 83 84 84 84 84 86 86 87 87 87 87 89 90 94 95 96 96 97 97 98 98 99

POTENTIAL IM PACTS OF CLIM ATE CHANGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introd Introduc uction tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 8.2 2 Terre rres strial trial eco ecos syste ystems ms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 8.2. 2.1 1 Agricu Agriculture lture (plant (plant crops crops)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 8.2. 2.2 2 Fore Fores sts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 8.2. 2.3 3 Des Deserts rts,, la land nd de deg gra rada dation tion and and de des sertifica rtification tion . . . . . . . . . . . . 8.3 8.3 Fre Fres shwa hwate terr res resource ources ma manag nage ement ment . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 8.4 4 Sea-leve -levell rise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 8.5 5 Storm torms s . . . . .. . .. . .. . .. . . .. . .. . .. . . .. . .. . .. . . .. . .. . .. . . . 8. 8.6 6 Huma Human n he hea alth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 .6.1 .1 Intro Introdu duc ction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 8.6. 6.2 2 Pote Potential ntial dire direct ct effect ffects s . . .. . .. . . .. . .. . .. . . .. . .. . .. . . . 8. 8.6. 6.3 3 Pote Potential ntial indire indirect ct effect ffects s . .. .. . .. .. .. .. . .. .. .. .. .. . .. 8. 8.6. 6.3. 3.1 1 Vect Vectoror-bo borne rne dise diseases . . . . . . . . . . . . . . . . . . . . . . . 8. 8.6. 6.3. 3.2 2 Wa Wate ter-b r-borne orne and food food-b -borne orne dis dise eases . . . . . . . . . . . 8.6 .6.3 .3.3 .3 Ag Agric ricultu ultura rall pro produ duc ctivit tivity ya and nd food food supplie upplies s . .. . .. 8.6 .6.3 .3.4 .4 Air p pollut ollution ion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.4 8.6 .4 Stratos tratospheric pheric ozone depletion and incre in crea ased Ea Earth-surfa rth-surface ce ultraviolet ultra violet ra radiat diation ion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

103 103 103 103 103 103 103 104 104 105 105 10 106 6 108 108 109 109 109 109 109 110 110 112 112 112 112 112 112 112 112 11 112 2

CONCLUDING REMARKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

114 114

REFERENCES REFE RENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

115 115

SU BJEC BJECT T IND I ND EX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 126 v



 The atmos atmosphe phere re is the essential physica physicall and chemica chemicall environment nvironment for li fe fe.. C hange hanges s, anth anthropoge ropogeni nic c or otherwise otherwise,, to the physical and chemical properties of the atmosphere have the potential of affecting directly the quality of life and even the very existence of some forms of life. Human-induced climate change, in particular, as well as other global environmental issues such as land degradation, loss of biological diversity and stratospheric stra tospheric ozon ozone e depleti pletion on,, threa th reatens tens our abilit ability y to meet very very basic basic h human uman nee needs, such as adequate food, water and energy, safe shelter and a healthy environment. A great majority of the scientific community, whilst recognizing that scientific uncertainties exist, believe that human-induced climate change is inevitable. Further proof of tthe he reality li ty of cli climate mate change was madeavail available able through the th e work of the th e IPCC, IPC C, estab tabli lis shed by WM O and UN UNEP EP in 1988 1988.. Th The e rece recentl ntly y relea released IIPCC PCC's 's  Third As Ass sessment ment Report port for forms ms the mos most com ompr pre ehe hens nsive ive pictu picture re of the sta tate te of the climate and the global environment yet published, confirming that earlier  judge  judg eme ments nts and project projections ions of global lobal mea mean te tempe mpera rature ture increa increases were were underestimated. The IPCC also concludes that human activity is having a discernible effect on the environment, and that global temperatures are projected to increase at a rate unprecedented in the last thousand years. A majority major ity of experts experts be beli li eve that important reductio reductions ns in net greenhouse greenhouse gas emissions are technically feasible due to a wide range of technologies and policy measures in the energy supply, energy demand and agricultural and forestry sectors. Besides, the anticipated adverse effects of climate change on socio-economic and ecological systems can, to some degree, be reduced through proactive adaptation measures. Consequently discussions are taking place under the UNFCCC and the Kyoto Protocol seeking how to best cope with this thi s iss issue ue,, particularly particularl y in deve developi lopi ng miti gatio gation n and adaptatio adaptation n strategie strategies s to prevent future generations from excessively negative impacts and to reduce the world worl d vul vulnera nerabil bility ity to tthes hese e change changes.  The me mete teorolog orologica icall comm community unity and, in partic particula ular, r, the Wea Weather ther Servic rvice es are now being increasingly solicited by the media, the general public and national and private institutions for information and guidance on climate issues. It is then essential that the technical and professional staff of weather services have the necess nece ssary backg backgroun round d and kn knowl owledg edge e of basic con conce cepts pts and approaches approachesof the the issue issue of climate cl imate changein order to pro provide vide authori authoritative tative respons pon ses. Professor David D. Houghton, from the Department of Atmospheric and Oceani Ocea nic cS Science ciences s, Uni U nive versity rsity of Wi Wis sconsin – M adison, adison, USA, kindl ki ndly y agree agreed, as part of  his sabbatical leave, to prepare these lecture notes. They aim at enabling meteorologists, hydrologists and oceanographers to gain a more comprehensive understandi understa nding ng of the topi topics cs re related lated to the th e issue issue of climate cl imate change. change. I wish to express my s sin ince cere re apprec appreciatio iation n and gratit gratitude ude to Profe Prof essor Houghton Houghton for the high quality and comprehensiveness of his text in such a difficult and complex subject. subject. I fee feell fully ful ly con confi fident dent that th the ese notes wil willl be highly highl y appreciate appreciated d by meteorologists and related professionals interested in expanding their knowledge and unders un derstanding tandi ng of the scie science nce of cli climate mate change change..

(G.O.P. Obasi) Secretary General vii



 The co coope opera ration tion and and s supp upport ort of many pers rsons ons and ins institut titutions ions we were re essential ntial for producing this book. I am deeply indebted to the University of Wisconsin – M adison and iits ts Depa Department rtment of Atmo Atmosphe spheri ric c and and Ocea Oceani nic c S Science ciences s for a sabbatical leave which gave me the time needed for the project and to the Educati Educa tion on and Train Trainii ng Progra Programme mme (ETRP) at at the Worl World d M Me eteorological teorol ogical Organization (WMO) in Geneva for providing work facilities. I thank Dr Gustavo Necco, Director of WMO ’s Education and Training Department, for his support and guidance and Eliette Tarry, also from the Education and Training Department, for her handling of logistical arrangements. I am very grateful for the comprehensive scientific reviews by Dr Hartmut Grassl, Director of WMO ’s World Climate Programme (WCP), and by Professor I gor Karol Karol,, V Voe oeiko ikov v M ain Geophysica Geophysicall Labora Laboratory, tory, St. Peters Petersburg, burg, Russian Russian Federation. They provided essential calibration, constructive comments and additional informa i nformation tion for the book book.. I appre appreciate ciated d th the e warm warm welcomes a and nd constructive constructi ve conve conversa rsati tions ons with wi th many persons in the climate research groups at the Hadley Centre, Bracknell, United Kingdom, Ki ngdom, and the M ax Planck IInstit nstitute ute for M eteorology, Hamburg, Ge Germany. rmany. I n particular, I wish to thank Geoff Jenkins and Lennart Bengtsson for hosting me on these visits. Thanks are also due to Lev Karlin, who hosted my visit to the WMO  Train  Tra inin ing g Cente Centerr at the Rus Russ sian Sta tate te Hydromete Hydrometeorologica orologicall I nstitute in St. Petersburg, and his assistant, Edward Podgaisky, and to Vilma Castro for hosting my visit to the WM O Train Trainin ing g Ce Center nter a att the th e Univers Un iversity ity of Costa C osta Rica, Rica, S San an  Jose  Jos e, Cos C osta ta R Rica ica.. Thes These e visits visits provide provided me with use useful insight insights s on the environenvironments in which this book may be used as well as constructive comments on the science of climate change. M any other pe pers rsons ons contri contribute buted d to thi this s projec project. t. T They hey incl include uded: d: M aurice Blackmon, Francis Bretherton, Jay Fein, Richard Hallgren, Ben Santer, Kevin  Tr  Tre enbe nberth rth and Warren rren Wa Was shingto hington, n, fo forr the their ir c comm omme ents in the form forma ative sta tag ges of  the project; David Parker, Thomas Spence, Paul Try and William Rossow for information they provided on meteorological observations; Ulrich Cubasch for his insights on climate change education; Walter Fernandez for calling to my attention a recent book about the role of the sun in climate change; Steve Hammond for information on computers; and Linda Hedges and Jean Phillips for helping wit with h clarifyi cl arifyi ng re refere ferences nces;; and Pete Pokrandt for helping me to scan figures into electronic files. I also want to thank Heather McCullough for her work on the index. Finally, I am very appreciative for the comprehensive editorial proofing of  the entire book by Linda Keller, my son Eric Houghton, and my wife Barbara Houghton and all others who assisted in editing. Barbara provided support and constructive review throughout the project that were critical for successful completion of the project.

David D. Houghton Professor, Department of Atmospheric and Ocean Sciences University of Wisconsin—Madison M ad adison, ison, W Wiscons isconsin in,, U nite ni ted d Sta State tes s of Ame A merica rica 10 July 2001 ix



Climate change and the need for environmental protection are global problems and call for a knowledgeable response from all countries in order to be effectively addressed. In recognition of this, the ten-year plan (1996–2005) of the Education and Train Trainin ing g Programme (ETRP) of the Worl d M Me eteorologica teorologi call O Orga rgani nization zation (WM O) places a high hi gh priori ty on enhancing nh ancing a g global lobal sys syste tem m approach approach iin n its education and training work in member countries, particularly in developing countri count rie es. The plan sta states tes that personnel wi ll need need to have a avail vailable able an an in integ tegrate rated d training programme for understanding the ocean-land-atmosphere system, whether for monitoring purposes under the Global Atmosphere Watch (GAW), for learning about physical concepts and understanding patterns, or for prediction of tomorrow’s weather and long-term climate changes. Considerable attention has been given to climate change by the scientific community, government bodies and the public media. However, many issues are not full f ully y unders understood. tood. IItt iis s important important th tha at the profe profes ssional ion al operational operational communi community ty of meteorologists, hydrologists, and oceanographers become more knowledgeable on this subject in order to be able to respond to the needs to monitor climate change, to incorporate climate change perspectives into their own work, to help governing bodies to understand the scientific issues, and to provide information to the general public.  Thes  The se lect lecture ure note notes s are inte intende nded d tto o enha enhance nce familiar familiarity ity w with ith the broa broad ds sco cope pe of topics topi cs relate related d to climate cli mate change. Th The ey provi provide de ma mate teri rial al on the science science of cli mate change assuming that the users already have a basic understanding of geophysical fluid dynamics, and relevant physical processes such as radiation transfer, diffusion, the hydrological cycle, and cloud physics along with some understanding of  air chemistry, hydrology, and oceanography. Individuals needing advanced level material for climate change studies or research should refer to the basic references listed at the beginning of the reference section on page 115.  The term term ‘climate change’ is used with different meanings and perspectives. In some cases it may refer to all environmental change or include natural variability. It is most useful to think of climate change as one of several symptoms of  human-produced environmental change with both global and local perspectives. A global perspective is appropriate in recognition of the global interactions involving the component physical systems fundamental to climate change. The local perspective is essential because it is the local impacts which are of significance to individuals and communities, and because it is at the local level where measurements must be obtained from all parts of the world in order to properly describe climate and predict its changes. With the current vigorous research programs and advances in observation technology and analysis it is clear that the specific assessments of climate change will be continually evolving. It is hoped that these lecture notes will give a background which remains relevant to understanding the advancing science of  climate change.  There  The re is a revie review w of tthe he cha chara ract cte eristics ristics and phy phys sica icall proc proce esses of the clima climate te system in Chapter 1 followed by a discussion of its variability first from natural causes in Chapter 2 and then from human activity in Chapter 3. The description of and advances made with numerical climate models are presented in Chapter 4 followed by a focus on climate predictability in Chapter 5. Chapter 6 presents important requirements for observations needed to identify and understand climate change. Progress in the isolation and analysis of recent climate change is discussed in Chapter 7 followed by examples of climate change impacts in Chapter 8. 1



 The prima primary ry s se even ven re refe fere rence nces ffor or the thes se le lect cture ure notes notes are list liste ed at at the top of  the list of references on page 115. They are labeled with numbers running from one to seve seven, n, and are cited in the text, particul particularly arly iin n table t ables s and and ffigures igures.. Note N ote that the book on long-term climate monitoring, the three reports of the Intergovernmental Panel on Climate Change (IPCC), and the climate system modelling book, numbered 2, 3, 4, 6 and 7, respectively, contain material from a very large number of contributors. A few other references are cited in the text. Figures and tables adapted from the source material contain numerous reference citations. A complete list of references is provided at the end of the text.  The third third re repor portt of Working Gr Group oup I of the IPCC which upda update tes s tthe he scie cienntific analysis and conclusions for climate change appeared just at this book was being finalized (July 2001). That report (cited as Reference no. 7 here) basically confirmed and strengthened the scientific conclusions from the previous reports used in this book. Some material was incorporated into this book; however, it was not considered necessary to include all of the details.








Climate is generally defined as the average state of the atmosphere for a given time scale (hour, day, month, season, year, decade and so forth) and generally for a specified geographical region. The average-state statistics for a given time scale including all deviations from the mean are obtained from the ensemble of conditions recorded for many occurrences for the specified period of time. Thus the mean tte empe mperature rature for the mont month h of o f M ay in A Ankara, nkara, Turke Turkey, y, is obta obtain ine ed fro from m measurements considered representative for Ankara averaged over the month of  M ay from a re record cord of many yea years. rs. Cl Climate imate des descriptors cript ors also also incl i nclude ude conditi condi tion ons s at the Earth’s surface such as ocean temperatures and snow cover.  The ave avera rag ge-sta -state te de des scr cription iption involve i nvolves a wide ra rang nge e of of va varia riable bles s de depe pending nding on what is of interest. Temperature and precipitation are the most commonly used; however the list may include wind, cloudiness and sunshine, pressure, visibility, humidity and elements with noteworthy human impacts such as severe storms, excessively high and low temperatures, fog, snow and hail. The method of description focuses on statistical parameters, the mean and measures of variability in time such as the range, standard deviation, and autocorrelations. It is important to identify the difference between weather and climate. Weather involves the description of the atmospheric condition at a single instant of time for a single occurrence. In general, climate may be thought of as an average of weather conditions over a period of time including the probability for distributions from this average.

1.1.2  The clima climate te syste ystem m is de define fined da as s the five compo compone nents nts in the geophys ophysica icall sys syste tem, m,


(a) (b) (c ) (d ) (e)



the atmosphere and four others which directly interact with the atmosphere and which jointly determine the climate of the atmosphere. The five components are listed below: At Atm mosphere here; Ocean; La Land nd surfa fac ce; I ce and s snow now surfa surface ces s (both lla and and ocean ocean a are reas as); ); a and, nd, Biosphe Biosphere re (bo (both th terre terres strial a and nd ma marine) rine).. Figure 1.1 shows the scope of the climate system. Note that the two-way arrows in the diagram identify explicit interactions between the atmosphere and other components. At this point it is appropriate to recognize that there are other factors, also variable in nature, which contribute to determining the climate. These are considered ‘external’  forcing factors and include the sun, Earth orbital parameters, land-ocean distribution, Earth topography (land and ocean), and basic composition ti on of the t he atmosphere atmosphere and oce ocean. an. Th The ese are i mportant determiners determiners of th the e cl cl imate which, except for the basic composition of the atmosphere and oceans, are not affected in return by the climate conditions. Climate change in these lecture notes is defined as the change in climate attributed ute d direc directly tly or iindi ndire rectly ctly to human ac activi tivity ty which, whi ch, in additi ddition on to na n atural climate variability, is observed over comparable time periods. The definition adopted by the United Nations Framework Convention on Climate Change (UNFCCC) focuses only on the human activity that alters the composition of the global atmosphere and excludes other human activity effects such as changes in the land surface. Sometimes the term ‘climate change’ is used to include all climate variability, which can lead to considerable confusion. Climate has variability on all time and space scales and will always be changing.




Changes in the Atmosphere: Composition, Circulation

Changes in the Hydrological Cycle

Changes in Solar input




Aerosols . H2O, N2, O2, CO2, O3, etc. Air-Ice Coupling

Air-Biomass Coupling


 T  Te err rre estr tria ial Radiation Heat Wind exchange Stress

Human influence inf luences s

Biomass Sea-ice

Rivers Lakes

Ocean Ocean

Landmass Coupling

Ice-Ocean Coupling text

Land Changes in the Ocean: Circulation, Biogeochemistry

Changes in/on the Land Surface: Orography, Land Use, Vegetation, Ecosystems

Fi gure 1.1 1. 1 — Sche Schematic matic vi ew of the th e ccom ompo pone nents nts of the global global climate cli mate system (bold), (bold), their proce processes and inte in teracti ractions ons (thi (th i n arrows) and some aspects that may change (bold arrows). [from page 55, Reference no. 3]. 1.2  The definition definition for the clima climate te s sys yste tem m make makes s it clea clear that that one has has to have have a an n


understanding of all of that system’s components (atmosphere, ocean, land surface processes, cryosphere, and biosphere) in order to understand it. In reality one needs to know a limited amount, dependent on the time scales considered, about the non-atmospheric components to understand the interactions of those components with the atmosphere. In general, these interactions occur primarily at physical interfaces so that, for example, for ocean interactions, it is necessary to know only the conditions at the oceanic upper boundary and for cryosphere interactions only at the surface of the ice. To know such conditions, of course, it is nece necessary to unders understand tand how they va vary ry iin n relation relations shi hip p to conditi ons with within in the ocean and ice. Unlike the other interactive components, the ocean is an easilymovable fluid, as is the atmosphere, so that understanding the ocean for climate system applications requires dealing with geophysical fluid dynamic and thermodynamic relationships as complex as those for the atmosphere. Hence, it becomes necessary to use numerical model representation for the ocean comparable to that used for the atmosphere. Current climate system research depends strongly on coupled atmosphere-ocean numerical models.  This chapte chapterr focuses on the for forcing cing a and nd iinte ntera ract ction ion pro proce cesses of pa partic rticula ularr relevance to climate change. Basic material on topics such as large-scale geophysical fluid dynamics, synoptic-scale weather systems, turbulence, or the hydrological cycle is not covered here. Radiation processes play a key role in the climate change scenario and are discussed first in some detail followed by discussion of relevant aspects of the five climate system components. Examples demonstrating the global connections of the climate change processes are then prese pres ented, and finall fi nall y, there is discus discuss sion io n of reg regio ionalnal-s scale cale aspe aspects cts of climate cl imate variability and climate change.







Electromagnetic wave energy transfer (radiation) accounts for nearly all energy transferr ffrom transfe rom the t he sun, and is i s the primary source of ene energy rgy for the atmos atmosphere phere a and nd the entire climate system. Such transfer is also the only way in which significant amounts of energy can leave the climate system. The energy of the global climate system is nearly in balance with incoming and outgoing radiation transfers. A change in one component will produce a different balanced state. The primary human impact on the energy balance is to alter the radiative properties of the atmosphere with respect to these two energy streams. This effect far out shadows other anthropogenic energy sources and sink effects such as the heating due to combustion and nuclear processes. Understanding the impacts of human environmental change on radiation transfer processes in the atmosphere and on the Earth ’s surface is crucial to understanding climate change. Because of this central role of radiation in climate change, a brief review of relevant radiation principles is given here even though it is assumed that the student already has a basic understanding of radiation. Radiation principles cover the production (emission) of radiation energy from the internal energy (heat) of material substance and the transformation of  radiation into the internal energy of material substance (absorption). These radiation principles also govern a number of processes that change the nature of the radiation, but do not convert its energy to internal energy of matter: reflection, refraction, diffraction and scattering. The production (emission) of radiation energy depends upon internal energy (temperature) as well as other properties of  the emitting material substance. The destruction (absorption) of radiation depe depends nds on the amount of incident in cident radiati radiation on energy and and p properties roperties of the t he a abs bsorborbing material substance except for its temperature. The properties of radiation depend on its wavelength. Radiation can exist for a wide and continuous range of  wavelengths referred to as the radiation spectrum.  There  The re a are re two primary primary form forms s of ra radia diation tion re rele leva vant nt to the e ene nerg rgy y balanc balance e properties of the climate system. The first is the ‘solar’  or ‘short-wave’  form predominant in the radiation from the sun. This is primarily in the wavelength range from 0.2 to 4.0 microns (a micron is one-millionth of a meter) which encompasses the visible part of the spectrum. This short-wave radiation provides a source of energy for the climate system as it is absorbed in the atmosphere, clouds, ocean, land surface, and by living matter. The second form is the ‘terrestrial’ or ‘long-wave’ type predominant in the radiation emitted by matter in the climate system. The primary wavelength range for this form is from 4 to 60 microns which is entirely in the invisible infrared part of the spectrum. Sometimes the solar and terrestrial radiation forms are called ‘visible’ and ‘invisible,’ respectively. respectively.  The ove overa rall ll differe difference nces s in predomina predominant nt wa wave veleng lengths ths are due to the dif differe ferences nces in temperature of th the ee emission mission sources for the radiati radiation, on, about 6000 K  for the sun and in the range of 190-330 K for the climate system components emitting terrestrial radiation. The terms ‘short-wave’ and ‘long-wave’ refer to the wavelengths of these radiation forms relative to each other and should not be confused with the same terminology used to describe the wavelengths of radiation used for communications (radio and television).  The rela relative tive role roles s of the two primary primary forms forms of ra radia diation tion in the ene nerg rgy y balanc balance e are complicated by the fact that the components of the climate system absorb as, well as emit, long-wave infrared radiation. This leads to a very complex description of the long-wave radiation energy transfer processes (Schwarzchild’s equation) which is complicated to solve for the atmosphere situation. Large-scale radiation effect variations in the climate system are most pronounce pronoun ced d with res respe pect ct to h he eight and latitude l atitude.. Horizon H orizontal tal variations in radiation radiation transfer do exist at the small scale as demonstrated by the difference in solar heating at the Earth ’s surface on the two sides of a hill, one facing the sun and the other facing away from the sun. For general applications to the climate system, only the vertical component of radiation and is considered. Energy transfer magnitudes are discussed with reference to energy crossing horizontal surfaces.





Solar radiation incident upon the Earth system coming into the atmosphere from RADIATIVE ENERGY BUDGET above leads to heating as it is absorbed by gases, aerosols and clouds in the atmos1.3.2.1 phere, and by the ocean, land, ice and biosphere elements at the Earth ’s surface. Solar radiation  The absorp bsorption tion is propor proportiona tionall to the inte intens nsity ity of the incide incident nt sola solarr ra radia diation tion a and nd depends on the properties of these substances. As discussed above, the relevant intensity is the component in the vertical direction. As the solar radiation is absorbed, there is less radiation available to be absorbed at lower levels. Although the radiation radiation is in initi iti ally ll y in a na narrow rrow be beam am trave traveli ling ng in one dire direction ction from th the e sun, reflection at surfaces and scattering within the atmosphere sends the solar radiation all directions. For this reason, when one looks outdoors in the daytime one sees light coming from all directions. The complete description of the absorption effects (heating) must include the cumulative effects from radiation propagating in from all directions.  The overa rall ll heating heating effe ffect cts s due to sola solarr radia radiation tion a abs bsorp orption tion on a horizont horizonta al surface surfac e in the cli climate mate system rel rel ate to the th e intensity o off th the es sol olar ar beam beam comin coming g in into to the atmosphere from space and the angle of the solar beam to the local vertical.  The inte intens nsity ity depe depends nds on the te tempe mpera ratur ture e of the sun a and nd the dista distance nce from tthe he sun to the Earth. The angle of the solar beam to the local vertical varies according to a number of astronomical factors: latitude on Earth (distance from the equator), longitude on Earth (time of day), and the orientation of the Earth’s axis with respect to the sun (the solar declination angle) which varies according to the time of year. Variations in these factors lead to large variations in heating from day to night, from equatorial to polar regions, and from summer to winter. Figure 1.2 shows the variations in total daily solar radiation energy received at the top of  the atmosphere atmosphere as a functi function on of l atitude and ti time me of ye year ar.. Variati Variations ons in i n the t he Ea Earth rth ’s orbit and solar conditions result in additional longer-term variations to be discussed later.  The deta details ils of sola olarr ra radia diation tion absorpt bsorption ion (he (hea ating) within the a atmos tmosphe phere re and at the Earth’s surface depend strongly on the properties of the absorbing substance. The albedo (reflectivity) of sunlight from the Earth ’s surface is indicative of (inversely related to) the absorption of radiation by that surface. A surface with a high albedo (high visible brightness) is heated much less than one with a low albedo (low visible brightness). At the Earth ’s surface, the albedo ranges from about five per cent for ocean surfaces (with the sun high in the sky) and the top surface of dark thick coniferous forests to 90 per cent for fresh snow.  Thick clouds clouds in the atmo tmos sphere phere can can als also o hav have e an albedo ne nea arly as as hig high h as fres fresh snow. Since much of the reflected and back-scattered solar radiation travels back out to space, it is never converted to heat in the climate system. The atmosphere (gases, aerosols, and clouds) absorbs less of the incident radiation than the Earth surface, so that solar heating effects are greater at the Earth surface than in the atmosphere.

Figure 1.2 — D aily total total of the  so  solar lar radiatio radiation n incident incident on on a unit  horizontal surface at the top of  the atmosphere as a function of  latitude and date in 106 J m-2 for  one day day ( 11.6 11 .6 W m-2 ). Shade Shaded  areas represent the areas that are not illuminated by the sun (from W allace allace a and nd Ho H obb bbs, s, 19 1977 77;; after  after  List, 1951). [from page 100, Reference no. 5, with permi permiss ssio ion n of Springe pri nger-Verlag] r-Verlag]. 6



 Terres rrestrial trial (long (long-wa -wave) ve) radia radiation tion is both both emitted and abs bsorb orbe ed by ma mate teria riall substances in the climate system. The absorption depends on the incident radiation intensity and physical properties of the substances (except for temperature) whereas the emission depends on the temperature and other physical properties of the substances. The Earth’s surface and clouds have radiative properties that tend to produce the maximum amount of terrestrial radiation given given by ‘black body’ values and to absorb incident terrestrial radiation completely. On the other hand, the radiation emission and absorption characteristics of atmospheric gases have a large variabilit variabil ity y depe dependi nding ng on wave wavelength, length, as shown i n Figure Fi gure 1.3. Th The es stron tronge gest e effects ffects are exhibited by minor constituents in the atmosphere: water vapour, carbon dioxide, ozone, nitrous oxide, and methane. These gases occur naturally and are known as ‘greenhouse gases.’  The ‘greenhouse gas’ radiative properties just noted are much more pronounced

 The ‘greenhouse effect’

for terrestrial radiation than for solar radiation. Panel b in Figure 1.3 shows the magnitude of absorptivity for the entire depth of the atmosphere. Note that the absorptivity effects are much larger for terrestrial radiation (wavelength range 4 to 60 microns micron s) th than an for the solar solar radiatio radiation n (wave (wavelength length range 0.15 to 4 micron microns) s).. Th The e large absorptivity of the atmospheric gases for the terrestrial radiation together with atmospheric temperatures in the 210-310K range [optimal for terrestrial radiation emission as shown in Figure 1.3, Panel (a)] leads to emission of significant

Figure 1.3 — (a) Black body  curves for the solar radiation ( assumed to have a tempe temperatu rature re of 6 600 000 0 K ) and the te terrestrial rrestrial radiation radi ation ( assumed assumed to have a temp tempe erature of of 25 255 5 K ); (b) low resolution absorption  sp  spe ectra ctra fo forr the entire ve verti rtica call exte xt ent of the th e atmosphere atmosphere;; and (c) for the portion of the atmosphere atmosphe re above 11 km after  G oody (1964 (1964);); and, and, ( d) the absorption absorption spectrum spectrum for  f or  the various atmospheric gases between the top of the atmosphere and the earth  s ’s  surface  surfac e after Howard et al. [upd  [updat ate ed with data from Fels and Schwarzkopf  (1988, personal communication) be betwee tween n 10 1 0 and 10 100 0 µm] µm].. [adapted from page 93, Reference no. 3]. 7



amounts of terres terrestrial tri al radiation radiatio n in all direction directions se eve ven n where clouds are not pres present. [Students should recall the Kirchhoff and the Wien displacement radiation laws.] It is the downward component which retains energy in the climate system and keeps equilibrium temperatures at the Earth ’s surface and in the lower atmosphere higher than would otherwise be the case. This enhanced temperature is said to result from the ‘greenhouse effect.’ On a globally-averaged basis the observed surface temperature is about 33K  above the 255K expected with no atmosphere at all. This enhancement value would be even greater (over 80K) if radiative effects for the clear atmosphere were the only on ly modi modifyi fying ng factors [Manabe a and nd Stri Strickler, ckler, 19 1964]. 64]. Se Sensibl nsible ea and nd latent he h eat fluxes from the Earth ’s surface to the atmosphere along with atmospheric convection partially offset the surface temperature enhancement due to radiation transfer back from the atmosphere.

Role of radiation in the overall energy balance

When considering only the vertical transfers of energy, radiation energy transfers have a dominant role in the overall energy balance of the globally-averaged atmosphere and the Earth ’s surface. Figure 1.4 summarizes these energy transfers. The top of the diagram represents the top of the atmosphere, the bottom the Earth ’s surface, and the atmosphere is in the middle. The solar radiation components are on the left side. The terrestrial radiation components are on the right side, and the sensible and latent heat transfers are shown in the center. Note that about 30 per cent of the incoming solar radiation is returned to space without being converted to heat (an albedo of about 30 per cent for the Earth–atmosphere system); about half is absorbed at the Earth surface, and only about 20 per cent is absorbed in the atmosphere. For the terrestrial radiation


Reflected Solar Radiation 107 W m-2

Reflected by Clouds, Aerosol and Atmosphere 77

Incoming Solar Radiation 342 W m-2



77 Emitted by Atmosphere 165


40 Atmospheric Window Greenhouse Gases

Absorbed by 67 Atmosphere 24

Outgoing Longwave Radiation 235 W m-2

Latent 78 Heat

Reflected by Surface 30 168 78   24 Absorbed by Surface Thermals   Evapotranspiration  


390 Surface Radiation


324 Back Radiation

324 Absorbed by Surface

Figure 1.4 — The Earth s  ’s radiatio radiation n and and ene nergy rgy bala balanc nce e. The net net a ave verage rage incoming incoming so solar lar ra radiatio diation n of of 342 W m-2 i s pa parti rtially  ally  refl refle ected cted by clouds and the atmosphere atmosphere,, or at the th e surface, sur face, but 49 4 9 per cent cent i s absorbed by by the surface. sur face. Some of that heat is is returned to the atmosphe atmosphere re as sensible hea heati ting ng and most most as evapotranspir vapotranspiration ation that t hat i s rre ealize ali zed d as latent heat heat i n preci preci pitati on. T he rest rest i s radiate r adiated d as thermal thermal i nfr nfrare ared d radiati on and most most of of tthat hat is i s absorbe absorbed d by the atmosp atmosphe here re whi which ch iin n turn tur n em emii ts radiation both up and down, producing a greenhouse effect, as most of the thermal radiation lost to space comes from cloud  tops and and parts of th the e atmosphe atmosphere re much colde colderr th than an the surface surf ace.. T he part partii ti oning oni ng of th the e annual gglobal lobal mean mean e energy nergy budget  and the accuracy accuracy of of the values values are gi ven ven iin n K i ehl and Trenberth Trenberth ( 19 1997 97)) . [from page 58, Reference no. 3]. 8



emit mitted ted from th the eE Earth arth on only ly ab about out 10 per ce cent nt is transmitte transmitt ed direc di rectl tly y to space space; the th e remaining part is absorbed in the atmosphere.  The energy rgy co compo mpone nent nt labe labele led d ‘back radiation’  is a key indicator of the greenhouse effect. Note also that the magnitude of terrestrial radiation emitted downward from the atmosphere to Earth and absorbed at the Earth ’s surface is nearly equal to the total solar radiation incident at the top of the atmosphere and is about double the amount of solar radiation absorbed at the Earth ’s surface. In general, the magnitudes of radiation energy transfer are considerably larger than those associated with the sensible and latent heat transfers (see Figure 1.4). A basic aspect of radiation forcing is the systematic variation with latitude. As had been shown in Figure 1.2, there is generally an overall reduction with distance from the equator in the daily total solar radiation coming into the Earth–atmosphere system, being more extreme in the winter season and nearly absent at the time of the summer solstice. Seasonal and annual means for solar radiation absorbed in the Earth-atmosphere system show poleward decreases in both the summer and winter hemispheres (see Figure 1.5).  The net net ra radia diative tive forc forcing, ing, of cours course e, depe depends nds on both the input input from s sola olarr radiation and losses due to terrestrial radiation emission to space. Latitude variations of terrestrial radiation emission are much less than for solar radiation (Figure 1.5). This Th is terre terrestrial strial emission depe depends nds on the tempe temperature rature (on tthe he abs absol olute ute Kelvi Kelvin n scale) both at the Earth ’s surface, and in the atmosphere which has a smaller percentage variation than that for the zenith angle factor change with latitude which affects solar radiation absorption amounts.  The res resulting net net ra radia diation tion for forcing cing for the Earth Earth–atmosphere system (see the

Figure 1.5 — Me  M eri diona dionall profiles at the th e top top of the th e atmosphere atmosphere,, for  annual, D JF and JJA JJA mea mean n conditions, of: (a) the zonal-mean albedo; (b) absorbed solar radiation; (c) emitted terrestrial radiation; and (d) ne nett rad r adii ation; ( based based on data from C ampbe ampbell and Vond Vonde er H aar, aar, 19 1980) 80) . N o corrections were made for global radiation balance. [from page page 128, Reference 128, Reference no. 5. W Wit ith h permissio permiss ion n of Springe pri nger-Verlag] r-Verlag].

last panel in Figure 1.5) has a net excess in the tropical latitudes and a deficit in the polar latitudes. If radiation transfer were the only process occuring, the equatorial regions would be hotter than observed and the polar regions colder than observed. However, the transport of heat from the equatorial to polar regions by atmospheric and oceanic circulations offsets this radiation imbalance and provides an overall energy balance at each latitude. In conclusion and as stated before, the primary connection between human activity and climate change is the alteration of the radiation transfer characteristics of the atmosphere. The change in greenhouse gas concentration and the addition of other gases with similar characteristics will change the terrestrial radiation transfer. In addition, a change in aerosol concentration and perhaps related change in cloud cover will change the solar radiation transfer. Except on a very local scale, the energy transferred by radiation is far greater than any production rate of energy due to human activity. 400


ALBE DO (%) (%) (Fr From om C amp bell and Vonde r Haar, 1980 1980))





60 DJ F


YEAR 200


20 80S



20 20  

100 0









NET (W (Wm m -2)




20 20  






50 0








-200 80S



20 20  

100 0










20 20  














A brief description of each of the five components of the climate system is presented. The information on the atmosphere is more extensive as climate is largely defined by conditions in the atmosphere. It is expected that students will already have background knowledge on the atmosphere, its circulations and physical processes. It is understood that students may have little or no background for the other four components. Therefore the material presented here deals only with aspects relevant to interactions with the atmosphere. A number of factors make the atmosphere a very complex fluid system. As a gas, it has compressibility and great mobility. It extends well above topographic barriers and can sustain global-scale circulations. A number of forcing factors including radiative heating and cooling, latent heat sources and sinks due to phase change of water, and variations in Earth ’s surface temperature give rise to significant temperature variations in all three space dimensions and in time.  Thes  The se te tempe mpera rature ture va varia riations tions give rise rise to horizonta horizontall pres pressure gra gradient dient force forces, approximately consistent with the hydrostatic relationship, which are the basis for horizontal atmospheric motions. Temperature variations also affect vertical press pres sure gra gradient dient forces which, whi ch, in i nstances nstances of ‘static instability,’ can le l ead to larg l arge e local-scale vertical motions. The large-scale horizontal motions are greatly modifie fi ed by C Coriol oriol is effec effects ts arising arising from the rotation of th the eE Earth arth and attain attain sufficie uffi cient nt magnitudes to force smaller-scale transient motions. Fluid transport and nonlinear processes together with interactions among the physical factors listed above lead to a complex global general circulation system and embedded smaller-scale systems with space and time scales all the way down to atmospheric turbulence which we see in the gustiness of winds and the dispersion of smoke plumes.  The hydrological hydrological cyc cycle le iis s an i mportant mportant pa part rt of the atm atmos osphe pheric ric sys yste tem. m. Evaporation and condensation of water can transfer considerable amounts of  energy by both vertically and horizontally. The cloud component of the hydrological cycle strongly affects transfers of both solar and terrestrial radiation.  The precipita cipitation tion is the source ource of fr fre esh wate waterr nee needed ded ffor or life on land land surfa surface ces. In the climate perspective, where statistics of atmospheric conditions are considered, the general circulation and associated temperature, cloudiness and precipitation patterns provide the primary bases for the mean climate conditions. Some of the transient features have systematic variations in time which are associated with the diurnal and annual cycles which are described directly in climate descriptions. Examples include average high and low temperatures for the day, monthly mean temperatures for each month of the year, and the annual range in monthly mean temperature. The random transient features such as extratropical cyclones, moist convection in the tropics and middle latitudes, and turbulence contribut contr ibute e to the th e clim climate ate des descript criptii ons for fo r extremes and and also for the mea mean n sta states tes if  appropriate appropri ate c correlatio orrelations ns exist amon among g the variables of the trans tr ansient ient systems systems..  This last last po point int des deserve rves s to be be deve develope loped d and and illus illustra trate ted. d. Le Lett us, us, for exa xamp mple le,, take the relatively random transient atmospheric feature, the cumulus cloud. If  the vertical circulations associated with cumulus convection have upward motions with systematically higher temperature than the downward motions, it would be expected that an ensemble of these weather systems would give a net upward transport of sensible heat. In the same way, if in association with extratropical cyclones, the winds from the south were typically warmer than winds from the north, an ensemble of these storm systems would give a net northward transport of sensible heat. In many situations randomly-occurring transient systems contribute significantly to the larger-scale conditions which determine the longer-term climate mean states. I t iis sa ass ssumed umed that the th e student student iis sa aware ware of the ge general neral climate cl imate condi conditi tions ons over the Earth. M any texts a and nd atlase atlases pre prese sent cli climate mate maps maps,, iincl ncludi uding ng the th e books re referferenced in the introduction. Nevertheless, a few climate charts are shown here to il lustra lu strate te differe dif ferences nces over the worl d as we well ll as to prese present seasonal asonal differe dif ferences nces.. I t iis s important to remember that climate conditions vary considerably over the world, cli climate mate change woul would d there therefor fore e affect affect people in differe dif ferent nt pl plac ace es of the worl world d qui quite te differently. Warming of summers in Canada may be welcomed as an enhancement




of the growing season whereas warming of summers in the Sahara, where it is already hot, may be an entirely negative development. Figures are shown for the climatology of surface temperature in January and July (Figure 1.6), precipitation in December-February and June-August (Figure 1.7), surface airflow (and pressure field) in December-February and June-August (Figure 1.8), and upper tropospheric airflow in December-February and June-August (Figure 1.9). The surface temperature and precipitation are key parameters for surface living conditions.  Thewind fie fields ldshig highlig hlight ht atm tmos ospheric tr tra anspo nsport rt cond onditio itions ns at thesurfa urfac ce wherewelive and in the upper troposphere where the primary maxima of kinetic energy exist in the circulation (the subtropical and polar jet streams). Both topography and ocean–land temperature differe differences ncesresult ult iin n wavy pattern rns s in the horizont hori zontal al mean fflo low w particularly particul arly in the North Northe ern H He emisphere.  The cir circu culat lation ion in the atmo tmos sphere phere is sufficie ufficiently ntly vig vigorou orous s that that mate materia riall injecte in jected d int i nto o on one e pa part rt of the atmosphere atmosphere c can an be sprea spread d quickly qui ckly over broad re region gions s. It may take just days for a volcanic smoke plume or radioactive products to circle the Earth. Constituents with sufficiently long lifetimes, such as carbon dioxide, would be expected to have relatively uniform concentration throughout the atmosphere. The large-scale north-south circulations are less vigorous than those in the the east-west direction (see Figure 1.9). Vertical motions generally are much smaller than horizontal motions so the vertical transport of atmospheric constituents may be quite limited. This accentuates the build-up of atmospheric pollu pol lutants tants in the lo lowe werr llaye ayers of the th e a atmosphere tmosphere espe peciall ciall y iin n l ocal areas areas wit with h l arge arge sources of the pollutants.  The interna internall insta instabilitie bilities s, fee feedba dbacks cks and nonlinear nonlinear na nature ture of the atmos tmospheric pheric system can result in circulation features that appear quite unrelated to the basic forcing due to energy fluxes at the Earth ’s surface and to radiation energy transfer. Examples of these include tropical cyclones and extratropical weather fronts. Furthermore, circulations may have more than one equilibrium for a given external forcing. Chaos theory deals with the variability characteristics in systems with such multiple equilibria. This characteristic of the atmosphere adds additional challenges and uncertainty to understanding and determining the outcomes for climate change scenarios.

Figure 1.6 — Global map of (a) the January surface temperature  Janua  January ry and and (b) July July surfac surface e temperature. (From Shea (1986), rre (1986), eproduced with wi th permiss permission ion from the th e National Centerr ffor Cente or A Atmos tmospheric pheric Research). [from page 7, Reference no. 1, with permission of Academic Press]. 11



Figure 1.7 — Global distribution of average precipitation rate for  D ecembe cemberr-Janua Januaryry-Feb Februar ruary  y  (D ,J,F; le left ft pa pane nel) l) and June June-July-July August (J,J,A (J,J,A ; ri ght pane panel) l) for  1988-1996 (from the Globa Gl oball Pre Precipitation cipitation Climatology Project [GPCP] of  the Global Gl obal En Ene ergy and Water Water Cycle Experiment Experiment [GEW [GEWEX]. EX].

Figure 1.8 — Global distri butions of of the he heii ght  anomalies of the 1000-mb  p  pre ressure ssure fi eld in gpm from the  stand  sta ndard ard atmo atmosphe sphere re height, height, 11 113 3  gp  gpm, m, and and ve vect cto or p plo lots ts of of the  surface  surfac e winds for for northe northern rn winter  winter  ( left panel) panel) and northern northern summer  summer  (right panel) mean conditions. Each barb on the tai taill of an arrow  ar row  rep represe resents nts a wi nd spee speed of of 2 m s-1. The isoheight lines can be i nterprete nterpreted d as iisob sobars ars ffor or surface sur face  pressure  pre ssure redu reduce ced d to sea sea le leve vel. l. [adapted from page 134, Reference no. 5, with pe permi rmiss ssio ion n of Springe pri nger-Verlag] r-Verlag]..

Figure 1.9 — Global distributions of the hei hei ght dif differe ference nce of the  200-mb  200 -mb pressure pressure fi eld in gpm  from 11, 784 gpm and ve vecctor  tor   plots  plo ts o off the 200 200-mb -mb winds for  for  northern winter (left panel) and  northern summer (right panel) mean me an co condi nditi tions. ons. Each E ach barb barb on the tail of an arrow represents a wind speed of 5 m s -1.

[adapted from [adapted f rom pages 151 and 152, Reference no. 5, with permissio permiss ion n of Springe pri nger-Verlag] r-Verlag].. 12






1.4.3  The oc oce ean ha has s a major major impact impact on the the clima climate te of the atmo tmos sphere phere.. It cove covers approxpprox-


imately 71 per cent of the Earth ’s surface and thus has a dominant role for transfers of energy and other properties between the atmosphere and the Earth’s surface. Its large heat capacity, made accessible for surface energy transfers by circulations within the ocean, provides a moderating effect on temperature variabili ty iin n the th e a atmosphere tmosphere.. O Oce ceanic anic currents transfe transferr l arg arge ea amoun mounts ts of hea heatt energ energy y away awa y fro from m equa equatori torial al regio regions. ns. Fin Finall ally, y, the ocean is i s an an i mportant source for atmos atmos-pheric water vapour, as well as a source and sink for other greenhouse gases.  The oc oce eanThis capacity exceeds that atmosphere byunit a factor of(the the ’s heat order of 1000. is due to differences bothof inthe heat capacity per mass specific heat of liquid water is about four times that of air), and in total mass between the ocean and atmosphere. The ocean ’s heat capacity bears upon atmospheric temperature through oceanic transports (both horizontal and vertical) that produce and maintain surface water temperatures warmer or colder than the atmosphere atmos phere res resulti ul ting ng iin n large hea heatt tr trans ansfers fers.. The T he de depth pth to wh whii ch the th e oceans int inte eract with the atmosphere depends on the time scale under consideration. For diurnal diu rnal variatio variations ns the depth depth is small small , of the order of five f ive to 10 mete meters. rs. For seasonal asonal variations variation s the de depth pth is 20-200 meters meters (the depth of the well well-mi -mixed xed oce oceanic anic surface layer). The ocean is a major component in determining the climate and its variations for annual, annually-averaged and longer period conditions.  The strong in fluence of th e oce ocea an on surfac urface ea air ir te tempe mperatu rature re is clea clearly rly evident. Figure 1.10 shows climatological sea-surface temperature conditions for  January  Janua ry a and nd July July.. Note that that thes these te tempe mpera ratur ture es are similar to to those those in the surface urface air over the oceans shown in Figure 1.6. Ocean temperatures are maximum in equatorial regions and decrease poleward. However, the poleward decrease in the

Figure 1.10 — Monthly  M onthly me mean an  Se  Sea-S a-Surfac urface e Temp Tempe erature raturess (SSTs) (SSTs)  for Janua January ry and and Ju July ly.. D ark are areas as indicate sea ice; stippling i ndica ndi cate tess lland and areas. areas. T he conto contour  ur  interval is 2 ° C ex cept cept for dashed  contours of 27 and 29 ° C . ( from from Shea et al., 1990). 14



winter hemisphere is not as rapid as that experienced for land surface temperatures. The large ocean heat capacity results in summer-to-winter temperature differences over the ocean are generally much less than the summerto-winter differences over land areas at comparable latitudes. Adjacent land areas downwind of the ocean, in fact, experience a moderation of winter cold temperatures and summer hot temperatures compared to other land areas at the same latitude. Note, for example, that in January at 50°N the mean surface air temperatures are well below freezing in eastern north America, well above freezing in western the central Atlantic is the sea-surface temperature), and still above freezing in Europe (see(as Figure 1.6). Ocean currents have a major influence on sea-surface temperature. It is important to understand these currents to fully appreciate the ocean ’s interactions in the climate system. The laws of basic geophysical fluid dynamics apply as they do for atmospheric atmos pheric moti motions. ons. As for the atmosphere atmosphere,, spa spatial tial variatio variations ns iin n hea heatin ting, g, primaril primarily y in the oce ocean an surface layer layer,, lle ead to h hori orizont zontal al pressure pressure gra gradi die ents wh whiich cause cause motio motion. n. However, the oceanic condition is different from the atmosphere in two fundamental ways. First, the primary forcing of the ocean is at the upper boundary, whereas the primary forcing for the atmosphere is at its lower boundary. Atmospheric winds above the ocean are a major factor in causing ocean surface currents through surface friction processes. In contrast, for the atmosphere frictional conditions at its lower boundary tend to reduce atmospheric motion. Second, the density of the ocean water is determined primarily by its salinity and temperature instead of pressure, temperature and water vapour content as in the atmosphere. The water vapour factor is relatively unimportant for atmospheric de density nsity e exce xcept pt in hot and h humid umid condi condition tions s. On the othe otherr hand, sa sali lini nity ty can can pl play ay a major role for ocean density especially when temperatures are near freezing in which case density changes very little with temperature. Salinity conditions in polar ocean regions are important for determining whether or not significant vertical motions occur in local areas. The variations in ocean water density according to temperature and salinity are shown in Figure 1.11. The density value is shown as the di differe fference nce from 1000 kg m-3. Thus, Th us, for example, for a tempe temperature rature of 10°C and a salinity of 16 parts per thousand, the seawater density is approximately 1016 kg m-3.  The major surfa urface ce-laye -layerr oc oce ean curre currents nts are s shown hown in Fig Figure ure 1.12 1.12.. Ocea Ocean currents result in significant equator-to-pole transport of thermal energy. The strong northward-moving currents off the east coasts of Asia and north America (the Kuroshio and Gulf Stream currents) take warm water away from the tropics. Currents towards the equator on the west side of continents (the California, Peru, and Benguela currents) transport cold water towards the equator. Currents at deep ocean layers form primarily due to pressure gradients from density variations (thermohaline currents). The density variations are strongly influenced by fresh water sources from land surface runoff and sea-ice melt and

Figure 1.11 — Contours of   se  seaw awate aterr de density nsity ano anoma malies lies (difference from a reference density of 1000 kg m-3 ) i n kg m-3 p  plo lotte tted d against against salini salini ty  and temperatu temperature. re. [from page page 175, Reference no. 1 with permission of Academic Press]. 15



Figure 1.12 — A map of the major surface currents in the world oce ocean duri ng the northern northern winter ( from from Tolmazin, 1985). [from page 177, Reference no. 5, with permission of SpringerVerlag].

fresh water evaporation from the sea surface. These currents provide a coupling between deep and surface ocean waters that involves a large portion of the ocean and provides ocean impacts on climate system variability on time scales of  centuries, millennia and even longer.  The oc oce eans play play a significant ro role le as a source ource and s sink ink for atmo tmos spheric pheric gases,

Figure 1.13 — The global carbon cycle, showing the reservoirs (in G tC) and and fluxe fluxess (G ( G tC/yr) rele releva vant  nt  to the anthrop anthr opoge ogeni ni c pe per tur turbati bation on as annual averages over the  peri  pe ri od 198 1980 0 to 198 1989 9 (Eswa (E swaran ran et  al., 1993 1 993;; Potter Potter et al. ,  , 1993, 199 3,  Siege  Sie gentha nthale lerr an and d Sa Sarmien rmiento to,, 1993 19 93)) . T he ccom ompo pone nent nt cycle cycless are  simplifi ed and and subje subject ct to co consi nsi de derable rable unce uncertai nty. I n addition, this figure represents average values. The riverine flux,  p  particula articularly rly the anthro anthrop pogenic  genic   p  po orti on, is curre currently ntly very very po poorly  quantified and so is not shown here.. Evi de here dence nce i s accumulati accumulating ng that many of the key fluxes can  fluctua  fluc tuate te signi fifica cantly ntly from ye year  ar  to year year (te ( terrestrial rrestrial sinks si nks and   so  sourc urce es: IIN N PE, 1992; 19 92; C iai iaiss et al., 19 1995 95;; export export ffrom rom the ma mariri ne biota;; W ong et al., 1993). In biota co contrast ntrast to the static vi ew  co conve nveye yed d by fi gures such as th thii s one, the carbon system is clearly  dynamicc and coupled dynami coupled to the cli climate mate system on on seasonal, interannual and decadal time scale  scaless (e (e.g. .g. Sc Schime himell a and nd Sulzman, Sulzman, 1995). [from page 77, Reference no. 3]. 16

including greenhouse gases. Changes in ocean temperature can change the holding capacity for gases and can result in a net outflow or intake from the atmosphere. Particularly noteworthy is the case for carbon dioxide. It is estimated that the carbon dioxide dissolved in the upper layers of the ocean is nearly 50 per cent more than the total amount in the atmosphere (1020 versus 750 gigatons of  carbon content). See Figure 1.13. Thus, there is much potential for effects on the atmospheric carbon dioxide concentrations and the resulting radiation impacts due to changes in the ocean. In conclusion, the ocean is a very important and interactive component in the climate system. The atmosphere forces oceanic motions through surface friction and affects oceanic temperature through surface sensible, latent, and   6  0   o  n o  c  t  i   u  d   oo   p    r   n .   1  6  a  r  y a  t  i  oo  s e  s     p  p     i   r  i  d  d    u   t   t    p       r  r   i   m    e e   n   l   aa   n e   r   a  l   a  n d   i  n g   b  g    o o   3 .  a  n   G  l   6  1   C  h Vegetation 610 .   0  5 Soils and detritus 1580

Atmosphere 750


5.5 Fossil fuels and cement production



Surface ocean 1020 50

1    0    0   

40 Marine biota 3


9    1    . 6   


Dissolved organic carbon <700

Intermediate and deep ocean 38,100

6 .  0  2

Surface sediment 150



radiative energy transfers at the ocean –atmosphere interface. The ocean affects atmospheric temperature by virtue of its large heat capacity which is enhanced by circulations that distribute its heat energy internally. As will be seen elsewhere in this chapter, it is also is a source and sink for atmospheric water vapour and other greenhouse gases. The ocean also has an important biosphere component.


Land surface is an important interactive component of the climate system. It


covers 29 per cent of the Earth ’s the surface. Significant exchanges of heat, moisture, and momentum occur between atmosphere and the land surface, including its biosphere. It is also the surface on which people live. The heat storage factor of land surface with respect to atmospheric temperature variations is much less than that for the oceans. Land has a lower specific heat than the ocean, and its rigidity restricts heat transport to deeper levels. As a result, the depth of the soil layer which is important for energy exchange interactions with the atmosphere is only several meters for the annual-cycle time scale. A cave 20 meters underground will remain at the same temperature all year round. Because of the small heat capac capacii ty of the lland and surface, variati variations ons in i n atmos atmospheric pheric te t empera mperature ture just above the surface are much larger over the land than over the ocean.  The energ nergy y and mom mome entum excha xchang nge es betwe between land land surfa urface ces and the atmosphere are similar to those for an ocean surface. Heat and latent heat (water vapour) exchanges depend on temperature and water vapour pressure differences between the land surface and the lower atmosphere, roughness of the land surface, and surface atmospheric wind speed. The latter may be characterized by wind conditions in the lowest ten meters of the atmosphere (the atmospheric ‘mixed layer’). Radiation transfer is the other important energy exchange. The amount of  solar radiation absorbed by a land surface depends on both the amount of solar radiation radiatio n comi coming ng th through rough th the ea atmosphere tmosphere (a highly hi ghly vari variable able qua quanti nti ty as discusse discussed before) and the albedo (reflectivity) of the land surface which is also highly variable. The albedo ranges from five to 90 per cent and depends on the type of cover for the land surface as shown in Table 1.1. The infrared radiation transfer is the net of the infrared radiation emitted by the land surface (which is close to the maximum ‘black body’ value and thus dependent only on temperature) and the total downward infrared radiation produced by the atmosphere. Because of the small heat capacity of the land surface, the radiative, sensible, and latent energy transfers come close to balancing most of the time.  Topog  To pogra raphy phy of the land surfa urface ce has has a pro pronounc nounce ed effect ffect on la larg rge e-s -sca cale le atmospheric circulations. particularly in the Northern Hemisphere. The Rocky M oun ountains, tains, which whi ch are are ori orie ented north north-south -south transe transect ct the No North rthe ern He H emisphere westerlies, and the Tibetan Plateau with its extreme height and aerial extent affects flow over a large area. Topography is a factor in the wave patterns in the upper tropospheric horizontal wind flow (shown in Figure 1.9), and also has major effects on surface temperature and rainfall. Alte Al teration ration of land surfa surface ce by human activi activity ty is an important fa f actor in climate change th that at adds to tthe he e eff ffe ects of h human-produced uman-produced change changes s in the radiati radiative ve charcharacteristics of the atmosphere. Urbanization, cultivation for agriculture, irrigation, and deforestation change the albedo of land surfaces and the surface sensible and latent heat transfers. These factors can also greatly influence the local aspects of climate change.

1.4.5  The cryos cryosphe phere re — the ice component — has significant impacts on the climate


system in several ways. It affects radiative and sensible heat transfers at the Earth ’s surface. It influences temperatures in the ocean and at the Earth ’s surface due to transfers between latent and sensible energy during melting and freezing. Finally, its melting and freezing influences water runoff from land and ocean salinity. Ice and snow exist primarily in the latitudes poleward of 30 degrees latitude and are thus are unfamiliar to the majority of the world’s human population.. Al population Although though only on ly about two pe perr ce cent nt o off all the wate waterr on Earth Earth is frozen, frozen, 17



Table 1.1 1. 1 — Albedos for various  surface  surfac es. [from page 88 in Reference no. 1, with permission of Academic Press].

 Surface  Surfa ce typ type e

Ra Range nge

Typic Typical al va valu lue e (in per cent)

Water Deep water; low wind, low altitude Deep water; high wind, high altitude

5–10 10–20

7 12

Bare M surfaces oist dark soil, high humus M oist gray soil Dry soil, desert Wet sand Dry light sand Asphalt pavement Concrete pavement

5–15 10–20 20–35 20–30 30–40 5–10 15–35

10 15 30 25 35 7 20

Vegetation Short green vegetation Dry vegetation Coniferous forest Deciduous forest

10–20 20–30 10–15 15–25

17 25 12 17

20–35 25–40 35–65 60–75 70–90

25 30 50 70 80

Snow and ice Forest with surface snowcover Sea ice, no snowcover Old, melting snow Dry, cold snow Fresh, dry snow

it covers an average of 11 per cent of the world ’s land surface and seven per cent of its oceans.  There  The re are ma many ny co cons nstitue tituents nts to the cryos cryosphe phere re:: land land ice in polar ic ice e shee heets ts,, glaciers, permafrost, frozen ground, seasonal snow cover and sea ice. Table 1.2 summarizes the amounts of ice in the various categories. Note that although Antarctica and Greenland between them account for 98 per cent of the world’s land ice, the total area covered by ice and snow can be much larger in the Northern Hemisphere winter. Figure 1.14 shows maps of ice coverage. The Northern Hemisphere has a much larger seasonal range than the Southern Hemisphere because of the larger amount of land area.  The albedos lbedos of ice ice and s snow now are highe higherr than than the albe lbedo do of tthe he la land nd or oce ocean surface that they cover (see Table 1.1). Thus, their seasonal variations in coverage will cause important seasonal variations in the Earth ’s surface albedo. The impact is less than might be first thought because in the winter season when coverage is maximum, the solar radiation is at a minimum and solar energy is less important in the atmospheric energy balance. Nevertheless, ice and snow introduce a process of positive feedback as the expansion of ice and snow coverage increases the albedo which, in turn, decreases solar heating. The resultant cooling acts to further enhance the ice and snow cover. Snow and ice cover have strong insulation effects for sensible heat transfer which reduces heat transfer from the Earth, oceans, and lakes to the atmosphere. Over the ocean (and lakes), snow and ice cover effectively cut off the moderating effects of the water so that air temperature over the ocean ice cover can fall well below freezing point. Cryosphere conditions may not be directly evident to most people of the world. However, because of the global nature of the climate system, the cryosphere component also influences lower latitudes.




Table 1.2 1. 2 — E  Esti stimate mated d global global i nve nventory ntory of land and sea i ce ce** . [After Untersteiner, 1984 from page 15 in Reference no. 1, with permission of Academic Press].

Land ice

* Not included in this table is the volume of water in the ground

Antarctic ice sheet Greenland ice sheet M ountain glaciers Continous

that annually freezes and thaws at the surface of permafrost (‘active layer’), and in regions without permafrost but with subfreezing winter temperatures.

 Area  Area (km 2 )

Vo Volu lume me (km 3 )

Per Per cent cent of tota totall ice mass

13.9 × 106 1.7 × 106 0.5 × 106

30.1 × 106 2.6 × 106 0.3 × 106

89.3 8.6 8.6 0.76

8 × 106

(ice content)


Sea snow (average maximum) Sea ice

0.2–0.5 × 106 Dis Discontinous

17 × 106


30 × 106


2-3 × 103 17 × 106

M ax.

18 × 106

2 × 104

M in. M ax.

3 × 106 15 × 106

6 × 104 4 × 104

M in.

8 × 106

2 × 104


Southern Ocean

Arctic Ocea O cean n

Figure 1.14 — Maxi  M aximum mum e extent  xtent  of snow and sea ice during winter  (a-panels) and maximum extent  of snow and sea sea iice ce during duri ng  summe  sum merr (b-p (b-pan ane els) in the the Northern Hemisphere (upper   p  pan ane els) a and nd So South uthe ern Hemisp Hemisphe here re (lower panels) after Untersteiner  (1 (1984 984).). Permafro Permafrost st regions regions are also shown in the Northern Hemisphere summer. The ice limit  was taken to be at concentrations 15 per per cent cent i n the th e Southe Southern rn Hemisphere. [from pages 208 and 209 in Reference no. 5, with pe permiss rmission ion of Springe prin gerVerlag].

1.4.6  The biosphe biosphere re is a co compo mpone nent nt o off the clima climate te syste ystem m tha thatt ha has s a dis distinct tinct role in the


i nterac nteracti tions ons of both the oceans and land l and surface wit with h the atmosphere. atmosphere. Ve Vege getation on the land surface and both plant and animal life in the oceans are all relevant elements of the biosphere component that interact with the atmosphere. Important exchanges between the terrestrial vegetation and the atmosphere are summarized in Figure 1.15. 19



Figure 1.15 — I mpo mportant  exchanges between the atmosphere and terrestrial ecosystem. [from page 174, Reference no. 6, with permission of Cambridge University Un iversity Pres Press] s]..

 A  Atmo tmo s ph ere Sensible Hheat Latent Heat Trace G as es C O2 C H4 N2O . . .

Momentum Radiation Precipitation Deposition NH4 NO3 S O4 O3 . . .

Key Chara cteristi cteristics cs   Leaf Area   Height (roughness)   Albedo   S oil Moisture   Nutrient Status

 Te  Te rres trial Ecos Eco s yst em s Climate conditions of the atmosphere have a direct effect on the type of  terrestrial plant growth at the Earth ’s surface, as summarized in Figure 1.16. The nature of the plant cover in turn feeds back on the atmospheric condition by influencing the sensible and latent energy transfers from a land surface, as well as surface layer turbulence in the atmosphere (through its roughness properties). Furthermore, land vegetation is a significant reservoir for carbon with a total carbon content nearly equal to that in the atmosphere (Figure 1.13). Changes in the amount of land vegetation due, for instance, to forest cutting and burning or simply seasonal changes have a direct impact on the carbon dioxide concentration in the atmosphere. Along with dissolved inorganic carbon and calcium carbonate solids, plant and animal life have key roles in the ocean, in

Figure 1.16 — An example of a  simple cla classifi ssifi cation cation of  vegetation types of the world  based on annual precipitation and mean annual temperature page (W hittake hittaker, r, 1975) . [from page 176, Reference Reference no no.. 6, 6, with permission of Cambridge Un University iversity Press] Press]..




the carbon cycle which influences the concentration of the greenhouse gas, and carbon dioxide in the atmosphere and results in a loss of carbon due to sedime di mentati ntation on of carbon carbonate ates sa att tthe he ocean ocean bott bottom. om.  The inte intera ract ctions ions betwe between the biosphe biosphere re and atmo tmos spheric pheric clima climate te have have produced a record of past climate conditions. Tree rings, fossil patterns, pollen counts in ocean and lake bottom sediments, and coal and oil deposits are records which give us information on past climates. Humans, themselves, are members of the biosphere. Humans alter the biosphere and forestry activities and indirectly by altering the climatedirectly systemby inagricultural which the biosphere exists. It is important to understand these various impacts of human activity in order to understand climate change.  The bios biosphe phere re mus mustt be included in the clima climate te syste ystem m analys analysis is in orde order to understand climate change. It is a component that interacts with other climate system components, and is the component where the effects of climate change will be clearly evident to people.

1.5  The  There re are numerous significa ignificant nt phys physica icall i ntera nteract ctions ions among among the compo compone nents nts of 


the climate system that are relevant to climate change. A brief overview of these components is presented below, they include: radiation energy transfer, heat energy transfer, and biosphere interactions.


Radiation is a primary mechanism for energy transfer in the climate system. At the same time characteristics of the climate system itself have a great impact on the magnitudes of radiative energy transfer. There are two key feedbacks as


described below.  Tempe  Tempera ratur ture e fe fee edb dba ack Albedo feedback

 The inte interre rrela lations tionship hip betwe between te tempe mpera ratur ture e and ra radia diation tion pro provide vides s a neg negative feedback whereby radiation transfer tends to reduce variations in temperature and to stabilize temperature conditions. This situation arises for two reasons. First, the magnitude of radiation emission from substance depends on the (absolute) temperature of the substance raised to the fourth power. Second, radiation emission represents a loss of energy from the substance causing its temperature to decrease. In the climate system the radiation involved is of the infrared (terrestrial) type. Thus increasing temperature will lead to increased radiative radia tive cooli ng. A primary energy source for the climate system is the absorption of solar (visible) radiation. The amount absorbed is dependent on the reflectivity (albedo) properties of the substance. Since there is a large variation of albedo for substances in the climate system, significant exist based amounts of  specific substances. Important feedbacks albedo feedbacks existon forvariations ice cover,incloud cover, and land-surface characteristics. The feedback is positive for the ice cover because an increase in ice cover raises the overall albedo at the Earth’s surface which tends to reduce surface temperature thereby making it possible for the ice cover to i ncrea ncrease se even ven m more. ore. An incre in creas ase e in cloud cove coverr woul d also tend to coo cooll the Ea Earth rth ’s surface temperatures temperatures bas based ed on albedo al bedo e eff ffec ects ts sin since ce cloud clo ud al albedo bedo tends to be hi gher than that of the Earth ’s surface. However, clouds also affect infrared radiation transfer so that the net effect on the Earth ’s surface temperatures may be a warming or a cooling. Overall land surface albedo varies according to land use, the type of plants and ice cover. This provides an important albedo feedback related to human activity, climatic conditions and biosphere cycles.



Sensible and latent heat energy transfers provide for important energy-related transfers between the components of the climate system and involve important feedbacks. Vertical energy transfers between the ocean and atmosphere have already been mentioned. To this must be added the energy transfers between land and atmosphere and between ice and ocean water. The latent heat component arises arise s from the phas ph ase e cha change nge of wate waterr betwee between it its s vapour, vapour, lliq iqui uid, d, and solid ol id form forms s. In many cases the transfer of latent heat energy can be as significant as that of  21



sensible heat. Recall that in the global mean, the latent energy transfer from the Earth to the atmosphere was much larger than the sensible heat transfer (Figure 1.4). The dependence of the equilibrium saturation vapour pressure of water on temperature introduces a significant role of temperature into the latent heat energy feedback. Because of their fluid nature, both the atmosphere and ocean transfer significant amounts of heat energy by horizontal motions. An important feedback between the atmosphere and ocean exists with regard to the latitudinal (poleward) energy for instance, thewould oceanbe poleward heat transport were to change due totransfer. internalIf, conditions, there a change in the oceanic latitudinal temperature variations which would affect the latitudinal temperature variations in the atmosphere. This, in turn, would alter atmospheric circulations and poleward po leward hea heatt transports.



Important two-way feedbacks between the atmosphere and biosphere were disc di scuss usse ed ea earl rlii er in i n Sectio ction n 1.4.6. On one hand, the bio biosphe sphere, re, as as a c ce entr ntral al component in the carbon cycle, is a key determiner of greenhouse gas concentration in the th e atmos atmosphere phere.. On O n the t he other hand, the atmos atmosphere phere,, iin n particular parti cular iits ts tempera temperature ture and precipitation, has a major influence on the biosphere. The biosphere in both the atmosphere and oceans plays a significant role in the carbon cycle which includes the atmospheric carbon dioxide.

1.6  The cir circu culat lations ions in the atmo tmos sphere phere and oc oce ean tra trans nsmit mit c cha hang nge es in one reg region of 




the climate system to broad sectors of the world. This means that many aspects of both natural climate variability and climate change are global in nature. This makes the climate change issue a global one which will require the understanding in g of people everywhere verywhere and the pa partici rticipa pation tion of all countries in dealin dealing g with its impacts. A few examples of this global scope are presented here.

1.6.2  The deple depletion tion of the stra tratos tosphe pheric ric o ozone zone lay laye er in rece recent years ha has s be bee en shown shown to


be due to chemical effects arising from the introduction of chlorofluorocarbons (CFCs) into the atmosphere. The CFCs were primarily used in the manufacture of  refrigeration systems, in plastics blowing agents and in aerosol spray-can propellants. Sources of these gases may have been originally in the industrialized countries (primarily in the Northern Hemisphere); however, the primary effect has been seen in the reduction of the stratospheric ozone concentrations at polar latitudes, particularly in the Southern Hemisphere, with associated impacts on human life in Australia and southern parts of South America. There has also been significant stratospheric ozone reduction in the high latitudes of the Northern Hemisphere. long lifetime of CFCs that the impacts are felt many years after theThe gases were released into means the atmosphere. Thus, overall, thefor impacts of CFC emissions by human activity extend far, both in time and space, from their source points.

1.6.3  The El Ni ño phenomenon in its original definition referred to warmer-than-


normal temperature conditions on the ocean surface off the coast of Peru. In recent times the definition has been expanded to refer to warmer-than-normal conditi condi tion ons s on and near near th the e equator in the eas eastern tern half of th the e Pac Pacif ifii c Ocean. Ocean. Figure 1.17 shows the typical pattern for the sea-surface temperature in the eastern tropical Pacific during El Ni ño. The specific example is for the very strong El Ni ño in 1997-98. This condition ties into an atmospheric oscillation called the Southern Oscillation to cause anomalous conditions in both the ocean and atmosphere in the tropical Pacific and Indian Ocean area. Figure 1.18 shows the patterns of  anomalies in surface atmospheric pressure of the Southern Oscillation which accompanies El Niño. Notice that pressure in the eastern tropical Pacific area is lower than the mean where the sea-surface temperatures are higher than the mean. Figure 1.18 actually shows correlations of surface pressure to that observed in Darwin, Australia, where the pressure is higher than the local mean value during El Ni ño.




Figure 1.17 — Anomalous sea surface  surfac e temp tempe erature rature for D ecemb cembe er  1997. 199 7. C ontour ntour i nte nterval rval is 1 ° C . D ashed ashed conto contours urs i ndica ndi cate te negati negative ve a anoma nomalili es. A nomali nomali es are departures from the adjusted  op opti timum mum interpo i nterpolati lation on climatology (Reynolds and Smith, [from page 24, Climate 1995). Prediction Center, 1997]. Observational data reveals ‘teleconnections’  (significant correlations) between ENSO conditions in the Pacific Ocean areas and variability in many other parts of the world. Correlations have been found not only in atmospheric conditions but also in ocean conditions such as for Indian Ocean sea-surface temperature anomalies as shown for the 1997-98 El Ni ñ o in Figure 1.17. Correlations are quite evident for weather conditions in the western coastal areas and southern region of the United States, the northeastern part of Brazil, and the eastern part of Asia as will be discussed in Chapter 2. Clearly the fluiddynamic processes in the atmosphere and ocean do much to give global-scale perspective to what may appear as a regional phenomenon.

1.6.4  The mons monsoon oon refe refers rs to quasi-sta i-stationa tionary ry cir circu cula lation tion pa patte tterns rns and associat ociate ed


weather that exist on a seasonal basis due to surface temperature contrasts betwee between conti nents and surroundi ng oceans. oceans. M onsoon onsoons s ca cause use large rainfall rain fall amounts over tropical and subtropical regions of Asia and Africa in summertime. Note, for example, the difference in precipitation amounts over south-east Asia between July (summer) and January (winter) as shown earlier in Figure 1.7. This regional rainfall pattern is a well-recognized aspect of a monsoon. However, further examination shows that the Asian monsoon, in particular, has impacts which extend over most of Asia, the Pacific Ocean and down into the Australian and Indi I ndian an Oce O cean an areas areas;; tru truly ly a g glo lobal bal sca scale. le. A schemati chematic c example example of the llow-l ow-le evel vel aspects of Asian monsoon circulation into these areas is shown in Figure 1.19. Variation in conditions in one part of the monsoon region may relate to conditions at quite distant locations. For instance, correlations have been found between monsoon rain intensities over south-east Asia and sea-surface temperatures in the eastern Pacific Ocean south of the equator.

1.6.5  The relat relative ively ly rece recent ma major jor eruptions ruptions of El Chichon in M exico in 1 198 982 2 and M ount


Pinatubo in the Philippines in 1991 serve good examples offrom the global impacts of volcanic eruptions. In both cases the as aerosols and gases the eruptions spread around the world in the latitude bands of the volcanoes within a few

Figure 1.18 — Horizontal distribution of the correlation coe coeff ffii ci cie ent betwee between n an annualnual-me mean an  se  sea-le a-leve vell p pre ressure ssure ano anoma malilie es o ove ver  r  the globe gl obe and th the e corr corre espondin spondingg  pressure  pre ssure ano anoma malilie es in Darwin, D arwin,  A  Austra ustralia lia (12 °   S, 131 ° E) E) as a me measur asure eo off the Southern Oscillation. The map shows that   globa  glo ball shifts o off atmo atmosp sphe heri ri c mass mass take place place duri ng E N SO epi epi sod sode es (adapted from Trenberth and   Shea,  She a, 1987) . A Are reas as with anomalies greater than 0.4 are  stipple  stipp led. d. [from page 422, Reference no. 5, with permi permiss ssio ion n of Springe pri nger-Verlag] r-Verlag].. 23



Figure 1.19 — Asia s  ’s mo monsoo nsoon circulation occurs in conjunction with the seaso seasonal nal shif shiftt in i n the I nter-Trop nter-Tr opii ca call C onvergence onvergence Zone (I TC Z) [Sh [ Sho own as as ITC in the diagram]. (a) In January, a  strong  stro ng high p pre ressure ssure deve develo lop ps o ove ver  r   Asia  A sia and and coo cool, dry contine continenta ntall a air  ir   ge  gene rates th the ethe dryonset winter  winte monsoon. monsoo n.nerate ( b) Wsi th onset of  or  f   summe  sum mer, r, the I TC Z migr migrat ate es northward and draws warm moii st air mo ai r onto the contin contine ent. [from page 179, Lutgens and  Tarb  Ta rbuc uck, k, 19 1995 95,, with with pe permi rmiss ssi on of Pears Pearson on Education Educ ation Publications].

we wee eks a and, nd, eventu ventuall ally y over se seve veral ral mo month nths, s, to most other ot her llatitudes atitudes.. IIn n many parts of the world these aerosols resulted in unusual colorations in the morning and evening skies. The dust from Mount Pinatubo persisting at stratospheric levels resulted in a decrease of solar radiation at the Earth surface and a measurable decrease in global-mean surface temperature of several tenths of a degree Celsius for the following year in the Northern Hemisphere.


Climate has a global nature, but it also has local-scale variability which is very important for its impacts on life. Local variations are caused by topography, differences in ground cover at the Earth ’s surface and organization of weather systems resulting from the atmospheric general circulation in tropical regions,



such suc h as the Inter-Tropical In ter-Tropical C Converge onvergence nce Z Zone one (I (ITC TCZ). Z). M Me ean tempe temperature rature and rainrain fall conditions change markedly with elevation of the land. Variations also exist upstream and downstream of topography with effects such as ‘rain forests’ or  or ‘rain shadows’ wh  whe ere the me mean an rain rainfall fall is gre greate aterr o orr lle ess ss,, respe respectively, ctively, than th an iin n surrounding areas. At the very small scale, farmers know that the slope of the surface and small valleys in fields can noticeably alter growing conditions. Even the areas of  shade and sun around one’s home provide for very small-scale climate variations (microclimates) which affect plant growth. These local variations are significant for defin defining ing one’s personal environment.

1.7.2  The  There re are a wide rang range e of c clima limate te conditions conditions on Ea Earth. It is us useful to c cha hara ract cte erize







them in terms of surface temperature and precipitation because these aspects relate directly to the biosphere and human activities. A classification system that has evolved from the original work of the Russian-born German climatologist, Wladimir K öppen, in the early 20th century is commonly used (K öppen, 1931).  The orig inalincla clas ssifica ification tion has has five ba bas sic cate categ gories ories de denote noted d by first firsafter t (uppe (upthe perr case)original letter the classification code. Subcategories denoted by the letters basic category letter provide details on seasonal variations in temperature and rainfall. Note that temperature criteria refer to monthly mean values. An update by Trewartha and Horn (1980) to this climatic classification is summarized below. Note that it has two additional basic categories. (T (Tropical ropical hum id): Warm enough and sufficient moisture (on an annual basis) for plant growth year round; essentially no winter with no frost in continental areas and mean temperature of coldest month 18°C or greater in maritime areas. (Dry): Plant growth li mit mite ed by mo moisture isture s suppl upply y alon alone e (Steppe and De D esert cl climates imates)) (Hum id m iddleiddle-latit latitude ude with lon g growing growing seas season): on): Sufficient moisture (on an annual basis) for plant growth; monthly-mean temperature equal to or greater than 10°C for at least eight months. (Hum id m iddleiddle-latitude latitude with short gr growing owing season): season): Sufficient moisture (on an annual basis) for plant growth; monthly mean temperature equal to or greater than 10°C for at least four months, but less than eight months. (Boreal subarctic): Very short summer with monthly mean temperature equal to or greater than 10°C for only one to three months.








(Polar): Esse Essenti all ally y no growi growing ng s se eason; ason; ffor or th the e warmest warmest month the th e mean-m n-mont onthl hly y temperature is less than 10°C in the Tundra climate and less than 0°C in the Ice Cap climate. (Highland): Not a type of climate; high elevations are an important factor in climatic clima tic condition conditions s Figure 1.20 shows shows th the e ove overall rall cli climate mate charac characteristics teristics for the land l and areas areas of the world in terms of the classification summarized above together with subcategories. The key in the diagram describes the subcategories.

M ean rainf rainfall all di distributi stributions ons in tropica tropi call are areas as can can h have ave large variabil variabilit ity y over over small small dista di stances nces.. The Th e rain rainfall fall in Afr Africa ica is one e example. xample. M Me ean annual and seasonal asonal rain rain-fall in the Sahel area of Africa can change rapidly over small distances in the north-south direction due to the quasi-stationary and small-scale structure of the ITCZ. Changes in annual rainfall amounts are as large as from roughly 1000 mm at 10°N to 50 mm at 17°N (a distance of roughly 700 km). Similar variations occur in the tropical region of South America. The largest variations are between the western-most portion of the Amazon River basin and the west coast of South America due to the Andes Mountains. As an example for subtropical areas the mean annual rainfall in the IndiaPakistan Pakista n are area a has large large va vari riation ations s due to the topo topogra graphi phi c inf i nflu lue ences of the Ti Tibe betan tan mountains and lower mountain ranges and from the structure and persistence of  the monsoon circulation. Rainfall values are as low as 200 mm in Pakistan and reach 1000 mm in the western part of India only 400 km to the south-east. Three hundred kilometers further down the west coast of India, rainfall values reach 3000 mm. Ocean islands, especially those in the trade-wind areas, can have large variations in mean annual rainfall from one side to another depending on topography.  The Haw Hawa aiian Is Isla lands nds are a good example xample..  Temperatur rature e va varia riations tions d due ue to a altitude ltitude or proximity proximity to oc oce eans ca can be larg large over

 Tempe  Te mpera rature ture

short distances. The nature of agricultural crops in tropical countries can change rapidly with elevation in mountainous areas. This is illustrated in countries such as Honduras on duras a and nd rreg egiions on s such as eastern astern Af Afri rica. ca. Mean wint wi nte erti me temperatur temperatures es in  Junea  June au, Ala Alas ska are 20°C warmer than places 100 km inland.




 .    )    l    l    i    H    w   a   r    G   c    M     m   o   r    f   n   o    i   s   s    i   m   r   e   p    h    t    i   w  ,   r   e   v   o   c   e    d    i   s   n    i  ,    0    8    9    1  ,   n   r   o    H  .    H  .    L    d   n   a   a    h    t   r   a   w   e   r    T  .    T  .    G     m   o   r    f    [

   h    t   r   a   e   e    h    t    f   o   e    t   a   m    i    l    C   —    0    2  .    1   e   r   u   g    i    F 26




2.1  The Ea Earth rth’s climate exhibits natural variability on all time scales. Some of this, for


instance, that has occurred in surface temperature is much larger than anything envisioned due to human impacts. It is a continuing challenge to demonstrate variations that are due to anthropogenic causes rather than natural causes. Variability magnitudes differ widely over the range of time scales. There are a number of time scales for which the variability is markedly larger than it is on slightly slight ly large largerr or smalle mall er ti me s sca cales les.. M any of thes these e local pea peaks ks in magni magnitude tude ca can n be ascribed to identifiable forcing processes. An idealized variance spectrum for the Earth’s s surface urface tempe temperature rature i s pres presented in Figure 2 2.1 .1 with wit h i denti denti fica fi cati tion on of tthe he time scales of the maxima, where time scale is measured by the period of oscillation ti on.. Th The e dail dail y and ann annual ual cycles can can be clea clearl rly y se see en. M any of the narrow peaks relate to o other ther astron astronomi omica call and ge geol ological ogical effec effects ts s such uch as va vari riation ations s in the Earth Earth ’s orbit, continental drift and mountain formation (forcing mechanisms ‘external’ to the climate system). Some of these are discussed in the next section. The broader peaks relate to variability enhancements that include the effects of interactions within the climate system (internal forcing mechanisms). The temperature spectrum, as for other variables in the climate system, is ‘re red d’ meaning that amplitudes of the variations are larger for the longer time-scales.  The natu natura rall v va aria riabilitie bilities s in tthe he ra rang nge e of seasona onall tto o mille millennia nniall ((thre three months months to th thousa ousands nds of years years)) may be considere considered d as the most relevant in o our ur di sc scuss ussiion about cli climate mate change. change. H Howeve owever, e eve ven n the di diurnal urnal temperature vari variabili abili ty iis sa an n im important portant factor in i n climate cli mate change as it iis s aff affe ected by change changes s in gre gree enhouse nh ouse gas conce concent ntratio ration. n. I n tthi his s chapter chapter ba basic sic forcing forci ng mechani mechanisms sms for climate cli mate variabil variabilit ity y are discus discusse sed, coveri cove ring ng both external and i nternal type types. s. Spe Specif cific ic example examples s of variabilit variabil ity y are then then presented.

2.2  The suns unspot pot cycle cycle is a we well-de ll-define fined dv va ariation riation in the sola olarr c condition ondition with a period period


of about 11 years. The three-hundred year record shown in Figure 2.2 shows the regularity in the periodicity of the variation. The amplitude of the peaks varies by a factor of two over the record and the periodicity itself ranges between 10


Astronomical effects Variations in solar radiation emission

Figure 2.1 — I de deali alized zed,,  sc  sche hema matic tic spe spectrum ctrum of  atmospheri c tempe temperrature atur e be betw twee een n 10 -4 and 10 10 yr ada adapt pte ed from  Mi tche tchell ll (19 (1976) 76) . [from page 25, Reference no. 5, with permissio permiss ion n of Springe pri nger-Verlag] r-Verlag].. 27



Figure 2.2 — Annual mean  sunspo  sunsp ot numb numbe ers from from 1700 170 0 to 1991. [from page 288, Reference no. 1, with permission of Academic Press].

and 12 years. Although the presence of a sunspot itself (a relatively darkened area on the sun’s surface) causes a reduction in solar radiation output, there are extra bright regions called faculae, that are found in conjunction with sunspots, that lead to an overall increase of solar radiation.  There  The re i s a dire direct ct relat relations ionship hip betwee tween the numb numbe er of suns unspot pots s and the incre in creas ase e in sol solar ar ra radiati diation. on. At tthe he top of the atmos atmosphere phere the dif differe ference nce in the solar -2

constant minimum and maximum of the order of 1.5  This diffebetween differe rence nce rre esthe ultssunspot in an a ve vera rag ge c cha hang nge e of sola olarr is ra radia diation tion absorb bsorbe ed Wm at the. Earth surface of about 0.2 Wm-2. This number is small, but not negligible, in comparison with the 2.45 Wm-2 estimated as the overall change in greenhouse gas forcing due to human-produced increases since the pre-industrial era.  The va varia riation tion in the suns unspot pot periodic periodicity ity itse itself has als lso o been shown shown to rre elate late to Earth surface mean temperatures, with shorter periods corresponding to warmerr tte warme emperatures mperatures.. An A n overa overall ll review review of solar ol ar e effects ffects on atmospheric clim cl imate ate is presented by Hoyt and Schatten (1997). diurnall and a annua nnuall cyc cycle les s are both both larg large e amplitude mplitude varia variations tions.. The rota rotation tion  The diurna Diurnal and annual cycles of solar of the Earth around its axis leads to the well-pronounced diurnal cycle which is ra radiation diation input i nput stronge strongest st in equatori quatorial al regio regions ns and does not exist at all at the pol pole es where the sun remains low in the sky (or just below the horizon) for the entire day.  The annua nnuall cyc cycle le o off s sola olarr ene energ rgy y input is primarily primarily ca caus use ed by the tilt of the Earth’s axis with respect to the plane of the Earth’s revolution around the sun.  This tilt is at p pre res sent a abou boutt 2 23. 3.5 5° to the normal of the plane and leads to seasonal variations in solar sunbeam zenith angle for all parts of the globe as well as variations in the length of the daylight period. Since the Earth ’s orbit around the sun is elliptical rather than circular, the variation in distance from the sun causes 28



Figure 2.3 — Sche  Schematic matic diagram di agram of the Earth s  ’s elliptica ellipticall o orbit  rbit  about abo ut the sun showing showi ng the th e criti cri tica call paramete parameters rs of e ecc cce entri city  (e), obliquity ( Φ Φ   ),  and lo longitude ngitude of peri peri heli heli on ( Λ ) defi defi ne ned d re relative lative to the vern vernal al eq equi uinox. nox. T he size siz e of  of  the orbit is defined by the  great  gre ate est distanc distance e betwe betwee en the ellipse and its center point, which is called the semi-major axis length, length, ao. T he Earth– sun distance at any time (d), the angle angl e betwee between n tthe he posi posi tion ti on of  Ear Earth th and peri peri heli heli on that we call call the true anomaly (v), and the angle angl e betwee between n tthe he posi posi tion ti on of  Ear Earth th and the vern vernal al equinox equinox ( ω ω   )  are also shown. [from page 303, Reference no. 1, with permission of Academic Press].

additional fluctuations in the amount of solar radiation received on Earth. This distance factor produces a variation of 6 per cent in solar radiation intensity between the July 4 minimum and the January 3 maximum, an annual variation that is largely masked by the axis-tilt effects for daily total solar radiation input.  The Earth Earth is curr curre ently close closest to the s sun un in the Northern Northern Hemis Hemisphe phere re in winter and furthest in summer. Thus, in the Northern Hemisphere this distance variation tends to offset the seasonal variability in solar radiation input. In about 11 000 years the sun wi will l be the closest closest in the North No rthe ern H emisphere summer summer and furthest away in the winter tending to make the Northern Hemisphere summers hotter and winters colder than they are at present.  The annua annuall cycle cycle of da daily ily total total solar olar ene energ rgy y input va varie ries sc cons onside idera rably bly with latitude as shown in Figure 1.2 earlier. The range is most extreme at the poles where there are six months with no energy input. At the equator the range of the annual cycle is very small and a small semi-annual variability is observed. The seasonal shift of the latitude where the sun shines straight down at noon (shown by the dashed line in Figure 1.2) results in significant shifts in the Intertropical Convergence Zone (ITCZ) and its associated weather. As a result, land areas very near to the equator (in parts of tropical Africa, for example) where the ITCZ crosses over twice in the year may have two rainy seasons each year.  Three of the Eart rth h’s orbital parameters have long-term variations that can cause large Variations in orbital parameters of  the Earth

variations in the range of solar energy input over the annual cycle. These are the eccentri ntricity city of the orbit, ti tilt lt of the th e Ea Earth rth’s axis (obliquity) and the positioning of the Earth’s axis (see Figure 2.3). The periods for oscillations in these three parameters are ap approximate proximately ly 10 100 0 000, 000, 41 00 000, 0, and 22 00 000 0 years years re res spective pectively. ly. M Mil ilutin utin M il ila ankovitch was the first person to theorize that these orbital variations could be responsible for climate cli matevariabili variability ty relate related d to ice ages. The positioni positioning ng iinvol nvolve ves s a precession of the Ea Earth rth’s axis that th at leadsto ch change anges in th the es se eason of th the e year when the the Earth is i s closest closest to th the e sun.  This pre prece cession e effe ffect ct may may be des describe cribed d by cha chang nge es in the long longitude itude of pe perihe rihelion lion relative relative to th the e vernal equin quinox. ox. Variations in these three orbital parameters cause significant effects on the amount of solar radiation received on the Earth as a function of season and latitude. It is possible for all three to be in phase and cause variations in seasonal insolation as large as 30 per cent in high latitudes (p. 307, Reference no. 1). Meteors

A far less predictable astronomical forcing effect is due to meteors. The impact of  l arg arge e me meteors teors wi with th the Earth Earth can can cause cause a ma majo jorr shortshort-term term variation in the Earth’s climate due to the production of large amounts of dust and smoke. Adding large amounts of dust and smoke can can gre greatly atly reduce the amount of solar radiatio radiation n that gets to the Earth surface causing surface temperatures to decrease. A current theory for the extinction of dinosaurs is that a giant asteroid hit the Earth about 29



65 million years ago. This introduced so much dust that surface conditions became much darker and much colder; the significant effect lasting for about three years.  Tect ctonic onic effe ffect cts s such uch a as s co contine ntinenta ntall drift and c cha hang nge es in mou mounta ntains ins produc produce e ve very ry

Geological effects  Tect  Tectonics onics

significant changes in climate. Their evaluation over time is important for determining past climates. The time scales associated with these changes are the order of millions of years or greater. Since they are far greater than those relevant for anthropogenic climate change effects, they are not discussed here. Volcanoes

Volcanoes can inject vast amounts of dust and gases into the atmosphere. An individual eruption may affect climate conditions for up to three years. On average newsworthy eruptions occur every 20 years; major eruptions with a significant impact on the global climate will perhaps take place every 100 years or so. (The statistics can be better restated as: in a given year the probabilities of  newsworthy and major eruptions are five and one per cent, respectively.) Volcanoes provide intermittent forcing impacts and are not generally associated with longer-term climate variations. Volcanic products which get into the stratosphere can have a significant impact. Products that remain in the troposphere, such as the dust, are subject to rather rapid re r emoval p proce rocess sse es by gravit gravitation ational al se settl ttlii ng and washout by precipi precipi tation.. On tion O n th the e othe otherr hand sulphate sulphates s forme formed d from the s sulph ulphur ur dioxi de injec in jecte ted d into in to the stratosphere by more severe eruptions can have lasting effects. They are small particles with very small settling speeds. The stratosphere has little up-and-down motion and little precipitation to remove the sulphates. The primary effect of  sulphate aerosols on radiation transfer is to reduce the short-wave solar radiation reaching the Earth ’s surface. This causes the cooling associated with volcanic events.


For variability time-scales of less than a month, the ocean generally has a INTERACTION INTER ACTION OF CLIMATE CLIM ATE damping effect on amplitudes of climate system oscillations. Its heat capacity is SYSTEM YSTEM COM PONENTS large compared with that of other interactive components in the climate system, and horizontal transport effects due to currents are small on these time-scales. Ocean effects  Thus  Thus,, oc oce ean tempe temperatur rature e tempo tempora rall varia variations tions are sma mall, ll, and va varia riability bility of e ene nerg rgy y exchange processes that depend on temperature differences will be primarily a function functi on of atmospheric atmospheric va variation riations s. On a seasonal time-scale, the ocean can increase the synoptic-scale transient fluctuations in the extratropical atmosphere by virtue of its slow temperature variations. variation s. B By y remai remai ning ni ng nea nearl rly y constant iin n tempe temperature rature as the th e continents conti nents cool o off  ff  in winter, surface temperature contrasts at the coasts of continents in the Northern Hemisphere increase. This fosters baroclinic processes in atmospheric disturbances and enhances synoptic-scale variability. Synoptic-scale activity can have a net effect on the general circulation due to correlations among synopticscale flow and temperature components. For longer time scales of variability, processes within the ocean begin to provide mechanisms that allow for two-way interactions with atmospheric processes. These interactions foster significant amplitudes of variability at the interannual and longer time scales. A striking example of this is the ENSO phenomenon which introduces into the climate system a pronounced global-scale variability on an interannual time-scale with periods ranging mostly from two to seven years. ENSO has both ocean and atmosphere components. The primary two-way interactions occur in the tropical area of the Pacific Ocean. The ocean component has variations in tropical sea-surface temperature that are related to vertical motion and changes in the depth of the ocean thermocline. The oceanic vertical motions (upwelling) are associated with horizontal ocean currents forced by atmospheric surface wi atmospheric winds. nds. Th The ea atmospheric tmospheric compon compone ent has va vari riation ations s in convective storm activity in the equatorial region caused by changes in the sea-surface temperature. The convective activity influences the surface atmospheric pressure and the associated surface winds. Processes involving horizontal advection, 30



upwelling, and wave propagation in the ocean are the primary determinants for the time scale of the ENSO cycle. In the climatological mean, the ocean temperature in the western equatorial region of the Pacific is warmer than in the eastern equatorial region. This is accompanied by a rainfall maximum, surface pressure minimum in the western Pacific and surface trade wind flow in the atmosphere from east to west. During El Niño events the temperature in the eastern Pacific is increased which enhances rainfall in the east, reduces surface pressure difference between the east and west, and reduces (or even reverses) the low-level atmospheric flow component from east ast to we west. st. In I n the opposit opposite e phas phase e of th the e os oscil cilll ation, ation , terme termed d ‘La Niña,’ the eastern Pacific sea-surface temperature becomes colder than normal, reducing precipitation in the region, enhancing the surface pressure difference between the east and west, and accelerating the east to west flow component at low levels. It is noteworthy that the atmospheric general circulation in the tropical Pacific area has important east-west variations along with the north-south variations normally considered. The east-west variations are associated with the ‘Walker Circulation’  and the north-south variations are associated with the ‘Hadley Circulation.’  Primary indicators for ENSO are the anomalies in the sea-surface temperature of the th e e eas astern tern tropi cal cal Pac Pacif ific ic O Oce cean an (e (eas astt of the in te ternati rnational onal date li ne) and and averaged atmospheric surface pressure difference between the western and eastern tropical Pacific Ocean regions (see Figure 1.17). The latter has traditionally been represented by the monthly mean pressure differences between Darwin, Australia and Easter Island in the eastern Pacific. Sometimes Tahiti, to the west of Easter Island, is used instead of Easter Island. Both of these are south of the equator. The surface pressure difference also provides a measure of the east to west surface airflow and the stress force that it applies to the ocean surface currents. During El Ni ñ o events the pressure difference between Darwin and Eas Easter ter Islan I sland d decreas decreases es,, th the s sur urface face win wi n d stress decrease decreases s an an d tthe he eastern eastern Pacific sea-surface temperature increases. Observatio Obse rvation n for the ENSO iindi ndica cators tors described above above are are s shown hown in Figure 2.4 for the period from 1949 to 1988. In the Figure it is easy to see the frequency of  occurrence of the El Ni ño and La Niña phases of the ENSO cycle. The major El Ni ño event years are marked with arrows in the pressure difference graphs. Note that the ENSO frequency increased in the 1990s with El Ni ño events in 1991, 1994, and 1997. Earlier reference was made to the global influences of the ENSO cycle in terms of correlated variations that have been observed at great distances (teleconnections) from the tropical Pacific area. Figure 2.5 shows some of these ENSO-related anomalies (for the El Ni ño or warm phase), primarily for precipitation. The sign of the anomaly is shown (wetter, dryer, or warmer) indicating the length of time of duration and the time of year. The plus sign refers to the year after the El Ni ño year.  There  The re are neg negative precipita cipitation tion a anoma nomalies lies in the we wes st Pacific Pacific are rea a and po pos sitive values in the central Pacific as the main precipitation area is displaced eastward. Also, conditions are wetter than normal on the west coast of South America. These wetter conditions and the warmer than normal sea-surface temperatures along the west coast of South America have major impacts on people living there, especially fishermen. The more distant impacts include altered monsoon precipitation in India, altered rainfall in central and southern Africa, reduced rainfall in Australia, reduced rainfall in Central America and the northern part of South America, increased rainfall in the Gulf of Mexico area, and warmer temperatures in Alaska and western Canada. Figure 2.5 also shows the locations of Darwin and Easter Island, locations that have been used to define a pressure gradient indicator for ENSO.  The oce ocean ha has s a ma major jor ro role le in deca decadal dal and and long longe er time time scale les s of va variability riability o of  f  climate. For these time scales, ocean circulations in the thermocline and deep waters over vast regions of the ocean are important factors. There is observational evidence of decadal variability in sea-surface temperatures in the Pacific and 31



Figure 2.4 — Two sets of  monthlymo nthly-me mean an ti me seri seri es of (a) a classi classica call i ndex ndex of the  Southe  So uthern rn Oscil Oscillat lation, ion, i.e., the  pressure  pre ssure differe differenc nce e betwe betwee en Eas Easte ter  r  I sland and D arwin arwi n in mb (afte (after  r  W yrtki , 1982 1 982,, update updated d with da data ta  from Rope Ropelewski, lewski, 1987 1 987;; (b) the zonalCEwind stress over the centr central al  τ  x  P equatori quatorial al Pacifi Pacif i c  region (8 °   S-4° N, N , 160° E-130 E-130 ° W ) in units of Pa (Pa = 10 dyn cm -2 ); and (c) the sea-surface temperature anomalies in the eastern equatorial Pacific region T  sEEP (20°   S-20  S-20° N, N, 180-80° W ) in ° C C.. The upper set of three panels cove coverr the years years 19 1949 49 to 1968, 196 8, the lower set covers 1969 to 1988. T he mo most st iimp mpo ortant El N i ño eve vents nts ( i .e., when warm water  water  covers covers tthe he e enti nti re eastern eastern equa quatori torial al Paci Pacififi c Ocean) Ocean) ac acco cordi rding ng to Rasmusson and  C arpente arpenterr (1 (198 982, 2, up upda date ted) d) are indicated by arrows at the bottom of (a). The thick solid lines i ndicate a 12-month runni running ng mean me an iin n (a) ( a) and 15-mo 15 -month nth weighted means in (b) and (c) (obtaii ne (obta ned d by using a G aussiantype filter with eights 0.012, 0.025, 0.040, 0.061, 0.083, 0.101,, 0.1 0.101 0.117, 17, and 0.122 at the central point). [fr [from om pages pages 424 and 425, Reference no. 5, with permissio permiss ion n of Springe pri nger-Verlag] r-Verlag]..




Figure 2.5 — Schematic  representation of typical ENSOrelated precipitation anomalies overr the gl ove globe obe.. Solid Soli d contours contours enclose relatively dry regions (l (lii ght shading) and dashe dashed  d  co contour ntourss enclose relati relati vely wet  re regi gio ons (hea (heavier vier shadi shadi ng). ng) . T he approx app roxii mate peri peri od o off extreme ext reme co condi nditi tions ons relative to the typical El Ni ño (0) year is also shown for  the various vari ous regions ( adapte adapted d from fr om Rope Rop elewski lewski and Halpe Halpert, rt, 19 1987 87).). [from page 427, Reference no. 5, with permission of Springer-Verlag].

Atlantic Oceans. Oceanic processes that have a decadal time scale include advective effects in the primary central gyre in the north Pacific and subduction processes within the thermocline layer between the middle latitude and tropical regions. It will require analysis with numerical models to sort out the role of these processes as there is onl y a short rec record ord of observation rvations s for subsurfac ubsurface e curre currents nts iin n the ocea ocean. n. C urrents in the de dee ep ocea ocean n laye layers rs belo below w the th e thermocli thermocline ne owe their existance to thermohaline density effects. Observational evidence has been obtained to show that the deep ocean currents have a large scale structure and connect the major ocean basins of the world. Figure 2.6 shows a schematic of this circulation which is referenced as the ‘great ocean conveyor belt.’ The interconnection of the deep ocean and surface waters brings the heat storage of the entire ocean into play and produces century- and millennium-scale variability in sea-surface temperature and the th e atmospheric atmospheric cl clim imate ate.. A key part part of the deep ocean ocean circul circulation ation system system is the downwelling regions where dense water sinks to deep or even bottom layers of the th e oc oce ean in relative relatively ly small are areas as of rapid downward motion in the vicini ty of  Greenland and Antarctica.  The  There re are a num numbe berr of importa important nt in inte tera ract ctions ions of the cryo cryos sphe phere re with the othe otherr

C ryosphere effects effects

components of the climate system that can affect variability characteristics. None of these introduce easily-defined time scales of oscillation, as is the case for the ocean interactions. Ice cover is an indicator for changes in climate. The net melting of mountain glaciers observed since 1850 is a clear example. As earlier discussed, ice cover on the Earth ’s surface has a dramatic feedback effect on climate due to its high albedo. Briefly, an increase in ice cover by virtue of increasing surface albedo causes less solar energy to be absorbed at the Earth ’s

Figure 2.6 — Sche  Schematic matic diagram di agram of the global ‘conveyor belt ’ depicting global thermohaline circulation (after (after B Broe roecke cker, r, 198 1 987) 7).. [from page 580, Reference no. 6, with pe permiss rmission ion of Camb C ambridge ridge Un Uniiversity versity Press] s].. 33



surface which helps to maintain the cool temperatures needed to sustain or increase the ice cover. This positive feedback process is considered to be a factor in the maintenance of previous ice age conditions.  There  The re are seve vera rall ot othe herr intera rac ctive proc processes. The me melting lting of ice over and nea near th the e oceans in th the e polar regions region s can in increa crease th the e supply uppl y of fresh wate waterr to th the e oceans.  This wate waterr will tend to sta tay y at the surface because use its low salt conte content nt te tends nds to ma make ke it less dense than sea water. Sufficient amounts of fresh water at the ocean surface in the polar regions have the potential to reduce or even prevent the occurrence of  strong downward convective currents that supply cold water to the deep waters and maintain the great ocean conveyor belt. Suppression or alteration of the great ocean conveyor belt would have major global, long-term climate impacts. An Anoth othe er interac i nteracti tive ve proce process ss in invol volve ves s the polar pol ar ice caps caps a and nd the oceans. M ost of the ice in the cryosphere is found in the polar ice caps over Antarctica and Greenl Gree nland. and. As thes these e ice are areas as li e on soli solid d ground and are no nott fl oatin oating g in the ocean, ocean, their melting will contribute directly to sea-level changes. It is estimated that if  these two ice fields melted completely, the sea level would rise more than 75 metres. It is also estimated that in the last major ice age (during the Pleistocene about 18 000 yea years rs ag ago) o) sea leve evells were were lower lo wer by about 100 metres. Such change ch anges s in sea level would significantly affect the shape of ocean coastlines and the impacts of ocean bottom topography leading to changes in the basic ocean circulation. Interactions involving the cryosphere have time scales that can range upwards to many thousands of years.  The bios biosphe phere re,, both both on la land nd a and nd in the oce ocean, ha has s a numb numbe er of inte intera ract ctions ions tha thatt

Biosphere Biosphe re iintera nteraction ctionss

can lead to climate variability. We are just beginning to understand some of the aspects that could lead to variability cycles. Biosphere relationships with albedo and greenhouse-gas concentrations provide two important factors for such cli climate mate syste ystem m variabil variabilit ity y. M any iinves nvesti tiga gati tions ons have focuse focused on the iimpac mpacts ts of  human alterations of the terrestrial biosphere. Albedo impacts on vegetation ground cover are shown in Table 1.1. Vegetation can have albedos that are higher or lower than ground cover without vegetation. It has been possible to show examples of the biosphere albedo interactive effects on the climate system through very simple models. The classical example is the ‘Gaia’ model developed by Watson and Lovelock (1983) summarized very nicely on pages 251-253 in Reference no. 1.  The ‘Gaia’ model has h as a worl world d with wi th three th ree regio regions ns covered covered by black daisie daisies s (low albedo), white daisies (high albedo), and Earth bare soil (intermediate albedo), respectively. The growth and death rates of the daisies are prescribed functions of  regional temperature. The temperature in turn is determined from equilibrium conditions for the overall global radiation budget (considering both the solar and terrestrial terre strial radiation radiatio n compon compone ents nt s) wh whii ch also satisfies local energy energy balance for each ach region. The local energy balance for each daisy type includes assumed rates of  energy transfer between the local areas depending on temperature differences.  The mode model de define fines s an equilibrium equilibrium solution in te terms rms of the perc perce enta ntag ge of the are rea a covered by each type of daisy for a prescribed value of solar luminosity. No oscillating solutions are produced in this simple model. Daisy regions exist only for an intermediate range of solar luminosity values. Values too low or too high give conditions which are too cold or too warm for growth. For solar luminosity values at the low end of the range the black daisies predominate. For luminosity values at the high end of the range the white daisies predominate. The albedo of the daisy is a key factor in determining the temperature of the daisy’s growth environment.  The land land-s -surfa urface ce biosphe biosphere re inte intera ract cts s direct directly ly with the gree reenhouse nhouse gases (waterr vapour, carbon di (wate dioxi oxide de a and nd methane) methane) iin n the atmosphere. atmosphere. Wate Waterr iintake ntake and and water vapour supply are part of the terrestrial biosphere life processes, and methane is a product of decay. The marine biosphere interacts with the carbon dioxi dio xide de absorbed in the t he water water (diss (dissol olve ved d inorga in organi nic c ca carbon rbon)) and converts it to soli d carbonates. The carbon dioxide gas can be transferred directly to and from the




atmosphere. The global carbon cycle presented earlier in Figure 1.13 shows components for both the terrestrial and marine biosphere.  Time scale cales s associat ociate ed w with ith biosphe biosphere re inte intera ract ctions ions are short for for re res sponse ponses of  the atmosphere to biosphere changes in comparison to those for the response of  the biosphere to ch change anges in atmospheric con condit ditii ons. The Th e for forme merr iinvo nvolves lves changes changes in albedo and surface energy and moisture transfer rates, whereas the latter involves life cycles of plant life. The terrestrial biosphere response to the annual cycle is clearly evident in the differences between summer and winter and between wet and dry seasons. Changes in climate can cause changes in the types of pl ants g growi rowing ng in a pa parti rticular cular plac pl ace e and th the e gra gradual dual mi migra grati tion on of o f th the e area where a given plant species exists. A good example is the northward advance of the boreall ffores borea orests ts in C anada and S Siberia iberia iin n th the e last 10 000 years follo fol lowin wing g the re rece cent nt ice age. Associated time scales vary from decadal to centennial to even longer. The terrestrial biosphere is also affected by human activity (such as agriculture, forest cutting, and urbanization) which introduces time scales dependent on the rate of  change due to human activity.  The  There re are a numb numbe er of low low-fre -freque quency oscilla oscillations tions in the atmos tmospheric pheric flow which

Internal atmospheric processes

are largely independent of interactions with other components of the climate system or effects of external forcing. These have a variety of oscillation periods and preferred latitudes for their existence, and may or may not have an impact on cli cl i mate mate.. M Many any of them include in clude non nonli li near near proces processe ses.  The stra stratos tosphe pheric ric Quas Quasi-Bie i-Biennial nnial Os Oscilla cillation tion (QB (QBO) O) is a var variab iabili ili ty in the stratospheric flow field most apparent in the upper and middle stratosphere in tropical latitudes. It has oscillations periods ranging from 22 to 34 months and has minimal impacts on tropospheric climate. It is believed to result from the interaction of vertically propagating gravity waves and the mean horizontal flow. Anothe Anoth er osc oscil il lation mos mostt pronoun pronounce ced d in i n th the e tropical region region is a tropos tropospheric pheric intraseasonal oscillation, sometimes referred to as the ‘Maddan-Julian Oscillation.’ This oscillation originates in the African area, develops as it moves eastward over the Indian Ocean, and then diminishes as it reaches the eastern Pacific Ocean. It has a period of oscillation ranging from 30 to 60 days. The oscillation modulates tropical convective activity and is related to break periods in the Indian monsoon. It results from interactions between eastward-moving Kelvin waves in the atmosphere and convective activity. A variability centered in the extratropical troposphere regions results from nonlinear processes in the tropospheric jet streams. Nonlinear effects give rise to ‘index cycles’  and ‘weather regimes’  which involve persistent trough and ridge patterns in the tropospheric jet streams and related modulations in extratropical cyclone activity. The periods of oscillation are generally in the range of 10-20 days.. A notable days not able a aspe spect of thi s va vari riab abii li ty iis s the deve develo lopment pment of a ‘blocking’ pattern where a persistent and anomalous anti-cyclonic flow pattern prevents cyclones and fronts from reaching given regions for a sustained period. This may cause a drought situation to become very severe. Nonlinear aspects of the atmospheric flow such as described for the extratropics above seemingly lead to erratic variability characteristics. A focus on the nonlinear aspects alone is described by mathematical chaos theory. Solutions found by Lorenz (1990) for an idealized system of three components (a zonal jet flow and two superimposed transient eddy components) showed some irregular characteristics that resembled extratropical latitude flow variability. Hi s soluti solutions ons for the t he s stre trength ngth of the zonal fl flow ow force forced d by a s smoothl moothly y varying annual cycle a are re s shown hown in Figure 2 2.7 .7 for a six six-yea -year period. period . The Th e wint winte er seas season on is on the left and right hand sides of the diagram and the summer season is in the middle. The irregular oscillations appear to have some systematic aspects which change as a function of season. The overall nature of the oscillation pattern changes from year to year. In general chaos theory the variability is of ‘fractal ’ nature meaning that it has similar complex structure for any time scale. Such theory may be relevant to describing some aspects of climate variability.




Figure 2.7 — The  T he variatio variati ons of X  (dimensionless variable representing the zonal flow   sp  spe eed) with time, time, t (months) (months) in a 6-year numerical solution of an i de deali ali zed thr t hre ee-compone -component  nt  nonline nonli near ar circulati ci rculation on mode model.l. Each row r ow be begi ns on 1 January, January, and, except except for the th e fifirst, rst, eac ach h rrow  ow  is a continuation of the previous one. [from page 383, Lorenz, 1990, with permission of  M unksga unksgaard]. ard].





For any time scale there are variations in the climate variables such as temperature, precipitation, and severe storms and in related climate system parameters such as sea-surface temperature and sea level. A few examples are shown here to highlight magnitude, range, periodicity, and extremes in the variations. In the examples you will see both systematic and irregular types of variability. It needs to be emphasized that observational information itself has quantitative uncertainty. The proper use of quantitative data requires being aware of their uncertainties and what region and time scale they actually represent. For the geological time scales a schematic for the Earth ’s overall surface temperature levels for the past 100 million years is shown in Figure 2.8. The curve shown for the futu future re (on th the e left left side id e) iis s spe speculati culative ve a and nd shows ho ws a an n early numerical n umerical model estimate for effects where a doubling of CO 2 was the only human impact considered. It is noted that climate change projections for temperature increases may be less important than natural variations over time; however, the rate of change might be larger in anthropogenic climate change compared to any natural rates of change. Pri Primary mary fea features tures of te t empera mperature ture va vari riation ations s in the pas pastt 100, 1 000, 000, 10 000, 000, and 100 000 ye years ars (up to about 1970 1970)) shown in Figure 2.9 reve reveal al iirreg rregul ular ar variations at all these time scales. Note that the region covered and the data source vary from panel to panel. Some of the recent variations relevant to the climate change discussion are identified: the warm period ‘thermal maximum of 1940s covering the 1930s-1950s; the particularly cold period ‘little ice age’ particularly in the 1 18 8th and 19th centuries; and the  Young  ‘Younge er Drya Dryas s cold cold inte interva rvall’ ab  about out 10 000 years ago. The range for temperature variations increases with the time scale which whi ch iis s consiste consistent wi th th the e ‘re red d’ nature of the variance spectrum discussed at the beginn beginnin ing g of tthe he chapter. chapter. For th the e last last 15 000 ye years ars the range is 10°C iin n the middle latitudes in the Northern Hemisphere (4-5°C for the global mean) whereas the temperature range is only 1.5°C for the llas astt 1 000 years years over e eas astern tern Europe.  The de deta tails ils of surfa urface ce globa global-me l-mea an te tempe mpera rature ture varia variations tions for the pe period riod since 1860 (up to 2000), used as a reference for current climate change impact studies,, iis studies s show shown n i n Figure 2.10 2.10.. T The he values are ac actuall tually y deviation deviations s from the 196119611990 199 0 time peri period. od. N Note ote that both yea yearl rly y and smooth smoothe ed de depicti picti ons are used. The Th e level of uncertainty in the values is suggested by the plotted standard error bars and the differences between two estimates, the dashed and solid curves, respectively. Note that the standard error is larger for the earlier years. Values for each hemisphere and the globe all show the general upward trend in temperature. Based on this data it is now estimated that global mean surface temperature has increased about 0.6 degrees C since the beginning of the 20 th century. It is also considered, with very high statistical confidence, that the last decade deca de (19 (1900-1999) 00-1999) has been the the warmes warmestt decade decade of th the e perio period. d. Furthermor Furthermore e,




Figure 2.8 — R  Rough ough schematic  schematic  co compa mpariri son of po possi ssi ble future  gree  gre enhouse nhouse warming warming with estimates of past changes in temperature. Pleistocene glaciali nter-glacial nter-glaci al cycles cycles are more more numerous than shown. The characte charac teriri stic sti c ampli ampli tude of of global global temperature change during  glacial-i nte  glacial-i nterglac rglacial ial cyc cycle less is 3-4 K . N ote that pre-Plei pre-Plei stoce stocene changess are not change not well fi f i xed in magnitude, but their relative warmth i s approxi approxi mately mately correc correct. t.  Maxi mu mum m warmi warmi ng in the C retaceous retaceous is i s based on on esti estimate matess by Barron Bar ron and and ccolle olleague agues. s. T i me i ntervals nterval s i n betwee between h have ave be been  sc  scale aled d acc accordingly (C (Crow rowle leyy, 1989). [from page 671, Reference no. 6, with permission of Cambridge Un University iversity Pres Press s].

Figure 2.9 — G eneral neral tr tre ends iin n global climate fo forr a vari ety of ti me scales. scales. ( a) C hanges hanges i n the 5-yea 5-yearr average surfac surf ace e te tempe mperatures ratures fr from om a abo bout ut 1 187 875 5 to 1970 19 70 averaged averaged from in instrumental strumental records records ove overr the regi regi on 0-8 0-80 0° N (from Mi tche tchell, ll, 196 1963). 3). (b) W i nter nter seve severi ty index index ffo or eastern astern Europ E urope e during the last 1000 100 0 years years up to ab abo out 1970 197 0 ( from Lamb, Lamb, 1 196 969) 9).. ( c) Mi M i d-latitude d-lati tude N orthern He H emisphe mis phere re ai ai r tempe temperature trends trends during duri ng the last 15 00 000 0 years years based based on on changes changes iin n tree li nes nes  [fro  [fr om La Marche Marche (1 (1974 974),), marginal fluctua fluctuations tions in alpi alpi ne and contine continenta ntall gla glacie ciers rs (from D ento nton n and and K arlén, 1973) , and  and   shifts in vege vegetat tation ion pat patte terns rns reco recorde rded d in polle pollen n spe spectra ctra (from va van n de der H Ham amme men n et al. ,  , 1971) 197 1),, (d) No N orthern rthern H He emisphe misphere re air  te tempe mperature rature trends duri ng the last 100 1 00 00 000 0 ye years ars based based on mid-lati mi d-latitude tude sea-surf sea-surfac ace e te temp mpe eratur rature e and and po pollen llen reco records rds and on worldwide worldwi de sea-leve sea-levell re r eco cords. rds. ( e) Fluctuati Fluctuations ons in global global i ce volume duri ng th the e last mi lli on years years as rre eco corde rded d iin n change changess i n i sotopic sotopic co compo mposi siti tion on o off ffossil ossil plankton plank ton in dee deep-sea p-sea core core V 28 28-2 -238 38 ( fr from om Shac Shackl kle eton a and nd Opdyke, Opdyke, 1973 19 73)) . [from page 256, Reference no. 1, with permission of Academic Press]. 37



Figure 2.10 — (a) to (c): Combined annual land-surface air and sea-surface temperature anomalie anomali es ( °°  C) C) from from 1986 to 1999, relative to the 1961 to 1999 mean value temperatures (vertical soli solid d bars) bars) for N orthern H emisphe mis phere, re, Southern Southern

(a) Northern Hemisphere 0.8

   0    9    9    )    1 0.4    C   t   o    (    1   y    6    l   a    9   m   1 0.0   o   o    t   n    A   e   v    i    t Hemisphere, and Globe    l   a−0.4   e respe resp ectively; twi twice ce their standard    r

error (denoted by the thinner  verti verti ca call li nes nes wi th small horizontal hori zontal bars at the th e top top and  bottom, where one end may be  p  partially artially obscure bscured d by the soli solid  d  bars); and time-smoothed curves ca calculate lculated d usi ng op opti timum mum averages and area weighted  averages (solid and dashed lines, re respe spective ctively). ly). I nformati nformati on is from data base basess at the U.K . M et. Office and the Climatic Research Unit at the Hadley Centre, both i n the Unite Uni ted d Ki ngdo ngdom. m. [from

Optimum average (Folland et al., 2001) Area weighted average (adapted from Jones

et al.,



−0.8 1 8 60

1 88 0

1 90 0

1 92 0









Year (b) Southern Hemisphere 0.8    0    9    9    )    1 0.4    C   t   o    (    1   y    6    l   a    9   m   1 0.0   o   o    t   n    A   e   v    i    t   a−0.4    l   e   r

Optimum average (Folland et al., 2001) Area weighted average (adapted from Jones

et al.,



page 114, Reference no. 7]

−0.8 1 8 60

1 88 0

1 90 0

1 92 0



Year (c) Globe 0.8    0    9    9    )    1 0.4    C   t   o    (    1   y    6    l   a    9   m   1 0.0   o   o    t   n    A   e   v    i    t   a    l −0.4   e   r

Optimum average (Folland et al., 2001) Area weighted average (adapted from Jones

et al.,



−0.8 1 8 60

1 88 0

1 90 0

1 92 0




based upon indicators for temperature, it is considered likely that the increase in temperature in the 20th century has been larger than that for any century in the pastt 1 000 ye pas years. ars.




Precipitation variability has quite different features from temperature variability. Figure 2.11 presents precipitation variability for all of the main land areas of the globe except for Antarctica for the past 90 years. Decadal variability is quite evident as it was for temperature. The data suggests systematic variations of landarea precipitation precipitation with latitude with incre in creas ase es in pre precipitation cipitation in the middle- and high-latitudes of the Northern Hemisphere and decreases in the tropics. It is difficult to see correlations between the temperature and precipitation variability shown in Figures. 2.10 a and nd 2.11, res respe pectively. ctively. Also Al so the pre precipi cipitation tation does not have a clear trend. Differences in precipitation variability from one area to another in the tropics and subtropics is shown in Figure 2.12. Note that in northern Africa multi mul tide deca cadal dal variati variations ons are qui quite te evident whe wh ereas reas in the t he region regions s of M Me exico xi co and India higher frequency oscillations are more evident.



Figure 2.11 — C hanges hanges i n land surface  surfac e precip precipii tation tation ave average raged  d  over regions between 55 °   S and  85 ° N. N. Annual precipitation depa departures rtures from the 1961-19 1961 -1990 90  peri  pe ri od are dep depi cted ted by the ho hollo llow  w  bars. The continuous curve is a  smoo  smo othing o off the sam same e data data.. [from page 28, Reference no. 3].

    ) 60    m    m     (    y    l 30    a    m    o    n    a 0    n    o    i    t    a    t    i    p-30    i    c    e    r    P-60






Year 2.3.3


It is important to be aware of the variability in extreme events such as droughts, floods and tropical storms. Drought conditions similar to those experienced in Africa over several years in the 1980s can be devastating in semi-arid areas. Longterm changes in water supply result in significant changes in areas covered by semi-arid and arid conditions with important impacts on civilization. An integrated picture of rainfall and evaporation in certain watershed regions may be provided by lake level information such as for Lake Victoria, Tanzania and the Greatt Sa Grea Salt lt Lake in the wes western tern U Uni nited ted S State tates. s. A 97-ye 97-year ar record record for f or l ake level level i n the Great Salt Lake shows a dramatic drop from 1925 to 1935 at the time of the very dry period in the central and western part of the country and a 4 metre increase during the wet 1982-85 period. As a second example, climatic variations in severe storm activity for the past 100 years up to 1988 are shown for the case of tropical storms and hurricanes in

Figure 2.12 — V  Vari ariati ations ons of of tropical and subtropi subtropi ca call lland-surf and-surfac ace ep prec recii pitati on ano anomalies malies base based d on the ave average rage o off the anoma ano malili es relative relative to 19 1961 61-19 -1990 90 means means from H ulme, ulme, 1 199 991, 1, H ulme et al., 1994, and the Global Historical Climate Network  (G HC N) [Vose [Vose et al., 1992; Eischeid et al., 1995]. Smooth curves are generated from nine-point binomial filters of the annual anomali anomali es. (from page 154, Reference no. 3).




Figure 2.13 — Time series of the annual number of Atlantic  tropical cyclones reaching at least  tropical storm stre str ength ( op ope en bar) and those reaching hurricane  strength  stre ngth (solid bar) bar) for 1886 1886-1988. The average numbers of  tropical storms and hurricanes  pe  per ye year ar are are 8.4 and 4.9 4.9,, respectively (adapted from N eumann umann et al., 19 1981 81,, up upda date ted). d). [from page 448, Reference no. 5, with permission of SpringerVerlag].

the Atlantic ocean area in Figure 2.13. The overall pattern in variations over time is quite different from that for global surface temperature as shown above in Figure 2.10.


Significant variations in sea-surface temperature exist at annual, interannual, decadal and longer time scales which are important for atmospheric climate. Pronounced interannual variability exists in the tropical Pacific sea-surface temperature as part of ENSO described earlier in Chapter 1 (Figures 1.17 and 1.18) and in section (Figure 2.4) above. Oceanic variability at decadal scales has been hard to detect and describe because of the short observational record. Recently decadal variability in sea-surface temperature has been detected in the central and northern Pacific Ocean area. In the north Atlantic the largest oceanic decadal signal detected so far has been found in association with the atmospheric North Atlantic Oscillation (defined by the atmospheric surface pressure difference between Iceland and the Azores or Lisbon). The decadal component compone nt of the th e North A Atlanti tlantic c Oscil Oscil lation correlate correlates s with decadal dal variation variations s in sea-surface temperature, currents, salinity and sea ice in the north Atlantic. The involvement of deep thermohaline currents can link this variability with other parts of the ocean system. Sea level itself is of interest to all shore land areas and ocean island inhabitants. Long-term records for sea level show a general rising trend in the major ocean regions in the range of 1-3 mm/yr along with decadal variations as large as 200 mm (see Figure 2.14).  The late lates st IPCC as ass sessme ment nt (2 (200 001) 1) conclud conclude ed tha thatt the globa loball me mea an s se ea-leve -levell th rise was between 10 and 20 cm in the 20 century and that this was mainly the result of a rise in ocean temperature.


Figure 2.14 — Six long sea level records records from fr om major major world regions: regi ons: Takorad Tako radii (A fri ca), H onolulu nolulu (Pacif (Pac ific), ic), Sy Sydne dneyy ((A A ustralia), ustralia), Bombay (Asia), San Francisco (North America) and Brest  (Europe). Each record has been offset vert vertii ca cally lly for pre prese sentati ntationa onall  p  purp urpo oses. ses. The obse bserve rved d tre trends nds (i n mm/yr) for each record over the  20 th century are, respectively,  3.1,, 1.5,  3.1 1.5 , 0.8, 0.8 , 0.9, 0.9 , 2.0, 2.0 , and 1.3 1.3.. The effect of post-glacial rebound  ( lowe loweriri ng relative sea sea level) level) as  simulate  simula ted d by the Pe Peltier ltier IC E-3 E-3G G model is less than, or of the order  of, 0.5 mm/yr at each site. [from page 367, Reference no. 3]. 40





As discussed in Chapter 1, the primary human activities which cause climate


change are those which influence radiative transfer in the atmosphere and radiation absorption on the Earth ’s surface. Other human activities, such as heating the atmosphere through combustion and changing surface wind flow by deforestation and building construction, are of secondary importance. There are four specific impacts to be considered: increasing greenhouse gas concentrations in the atmosphere, adding aerosols to the atmosphere, changing cloudiness and changing surface conditions. The first two clearly have global effects. There is incomplete understanding for climate impacts from changes in cloudiness due to human hum an activity. activi ty. M More ore obs obse ervational rvatio nal and modell modellin ing g studi studie es a are re needed. ded. Th The ee effect ffect could be large and global, particularly if the cloud changes are related to the aerosols added by human activity. Land surface changes may have large impacts on local climate; but have less impact globally as land covers only 29 per cent of  the earth’s surface.  There  The re are seve vera rall me mea asures ures of the mag magnitude of huma human n impa impact ct.. An overa rall ll measure is accomplished by comparison of current conditions of the atmosphere and the Earth ’s surface with those that existed before the industrial revolution (considered to be before 1750). A measure used for gases and aerosols is the current rate of increase in atmospheric concentration. This is expected to relate dir dire ectly tto o le l evel vel o off poss possibl ible e im impac pactt iin n the t he future. A convenie conveni ent way w ay to de desc scri ribe be and compare the overall impact of human-caused changes in greenhouse gases or aerosols on radiation exchanges is by their ‘radiative forcing.’  Radiati Radiative ve forcin forcing g here is de defi fined ned as the change in ave averag rage e net total radiatio radiation n in the planetary radiation budget at the top of the troposphere due to changes in solar and and iinf nfrare rared d radiatio radiation n wi with th fix f ixe ed verti vertica call structure for temperature temperature.. Radiative forcing can be used to describe the impacts of both anthropogenic and natural changes change s in the physical sys system. tem. A positi positive ve radiative forci forcing ng (i (increa ncrease se in net downward radiation) tends to cause average warming of the Earth ’s surface and a negative radiative forcing, average cooling. This definition was made by the IPCC in 1995. As stated in that report: “For a range of mechanisms there appears to be a similar relationship between global mean radiative forcing and global mean temperature change. However, the applicability of global mean radiative forcing to me m echani chanisms sms such such as change changes s in ozone ozon e or tro troposphe pospheri ric c ae aerosol rosol (concentrations (concentration s), which are spatially very inhomogeneous, is unclear.” [p. 16, IPCC, 1995].

3.2  The natur natura al atmo tmos sphere phere co conta ntains ins gre ree enhouse nhouse gases (p (prima rimarily rily wate waterr va vapou pourr,


carbon dioxide, ozone, nitrous oxide, and methane) which have a major impact on determining temperatures in the atmosphere and at the Earth’s surface. Human activity has provided additional sources for these and other gases that have greenhouse-gas characteristics. The result is an enhanced greenhouse effect which is expected to force increased temperature at the Earth ’s surface and in the lower atmosphere. All greenhouse gases have sources and sinks which may include chemical conversions in the atmosphere. The effect of human activity on concentration levels in the atmosphere depends on the cumulative amounts added by human activity and the strength of the sinks. For each greenhouse gas constituent one can define an ‘adjustment time’ which describes the rate of reduction of a concentration enhancement. For instance, if an unusual event were to suddenly increase carbon dioxide concentration in the atmosphere by 100 parts per million by volume (ppmv), the adjustment time may be defined as the time it takes to reduce the concentration enhancement to 1/e of its initial value (where e is the base of natural logarithms 41




or about 2.71) or to roughly 37 per cent of its initial value, namely 37 ppmv. This definition assumes that the rate of depletion of the gas concentration is proportional to the concentration. Sometimes the adjustment time is equated to the ‘lifetime’  of the constituent. The lifetime is defined as the total content of  the constituent divided by the current rate of removal. The lifetime definition is used for stationary input of a tracer into the atmosphere.

3.2.1  Thre  Three e of the five natur natura al greenhous nhouse e gas cons constitue tituents nts are underg undergoing oing we well-doc ll-docuu-


Table 3.1 (below) — Average annual budget budget of C O 2  p  pe erturba rturbations tions fo forr 1980 198 0 to 1989. 198 9. Flux es and re r eservoi servoirr change changess of  ca carbon rbon are expresse expressed d iin n G tC /yr, err rror or li mits mi ts correspond correspond to an an estimated 90 per cent confidence interval. [from page 79, Reference no. 3].

mented increases in concentration due to human activity: carbon dioxide, methane,, and n methane nit itrous rous oxi oxide de.. Th The e other two n natura aturall ll y occurring occurri ng gre gree enh nhouse ouse ga gase ses, water vapour and ozone, are less directly linked to human activity. The role of  ozone is small. The relevant concentration for ozone includes both tropospheric and stratospheric stratospheric compone compon ents and n not ot just j ust the stratosphe stratospheri ric c conce concentr ntration ation values for which there has been an observed decrease in polar regions. Water vapour is the dominant greenhouse gas and accounts for about 75 per cent of the overall greenhouse effect. However, its modification results primarily from changes in evaporation rates from the oceans due to temperature and surface wind variability and not from human activity. Carbon dioxide enhancement is the most important human impact on the greenhouse gases. This enhancement accounts for more than half of the total enhanced greenhouse effects due to human activity. A continuous observational record rec ord for f or atmospheric carbon di oxide oxi de has bee been obtain obtaine ed since sin ce 195 1958 8 at Mauna M auna Loa, Hawaii and since 1957 at the South Pole. Concentrations prior to that time have been measured from air trapped in ice in Antarctica. Both of these measurements are considered to be representative of global mean concentration. Carbon dioxide concentration is rather uniform in the atmosphere.  The chang hange e in conc once entr ntra atio tion n values from from thepr pre e-indus -industr tria iall pe period riod to abou outt 1989 reveals that the rate of increa i ncrease seitself is iincrea ncreasing sing wi with th tim time e asshown in i n Figure Fi gure 3.1. Th The e figure also shows estimates for the production of carbon dioxide from fossil fuel combustion and cement manufacturing since 1860. On a linear scale the shape of the production curve would be simi imilar lar to th tha at shown ffor or carbon carbon dioxi dioxide de concentration. The rate of producti production on has in incre creas ased by ne n early a factor o off 10 sin since ce 1900.  The ove overa rall ll h hum uma an imp impa act on carb rbon on dio dioxid xide e conc once entr ntra ation has has been to inc incre rease it from roughly 278 ppmv to 365 ppmv (the value in 1998), an increase of 87 ppmv or almost 31 per cent. The current level is estimated to be higher than at any time since the las l ast interglac i nterglacial ial warmin warming g ab about out 120 000 years years ago. At the current rrate ate of in incre creas ase e, the present (1998) carbon dioxide concentration will double in less than 100 years. Estimates for the budget for the carbon introduced into the atmosphere by human activity from 1980 to 1989 are presented in Table 3.1. Shown are

CO 2 sources (1) (1) Em Emissio ions ns fr from om fo fos ssil fue fuel com combu bus sti tion on and c ce ement prod produc ucti tion on (2) Net emissions from changes in tropical land-use (3) Total anthropogenic emissions = (1) + (2)

IPCC 1992† IPCC 1994* Estimates for 1980s budget 

IPCC 1995

5.5 5.5 ±0 ±0.5 .5∆ 1.6 ± 1.0∆ 7.1 ± 1.1

5.5 ± 0.5 1.6 ± 1.0 7.1 ± 1.1

5.5 ± 0.5§ 1.6 ± 1.0§ 7.1 ± 1.1

3.4 ± 0.2∆ 2.0 ± 0.8∆ not accounted for

3.4 ± 0.2 2.0 ± 0.8 0.5 ± 0.5

3.3 ± 0.2§ 2.0 ± 0.8§ 0.5 ± 0.5§

1.7 ± 1.4

1.4 ± 1.5

1.3 ± 1.5

Partitioni Parti tioning ng amo ngst reservoir reservoirss (4) Storage in the atmosphere (5) Ocean uptake (6) Uptake by Northern Hemisphere forest regrowth (7) Other terrestrial sinks =(3)–((4)+(5)+(6)) (CO2 fertilization, nitrogen fertilization, climatic effects)

† Values given in IPCC (1990, 1992). * Values given in IPCC (1994). ∆ Values used in the carbon cycle models for the calculations presented in IPCC (1994). § Values used in the carbon cycle models for the calculations presented here.




Figure 3.1 — (  (a) a) G lob lobal al annual annual emissi ons o off C O 2 from fossil fossil fuel fuel combustion and cement  manufacturing expressed in GtC   ye  year  ar -1 (R otty and Marland, 1986; Marland Marland,, 1989). The average rate of increase in emissions between 1960 and  1910 and between 1950 and  19 1970 70 i s about about 4 pe perr cent cent per per ye year. ar. N ote: ote: the o ordi rdinate nate scale scale is logarithmic. (b) Atmospheric  C O 2 concentration for the past   250 years years as indicat indicate ed by  measurem me asureme ents i n ai r trapped trapped iin n ice from Siple Station, Antarctica (squares, Neftel et al., 1985; Friedli et al. ,  , 1986) 198 6),, and by  di direct rect atmospheri atmospheri c measur measure eme ments nts at Mauna Loa, Loa, H awai awai i  (triangles, Keeling et al. al.,, 1990). N ote: ote: ppm ppmvv means part pe per  r  million by volume. [from Watson et al. , 1990, from page 323 in Reference no. 1, with permission of Academic Press].

apportionments for the sources and reservoir dispositions. Fossil fuel combustion and cement manufacturing account for more than 75 per cent of the total carbon dioxide input. The remainder (less than 25 per cent) is due to net effects of deforestation and other land-clearing operations mainly in tropical areas. Of the total carbon dioxide input into the atmosphere, almost half remains in the atmosphere, an estimated 30 per cent goes into the ocean, seven per cent into Northern Hemisphere forest regrowth, and the remainder is assumed to go into other parts of the terrestrial biosphere. Note that the remainder component in the budget estimates has been reduced between the 1992 and 1995 IPCC reports. The 2001 IPCC report (Reference no. 7) shows that for the decade 1990-1999 the land ecosystem uptake of CO2 is larger and is now nearly as large as the ocean uptake.  The estima timate tes s for tthe he land and oc oce ean we were re 1. 1.4 4 and 1.7 1.7 GtC/yr res respe pect ctive ively ly..  Two  Tw o othe otherr ‘natural’ greenhouse gases that are increasing due to human activity are methane and nitrous oxide. As shown in Table 3.2, their relative increases between pre-industrial times and 1990 are significant especially for methane which has more than doubled in concentration since preindustrial times. Even though the absolute values of the concentration of both these gases are less than one per cent of that of carbon dioxide, the radiative effects of the human-caused increases in these two gases together amount to nearly 40 per cent of that of  carbon dioxide.




Table Tab le 3.2 — C haracte haracteriri sti cs of some key greenhouse greenhouse gases gases that are influenc inf luence ed by human activi tie ti es a. Parameter Pre-industrial atmos Pre-industrial atmospheric pheric concentra concentration tion (1750–1800) Current atmospheric concentration (1990)c Current rate of annual atmospheric accumulation Atmospheric Atmosphe ric lifetimed (years)

CO 2

CH 4



N 2O  

280 ppmvb

0.8 ppmv



288 ppbvb

353 ppmv 1.8 ppmv

1.72 ppmv 0.015 ppmv

280 pptvb 9.5 pptv

484 pptv 17 pptv

310 ppbv 0.8 ppbv

(0.5%) (50–200)

(0.9%) 10

(4%) 65

(4%) 130

(0.25%) 150

(from page 320, Reference no. 1). a. Ozone has not been included in the table becau because se of a lack of precise data. b. ppmv =p =parts arts per million by volume volume;; ppbv = =parts parts per billion b by y volume; volume; pptv =parts per trilli trillion on by volume. volume. c. The current (1990) concentra concentration tion have have been estimated from an extrapolation of measur sureme ements nts reported for earlier ye years ars, assuming that the recent trends remained approximately constant. d. For e eac ach h gas in the ta table ble (ex (exce cept pt CO 2), the ‘lifetime’ is defined here as the ratio of the atmospheric content to the total rate of  removal. This time scale also characterizes the rate of adjustment of the atmosphereric concentrations if the emission rates are changed abruptly. Carbon dioxide is a special case since it has no real sinks, but is merely circulated between various reservoirs (atmosphere, (atmos phere, ocean, biot biota). a). The ‘lifetime’ of CO2 given in the table is a rough indication of the time it would take for the CO 2 concentration to adjust to changes in the emissions.

3.2.2  The introduc introduction tion of new gre gree enhous nhouse e gases b by y human human ac activity tivity ha has s had had a not notice ice--


able impact on the gre gree enh nhouse ouse effe ff ect, accounti accounting ng fo forr over 10 per ce cent nt of the total human impac im pactt on the gree greenhouse nh ouse effect. M any di differe fferent nt ga gase ses have bee been in introtroduced. They are mainly halocarbons (compounds containing carbon together with haloge halogens ns s such uch as chlori ne, fluori ne, bromine, a and nd i odine odin e) such such as chlorof chlorofluluorocarbons (CFCs) and hydrofluorocarbons (HFCs). As stated before, these compounds compoun ds we were re ma manuf nufac actured tured for use in refrigeration refrigeration uni ts, foamin foaming ga age gents nt s and and solvents.  The ha haloca locarb rbons ons are strong gre ree enhouse nhouse gases and the their ir life lifetime times s are poss possibly longer than those of the long-lived natural greenhouse gases. In addition, ultravio violet let radiati radiation on fro from m the th e sun can disas disassoc socii ate th the ei r mole mol ecules and relea release se chlori chl orine ne and bromine which will interact with and cause the destruction of stratospheric ozone. The potential harmful effects were so obvious that international agreements have already been put in place to reduce the production of these gases, e.g. the 1987 1987 Mon Montre treal al protocol as pa part rt of the Vienna C Convention onvention to Protec Protectt the th e Ozone Layer and its subsequent amendments. A nearly-complete inventory of human impacts on the greenhouse gases is presented in Table 3.3 (pages 46–47). This list includes a large variety of halocarbons and hydrocarbons. The entire list is presented to emphasize that there are a large number of ga gase ses human iimpacts mpacts and to give s some ome te termi rminol nol ogy for the hal halooand hydro-carbons. Note the numbers used to identify the halocarbon types in the first column. The list does not contain data for ozone itself because much of  the change in ozone is not due directly to emissions from human activity but from subseque quent nt chemical reactio reactions ns in the atmosphere atmosphere.. Th The e list iide denti nti fie fi es: the th e life lif etime of each gas; concentration change since the pre-industrial era; current growth rate r ate;; and radiative radiati ve forcin forcing g due to change ch anges sin since ce the th e pre-i pre-industri ndustrial al era. era. N Note ote that the concentrations of some of the gases are so small that no concentration is listed. Several already show reductions in concentration through compliance with internation in ternational al ag agree reements. ments. Key information for the relative importance of the gases listed in Table 3.3 for greenhouse effects is given by the radiative forcing shown in the very last column of tthe he ta table. ble. Wi With th thi this s info informa rmation tion alone lon e one can can easil ily y sort out th the es small mall subset of gases important for the enhancement of the greenhouse effect. Numbers are omitted for radiative forcing if they are less than .001 Wm-2. As stated before, the radiative forcing defines the change in the amount of radiative energy transfer at the tropopause due to the changes in concentrations of the gas in the atmosphere, in this case since the pre-industrial era. A positive sign means




increased energy in the downward direction which would lead to higher temperatures at the ground on average.  The gases CFC-11 CFC-11 and CFC-1 CFC-12 2 are res responsible ponsible for most most of the radiat radiative ive forcing due to the new greenhouse gases. They account for a total of 0.20 Wm -2 or 8 per cent cent of the t he total radiative forci forcing ng (+2.45 Wm-2) caused by human enhancement of the greenhouse gases. Note from Table 3.2 that the percentage rate of increase of these two halocarbons was much larger than for the naturally occurring occurri ng gre gree enh nhouse ouse ga gase ses be before fore the th e Mont Mo ntrea real Protocol Proto col was enacted. nacted. A All ll of the halocarbons together result in a radiative forcing of about 0.27 Wm -2 or 11 per cent of the total radiative forcing in the early 1990s.



Fossil fuel combustion and biomass burning are the primary sources of aerosols due to human h uman activi ty. Th The ese sources produce both soot (parti (particulate culate black ca carbon rbon aerosols), gaseous sulphur dioxide and nitrogen oxides. The latter two are partially transformed by chemical processes into sulphate and nitrate aerosols. Additional sources for human-produced aerosols include dust from changes in land use. All of these aerosol products remain primarily in the troposphere (unlike those produced by major volcanic eruptions) and thus are subject to rapid removal by precipitation and settling processes. Table 3.4 summarizes source strength, atmospheric loading, the radiative mass extinction coefficient and radiative optical depth for the main aerosol constituents for both natural- and human-produced components.  The ma mas ss extinct xtinction ion co coe efficie fficient nt is a me mea asure of the effect ffective ivene nes ss of radia radiative tive absorption and scattering per unit mass of aerosol, and the optical depth is a measure of the overall reduction in radiation due to absorption and scattering while passing through the atmosphere. The optical depth depends on both the mass extinction coefficient and the total mass of the aerosol in a vertical column of atmosphere. The optical depth gives a measure of the overall direct radiative forcing on the atmosphere due to the aerosol.  The human human pro produc duction tion of sulpha ulphate te ae aerosols rosols can can be measured dire direct ctly ly by sulphur dioxide emissions from fossil fuel combustion. The dominant source regio reg ions ns are i n the t he Nor Northern thern H emi misphe sphere. re. This Thi s emission emission has increa increas sed dramatically over the past 130 years as shown in Figure 3.2. Concerns about other impacts such as acid rain have led to a levelling off of production rates in recent years in some areas such as Europe and north America.


Regional Anthropogenic Flux Asia Europe N America USSR


Figure 3.2 — N  Natural atural and and fossi fossill  fuell cco  fue ombustio mbustion n so sourc urce es o off SO 2 in the Northern Hemisphere (after  D i gnon a and nd Hame Hamee ed, 19 1989 89;; 1992). [from page 106, Reference no. 3].

   )   r   y    /    S50   g    T    (   s   n40   o    i   s   s    i   m30   e   r   u    h   p20    l   u    S 10

Northern Hemispheric Anthropogenic Flux

Northern Hemispheric Natural Flux

0 1860











S p e cie s

Life t im e Ye a r

U n ce rt .

Co n c e n t ra t io n ( p p b v) 1992 p re -in d .

Cu rre n t g ro wt h p p b v/ yr

Ra d ia t ive fo rcin g -2 Wm / p p b v Wm -2

Natural and anthropo ge nical nically ly influ influenced enced gases carbon dioxide methane nitrous oxide methyl chloride

CO2 [email protected] N2O CH3Cl

variable 12.2 120 1.5

methyl bromide chloroform methylene chloride carbon monoxide


1.2 0.51 0.46 0.25

25% 25% 32% 300% 200%

356 000 1714 311 ~0.6

278 000 700 275 ~0.6

1 600 8 0.8 ~0

0.010 ~0.012 ~0.030 50-150


~0 ~0 ~0 ~0

1.8 × 10-5 3.7 × 10-4 3.7 × 10-3

1.56 0.47 0.14 0 0

0.017 0.03 $

Gases phased out b efor eforee 2 000 under the Mo ntr ntreal eal Protocol Protocol and its amendme nts CFC-11 CFC-2 CFC-113 CFC-114 CFC-115 carbon tetrachloride methyl chloroform halon-1211 halon-1301 halon-2402


50 102 85 300 1700 42 4.9 20 65 20



0.268 0.503 0.082 0.020 <0.01 0.132 0.135# 0.007 0.003 0.0007

0 0 0 0 0 0 0 0 0 0

+0.000** +0.007** 0.000**

-0.0005** -0.010** .00015 .0002

0.22 0.28 0.28 0.32 0.26 0.10 0.05

0.06 0.14 0.02 0.007 <0.003 0.01 0.007


Chlorinated hydrocarbons controlled by the Montreal Protocol and its amendments HCFC-22 HCFC-123 HCFC-124 HCFC-141b HCFC-142b HCFC-225ca HCFC-225cb


12.1 1.4 6.1 9.4 18.4 2.1 6.2

20% 25% 25% 25% 25% 35% 35%


0.002 0.006

0 0 0 0 0 0 0


0.001** 0.001**

0.19 0.18 0.19 0.14 0.18 0.24 0.28


0.64 0.10 0.23 0.24 0.31 0.39 0.46 0.32

0.002 0.007

Perfluorinated compounds sulphu phur he hexafluorid ide e perfluoromethane perfluoroethane perfluoropropane perfluorobutane perfluoropentane perfluorohexane pe perrfluor luoroc ocy yclo lobu buta tane

SF6 CF4 C2F6 C3F8 C4F10 C5F12 C6F14 c-C4F8

3200 50000 10000 2600 2600 4100 3200 3200

0.032 0.070 0.004

0 0 0 0 0 0 0 0

+0.0002 +0.0012

Anthropogenic greenhouse gases not regulated (proposed or in use) HFC-23 HFC-32 HFC-41 HFC-43-10mee HFC-125 HFC-134 HFC-134a HFC-143 HFC-143a HFC-152a HFC-227ea HFC-236fa


264 5.6 3.7 17.1 32.6 10.6 14.6 3.8 48.3 1.5 36.5 209

45% 25%

HFC-245ca HFOC-125e HFOC-134e trif triflu luor oroi oiod odom ome eth tha ane


6.6 82 8 <0.005

35% 300% 300%

35% 35% 200% 20% 50% 35% 25% 20% 50%

0.18 0.11 0.02 0.35 0.20 0.18 0.17 0.11 0.14 0.11 0.26 0.24 0.20





Table 3. 3.3 3 — Li  L i fet fetii mes mes for  radiati radi ative vely ly active acti ve gase gasess and  halocarbons. [from pages 92 and 93, Reference no. 3].

Table Tab le 3.4 — Source strength, atmospheriri c burde atmosphe burden, n, exti nction ncti on efficiency and optical depth due to the various types of aerosol  p  particle articless (after (after IPC I PCC C , 199 1994; 4;  Andreae  Andre ae,, 199 1995; 5; and C ook and  W ilson, ilson, 1996). [from page 104

Notes:  This  Th is ta tabl ble e lists only only the the di dire rect ct ra radia diativ tive fo forc rcin ing g fro from m emi mitte tted ga gases. The The in indir dire ect eff ffe ects cts du due e to subsequent changes in atmospheric chemistry, notably ozone (see below), are not included. The Wm-2 column refers to the radiative forcing since the pre-industrial, and the Wm-2/ppbv column is accurate only for small changes about the current atmospheric composition (see Section 2.4 of Reference no. 3 and IPCC, 1994). In particular, CO 2, CH4 and N 2O concentration changes since pre-industrial times are too large to assume linearity; the formulae reported in IPCC (1990) are used to evaluate their total contribution. A blank entry indicates that a value is not available. Uncertainties for many lifetimes have not been evaluated. The concentration of some anthropogenic gases are small and difficult to measure. The pre-industrial concentrations of some gases with natural sources are difficult to determine. Radiative forcings are only given for those gases with values greater than 0.001 Wm-2. @ M ethane increa increase ses sa are re ca calculated lculated to cause increases ses in tropospheric ozone a and nd stratos stratos-pheric H2O; these indirect effects, about 25 per cent of the direct effect, are not included in the radiative forcings given here. $ The dir direc ectt radiative forcing forcing due to cha changes nges in the CO concen concentra tration tion is unlik unlikely ely to reach a few hundredths of a Wm-2. The direct radiative forcing is hard to quantify. ** Gases Gases with rapidly changing growth rates ove overr the pa past st deca decade, de, re recent cent tre trends nds since 1992 are reported. # The change nge in CH3CCl3 concentration is due to the recalibration of the absolute standards used to measure this gas. Stratospheric ozone depletion due to halocarbons is about -2 per cent (globally) over the period 1979 to 1990 with half as much again occurring both immediately before and since; the total radiative forcing is thus now about –0.1 Wm-2. Tropospheric ozone appears to have increased since the 19th Century over the northern mid-latitudes where few observational records are available; if over the entire Northern Hemisphere, tropospheric ozone increased from 25 ppb to 50 ppb at present, then the radiative forcing is about +0.4 Wm-2.

in Reference no. 3].

 Sourc  Source e

Flu luxx ( Tg /yr)

Globa Globall mean mean column burden (mg m-2 )

Mass extinc xtinctio tion n coefficient (hydrate (hydrated) d) (m 2 g -1 )

Glob Global al mean mean optical depth





1300 33 50

7.0 0.7 1.1

0.4 2.0 2.0

0.003 0.001 0.002

102 55 22

2.8 2.1 0.5

5.1 5.1 2.0

0.014 0.011 0.001

Primary Industrial dust, etc. Soot (elemental carbon) from fossil fuels Soot from biomass combustion

100 8 5

2.1 0.2 0.1

2.0 10.0 10.0

0.004 0.002 0.001

Secondary Sulphates from SO4 as (NH4)2SO4 Biomass burning Nitrates from NOx

140 80 36

3.8 3.4 0.8

5.1 5.1 2.0

0.019 0.017 0.002

Natural Primary Soil dust (mineral aeorosol) Sea salt Volcanic dust Biological debris Secondary Sulphates from natural precursors, as (NH4)2SO4) Organic matter from biogenic VOC Nitrates from NOx






RADIATIV RADIATIVE E IMPACTS Di Dire rect ct impa imp acts

Aerosols have a pronounced effect on solar radiation transfers in the atmosphere and a small small er one o ne on terre terres stri trial al radiation radi ation transfers transfers in the atmosphere. atmosphere. They caus cause e scattering and absorption of solar radiation with the result that less solar energy gets to the Earth ’s surface, more is absorbed in the atmosphere, and more is backscattered to space. Only for the case of soot (black carbons) is there such strong absorption of solar radiation in the lower atmosphere that solar radiation backscattered to space is reduced, producing a positive radiative forcing. Aerosols also enhance absorption and emission of terrestrial radiation and thus slightly enhance the greenhouse effect. However, the effect on solar radiation is generally the dominating factor so that the net aerosol effect is estimated to be opposite to the greenhouse effect. Since the lifetime of tropospheric aerosols is short, their concentration is not uniform over the globe; they are concentrated over the land areas. A model simulation for lo lowe werr tro troposphe pospheric ric sulph ulphate ateaerosol concentrations in in the 1980s 1980s is shown in Figure 3.3. (In Europe, the c current urrent values are much lower l ower than iin n th the e 1980s 1980s). ). Figure 3.3 highl hi ghligh ights ts the areas whe where re anoth nothe er envi environ ronmental mental effect of sulphate sulph ates s hasoccurred, the acidification of rainwater leading to ‘acid rain.’ Acid rain became a serious problem in place places s such uch as eas eastern tern north n orth America, Euro Europe pe a and nd eas astern tern Asia. A sia. The Th e s sho hort rt l ife if eti time me of  aerosols implies that if the human sources were turned off, the human-produced aerosol concentration concentrati ons s woul would d decre decreas ase very fas fast, t, with wi th a time time scal cale of about a week. In summary the ove overall rall direct rad radiative iative forcin forcing g from h human-produce uman-produced ae aerosols rosols is -2 negative, estimated as -0.5 Wm . Estimates (from numerical models and not obse obs ervation rvations) s) for the radiative forcing forci ng fo forr th the e threemain components, sulphate ae aerosols, -2 -2 fossil fuel soot, and biomass burning are, -0.4 Wm , +0.1 Wm W m and -0.2 Wm-2, respectively. For comparison, the estimated peak values for radiative forcing of  stratospheric aerosols put there by volcanic activity at times during the last 130 years have nearly nearly tte en ti time mes s the current current neg negative ative radiative forci forcing ng due to ttropospheric ropospheric ae aerosols rosols from human h uman activi activity, ty, a as ss shown hown in Figure 3.4. The las lastt major pe peak ak of roughly roughl y -3.5 Wm-2 wasin 199 1991 1 due to the e erupti ruption on of M t. Pi Pinatubo. natubo. Howe Howeve ver, r, volcano-produced volcano-produced n ne egati tive ve forcing forci ng lastsonly onl y for a few few ye years arsand it occurs only onl y spora sporadicall dically y. O Ove verr multi mul ti-ye -year ar pe peri riods, ods, the mean volcanic forcing is comparable to anthropogenic aerosol.

Indirect impacts

Assessment of the overall impact of human aerosol enhancement is complicated by transformations that occur due to chemical processes with other gases and with the water droplets in clouds. These transformations result in ‘indirect’ impacts. The impact on clouds and their optical properties is discussed below.


Since aerosol particles act as cloud condensation nuclei and freezing nuclei, they


have important impacts on the radiative properties of clouds. Aerosols may change chang e the d drop-s rop-size ize distribution in clouds which can can influence in fluence the optical properties. The magnitude of this effect depends on the properties of the aerosols present which can vary from region to region. Nucleation leading to an increase

Figure 3.3 — Model simulation of  the annual mean sulphate (SO4 2- ) co conce ncentr ntrati ation on at 900 90 0 mb. C ontours ntours are sho shown wn at 25, 25 , 5 50, 0, 100, 250, 500, 1000, and 2500  pptv  pp tv.. C once ncentrati ntrati ons o ove verr east easte ern North America and eastern E urope excee exceede ded d natur natural al ( nonanthrop anthr opoge ogeni ni c) le l evels by a factor  of 10. [from Langner and Rodhe, 1991, from page 328, Reference no. 1, with permission of Academic Press].




Figure 3.4 — Vari ation o off glo gl obal mean mean vi visi sible ble opti optical cal depth, depth, and the co conseq nseque uent nt rradiati adiati ve forcing forci ng (W m-2 ) resulting sulting fro from m  strato  stra tosp sphe heric ric aeroso rosols ls of volc volca anic  oririgin gin fro f rom m 1850 to 1993, 1993 , as esti estimated mated by Sata et al. ,  , 1993. The radiative forcing has been estimated using the simple relationship given in Lacis et al. al.,, 19 1992 92,, where the radiative radiati ve forcing is is -30 times the visible optical depth. [adapted from page 116, Reference no. 3]. in cloudiness and/or the prolonging of existing clouds are other possible influences of aerosols on clouds. Effects are believed to be quite different between ice clouds and water clouds. The increase in high-level cloudiness due to jet aircraft contrails is a specific example of increase in cloudiness due to the introduction of  aerosols and water vapour. Additional observations and theoretical studies are needed before the changes in the optical properties of clouds can be quantified. At the present time, the IPCC assessment judges that, overall, the altered properties of clouds due to aerosols produced by human activity will give a negative radiative forcing on the global scale. Estimates of magnitudes range as high as -1.50 Wm -2 which would be much llarg arge er th than an the dire dir ect aerosol iimpact mpact and and coul d off offse set the th e positi positive ve forci forcing ng attributable to greenhouse gases by nearly a half.





It is recognized that humans have greatly altered the Earth ’s land surface by settlements, deforestation and agriculture. These changes have resulted in both increases and decreases in local albedo values. Replacing forests with crops that are pres prese ent only on ly part of the year year woul d likely l ikely in incre creas ase ea all bedo bedo effects effects.. U Urbanizati rbanization on could either increase or decrease the albedo depending on the reduction in trees and on the materials used for roofs and streets.  Thes  The se a albe lbedo do chang change es cle clea arly have have a role in the local ene nerg rgy y balanc balance ea and nd contribute to microscale climate changes such as the heat ‘islands’  over urban regions. However, other factors such as evaporation, precipitation and wind flow changes could have large impacts on local or even regional climate change. On a global basis the change in the radiative properties of land surfaces is not considered to be a major factor in the energy balance. Figure 3.5 provides an overall summary of the relative importance of the many ways in which human activity can alter radiative energy transfers in the atmosphere. Confidence in the quantitative values is indicated at the bottom. The depiction includes ozone effects and, for comparison, solar variability effects due to the sun spot cycle. Note that increases in tropospheric ozone are estimated to give a radi radiativ ative e fo forci rcing ng valu value e of +0.4 W Wm m-2 which is equal and opposite to the forcing due to tropospheric sulphate increases. The cloud change impacts due to aerosols are shown in terms of the range of estimated values of radiative forcing. Further understanding is needed to establish a mid-range estimate as is done for all the other radiative forcing components. No entry is made for volcanic effects, which are quite variable in time, or for the impacts of changes in surface albedo as these these e effects ffects a are re considere considered d iimportant mportant pri primaril marily y on a re region gional al sca scale. le. M uch research is needed to reduce these uncertainties especially with respect to the indirect aerosol effect on clouds.




The global mean radiative forcing of the climate system for the year 2000, relative to 1750 3

   )   e 2   r    t   e   m   e   r   g   n   a    i   u   m   q   r 1   s   a   r    W   e   p   s    t    t   a 0    W    (   g   n    i   g   c   n   r    i    l   o −1    f   o   o   e    C   v    i    t   a    i    d   a −2    R

Halocarbons N O




Tropospheric ozone

Black carbon from fossil fuel burning

Mineral Dust



Contrails Cirrus Stratospheric ozone


Organic  carbon Biomass burning Sulphate from fossil fuel burning

  Medium Medium Med Medium ium


Very Low

Very Low

Landuse (albedo)   only

Aerosol indirect effect

Very Very Low Low

Very Low

Very Low

Very Very Low Low

Level of Scientific Understanding Understandi ng Figure 3.5 — G lobal, lobal, annual me mean radi radi ative forci forci ngs (W ( W m-2 ) due to a numbe numberr o off ag age ents for the peri od from pre pre-i -indu ndustrial strial (1750) to present (late 1990s; ~2000). The height of the rectangular bar denotes a central or best-estimate value while its absence denotes no best estimate is possible. The vertical lines capped with horizontal lines indicate an estimate of the uncertai unce rtai nty range r ange,, for th the e mo most st part gui de ded d by the sprea spread d iin n the publi publi shed shed values values of the forcin forcing. g. T he unce uncertai nty rrange ange  sp  spe ecified if ied he here re has has no statisti statistica call b bas asii s. At the botto bottom m a ‘level of scientific understanding’ index is accorded to each forcing. T hi hiss re r eprese presents nts a subj subje ective judgeme judgement about about the reliabili reliabi li ty of the forcing esti mate, mate, i nvolving nvolvin g ffacto actors rs such as the assumptions assumptions necessary to evaluate the forcing, the degree of knowledge of the physical/chemical mechanisms determining the forcing, and  the uncertai uncertai nti es surrounding sur rounding the quanti quanti tati tative ve esti esti mate of the forcing. forci ng. T he wellwell-mi mixed xed gree greenhouse nhouse ga gase sess are group gr oupe ed toge togethe ther  r  into a single rectangular bar with the individual mean contributions due to CO 2 , CH  C H 4 , N  2O and halocarbons. The sign of the effects due to mineral dust is itself an uncertainty. The forcing due to stratospheric aerosols from volcanic eruptions is highly  vari able ove overr the t he pe peri od and is not co consi nsi de dered red for for tthi hiss plot. A ll the forci forci ngs shown s hown have dist distii nct spatial and seasonal seasonal features features  such  suc h that that the globa global, l, annua annual-me l-means ans do no nott yyield ield a comple mplete te picture of the rad radiative iative perturba perturbations. tions. The Theyy a are re intende intended d to give a  fi rst-o rst-orde rderr pe perspect rspectii ve on a globa global, l, ann annua ual-me l-mean an scale scale and and canno annot b be e re read adii ly emp emplo loye yed d to obtain btain the tota totall re resp spo onse to  forcii ngs. It is  forc is e emp mphas hasized ized that that th the e positi ve and and ne negat gative ive forci forci ngs cann canno ot b be e adde added d up and viewe viewed d a pri ori as p prov rovii ding offse offsets ts i n terms of th the e comple complete te globa globall cli mate i mpact. mpact. [from page 8, Reference no. 7].





4.1  This cha chapte pter ha has s two bas basic p purp urpos ose es. The firs firstt is to summa ummarize brie briefly fly s some ome of the the


fundamentals for n fundamentals nume umerical rical modelli modelling ng of cli clima mate te.. This Th is provides li lite terac racy y for te termirminology used in the discussion of climate models and some understanding of the numerical modelling approach including its strengths and weaknesses. The second is to present assessments of current climate model performance. This is very very i mportant as most proje proj ections ctio ns for futur future e cli cli mate change are base based on numerical model solutions. M athe athematical matical simul simulation ation of tthe he climate cli mate system is ess esse enti al for unders understandi tanding ng climate change outcomes from human impacts such as those described in the last chapterr and for making chapte makin g e estimate stimates s of futur future e cli climate mate c change hange.. The T he cli mate system system is very complex with many interacting components and a large number of variables and processes that need to be represented. A quantified description of the system and its changes requires obtaining solutions to a large number of governing mathemati mathema tica call equation quations. s.  The phys physica icall comp compone onent nt s sys yste tems ms are continuous continuous in space pace and the scale cales so of  f  phenomena that exist range from global to molecular. The governing equations include partial differential and integral relationships. Many processes are not fully described in the equations such as turbulence in the atmosphere and ocean, precipitation growth in clouds, cumulus convection, radiation transfer in and around clouds, and CO 2 transfer processes in heterogeneous biosphere canopies.  The co comp mple lexity xity o off the sys yste tem mp pre reclud clude es obta obtainin ining g genera nerall s solutions olutions by ana nalytic lytica al mathemati mathema tica call me metho thods. ds. In order to obtain solutions, it is necessary to make approximations in both the governing equations and the numerical methods. Approximations in the equations are guided by understanding the relative importance of various processes represented in the equations. Computer capacity limits the spatial resolution available for the physical system in the numerical model. A climate model must represent the entire globe, and the resolution limitation means that there is a lower limit on the size of scales that can be explicitly represented in a model. Current computer capacities limit overall atmospheric resolution to the order of  100 km in the horizontal for full climate system models. The effects of all smaller scales must be parameterized, i.e. represented in terms of conditions which are resolved by the model.  The most most effe ffect ctive ive c climat limate e mod mode elli ng work utilize utili zes s a hiera hierarchy rchy of mod mode els including those which are simplified more than required by the computer system. The simpler models make it easier to isolate physical processes in the climate system and to give a general indication for overall impacts of climate change forcing. The complete models provide the best quantitative estimates of  climate change on a detailed regional basis. Simple, one-dimensional or volumeintegrated models of the atmosphere with prescribed ocean, cryosphere, biosphere, and land surface specifications, or with an interactive ocean using simple diffusion, radiation and convection, and energy balance relationships are important. They provide useful first indications of atmospheric changes due to human enhancements of radiatively-active constituent concentrations in the atmosphere. The model hierarchy also includes models that represent only a part of the climate system to give more details on that part. Examples are regional models which use information from the global models as external forcing conditions. I t iis se ess sse enti al th that at numerical cl clim imate ate models be cal cal ibrated by compa compari ring ng solutions with observational information. This should be done for as wide a range of  variables in as many locations as possible. A numerical model may give a reasonable solution for surface atmospheric temperature, sea-surface temperature, or




sea-ice coverage while at the same time its solution for precipitation over a given area can be very poor. Obtaining and analysing the relevant observational information is a considerable undertaking. Paleoclimatologists have made great progress in meeting the challenge to make analyses of conditions from geological evidence for comparison with model predictions of past climates. For current-day analyses, observation rvational al data from fr om many di differe fferent nt sources are are me merge rged d to toge gether wi th in internal ternal consistency provided by numerical model solutions. This process, termed data assimilation, is used by many operational weather prediction groups in the world today. As a result, the observational information used to compare with the model prediction solutions may not be fully independent of the model.  The ba bas sic mod mode elling lli ng stra trate teg gy for clima climate te chang change e prediction diction ha has s the following key components. The first step is to understand the quality of model performance based on comparison of model solutions with observations of existing conditions for a variety of situations. The second step is to obtain model solutions where anticipated future forcing of the climate system due to human activity is introduced into the model. The final step is to interpret, evaluate, and establish the level of uncertainty for the model predictions based on the understanding of  model pe perfo rformance rmance..

4.2  The equations use used tto od de efine the clima climate te mode model are are thos those e associat ociate ed w with ith ea each of 



the five components of the climate system plus additional relationships for the interactions between the components. Needless to say, the list of governing equations is extensive. Formulation of basic equations is still under way particularly for the cryosphere, biosphere and land-surface components. M any of th the e equation quations s for the atmos atmospheric pheric and oceani oceanic c components have been published in textbooks including the basic references listed in the introduction ti on to the th ese le lecture cture notes. notes. The Th e g governi overning ng equation equations s incl include ude those whi ch des describe cribe mass continuity, Newton ’s laws laws of motion for a fluid fl uid (wi (with th th the e ve vertical rtical component equation replaced by the hydrostatic relationship), the equation of state, first law of thermodynamics, radiation transfer and, for the atmosphere, the hydrological cycle. The equation of state for the atmosphere is that for a compressible gas including water vapour effects. The equation of state for the ocean is that for an incompre in compres ssible ibl e flui fluid d includi i ncluding ng sa sali lini nity ty effects effects.. Equations for chemistry must be included with the atmospheric equations. Chemistry is very important as it describes the concentrations, life cycles, and interactions involving the greenhouse gases and aerosols, key forcing agents of  the climate system. An example of the important areas and processes involving chemistry relationships in the climate system for water vapour is shown in Figure 4.1. This figure outlines the many chemical and physical interactions in the climate system. No attempt is made to summarize here the governing equations for all the chemical processes. Equations for the land surface component in the climate system include much more detail for variations in surface composition than are used in atmospheric weather weather predicti prediction on models. Terres Terrestri trial al biosphe bi osphere re condi conditi tion ons s are are close closely ly ti tie ed to the overall land surface condition. The low heat capacity of the surface means that small changes in albedo can have large impacts on surface temperature. Thus, governing equations are needed for ground cover canopy types and coverages, and for the resultant albedo. Equations to describe details for hydrological processes are essential both for the condition of the surface biosphere and for evaporation rates into the atmosphere. Key parameters for discussion of the Earth’s surface biosphere properties are the ‘resistances’ (resistance to transfer) for water vapour and carbon dioxide. Comprehensive data on land cover types and the details of vertical structure in the terrestrial biosphere are still needed to calibrate a full set of equations for the land surface. A land surface model must represent and quantify an intricate array of  processes to define the key surface parameters (albedo, roughness length and soil moisture) which can lead to large-scale climate system feedbacks. There are numerous physical interactions related to each of these quantities from which it




Figure 4.1 — Sche  Schematic matic diagram di agram of the physical and chemical interactions involving water  vapour vap our that might mi ght be included i n a comprehensive global climate mode mo del.l. T he key physi physi cal climate cli mate  param  pa rame eters ters a are re sho shown wn in bo boxe xes. s.  Atmo  A tmosp sphe heri ri c che chemical mical sp spe ecie ciess a are re enclosed in elipses. Chemical mechanisms that may involve mechanisms i nvolve waterr vapour wate vapour are divi de ded d iinto nto classes, classe s, i de denti nti fi ed at the bottom bottom of the figure, fi gure, and the pathways pathways and modes of interactions are indicated by arrows. ‘Photo’ encompasses those chemical processes driven by   so  solar lar radiatio radiation n or or involving involving the reaction of species produced when ambient air is exposed to solar  ultraviolett radiation; ultraviole ‘hetero’ refers to t o cchemical hemical  p  proc roce esse ssess o occ ccurri urring ng in aqu aque eous  solutions  solutions,, principally principally in clo cloud  ud  droplets, or on particle surfaces,  parti  pa rti cularly ularly soli solid d aero aeroso sols ls and and i ce crystals; ‘anthropo’ to emissions and   p  pro roce cesse ssess a asso ssociat ciate ed mainly mainly with human huma n activiti activi tie es; ‘bio’ refers to processes related to the assimilation and respiration of atmospheric constituents by  living organisms; and   geo ‘geo’ refers to chemical processes occurring on surfaces or in media at the interface between the atmosphere and the oceans and  land. [from page 213, Re Reference ference no no.. 6, wi with th permission of Cambridge University Un iversity Pres Press] s]..

is possible to argue both positive and negative feedbacks. It remains a challenge to develop a land surface model that can resolve quantitatively the interaction impacts. Representation of marine biochemical processes is important to define the gas exchanges with the atmosphere for the radiatively-active gases such as carbon dioxide, nitrous oxide, and dimethyl sulphide. Geological data document variations in the marine biosphere associated with carbon dioxide variations that have accompanied large climate changes in the past. There is a wide range of processes, including biosphere interactions, associated with the carbon cycle in the oceans. Four forms of carbon are involved: gaseous carbon dioxide, its dissolved counterpart (Dissolved Inorganic Carbon, DIC), solid organic compounds and solid inorganic compounds (carbonates). Ocean temperature and motions, and solar radiation radiatio n penetrating below th the e oce ocean surface surface a are re import important ant factors in the oce oceanic anic carbon cycle.  The cr cryos yosphe phere re ha has s seve vera rall compo compone nents nts tha thatt must must be re repre presented nted by geoph ophysica ysicall gove governi rni ng equa equati tions. ons. For glac glacii ers and ice i ce domes on land, there a are re the equations for motion that can be simplified because acceleration terms can be neglected. The motion of glaciers and ice fields is an important factor in determining coverage and total mass content for ice. Hydrodynamic equations are needed to describe the motion of sea ice as it responds to ocean currents, surrounding ice and atmospheric winds. There is an important instability hypothesis (positive feedback mechanism) for the west Antarctic ice sheet that could occur with an increase in sea-surface temperature. In this hypothesis, warming temperatures increase the slippage of ice into the ocean which raises sea level thus incre in creas asii ng furth furthe er the s sli li ppag ppage e of ice i ce in into to th the e ocean. ocean. I t will wi ll be a chall chall enge to repre repre-sent this with deterministic equations.  The linkage linkage between the oc oce ean and atmos tmosphere phere is central ntral for the climate climate model. It is necessary to accurately represent sensible and latent energy transports. The sensible energy transport depends on turbulent boundary layer processes in both the ocean and atmosphere. Latent energy (water vapour) transport depends on the boundary layer processes only in the atmosphere. In addition, atmospheric forcing of ocean currents must be represented. This requires proper specification of wind stress at the surface — another aspect of the atmospheric boundary layer. Radiation transfer must take into account that solar radiation penetrates into the ocean, thereby distributing the heating effect into




the upper layer of the ocean. The sensible and latent energy transport specifications involve approximations because of parameterization for turbulence in the boundary bounda ry layer layer.. Be Beca cause use of such approximation approxi mations s in both the t he a atmosphere tmosphere and ocean, special attention must be given to make sure that the vertical energy transports are the same on both sides of the oceanic-atmospheric interface.

4.2.2  The last last se sect ction ion ga gave ve a brie brieff ove overvie rview w of the larg large numb numbe er of ma mathe thema matica ticall relarela-


tionships needed for a complete climate model. It was noted that some of the


relationships have yet to be developed. In the equations there are numerous processes, such as turbulence, that cannot be described because of limitations in the numerical model resolution. These processes are described as subgrid-scale processes. Parameterization is the representation of the effects of the subgrid-scale processes on the model-resolved conditions. It is based on the model variables and specified proportionality constants.  The be bes st spa spatia tiall re res solution atta attaine ined d in c curr urre ent clima climate te mode models is on the ord orde er of 100 km in the horizon horizontal tal direc direction tion and up to 40 lay laye ers in the th e ve vertical dire di rection ction in both the ocean and atmospheric components. This means that many phenomena which have important influences on the climate system cannot be fully resolved explicitly and must be parameterized. Examples of these include: Radiation (both solar and terrestrial); Cumulus Cumul us c convec onvection tion,, includi i ncluding ng thun thunde ders rstorms torms;; Oceanic convection; Surface layer (including plant canopies) momentum, water vapour and heat transfers; Planetary boundary layer momentum, water vapour and heat transfers;  Turb  Tu rbule ulence nce in the fre free e atmo tmos sphere phere and oce ocean; Precipitation processes; Atmospheric gravity waves over mountains and in the stratosphere; Weather fronts; Oceanic jet streams; Sea-ice thickness changes; and, M arin arine e biosphere processe processes. s. Improvement of parameterization is an essential part of improving climate models. Parameterization schemes may be interrelated so that changing one scheme may require simultaneously changing another to get overall improvement in the model. An example is the interconnection between cumulus convection parameterization and planetary boundary layer parameterization. Changes in parameterization can have significant effects on the simulated climate.

• • • • • • • • • • • •

4.2.3  The ma mathe thema matics tics require required d for s solving olving the the equations quations for the curre current nt com compre prehe henn-


sive climate system models are quite complicated in practice. Thus, many scientists as well as other persons do not have a working knowledge of such detail details s and must re rely ly up upon on spe speciali ciali sts. Much Mu ch of the th e modell modellin ing gc compl omple exity xi ty arises arises from dealing with the atmospheric and oceanic components of the climate system. As noted before, the atmospheric and oceanic general circulation equations tion s include a la large rge number number of nonli non linea near, r, pa partial rtial diffe diff erenti rential al equa equation tions s includi including ng integrals and do not have exact analytical solutions. Different numerical approximation approaches have been used to represent the partial differential equations as a finite set of algebraic equations that can be handled by the computer. These approaches include fi nite nite-differe -differenc nce e, sp  spe ectral ctral and  fi nite nite-e -ele leme ment  nt metho methods. ds. Bas Basiic vari variables ables of th the e syste system m such as pre press ssure, ure, tempe temperatur rature e, wind, water vapour and radiation are prescribed by a discrete and finite set of  numbers in the spatial and temporal domain of the climate system. These numbers represent values of the variables in different ways. In the finite-difference method these number are grid point values for basic variables of the system. In the spectral and finite-element methods the numbers are the amplitudes (transform values) of specified continuous functions which when added together describe the spatial variations of the basic variables. For each method of 




representation appropriate algebraic expressions are derived for the spatial and temporal partial derivatives and integral functions in the governing equations.  The ‘resolution ’ of the model is an indicator of the number of grid point positions or specified continuous functions that are used in the model. Resolution increases as the number of the positions or functions used is increased. It is expected that the accuracy of the solution will be improved as the resolution is increased. ni te-diff -diffe ere rence nce method is th  The fi nite the e eas easiest iest to unders un derstand tand and to u use se.. Take, Take, for f or example, the algebraic expression used for the first derivative. The first derivative (gradient) for a variable f in the x-d x-direc irection tion at pos positi iti on x may be expressed as the difference difference in value be betwee tween f at the grid point poin t position on th the e plus side side of x (position  x +1) +1) and f at the grid point poin t position on the th e minus min us side of x (position x -1) -1) divided by the dista di stance nce be betwee tween th the ese two poi points. nts. For an iint nte egral gral over a g giive ven n spati tial al range the fini fi nite te differe difference nce expressio ion n coul could d be asummatio tion no off products products of all f grid point values within with in this thi s ra range nge,, eac each h mul multipl tiplied ied by the increme increment nt of dis di stance for which wh ich that value is representative. There are many choices for the finite-difference formulation depending on the accuracy desired in the approximation.  spe ectral method is more commonly used in Of the other two methods, the sp climate models. The concept of this approximation and definition of its resolution are illustrated in Figure 4.2. The thick line with right-angle bends, an example of a physical variable functional relationship, is represented by sine and cosin cos ine e fun functio ctions ns of appropri appropriate ate ampli amplitudes tudes (transform values) values) so that the functi function on obtained by adding them together (represented by thin lines) is as close as possible to the original function (thick line). Results are improved if one increases the resolution by increasing the number (K) of the sine and cosine functions added together. Figure 4.2 shows results using 1, 2, 3, and 5 functions, respectively. I f the squa square re functi on in Figure 4.2 we were re considered considered repre repres sentative ntati ve of a sharp mountain on earth, note that the spectral approximation will give regions where the ground level actually is lower than anywhere in the original representation.

Figure 4.2 — Spectral ap appro proxi xi mati matio ons (thi ( thin n li ne nes) s) to the top-ha top-hatt function (thi ck li ne nes) s) for   se  seve veral ral dif diffe fere rent nt wav wave e numbe number  r  truncations. [from page 296, Reference no. 6, with permission of Cambridge U ni nive versity rsity Pres Press] s]..




In all fairness, it should be noted that the finite difference method will also have an obvious deficiency in this case. Namely, at each of the positions 0 and 0.5 on the x-axis in Figure 4.2 only one value can be used for a grid point value so that the vertical lines must be replaced by (steeply) sloping lines.  The ma mathe thema matics tics for the solution of tthe he spe pect ctra rall me method thod equations quations are ve very ry complex. Originally it was not even possible to use them for accurate representation of nonlinear or local phenomena because of extensive computer requirements. However, new mathematical procedures such as the ‘fast Fourier transform’ and the combination of the finite-difference method with the spectral method have made it possible to incorporate the spectral method into the comprehensive comprehe nsive cl climate imate models. Th The e ‘fast Fourier transform’ requires a grid point array with grid point spacing specified according to the number of spectral components in the model. This grid is called the transform, Gaussian, or equivalent grid. Many climate models incorporate a mixture of the finite difference and spectral formulations. In these cases the resolution may be described in terms of  either the number of spectral components or the spacing of grid points in the equivalent grid.  There  The re a are re a number number of ma mathe thema matica ticall propertie rties s of the nume numerica ricall model model equations that must be considered to produce a satisfactory climate model. Nume Num erica ri call in stabili ty iis s the one feature feature that ne n eeds to be avoided at at all cost. cost. Th is insta in stabil bil ity it y ca can n resul resultt iin n very rapid and unrea unreali li sti tic c in incre creas ase es in magni magni tudes of the th e model variables which render the solution meaningless. This instability arises from the th e mathem mathematica aticall formulation itse itself lf.. M ethods to pre preve vent nt it include in clude decre decreas asin ing g the size of th the e time s steps teps,, adopti adopting ng fi ni te diffe diff erence equation formulations that have special energy-conserving properties, and adding smoothing properties to the equation system to reduce amplitudes of the solution components most likely to be unstable. It is desirable to have mathematical formulations so that the model solutions have similar properties to those of the partial differential equations. A basic feature is the constancy of total mass and total energy expected if the only physical process operating is transport (advection due to fluid motion) within the model domain. Also, it is known that transport due to a uniform flow field will move another dependent variable feature with the velocity of the uniform flow and without distortion of the structure (shape and peak magnitude value) of the othe oth er depe dependent ndent variable. Num Nume eri rica call model errors with wit h respe respect ct to thes these e two characteristics are referred to as ‘phase’ (position) and ‘amplitude’ errors.



Electronic computing machines which included stored-programme technology came into being in the late 1940s. This was followed by a rapid growth in the capability of single-processor machines starting in the early 1950s and still continuin contin uing g toda today y. Application Appl ication to wea weather ther prediction prediction wa was s one motiva motivatin ting g fa factor ctor in this development. The first operational numerical weather prediction model began operation in 1955. Speed of single-processor machines, one measure of  computer power, has increased by many orders of magnitude from the first electronic machines to the present time. Figure 4.3 shows the increase in electronic computer speeds from the mid-1950s to 1990. The pace of increase in computer speed has increased even more since 1990 and current (1998) single-processor machines have reached sustained speeds as high as 600 million operations per second (600 megaflops) [Hammond, 1998, private communication]. Advances in computer power continued in the 1980s with the development of parallel-processor vector machines wherein single-processor machines were combined to work on parts of the same problem simultaneously. This interconnection capability has made it possible to achieve computer system speeds as high as 20 billion operations per second by 1998. Projects are now under way in the United States and Japan to increase computer system peak speeds to 32 trillion operations per second (32 teraflops) by the year 2004 [private communication H. Grassl,1998 Grass l,1998 with wit h strate trategy gy docume document nt for ECM W F 1999-2008 1999-2008]. ]. At the same same time communications and networking capabilities have led to systems that may be physically separate but can combine to give large computer capability.




Figure 4.3 — Tren  Trend d in si ngle  p  proc roce essor ssor cco omputa mputationa tionall  performa  pe rformanc nce e up to 1990 199 0 (after  (after  Worlton, 1987, personal communication). [from page 288 , Reference no. 6, with permission of Cambridge Un University iversity Pres Press] s]..

General circulation modelling became an important use of computer power by 1960. 1960. Expansion of such such models to cl climate imate model model s a and nd the subse subsequent growth of climate modelling work has continued the pressure to expand computer power even further.




Both simple and comprehensive numerical climate models have been used extensively in climate change research. Simple climate models based on (oceanic) Upwelling Diffusion and Energy Balance (UD/EB) have played a key role in sensitivity studies for climate change. These UD/EB models represent the atmosphere and oce ocean an by se several veral llarge arge domai domains ns res respectively. pectively. M ean temperatures are foun found d which balance the energy transfers between the domains for a prescribed radiative forcing in the atmosphere. Energy transfers within the ocean domains are represented by simple upwelling and diffusion processes. Descriptions of the UD/EB model used in climate change studies appear in publications by Wigley and Raper (1987, 1992). It has been possible to get solutions for global mean temperatures in the UD/EB model that correspond to comprehensive climate model results by adjusting the structure and parameter values in the UD/EB model. Since the UD/EB models have much smaller computer requirements than the comprehensive




models, it has been possible to perform a large number of sensitivity experiments to quanti qu antify fy th the e range range in global tempe temperature rature c change hanges s due to unce un certain rtainti tie es in radiative forcing effects and future concentrations of aerosols and greenhouse gases.  Thes  The se res results have have be bee en comp compa ara rable ble to the re res sults for globa globall me mea an tempe tempera ratur ture es from the comprehensive climate models. Comprehensive climate models containing representation of both the ocean and atmosphere have been developed by over two dozen research groups worldwide for climate change research and assessments. These models differ in details of model formulation including resolution and parameterization. The representation of the ocean is comprehensive including currents, temperature and salinity distributions, sea ice, turbulent mixing processes, radiation transfer and bottom topography. Systematic efforts have been made to compare model simulations with current and past observed conditions and with each other. Such model intercomparisons help us to isolate and understand the impact of different model approaches and to document me nt the range range of un unce certainty rtainty in cli clima mate te mode modell simula imul ations. tio ns.  Three  Thre e kinds of mode modell eva evalua luation tion ha have ve bee been ma made de.. The first first is an e eva valua luation tion of the overall full climate model where the components are interactive. The second approach is to examine individual components of the climate model. The thi third rd iis s to look at the s se ensiti nsitivity vity of the mode modell res results to its i ts formulation, bounda boun dary ry conditions and parameterizations. These three approaches are presented in the next three sections. The final part of the section on model sensitivity summarizes the model factors which are currently believed to be most responsible for the uncertainty unce rtainty i n climate model simul imulations. ations.


Sixteen comprehensive climate models presented in the 1995 IPCC report included full two-way interactions between the atmospheric and oceanic components have provided a basis for overall climate model evaluation and intercomparison. These evaluations have been based on the climate produced by climate climate conditi conditions ons the models, all with the same forcing parameters corresponding to current conditions. Extended time simulations ranging in length from 100 to 1000 years have bee been made to es establi tablis sh what was c considere onsidered d to be equili qui li bri brium um clim cl imate ate condi conditi tion ons s. M odel result results s have be been en compared to obse observati rvation ons s and tto o ea each ch ot other. her.  The sixte ixtee en g grou roups ps are listed in Table 4. 4.1 1 along with a few few ke key y des descriptor criptors s for each model such as: (a) Res Resolution of the a atmos tmosphe pheric ric gene nera rall circulation circulation mode modell compone component nt (AGC (AGCM M ).  Terminology rminology for horizonta horizontall re res solution is given iven by the l atitude titude-longitude -longitude g grid rid point spacing for the grid point models and with numbers representing the number of spectral components for the spectral models (R and T refer to rhomboidal and triangular, respectively, descriptors for the details of the spectral


Current Curre nt

components). The vertical resolution is given by the number of layers written after ‘L.’ Note that the model summary given in the 2001 IPCC report (Reference no. 7) shows improved resolution for many of these models. (b) Resolution of the Ocea Ocean G Ge enera nerall Circu Circulat lation ion M odel Compone Component nt (OGCM ).  Terminology rminology is the same a as s for the atmo atmos spheric pheric compo compone nent. nt. (Note (Note:: only grid point formulations are used since the spectral approach is not practical for a domain doma in broke broken n up by conti continents nents.). .). (c ) Descriptio Descriptions ns for the sea-ice and la land-surfa nd-surface ce compone components. nts. (d ) Ind Indica icatio ion n if ‘flux corrections’ are used at the ocean-atmosphere interface.  The us use o off flux co corre rrect ctions ions has has b be een ne n ecessary for some some of the mo mode dels ls to offs offse et discrepancies in the vertical momentum, heat, and water vapour fluxes at the ocean-atmosphere interface which could cause an unnatural drift away from observed climate conditions. It is anticipated that the need for flux corrections will decrease as models are further improved. A number of variables from these climate simulations are compared with observations and with each other in the group of the 11 climate models that had completed the simulation at the time of the 1995 IPCC report. The variables are surface air temperature, precipitation, Northern Hemisphere snow cover, sea-ice cover, mean sea-level pressure, surface heat flux over the ocean, and strength of  the north Atlantic ocean thermohaline circulation. Comparisons are made in




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terms of global means or global coverage, spatial distributions, and zonal averag ages es.. M Mea ean val values ues were shown for for all variables variables.. Al so exami examined ned is is s surf urface ace temperature variability on monthly, seasonal, interannual and decadal time scales scal es.. Some comparisons of these climate descriptors are shown in the following table and two figures with information on observed values: Table 4.2 for global average surface air temperature and precipitation for December-February and  June-Aug  June -Augus ust; t; Fig Figure ure 4. 4.4 4 for zona zonally-a lly-ave vera rag ged surface urface te tempe mperatur rature e fie fields lds for December-February and June-August; and Figure 4.5 for zonally-averaged mean precipitation fields for December-February and June-August.  Thes  The se fi gures ures illus ill ustra trate te the diffe differe rence nces s betwe between mode models ls.. For For global lobal mea mean temperature, differences are on the order of several degrees Celsius (Table 4.2) which is in the same range as the overall temperature increases discussed for global climate change. For zonally-averaged surface temperature, model errors tend to be larger at higher latitudes (Figure 4.4). Global mean precipitation simulation values vary from observed values by up to 30 per cent (Table 4.2). Spatial variations in model-simulated precipitation differences from observed values tend to be largest in the tropical latitudes. For the models using surface heat flux corrections over the oceans, the typical values required to give equilibrium consistent with current climate conditions were on the order of tens of watts per square metre. These values are larger than any associated with radiative forcing associated with climate change. Since 1995 great progress has been made to reduce or eliminate the need for surface heat flux corrections (Reference no. 7).  Thes  The se e exa xample mples ss show how tha thatt mode modell errors rrors a and nd va varia riability bility be betwe tween mode models in simulations for current climate exceed changes expected with climate change. The proper use of models for cli climate mate-change -change de determin termination ation is to examine xamin e the changes in model climate when radiative and other forcing associated with climate change are adde add ed, inste i nstead ad of comparin comparing g the mode modell predicti prediction on of future cli climate mate directly to curre current nt observed cli climate mate.. It I t iis s assumed that as long ong as the ove overall rall cli climate mate conditi condi tion ons s simulate mul ated d by the model are close to observed conditions, then the modelled changes will be valid indicators in dicators for cli climate matechang change e in the the real real world. worl d.  The varia variability bility in the mod mode el-simula l-simulate ted d climate climates s ha has s als lso o be bee en exa examine mined. d. This is an important but more difficult aspect of the climate system to simulate. In one study it was shown that the modelled intermonthly standard deviation of lower tropospheric temperature temperature wa was s larger tthan han observed in the tropics trop ics and smaller th than an observed in the southern high latitudes. In another study it was found that the model simulation of the ENSO oscillations tended to underestimate the magnitudes of the oscillations. Decadal variability also has been found in model simulations for current climate conditions having amplitude increases with latitude consistent with observations.

Table 4.2 4. 2 — C oupled model model  simulations  simula tions of globa globall ave average rage temperature and precipitation.

 Surfac  Surf ace e air Te Tempe perat rature uress ( °  ° C) D JF JJA

Precip Precipitat itation ion ( mm/day) DJF JJA

[from page 238, Reference no. 3].

* models without flux adjustment Here the observed surface air temperature is from Jenne (1975) and the observed precipitation from Jaeger (1976).


12.7 12.0 12.6 12.1 9.6 13.0 11.0 11.2 13.4 15.5 12.0 12.4

16.7 15.7 15.5 15.3 14.0 15.6 15.2 14.8 17.4 19.6 15.0 15.9

2.79 2.72 2.64 2.73 2.39 3.14

2.92 2.86 2.67 2.82 2.50 3.13

2.64 2.89 3.78 3.02 2.74

2.73 3.03 3.74 3.09 2.90




Figure 4.4 – (a) The zonally-averaged difference of 11 coupled models’ surface air temperatures from observations for  D ecemb cembe er-Fe r-F ebruary; ((b) b) as iin n (a) ( a) but for June June-A -August. ugust. Uni ts ° C . [from page 239, Reference no. 3].

Figure 4.5 – ( (a) a) T he zo zonally-average nally-averaged d precipi precipitati tation on rate from 11 1 1 co couple upled d mod mode els and that from f rom obse observati rvati ons according according to  Jae  Jaeger ger (19 76) ( solid solid line) line) for Dece Decembe mber-Februa r-February; ry; (b) as in (a) but for June June-A -August ugust.. Units mm/d mm/day ay.. (See (See Fig Fi g 4.4(a) 4.4( a) for for mode modell identification.) [from page 241, Reference no. 3] In conclusion, as already judged in the 1995 IPCC report (Reference no. 3), climate models have been shown to simulate satisfactorily as a group the largescale features of current climate. The IPCC report also concludes that the: “different coupled models simulate the current climate with varying degrees of  success, and this affects the confidence that can be placed in their simulations of  climate change.”  Testing clima climate te mod mode els on p pa ast climate climate situations ituations (p (pa ale leoc oclimat limate e) ma make kes s it pos poss sible

Past climate conditions

to evaluate model performance for a wider range of conditions of forcing of the cli climate mate sys system. tem. Chapte Ch apterr 2 reviewe reviewed d variabilit variabil ity y of pas pastt clim cl imate ates s in terms of surface surface




atmospheric te atmospheric t empe mperature rature.. C Change hanges ss sin ince ce the llas astt glacial maximum (about 20 000 years ago) have been documented from ice cores, tree rings, fossil pollen, mountain glaciers, ancient soils, closed-basin lakes, and sediments in lakes and oceans.  Thes  The se re reco cords rds ha have ve p prov rovide ided d enoug nough h informa information tion for climate climate in te terms rms of of time changes of spatial patterns for temperature and precipitation so that validation of  the general comprehensive climate models is possible.  The gene genera rall approa pproach ch has has be bee en to examine xamine model-s model-simula imulate ted d equilibrium climates applying fixed forcing conditions for given times in the past. By this means the evolution of climate changes corresponding to changes in the forcing conditions has been examined. It has only been possible to evaluate the general climate regimes produced by the model and not the details of processes and local variations because of the limitations in the paleoclimatic data.  The va valida lidation tion of climate climate m mode odels for pale paleoc oclima limate te situations ituations ha has sp prov rove en to be useful for testing and understanding climate models. This has led to the use of  more sophisticated climate models which include interactions with simple ‘mixed-layer’ oceans and the terrestrial biosphere. The establishment of a comprehensive hens ive Paleoc Paleocli li ma mate te Modelli ng IInterc ntercompa omparison rison Projec Projectt (PM I P) wil l provide eve even n more understanding of model performance in the future.


M odell odellin ing g of the a atmospheric tmospheric compon compone ent i s the most compre comprehensive hensive and and deve devellEVALUATION OF CLIMATE oped of all the climate system components. Atmospheric general circulation MODEL COMPONE COM PONENTS NTS models have been developed over a 40-year period in connection with weather prediction on and cli climate mate modell modellin ing. g. M ore than two dozen model version versions s exist, xi st, predicti and these have been subject to extensive intercomparison such as in the internaAtmospheric component tional Atmospheric Model Intercomparison Project (AMIP) [Gates, 1992]. In the simulations, sea-surface temperature is normally prescribed with climatological mean annual cycle values. This means that surface atmospheric temperature over the oceans is constrained to be close to climatological values because of the strong influence of ocean temperature on the temperature of the atmospheric surface layer. M odel s sim imul ulation ations s for surface air tempe temperature rature over land, precipi precipi tation, tation , and Figure 4.6 — The zonally- sea-level pressure have the same general quality as obtained in the coupled averaged ave raged zo zonal nal wi nd (ms ( ms-1 ) at  cli climate mate mode models ls di disc scuss usse ed i n the llas astt se sectio ction. n. Si mulate mul ated d tropospheric tr opospheric te t emperature mperature  200 hPa as as o obs bse erve rved d (blac (blackk line) is close to that observed, but there is a tendency for the models to be too cold at and as as si mulate mulated by the A MI P lower levels in the troposphere in the tropics and too warm in the tropical lower mode mo dels ls for (a) ( a) D JF and (b) JJ JJA A . stratosphere. The associated zonally- and seasonally-averaged zonal winds at the T he mean mean of th the e models models ’ res  results ults iiss upper troposphere represent the observations well. The mean zonal wind values  given  give n by by the the full white lines, lines, and  found by averaging all the model results together have errors of less than 5 ms-1 the 10, 20, 30, 3 0, 7 70, 0, 80 an and d 90 at all of the latitudes including the winter hemisphere latitudes where the jet

 p  pe ercentile rcentiless a are re given given by by tthe he stream magnitudes reach 40 ms-1. There are differences among the models for jet  shading surroun  shading surrounding ding the mode modell stream speeds at given latitudes also in the order of 5 ms-1. Figure 4.6 shows the me mean. an. T he observ observe ed data data are are observed a and nd A AM M I P-model di distributi stribution on percenti percenti les for the t he Dec Dece embe mber-J r-January anuary a and nd  from Schub Schube ert e ett a al. l. (1992 (1 992).).  June-Aug  June-Augus ustt z zona onall wind s spe pee ed m me ean value values s. [from page 250, Reference no. 3].




M odel s sim imul ulation ations s for cl cloudi oudines ness s are not so good. Cl Cloudi oudines ness s is an an iimpormportant compone compon ent in a climate clim ate model becaus because e of its i ts e effects ffects on radiatio radiation n ttransfe ransfer. r. On a zonally- and seasonally-averaged basis, errors in cloud cover and differences between models have magnitudes in the order of 20 per cent. Generally the models tend to underestimate the cloudiness in the winter and summer seasons in low and middle latitudes, and overestimate cloudiness in polar regions.  The quality of of tthe he simula imulate ted d ra radia diation tion ttra rans nsfe fers rs is a ke key y to the overa rall ll climate climate of the atmosphere and the capability of the atmosphere model to simulate properly the effects of climate change forcing. An important measure is the amount of terrestrial radiation emitted to space from the atmosphere-Earth system. In terms of zonally- and seasonally-averaged values, the models simulate outgoing terrestrial radiation values that generally differ by no more than an order of 10 Wm -2 from observations at any latitude. It is noted that the models are able to represent the local minimum of outgoing terrestrial radiation values near the equator. This minimum results from the effects of deep convective clouds in tropical areas. This convective activity is an important component for the general circulation of the atmosphere.  The overa overall ll effe effect ct of the clouds clouds on the ra radia diation tion b budg udge et of the atmos tmosphere phere is a key key factor iin n the climate cli mate syste ystem. m. Clo C louds uds affect affect both the solar and terre terrestrial strial radir adiation transfer, and reflect solar radiation which leads to a reduction of input energy; whereas they absorb terrestrial radiation which leads to a reduction in output energy. These effects offset each other so that the net radiative forcing due to clouds can be positive or negative.

Figure 4.7 — A  Ass in Fi gure 4.6 except for net cloud radiative  fo  forc rcing ing (W m-2 ). The T he obse bserva rvation tional al estimates are from ERBE data for  1985-88 (H arri son son et et a al., l., 19 1990). 90). [from page 251, Reference no. 3].

M odel simul simulation s for net ra radiati diative ve forci forcing ngstructure due to clo clouds uds a are re compared compare d to observation obse rvations s inations Fi Figure gure 4.7.the The ove overall rall l atitudinal atitudi nal is re reproduced produced by the models fairly well. However, differences at individual latitudes such as those: 1) betwee between tthe he averag rage e of the model res resul ults ts and and obs o bse erved condi conditi tions; ons; and -2 2) among the mode modell res resul ults ts typicall typically y range betwee between 10 and 20 Wm W m . Global and annual mean values of the differences are less because of offset between plus and minus values. Nevertheless, values are comparable to those for global mean radiative forcing due to the greenhouse gases. This shows that cloud cover is an important factor for model simulations for climate. The most obvious discrepancy in model performance is the systematic underestimation of negative radiative forcing due to clouds in the tropical and subtropical latitudes (30°S to 30°N) in both the December-February and June-August periods and the overestimation of  the negative forcing near 60° latitude in the summer hemisphere. Overall the atmospheric general circulation model simulations show realistic variability for atmospheric surface temperature. The observed and simulated diurnal ranges in surface temperature over land are similar, although simulated values are too large over the high northern latitude areas in January and over the northern continents and deserts in July. The simulated annual cycle for surface temperature over the continents is generally reasonable although there is an overestimation of the seasonal amplitude in the drier




climate regions. There are large differences among the models for the seasonal amplitudes in the higher latitudes. Variability associated with processes internal to the atmosphere is also present in the models but with amplitudes smaller than observed. This is true both for extratropical variability due to synoptic storm activity and for the M adde dden-J n-Juli ulia an 30-60 d day ay os oscil cillation lation in the tropics tropics.. Finally, the recent trends in surface temperature have been approximately represented in atmospheric model simulations for the past 45 years using observed values of sea-surface temperature and, in some cases, observed carbon dioxide and tropospheric aerosol changes. The results obtained by five modelling groups for mean surface temperature over land areas compared to each other and to observations are shown in Figure 4.8.

Ocean Oce an compon compone ent

(a) (b) (c ) (d ) (e) f ) (  f  (  gg)

Ocean general circulation models have been developed by a number of research groups. The highest resolution versions of these models have horizontal resolutions as small as 20 km and vertical resolution up to 60 layers (as of 1995). At present, computer size limits the resolution that can be used in coupled oceanatmosphere models. Ocean general circulation models have been able to simulate interannual variability of the type related to ENSO. In the coupled ocean-atmosphere models the ENSO-like ENSO-l ike variabil variabilit ity y iis s much we weake akerr and much more re regular gular than i n obs o bse erved conditi condi tions. ons. Some oc oce ean ge genera nerall circul circulation ation models have bee been able able to simulate simul ate the Rossby waves and equatorial Kelvin waves that are part of the ENSO variability cycle. The and newest coupled models show ENSO rather nearsimulated to realityinboth in frequency intensity. Decadal variability has also been ocean basins where deep water formation occurs (due to downward convective motions of cold water). As concluded concl uded in the 1995 IPC IPCC C report [Re [Refere ference nce no. 3], oce ocean an genera generall circulation models are able to portray realistically the large-scale structure of the oceanic oce anic gyres gyres and and th the e main fea features tures of the t he thermohali ne circulation. circulati on. Pri Primary mary defi defi-ciencies are in the representation of mixing processes and the structure and strength of the western boundary currents, the simulation of meridional heat transport, and the simulation of convection and subduction. Accordingly, a number of problem areas and deficiencies remain to be addressed, these include: Repre Repres sentation ntation of ge geome ometry try and bathyme bathymetry; try; Parame Parameterization terization o off subgrid-sca subgrid-scale le proce process sses as convection, convection , mixi mixing, ng, and and mes mesosc oscale ale eddies; Errors Errors in de defin fining ing s surfa urface ce forc forcing ing by the atmos tmosphe phere re;; A thermoc thermocli line ne tha thatt is too deep a and nd too d diffuse iffuse; Wea Weak pole polewa ward rd he hea at tra trans nspor port; t; Distort Distortion ion of upper upper oce ocea an and de dee ep boundary boundary cu curre rrents nts;; a and nd Te Tempe mperature rature and salin alinity ity e errors rrors in deep deep wate water. r. An important chall challe enge nge,, adding to th the e list ab above ove,, for further improvi improving ng and understandin unders tanding g ocean genera nerall circulation mode modell llin ing g is obtaining obtaini ng observational data for the oceans. Observations for the ocean are insufficient in coverage especially in the deep waters for comprehens comprehensive ive de desc scri ripti ption on of curre currents nts and th the ermoh rmohali aline ne structure structure a and nd for their variability. In many parts of the ocean the observational record is insufficient to define the key decadal and longer-term variability characteristics.

land-s -surfa urface ce co compo mpone nent nt is depe depende ndent nt on the para parame mete teriza rization tion for a numb numbe er  The land

Land-surface component

of processes that involve water vapour, evaporation, liquid water distribution and storage, soil moisture, heat and momentum transfer, ground temperature, and radiative exchanges. The parameterizations depend in part on resolved variables in the atmospheric model, so that deficiencies in the atmospheric model will influence the performance of the land-surface parameterizations. Differences between parameterization methods have been shown by the Project for Intercomparison of Land Surface Parameterization Schemes (PILPS). In this project 20 land-surface models were compared for computed values of surface heat and moisture fluxes and liquid water runoff and drainage. All of the models




Figure 4.8 — T he o observed bserved global annuall 1. 5 m land a annua aii r  temperature for 1949 to 1993 0.4 0. 4  from the Jo Jone ness (1994) (199 4) dat datas ase et  (shaded) and the corresponding     )     C modelled 1.5 m air temperature    O     (    y de deviati viati ons fro fr om the 1950 to    l 19 1959 59 average average fo for: r: (i ) the ave average rage    a of ffo our si mula mulati tio ons with wi th the UKMO model without progressive change cha ngess i n rad r adii ative forcing; forcing; (ii) the average of four   simulations  simula tions with chan changing ging C O 2 ; (iii) the average of four models with a representation of  tropospheric aerosols; and (iv) the averages from four other models wi with th no change changess in i n radiati radiative ve  forcii ng. [from page 258,  forc Reference no. 3].

   m    o 0. 2    n0.2     A    e    r    u    t    a    r    e0.0 0. 0    p    m    e    T


OBSERVED UKMO Fixed Fixed C O 2 UKMO Changing CO 2 UKMO Changing CO 2 + S O 4 BMRC GSIRO COLA J MA



1970 YEAR



were subjected to the same observed atmospheric forcing inputs for rainfall and surface radiation, and for low-level temperature, moisturewas andused winds. of  atmospheric observations at Cabauw, the Netherlands, forOne the year forcing inputs and for verification of the land-surface quantities computed. Each model was run for as many annual cycles as necessary to obtain a near-constancy for the annual averages of the computed quantities. General results are shown in Figure 4.9. The variations have certain correlations. For instance, an increased sensible heat flux relates to decreased latent heat flux implying that the total of sensible plus latent heat flux has less variation. Similarly, the increase in runoff relates directly to decreases in evapotranspiration. Nevertheless, the range in sensible and latent heat fluxes individually was on the orderr of 20 Wm-2 wi orde with th values roughl roughly y ce cent nte ered on tthe he observed a amoun mounts. ts. Th The e range range in runoff and evaporation amounts was in the order of 300 mm (for a year). Note that for the runoff and evapotranspiration, results are included for several cases where the land surface model w was as forced by resul results ts fro from m atmospheric models (refer(referenced AM AMII P/PI P/ PILPS). LPS). The g ge enerall nerally y wider s sprea pread d of th the e later poin points ts il illu lustra strates tes the error contribution from the atmospheric model. Anoth An othe er intercompa i ntercompari ris son experi xperime ment nt was made for the annual cycle of soil moisture using 13 land-surface models and observational data from an experimental site in southern France. The range for computed soil moisture amounts among the simulations was about 100 mm. The range was the greatest in the summer when the schemes tended to underestimate soil moisture.

Cryosphere component (sea-ice models)

Sea-ice models have been developed that can simulate sea-ice thickness and coverage. An example of capability is shown in Figure 4.10. In current climate models, only on ly very very approx approximate imate se sea-ice models models h have ave be bee en used. M ajor simul simulation ation discrepancies exist in climate models such as the ice edge being too far south in the Atlantic Ocean and not far enough north in the southern ocean. It is necessary to develop more comprehensive sea-ice models for climate modellin modell ing. g. Sea-ice affec affects ts ocean ocean s sali alini nity ty and oce oceanic anic convection that sends surface water down to the ocean depths. Furthermore, sea-ice transport results in important heat transport. There is some evidence from preliminary studies that incorporating sea-ice dynamics reduces the climate (change) sensitivity of the climate model. Recently the Sea-Ice Model Intercomparison Project (SIMIP), which was undertaken within the Arctic Climate System Study (ACSYS) has completed an intercomparison of sea-ice models and promoted a ‘viscous-plastic’ sea-ice model for climate models [Lemke et al. , 1997].




Figure 4.9 — (a) T he annua annually  lly  averag ave rage ed latent and an d sensi sensible ble hea heat  t   fluxess p  fluxe pre redicte dicted d by the PILPS PI LPS land-surface schemes. A single  ye  year  ar  s  ’s obs obse ervations rvations from from Cabauw, The Netherlands, were used for as many annual cycles as was required for each land surface sche  surface scheme me to conse conserve rve ene nergy  rgy  ( ≤ 3 W m-2 ) and and wate waterr ( ≤ 3 mm mm//yr). (b) As in (a) but for the annual totals of evaporation and runoff   p  plus lus drainage drainage.. The A MI P/PI P/PILPS LPS models’ 10 1 0-ye year ar mean mean values are the weighted average for the close closest st G C M gri d po poi nt and as as many of the surrounding eight   gri  grid d points that that a are re designate designated d as land.. (T he A MI P precip land precipii tation tation totals are given in parentheses and differ dif fer from f rom those presc prescriri be bed  d   for the off-li ne forcing.) forcing.) [from

(a) 15




   )    2   –

  m    ( 0   x   u    l    f    t   a  –5   e    h   e    l    b    i   s –10   n   e    S  –15





 –25 25








Latent heat flux (Wm –2)

page 259, Reference no. 3].

(b) 500

CCC (1191)

400    )   m   m    (   e   g300   a   n    i   a   r    D   +    f    f 200   o   n   u    R


BMRC (1102)




PILPS (776) AMIP (precipitation) 0 300







Evapotranspiration Evapotrans piration (mm) climate te mod mode el is a ve very ry co comple mplex x entity. ntity. Improv Improve eme ment nt in its solutions re require quires 4.3.4  The clima


improving many of the subparts. Improvements cannot be made in isolation as the parts affect each other, and the final solution includes approximations that balance each ach oth othe er. A parame parameterization terization scheme scheme that is iimproved mproved in isolation isol ation may, may, in fact, reduce the quality of the solution of the model system because it changes inputs to other parameterization schemes and that could offset existing compensations within the model.  The ke key y stra trate teg gy is to focus focus on impr improving oving the parts parts of a mod mode el s sys yste tem m which which contribute the most uncertainty to the model simulations. This avoids giving




Figure 4.10 — ( (a) a) Simulate Si mulated, and  (b) observe observed d sea-ice sea-i ce cconc once entrati ntr ations ons (i n per per cent) cent) i n the We W eddell ddell Sea for   Septemb tembe er 1987 (Fisc (Fische herr and  Le Lemke mke,, 1994). 1994 ). [from page 268, Reference no. 3].

excess attention to improving parts of a model which would not make much difference to the quality of the solution. An important part of model evaluation is to identify the aspects of a model most responsible for its simulation deficiencies, i.e., ‘model sensitivity.’ The 1995 IPCC report (pp. 271-274, Reference no. 3) lists five key areas of sensitivity given below. (a) Representation of water vapour

Water vapour content has a large range of values in the troposphere. Because of 

this, numerical approximation errors for advection of water vapour can lead to large errors in water vapour distribution. Such errors can lead to errors not only in cloud coverage and precipitation but also in latent heating and radiation transfer. Special numerical techniques are needed to deal with this problem. (b) M odel res resol oluti ution on in both the atmosphere atmosphere and the th e ocean ocean model is i s considere considered d to Model resolution have important impacts on simulation quality. Resolution affects both the numerical errors in the resolved phenomena of the model and the behavior of  parameterization schemes. It may be hard to separate these two effects. It is appropriate to consider a resolution that varies in space such as is usually done with vertical resolution near the atmosphere-ocean interface and near the tropopause in the atmosphere. (c )  The sens nsitivity itivity of c clima limate te mo mode del so solutions lutions to the cumu cumulus lus conv onve ection para rame mete teririConvection and clouds zation scheme has already been demonstrated. Cumulus convection parameterization quality is very important in tropical regions as it affects precipitation, cloud cover, cloud radiative forcing, and the overall hydrological cycle. Climate model solutions are also sensitive to microphysical processes for all clouds. (d )  The earlie rlierr dis discu cus ssion sho showe wed d the w wide ide ra rang nge e of of so solutions lutions give iven n by curr curre ent land Land surface processes surface model parameterization methods. The overall climate solution may be less sensitive to the details of the land surface because land covers only 29 per cent of  the Earth’s surface; however, the land surface is where people live so it will be




(e) Initial conditions and surface boundary conditions

necessary to have more detailed description of climate change effects for the land areas. In o orde rderr to off offse set the th e effects of ch chaos aos sensiti nsitivit vity y due to the nonli non linea nearr charac characte terr of the fluid dynamic equations for the atmosphere and ocean, it is considered necessary to employ ensemble simulation techniques to reduce the sensitivity to initial conditions. This is considered important for aspects of the climate such as interannual variability. It is believed that the boundary conditions at the ocean surface play an important role in the stability of a simulated thermohaline circulation in the ocean. Of the points listed above, it is felt that clouds, convection, the hydrological cycle, and land surface processes — points (c) and (d) above — are the areas of  largest uncertainty in climate models.

4.3.5  The 20 2001 IPCC I PCC repo report rt su summ mma arize rized re rec cent adva dvanc nce es in mod mode elling st stud udie ies s. Thes These

UPDATE FROM 2001 2001 IPCC REPORT (Reference no. 7)

have improved even further confidence in the ability of models to predict future climates. These advances include increased computer power to enable higher resolution models and multiple runs (ensembles) of models, better coupled model simulation of lower frequency events such as the El Ni ño-Southern Oscillation (ENSO), and model calibration for climate changes using paleoclimate simulations.






Climate prediction covers many ranges of time. Strategies for handling these can


be organized into three time-scale categories: short-range (covering seasonal and i nterannual time ti me s sca cales les), ), medium-range medium -range (covering (covering tim t ime es sca cales les larger larger tthan han in intera terannnual up to a ce centu ntury), ry), and llong-range ong-range (cove (coveri ring ng ti time me scales cales on th the e order of 10 000 years) [see Bengtsson in Chapter 23 of Reference no. 6]. Prediction strategy can build on experience of day-to-day weather prediction, and it must also consider the nonlinear and chaotic characteristics of the atmospheric and oceanic components in the climate system. ‘Predictability, ’  defined as the length of time for which the model can provide useful, deterministic information, is greatly influenced by the nonlinear characteristics of the system.


Predictability limits in numerical weather prediction models have been measured by the th e g growth rowth in differe di fferences nces,, or convers converse ely the reduc reducti tion on in correlation correlations s, betwee between the model forecast and the corresponding observational information. Commonly the atmospheric variable examined has been the 500 mb height field. Typically, the growth of model errors in time is exponential when the errors are very small


compared to overall spatial variability. Thesimilar error growth gradually decreases to zero as error magnitudes reach values to overall spatial variability. Conversely, the correlation between the time-dependent spatial patterns (anomaly structure) in the model field and those in the observations will decrease with time and eventually approach zero.  The pre predic dicta tability bility of the mode model can can be me mea asured by the le leng ngth th of time tthe he above correlation remains greater than a specified base value. In setting the base value, an effort is made to distinguish model prediction skill in the forecast from other factors which might happen to give a correct forecast. For instance, a persistence prediction (predicting continuation of existing conditions) can be correct some of the time. The skill in the model forecast is represented by the increase in anomaly structure correlations between the model forecast and observations over those obtained with a persistence or climatological forecast.  The pre predic dicta tability bility of a nume numerica ricall wea weather ther pre prediction diction model depe depends nds on the ac accura curacy cy of i ts nume numerical rical for formulation, mulation, the a acc ccurac uracy y of the in initi iti al conditions, conditi ons, and the rate at at wh which ich smal smal ler sca scales les unresol unresolve ved d iin n the model actually actuall y affect the resol resolvvable scales. No matter how well the first two conditions are met, predictability will always be limited by the third factor in a geophysical fluid system because the model can ne n ever ver re r esolve a all ll scales cales.. Even iin n the ge geoph ophys ysical ical flui fl uid d sys system tem itse itself lf there is no complete determinism from an initial state because of the inherent uncertainty in the initial state and of forcing effects. Lorenz (1969) gave a graphic example of the concept of predictability limits by stating that it could be argued that the flapping wings of a single butterfly could completely alter the details of  the entire atmospheric system if given a sufficiently long time. For weather prediction, the inherent limit to predictability has been estimated to be on the order of two to three weeks based on theoretical studies by Lorenz (1969, ob cit. ci t. ). The predictability achieved by weather prediction models for the th e entire nti re region north n orth of 20°N has improved greatly over the past 30 years as shown in Figure 5.1. From data sources such as those shown in Figure 5.1, it has been estimated that the ‘useful’ predictability skill achieved by models increased from slightly more than half a week in the mid-1960s to nearly ten days in the 1980s. Weather prediction models have become so accurate for one-day forecasts that it is now possible to approximate closely the predictability limits to which weather prediction models may be compared. The procedure proposed by Lorenz (1982) compares forecasts of one-day difference in length for each day in the




Figure 5.1 — T he p pre redictive dictive ski skill ll i n numerica numeri call weathe weatherr predicti prediction on and its improvement from (a) the mid-1960s to (b) the late 1980s.  Shown  Sho wn is the decre decreas ase e with time of the correlation coeff coeffii cient  between betwee n pre pr edi dicted cted and observed   500-mb  500 -mb geo geopotential tential height  height  anomalies for 12 two-week   pre  predictio dictions ns in (a) and and 90 ten-da ten-day  y   p  pre redict dictions ions in (b). T he corre rrelatio lation n coefficients were computed using all grid-point values north of   20 ° N. N. The thick solid lines show  the mean correlation values averaged ave raged o ove verr all i ndivi ndi vidual dual cases, whereas the shaded areas  give a meas measure ure of the range of the individual cases (i.e., about 5 per  ce cent nt of the predi predicti ctions ons li e be below  low  the shaded area and 5 per cent  above it). For comparison, the  pe  persi rsiste stenc nce e curve (das (dashe hed) d) forecast period. Likewise this procedure specifically compares the two-day and indicates the(useful) no-skillpre forecast. T he growth in predictive dictive  ski  skill ll from from less less ttha han n one one wee week in the mid-196 mid-1 960s 0s to about about ten ten days i n the 19 1980 80ss is evide evident. nt. T he da data ta i n (a) are from Mi yakoda yakoda et al., 1972, 197 2, and i n (b) from unpub unp ublilishe shed d EC ECMW MW F stat statisti isti cs (courtesy (cou rtesy L. Bengtsso Bengtsson). n). [from page 461, Reference no. 5, with permi permiss ssio ion n of Springe pri nger-Verlag] r-Verlag]..

one-day forecasts tomorrow, three-day and two-day forecasts for the after tomorrow, thefor four-day and the three-day forecasts for the day after that, andday so forth. Thi s provide provides s a pre predictab dictabil ility ity upper-li upper-limit mit estimate containi containi ng onl y one on e-da -day y forecast errors for the whole period of time. Figure 5.2 shows an example of forecasts for a winter season. This procedure provides estimates on how close the ten-day forecast is to the best that could be obtained (the solid and dashed lines, respectively, in Figure 5.2). Predictability considerations for climate are quite different from those for weather. Climate prediction is made for time periods longer than the two to three week weather predictability limits. This is possible because climate prediction is for statistical descriptions of the weather conditions, such as monthly means, instead of instantaneous conditions. Climate predictability extends to periods longer than two or three weeks; this is especially true in situations influenced by slowly varying oceanic conditions such as ENSO. However, nonlinear processes add complexity to climate predictability. Specif pecificall ically, y, there ma may y be more than one cli mate for a g given iven set o off forcin f orcing g para parammeters. In such a case, the climate is not unique and is termed ‘intransitive.’ If the climate is unique, it is termed ‘transitive.’ The first case is an example of chaos theory where ‘multi multiple ple attractors attractors’ exist. The ‘attractor’ is mathematical terminology that may be considered a frame of reference for a ‘given climate.’ The theory goes on to describe how the shift from one ‘attractor’ (or climate) to another may be random and unpredictable. It is not known what, if any, aspects of climate are intransitive. However, this characteristic would imply that a small additional forcing, such as that due to human activity, could potentially cause a general shift to a new climate pattern which is not necessarily reversible even if the small additional forcing is removed. Clearly, this is an important area for research. An ‘ensemble forecasting’  modelling strategy has been developed to deal with numerical predictability in situations which are highly sensitive to nonlinear processes. In this method a large number of numerical simulations are made for a forecast period, using the same model. The simulations differ slightly only in sde details tails of tthe hecan in initi itial al de condi condition tions s or forcing ge effec ffects . Th The enand tatis tatistics tics. T for model fore for eca cas t variabili vari abili ty be determin termine ed asforcin a functi function on space ce asnd ti time me. Thes hese e statistics statistics show whe wh ere the model soluti on is mor more e va vari riable able and and there therefore fore lle ess reliable for estimating values. The statistics also provide information on the probability of  occurrence for specific forecast states.



appr pproa oach ch has has bee be en us use d for ope rationa we wea ather ther period. pre predict diction ion whic which h up to 50 This separate forecasts may beemade forra ational givenl forecast It is in just as valid for climate models. Ensembles of up to ten forecasts are currently used in short range climate prediction (seasonal forecasts). The intercomparison tests among climate models, such as those discussed in Chapter 4, serve a similar function. Howe Ho weve ver, r, in i n suc such h interc in tercomparison omparison experiments, it is not n ot po poss ssibl ible e to separate parate in interternal nonlinear processes from effects due to differences in model formulation and parameterization. Short-term climate forecasting (seasonal to interannual time scales) is a direct extension of deterministic long-range weather forecasting. Currently, long-range weather forecasting has been extended to seasonal forecasting by focusing on the pronounced ENSO variability in the tropical Pacific ocean area and its relation-



– – – – – – – – – –

Winter precipitation in central Chile; Central England summer precipitation (based on Atlantic Ocean sea-surface temperature);  Tr  Tropic opica al Pa Pacific cific and Indian Oc Oce ean s se ea-s -surfa urface ce te tempe mpera ratur ture es; No North rthe ern tropi ca call Atlanti Atl antic c se sea-s a-surface urface te tempera mperatures tures;; Atlantic tropical storm activity; Southern Oscillation index time series; Summe ummerr mon mons soon rainfall in ce central-e ntral-eas astt Chi C hina; na;  Tr  Tropic opica al Pa Pacific cific island island precipita cipitation; tion; Canadian wintertime temperature and precipitation; and U.S. temperature and precipitation. A range of methods is often used in seasonal forecasting. For example, a number of groups currently supply seasonal forecasts for rainfall in north-east Brazil. These forecasts use January conditions for sea-surface temperature, particularly in the Atlantic Ocean, to predict rainfall anomalies in the following spring. M etho thods ds us use ed iincl nclude ude s statistical tatistical l in ine ear regre regress ssio ion, n, discrimi d iscrimi nant analys anal ysii s a and nd dynamic approaches. Correlation skills are generally in the 0.9 range for this seas ason onal al fo foreca recast. st.  The long-ra long-rang nge e for fore ecas casts for trop tropica icall Pacific Pacific s se ea-s -surfa urface ce te tempe mpera rature tures s show how skill. As an example, forecasts from a coupled ocean-atmosphere model at the Climate Prediction Center, U.S. National Centers for Environmental Prediction (NCEP), are presented in Figure 5.3. These are mean values of ensemble forecasts for sea surface temperature anomalies in the eastern tropical Pacific for the area between 5°N and 5°S latitude and longitudes ranging from 150° to 90°W for the ‘NI Ñ O’ index and 170° to 120°W for the ‘NI Ñ O3.4’ index. Forecasts made three, six, and nine months in advance are compared with observed values. Note that the fore for eca casts sts for late 199 1997 7 incl i nclude uded d the th e large El Ni ño event event that did di d ac actuall tually y occur. Since January 1998 global seasonal forecasts have been made available from the European European C Ce entr ntre e for Me M edium diu m Range Weather Weather Forecas Forecasts ts (ECM (ECMW W F) on the t he I nterne nternett at the web web site: <http:/ http://www.e / html html// seas asonal/ onal/in info/ fo/in info.html fo.html> >. An example of their six-month forecast for the NI Ñ O-3 inde in dex x anoma anomaly ly duri during ng the onset of the major 1997/98 El Ni ño event is shown in Figure 5.4. The monthly forecast ensemble is based on forecasts made three times a week, which gives an ensemble of 12 to 15 members for each month. Forecasts for climate conditions in other parts of the world which have some observed correlation with the ENSO generally have limited, but useful, accuracy over subregions of the forecast area. The results of an experimental programme to predict seasonal rainfall over northern Africa one month prior to the season are are a good example. (A joint programme among operational weather prediction

Figure 5.3 — NCEP coupledmodel, monthly-mean SST  anomaly forecast forecast ti me seri serie es for  N i ño-3 and N Ni  i ño-3.4 forecasts  from 1994 199 4 to 1998 199 8 for three three, six , and nine ni ne month month lea lead d ti time mess i n the upper, middle, and lower panels respectively. These monthly  values were used in three-month mean SST anomaly forecast   p  prod roduc ucts. ts. The pre predict dictions ions represent the mean of three ensemble-mean forecasts, each for  one of the three most recent  months, respectively, and each  p  pro rodu duccindividual ed by fore forec cast astsstofro from m two to three one two-weekapart apart i ni niti tial al conditi conditi ons per  per  month. [from page 13, Ji. et al. (1997)].

uni ts in England, France, France, and ECM W F.) An ense ensemble mble predicti predicti on method was




NIÑO 3 anomaly Figure 5.4 — Monthly sea-surface temperature (SST) ensemble 30 N  predict  pre dictions ions for for the easte astern tro tropica picall 20 N Pacif Pac ific ic (the ( the N I Ñ O-3 O-3 region region)) for  ~ 2 Nino 10 N the onse nsett o off the 1997 -98 El N i ño. ~ 3 ~ 4 0 Nino Nino *  Six-mo  Sixmonth nth pre predict dictii ons from from a 10 S * ~ 1 20 S coupled ocean-atmosphere model Nino 30 S (fine lines) compared with 140 E 180 E 180 180 W 140 W 120 W 100 W 80 W Longitude observations (heavy line) are  shown  sho wn fo forr the N ove vemb mbe er 1996 199 6 to  Se  Sep ptemb tembe er 1997 199 7 time peri peri od. Predictions are initiated over an extended pe perri od from Nove N ovember  mber  1996 199 6 to March 1997. 199 7. T hree hree  foreca  fore casts sts are ma made de each ach we wee ek  ( only one i s plotte plotted d iin n order order to avoid clutter) which gives an ensemble of 12 to 15 members each month. N ote that the t he onset  onset  of El N i ño in A pril 1997 is we well ll  fo  fore reccast. ast. The subse subseq que uent  nt  evolution is also well forecast, although one can see a tendency  appli applie ed to the 15-year 15-year pe peri riod od ffrom rom 1979 1979 to 1993. 1993. C Correlatio orrelations ns betwe betwee en the ense ensemo



   e     d    u    t    i    t    a    L




toevent. underpredict the amplitude of  the eve nt. [from ECM W F, 19 1998]. 98].

ble mean rainfall and the observed rainfall were made by Harrison, et al. [1997]. Aft Afte er calibratin cali brating g the e ense nsemble mble forec fo recas astt vari variance ance,, th the ey ffoun ound d th that at in some are areas as the observed rainfall was within the ensemble range for most of the 15 years while in others this was the case for only ten of the 15 years. In summary, useful seasonal prediction is now possible for selected regions of the globe. The prediction is generally for anomalies in monthly mean temperature or precipi precipitation tation.. Th The e ski kill ll level level in some case cases ma may y be suffici uff icie ent to be of value as an operational product for the public. It is important that operational meteorologists become acquainted with possible benefits for their area of service.  The World M ete teorologica orologicall Orga Organi niza zation tion has in ins stituted tituted a Cl imate I nformation and Pre Prediction diction Service rvices s (CLI PS PS)) proje proj ect whi which ch wi will ll make make s short-range hort-range climate forecast information available to the world meteorological community. It is important to understand the strengths and limitations in forecasting to be able to integrate CLIPS most effectively with national service and policy.


Forecasting climate variability for decadal to century time scales does not deal


with spe pecifi cific c va variabil riability ity ph phe enomena b but ut rathe ratherr with wi th simulation simul ation of ove overall rall cl climate imate processes and changes due to external forcing that causes climate change. It is expec xpected ted that the modell modellin ing g of all compone compon ents of the cli climate mate system ystem will wi ll have an important role in forecast quality. For such forecasts, major variations in some of  the external forcing conditions, for example changes in the major ice sheets that occurred in the last ice age, may be excluded. It is important that the climate models used for simulating this time scale have reasonable equilibrium characteristics for the control state. Climate drift due to energy transfer discrepancies must be counteracted with ‘flux fl ux ad adjustme justments nts’ as discussed in Chapter 4. For this scale of forecasting and certainly for long-range climate prediction, the possible intransitive nature of the ocean thermohaline circulation can present a challenge. There is evidence that a significantly different circulation existed in the Atlanti Atl antic c Ocea Ocean n aroun around d 11 000 years ag ago. o. T The he incre in creas ase e in fres fresh h water runof runoff  f  from north n orth Ame America rica in into to th the e north Atl Atla anti ntic c caus cause ed oce oceanic anic sub subduction duction and downward convection to stop. This that in turn prevented the formation of cold ocean waters and reduced currents provide the northward transport of deep heat by the oceans. This resulted in a lowering of atmospheric temperatures in the northern polar latitudes. Modelling studies have shown that this large change in circulation could have occurred on a decadal time scale.




Calibration of medium-range climate forecast products is provided by testing the model on the climate of the recent past, for a period for which sufficient observational data are available. An example was shown in Figure 4.9. A model simulation of global mean surface temperature for the entire period of climate change since the pre-industrial era is shown in Figure 5.5. In this simulation the greenhouse gas and aerosol forcing is based on the observational estimates of the concentration of these forcing constituents. Note that the model forecast for overall trends is reasonable; however, decadal variability, especially in the period from 1920 to 1960, is not forecast well.



Forecasting Forecasti ng fo forr ti time me scales scales of 10 000 years years and greater greater requir requires es cli mate model models s with the full representation of the components of the climate system. Effects due to the t he dee deep circul circulation ations s in the oce oceans ans (with ti time me sca scales les of 1 000 ye years ars or more) mo re) and major changes in the cryosphere (glaciers and ice fields) must be included. A numbe num berr of o f proces processe ses, such as the oce oceanic anic thermohali thermoh aline ne circul circulation ation,, may caus cause e the climate to be intransitive. This would mean that there is not a unique climate for a specified climate forcing, so that the model prediction would depend on the initial conditions. M odel studi studie es to simul ate pa past st climate cli mates s have demon monstra strated ted that climate cl imate models can represent different climate conditions. However, these studies have not reproduced all of the process cycles in the evolution of the climate system. As discussed by Bengtsson [p. 721, Reference no. 6], the time scales that must be handled for long-range prediction range from hours for the atmosphere to centuries and millennia for the cryosphere, ocean, and astronomical forcing. Bengtsson goes on to describe a modelling technique originally suggested by Hasselmann (1988) whereby the ‘slow system’  representing the land ice, deep ocean, and astronomical forcing are solved separately from the ‘fast system’ consisting of the atmosphere, land surface and the upper ocean. An equilibrium condition would be found in the fast system for a given state of the slow system.  The forc forcing ing effe effect cts s of the fa fas st s sys yste tem m due due to this equilibrium condition condition wou would ld be maintained as constant on the slow system for a time duration comparable to the time scale for changes in the slow system. Periodically a new equilibrium would be computed for the fast system to be consistent with changes in the slow system.

5.6  The prima primary ry c clima limate te simula imulation tion mod mode els are globa lobal. l. Curre Currently ntly ava availab ilable le compu compute terr


power limits the resolution of the global climate models to a few hundred kilomete me ters rs in the horizontal. hori zontal. Th This is me mea ans that lo loca call cli clima mate te c condi ondition tions sa and nd variations cannot be represented. Yet the local conditions are those which relate most directly to climate-impact assessments. Local variations and changes in climate


Figure 5.5 — Simulated global annual mean warming from 1860 186 0 to 1900, allowing allowing for  increases in equivalent CO 2 only  (dashed curve) curve) and allowi allowing ng ffor  or  increases in equivalent CO 2 and  the dire dir ect e eff ffe ects of sulphate sul phatess (f (fle lecke cked d curve) curve) (M i tchell tchell et al., 1995). observed changes  fromThe Parker Parke re ett a al. l. (1994 (1 994).). Tare he anomalies are calculated relative to 1880-19 1880-1920. 20. [from page 297, Reference no. 3].

   )    C    °    (   e   g1.0   n   a    h   c   e   r   u    t 0.5   a   r   e   p   m   e    t    l  0.0   a    b   o    l    G -0.5

Equivalent CO2 only Equivalent CO2 and the direct effect of sulphate aerosol Observed












are much larger than averages measured over large spatial scales. Below, an example of the limitations of the global climate models for regional climate prediction is presented followed by a brief discussion of two methods that can provide useful predictive information on local climate from climate models: statistical ‘downscaling’ and higher resolution regional models.



Analysis of regional results in global simulation models demonstrates the large variability in comparing model results with each other and with observations. Nine modelling groups examined seasonal climatologies for surface temperature and precipitation in control runs intended to represent existing current conditions. ti ons. C Comparisons omparisons were made for seven ven reg regio ions ns over land areas areas wit with h dimensions dimension s of very roughl roughly y 2 00 000 0 km by 2 000 km. They were were from central central n north orth Ame America rica (CNA), south-east Asia (SEA), the Sahel (SAH), southern Europe (SEU), Australia (AUS), northern Europe (NEU), and east Asia (EAS). Results shown in panels b, d, f, and h of Figure 5.6 illustrate the large difference between the global models.

5.6.2  The sta statis tistica ticall method method ha has s tthe he fo following llowing two two-s -ste tep p appr approa oach: ch: 1) 1) Deve Developme lopment of 


statistical relationships between local climate variables and large-scale predictors from the global climate model; and 2) Application of such relationships to the output from th the e climate climate mode models ls to estimate local climate cli mate c characte haracteri ristics. stics. This Th is approach has h as be bee en quite successful in relating weather-prediction model simulations to local surface weather conditions. The method requires a large number of realizations where model output is related related to cor corre respondin sponding g obs obse erved s surface urface condi conditi tions ons in in o order rder to determine rmine the appropriate model predictors and statistical coefficients.  This me method c ca an be applie pplied d to any any predicte dicted pa para rame mete terr that that ha has s a phys physica icall relationship to the model variables, as long as there is an observational record for


Winter, Temperature 7.0

   ) 6.0    C    °    (   e 5.0   g   n   a    h 4.0   c   e   r   u 3.0    t   a   r   e 2.0   p   m   e 1.0    T 0.0


Winter, Temperature

CO2  –  –  Control  Control

   )    C    ° 15.0    (   e   c 10.0   n   e   r   e    f    f    i 5.0    d   e 0.0   r   u    t   a  – 55.0 .0   r   e   p  – 1 10.0 0.0   m   e    T  – 1 15.0 5.0



Summer, Temperature 7.0


Control –  Control  –  Observed  Observed


Winter, Precipitation 60.0

   )    l   o   r    t   n 40.0   o    C    f   o 20.0    %    (   n   o 0.0    i    t   a    t    i   p    i 0.0   c – 220.0   e   r    P  – 4 40.0 0.0

CO2  –  –  Control  Control


   ) 200.0    d   e   v 150.0   r   e   s    b    O 100.0    f   o    % 50.0    (   n   o 0.0    i    t   a    t    i   p  – 550.0    i   c 0.0   e   r    P – 1100.0 00.0

Winter, Precipitation Control –  Control  –  Observed  Observed




MRI(p )

GFDL(g )

BMRC(a )

NCAR(r )

Control –  Control  –  Observed  Observed

 – 1 15.0 5.0




Summer, Precipitation


Summer, Precipitation 200.0


   )    l   o   r    t   n 40.0   o    C    f   o 20.0    %    (   n   o 0.0    i    t   a    t    i   p    i 0.0   c – 220.0   e   r    P  – 4 40.0 0.0


   )    C    ° 15.0    (   e   c 10.0   n   e   r   e    f    f    i 5.0    d   e   r 0.0   u    t   a 5.0   r  – 5.0   e   p 0.0   m – 110.0   e    T


(g) (e)

CO2  –  –  Control  Control



Summer, Temperature 20.0

   ) 6.0    C    °    (   e 5.0   g   n   a    h 4.0   c   e   r   u 3.0    t   a   r   e 2.0   p   m   e 1.0    T



CO2  –  –  Control  Control


   )    d   e   v 150.0   r   e   s    b    O 100.0    f   o    % 50.0    (   n   o 0.0    i    t   a    t    i   p  – 50.0    i   c 50.0   e   r    P – 1100.0 00.0

MPI(x )




UKMO(t )

Control –  Control  –  Observed  Observed

NCAR(q )

UKMO(s )

MPI(m )

Figure 5.6 — D i ff ffe erences rences betwee between n averages averages at ti me of C O 2 do doublin ublingg and co control ntrol run r un ave average ragess ((C C O 2 –Control) and difference betwee between n contr control-r ol-run un ave averages rages an and d observed observed averages averages ( C ontrol –Obse Observed rved)) as simulate si mulated d by nine A AOG OG C M runs ov ove er seven seven regions. winter; (c), (d) temperature, (f) rages precipitation, winter; Units are(a), ° C(b) forTemperature, tempe temperature ratur e and perce rcentage ntage of of control rrun un summer; or observ observe e(e), d averages ave for prec precii pit pitati ation. on.(g), I n(h) ((f) f) precipitation, and (h) (h ) vvalues aluessummer. in excess excess of 200 20 0 per cent cent h have ave be bee en rrep eported orted at the top end end of the ver verti cal scale. I n ((e e) values values in excess of 60 6 0 per cent cent h have ave be been en report reporte ed  at the top end of of the verti verti ca call scale. scale. C N A =central =central north A meri meri ca can, n, SEA =south-ea =south-east st Asi A sia; a; SAH SA H =Sahe =Sahel;l; SEU=southe SEU=southern rn Euro Eur ope pe;;  AUS=Australia;  AUS=Austra lia; NE U=northe U=northern rn Europ Europe; EAS= EA S=e east ast Asia. [from page 338, Reference no. 3].




calibration. The method can provide results for situations where small spatial structure is expected, such as for temperature and precipitation in regions with large topography. On One e example of appli cati cation on is th the e predi predictio ction no off se sea a level at tidal ga gauge uges s in J apa apan n from f rom m modelodel-simul simul ate ated d sea-level pressure pressure anomal anomaliies [Maochange [M aochange et al. , 1995].

5.6.3  The re regiona ionall mod mode elling lli ng a appr pproa oach ch us use es outp output ut from the globa loball climat climate e mode model to




(c )

(d ) (e)

f ) (  f  (  gg)

provide initial and boundary conditions for a regional climate model. These regional models are forced ‘one way’ by the global climate model, i.e. the global climate model determines forcing conditions for the regional model but the regional regional model does not, in turn, infl in flue uence nce the g global lobal model.  Thes  The se mod mode els can have have a muc much h higher higher resolution and and c ca an incorp incorpora orate te physphysical processes not in the global model. Regional climate models have included coupling to lake models, dynamic sea ice models, coastal ocean models and ecosystem models. Experiments using regional climate models for present-day climate experiments have shown the following results for regional climate models [see pp. 340-341, Reference no. 3]. These models had horizontal resolutions ranging from 15 to 125 km. Re Rea ali stic synoptic events have bee been simul imula ate ted d with wi th small small biase biases in te tempe mperature rature and precipitation when initial and boundary conditions were provided by observations. Biases were in the range of a few °C for temperature and 10-40 per cent for precipitation. Pe Performance rformance was was d de egraded when the qua quali lity ty of th the e model model forcing f orcing (i.e. s spe pecifi cifica ca-tion of initial and boundary conditions) was reduced by using general circulation model simulations instead of observations to force the regional climate model. Simulations imul ations produ produce ced d more realistic realistic de detail tail than in the global global clima cli mate te models models us use ed to force the regional models; however, regionally-averaged values could be more or less realistic than those in the driving climate model. M odels odels performe performed d bette betterr at mid-latitude mid-latitudes s than than i n tropica tropicall reg regions. M odel odel performa performance nce improve improved d a as s the re res solution of the d drivin riving g globa globall cli clima mate te model increased. Seasonal asonal as we well ll as diu diurnal rnal tempe temperature rature range ranges s we were re simulate simul ated d reas reason onab abll y we well ll . Validation data data from ade adequa quate tely ly de dense nse obse observational n ne etworks was was lac lackin king g, espe espe-cially in mountainous areas.

5.7  The 2001 2001 IPCC repor reportt lis li sts te ten n hi ghlights in mode modelli lli ng adva advance nces s since the 1995 1995


• •

IPCC report. These are listed below. • Coupled models can provide credible simulations of both the present annual mean climate and the climatological seasonal cycle over broad continental scales for most variables of interest for climate change. Clouds and humidity remain sources of significant uncertainty but there have been incremental improvements in simulations of these quantities. • Confidence in model projections is increased by the improved performance of  several models that do not use flux adjustment. These models now maintain stable, multi-century simulations of surface climate that are considered to be of sufficient quality to allow their use for climate change projections. •  The  There re is no s sys yste tema matic tic diffe differe rence nce be betwe tween flux-adjus flux-adjuste ted d and and non flu flux-a x-adjus djuste ted d models mode ls iin n the s simul imula ation of i nterna nternall cli clima mate te va variabil riability. ity. Th This is supports supports the use use of both types of model in detection and attribution of climate change. • Confidence in the ability of models to project future climates is increased by the ability of several models to reproduce the warming trend in 20th century surface air temperature when driven by radiative forcing due to increasing greenhouse gases and sulphate aerosols. However, only idealised scenarios of  sulphate aerosols have been used. Some modelling studies suggest additional forcings such as solar variability and volcanic aerosolsthat mayinclusion improveofsome aspects of the simulated climate variability of the 20th century. Confidence in simulating future climates has been enhanced following a systematic evaluation of models under a limited number of past climates.




•  The perform performa ance of co couple upled d mod mode els in simulating imulating the El Ni ño-Southern Oscil lation Oscillati on (ENSO) has h as improved; im proved; howeve however, r, the re region gion of m maximum aximum se sea-s a-surface urface temperature variability associated with El Ni ño events is displaced westward and its strength is generally underestimated. When suitably initialised with an ocean data assimilation system, some coupled models have had a degree of  success in predicting El Niño events. • Other phenomena previously not well simulated in coupled models are now handled reasonably well, including monsoons and the North Atlantic Oscillation. • Some palaeoclimate modelling studies and some land-surface experiments (including deforestation, desertification and land cover change) have revealed the importance of vegetation feedbacks at sub-continental scales. Whether or not vegetation changes are important for future climate projections should be investigated. • Analysis of, and confidence in, extreme events simulated within climate models is emerging, particularly for storm tracks and storm frequency.   Tropic ‘Tropica al cyc cyclone lone-like -like’ vort  vortices ices are bein being g si si mulate mul ated d iin n cli climate mate models, a alt lthough hough enough uncertainty remains over their interpretation to warrant caution in projections of tropical cyclone changes.







Observations Observation s are a cri criti tica call compo component nent iin n the th e di dis scussion, cussion, u unders nderstandi tanding, ng, and identification of climate change. There are uncertainties in the observational data which whi ch contri contribute bute to th the e ove overa rall ll uncertainty uncertainty about cli climate mate variabil variability ity both natu n atural ral and anthropoge anth ropogeni nic. c. M Many any pers persons ons mi might ght as assume sume that the th e c current urrent observation observational al network is sufficient. A global observing system has been in place for years to support operation operational al we weathe atherr prediction; it forms part part of W M O’s World Weather Watch (WWW) programme. In recent decades, the observations have been substantially reinforced by observations from new systems such as satellites. Ocean measurements are an indispensable part of the global climate observing system. sys tem. Mea M easureme surement nts sa are re needed to pro provi vide de spati patial al and ttemporal emporal de d escript scriptiions on s of  temperatur tempera ture, e, s sali alini ni ty and currents. cur rents. M Mea easure surements ments for sea-s a-surf urface ace temperatur temperature e have been comprehensive; however, measurements for conditions below the ocean surface are currently insufficient. A fully four-dimensional observational system is needed for the ocean as exists for the atmosphere.  There are serious deficie  There deficiencie ncies s in, and critica criticall iis ssues ues for for,, the obs observa rvationa tionall sy sys ste tem m for climate monitoring for the atmosphere, as well as the ocean. It is important that these deficiencies and critical issues should be understood and addressed. In recognition of the needs for climate monitoring, a new international programme was established by the Second World Climate Conference in 1990, the Global Climate Observing System (GCOS). Local weather observations provide a key foundation for climate-in climate-information formation syste ystems ms.. M ete teorologists orologists throughout the th e world need to understand the importance of long-term and well-documented local observations observations for climate monitori monitoring. ng.  There  The re ar are e seve vera rall underlying underlying princ principle iples s for a clima climate te monitoring sys syste tem. m. First, the system must be underpinned by the scientific community in terms of  development, calibration, and monitoring. This is true even for automated observing systems. Second, it must be understood that climate observations have requirements that go beyond those for weather observing. Third, the observational records need to be long-term and have a consistent, homogeneous, and documented frame of reference to be able to detect trends in climate conditions.  This cha chapte pterr lea leads off with elab labora oration tion on the ke key y specific pecific princ principle iples s for long-term climate monitoring. Examples of the status of selected observations relevant to climate change are then presented followed by discussion of strategies for improving long-term climate monitoring. The primary reference for this chapter is Reference no. 2 listed in the introduction.

li st of te ten n principles principles below below cov cove ers the deta details ils for a as ssuring that that obse observa rvations tions 6.2  The lis


will be of value for long-term climate monitoring. They apply to observations taken anywhere in the world. Staff meteorologists, weather station managers, meteorological service directors, and the appropriate government agencies need nee d to become aware aware of and un unde dersta rstand nd the t he importance impo rtance of thes these e principles princi ples..  Th e pr i nci pl es appl apply y to both sma small l and simpl simp l e and l arge and elaborate observing operations. The list is presented in complete form on pages 86-87 in Reference no. 2.

ffects so on n the climat climate e reco record rd du due e to chang change es in instr instrume umentation, ntation, obse observing (a)  The effect practices, observation location, etc., must be known before implementing such System changes changes. These effects can be determined by a period of overlapping measurements between the old and new system or by comparison of the old and new systems with a standard reference. Sites chosen for in-situ measurements should




have expectations of long and uninterrupted use and expectations that there will be little change of the nearby physical environment over time. (b) Processing algorithm description

Processing algorithms for determining data values from the instrument system must be well documented and archived with the original data.

(c ) Observing system description

Information on instrument, station, platform history, changes in sampling time and local environmental conditions, and all other factors relevant to interpretation of the data should be recorded as part of the observing routine and archived with the original data.

(d ) Length of record

Observations that have a long, uninterrupted record should be maintained and kept uniform uni form (homoge (hom ogeneous) neous) in terms of me meas asureme urement nt procedures. procedures. Lon Long-te g-term rm ffor or space-based measurements is measured in decades, but long-term records for more conventional measurements may be a century or more.

(e) Climate record homogeneity

Calibration, validation and system maintenance should be used to provide a constant frame of reference for the climate record.

f ) (  f  System backup

Some form of ‘low-technology’ back-up to ‘high-technology’ observing systems should be put in place to safeguard against unexpected operational failures.

(  gg)

Highest priority should be given to the design and implementation of new

Observing system priorities

climate observing systems for; 1) data-poor regions; 2) variables and regions sensitive to climate change; and 3) key measurements with inadequate spatial and te temporal mporal res resoluti on.

(h) Network de design sign

Long-term climate requirements should be made known to the designers and engineers at the outset of designing a new network.

(i ) New observation system development

A long-term association and cooperation commitment is needed between the research group whose needs require developing a new instrument system and the group that will eventually handle the system in operational mode. A clear plan for the transition from research to operational applications should be made.

j ) (  j  Data management

It is essential to have data management systems that facilitate access, use and interpretation of the data. Data management should have freedom of access, low cost, and user-friendly interfaces (directions, catalogues, browsers, metadata on station history, algorithm accessibility, documentation, etc.). International cooperation is very important.

6.3  The solar olar radia radiation tion e ente ntering ring the the Earth Earth’s atmosphere is the primary input forcing



Solar radiation

factor for the climate system and its measurement is essential for analysis of  climate. Variations in the magnitude of this irradiance have a direct impact on equilibrium energy levels in the climate system. Satellite systems are optimal for its measurement because they are above the atmosphere. However, measurement of the solar radiation made by multiple satellite systems may show differences from system to system that are nearly as large as 10 Wm -2 . A difference of 10 Wm-2 is quite significant. It exceeds by quite a bit the 1-2 Wm -2 variation in irradiance due to the sun spot cycle as discussed in Chapter 2. A solar irradiance difference of 10 Wm-2 is equivalent to a mean radiative forcing factor of 1.75 Wm-2 if the Earth ’s sphericity (a factor of 0.25) and albedo (a factor of 0.7) are taken into account. The estimated overall change in radiative forcing due to humanproduced greenhouse gas enhancements is of the same order of magnitude. Clearly, great care is required to obtain a valid (homogeneous) long-term climate record of solar radiation intensity with calibration to a fixed reference. A ne n ew 20-ye 20-year ar re record cord has h as bee been produced with appropriate ca cali li bration correccorrections which has less variability and clearly depicts the 11-year sunspot cycle (see Figure 6.1).




Figure 6.1 – Comp  C omposi osite te total total  so  solar lar irradiance irradiance for for 1978 197 8 to 19 1997 97.. T he who whole le ti time me seri seri es is adjusted to the Space Absolute Radiom Radi ome eter ter Referenc Reference e (SA (SARR RR)) which does not improve absolute accuracy, but allows comparison of rrep epeate eated d space ex ex peri peri ments with the same radiometer. [from Fröhl hlich ich and Lean, 1988 1988,, with wi th pe permis rmiss sion of Rob Robe ert B. Le Lee e II I and Kluwer Academic Publishers].

C arbon di dioxi oxide de c con once centr ntration ation i n tthe he atmosphere atmosphere has been me m easured asured a att M auna

Gree Gr een nhouse gases gases

Loa Observatory, Hawaii and the South Pole from around 1957 onward. This has provided consistent long-term, single-point records of an important climateforcing parameter. In climate change study it is necessary to expand the measurement programme to improve our understanding of the global carboncycle budget. This requires measuring spatial variations in carbon dioxide concentration in the atmosphere to identify the details of sources and sinks with respect to the biosphere and ocean. Investigation of the sources and sinks of  carbon dioxide will also be facilitated by having measurements for spatial variations of O 2 concentrations. The extremely high accuracy needed for the O 2 measurements has restricted these records to the last few years at only a few stations.

Carbon dioxide


Water vapour

Both vertical and horizontal distributions of ozone need to be measured in order to understand implications for climate change forcing. Total ozone (in a vertical column) has been measured by surface-based Dobson spectrophotometers and by satellites. It has been necessary to calibrate Dobson measurements and to adjust for changes in the calibration factor for satellite measurements. Now it is important to develop measurement systems to determine ozone concentrations in the troposphe tropo sphere. re. Th The ere are also sa satelli telli te ins in struments measurin measuring g the vertical profil prof il es of  ozone. Water vapour is the most important greenhouse gas, accounting for roughly 80 per cent of the total greenhouse effect. Water vapour concentration is expected to increase if temperature increases because of enhanced evaporation from natural sources. It will be important to observe variations in water vapour concentrations on a global basis to understand observed climate change trends and to validate climate model simulations. Current water vapour measurements are not as accurate as those for other basic variables of the atmosphere. The slow response to relative humidity of the sensors in radiosondes me means ans that vertical vari variation ation of water water vapour are smoothed out. In addition, processing procedures have changed over time. For instance, starting in 1993 1993 the c calcula alculation tion for relative humidi humidity ty from VI Z s sondes ondes,, whi which ch are part part of the global global n ne etwork, was modifi ed to include in clude calculation calculation o off humidi h umidity ty value values s below 20 per cent and upward adjustments in humidity values over 80 per cent.  The net net effe ffect ct wa was s to increa increase the me mea asured ured value value of total total wate waterr vapou vapourr and to add an artificial discontinuity to the time-series record for water vapour. The number




of different kinds of radiosonde systems currently used in the global observing system also makes it necessary to apply calibration adjustments in the data processing. Satellite observations are good for showing horizontal variations of humidity; however, vertical variations are smoothed out even more than with radiosonde measurements. Intercomparison of satellite measurements with in-situ (radiosonde) measure sureme ments nts wi will ll be requi required red to assure assure th the e vali vali di dity ty of trend studi studie es [page 70, Reference no. 7].





Improved measurements are needed for aerosol concentrations in the atmosphere. Such measurement systems must include monitoring the aerosol type and i ts s size ize spe spectrum. ctrum. Turbi Turbidit dity y mea meas surements urements alone alon e are not sufficient. suffi cient. M oni toring tori ng of  source magnitudes including those from biomass burning will be essential for understanding the budgets for aerosols. It is expected that remote sensing from satellites will be essential for making the required observations. Cloud observations are critical for understanding recent and future climate change. The measurements need to include not only total cloud cover, but also quantative data on the level and composition of the cloud. Ice clouds have quite different radiative properties than water clouds. Continuity in the climatology record for clouds has been degraded by changes in observational methods. Viewing clouds from the Earth ’s surface surface is qui quite te diffe diff erent fro from m vi vie ewin wing g them from space. Conversion to automated cloud-observing systems introduces a major discontinuity into the climate record.  The Inte Interna rnatio tiona nall Satellite Clou Cloud d Clima Climato tolog logy y Proje Projec ct (IS (ISCCP) CCP) has has be been es esta tab blished to construct a valid climatology of cloud coverage. Observational records from the 1980s had shown considerable uncertainty in the observations even when made by satellite systems. Reductions in percentage cloud cover of approximately three per cent over a seven-year period were found for both nimbostratus and deep convective clouds. The changes were primarily in the periods when there was conversion from one satellite system to another. This conversion effect has now been eliminated by reprocessing the data. Cloud data are now considered reliable for studies of shorter term and regional variations of clouds, even if we cannot monitor long-term trends. Attempts have been made to use surface solar radiation measurements to give an indication of cloud cover. However, the two are poorly correlated because of measurement system changes for both methods and because of the interference effect of air pollution.


M eas asureme urement nts s of tempe temperature, rature, s sali alini nity ty and curre curr ent nts s are requi required red to unders un derstand tand oceanic processes and overall how much heat the oceans will store or give up in a climate change scenario. It is essential to measure the current systems in the deep ocean as well as in the upper ocean (to depths of several hundred meters) to descri des cribe be the hea heatt tr transports ansports withi wit hin n th the e ocean ocean wh whiich affe aff ect se sea-s a-surf urface ace temperature. temperature. M easure asureme ments nts in the dee deep oce ocean an wil willl make it possibl ible e to monitor moni tor th the e Atlanti Atlantic c thermohaline circulation which is a key factor for identifying decadal-scale shifts in th the e ocean circul circulatio ation. n. M easureme asurement nts s of tempera temperature ture iin n the t he deeper ocean ocean may provide a detection of climate change less obscured by seasonal cycle and shorter period variability. For example Figure 6.2 shows a warming trend between 1957 and 1992 that has been observed in subsurface north Atlantic waters at 24 °N and is most pronounced between depths of 0.7 and 2.5 km with values up to 0.5 °C. In limited areas, comprehensive ocean-measurement networks exist. An example is the network to support the research, detection, and prediction of El Ni ño, the Tropical Ocean Atmosphere (TAO) array in the tropical Pacific Ocean.  This netwo network rk of moor moore ed i nstru nstrume ment nt syste ystems ms pr provide ovides te tempe mpera ratur ture e and and curre current nt measurements in the upper levels of the ocean across the Pacific between 10°S and 10°N. However, that network does not involve deep ocean water. For decadal-scale variability, long observational records will be required in both the Pacific and Atlantic ocean areas. In the Atlantic Ocean area, the decadal-scale variability involves deep water conditions which need to be thoroughly measured.

involves deep water conditions which need to be thoroughly measured. 81



Sea-level observations are needed for several reasons. First, spatial sea-level variations relate to ocean currents and provide information useful for understanding ocean dynamics. Spatial sea-level variations can be measured from satellites. Second, sea-level changes can have potentially deleterious impacts on coastal coas tal regio regions. ns. L Lon ong g term in-situ monitoring is needed to isolate changes due to climate change from many other factors that can affect sea level in order to make projections for future impacts.

Surface hydrology

Surface land cover Cryosphere

Routine long-term soil moisture observations are woefully scarce over the world. Only a few countries such as the Russian Federation have observations that extend over many decades. Understanding climate change processes over land areas will require such information. Remote sensing from satellites together with calibration from local surface observations is providing an important monitoring of land surface changes. This information helps to define changes in the land surface forcing of the atmosphere and the response of the terrestrial biosphere to climate variations. It is important to maintain this observing activity for monitoring and studying climate change.

Satellite observations are important for monitoring snow and ice cover over land, and sea-ice extent over the oceans. However, many of the records to date are undocumented with regard to processing procedures and are of short duration. It will be important to give more attention to data quality and continuity for

climate monitoring purposes. 82



6.3.3  The surface urface tempe tempera ratur ture e of the Ea Earth rth is a ke key y des descriptor criptor for clima climate te.. Surfa urface ce


Surface temperature

station reports are important for providing the observational data as satellites are incapable of measuring the details of temperature in the surface boundary layer (the lowest several meters in the atmosphere). There are numerous deficiencies in these observational records. Over land, the density of reporting stations varies by continent. Africa, Central America, and South America have large areas which are not adequately covered. Figure 6.3, which presents mean maximum temperatures reported by surface stations, shows the uneven spacing in station distribution. To compound the problem, the number of reporting stations is decreasing even in those areas that are already sparsely represented. As shown in Figure 6.4, the decrease was more than 10 per cent from 1989 to 1994.  There  The re are ma many ny fa fact ctors ors which introd introduc uce e non-unifor non-uniform ms spa patia tiall and tempo tempora rall biases into land surface temperature data. These include changes in instrumentation, instrument shelters, station location and time of observations. Changing an observation site from a city to a nearby airport can cause systematic changes in the temperature due to the ‘heat island’ effect of the urban area. In many cases, the principles for long-term climate prediction presented in Section 6.2 were not observed. A common omission has been the overlapping of measurements between old and new observing systems when the observing system is changed. M any of th the e temperature temperature bi bias ases es int ntrod roduced uced by thes these e chan change ges s are are of magni magnitudes tudes similar to those expected with climate change. Thus, it is important to define these biases and remove them from the climate record. Sea-surface temperatures have been measured extensively over large parts of  the oceans from ships that voluntarily make temperature measurements (voluntary observing ships and ships of opportunity), as well as from former ship weather stations, and from buoy systems. Ships of opportunity and voluntary observing ships have provided considerable data for more than 100 years in some parts of the world. However, the method of measurement has changed over the years from sampling with buckets of water drawn from the ocean to sampling of water coming into the engine intake. Efforts have been made to adjust data to a common frame of reference. However, information is lacking about the details of the measurement so it has been difficult to make precise corrections to historical data. A good review is provided by Parker, et al., in Reference no. 2 [pp. 429-470]. Satellites provide virtually complete coverage of the oceans for sea-surface temperature measurement. It is a challenge to combine this data with that obtained by in-situ measurements because the satellite is measuring temperatures at a very thin surface layer (‘skin’ temperatures) whereas in-situ measurements are for a deeper surface layer. In addition satellite measurements are affected by atmospheric atmos pheric tur turbidi bidi ty whi ch requires adjustments adjustments to th the e data. data. Th The e rece recent volcani vo lcanic c eruption erupti on of M t. Pinatu Pi natubo bo caus caused ed bias biases es as large as 1°C in the satellite temperature data.

Figure 6.3 — C li mato matologica logicall  stations  sta tions with 1961-90 196 1-90 normals normals data for mean maximum te temp mpe erature ratur e. A ll station stati on da data ta has bee been re r ecei cei ved ved by the C Clili mate Research Unit at the University  of East Anglia from National  Meteorolo  Mete rological gical A genc gencii es. A total total of 6 63 632 2 locations locations are are shown. shown. [fr [from om page 418, Refe Reference rence no. 2, with pe permis rmiss sion of 

Kluwer Academic Publishers]. 83



Figure 6.4 — N umber umber of  ‘CLIMAT ’ messages (containing monthly temperatu temperature re summari summarie es) received at the UK Met. Office thr through ough the G Global lobal Teleco Telecommummuni nica cati tions ons Syste Systems ms for the pe peri od  of 1977-1993. [from page 72, Reference no. 2, with permission of Kluwer Academic Publishers].


Rainfall is a very important climate parameter for all forms of life on land. Nevertheless, its measurement is very inadequate. Precipitation has much localscale variability which makes it difficult to find measurements representative for an area. Some of this variability is due to topographic effect. Rain gauge measurements are quite sensitive to rain gauge design, to wind conditions and to whether the precipitation is rain or snow. For snow conditions gauges can seriously underestimate precipitation amounts. Changes in rain gauge design and observing practices which have been common throughout the world result in a major task task fo forr pro producin ducing g homoge homogeneous neous c cll imate da data. ta. Figure 6.5 show shows s a sampl samplin ing g of changes that have been made throughout the world with estimates on the biases introduced into the data. Satell ite and radar s se ensin nsing g sys syste tems ms provide additional additio nal in informatio formation n on precipprecipitation, and this information should become better quantified with time. However, these data are available only for short times and the radar data has limitations in the observations of diurnal cycle variability. It will be essential to calibrate and combine gauge and remotely-sensed data to get long-term climate records. The gauge data will remain important as calibration for the remote-sensed data. For reasons mentioned above, there is considerable uncertainty in climate records of precipitation. Nevertheless, there are first estimates of precipitation trends as reported by IPCC [pp. 152-156, Reference no. 3]. For example, analysis of data suggests an increase in precipitation in high northern latitudes during this century and an increase of winter precipitation in northern mid-latitudes. It will require considerable effort to obtain climate records for precipitation that will be useful for identifying trends due to climate change.






A number of steps are being taken to improve long-term climate monitoring. A few are briefly presented here.  There are ong  There ongoing oing efforts to g ga ather ther da data from archive rchives s throug throughout the world world a and nd to render it into a form that allows it to be easily accessed and incorporated into the climate data files. Digitizing hand written records is an important aspect of  this work. For all such data it is also necessary to study existing metadata (documentary records about site changes, exposure details, etc.) to correct for artificial biases in the data. data. WM W M O has its Data Re Rescue scue prog programme ramme (DARE), (DA RE), to help in thi s area. Construction of analysis (spatial distribution maps) from data is important for describing and understanding processes in the climate system. The process of  analysis also also helps to fi fill l i n wh whe ere obse observational rvatio nal data is miss missin ing g and and to provide provi de the information database needed for numerical model studies. Numerical models themselves are important components of the analysis process. Adjustments in the patterns that evolve in numerical models as observational information is introduced achieves a ‘four dimensional data assimilation ’ whereby best-estimate and internally consistent initial analyses for prediction models are produced from observational data. This has been the practice since the advent of operational Numerical Weather Prediction (NWP) in the 1950s. The improvement of NWP models has improved the quality of the analysis fields

representing observations. 84



Figure 6.5 — T  Tii me-varyi me-varying ng biases biases of precipitation measurement for  various co countri es (from ( from K Karl arl et al., 1993. [from page 77, Reference no. 2, with permiss permission ion of Kluwe Kl uwerr Academic Acade mic Publi shers] shers]..

Changes of the model formulation over time have led to variations in the quality of the analysis and have introduced artificial time-varying biases into the data. In order to obtain an observation analysis record that has a uniform (homogeneous) frame of reference in time, reanalysis has been conducted using the same numerical prediction model. For the purposes of NWP, a reanalysis has been carried out to obtain a consistent dataset of atmospheric observations over a significant portion of the world from around 1957. Generall rea Genera reanalysis nalysis is is c currentl urrently y bein being g done by two programmes. programmes. In the Unit Un ite ed State tates s, the Environmental Enviro nmental M ode odell ll in g C enter of the th e National Nati onal Ce C enters for Environmental Prediction (NCEP) together with the National Center for Atmospheric Research (NCAR) are using a forecast model that became operational in 1994 to reanalyse initial conditions for daily data at many levels in the atmosphere. Their plan is to go back to 1957. In Europe, the European Centre for M edium-Range di um-Range Wea Weather ther Foreca Forecas sts (ECM W F) is is a also lso con conductin ducting g a re reanalysis analysis of 

daily data at many atmospheric levels using one of their recent comprehensive 85



forecast models. Their reanalysis period starts in 1979. These reanalysis projects are of central interest to the World Climate Research Programme (WCRP). The reanalysis data will greatly facilitate long-term climate monitoring. Further reana reanalysis lysis infor informa mation tion is g given iven in W M O (1997 (1997). ).

6.4.3  The incre increa ase of in-situ measurements for greenhouse gases, aerosols and ozone is


considered a relatively low-cost enhancement of great value for observations directly related to climate change. In particular, increases in the number of places where flask measurements for CO2 concentration are obtained are needed to provide provi de validation vali dation poi points nts for future f uture sate satell ll it ite e meas measureme urements. nts. More M ore in-situ measurements of aerosol chemical and physical characteristics would greatly benefit determination of its radiative properties. An increased number of balloon-borne measurements of ozone and water vapour in the stratosphere would provide important validation data for future satellite measurement.


Satellites will play a key role in new measurements for climate monitoring. Many new systems are being developed for satellites to improve measurements of the radiation budget, clouds, trace gases, surface changes on Earth and so forth. Careful attention will have to be given to calibration of the data and overlapping measurements between different satellite systems.  The Ea Earth rth Obse Observing Sys yste tem m (E (EOS OS)) progra programme mme of the U.S U.S.. Nationa Nationall Aeronautics and Space Administration (NASA) is a good example of the advancement me nt in remote sensin nsing g from satelli satelli tes. tes. The T he EOS progra programme mme is developin lo ping g over over 20


new satellite systems to obtain or improve measurements of many climate system parameters (NASA, 1995). The list includes: solar radiation (total irradiation and ultraviolet component only); atmospheric water vapour vertical distribution; images of the land surface, water, ice and clouds; Earth radiation budget; topography of the sea surface and ice sheets; flux of trace gases (including carbon dioxide) at the air-sea interface; global aerosol distribution; cloud properties such as optical thickness; cloud heights; planetary boundary layer heights; global distributions of numerous trace gases and aerosols in the upper troposphere, stratosphere and mesosphere; location and radiant energy of lightning flashes; precipitation rate; cloud water; sea-surface temperature; soil moisture; angular solar reflectance reflectance functi function ons s from th the e top of the t he atmosphere atmosphere,, clo clouds uds a and nd the Earth’s surface; biological processes (chlorophyll concentration, vegetation productivity, etc); tropospheric pollution; and sea-surface winds. Innovative approaches for temperature measurements are being considered. On One e idea is to mon monit itor or global tte emperature mperature change changes s by me meas asuri uring ng the th e Earth Earth’s electrical fields. Overall electric potential variations with height are related to the number and intensity of thunderstorms in the world. To the extent that thunderstorm derstorm activi ty rre el ate ates s to temperature rature,, m me easureme asurement nt of electric potential potenti al wo would uld provide a measure of temperature conditions. Another idea is that the measurement of temperatures at great depths (like 600 m) below the surface of the Earth could provide signals for long-term temperature changes. This is already being done in the ocean as shown in Figure 6.4. Finally, the travel time for acoustic waves in the ocean over great distances may provide useful information on temperature changes in the deep ocean since the sound speed depends, in part, on temperature.





7.1  The project projections ions for future future clima climate te cha chang nge e ar are e continually continually upda update ted d as climate climate


models are improved observational records are expanded, understanding of the climate system is improved and estimates of the climate forcing due to human activity are refined. All of these areas involve complex considerations and uncertainties. A large number of research groups throughout the world are focused on improving the projections for future climate through the coordination efforts of  the Intergovernmental Panel on Climate Change (IPCC), the World Climate Research Programme (WCRP) and other substantive programme activities of the World Meteorological Organization (WMO) and the International Council of  Scientific Unions (ICSU).  The res results pub publishe lished da att a any ny one one time will be supers rse ede ded d by by ne new w rre esults. ults. The differences between successive conclusions may be confusing to those outside the community working on climate change. One must appreciate that the changes in conclusions are incremental and part of a coordinated approach to finding answers. Intercomparisons among many model experiments involving long-term simulations are required to establish a meaningful understanding of their characteristics, sensitivities and uncertainties for any given specification of human climate-forcing impact. As a result, only a limited number of forcing scenarios can be modell modelle ed and iitt take takes s time for tthe he gain gains s ma made de in cli climate mate-change -change proj proje ections ctio ns to become apparent as models and forcing specifications are improved.  This chapte chapter pro provide vides s a pers perspe pect ctive ive on the scope cope and typ type e of conclus conclusions ions that that are being reached. The material presented is based primarily on the material in chapters chapte rs 6 and 8 of Refere Reference nce no. 3. Current C urrent work wo rk and concl conclusion usions sa are re ove overvi rvie ewe wed d here with full recognition that the details will be different in the future. Some updates from chapter 9 of the 2001 IPCC report (Reference no. 7) are also included.  This cha chapte pter inc include ludes s discu discus ssion of re rece cent clima climate te chang change e be beca caus use e tha thatt is the direct antecedent for calibration and understanding of projections for the future. It first presents modelling results and then moves to the detection (analysis of  observations) and attribution (understanding the causes) for climate change. Attribution is the key factor for isolating human-produced effects from natural changes in the climate system.


Figure 3.5 in Chapter 3 identifies the primary anthropogenic impacts on the MODEL RESULTS FOR cli climate mate syste system m up to th the e pres prese ent. I ni niti tial al model simul simulation ation studies considered considered onl y CLIM ATE CHANGE the carbon dioxide component. More recent model simulation studies have 7.2.1 included the sulphate aerosol component. Note that in the description of these RECENT CLIMATE CHANGE experiments, the carbon dioxide concentration used may be an equivalent concentration to represent all of the greenhouse gases. M odel simul simulation ations s for rre ecent cent global mean mean temperature change changes s in the las l astt 45 and 120 years have already been shown in Chapters 4 and 5, respectively.  The predic dicte ted dg globa loball me mea an te tempe mpera ratur ture e increa increases when when the observe rved d incr incre eases in both carbon dioxide and sulphate aerosol are included in the model (see Figure 5.5). The sulphate aerosol itself leads to a cooling effect which partially offsets the warming due to carbon dioxide. Earlier model studies which had not included sulphate aerosols gave larger values for predicted warming. The general upward trend appears quite realistic when compared with observations. The observed temperature record shows much more variability within the 1860-1990 period than is simulated in the model. However, the model result has been

smoothed over time. Yearly mean model values would show similar variability. 87



 So  Sourc urce e Simulated, increase in equivalent CO2 since 1900 Simulated, aerosol forcing and equivalent CO 2 increase since 1990 Observations of recent change (1981 to 1990 mean less 1951 to 1981 mean)






–0.46 –0.43

–0.35 –0.27

–0.08 –0.16

–0.34 –0.32

–0.29 –0.27






* DJF = Dece December mber, Janua J anuary ry,, Februa February; ry; M MAM AM = M arch, arch, April, M ay ay;; JJA JJ A = =June, June, July, August; August; SON = Septembe September, r, October, N Nove ovember mber.

Table 7. 7.1 1 — C  Change hanges iin n diurnal range rang e of 1. 1.5 5 m tempe temperature ratur e averaged over over seasons and the the annual cycle. cycle. T he si simulated mulated and  observed values are averaged over  the regions whe wh ere obse observ rvati ations ons are available avail able.. T he obse observed rved data are  from  fro m Horton Horton (1995) an and d the  simula  simu latio tions ns fro from m Mi tche tchell ll et al. (1 (199 995) 5).. The T he change changess iin n greenhouse  ga  gass forc forcing ing (rep (represe resent nte ed by an equivalent increase in CO 2 ) and  and  direc di rectt sulphate sulphate aerosol aerosol forci forcing ng ar are e

Diurnal temperature range decreases are predicted by theory when the greenhouse g gas ases es and sul sulph phate ate aerosols erosol s are in increas creased. Mod M odel el result results s show how a decreas decrease e in diurnal temperature range since 1900 over Northern Hemisphere continental areas as observed increases in carbon dioxide and sulphate aerosols are included in the model. The observations also show decreases in diurnal temperature range (see Table 7.1). However, other processes also affect diurnal temperature ranges such as cloudiness and surface evaporation. The relative importance of all these effects is still uncertain. In summary, model simulations generally have suggested that the human production of carbon dioxide and sulphate aerosols, starting with the industrial era, has already caused climate change in terms of global warming. Recent numerical model simulations have given estimates for the global-mean warming that range from 0° to +1.6°C, as shown in Table 7.2. These results imply that such

those esti esti mated to have o occurr ccurred  ed   since 1900. No  since Note te th tha at theobse bserve rved  d  changes are available only over the latter half of this period and are the diff di ffe erence be betwee tween n tthe he me mean an ffor  or  19 1981 81 to 1990 199 0 and the mean mean for  1951 to 1980. [from page 295,

changes are currently ongoing and that the atmosphere and the climate system currently are not in equilibrium. As reported in the 2001 IPCC report (Reference no. 7) recent studies further confirm this conclusion.  This pre pre-e -exis xisting ting cha chang nge e and c curr urre ent lack lack of equilibrium must must be be cons conside idere red d when designing model experiments for future climate change that start from present conditions. The year 1990 has often been used as the initial time for future climate-change simulations. If the climate model is defined as being in equilibrium before starting the future climate-change experiment, the initial rate of simulated temperature increase will be erroneously suppressed as the model develops the radiative imbalance conditions. This is called the ‘cold start’

Reference no. 3].

 Study  Stu dy

S1 S2 S3 S4

Se Sensit nsitivity ivity to do doubl ubling ing CO 2 ( °  ° C)

Direct Direct ae aerosol rosol forcing ( Wm-2 )

Tempe peratu rature re re resp spo onse of equ quililibrium ibrium du due e to aerosols ( ° C)

Tempe perat rature ure re resp spo onse of  equ quililibr ibrium ium to combine bined ae aero roso soll and CO 2 forc  forcing ing sinc since e 1900 ( °°  C C))

2.8 5.2# 5.2 3.9

-0.7 –0. 0.9 9 –0. 0.6 6 –0.3 (direct) –0.8 (indirect)

–0.9 0.9* –0.9 –0.8 1.6$ –1.6

0.5 0.5 0.6+ 1.6 0.0 0.0$

Roe oec ckner ner et al. (1995) Taylor and Penner (1994) Mi Mittchell et al. (1995a) Le Treut et al. (1995)

Table Tab le 7.2 — Equilibrium global mean mean rre esponse to the increa in crease se in in  gree  gre enhouse nhouse gase gases a and nd sulphat sulphate e aerosol concentrations over the  20 th century. The experimental designs in the four studies differ,  so some some of the entries are deri deri ve ved  d  underr specif unde specifii c assumpti assumptions ons defined below. [from page 293, Reference no. 3].

S1 and S3 use the aerosol distribution of Langer and Rodhe (1991) and represent aerosols as an increase in surface albedo. S2 derives the sulphate loading from a coupled atmosphere sulphur cycle model and includes an explicit radiative scattering treatment of aerosols. * As Ass suming 40 per per ce cent nt incre increas ase of CO2 gives 50 per cent of the warming due to doubling, and subtracting this value from the combined forcing experiment in the final column. # Assum Assuming ing 25 per ce cent nt increa increase se in CO 2 gives 29 per cent of the warm warming ing due due to doubli doubling. ng. + Using a 25 per c cent ent incre increas ase in CO2, whereas S1 and S3 use a 40 per cent increase to allow for changes in all greenhouse gases. $ The forcing forcing used includes both the es estimated timated direc directt and indirect forcing. In the las lastt column, colum n, C CO O2 was increased by 25 per cent. Substantially higher sensitivities are found if  a colder simulation is used. Although the global mean temperature change in S4 is zero, the model gives a cooling in the Northern Hemisphere and a warming in the Southern

Hemisphere. 88



  m    W    (   g   n    i   c   r   o    f   s   s   o   r    G

Actual Model



(b)   e   s   n   o   p   s   e   r   e   r   u    t   a   r   e   p   m   e    T





Cold start Model

 Ye  Year

  m    W    (   g   n    i   c   r   o    f    t   e    N



1900 1900


T a





Figure 7.1 — Sche  Schemati maticc diagr diagrams ams of radiati radi ative ve forcing and tempe temperature ratur e respo response, nse, showing showing the eff effe ect of negle neglecti cting ng tthe he eff effe ect of past   fo  forc rcing ing (the‘cold cold start  star t ’ prob  proble lem). m). Pane Panell ((a) a) sho sh ows fo f orcing rcin g due to i ncrease ncreasess i n C O 2 , with with a rate rate of incre increa aserising gra grad dually ually to 1990 as as obse observed, rved, and maint maintai ained ned at one one pe per cent/yr cent/yr tthere hereafter after (left ( left curve) and fr from om a one one p pe er ce cent/yr nt/yr i ncrea ncrease se star startiting ng abruptl abruptlyy iin n 199 1990, 0, as in in idealized experiments (right curve). Panel (b) shows temperature response to the forcing in (a). The upper curve, which is the response in the case with the gradual initial increase as observed, has been transposed vertically to zero at 1990 (dashed curve) to highlight the i ni nititial al slow re response sponse in the case case of an abrupt increa i ncrease se used used iin n iide deali alized zed experi xperime ments nts ((lowe lowerr curve cur ve)). T he di di ffe ff erencebe betwee tween the two curvess iiss kno curve kn own as ‘the cold cold start  start ’ and is an artefact of the experimental design. Panel (c) shows the net forcing (which allows for the i ncrea ncrease sed d loss of radi radiati ation on to space spaceas the mode modell warms) war ms) i n the case case with a gradual start start to to the forcing. forci ng. T he lowe lowerr cur curve ve sho shows ws the the net   fo  forc rcing ing (upp (upper curv urve e) in theidea idealized lized case. se. Note Note th the e ne nett he hea ating at 1990 which which ma mainta intains ins thewarming warming of theocean in tthe he upp upper curve urve i n (b). ( b). To heat heat 300m 300 m depth of of water water by 0.3 0. 3° C/decade C/decade (typical of the AOGCM experiments in climate change assessments) requires a net net he heating ating of of 1.5 1 .5 W m-2 (c (cf. f. 4 W m-2 fo  forr a doub ubling ling of CO 2 ) whic which h take takess seve several decades to build up with a 1 pe per cent/yr nt/yr incr incre easein CO 2. [from page 313, Reference no. 3]. problem. It is estimated that the lag effect on predicted temperature changes will last for several decades, a time scale determined by the adjustment time of the oceans. Rates of temperature change would be underestimated during this time as shown in Figure 7.1.



A range of climate model predictions have been made. The largest group dealt with greenhouse-gas impacts alone. In the description of these experiments, as noted before, reference may be made to the changing concentration of carbon dioxide alone, or to an equivalent carbon-dioxide concentration, that is calculated to represent the effect of all the greenhouse gas changes attributable to human activity. Experiments have been carried out to examine the effects of  doubling equivalent carbon dioxide concentrations either all at once or gradually with rates of increase varying from 0.25 per cent per year to 4 per cent per year. Note that the current observed rate of increase of carbon dioxide itself is about 0.5 per cent per year, and the overall increase from the pre-industrial period to 1990 has been 26 per cent (see Table 3.2). A current rate of 1.0 per cent per year 4


Fig. 7.2 — C ompari ompari son be betwee tween n  se  seve veral ral AOG C M simu si mulatio lations ns (climate sensitivities between 2.1 and 4.6° C) C) and two versions of  the simpler UD /EB-type /EB -type mod mode els (wi th climate sensi sensiti tiviti viti es o of  f   2.5 ° C and 2.2° C). C). All models were forced with one per cent/yr  (compound) increase of  atmospheric CO 2 concentration  from equili brium or nearnearequilibrium in 1990. [from page

   )    C             °    (   e   g   n 2   a    h   c   e   r   u    t   a   r   e   p   m   e 1    t    l   a    b   o    l    G

Coupled AOGCMs GISS (k ) CSIRO (d ) MRI (p ) UKMO (t1) UKMO (t2 ) GFDL ( j ) BMRC (a ) UKMO (s ) NCAR (r ) COLA (c ) UD/EB models Section 6.3 Section 7.5.3


-1 0






300, Reference no. 3].

Year from start of experiment





for equi quivalent valent carbon di dioxi oxide de is close to reality. realit y. More Mo re re rece centl ntl y, experiments experiments have included sulphate aerosols and other greenhouse gases more explicitly. Other experiments have looked at factors of two and four for carbon dioxide increases. Projections have been generally made to about the year 2100, although some studies studi es have g gon one e to th the e yea yearr 2500. M Many any predicti prediction ons s have s started tarted wit with h 1990 conditions; others have gone back to pre-industrial time, eliminating the ‘cold start’ problem.

Mean conditions

Results from experiments with fully-coupled ocean-atmosphere climate models have shown large differences in the simulations for climate change impacts even for globally-averaged surface temperature. A comparison was made among ten models for the case where (equivalent) carbon dioxide concentration was increased at the rate of one per cent per year starting from 1990 values and the model conditions were initially in equilibrium. This rate of increase gives a doubling of CO2 in about 70 years. Results for global surface temperature are shown in Figure 7.2. Also shown are results from the simpler type of climate model labelled (UD/EB) discussed in chapter 4. The warming after 70 years ranges from 1.5° to 3.8°C. This va varia riability bility of  model response has led to a calibration descriptor for climate models called ‘climate sensitivity.’ Climate sensitivity is defined as the increase in the equilibrium value of global mean surface temperature produced by the model with a doubling of carbon dioxide concentration. The climate sensitivity value is larger than the values shown in Figure 7.2 after 70 years because the 70-year value is not an equilibrium value. It would be necessary to run the model for many years with the CO2 concentration held constant at its doubled value in order to get the equilibrium value of temperature. Differences between the model results are even more dramatic for spatial patterns and local/regional temperature change values. Figure 7.3 shows a comparison of annual-mean temperature spatial patterns between two of the models shown in Figure 7.2 at the time the CO 2 reaches a doubled value. Note that the increases tend to be larger over land than over water and larger at higher latitudes in both models. However, differences in the details between the models can be large as is seen over northern Africa. Some experiments for CO 2 concentration doubling and with sulphate aerosol effects included have simulated mean temperature decreases in regional areas in both summer and winter seasons. These areas included parts of China and the United States where aerosol concentrations were high. However, the uncertainty in results for regional areas is large, as shown by the range of values in the model intercomparisons in Figure 5.6 and Figure 7.4. Differences in predicted changes of seasonal mean temperatures ranged up to 5°C. For the same simulations, predictions of seasonal mean precipitation changes differred by up to two

mm/day. In some cases, different models even predicted changes of opposite sign. 90



Figure 7.4 — Si mulated mulated regional changes from 1880-1889 to  2040-20  204 0-2049 49 (e ( experi xperi me ments nts x,y) or   from pre-i pre-indu ndustrial strial to 2030-20 203 0-20 50 (experiments w,z). Experiments x  and w include greenhouse gas  forcing  forc ing only only,, whe where reas as y a and nd z a also lso include direct sulphate aerosol effects. The x, y, w and z  indicators are in the model list at  the botto bottom m of the figure. fi gure. (a) ( a) Temperat rature ure (D ( D ece cembe mberr to February); (b) Temperature (June to August); (c) Precipitation ( D ecembe cemberr to February) February) ; ((d) d) Precii pitation (June Prec ( June to August); (e)  Soilil mo  So moisture isture (D ecemb cembe er to February); (f) Soil moisture (June to August). CNA=Central North  Ame  A meri ri ca; ca; SEA SEA=S =So outh East A sia;  SAH =S =Sah ahe el; SEU=So SEU=Southern uthern Europe; AUS=Australia. [from page 306, Reference no. 3]. Seasonal soil moisture predictions were also in varying directions with differences as large as three cm. Note that the model simulations discussed in the previous paragraph cover different time periods and have different CO 2 concentration variations than do the model simulations discussed in the two preceding paragraphs. This does not change the overall characteristics discussed here. Climate-change assessments have been made with the climate models using standardized projections for the changes in anthropogenic forcing of the climate system. For the 1995 IPCC reports six emission scenarios established in the 1992 IPCC report were used in the model studies. The assumptions for these scenarios (referenced IS92a-f) are described in Table 7.3. The scenarios cover the period from 1990 to 2100 and provide examples of low (IS92c), medium (IS92a), and high (IS92e) impacts. A new set of 40 emission scenarios (referenced SRES for ‘Special Report on al.. , 2000) to define Emissio Emiss ion n Standards’) was esta stabli blishe shed d in 200 2000 0 (Naki (N aki c ´ enovic´ et al future projections for anthropogenic emissions. Of these, 35 scenarios which contain data on the full range of gases required for climate modelling, define the full ful l set of sc sce enarios nari os us use ed for climate cli mate-change -change proj proje ections. ctio ns. A subs subse et of these these was used in studies for the 2001 IPCC report. These scenarios generally define the range of possibilities in terms of demographic, economic and technological development. A group of six ‘marker’ or ‘illustrative’ scenarios from this set serves to desc descri ribe be the pri primary mary range of un unce certain rtainty ty fo forr fut future ure projection projections. s. The Th ese six scenarios are highlighted in Table 7.4 (from Chapter 9, Reference no. 7). Basically the A categories have larger emission outcomes than the B categories. The three subsets of the A1 category (A1FI, A1T, and A1B) refer to fossil fuel intensive, non-fossil fuel sources, and reliance not on only one energy source cases, respectively. The projections for total radiative forcing out to 2100 for the six SRES ‘marker’ categories along with three of the scenarios examined in the IPCC 1992 studies (i.e. IS92c, IS92a, and IS92e for low, medium and high impacts, respectively) are shown in Figure 7.5. Note that the IPCC Working Group II, which examined impacts, adaptations, and mitigations of climate change, has adapted revised anthropogenic forcing scenarios based on anticipated new efficiencies for anthropogenic energy production systems that will cut back on increases in carbon dioxide

production. See the 1995 IPCC Working Group II report (pp. 47-50, Reference 91



 The e A1 stor tory yli line ne and scenari nario o fa fami mily ly de des scrib cribe e a futu future re wo worl rld do off v ve ery ra rapi pid d eco econo nom mic gr grow owth th,, glo globa ball p pop opul ula ation tion ttha hatt pe peaks A1.  Th in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies. Major underlying themes are convergence among regions, capacity building and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income. The A1 scenario family develops into three groups that describe alternative directions of technological change in the energy system. The three A1 groups are distinguished by their technological emphasis: fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance across all sources (A1B) (where balanced is defined as not relying too heavily on one particular energy source, on the assumption that similar improvement rates apply to all energy supply and end-use technologies).

A2. Th  The e A2 story torylin line e and scena nari rio o fami family ly de des scribe cribe a very hete hetero roge gene neou ous s wo worl rld. d. The The un unde derl rly yin ing g th the eme is self lf-r -re eli lia anc nce e and pr pre eservation of local identities. Fertility patterns across regions converge very slowly, which results in continuously increasing population. Economic development is primarily regionally oriented and per capita economic growth and technological change are more fragmented and slower than in other storylines. B1.  Th  The e B1 B1 s sto tory rylin line ea and nd s sce cena nari rio o fa fam mily de des scribe cribe a con conv verg rge ent wo worl rld d wit with h the same glo globa ball popul popula ation, tion, th tha at pea peaks iin n mi middcentury and declines thereafter, as in the A1 storyline, but with rapid change in economic structures toward a service and information economy, with reductions in material intensity and the introduction of clean and resource-efficient technologies.  The  Th e empha emphasis is o on n gl glob oba al solu olutio tions ns tto o econo conom mic, ic, socia ociall and env envir iron onm ment nta al susta tain ina ability bility, in incl clud udin ing g impr improv ove ed equ quit ity y, bu butt without additional climate initiatives.  The e B2 story toryli line ne and scena cenari rio o fa family de des scribe cribe a worl world d iin nw whi hich ch the the emp mpha has sis is on loca local s sol olut utio ions ns to eco cono nomi mic, c, socia ociall a and nd B2.  Th environmental sustainability. It is a world with continuously increasing global population, at a rate lower than A2, intermediate levels of economic development, and less rapid and more diverse technological change than in the B1 and A1 storylines. While the scenario is also oriented towards environmental protection and social equity, it focuses on local and regional levels.

Table 7.4 7. 4 —  The Emissi ons  Sc  Sce enarios of the Sp Spe ecial Re Rep port on on Emissions Scenarios (SRES). (from page 554, Reference no. 7).

no. 4) for a description of the low CO2-emitting energy supply system (LESS) scenarios.  The unc unce erta rtainty inty in c clima limate te-mo -mode dell p pre redic dictions tions for climate climate chang change e is larg large. The sources for this uncertainty include the uncertainty in the forcing to be prescribed for the system (as shown in Figure 7.5), and uncertainty due to model formulation (as demonstrated by the range in climate sensitivities in the model intercomparisons shown above in Figure 7.2). Note that the uncertainty in the forcing specification is even larger than that due to the model formulation. As a result of these uncertainties, it has been necessary to examine a wide range of forcing scenarios. A simple reference climate model of the UD/EB type described in Chapter 4 has been used to make an overall initial analysis for the many cases to determine general characteristics of climate change impacts because of limitations in the computer resources needed to use the comprehensive climate models for so many experiments. The comprehensive climate

(a) 10 9

Figure 7.5 — Estimated  historical anthropogenic  radiative rad iative fo forcing rcing (W m-2 )  followe  follo wed d by proje roject ctions ions for fu future ture  forcing  forc ing base based d on the the six SRES SRE S marker scenarios and three of the  sc  sce enarios from the 199 1992 2 IIPC PCC  C  (I S92a, S92a, I S92c S92c,, IS92e I S92e). ). A lso  shown  sho wn is the total total range of  variation (shaded area) for the  35 SRES sce scenarios that that co could be used for climate modeling  studies.  stud ies. (from Figure 9.13a on

8    )




  m 6    W    (   g 5   n    i   c   r   o 4    F

A1FI A1B A1T A2 B1 B2 IS92a IS92c IS92e

Model ensemble all SRES envelope

3 2

ars show the


ange in 2100 produced by everal models

0 1800





page 554 in Reference no. 7).




Figure 7.6 — Si  Simple mple model model results for the estimated historical anthrop anthr opoge ogeni ni c global-mea global-mean n  surface  surfac e temp tempe erature rature chan hange ge ( °°  C )  followe  follo wed d by proje roject ctions ions for future future changess based change based on the six SRES SRE S marker scenari scenari os and middlerange of the scenari scenari os from the 1992 I PCC (I S92a S92a)) using a  simple  simp le climate climate mode modell tu tune ned d to  seve  seven n oce ocean-a an-atmo tmosp sphe here re clima climate te mode mo dels. ls. A lso shown is i s the total total range of vari ati ation on (shade (sh aded d area) area)  for all SRES. (from Figure 9.1 9.13b 3b

(b) 7

on page 554 in Reference no. 7).



   )    C        °    (   e 5   g   n   a    h    C 4   e   r   u    t   a   r 3   e   p   m   e    T 2

A1FI A1B A1T A2 B1 B2 IS92a

Several models all SRES envelope Model ensemble all SRES envelope

Bars show the range in 2100 produced by several models







models are used to examine the details of climate change for selected cases. The UD/EB model was calibrated using the comprehensive climate models and set to represent a model with ‘climate sensitivity’  of 2.5°C. The global-mean surface temperature change predictions out to 2100 for the scenarios used in Figure 7.5 (except for IS92a and IS92e) are shown in Figure 7.6. These figures also show the full range of variability ‘envelopes’ for the full set of 35 SRES scenarios described above.  The UD/ UD/EB EB-ty -type pe mode model ha has s als lso o bee been u us sed to exa xamine mine other other pos poss sible impact impacts s due to projected future increases in the greenhouse gases. As an example, possible impacts on the thermohaline circulation in the oceans were studied in experiments that required 1000-year model simulations (see Stocker and Schmittner, 1997). Use of the UD/EB-type model made it possible to perform the set of very long simulations needed for the study. Stocker and Schmittner showed an example where the thermohaline circulation change depended on the rate of  increase of greenhouse-gas concentration in the atmosphere. A slow rate of  increase to a final enhanced value caused the thermohaline circulation to be reduced. A rapid rate of increase to the same final enhanced value resulted in a total ces cessation of the thermohali thermohaline ne circulation. Studies with more sophisticated models reported in the 2001 IPCC report (Reference no. 7) still show considerable variation among models for the same forcing scenario, a doubling of CO 2 in 70 years. See Figure 7.7 for a comparison of the results from 19 models for both temperature and precipitation changes.  The chang change e in globa loball me mea an te tempe mpera rature ture rang range es from +1. 1.1 1 to +3. 3.1 1°C and the change in global mean precipi precipitatio tation n range r anges s from -0.2 to +5.6 per per cent cent.. Th Thii s variability is similar to that reported in the 1996 IPCC studies shown in Figure 7.2 before.  There re have have bee been c climat limate e-cha -chang nge e analys nalyse es for ma many ny s spe pecific cific aspect pects s of tthe he clima climate te  The


system other than mean conditions. Studies have generally been with individual climate model simulations and not the whole group of climate models. Thus, results are quite preliminary.  The de decr cre ease of diurna diurnall varia variability bility ove overr continenta continentall are rea as has has a alre lrea ady been discussed. For longer-term variability, it is sufficient to reproduce the essence of  the summary comments from page 330 in the 1995 IPCC report (Reference No. 3). (i) Exp Expe erime riments nts with dif diffe fere rent nt mode modell configura configurations tions indi indica cate te tthat hat zona zonal-me l-mea an, mid-latitude, intermonthly temperature variability may be reproduced, but there are no consistent results in regard to changes in persistent anomalies called ‘blocks,’  one of the contributors to intermonthly variability. Generalization of climate change impacts from these experiments is difficult since different definitions of blocking have been used and different models

show different changes of geographical patterns of blocking. 94








(ii) (ii ) One On e study s shows hows that that in inte termonth rmonthly ly and intera interann nnua uall variab variabil ility ity diffe dif ferrences be betwee tween GC GCM M s with a simp simple le mixed-laye mi xed-layerr (ocean (ocean model) and tho those se coupled to a full ocean model are larger than changes in either type of  model due to increased CO2 alone, pointing to the importance of using a model model wi with th some repres representation ntati on of EN ENS SO-l ike phenomena (mixe (mix ed llaye ayerr ocean models cannot represent ENSO processes). (ii i) ENSO-l ENSO-like ike variabil variabilit ity y in Sea-Surface a-Surface Te Tempera mperatures tures (S (SS STs) Ts) foun found d in i n se seve veral ral Atmospheric-Ocea Atmosphe ric-Ocean nG Ge eneral neral Ci Circulation rculation M ode odels ls (AOGCM (AOG CM s) still exists with increased CO 2 conditions. The variability shows either little change or a slight decrease in the eastern tropical Pacific Ocean. (iv) Pre Precipi cipitation tation variability variabil ity as ass sociated ociated with EN ENS SO eve events nts increas increased in the climate change simulations, especially over the tropical continents. It was suggested that this could be associated with the mean increase of  tropical SSTs. (v) Seve veral ral mode models ls in indicate dicate enha nh ance nced d iintera nterann nnua uall variabili variabili ty of area-a rea-ave verraged summer rainfall in the South Asian monsoon. (vi) In a numbe numberr of simula imul ations tion s decada cadall and lon long ger ti time me-s -sca cale le va variabili riability ty obscured the signal of climate change in the rates of global warming and patterns of zonal mean temperature change. In one case multi-century variability in ENSO phenomena was as large as the mean change caused by CO2 increase. This indicates that natural climate variability on longtime scales will continue to be problematic for CO 2 climate change analysis and detection.


Extreme event statistics are an important aspect of climate. Predicting such events requires predicting changes in the probability distributions. Changes in variability will strongly affect the occurrence of extreme events and, in some cases, even more than would result from changes in mean values. For instance, decreasing diurnal variability would be expected to decrease the probability of extreme temperature events (hot and cold). Global climate models do not have the resolution needed to predict most extreme events. Techniques such as statistical downsc down scali aling ng and high h igh res resol oluti ution on reg regio ional nal cli climate mate models a are re neede ded. d. T Thes hese e techtechni ques we were re discuss discussed ed earli earli er. er. A summary of some obs observe erved d and modell modelled change changes s

listed in the 2001 IPCC report (Reference no.7) are shown in Table 7.5. 95



Confidence in observed changes (latter half of the 20th century)

Changes in Phenomenon

Confidence in projected changes (during the 21st century)


Higher maximum temperatures and more hot days a  over nearly all land areas

Very likely

Very likely

Higher minimum temperatures, fewer cold days and frost days over nearly all land areas

Very likely

Very likely

Reduced diurnal temperature range over most land areas

Very likely

Likely, over many areas

Increase of heat index b over la lan nd ar area eass

Ver ery y lik likel ely y, ov over most most are reas as


Likely, over many Northern Hemisphere middle to high latitude land areas

More intense precipitation events

Likely, in a few areas

Increased summer continental drying and associated risk of drought

Likely, over most mid-latitude continental interiors. (Lack of consistent projections in other areas)

Not observed in the fe few w analyses available

Increase in tropical cyclone peak wind intensities d Increase in tropical cyclon lone mean and peak precipitation intensities d

Likely, over some areas

Insufficient data for assessment

Very likely, over many areas

Likely, over some areas


Hot days refers to a day whose maximum temperature reaches or exceeds some temperature that is considered a critical threshold for impacts on human and natural systems. Actual thresholds vary regionally, but typical values include 32°C, 35°C or 40°C. b Heat index refers to a combination of temperature and humidity that measures effects on human comfort. c

d For

other areas, there are either insufficient data or conflicting analyses. Past and future changes in tropical cyclone location and frequency are uncertain.

Table 7. 7.5 5 — Estimates of  confidence in observed and   proje  projeccted ted chang change es in extre extreme me weather wea ther and an d cli mate events. events. (from (from page page 575, 575, Reference no no.. 7). Wind

Some of the examples below from the 1995 IPCC report (Reference no. 3) show where changes in extreme weather events were inferred from overall weather or wind pattern changes in the experiments with CO 2 global warming. As in the previous section, these examples are intended to illustrate areas of interest and not areas where conclusions of high certainty have been reached. One source of extreme wind events in the middle latitudes is synoptic-scale storms. The occurrence and intensity of such storms relates to baroclinic field intensity and moisture supply. Changes in middle-latitude storm intensity and tracks have been been exa exami mined ned iin n some c cll imate im ate-change -change sim simul ulation ations. s. Th The ere has be bee en some evidence in the models of storm tracks being displaced poleward and storms being more intense as global warming occurred.  T  Tro ropic pica al c cyc yclone lones are the pr prima imary ry cause of extre xtreme wind events (as well a as s he hea avy rain events) in tropical and subtropical regions. Some global climate models can simulate imulate aspects pects of ttropi ropica call cyclone occurrence occurrence.. H Howeve owever, analysis of global warmi warming ng simulation simul ations s has not sho hown wn any clear-cut clear-cut patterns of ch change anges in freque frequency, ncy, are area a of  occurrence, time of occurrence, mean intensity, or maximum intensity of tropical cyclones. Recall that the observations for Atlantic tropical storm activity have shown no systematic change in the past 50 years (see Figure 2.13).  The 1995 1995 IPCC re repor portt goe goes s on to hig highlight hlight tthe he following pro proble blems ms for clar clariifying climate change impacts on tropical cyclones: (i) Tropica Tropicall cyc cyclon lone es cannot cannot be s simul imulate ated adequate quately ly iin n prese present genera generall ci circula rcula-tion climate models. (ii) (ii ) Some as aspe pects cts of ENSO are not simul simulate ated well well in general neral circulation ci rculation cli mate models. (iii) (ii i) Oth Othe er la l arge-sca -scale le chang change es in the atmos atmospheric pheric ge general neral circulation whi which ch could affect tropical cyclones such as jet stream activity cannot yet be discounted. (iv) Natura Naturall variabili variabili ty of tropical storms is ve very ry larg l arge e, so s sma mall ll tre trends nds are are likely to be lost in the noise.  The effe effects cts of doublin doublin g tthe he c conce oncentra ntration tion of CO 2 in climate models have been  Tempe  Tempera rature ture analysed in terms of changes in daily maximum and minimum temperatures.

Ch ang ange es of up to 10°C were found in regions over land areas. The larger changes 96



were related to modelled alterations in snow cover, soil moisture, and cloud cover. An analysis of climate-model simulations for Victoria, Australia, in a global warming experiment, clearly documented the large change in occurrences of  extremes associated with a small change in the mean value. In a low-warming scenario where the mean temperature increased about 0.5°C in that area, there was a 25 per cent increase in summertime days with temperatures over 35°C and a 25 per cent decrease in wintertime days when the temperature went below 0 °C.  The pro proba bability bility tha thatt five co cons nse ecutive cutive da days ys would would exhibit such uch extreme high or low temperature conditions also showed notable changes. Precipitation

A warmer climate is expected to have a more active hydrological cycle as increased evaporation generally leads to higher water vapour content in the atmosphere. It is believed that this would lead to increases in rainfall, including extreme rainfall events. Recent model studies for doubled CO 2 cases have shown an increase in the intensity of single precipitation events along with an overall precipitation increase with temperature. In general, the resolution in the climate models is not good enough to represent the atmospheric convective elements th that at actuall actually y caus cause e he heavy avy ra rain ins. s. Mo Mode dell experiments for doubled dou bled CO 2 cases have shown both decre decreas ase es a and nd incre in creas ase es in rain rainfall fall for are areas as that n normall ormally yh have ave much rain. In some cases, the predicted rainfall increased while, at the same time, the number of days with rain events decreased. In one experiment where the mean precipitation decreased by 22 per cent in a southern Europe area, the frequency of occurrence for 30-day dry spells more than doubled in the summer. It will take considerably more research and model experimentation to clarify the expectations for mean and extreme precipitation associated with climate change.

7.2.3  The discu discus ssion in Chapte Chapterr 4 on the a as spect pects s of curr urre ent climate limate mode models which which


contri bute the mos contribute mostt un unce certaint rtainty y to model model simul ations provide provi des s the ba bas sis for de deve velloping the model needed to improve climate change assessments. The 1995 IPCC report [pp. 345-348, Reference no. 3] discussed nine areas considered to be most important for improving global climate models; these are summarized below.

(a) Cloud modeling

It is important to improve the parameterization of cloud formation and dissipation, as radiative energy transfer is very sensitive to cloud cover. This will require improveme imp rovements nts iin n microphysical mi crophysical para parame meterization terization to better better represe represent the ice i ce phase in cloud, particle size distribution and the type of precipitation (rain or snow). Particle size distribution and ice phase components are all important for determining the radiative properties of clouds, particularly for solar radiation. The proper simulation of snow is important for impacts on surface albedo and ice field growth. Furthermore, improvement is needed in parameterization for cloud scale dynamics to include deep convection and turbulence effects. Additional observational information will be necessary for this work.

(b) Ocean component

Improvement of resolution is essential to improving the ocean component. It is felt that the horizontal resolution needs to be reduced to much less than a 1 ° l atitude-lon atitude-longitude gitude g gri rid d to rre esolve the s small malle er-sc r-scale ale e eddi ddie es that infl in flue uence nce the circulation with even smaller grid spacing in tropical areas. Currently, some ocean genera generall circul circulation ation models have gri grid d spa spacin cing g as as small small as 1/6° in both latitude and longitude. Improving the thermohaline circulation simulation is necessary for representation of the dynamics of the full ocean and long time-scale interactions provided by the oceans in the climate system. Observation of the temperature, salinity and motions in the deeper ocean areas will be required for this modelling improvement work. Finally, parameterization for sub-grid scale processes needs to be improve impro ved. d.

(c ) Flux adjustments  To mainta intain in approp ppropria riate te bala balance nces in the c coup ouple led d oc oce ean-atmo n-atmos sphe phere re sys yste tem, m, it is

desirable to avoid the need for using flux adjustments at the ocean-atmosphere 97



interface in the climate models. The use of a flux adjustment makes it more difficult to interpret variability in the model simulations; many of the major modelling centers no longer use it, because the realism of their climate models has improved. (d ) Longer periods for simulations

M ore genera nerall ava avail il abili ty o off ense ensembles mbles of 100- to 1000-ye 1000-year ar (or even even longe lo nger) r) simulations will help to calibrate and validate climate models using past-climate variations. It is expected that computer capacity will continue to grow to make this possible.

(e) Sea ice component

Sea-ice modelling should include motion effects to properly represent dynamic and thermodynamic feedbacks to the ocean and atmosphere.

 f ) Land surface processes ( f 

A fully interactive land-surface component should be incorporated into the global climate models used for climate-change assessments particularly for land areas.  This will will require require mod mode el formula formulations tions that that repre represent land-s land-surfa urface ce struct tructure ure (land(landsurface type) and functions (processes within the land-surface features).

 g) Radiation ( g Radiation computa computation tion

It is necessary to improve the radiation computational scheme so that, in particular, the water vapor and aerosol effects on solar radiation are represented better.

(h) Global carbon cycle  The oce oceanic a and nd la landnd-s surf urfa ace comp compone onents nts of the the carbon rbon c cyc ycle le should be incorpoincorporated rate d iinto nto the climate cli mate models. models. Th This is in incorporation corporation de depe pends nds on o other ther impro improve veme ments nts in the oceanic and land-surface components mentioned above. In the ocean, deep circulation circulati on effects are important. import ant. It I t wi ll be nece neces ssary tto oo obtain btain better better obs obse ervations rvation s for the deep ocean currents and carbon chemical components. (i ) Tropospheric chemistry

 The ra radia diative tive effect ffects s of ttrop ropos osphe pheric ric sulphate ulphates s, dus dust, t, ozone ozone and othe otherr gre gree enhouse nhouse gase gases (CFCs, methane methane,, and n nit itrous rous oxi oxide de)) shoul should d be incorpo incorporate rated d int i nto o the th e cli climate mate model. This will require modelling the appropriate chemical processes for these substances and predicting the size distribution for the aerosol particles.


A key challenge for climate change assessment is to determine how much of the observed changes in climate are, in fact, due to the effects of human activity (anthropoge (anthr opogeni nic c factors) and to desc descri ribe be these these aspe aspects. cts. I t iis s important impor tant to t o make the climate change issue understood in order to gain public interest for taking appropriate responses to limit climate change and to deal with impacts on society. M ete eteoro orollogists need need to be able to give a clear respon response se to tthe he questi question on:: “ha has s climate change already occurred?”. It is recognized that humans have already had significant impact on environmental conditions, particularly since the beginning of the industrial era. The discussion about climate change has included changes up to the present as well as those anticipated in the future. Considerable attention is now being given to showing showin g that cl clim imate ate change is already in progre progres ss. The tas task kh has as two parts p arts:: fi rst to isolate signals due to anthropogenic influences from the background ‘noise’  of  natural variability (detection), and second to define the specific causes of these effects (attribution).  The stud studie ies s us use ed to dete detect ct clima climate te cha chang nge ea and nd to attrib ttribute ute its ca cause uses ha have ve become increasingly sophisticated. The approaches, from simplest to most complex, may be described by four ‘stages’ listed below:



Stage 1:

Examine global or hemisphere-mean values of a single atmospheric descriptor, (most commonly used has been annual-mean surface temperature);

Stage 2:

Examine spatial patterns of a single atmospheric descriptor, (again, most commonly used has been surface temperature averaged over a season or a year, although three dimensional temperature patterns have been used);

Stage 3:

Same as Stage 2 but the data (model or observed) have been filtered in space and/or

in time to make the anthropogenic component signal more detectable; and, 98



Stage 4:

Simultaneously use more than one atmospheric descriptor in the analysis made for any of the first three stages. M odel res resul ults ts a are re often often use used d as signals for the attributi on o off th the e data. data. It I t iis s possibl poss ible e to isolate i solate the cause causes for model signals by mo mode del-se l-sensiti nsiti vit vity y studies where hypothesized causal factors are varied one at a time. Results can be extrapolated from observed conditions where there is correspondence of model signals to observed change signals.


Recent progress in detection and attribution studies has been made possible by a number of advances. First, more realistic climate simulations have become available from improved climate models. Anthropogenic forcing factors such as for sulphate aerosols have been added. The first experiments had carbon dioxide or general greenhouse-gas forcing only. Second, more and longer control simulations have provided more reliable statistics for natural variability. Third, more sophisticated statistical analysis techniques have been developed. Nevertheless, there are many challenges and uncertainties that must be dealt with for the detection and attribution of climate change. Correlations of climate variations with natural forcing factors may appear sufficient to explain all of the observed variability and trends. For instance, Friis-Christensen and Lassen (1991), found a remarkable correlation between the length of the solar sunspot cycle and Northern Hemisphere misphereland ttempe emperature anomali anomalie es from from 1860 to 1985 (se (see Figure 7.8). Subsequent study has suggested that although solar impacts are important in climate variations, they do not explain most of the recent warming trend (Kelly


and Wigley, 1992). Uncertainties about natural variability exist because of limitations in the observational data as discussed in Chapter 6. Instrument data with sufficient quality to make variability estimates for the atmosphere go back as far as 150 years, but the coverage over the globe during the early years was quite limited; it still has deficiencies. Paleoclimatological data are difficult to interpret and very incomplete in coverage of space and time. Very long climate-model control-case simulations have helped to fill in the gaps for natural variability. The uncertainties of climate model simulations for climate change signals have already been discussed. Disc Di scuss ussio ion n of several veral rre ecent cent studies can can help to i ll ustrate ustrate a accompli ccomplishme shments nts in in detecti dete ction on and an d attri attributi bution on.. M Many any studi studie es have been made at the Stage tage-1 -1 level usin using g globally-averaged surface temperature observations. The likelihood that observed trends in the observational record for global-mean surface temperature could result res ult fro from m natura n aturall variabil variabilit ity yh has as bee been evaluated. valuated. In I n thi s a analysis nalysis it i t was nec nece essary to use variability statistics from climate models because observational data for variability was insufficient.  The obse observe rved d globa lobal-me l-mea an te tempe mpera ratur ture e trend trend for the pa pas st 100 100 yea years was was compared with values expected from linear trends of natural variability characteristics defi defined ned by th three ree cli climate mate models. Linea Li nearr tr tre ends were fitted fi tted to a n numbe umberr of  diff di ffe erent ass asse embl mbled ed tim time-s e-series eries se segme gment nts s (overlapping (overlappi ng ‘chunks’) taken from fr om multi multi-century model simulations made by these models with no anthropogenic radiative forcing changes. This procedure produced a statistical distribution of  possible trends that could be found in the model-simulation data for time scales

Figure 7.8 — Variations in solarcycle cyc le length length and N Northe orthern rn Hemisphere temperature anomalies. The two plotted  vari ables ables parall paralle el each each other  quite re r emarkably (from FriisChristensen and Lassen, 1991, with permission). [from page 186, Hoyt and Schatten, 1997, with permission of Oxford

University] 99



Figure 7.9 — Si  Signi gnififi ca cance nce of  observed changes in global mean, annually averaged averaged ne near-sur ar-surface face te temp mpe erature ratur e. T he so solili d li ne gives the magnitudes of the observed  tempe temp erat rature ure tre tr end ( °°  C /ye /year) ar) over  over  the recent record — i i.e. .e. ov ove er 1 10 0  years  years (1 (1984 984 to 199 1993), 3), 20 years years (1 (1974 974 to 1993), 1993) , etc etc.. to 100 ye years. ars. Observed data are from fr om Jones Jones and  Bri ffa (1992 (1 992).). Mode Model results results ( dashed dashed li nes) nes) are from three  AOG  A OG C M contro controll inte integratio grations: ns: the G FD L control control run (Sto (Stouffer uffer et al.  ,, 1994), 199 4), the fi rst 600 ye years ars of of the 1000-year ECHAM-1/LSG control i ntegrati ntegratio on ( H asselmann asselmann e ett al., al. , 1995), and the first 310 years of  the UKMO control run (Mitchell et al., 1995). Linear trends were  fi tte tted d to ove verlap rlapp ping ‘chunks’ o  of  f  the model temperature series, thus allowi allowing ng sampli sampli ng distributions of trends to be  ge  gene nerate rated d for the same same 10- to 100-year time scales for which observed temperature trends were estimated. The 95th p  pe ercentile rcentiless o of  f  these distri dis tri butions buti ons are plotte plotted  d  wi with th dashed lines li nes for each each model model control run and each trend  length. [from page 423, Reference no. 3].

ranging from 10 to 100 years. The observed temperature trend for various lengths of record ending at the present time was then compared with the distribution of  trends found foun d in i n th the e mode modell data for th the es same ame length o off re r ecord (se (see e Figure 7.9). Th The e results show that for any rec record longe longerr than 20 years, years, the o obs bse erved rved wa warming rming trend trend is

higher hi gher than tthe he leve levell that th at might mi ght be expe expecte cted d fr from om natural vari abili ty alone alone,, for more than 95 per per ce cent nt of the natural natural vari abi abi li ty possi possibili bili tie ti es. This, of course, is not certain proof  of climate cl imate change, but tthe he re results sults in indi dica cate te probab probabil il it ity y is i s low that t hat natura n aturall proces processe ses alone could account for the observed trend. The conclusion may be exaggerated because the model variability an underestimate of natural variability in the real climate system. The italicized part in the sentence above is an answer that can be given to the question: ‘has climate change already occurred?’ Sensitivity of climate variation to forcing can provide information on how anthropogenic forcing may account for observed climate variations. Again with attention to globally-average surface temperature, studies with simple climate models have identified aspects of observed variations that can be attributed to human forcing effects. Figure 7.10 shows the matching between observed and modelled temperature obtained by adding anthropogenic greenhouse gases and aerosol forcing to a model system. In this case, an upwelling diffusion-energy balance model (UD/EB) was used. The adjustment factor is the ‘climate sensitivity’  of the model. Results suggest that there is some correspondence of  overall trends in the observations to those attained in the model. In the Stage 2 level of analysis, spatial patterns of change are evaluated. This represents a more rigorous analysis and one that can provide more confidence in the attribution of the observed changes to human-produced climate change. The patterns are called ‘fingerprints.’ They may involve the total value of the variable or structures associated with variable magnitudes that have certain internal coherency such as Empirical Orthogonal Functions (EOFs). An example of one fingerprint is the vertical structure for temperature in the atmosphere. Enhanced greenhouse forcing, such as that due to increased carbon dioxide, causes a warming in the troposphere and a cooling in the stratosphere. Forcing due to solar radiation increase would be expected to increase temperature at all levels. Figure 7.11 shows the vertical pattern of temperature changes due to changes in anthropogenic forcing from the pre-industrial age to the present in both observations and model simulations. The observed pattern of change has a vertical structure qualitatively similar to that expected from an enhanced greenhouse effect and is not of the form expected for solar radiation increase. Specifically, a common pattern of stratospheric cooling and tropospheric warming is evident in the observations and in both model experiments. In the model data, this pattern primarily reflects the direct radiative effect of changes in atmospheric CO . Temperature changes in both the observations and in the experiment with


combined CO2+aerosol forcing forci ng also s show how a common pattern of hemisphericall hemispherically y 100



Figure 7.10 — Obse  O bserr ved ved chang change es (a) i n gl glob obal al mean mean te t emperatu mperature re ove over  r  18 1861 61 to 1994 co comp mpare ared d with those si simulate mulated d using usi ng an up upwe welli lli ng dif diffusi fusion-e on-ene nergy  rgy  balance cli climate mate mod mode el. T he mode mo dell was run fi rst wi th forcing due to greenhouse gases alone (a), (a) , then wit with h gr gre eenhouse gases gases and ae aeroso rosols ls ((b), b), and finally wi th  gree  gre enhouse nhouse gase gases, aero aeroso sols, ls, and  and  an estimate of solar irradiance change cha ngess (c). ( c). T he radiative  forcings  forc ings were were the best-gue best-guess ss values recommended in this (b) report. Simulations were carried  out wi with th clima cli mate te sensi sensiti tiviti viti es (T 2X ) of of 1.5, 2. 5 and and 4.5 ° C ffo or  the equilibrium CO 2-doubling temperature change. [from page page 424, Reference no. 3].

0.75 OBS    )    C   o    (   y    l   a   m   o   n   a   e   r   u    t

∆T 2x =



2.5oC o ∆T 2x = 4.5 C

∆T 2x =


0.00   a   r   e   p   m   e    T -0.25

-0.50 1850






0.75 OBS ∆T 2x =

   ) 0.50    C   o    (   y    l   a   m 0.25   o   n   a   e   r   u    t   a 0.00   r   e   p   m   e    T -0.25

-0.50 1850


2.5oC o ∆T 2x = 4.5 C

∆T 2x =












(c)    )    C   o    (   y    l   a   m   o   n   a   e   r   u    t   a   r   e   p   m   e




   T -0.25

-0.50 1850

Time (years)

asymmetric warming in the low- to mid-troposphere, with reduced warming in the Northern Hemisphere. This asymmetry is absent in the CO 2-only case. Each forcing component has its own fingerprint. To the extent that fingerprints are different, it is possible to identify causes for patterns of change seen in both observations and model simulations. For example, horizontal fingerprints for surface-temperature climate change expected with greenhouse-gas enhancement me nt woul would dh have ave large ampli amplitude tude over over pol ar and contin cont ine ental are areas as such as sho hown wn in Figure 7.3 7.3.. In I n contras contrast, t, th the e horizontal finge fi ngerprin rprintt for aerosol effects effects would more likely have large amplitudes over industrialized continental areas and resemble the aerosol concentration variations shown in Figure 3.3. Analysis of the correspondence of observed or modelled patterns of climate change with the fingerprints defined for each forcing component can be done by statistical methods. An example is the EOF approach where the statistically ‘most

dominant’ one or two component structures (EOFs) are identified for the patterns 101



Figure 7.11 — Modelled and  observed changes in the zonalmean, annual-average temperature  st  struc ructu ture re of theatm tmo osp sphe here re ( °°  C). C ). (a) Model results are from equili quil i brium bri um response response experi experime ments nts  p  pe erfo rforme rmed by Ta Tayl ylo or and Pe Penne nner  (1994),, in which (1994) which an an AG CM with a mi mixed-l xed-laye ayerr ocean was co coupled upled to a troposphe tropo spheriri c che chemi mistr stryy model model and   fo  forc rce ed with prese resentnt-d day atm tmo osphe spheric  ric  concentration of CO 2 (b) and by the the ccombin ombine ed eff effec ects ts of prese presentntday C O 2 leve levels ls and sul sulphur  phur  emissi ons (c). (c) . M ode odel cha change ngess are expressed relative to a control run wi with th pre pre-i-industri ndustrial al le leve vels ls of CO 2 and no anthrop anthropoge ogeni nicc sulphur  emissions. Observed changes (c) are radiosonde-based temperature measur mea sure ements ffrom rom the the data data set by  Oort Oort and Li u (1993) (19 93) and are expressed as total least-squares linea li nearr tr tre ends over th the e 25-year 25 -year peri od  exte xtending nding fro fr om May 1963 19 63 to April 1988 (i .e., .e., ° C/25 C/25 years). years). D ark   shad  sha ding at and aboveth the e 150 h hPa Pa  p  pre ressu ssure re leve levell highlights highlights regio regions ns of  coo cooliling. ng. D ark shading gene generally  rally  below below the 200 20 0 h hPa Pa leve levell hi ghli ghts warming warmi ng except except for the re regi gion on at   300 hPa hPa and 60° N which ha hass co coo oling. li ng. For further detail detailss refer refer to  Sante  Sa nter et al. al. (1995). [fro [from m page page 428, Reference no. 3].

(a)    )   a    P    h    (   e   r   u   s   s   e   r    P

50 100 200 300




(b)    )   a    P    h    (   e   r   u   s   s   e   r    P




























50 100 200 300




(c)    )   a    P    h    (   e   r   u   s   s   e   r    P

50 100 200 300




-1.5 -1.8

-0.9 -1.2

-0.3 -0.6

0.3 0

0.9 0.6

1.5 1.2


and fingerprints along with an amplitude factor for each. Then correspondence of  the overall patterns can be measured by examining the amplitude factors. In conclusion, the overall assessment of the scientific community involved with the 1995 Intergovernmental Panel on Climate Change report concerning the existence of climate change was that: ‘the balance of evidence suggests a discernible human influence on global climate’. The detection of and attribution for climate change will continue to receive attention from many scientists. One can expect many new results with more definitive conclusions in forthcoming years. The subsequent IPCC assessment of 2001 (Reference no. 7) had a stronger conclusion about the human influence on global climate stating that: ‘there is new and stronger 

evide vi dence nce that most of of the warming warmin g over over the last 50 years years i s attri attr i butable butable to human human activities’ (Summary  (Summary for Pol Policy icy M ake akers rs in Refere Reference nce no. 7).







Climate change is expected to have an impact on a wide range of ecological and socio-economic areas including human health. It has been considered important to investigate these impacts at the same time as climate change itself is assessed. In its 1995 second assessment, IPCC provided an extensive report on potential impacts of climate change (Reference no. 4). Impact assessment is still in a preliminary stage as it is difficult to quantify and most studies have been quite limited in scope. Analyses generally have used simple assumptions about climate-change conditions and have considered only limited aspects of the complex interactive stress factors. The overall impact on a system depends on both sensitivity to the climate-condition changes and the adaptability and compensating factors that the system itself possesses. In many cases actual impacts will depend on regional climates for which the estimates of  change are far more uncertain than for global-mean conditions. Some examples of potential impacts are presented here. These were chosen to focus on areas in which operational meteorologists may be directly involved in discussions with government agencies or citizens.

8.2  The  There re are a num numbe ber of ways in whic which h clima limate te chang hange e will affect terr rre estr tria iall ecosys osystems.


 They a  They are re dis discu cus ssed b brie riefly fly be below low a and nd followed followed by a foc focus use ed dis discu cus ssion of s se evera verall specific ecosystems.  T  Te err rre estr tria iall e ec cosys osystems tems depend d dire irec ctly on te tempe mpera ratu ture re and pre precipitation clima lima-tology as shown in Figure 1.16. Changes in temperature and precipitation, including their extremes and seasonal or daily variations, will influence the distribution of  biomes in the world, including those in agriculture. Rising temperatures alone would be expected to foster the poleward migration of biome species. For example, a warming could be expected to improve options for agriculture in subarctic regions. Changes in climate will influence other determinants of the ecosystem condition such as disease, pest cycles and the incidence of fires. The increase in atmospheric carbon dioxide concentrations is expected to increase the primary productivity of plants, i.e. make them grow faster. This could change the balance among plants competing for the same space.  The large largest advers rse e impa impact cts s on the e eco cos syste ystem m are a anticipa nticipate ted d to be fro from m direct human activity itself. The clearing of land for agriculture and urbanization and the th e s se egme gmentati ntation on of ecosyste cosystems ms wil willl be important imp ortant factors. Mo More re unfavourable impacts are likely in tropical and subtropical developing countries compared to developed countries because of population pressures and lack of resources for adaptation and mitigation responses to climate change impacts.


Climate change will affect crops in a number of ways. These include the growth proce pro cess ss of crop crop plants pl ants and th those ose of insec nsects, ts, weeds weeds and di dise seas ases es as discuss discussed ed above above.. Growth will be affected by changes in CO 2 concentration as well as those in temperature, moisture supply and severe weather. CO2 increases alone are expected to increase the productivity of annual crops, particularly those that may be limited by existing concentrations of carbon dioxide (i.e. most crops including wheat, rice, barley, cassava and potato, and most trees). For these crops, increases on the order of 30 per cent would be expected for a doubling in CO 2 if there we were re no other changes in conditions. The increases would be less for plants that have


a special CO 2-concentrating mechanism (such crops as maize, millet, sugar cane, sorghum, and many tropical grasses).  Tempera ratur ture e and moistur moisture e supply upply have have a dominant dominant influe influence nce on crop crop

growth. Each crop type has an optimal opti mal tempe temperature rature range range for growth. M oisture oi sture supply is critical throughout the growth period of the crop. The moisture supply 103



depends on both precipitation and evaporation (evapotranspiration from the plant). The diurnal and day-to-day variability for temperature and moisture supply along with mean values are important climatic factors for plants because of the negative impact of extremes. Very preliminary impact assessments have been made for a selection of crops using simple estimates of climate change. The estimates were derived from climate-model simulations, historical data, or just an outright specification of a temperature change. The impacts show extreme variability, often ranging from increases to decreases of yields for a given crop. Some examples are shown for areas are as in Afr Africa, ica, south Asia, Lati Latin n America, and wes western tern Europe in i n Tables Tables 8 8.1 .1 to 8.4. One may find comparable summaries for all other major regions of the Earth in chapter 13 in the 1995 IPCC Working Group II report (Reference no. 4). The climate models referenced are briefly described earlier in Chapter 4. It is clear from these tables that there is great uncertainty in the quantitative impact assessments of climate change on agricultural crops. It is strongly suggested that crop yields and productivity producti vity will wil l vary a g grea reat deal from one regio ion n to anothe anoth er. Some a area reas wil willl see improveme improvement nt in yields, oth othe ers wil willl see decreas creases. Agricultural Agricultural patte patterns rns wil will change. It It is likely that overall impacts will be significant. The overall effects for a region will de depe pend nd o on n many fac f actors tors in that reg regio ion n such as irrigation irrigation opti options, ons, curre current nt ag agri ricultural cultural infras in frastructure, tructure, adaptati tions ons in farming farmin g practicesand so so for forth. th. IItt wil willl take seri rious ous planning planni ng efforts to deal most constructively with the changes.


Overall regio ional nal im impac pacts ts of climate climate cha change nge for for fore estsarebriefly briefly summa summari rize zed d on p. 26 of 

FORESTS Table 8.1 — Selected crop studies  fo  forr Africa Afr ica and the Mi ddle ddle East for  for  climate-change scenarios from climate models, observations, and   p  pre resc scri ri bed change hanges. s. [from page 438, Reference no. 4].

Re Refere ference nce no no.. 4. As the th e proje projected cted te tempera mperature ture increa increase ses are are smaller in i n tropical tropi cal llatitud atitude es, tropi call fores tropica forests ts will wil l be affe affecte cted d lle ess than tho thos se in othe oth er llatitudes atitudes.. H Howev oweve er, cli climate mate change in terms of tthe he amount and seas seasonal onalit ity y of rain rainfall fall could have larg large er iimpac mpacts. ts. Even Eve n so, other human impacts will li like kely ly affect affect tropi tropica call fores forests ts more than doescli climate mate change. Temperate forests will be impacted by temperature, precipitation and CO 2 changes differently from region to region. However, the negative aspects of such changes on temperate forests will be minimized by reforestation and forest management programmes, since most temperate forests are located in developed countries. Boreal forests will be most affected by climate change as warming is expected

 Study  Stu dy

Sce Scena nario rio

Geograph Geographic ic sc sco ope

Crop(s) Crop(s)

Yiel Yield im impa pact ct in per cent 

Eid. 1994



Wheat M aize

-75 to -18 -65 to +6

Schulze et al., 1993

+2°C (1)

South Africa

Biomass M aize

M uchena, 1994



M aize

-40 to -10

w/ CO2 effect; also temperature and precipitation sensitivity; adaptation (fertilizer and irrigation) unable to fully offset yield loss.

Downing, 1992

+2/ +4°C, Zimbabwe ± 20 per cent Senegal precipitation Kenya

M aize M illet M aize

-17 to -5 -70 to -63 decrease

Food availability estimated to decline in Zimbabwe; carrying capacity fell 11 to 38 per cent in Senegal; overall increase for all crops in Kenya with zonal shifts.

Akong’a et al., 1988 broader socio-

Historical droughts,


M aize,



of drought

economicaim pactsimplications. , small-holder impacts, policy

1945-1964 vs. Niger 1965-1988 West Africa

Growing season

reduced 5-20 days

Crop variety development, timely climate information seen

Sivakumar, 1993

decrease increase

Other Other com comm ments w/ CO2 effect; also temperature and precipitation sensitivity; adaptation would require heatresistant variety development. M apped results, not summarized as average change for entire region.

negative effects


as important adaptation strategies. 104



 Study  Stu dy

Sce Scena nario rio

Rosenzweig and Iglesias (eds.), 19941


Qureshi and Hobbie, 1994

average of five GCM S

Geo Geograph graphic ic sc sco ope Pakistan India Bangladesh  Tha  Th ail ila and Philippines

Bangladesh India Indonesia

Pakistan Philippines Sri Lanka

Parry et al., (eds), 1992

Matthews et al., 1994a, 1994b


three GCM s

Crop(s) Crop(s)

Yiel Yield im impa pact ct in per cent 

Other Other com comm ments

Wheat Wheat Rice Rice ice Rice

-61 to +67 -50 to +30 -6 to +8 -17 to +6 -21 to +12

UKM O, GFDL, GISS, and +2°C, +4°C, and ±20% precipitation range is over sites and GCM scenarios ios wi with th di dirrect CO2 effect; scenarios w/ o CO2 and w/ adaptation also were considered; CO2 effect important in offsetting losses of climate-only effects; adaptation unable to mitigate all losses.

Rice Wheat Rice Soyabean M aize Wheat Rice Rice Soyabean Co Coa arse grain Coconut

+10 decrease -3 -20 -40 -60 to -10 decrease -6 -3 to +1 decrease decrease

GCMs included UKMO, GFDLQ, CSIRO9, CCC and BM RC; GCM results scaled to represent 2010; includes CO2 effect.


Rice Soyabean M aize

approx. -4 -10 to increase -65 to -25

M alaysia

Rice M aize Oil palm Rubber

-22 to -12 -20 to -10 increase -15

Rice Rice

-5 to +8 -3 to +28 -9 to +14 +6 to +23 +2 to +27 -14 to +22 -14 to +14 -12 -12 to +9

Thailand sites India Bangladesh Indonesia M alaysia M yanmar Philippines  Tha  Th ail ila and

Low estimates consider adaptation; also estimated overall loss of farmer income ranging from $10 to $130 annually. M aize yield affected by reduced radiation (increased clouds); variation in yield increases; range is across seasons. Range across GISS, GFDL, and UKMO GCM scenarios and crop models; included direct CO2 effect; varietal adaptation was shown to be capable of   ameliorating the detrimental effects of a temperature increase in cur urrrent ntly ly hi high gh-t -te emperatu turre environments.

1 Buendia, Count Country ry s studies tudiesfor were by Qures Qureshi hi and Iglesia Iglesias, s, 1994; Rao a and nd Sinha, 1994; Ka Karim rim et al. , 1994; Tongyai, 1994; and Escaño and 1994, Pakistan, India, Bangladesh, Thailand, and the Philippines, respectively.

Table Tab le 8.2 — Sele  Selected cted crop studi es  fo  forr south south and south-e south-east ast A sia fo for  r  climate change scenarios from cli climate mate mode models. ls. [from page 439, Reference no. 4].



to be largest at high latitudes. Increased fire and pest outbreaks will negatively impact the southern regions of boreal forests, whereas the increased temperature and moisture supply in the northern regions of the boreal forests will enhance the forest, which is expected to advance northward into the tundra. Forest fires are a matter of special interest. Forest fires occur commonly in seasonally dry forest areas due to human and natural causes. They play an important rol role e in the the e ecos cosyste ystem dynamics dynamics in ttropi ropica cal, l, temperate and boreal boreal zo zones nes.. Handl Handlin ing g them is an import i mportant ant part of for fore est manage management. IIn n some case cases of major fires fi res th the ere can be extreme danger to human human llif ife e and p property roperty llocall ocally, y, a as s well asregion-wi on-wide de health health effe ffects from air pollution. Temperature increases due to climate change are expected to increase drought conditio conditions, ns, which wo would uld lle ead to more favourable favourable c condi onditio tions ns for fores forestt fi fire res s in seasonal asonally ly dry area areas s. Thi T his s would be of particul particular ar conce concern rn to area areas s that do n not ot h have ave integrated fire, pest and disease management. Roughl y 30 per Roughly per cent of th the e earth’s land surface is desert or semi-desert; as shown by the dry climate cli mate areas (BS and BW classifica fi cati tion ons) s) in Figure 1.20. The Th ese are areas areas where the


lack ack of moisture moistureis a se serious rious impediment to the growth of plant plants s. Adj Adjac ace ent to th the eseareas and elsewhereare regi regions, ons, e estimated stimated at 17 per cent of th the e total earth land surface surface, where wh ere 105



 Study  Stu dy

Sce Scena nario rio

Geo Geographic graphic sc sco ope

Crop(s) Crop(s)

Yiel Yield imp impac actt in per cent 

Baethgen, 1992, 1994



Barley Wheat

-40 to -30 -30

Baethgen and




-10 to -5

M agrin, 1994 Siquera et al., 1994; Siquera, 1992


Uruguay Brazil

Wheat M aize Soybean

-50 to -15 -25 to -2 -10 to +40

Othe Otherr com comm ments w/ and w/ o CO2; with adaptation, losses were 15 to 35 per cent; results indicate increased increase d va variabil riability. ity. w/ CO2; high response to CO2, high response to precipitation. w/ CO2; w/o adaptation; adaptation scenarios did not fully compensate for yield losses; regional variation in response.

Liverman et al., 1991, 1994


M exico

M aize

-61 to -6

w/ CO2; adaptation only partly mitigated losses.

Downing, 1992

+3°C. 25 per cent precip.

Norte Chico Chile

Wheat M aize Potatoes Grapes

decrease increase increase decrease

The area is especially difficult to assess because of the large range of climates within a small area.

Sala and Paruelo, 1992, 1994



M aize

-36 to -17

w/ and w/ o CO2; better adapted varieties could mitigate most losses.

1  The  Th ese stu tudie dies s als lso o co cons nsid ide ere red dy yie ield ld sensiti itiv vit ity y to +2 and +4°C and -20 and +20 per cent change in p precipitation. recipitation.

Table 8.3 — Sele Table  Selected cted crop studi es  for Latin Ame Ameri ri ca for clima climate te-change sce scenari os fr from om cli cli mate mode mod els and prescri bed bed change chang es. [from page 444, Reference no. 4].



‘desertification’ as  ascribed cribed to human h uman activity activi ty is i s occ occurri urring. ng. Th This is proces process s is an an ecological cological degradation which causes economically-productive land to become less productive, more desert-like and incapable of continuing to sustain an existing community of  pe peopl ople e. Curre C urrentl ntly y about one s sixt ixth h of tthe he e earth arth’s population lives in regions where such de des serti rtifi fica catio tion n is occurrin occurring. g. Desertification involves a number of factors. Soil that is cultivated for agriculture can be eroded by water flow and wind. Salinization of soils can occur near coastlines due to sea-level rises. Inland salinization can occur due to salt accumulation related to erosion, seepage and wind deposition. Overall significant causes of desertification can be traced to overcultivation, overstocking, fuel and wood collection, salinization and urbanization. It is difficult to define the effects of global climate change in these arid areas, especially where direct effects due to human activity are already taking place. Generally climate-change temperature increases would be expected to increase stresses on plants. This would tend to make desert conditions more severe and to accentuate desertification processes. The increase in CO2 would be expected to reduce plant transpiration and to increase the water use efficiency of plants. Climate model projections for changes in precipitation are very uncertain. Impacts of precipitation changes depend critically on changes in distribution throughout the year and in extreme events, aspects which are not reliably handle handl ed by the models. Most model simul simulation ations s to da date te do not sug sugge gest st s signi igni fifi cantl cantly y we wette tterr condi condition tions s in arid reg region ions s. In summary, local environmental change due to human activity (desertification) is significant in many arid areas and is independent of global climate change. It is not expected that climate change will offset the desertification process, but rather that climate-change-related factors, such as increased drought conditions resulting from rising temperatures, will increase the vulnerability of  land to des dese erti rtifi fica cati tion. on. M any arid and semi mi-des -dese ert areas areas are are in deve develo lopi ping ng countries where the negative impacts would be most severe. Analysis of climate-change impacts on freshwater resources must include their effects on both water supply and water demand.


A rough analysis of water supply can be based on the runoff of surface water, which depends on the difference between precipitation over a river catchment




 Study  Stu dy

Sce Scena nario rio

Geo Geograph graphic ic sco scope pe

Crop Crop(s) (s)

Yield Yield impa impact ct in per cent 

Oleson et al., al., 1993


N orthern Europe



Quality affected by temperature; longer season.

Goudriaan and Unsworth, 1990


N orthern Europe

M aize (fodder)


Shift to grain production possible.

Squire and Unsworth, 1988


N orthern Europe



Kettunen et al., 1988 GCM s


Potential yield

+10 to +20

Range is across GISS and UKM O GCMs.

Rötter and van Diepen, 1994

+2°C (winter), +1.5°C summer

Rhine area

Cereals, sugar beet, potato, grass

+10 to +30

Also +10 per cent winter precipitation; includes direct effect of CO2; range is across crop. agroclimatic zone, and soil type; decreased evapotranspiration (1 to 12 per cent), except for grass.

U.K. Dept. of Environment, 1991

GCM s +1, +2°C


Grain, horticulture

Wheeler et al., 1993





Quality affected; more crops per per season possible.

Semonov et al. , 1993


U.K./ France


Increase or decrease

Yield varies by region; UKM O scenario negative; includes adaptation and CO2.

Del Delécolle et al., 1994

GCM s** +2, +4°C


Wheat, maize

Increase or level

N orthward shift; w/ adaptation, w/ CO2; GISS, GFDL and UKMO GCMs.

Iglesias and M inguez, 1993

GCM s**


M aize

-30 to -8

w/ adaptation, w/ CO2; irrigation efficiency loss; see also M inguez and Iglesias, 1994.

Santer, 1985


Italy/ Greece


-5 to +36

Scenarios included -10 per cent precipitation.

Bindi et al., 1993

+2, +4°C and *


Winter wheat N ot estimated

Increase or level increase

Othe Otherr com comments

Increased pest damage; lower risk of crop failure.

Crop growth duration decreases; adaptation (using slower developing varieties) possible.

* Cli Clima mate te s scena cenarios rios included GISS GISS,, GFDL a and nd UKM UKMO O and time-depende time-dependent nt sc scena enarios, rios, using GC GCM M methodology, ba base sed d on emission scenarios proposed by the IPCC in 1990. Composite scenarios for temperature and precipitation were based on seven GCMs and scaled by the global-mean temperature changes associated with the IPCC 1990 emissions scenarios for the years 2010, 2030, and 2050 (Barrow, 1993). ** These studies a also lso considered yield sensitivity to +2 and +4°C and -20 and + +20 20 per cent change in precipitation precipitation..

Table 8.4 8. 4 — Sele  Selected cted crop studi es  for W estern stern Europ Europe e for clima climate te-change sce scenari os fr from om cli cli mate mode mod els and prescri bed bed chang change es. [from page 445, Reference no. 4].

and evaporation over the same area. This approach would neglect the effects due to groundwater recharge and salinization due to sea-level rise and so forth. As discusse discus sed ea earl rlier, ier, cli mate mate-change -change e estimate stimates s for prec precip ipit itation ation and evaporatio evaporation n from climate models have considerable uncertainty. It is useful to describe the water supply in i n terms of th the e amount available pe perr person person so that popul population ation impac im pacts ts a are re included in cluded but separa parated ted from cli climate mate-change -change impacts. impacts. An impact analysis was made for the year 2050 using the results of three climate-model simulations for climate change. Results for selected countries are shown in Table 8.5. The values for the three quantities shown are for: current (1990) fresh water availability in m3/yr/person; availability in the year 2050 assuming assuming current wate waterr amoun amounts ts but wi with th the increa i ncreas sed popu populati lation on expected; xpected; a and nd the range of water availability in the year 2050 based on three transient climatemodel scenarios. Comparison of the second and third quantities shows the predicted impact of climate change on water availability.

As can be seen in Table 8.5, there is a wide range of estimates for the water availability with climate-change conditions. This is due to the significant 107



variations in precipitation and evaporation among the three climate models. Predictions for any given country range from increases to decreases in most cases. Clearly there is great uncertainty in the results. However, overall the number of  countries with shortages in water supply is expected to increase by the year 2050.  This is due to p popu opulat lation ion increases which are not o offs ffse et by by p pre recipita cipitation tion incr incre eases (actually precipitation minus evaporation increases) even for the most favourable of the three model predictions. 3

value of 1000 m /yr/person has been used for minimum waterArequirement. Based on this number, only twoasofthe thebaseline 21 countries listed in  Table 8.5 8.5 ha had d a wat wate er short shorta age in 19 1990 90,, eight eight of the thes se c countr ountrie ies s wou would ld have have a water shortage in 2050 due to population increase alone, and anywhere from seven to eleven would have a water shortage in 2050 based on both population and climate change projections. Freshwater demands are expected to increase with the warming associated with climate change. However, this impact will be combined with population growth, economic factors, and changes in agricultural, industrial and domestic practices, acting at the same time. Irrigation for agriculture is the biggest user for water extracted from the natural reservoirs (rivers, lakes and ground water).  Tempera ratur ture e incre increa ases are expecte cted tto o incre increase irrig irriga ation require quireme ments nts more more than than precip prec ipit itation ation enhance nh ancement ment woul d decreas decrease es such uch rre equireme qui rements. Impacts I mpacts of cli climate mate change on municipal uses for domestic and industrial purposes are not well defined.



Table 8.5 8. 5 — Water availability   3 (m  /y  /yr/p r/pe erso rson) n) in i n 2050 for the the  p  pre rese sent nt cclilima matic tic co cond ndii tions and   for thre three e transient transient clima climate te mode modell  sc  sce enarios (G FD L, UK MO, MO , MPI ) compared with the present. The range in simulated values for the three cli climate mate mo mode dels ls i s shown i n the right hand column. [from page 478, Reference no. 4].

Observations have detected sea-level rises over the last 100 years of between eight and 31 cm (cf. Figure 2.14). The overall global mean value is estimated to be between 10 and 25 cm. This is related to the observed net melting of glaciers and ice fields and the net warming of the surface air temperature. Temperature increases in the ocean lead to change in sea level due to thermal expansion. Thermal expansion and ice i ce melting ti ng are estimate stimated d to be of comparable imp importance ortance for th the es se ea-leve a-levell changes. There are local variations in the rate of sea-level change due to geological factors of ‘post-glacial rebound’ (rising of earth surface as the weight of an overlying ice mass is removed) and other earth crust movements.

Present Climate ( 1990)

Present Climate ( 2050)

Scenario Rang e ( 2050)

China Cyprus France Haiti India  Japa  Japan n Kenya M adagascar M exico Peru Poland Saudi Arabia South Africa Spain Sri Lanka  Tha  Th ail ila and

2,500 1,280 4,110 1,700 1,930 3, 3,21 210 0 640 3,330 4,270 1,860 1,470 310 1,320 3,310 2,500 3, 3,38 380 0

1,630 820 3,620 650 1,050 3,06 3,060 0 170 710 2,100 880 1,250 80 540 3,090 1,520 2,22 2,220 0

1,550–1,780 620–85 850 0 2,510–2,970 280–84 840 0 1,060–1,420 2,94 2,940 0–3,470 210–25 250 0 480–73 730 0 1,740–2,010 690–1,020 980–1,860 30–14 140 0 150–50 500 0 1,820–2,200 1,440–4,900 59 590 0–3,070

 T  Togo ogo  T  Tur urk key Ukraine

3, 3,40 400 0 3, 3,07 070 0 4,050

90 900 0 1,24 1,240 0 3,480

55 550 0–88 880 0 70 700 0–1,910 2,830–3,990


United Kingdom Vietnam

2,650 6,880

2,430 2,970

2,190–2,520 2,680–3,140




Climate-change warming is expected to cause a sea-level rise of between 20 and 100 cm by the year 2100 as ocean warming and ice melting occurs at a faster rate than in the past 100 years. Figure 8.1 shows the wide range in the estimates which have been made made.. Thi This s wide range results fro from m gre great at uncertain uncertainty ty about how how iice ce melt will proceed in polar regions. The rate of sea-level rise is projected to be anywhere from the same to five times greater than that experienced in the last 100 years.  The rise rise in s se ea leve levell will h ha ave importa important nt impact impacts s on coa coas sta tall zone zones s and s sma mall ll islands. Coastalthat erosion, flooding, saltwater intrusion and sedimentation will be factors impact on human settlements, agriculture, freshwaterchanges supply and quality, fisheries, and human health. Millions of people will be affected in countries such as Bangladesh, Benin, China, Egypt, India, Japan, The Netherlands and Nigeria. In some, such as the Marshall Islands, entire countries will be affected. Table 8.6 shows estimates for impacts for a 100 cm sea-level rise.



Changes in the frequency, intensity and location of storms may be an important as aspe pect ct of climate cli mate-change -change im impac pacts ts ove overr many parts of th the e world. Tropica Tropi call cyclon cyclone es, i.e. typhoons and hurricanes, affect many areas in and adjacent to the Indian, Pacific and Atlantic Oceans, primarily in the Northern Hemisphere. Damage caused by flooding and high winds can be devastating for land areas. In the middle latitudes, high winds and precipitation associated with extratropical cyclone cyclon es ca can n have ma majj or i mpacts eve ven n at lo loca cati tion ons s dista distant nt from the oceans. Some comments on the effects due to climate change were made in Chapter 6 as part of the discussion on changes in extreme events. Both tropica tropi call cyclon cyclone es and th the es se eve vere re weather weather aspects pects of extratropica xtratropi call cyclon cyclone es are of regional scale (mesoscale) and are difficult to represent in global-climate models. Asse Ass essment ments s of mo modi difi fica cati tion on of thes these es syste ystems due to cli climate mate change up tto o now n ow h have ave been based primarily on inferences from changes in large-scale conditions.  The ENS ENSO O cy cycle cle in the ttrop ropica icall Pa Pacific cific Oce Ocea an are rea a is know known n to affe affect ct b both oth tropical and extratropical cyclones. In particular the warm El Ni ño phase of ENSO is associate associated d with wi th enhance nh anced d extratropi extratropica call stormin stormine ess in the th e weste western rn and southern United States. Accordingly, it is important to identify the climate-change impact on ENSO. Global climate models have been able to represent ENSO, but further experimentation is needed to isolate climate change impacts.




It is anticipated that global climate change will have a wide-ranging and net adverse impact on human health, including increased loss of life. This will be due both to direct causes such as the increased severity of heat waves and to indirect effects such as changes in local food productivity and in the range of diseases transmitted by organisms in air or water. Direct health impacts will also result from concurrent environmental changes such as in the concentration of toxic and carcinogenic air pollutants. Figure 8.2 summarizes some of the major direct and indirect impacts. M any iimpac mpacts ts wil willl result ult from from di disturba sturbances nces in ecologi cologica call systems. S Such uch chang ch ange es could bring disease conditions to a human population that was formerly outside the range of such conditions. As an example, atmospheric warming could lead to mosquitoes reaching higher levels in mountainous areas introducing malaria to people living there. Populations in developing countries may require additional resources to deal effectively with such changes in disease patterns. 100

Figure 8.1 — Sce  Scenari nari o IS9 IS92a 2a seaseale leve vell ri se from 1990 199 0 to 2100 210 0 fo f or  high, medium and low ice-melt   p  para arame mete terr spe specifi cations. cations. (Se (See e Chapter 7 in Reference no. 3 for 

   )   m80   c    (   e   g 60   n   a    h   c    l   e   v   e 40    l   a   e 20    S


Including changes in aerosol beyond 1990 Constant 1990 aerosol



55  MID 49

23 LOW 20

more details.) [from page 296 in

0 2000

Reference no. 4].






Year 109



People affected no. of people % ( 1000s) Total Antigua2, (Cambers, 1994) Argentina (Dennis et al.,1995a)


Bangladesh (Huq et al.,1995: Bangladesh Government, 1993) Belize (Pernetta and Elder, 1993) Benin3 (Adam, 1995) China (Bilan, 1993; Han et al., 1993) Egypt (Delft Hydraulics et al., 1992) Guyana (Kahn and Sturm, 1993) India (Pachauri, 1994)  Japa  Japan n (Mim (Mimur ura a et al., 1993) 2 Kiribati (Woodroffe and M cLean, 1992) M alaysia (M idun and Lee. 1995) M arshall Islands2 (Holthus et al., 1992) Mauritius4 (Jogoo, 1994)  The  Th e Ne Neth the erl rla and nds s (P (Pe eerb rbol olte te et al., 1991) Nigeria Ni geria (Fre (French nch et al., 1995) Poland (Pluijm et al., 1992) Senegal Sene gal ((Dennis Dennis et al., 1995b) St. Kitts-Nev Kitts-N evis is2 (Cambers, 1994) 2  T  Ton onga ga (Fifita et al., 1994) United States (Titus et al., 1991) Uruguay5 (Volonté an  and Nicholls, 1995) Venezuela (Volonté an  and Arismendi, 1995)

Table 8. 8.6 6 — Synthesi  Synthesize zed d results of country country case case studie studi es. Results are for existing development and  a one metre rise in sea level. People Peo ple affe aff ected, cted, cap capii tal value at  loss, land l and at loss, and wetland a at  t  loss assume no measures measures (i .e. n no o human response), whereas adaptation assumes exce xcept pt in i n areas wi with th low  lprotection ow   pop  po pulation ulation density. density. Al Alll cco osts have been adjusted to 1990 US D ollars (adapte (adapted d fro fr om N i cholls, cholls, 1995). [from page 308, Reference no. 4].




50 -

71 000 70 1350 72 000 4 700 600 7 1006 15 400 9 20 3 10 000 3 2006 240

60 35 25 7 9 80 1 15 100 100 <1 67 4 1

1106 30 136 566

>1 47 <1 <1

Capital value at loss M illion % US$1 GNP >50007


118 12 59 000 204 4 000 1 115 849 000 72 2 8 160 324 186 000 69 7 17 000 52 22 000 24 >5007 1 7007 3307

>12 26 1

Land at loss km 2 5 3400

Wetland at loss

% Total 1.0 0.1

km 2

Adaptation/   Protection Costs M illion % US$1 GN P

3 1 100

71 >1 800

0.32 >0.02

25 000 17.5 1 900 8.4 230 0.2 35 000 5 800 1.0 2 400 1.1 5 800 0.4 2 300 0.6 4 12.5 7 000 2.1 9 8 80 0 5 0.3 2 165 5.9 18 6 60 00 2.0 1 700 0.5

5 800 >1 00010 85 >40010 13 10011 500 200 >156 000 3 6 000 >360 642 12 300 16 0 00 00 >1 4 40 00 36 1 400

>0.06 >0.41 0.45 0.26 >0.12 0.10 >7.04 0.05 >0.04 0.02

6 100 1 7 31 6008 96 5 700

6 000 >1 000 1 50 17 000 >156 000 23 >1 000 5 600 >1 600

>0.21 2.65 >0.03 >0.12 >0.03

3.1 1.4 2 2..9 0.3 0.1 0.6

1 2 3 4 5 6 7 8

Costs have been adjusted to reflect 1990 US Dollars. M inimum inimum e estimates stimates-i -incompl ncomplete ete nation national al coverage. coverage. Precise year for financial values not given are assumed to be 1992 US$. Results are linearly interpolated from results for a two metre sea-level rise scenario. See also review in Nicholls and Leatherman (1995a). M inimum estima estimates tes—number reflects estimated people displaced. M inimum estima estimates tes—capital value at loss does not include ports. Best estimate estimate is that 20 000 kM 2 of dry land are lost, but about 5.400 kM 2 are converted to coastal wetlands. 9 Adaptation only o nly provides protection against against a one-in-20 one-i n-20 year year ev event. ent. 10Adaptation costs are linearly extrapolated from a 0.5-m sea-level rise scenario. 11Adaptation costs include 30-year development scenarios.

A range of possible health impacts to climate change are summarized here. Research has yet to quantify such impacts and it may be difficult to isolate them from overall trends in world health (except for stratospheric ozone reduction). Nevertheless, it is important to be aware of the possibilities because of the potential impacts on whole communities or populations. Global warming is expected to increase the frequency of extremely hot days as discussed in Chapter 6. Recognizing that extremely hot spells are related to increases in mortality rates, it is logical to conclude that mortality due to excessively hot conditions will increase with global warming. Estimates of changes in heat-related mortality rates for selected large cities using temperatures predicted by global climate models show that by the year 2020 the rate will double and nearly quadruple by the year 2050. These results are based on current mortality rate increases due to hot weather. They neglect other factors that may operate in

a hot spell such as increased air pollution and mitigation programmes that might be implemented with global warming. 110



M e d ia t in g p ro c e ss

He a lt h o u t c o m e s DIRECT

Altered rates of heat- and coldrelated illness and death (especially cardiovascular and

Exposure to thermal extremes (especially heatwaves)

respiratory diseases)


Alt lte ered frequ reque ency ncy and/or nd/or inte intens nsit ity y of oth other ex extreme wea weather ev events (floods, storms, etc.)

Dea Deaths ths, inju injurrie ies s and psy psycholo hologi gi-cal d diisorde derrs; da damage to pu publi blic health infrastructure



Figure 8.2 — Way  W ayss in which cli mate change ccan an affe aff ect human health. (from page 565 of  Reference no. 4).

Effects on range and activity of  vectors and infective parasites

Changes in geographic ranges and incidence of vector-borne diseases

Altered local ecology of waterborne and food-borne infective agents

Changed incidence of diarrheal and certain other infectious diseases

Altered food (especially crop) productivity due to changes in climate, weather events, and associated pests and diseases

Regional malnutrition and hunger, and consequent impairment of child growth and development

Sea-l -le evel rriise wit with po popu pullation displacement an and da damage to infrastructure (e. (e.g., g., sa sanitation) on)

Inj Injuries, iin ncreased rriisks of various infectious diseases (due to migration, on, c crrow owd ding ing, c co ontamination of drinking water), psychological disorders

Levels and bi biol olo ogi gic cal impa pac cts of air pollution, including pollens and spores

Asthma and alle lerrgic disorders; other acute and chronic respiratory disorders and deaths

Social, economic and demographic dislocations due to adv dve erse cli lim mate cha hang nge e iim mpac pacts on economy, infrastructure and resource supply

Wide range of public health cons onseque quence nces (e.g (e.g., ., me menta ntal health, nutritional impairment, infectious diseases, civil strife)

NOTE: Populations with different levels of natural, technical and social resources would differ in their vulnerability to climate-induced health impacts. On the othe oth er h hand, and, global warmin warming g is also e expec xpected ted to reduce the occurrence of cold conditi conditions ons in parts parts of the world. IIn n such areas morta mortali lity ty due to cold con condiditions, including accompanying respiratory diseases, is expected to decrease. On balance for the world it is felt that the sensitivity of death rates to hotter summers may be greater than that for the warmer winters. Thus, the overall temperature

impact is negative. Considerably more research, especially in developing countries, is needed to quantify the impacts of temperature on health. 111



Other weather extremes such as droughts, floods and high winds also cause adverse health effects including deaths for humans. Understanding the impact of  these factors due to climate change will require defining the changes in such weather extremes.



It is expected that climate change will have a significant impact on vector-borne diseases particularly in tropical and subtropical countries. ‘Vector-borne’ refers to the th e process processe es byvector) which whi ch tthe he iinf nfe ective ag age ent of the dise di sefly, ase asebug is transmitted by a livi l ivi ng organism (the such as a mosquito, tsetse or tick. The climate change may directly affect the living patterns of the vector which in turn will affect the spread of the disease among humans as a secondary or indirect effect Vectors such as mosquitoes are extremely sensitive to temperature. For instance, the anopheline mosquito species, which transmits malaria, normally does not survive urvi ve if the mea mean n win winter ter te tempera mperature ture is below 16-18°C, or if nighttime temperatures in summer are sufficiently low. Furthermore, the temperature at which whi ch th the e mos mosqui quito to can iincubate ncubate the malaria para paras si te has a lower limi l imi t, e.g., e.g., 18°C Plasmodium dium vivax parasites, respecand 14°C for the Plasmodium falciparum and Plasmo tively. If climate change increases temperature, then the mosquito can become an active vector for malaria at higher levels in mountainous country and at latitudes more poleward of the tropical areas. This can expand the malaria area to include populations which were not previously exposed and which would lack naturallyacquired immunity. A large number of vector-borne diseases are active in the tropical and subtropical areas as listed in Table 8.7. The number of people likely to be exposed is enormous. Alteration (expansion) of the risk areas due to climate change will affect a large number of additional persons. The diseases considered to be most likely to be involved are malaria, schistosomiasis, onchocerciasis, dengue fever and yellow yell ow fever. fever.

Water-borne Wate r-borne and food-borne food-born e diseases

Alteration of water-borne and food-borne infectious diseases is another major potential indirect impact of climate change. Diarrheal diseases such as cholera and dysentery are spread by untreated water and water systems infiltrated by run-off water. Climate change would have an impact to the extent that it changes or increases flooding situations and by providing warmer environments for bacterial development. The rise in sea level due to climate change would be expected to increase coastal flooding and to degrade sewage disposal systems. Agricultural productivity and food supplies

Climate-change impacts suchcoastal as increased desertification, increased severe weather and increased flooding drought, could have serious impacts on food supply, especially in developing countries. This could have negative impacts on nutrition and health of the population.

Air pollution effects may be increased by climate change. This is in addition to the human-produced air pollution which is a factor in producing the climate change in the first place. Climate changes may impact levels of pollen and other biotic allergens from birch trees, grasses, oilseed rape crops and ragweed. These effects would be further enhanced by pollutants, such as ozone, that are generated by fossil fuel combustion and the action of solar radiation.

Air pollution



A serious health hazard has arisen from reduction of ozone in the stratosphere due to human-produced CFC and other chlorine- and bromine-containing gases. Ozone in the stratosphere helps to protect life at the Earth ’s surface by absorbing most of the harmful ultraviolet radiation in the sunlight. It has been estimated that the ozone concentration in the middle and high latitudes has, on average, been reduced by 10 per cent in the past ten years. The increase of ultraviolet radiation causes skin cancer, eye cataracts and damage to the local immune system in

the skin. The suppression of the immune system leads to increased susceptibility to infectious diseases. 112



D isease


Population at risk ( million) 1

N umber of people currently infected or new cases per year

M alaria Schistosomiasis

M osquito Water snail

24002 600

Lymphatic filariasis African Trypanosomiasis (Sleeping sickness) Dracunculiasis (Guinea worm)

M osquito Tsetse fly

10944 55

Crustacean (Copepod)



Phlebotomine Sand fly


Black fly


12 million infected. 500 000 new cases per year6 17.5 million

Triatom tomine ine bu bug g

10 100 07

18 million

Present distribution

Likelihood   of altered   distribution with climate chang e

300-500 million 200 million

Tropics/ Subtropics Tropics/ Subtropics

+++ ++

117 million 250 000-300 000 cases per year 100 000 per year

Tropics/ Subtropics Tropical Africa

+ +

South Asia/ Arabian Peninsula/ Central-West Africa Asia/ Southern Europe/ Africa/ Americas Africa/ Latin America



Onchocerciasis (River blindness) Americ ica an Tr Trypanosom omia iasis (Chagas‘ d  diisease) Dengue  Y  Ye ello llow w ffe ever

M osquito Mos Mosqu quito ito

Table8.7 8. 7 — Major tropical vectorborn borne e diseases diseases and the lilikeli kelihoo hood d of  change cha nge of the theii r distri di stri bution with climate cli mate change change.. [from page 572, Reference no. 4].

1800 45 450 0

10-30 million per year <5 00 000 0c ca ases pe perr y ye ear

Central and South America All Tropical Countries Tro ropic pica al S Sou outh th America and Africa


++ + ++ ++

+ = likely. ++ = very likely. +++ = highly highly likely. ? = unknow unknown. n. 1  T  Top op thre three e ent ntri rie es are pop popul ula ation-p tion-pro rora rated ted p pro roje ject ctio ions ns (based o on n1 198 989 9e es stima timates tes). 2 WHO, 1995b. 3 M ichae ichaell and Bundy Bundy,, 1995. 4 WHO, 1994a. 5 Ranque, personal communication. 6 Annual incidence of visceral leishmaniasis; annual incidence of cutaneous leishmaniasis is between one and one and a half million cases/yr (PAHO, 1994). 7 WHO, 1995c.

 The estima timate ted d incre increa ase in skin ca cance ncerr is of ma major jor conc conce ern. Eff Effe ects cts a are re more more notable at high latitudes than in tropical regions because the depletion of ozone has been greater at high latitudes. As an example, it is estimated that if the current latitude-dependent in ozone maintained forincrease the nextby few decades the incidence of the skinreduction cancer, basal cell is carcinoma, will a factor of one to two t wo per ce cent nt at ve very ry low l ow latitudes l atitudes,, three th ree to five f ive per cent cent for the 15-25° latitudes, eight-12 per cent at the 35-45° latitudes, and 13-15 per cent at the 55-65° latitudes (Madronich and de Gruijl, 1993).




 The broa broad ds sco cope pe of the thes se lect lecture ure notes notes s should hould make make it cle clea ar that that the there re is a wide range of topics related to the study and understanding of climate change. It requires collaboration among persons from many different professions to answer the th e ques questions ti ons about th the e ma magnitu gnitude de of cli mate change and th the e effe ff ects it iis s li likely kely to have on our way of life.  The lect lecture ure note notes s were were des designe igned d to prom promote ote sufficie ufficient nt unde unders rsta tanding nding of the science and technology of climate change so that the student will be equipped to critically evaluate reports concerning climate change and to explain material to government representatives and lay persons. The material also gives background information to those who wish to pursue further study. It should be emphasized again that data and assessments of climate change are being continually updated.  Thus,, it is nece  Thus necessary to seek out curre current nt re repor ports ts and publica publications tions to ke kee ep up to date date on the topic.  The lect lecture ure note notes s ha have ve give iven n some example xamples s of the many type types s of impa impact ct that climate change would have on the human community, and on plant and animal communities. Some of the impacts may be considered to be favourable, but a large number are unfavourable and would require actions to reduce their negative neg ative e effects ffects.. C Cli li mate change clea clearl rly y iimpl mplies ies many many other o ther kinds ki nds of change changes. s. The world community needs to be alert and provide assistance to those in the world who lack the resources to respond to the negative impacts of climate change. Some people still doubt that climate change has occurred despite statistical evidence at the 95 per cent significance level. Despite their doubts relating to climate change, environmental impacts of humans on the atmosphere, oceans, and biosphere are recognized. The net result of these impacts has already been an overall degradation of our environment. It is important for the meteorological community to do its part to raise awareness of projected human effects on world climate.




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A absorption . Se  See e also radiation solar, 5-6 spectrum, 7 terres terre stri trial, al, 7 acid rain, 48 acoustic wave measurements, 86 aerosols. Se  See e also nitrate aerosols; sulfate aerosols human impac imp actt enh enhance ancement, ment, 45-48 observational measurements, 81 agriculture, 103-107, 112 air pollution, 110-112 airflow. Se  See e winds albedo, 6, 8 climate feedbacks, 21 cryosphere, 18 human impact, 49-50 land surfaces, 17 meridional profiles, 9 numericall modell numerica modellin ing, g, 52 various surfaces of, 18 vegetation, 34 annual cycles, 28-29 astronomical effects, 27-29 atmosphere climate conditions, 10-11 constituent cons tituent circulation circulation,, 11 forcing factors, 10 gas absorption spectra, 7 internal processes, 35 land surface exchange, 17, 20

C California (ocean current), 15-16 carbon cycle annual averages, 16 biosphere interaction, 34 numerical modelling, 53, 98 vegetation relationship, 19 carbon dioxide. Se  See e als also o greenhouse gases agricultural influence, 103 annual global emissions, 43 human im impac pactt enhanceme enhancement, nt, 42-44 model predictions, predicti ons, 65, 69, 87-88, 90-94 90-94 observational measurements, 80 oceanic influence, 16 sources and reservoirs, 42 cement manufacturing, 42-43 chaos theory, 11, 35-36, 69 chlorofluorocarbons (CFCs), 22, 44-45, 112 climate class clas si fica fi cati tions, ons, 24-26 definition, 3 feedbacks, 21-22 geography, 11, 24 global variations, 11-12, 22-23 local variations, 23, 25, 75 observational data, 81 predictability, 69-71 regional variations, 24-25 transient features, 10 variables, 3 cli climate mate change change

numerical modelling, 62-64 oceanic interaction, 53 Atmospheric General Circulation Model (AGCM), 58, 102 Atmos Atm ospheric pheric M Mode odell I ntercomparison Project (AMIP), 62, 66

definition, 3 detection and attribution, 98-99 recent progress, 99-100 model results, 87-98 improveme im provements, nts, 97-98 mean conditions, 90-94 predictions, 89-97 recent rece nt changes, changes, 87-90 scenarios, 91, 93-95 variability, 94-95 numerical modelling, 51-68 perspectives, 1 potential potenti al iimpac mpacts, ts, 103-113 103-113 Cli Climate mate Information Inf ormation and Prediction Service Services s (CLIPS), 73 climate models. Se  See e als also o numerical modelling model comparisons, 89 model evaluation, 58-61

B back bac k r adiation, 8-9 8-9 Benguela (ocean current), 15-16 biomas biom ass s burn in ing, g, 45, 47-48. Se  See e als also o deforestation biosphere, 19, 21 climate feedbacks, 22 interactions, 34-35 numericall modell numerica modellin ing, g, 52 black body radiation, 7, 17

boreal forests, 104 104 buoy obse observati rvat i on measur measure ements, 83

sensitivity, 90 improveme im provements, nts, 66-68 uncertainti es, 93




Upwelli ng Di Upwelling Diffusion ffusion and Ene Energy rgy B Ba alance (UD (UD// EB) EB) model, 57, 89, 93-94, 100-101 validation by past climates, 61-62 climate monitoring clouds, 81 cryosphere, 82 defi deficiencies ciencies,, 78

diseases agricultural, 103 human, 112 diurnal cycles, 28, 63 temperature ranges, 88 downscaling, 75-76

greenhouse gases, 80 improvement strategies, 86 introduction, 78 long-term climate modelling, 78-79 oceans, 81-82 precipitation pre cipitation,, 8484-85 85 principles, 78-79 solar radiation, 79 surface hydrology, 82 surface land cover, 82 surface temperatur temperature e, 83 climate clim ate pre prediction, diction, 69-77.  See See also climate change, model results limitations, 70-71 long-range forecasts, 74 medium-range forecasts, 73-74 regional forecasts, 75-76 short-term forecasts, 71-73 climate system components, 3-4 atmosphere, 10-11 biosphere, 19-21 characteristics, 10-21 cryosphere, 17-18 interactions, 30-35 land surface, 17 observational data, 81 ocean, 14-16 definition, 3 forcing factors, 3 human impacts, 41-50

E Earth Observing System (EOS), 86 eccentr ccentrici icity ty (Earth (Earth's 's orbi orbit), t), 29 El Niño Niñ o - S Southern outhern Oscil Oscillati lati on (ENS (ENSO) O) atmospheric interaction, 30-31 cyclone effects, 109 foreca forecasts sts,, 71-73 global variability, 22-23 model results, 95 numericall modell numerica modellin ing g, 64 observational indicators, 32 precipitation anomalies, 31, 33 el ectri cal f i el d measur measure ements ments,, 86 emission, 5. Se  See e als also o radiation Empiri Empi rical cal Ort Orthogonal hogonal Functions (EOF (EOFs s), 100 100 energy bud budge gett radiative prope pro perti rtie es, 6-9 vegetation, 34 ensemble forecasting modelling strategy, 70-71 equati on of state, the, the, 52 European Centre for Medium Range Weather Forecasts (ECMW (ECM W F), 72, 85 evaporation. Se  See e latent heat external forcings for cings,, 27-29 extreme events, 95-97. Se  See e als also o severe weather precipitation pre cipitation,, 97 temperatur tempera ture, e, 96-97 wind, 96

clouds aerosol and radiative effects, 48-49 changes change s due to human im impac pact, t, 41 numerical modelling, 63, 67, 97 observational data, 81 cold start (climate models), 88-89 computers compu ters,, advancement advancement of, 56-57 crops. See  See agriculture cryosphere, 17-18 effects,, 33-34 effects numerical modelling, 53. 65. 74 observational data, 82 currents cur rents (oce (ocean), an), 15-16, 33

D data recovery, 84

F fast system, 74 fi nge ngerpr rprin in ts (spatial pattern patterns s), 100 100 finite-difference numerical modelling, 54-55 food-borne diseases, 112 112 forecasting.. Se forecasting  See e climate prediction; weather prediction modelling forecasts climate long-range lo ng-range,, 74 medium-range,73-74 regio reg ional, nal, 75-76 short-range (weather), 71-72 ENSO, 71-73 forests, 104-105

deforestation, 17, 41, 43, 49. Se  See e also biomass burning desertification, 105-106

fossil fuel combustion, 42-43, 45, 47 foss Fourier transform, 56 fresh water resources, 106-108 127



G Gaia mode model, l, 34 General Ge neral Circul Ci rculation ation Models Models (GCM). Se  See e numerical modelling, GCMs geological effects, 30 glaciers. See  See cryosphere global cli climate mate models, models, 75-76. Se  See e also climate

I ntergo ntergove vern rnme mental ntal Panel Panel on Clim Cl imate ate Change (IPCC), 87 current climate modelling, 58 emission scenarios, 92-93 human impact, 41-42 model i mprovements, mprovements, 97-98 ocean oce an genera generall circulation circul ation models, 64

models Gl Global obal Cli Cl i mate Obse Observi ng System (GCOS (GCOS), ), 78 gravity waves, 35 greatt ocean grea ocean conveyor conveyor bel bel t, 33 greenh gree nhouse ouse e eff fect, 7-8 7-8 comparison with past climates, 37 energy balance, 8 human impacts, 41-45 greenhouse gases. Se  See e als also o under specific type absorptivity, 7 concentrations, 46-47 constitue constit uent nts s, 7, 42-43 enhancement due to human impacts,41-45, 50, 88 future scenarios, 91-93, 101 interaction with biosphere, 34-35 li fetimes, fetimes, 42, 46-47 numerical modelling, 52-53, 98 observational data, 80-81 radiative forcing, 41, 44-47 sources, 41, 47 Gulf Stream, 15-16

potential impacts, 103 precipitation pre cipitation unce uncertainty rtainty i n models, models, 84 report summary, 94-95 International Satellite Cloud Climatology Project (ISCCP), 81 I ntertropi ntertropical cal Conve Converge rgence nce Zone ((II TCZ), 25, 29 in transiti transitive ve cli climate mates s, 70

H Hadley Circulation, Circulati on, 31 halocarbons,, 44-47. Se halocarbons  See e also chlorofluorocarbons; hydrofluorocarbons health, human, 109-113 heat capacity cryosphere, 18 land, 17 ocean, 14, 16 heat energy transfer, 21-22 heat island effect, 83 heating (absorption), 6 human impacts, 41-50 biosphere, 21, 103 measurements, 41 radiative forcing, 41 summary of radiative energy transfers, 50 hurricanes.. Se hurricanes  See e tropical cyc cyclon lone es hydroflu hydrof luorocarbons orocarbons (HFCs), (HFCs), 44, 46-47. Se  See e als also o chlorofluorocarbons hydrol og ogic ic cycle, 10


K  Kelvin waves, 35 K öppen, W., 24 Kuroshio, 15-16

L La Ni ñ a, 31 land lan d de degradation, gradation, 105 105–106 106 land surface, 17 exchange with atmosphere, 17 human effects, 17 numerical modelling, 52, 64-65, 67 observational data, 82 radiative prope pro perti rtie es, 49 temperature anomalies, 38 topographica topographi call effects ffects,, 17 latent heat, 21, 53, 65 latitude energy ene rgy ttransfer, ransfer, 22 radiation variations, 9, 29, 63 Little ice age, 36-37 . See also past climates local vari variabili ability ty in cli mate, mate, 23, 75 l ong-range for ecas casts ts climate, 74 weath wea ther er,, 71-73 Lore Lor enz, E. N., 35-36, 69

M Madden-Juli an Os Oscill cill ation, 35, 64 marii ne biochemi mar biochemical cal proces p rocesses numericall modell numerica modellin ing g, 53 mass extinction coefficient, 45, 47 medium-range medium -range cli climate mate for fore ecas casts ts,, 73-74 meteors, 29 methane, 42-44. See also greenhouse gases

i ce cover. cover. See  See cryosphere i ndex cycle cycl es, 35 infrared in frared radiation radiation.. See  See rad radiation iation,, llong-wa ong-wave ve

Mil ankovitch, M. M.,, 29 modelling. Se  See e numerical modelling monsoons, 23-24, 95




Montreal Protocol (1987), 44, 46-47 mortality mortali ty rate r ate,, 110-112 mosquitoes, 11 112 2 multiple attractors, 70

N National Aeronautics and and Spa Space ce Admi ni nis strati tration on (NASA), 86 National Center for Atmospheric Research (NCAR), 85 National Centers for Environmental Prediction (NCEP), 85 naturall va natura variabili riabili ty, ty, 27-39, 98 NINO Index, 72. Se  See e als also o El Niño - Southern Oscillation (ENSO) ni tr trate ate ae aerosols rosols,, 45, 47. Se  See e also aerosols nitrous oxide, 42-44. Se  See e also greenhouse gases nonlinear effects (atmospheric), (atmospheric), 35 35 North Atlantic Atlanti c Oscill ation, 40 numeri nume rica call insta instabil bility, ity, 56 numeri nume rica call mode modell ll ing. Se  See e als also o climate models approximations, 51, 54-55 climate change, 51-68 climate monitoring, 85 computer advancement, 56-57 finite-difference method, 54-55 flux corrections, 58, 60, 97 GCM s, 63 governi gove rni ng equati equation ons s, 52-53 initial conditions, 68, 88 mathematics, 54-56 model evaluation, 58-62, 74 model variability, 60-61, 64 numericall in numerica ins stab tabil ility, ity, 56 parameterization, 54, 64, 66-67 physical and chemical interactions, 53 sensitivity improvements, 66-67 spatial resolution, 51, 54-55, 67 spectral method, 54-56 surface parameters, 52 transport, 56 use of observational data, 51-52 variables, 58-60 weather prediction, 56, 69

O obliquity obliqui ty (til (tiltt of Earth's axis axis), ), 29 obse obs er vati vational onal mea meas sure ur ements climate forcing factors, 79-84 new techniques, 86 number increase, 86 principles for long-term climate modelling, 78-79

density, 15 energy transport, 15 forcings, 15 heat capacity, 14, 17, 30 interaction with atmosphere, 53 numerical modelling, 57-58, 6, 73 observational data, 64, 78, 81 role with greenhouse gases, 16 salinity, 15-16, 34 surface currents, 16 thermohaline currents, 15, 33, 64, 73 ti time me scal scales, 30-32 Ocean Oce an Gene General ral Cir culati culation on Model Model (OGCM), 58 optical depth, 45, 47, 49 orbital orbi tal paramete parameters, rs, 29 oscillations (atmospheric), 35 ozone, 42 depl deple eti tion, on, 22, 44, 112-1 112-113 13 halocarbon haloca rbon relation relations shi hip, p, 44 observational data, 80

P Paleocli Paleo climate mate Modell Modell in g Inte In tercompari rcompari son Project (PMIP), (PMI P), 62 paleoclimates. Se  See e past climates paral lel-pr lel-proce oces ssor ve vector ctor machi nes nes,, 56 pastt cli mates, pas mates, 21, 34, 36-38, 52 long-range forecasts, 74 numerical modelling, 61-62 Peru (ocean current), 15-16 pests, 103, 105 polar pol ar i ce caps caps,, 34 precipitation. Se  See e als also o rainfall climatology, 11-12 ENSO anomalies anomal ies,, 31, 33 extreme events, 97 fresh water resources, 107-108 land surface anomalies, 39 numerical modelling, 60-62 observational data, 84-85 observed cli climate mate vari riab abii li ty, 27, 36, 39 regional forecasts, 75 seasonal forecasts, 71-73, 92 predictability, 69-71 Project for Intercomparison of Land Surface Parame Param ete teri ri zation zati on Schemes Schemes (PI (PILPS) LPS),, 64 64–66

Q Quasi-Biennial Oscillation (QBO), 35


observed climate variability, 36-38 ocean, 14-16 deca decadal dal vari variab abii li ty, 40

radar measurements, 84 radiation, 55-9 9 absorption spectra, 7 129



albedo, 6, 8-9 energy balance, 8-9 forms, 5 horizontal variations, 5 human effects, 5 latitude variations, 9 long-wave lo ng-wave,, 5, 7 meridional profile, 9 net forcing, 9 principles prin ciples,, 5 short-wave, 5 vertical variations, 6 zenith angle, 6 radiation energy transfer, 21, 48, 63 radiative forcing, 41, 44-47, 50 di direc rect, t, 48-49 in indirec direct, t, 48 numericall modell numerica modellin ing, g, 63 radiative radi ative prope properti rti es clouds, clou ds, 48-49 48-49 land surface, 49 rain gauge measurements, 84 rainfall. See  See also precipitation local variabil variability, ity, 25 severe events, 39 reanalysis rea nalysis (cli (climate mate monitori ng), 84-85 recent rece nt cli mate change, change, 87-89 regional climate models, 76 regional climate predictability, 75 rehabilitati rehabil itati on (data re r ecovery), 84 r emot mote e se sensin g, 82 . Se  See e als also o sa satell telliite mea measureme surement nts s

S salinity, 15, 34 satellite measurements cryosphere, 82 International Satellite Cloud Climatology Project (ISCCP), 81 land surface, 82 new systems for climate monitoring, 86 ozone, 80 precipitation pre cipitation,, 84 sea level, 82 solar radiation, 79 surface temperature, temperature, 83 water vapour, 80 sea ice. See  See also cryosphere global inventories, 19 models, 65,67 winter extent over hemispheres, 19 Sea Ice Model Intercomparison Project (ACSYS), 65 sea level, 34, 40 observational data, 82

relation to ENSO, 22-23, 30-31 sea surface sur face temperat temperatur ure e (S (SS ST ) annual anomalies, 38 climatology, 14–15 de deca cadal dal variabil i ty, 40 fo foreca recasts sts,, 71-73 model predictions, 95 observational measurements, 83 relation to ENSO, 22-23, 30-31 thermal expansion (sea level rise), 109 seasonal forecasts, 71-73 sensibl nsib l e heat, 22, 53, 65 severe weather, 39-40, 109 short-te hor t-term rm cli mate foreca f orecas sts ts,, 71-73 single-processor machines, 56-57 skin cancer, 113 113 sl ow sys ystem, tem, 74 snow, now , seas asonal onal ext xte ent nt,, 19 soil moisture, 65, 82, 92. Se  See e also deserts and desertification; land surface solar beam, beam, 6 solar luminosity, 34 solar radiation, 6 . See See a all so radiation r adiation,, short-wave short -wave aerosol effects, 48 annual cycles, 28-29 black body curves, 7 diurnal cycles, 28 energy ene rgy bal balance, ance, 8 land surface a abs bsorpti orption, on, 17 meridional profile, 9 numericall modell numerica modellin ing g, 63 sunspots, 27 top of atmos atmosphere phere,, 6 solar-cycle length, 99 spatial distribution maps, 84 spatial patterns, 10 100 0 spectral numerical modelling, 54–56 SST. Se  See e sea surface temperature statistical downsc downscali aling. ng. Se  See e downscaling storms, 109. Se  See e also severe weather; tropical cyclones stratosphere ozone depletion, 22 volca vol cani nic c effec effects, ts, 30 sulfate aerosols, 30, 45, 47-48. Se  See e also aerosols sunspots, 27-28, 80, 99 surface ur face obs obse ervat rvatii on stations, tati ons, 83 surface ur face tte emperatur e. Se  See e tempe temperatur rature e, surface

T tectonics, 30 teleconnections, 23, 31, 33 temperate f ore or ests, 10 104 4 temperature

potential impacts from climate change, 109-110 sea level pressure numericall modell numerica modellin ing, g, 62

climate feedback, 21 deep ocean, 81-82 diurnal diu rnal range ranges, s, 88 88,, 96




electrical field measurements, 86 extreme events, 96 surface climatology, 11 global mean changes, 74, 88-95, 99-100 local variabil variability, ity, 25 meteor effects, 29 numerical modelling, 60-63 observational data, 83-84 observed cli climate mate va vari riabili abili ty, 28-29, 36-38 relation to sunspots, 28 seasonal forecasts, 71-73 spatial variations, 10 temporal variations, 27 vertical changes, 100, 102 terre terr estr trii al ecosyste cosystems, ms, 20, 103-107 terrestrial radiation, 7 . Se  See e also radiation, longwave aerosol effects, 48 black body curves, 7 energy balance, 8 meridional profile, 9 numericall modell numerica modellin ing, g, 63 the th erm rmal al expan xpans si on (se (sea le l eve vell ), 10 108 8 The Th erm rmal al maxi maximum mum of th e 1940 1940's 's,, 36-37. Se  See e als also o past climates topography, 17, 24-25 transform values, 54-55 transitive climates, 70 tropical tropi cal cyclones cyclones, 96, 109. Se  See e also severe severe w wea eath ther; er; storms Atlantic, 40 tropical forests, 104 104

V vector-borne diseases, 112 112 vegetation, 19-20 exchange with atmosphere, 20 potential impacts from climate change, 103-104 types, 20 Vie Vi enn nna a Conventi Convention on to Pr ote otect ct th the e Ozone Layer, 44 volcanoes, 23, 30, 48-49

W Walker Circulation, 31 water vapour. Se  See e also greenhouse gases numerical modelling, 52-53, 67 observational data, 80 relation to greenhouse effect, 42 water-borne diseases, 11 112 2 weather definition, 3 long-range forecasts, 71-73 regimes, 35 weather prediction modelling, 56, 69 winds climatology, 11-12 numericall modell numerica modellin ing g, 62 WMO World Weather Watch (WWW), 78 World Climate Research Programme (WCRP), 86-87 World Meteorological Organization (WMO), 1, 73

 Y   Younge  Yo unger r Dryas cold cold interval, interval, 36-37. Se  See e also past climates U ultr aviole aviolett radiation, 112-113 Upwell in Upwell ing g Diffus Dif fusion ion and Ene Energy rgy Balance (UD/ EB) mode models, ls, 57, 89, 93-94

Z ze zeni nith th angle, angle, 6, 28

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