eschweizerbartxxx_ingenta

Meteorologische Zeitschrift Vol 19 No 2 135-141 (April 2010) Open Access Articleccopy by Gebruder Borntraeger 2010 (published online)

Observed and projected climate shifts 1901ndash2100 depicted byworld maps of the Koppen-Geiger climate classification

FRANZ RUBEL1lowast and MARKUS KOTTEK2

1University of Veterinary Medicine Vienna Austria2Carinthian Institute for Climate Protection Klagenfurt Austria

(Manuscript received September 7 2009 in revised form December 15 2009 accepted February 2 2010)

Abstract

In a previous paper we presented an update of the highly referenced climate classification map that ofWladimir Koppen which was published for the first time in 1900 and updated in its latest version by RudolfGeiger in 1961 This updated world map of Koppen-Geiger climate classification was based on temperatureand precipitation observations for the period 1951ndash2000 Here we present a series of digital world mapsfor the extended period 1901-2100 to depict global trends in observed climate and projected climate changescenarios World maps for the observational period 1901-2002 are based on recent data sets from the ClimaticResearch Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre(GPCC) at the German Weather Service World maps for the period 2003ndash2100 are based on ensembleprojections of global climate models provided by the Tyndall Centre for Climate Change Research Themain results comprise an estimation of the shifts of climate zones within the 21st century by consideringdifferent IPCC scenarios The largest shifts between the main classes of equatorial climate (A) arid climate(B) warm temperate climate (C) snow climate (D) and polar climate (E) on global land areas are estimatedas 26ndash34 (E to D) 22ndash47 (D to C) 13ndash20 (C to B) and 21ndash32 (C to A)

Zusammenfassung

In einer vorangegangenen Arbeit prasentierten wir eine Aktualisierung der am haufigsten verwendetenKarte der Klimaklassifikation jener von Wladimir Koppen die erstmals 1900 vorgestellt und in der letztenAuflage von Rudolf Geiger 1961 aktualisiert wurde Diese aktualisierte Weltkarte der Koppen-GeigerKlimaklassifizierung basierte auf Beobachtungen der Temperatur und des Niederschlags der Periode 1951ndash2000 Hier stellen wir eine Serie von digitalen Weltkarten fur die erweiterte Periode 1901ndash2100 vor umglobale Trends des beobachteten Klimas sowie Trends in vorhergesagten Klimaszenarien zu beschreiben DieWeltkarten des Beobachtunszeitraums 1901ndash2002 basieren auf aktuellen Datensatzen der Climatic ResearchUnit (CRU) der Universitat von East Anglia und des Weltzentrums fur Niederschlagsklimatologie (WZN) amDeutschen Wetterdienst Weltkarten fur den Zeitraum 2003ndash2100 basieren auf Ensemble-Projektionen vonglobalen Klimamodellen die vom Tyndall Centre for Climate Change Research bereitgestellt werden Diewichtigsten Ergebnisse beinhalten eine Abschatzung der Verschiebungen der Klimazonen im 21 Jhd unterBerucksichtigung verschiedener IPCC Szenarien Die groszligten Verschiebungen zwischen den Hauptklassenaquatoriales Klima (A) arides Klima (B) warm gemaszligigtes Klima (C) Schneeklima (D) und polares Klima(E) der globalen Landflachen werden mit 26ndash34 (E zu D) 22ndash47 (D zu C) 13ndash20 (C zu B) und21ndash32 (C zu A) abgeschatzt

1 Introduction

Recently a world map of Koppen-Geiger climate classi-fication based on temperature and precipitation observa-tions for the period 1951-2000 was published by KOT-TEK et al (2006) Both the printed map and the underly-ing digital data have been provided to the scientific com-munity free of charge via the website httpkoeppen-geigervu-wienacat Already a short-time after the re-lease of the digital world map of Koppen-Geiger climateclassification it was used in various studies mostly to re-late research results to regional climates It became pop-ular mainly in the fields of zoology parasitology andpublic health (DIAZ et al 2007 LLOYD et al 2007)

lowastCorresponding author Franz Rubel Biometeorology and Mathemati-

cal Epidemiology Group Institute for Veterinary Public Health Uni-

versity of Veterinary Medicine Vienna 1210 Vienna Austria e-mail

franzrubelvetmeduniacat

Direct use of the digital data set was made for exam-ple to review micrometeorological flux measurements inAsia (MIZOGUCHI et al 2009) to investigate the ef-fect of particulate air pollution on mortality in the USA(ZANOBETTI and SCHWARTZ 2009) and to identify cli-mate type represented by fossil flora (UTESCHER et al2009) KOTTEK and RUBEL (2007) applied the digitaldatabase to regionalize spatial auto-correlation functionsapplied to objectively analyze global daily precipitationfields by block kriging Even the first geographical text-books (RAW 2008 KUTTLER 2009) desisted from de-picting the historical hand-drawn map as usual in ge-ography and made use of the new digital world map ofKoppen-Geiger climate classification

Here additional world maps of Koppen-Geiger cli-mate classification depicting the shift of climate zonesin the past present and future are presented Because ofthe lack of global datasets the pioneers of the effective

0941-294820100430 $ 315

DOI 1011270941-294820100430 ccopy Gebruder Borntraeger Berlin Stuttgart 2010

eschweizerbartxxx_ingenta

136 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 Meteorol Z 19 2010

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dra

Res

olu

tio

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05

deg

lo

nl

at

Figure 1 World Map of Koppen-Geiger climate classification calculated from observed temperature and precipitation data for the period

1901ndash1925 on a regular 05 degree latitudelongitude grid

eschweizerbartxxx_ingenta

Meteorol Z 19 2010 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 137

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yn

dall

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ture

an

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pit

ati

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rio

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d 2

07

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00

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d

C

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dra

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05

deg

lo

nl

at

Figure 2 World Map of Koppen-Geiger climate classification projected with Tyndall temperature and precipitation data for the period

2076ndash2100 A1FI emission scenario on a regular 05 degree latitudelongitude grid

eschweizerbartxxx_ingenta

138 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 Meteorol Z 19 2010

climate classification the German scientists WladimirKoppen (1846ndash1940) and Rudolf Geiger (1894ndash1981)were not able to compile such time series of climate clas-sification They had to collect all available observationsto prepare one single world map (GEIGER 1961) Ob-served global temperature and precipitation datasets col-lected during recent years offer the possibility to com-pile a 100-years time series of global maps of Koppen-Geiger climate classification Associated temporal andspatial trends were investigated in previously publishedpapers for example by FRAEDRICH et al (2001) and ndashfocused on Europe - by GERSTENGARBE and WERNER

(2009) In this paper the observational period was ex-tended by global climate model (GCM) projections tocover the 200-year period 1901ndash2100

One of the first applications calculating Koppen-Geiger climate classifications from temperature and pre-cipitation projections was presented by MANABE andHOLLOWAY (1975) They used the climate classificationto simply verify the output of a GCM Further climateclassifications provide an excellent method for model in-tercomparison as demonstrated by DE CASTRO et al(2007) to investigate climatic change in Europe usingan ensemble of nine regional climate models Here en-semble projections from 5 GCMs applied to 4 emis-sion scenarios defined by the Intergovernmental Panelon Climate Change (IPCC) and described in the Spe-cial Report on Emission Scenarios (SRES) were usedUnlike previous studies climate models are not investi-gated concerning their differences but they are used toprovide ensemble means of global temperature and pre-cipitation distributions to estimate Koppen-Geiger mapsof possible future worlds

Therewith fundamental climate trends become visi-ble These trends comprise for example the decreasedpermafrost in high-latitudes of the Northern hemisphere(WANG and OVERLAND 2004) or the increased arid-ity in the Mediterranean area in Southern Europe (GAO

and GIORGI 2008) However the spatial resolution ofthe world maps of climate classifications is not yet highenough to depict small-scale features such as the dis-appearing ldquoalpine tundrardquo Koppen climatic type in thewestern United States as described by DIAZ and EIS-CHEID (2007)

2 Data and Method

Two global datasets of climate observations were se-lected to calculate world maps of Koppen-Geiger cli-mate classes Both are available on a regular 05 de-gree latitudelongitude grid with monthly temporal res-olution The first dataset is provided by the ClimaticResearch Unit (CRU) of the University of East An-glia (MITCHELL and JONES 2005) and delivers gridsof monthly climate observations from meteorologicalstations comprising nine climate variables from which

only temperature is used in this study The tempera-ture fields have been analysed from time-series obser-vations which are checked for inhomogeneities in thestation-records by an automated method This datasetreferred to as CRU TS 21 covers the global land ar-eas excluding Antarctica The second dataset providedby the Global Precipitation Climatology Centre (GPCC)located at the German Weather Service is the so-calledGPCCrsquos Full Data Reanalysis Version 4 for 1901ndash2007(FUCHS 2008) This recently updated gridded precip-itation dataset covers the global land areas excludingGreenland and Antarctica Most of the meteorologicaland hydrological services of the world as well as thehosts of the global precipitation station databases - CRUthe Global Historical Climatology Network (GHCN) atthe National Climatic Data Center (NCDC) Food andAgriculture Organization (FAO) of the United Nationsndash and the hosts of some regional precipitation datasetscontributed to the GPCC precipitation data base All ob-servations in this station data base are subject to a multi-stage quality control to minimise the risk of generat-ing temporal inhomogeneities in the gridded data due tovarying station densities Both CRU TS 21 and GPCCrsquosfull data reanalysis version 4 are publicly available andcover the period 1901 to 2002 selected in this study tocalculate Koppen-Geiger maps based on observations

Global temperature and precipitation projections forthe period 2003ndash2100 were taken from the Tyndall Cen-tre for Climate Change Research dataset TYN SC 203(MITCHELL et al 2004) It comprises a total of 20GCM runs combining 4 possible future worlds of emis-sion scenarios described by SRES (ARNELL et al 2004)with 5 state-of-the-art climate models The emissionscenarios were developed in the mid 1990s and are basedon 4 different storylines to describe consistently the rela-tionships between the forces driving emissions and theirevolution and to add context for the scenario quantifica-tion Each storyline represents different world futures Aworld with quick economic growth and a quick launchof new and efficient technologies (A1) a very hetero-genic world with focus on family values and local tra-ditions (A2) a world without materialism and launch ofclean technologies (B1) and a world with focus on lo-cal solutions for economic and ecological sustainabil-ity (B2) The variables in each model include popu-lation growth economic development energy use ef-ficiency of energy use and mix of energy technolo-gies respectively The TYN SC 203 dataset consid-ers the scenarios A1FI (fossil fuel intensive) A2 B1and B2 Five general circulation models were used tosimulate climatic changes The Hadley Centre CoupledModel Version 3 (HadCM3) the National Center forAtmospheric Research-Parallel Climate Model (NCAR-PCM) the Second Generation Coupled Global ClimateModel (CGCM2) the Industrial Research Organizationndash Climate Model Version 2 (CSIRO2) and the EuropeanCentre Model Hamburg Version 4 (ECHam4) Since

eschweizerbartxxx_ingenta

Meteorol Z 19 2010 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 139

the GCMs had a substantially coarser spatial resolutionthan 05 degree MITCHELL et al (2004) interpolatedthe GCM-patterns to the higher resolution by applyingan Delaunay triangulation and by relating them to theobserved temperatures 1961ndash1990 To estimate futureKoppen-Geiger climate classifications ensemble-meansfrom the 5 GCM runs for temperature and precipitationhave been calculated for each of the 4 emission scenar-ios

The calculation scheme for the Koppen-Geiger classesapplied here was briefly described by KOTTEK et al(2006) It comprises a total of 31 climate classes de-scribed by a code of three letters The first letter de-scribes the main classes namely equatorial climates (A)arid climates (B) warm temperate climates (C) snowclimates (D) and polar climates (E) The second letteraccounts for precipitation and the third letter for temper-ature classes (see also the legends of Fig 1 and Fig 2)

In the past Wladimir Koppen and Rudolf Geiger re-garded the climate as constant and used all of the sparseclimate information available at their time to compile asingle map1 To consider climate change the questionabout the shortest period to be selected for a representa-tive world map of climate classification arises More pre-cisely one has to define the lower limit of the length ofan averaging interval to get a representative climate mapFRAEDRICH et al (2001) solved this by statistically in-vestigating the climate shift during the observational pe-riod 1901ndash1995 for 15 Koppen-Geiger climate classesTheir result was an optimal averaging interval of above15 years and they stated further that longer time inter-vals do not lead to a significant change in the trend Ac-cording to this considerations the Koppen-Geiger cli-mate classification maps presented here were calculatedfrom temperature and precipitation data averaged overperiods of 25 years

3 Results

Applying a 25 year running average to the observedand projected monthly temperature and precipitationdata a total of 176 Koppen-Geiger climate classificationmaps was compiled for each of the 4 emission scenar-ios covering the period 1901ndash2100 They are archivedas computer animations and available via the inter-net (httpkoeppen-geigervu-wienacat) Two of theseworld maps one for the observational period 1901ndash1925and one for the projection period 2076ndash2100 are de-picted in Fig 1 and Fig 2 The map representing theIPCC A1FI emission scenario was selected for Fig 2 toshow the scenario with maximum climate shift For afurther printed map we refer to KOTTEK et al (2006)who published a map representative for the current cli-mate period 1951ndash2000

1The first map of climate classification was published by KOPPEN (1900)

the latest version by GEIGER (1961)

1515

-411 plusmn 114

1104

2914

268 plusmn 254

3182

2162

-214 plusmn 466

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1467

053 plusmn 337

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304 plusmn 144

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1221

2914

093 plusmn 127

3007

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2175

1467

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1429

1942

227 plusmn 101

2169

Figure 3 Shifts between main Koppen-Geiger climate classes of

the periods 1976ndash2000 vs 2076ndash2100 for IPCC A1FI (top panel)

and B1 (bottom panel) emission scenario Mean values and range of

5 GCM projections are given in percentage of the global land area

covered by climate data Note that 1 corresponds to an area of

143 middot 106 km2

Most visible climate change may be found in theNorthern hemispheric 30ndash80 belt where B C D andE climates successively shifts to the north Regardingthe whole globe the shift of climate zones within the

21st century exactly for the 100 years period 1976ndash2000 vs 2076ndash2100 is depicted in Fig 3 Again theextreme scenarios have been selected to demonstrate cli-mate change The A1FI scenario shows the largest shiftsthe B1 scenario the smallest shifts between the mainKoppen-Geiger climate classes Values for A2 and B2scenarios (not shown) are within this range

In the observational period 1976ndash2000 a total of 2914 of the global land area is covered by climates of typeB followed by 2162 D climates 1942 A climates1515 E climates and 1467 C climates respec-tively Assuming an A1FI emission scenario for the pe-riod 2076ndash2100 (Fig 3 top panel) the ensemble meanof the 5 GCM projections results in an increased cover-age of 2246 of A climates (+304 ) 3182 of Bclimates (+268 ) 1520 of C climates (+053 ) aswell as a decreased coverage of 1104 of E climates(ndash411 ) and 1948 of D climates (ndash214 ) Sig-nificantly smaller shifts of climate zones were projectedfor the B1 emission scenario (Fig 3 lower panel) Ad-

eschweizerbartxxx_ingenta

Meteorol Z 19 2010 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 137

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eig

er C

lim

ate

Cla

ssif

ica

tio

np

roje

cted

usi

ng I

PC

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I T

yn

dall

SC

20

3 t

emp

era

ture

an

d p

reci

pit

ati

on

sce

na

rio

s p

erio

d 2

07

6 -

21

00

Ma

in c

lim

ate

s

A

equ

ato

ria

l

B

ari

d

C

wa

rm t

emp

era

te

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sno

w

E

po

lar

Pre

cip

ita

tio

n

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des

ert

S

step

pe

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ull

y h

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id

s s

um

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dry

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win

ter

dry

m

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nso

on

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Tem

per

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re

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ho

t su

mm

er

b

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rm s

um

mer

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oo

l su

mm

er

d

extr

emel

y c

on

tin

enta

l

h

ho

t a

rid

k

cold

ari

d

F

po

lar

fro

st

T

po

lar

tun

dra

Res

olu

tio

n

05

deg

lo

nl

at

Figure 2 World Map of Koppen-Geiger climate classification projected with Tyndall temperature and precipitation data for the period

2076ndash2100 A1FI emission scenario on a regular 05 degree latitudelongitude grid

eschweizerbartxxx_ingenta

138 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 Meteorol Z 19 2010

climate classification the German scientists WladimirKoppen (1846ndash1940) and Rudolf Geiger (1894ndash1981)were not able to compile such time series of climate clas-sification They had to collect all available observationsto prepare one single world map (GEIGER 1961) Ob-served global temperature and precipitation datasets col-lected during recent years offer the possibility to com-pile a 100-years time series of global maps of Koppen-Geiger climate classification Associated temporal andspatial trends were investigated in previously publishedpapers for example by FRAEDRICH et al (2001) and ndashfocused on Europe - by GERSTENGARBE and WERNER

(2009) In this paper the observational period was ex-tended by global climate model (GCM) projections tocover the 200-year period 1901ndash2100

One of the first applications calculating Koppen-Geiger climate classifications from temperature and pre-cipitation projections was presented by MANABE andHOLLOWAY (1975) They used the climate classificationto simply verify the output of a GCM Further climateclassifications provide an excellent method for model in-tercomparison as demonstrated by DE CASTRO et al(2007) to investigate climatic change in Europe usingan ensemble of nine regional climate models Here en-semble projections from 5 GCMs applied to 4 emis-sion scenarios defined by the Intergovernmental Panelon Climate Change (IPCC) and described in the Spe-cial Report on Emission Scenarios (SRES) were usedUnlike previous studies climate models are not investi-gated concerning their differences but they are used toprovide ensemble means of global temperature and pre-cipitation distributions to estimate Koppen-Geiger mapsof possible future worlds

Therewith fundamental climate trends become visi-ble These trends comprise for example the decreasedpermafrost in high-latitudes of the Northern hemisphere(WANG and OVERLAND 2004) or the increased arid-ity in the Mediterranean area in Southern Europe (GAO

and GIORGI 2008) However the spatial resolution ofthe world maps of climate classifications is not yet highenough to depict small-scale features such as the dis-appearing ldquoalpine tundrardquo Koppen climatic type in thewestern United States as described by DIAZ and EIS-CHEID (2007)

2 Data and Method

Two global datasets of climate observations were se-lected to calculate world maps of Koppen-Geiger cli-mate classes Both are available on a regular 05 de-gree latitudelongitude grid with monthly temporal res-olution The first dataset is provided by the ClimaticResearch Unit (CRU) of the University of East An-glia (MITCHELL and JONES 2005) and delivers gridsof monthly climate observations from meteorologicalstations comprising nine climate variables from which

only temperature is used in this study The tempera-ture fields have been analysed from time-series obser-vations which are checked for inhomogeneities in thestation-records by an automated method This datasetreferred to as CRU TS 21 covers the global land ar-eas excluding Antarctica The second dataset providedby the Global Precipitation Climatology Centre (GPCC)located at the German Weather Service is the so-calledGPCCrsquos Full Data Reanalysis Version 4 for 1901ndash2007(FUCHS 2008) This recently updated gridded precip-itation dataset covers the global land areas excludingGreenland and Antarctica Most of the meteorologicaland hydrological services of the world as well as thehosts of the global precipitation station databases - CRUthe Global Historical Climatology Network (GHCN) atthe National Climatic Data Center (NCDC) Food andAgriculture Organization (FAO) of the United Nationsndash and the hosts of some regional precipitation datasetscontributed to the GPCC precipitation data base All ob-servations in this station data base are subject to a multi-stage quality control to minimise the risk of generat-ing temporal inhomogeneities in the gridded data due tovarying station densities Both CRU TS 21 and GPCCrsquosfull data reanalysis version 4 are publicly available andcover the period 1901 to 2002 selected in this study tocalculate Koppen-Geiger maps based on observations

Global temperature and precipitation projections forthe period 2003ndash2100 were taken from the Tyndall Cen-tre for Climate Change Research dataset TYN SC 203(MITCHELL et al 2004) It comprises a total of 20GCM runs combining 4 possible future worlds of emis-sion scenarios described by SRES (ARNELL et al 2004)with 5 state-of-the-art climate models The emissionscenarios were developed in the mid 1990s and are basedon 4 different storylines to describe consistently the rela-tionships between the forces driving emissions and theirevolution and to add context for the scenario quantifica-tion Each storyline represents different world futures Aworld with quick economic growth and a quick launchof new and efficient technologies (A1) a very hetero-genic world with focus on family values and local tra-ditions (A2) a world without materialism and launch ofclean technologies (B1) and a world with focus on lo-cal solutions for economic and ecological sustainabil-ity (B2) The variables in each model include popu-lation growth economic development energy use ef-ficiency of energy use and mix of energy technolo-gies respectively The TYN SC 203 dataset consid-ers the scenarios A1FI (fossil fuel intensive) A2 B1and B2 Five general circulation models were used tosimulate climatic changes The Hadley Centre CoupledModel Version 3 (HadCM3) the National Center forAtmospheric Research-Parallel Climate Model (NCAR-PCM) the Second Generation Coupled Global ClimateModel (CGCM2) the Industrial Research Organizationndash Climate Model Version 2 (CSIRO2) and the EuropeanCentre Model Hamburg Version 4 (ECHam4) Since

eschweizerbartxxx_ingenta

Meteorol Z 19 2010 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 139

the GCMs had a substantially coarser spatial resolutionthan 05 degree MITCHELL et al (2004) interpolatedthe GCM-patterns to the higher resolution by applyingan Delaunay triangulation and by relating them to theobserved temperatures 1961ndash1990 To estimate futureKoppen-Geiger climate classifications ensemble-meansfrom the 5 GCM runs for temperature and precipitationhave been calculated for each of the 4 emission scenar-ios

The calculation scheme for the Koppen-Geiger classesapplied here was briefly described by KOTTEK et al(2006) It comprises a total of 31 climate classes de-scribed by a code of three letters The first letter de-scribes the main classes namely equatorial climates (A)arid climates (B) warm temperate climates (C) snowclimates (D) and polar climates (E) The second letteraccounts for precipitation and the third letter for temper-ature classes (see also the legends of Fig 1 and Fig 2)

In the past Wladimir Koppen and Rudolf Geiger re-garded the climate as constant and used all of the sparseclimate information available at their time to compile asingle map1 To consider climate change the questionabout the shortest period to be selected for a representa-tive world map of climate classification arises More pre-cisely one has to define the lower limit of the length ofan averaging interval to get a representative climate mapFRAEDRICH et al (2001) solved this by statistically in-vestigating the climate shift during the observational pe-riod 1901ndash1995 for 15 Koppen-Geiger climate classesTheir result was an optimal averaging interval of above15 years and they stated further that longer time inter-vals do not lead to a significant change in the trend Ac-cording to this considerations the Koppen-Geiger cli-mate classification maps presented here were calculatedfrom temperature and precipitation data averaged overperiods of 25 years

3 Results

Applying a 25 year running average to the observedand projected monthly temperature and precipitationdata a total of 176 Koppen-Geiger climate classificationmaps was compiled for each of the 4 emission scenar-ios covering the period 1901ndash2100 They are archivedas computer animations and available via the inter-net (httpkoeppen-geigervu-wienacat) Two of theseworld maps one for the observational period 1901ndash1925and one for the projection period 2076ndash2100 are de-picted in Fig 1 and Fig 2 The map representing theIPCC A1FI emission scenario was selected for Fig 2 toshow the scenario with maximum climate shift For afurther printed map we refer to KOTTEK et al (2006)who published a map representative for the current cli-mate period 1951ndash2000

1The first map of climate classification was published by KOPPEN (1900)

the latest version by GEIGER (1961)

1515

-411 plusmn 114

1104

2914

268 plusmn 254

3182

2162

-214 plusmn 466

1948

1467

053 plusmn 337

1520

1942

304 plusmn 144

2246

1515

-294 plusmn 133

1221

2914

093 plusmn 127

3007

2162

013 plusmn 125

2175

1467

-038 plusmn 097

1429

1942

227 plusmn 101

2169

Figure 3 Shifts between main Koppen-Geiger climate classes of

the periods 1976ndash2000 vs 2076ndash2100 for IPCC A1FI (top panel)

and B1 (bottom panel) emission scenario Mean values and range of

5 GCM projections are given in percentage of the global land area

covered by climate data Note that 1 corresponds to an area of

143 middot 106 km2

Most visible climate change may be found in theNorthern hemispheric 30ndash80 belt where B C D andE climates successively shifts to the north Regardingthe whole globe the shift of climate zones within the

21st century exactly for the 100 years period 1976ndash2000 vs 2076ndash2100 is depicted in Fig 3 Again theextreme scenarios have been selected to demonstrate cli-mate change The A1FI scenario shows the largest shiftsthe B1 scenario the smallest shifts between the mainKoppen-Geiger climate classes Values for A2 and B2scenarios (not shown) are within this range

In the observational period 1976ndash2000 a total of 2914 of the global land area is covered by climates of typeB followed by 2162 D climates 1942 A climates1515 E climates and 1467 C climates respec-tively Assuming an A1FI emission scenario for the pe-riod 2076ndash2100 (Fig 3 top panel) the ensemble meanof the 5 GCM projections results in an increased cover-age of 2246 of A climates (+304 ) 3182 of Bclimates (+268 ) 1520 of C climates (+053 ) aswell as a decreased coverage of 1104 of E climates(ndash411 ) and 1948 of D climates (ndash214 ) Sig-nificantly smaller shifts of climate zones were projectedfor the B1 emission scenario (Fig 3 lower panel) Ad-

eschweizerbartxxx_ingenta

138 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 Meteorol Z 19 2010

climate classification the German scientists WladimirKoppen (1846ndash1940) and Rudolf Geiger (1894ndash1981)were not able to compile such time series of climate clas-sification They had to collect all available observationsto prepare one single world map (GEIGER 1961) Ob-served global temperature and precipitation datasets col-lected during recent years offer the possibility to com-pile a 100-years time series of global maps of Koppen-Geiger climate classification Associated temporal andspatial trends were investigated in previously publishedpapers for example by FRAEDRICH et al (2001) and ndashfocused on Europe - by GERSTENGARBE and WERNER

(2009) In this paper the observational period was ex-tended by global climate model (GCM) projections tocover the 200-year period 1901ndash2100

One of the first applications calculating Koppen-Geiger climate classifications from temperature and pre-cipitation projections was presented by MANABE andHOLLOWAY (1975) They used the climate classificationto simply verify the output of a GCM Further climateclassifications provide an excellent method for model in-tercomparison as demonstrated by DE CASTRO et al(2007) to investigate climatic change in Europe usingan ensemble of nine regional climate models Here en-semble projections from 5 GCMs applied to 4 emis-sion scenarios defined by the Intergovernmental Panelon Climate Change (IPCC) and described in the Spe-cial Report on Emission Scenarios (SRES) were usedUnlike previous studies climate models are not investi-gated concerning their differences but they are used toprovide ensemble means of global temperature and pre-cipitation distributions to estimate Koppen-Geiger mapsof possible future worlds

Therewith fundamental climate trends become visi-ble These trends comprise for example the decreasedpermafrost in high-latitudes of the Northern hemisphere(WANG and OVERLAND 2004) or the increased arid-ity in the Mediterranean area in Southern Europe (GAO

and GIORGI 2008) However the spatial resolution ofthe world maps of climate classifications is not yet highenough to depict small-scale features such as the dis-appearing ldquoalpine tundrardquo Koppen climatic type in thewestern United States as described by DIAZ and EIS-CHEID (2007)

2 Data and Method

Two global datasets of climate observations were se-lected to calculate world maps of Koppen-Geiger cli-mate classes Both are available on a regular 05 de-gree latitudelongitude grid with monthly temporal res-olution The first dataset is provided by the ClimaticResearch Unit (CRU) of the University of East An-glia (MITCHELL and JONES 2005) and delivers gridsof monthly climate observations from meteorologicalstations comprising nine climate variables from which

only temperature is used in this study The tempera-ture fields have been analysed from time-series obser-vations which are checked for inhomogeneities in thestation-records by an automated method This datasetreferred to as CRU TS 21 covers the global land ar-eas excluding Antarctica The second dataset providedby the Global Precipitation Climatology Centre (GPCC)located at the German Weather Service is the so-calledGPCCrsquos Full Data Reanalysis Version 4 for 1901ndash2007(FUCHS 2008) This recently updated gridded precip-itation dataset covers the global land areas excludingGreenland and Antarctica Most of the meteorologicaland hydrological services of the world as well as thehosts of the global precipitation station databases - CRUthe Global Historical Climatology Network (GHCN) atthe National Climatic Data Center (NCDC) Food andAgriculture Organization (FAO) of the United Nationsndash and the hosts of some regional precipitation datasetscontributed to the GPCC precipitation data base All ob-servations in this station data base are subject to a multi-stage quality control to minimise the risk of generat-ing temporal inhomogeneities in the gridded data due tovarying station densities Both CRU TS 21 and GPCCrsquosfull data reanalysis version 4 are publicly available andcover the period 1901 to 2002 selected in this study tocalculate Koppen-Geiger maps based on observations

Global temperature and precipitation projections forthe period 2003ndash2100 were taken from the Tyndall Cen-tre for Climate Change Research dataset TYN SC 203(MITCHELL et al 2004) It comprises a total of 20GCM runs combining 4 possible future worlds of emis-sion scenarios described by SRES (ARNELL et al 2004)with 5 state-of-the-art climate models The emissionscenarios were developed in the mid 1990s and are basedon 4 different storylines to describe consistently the rela-tionships between the forces driving emissions and theirevolution and to add context for the scenario quantifica-tion Each storyline represents different world futures Aworld with quick economic growth and a quick launchof new and efficient technologies (A1) a very hetero-genic world with focus on family values and local tra-ditions (A2) a world without materialism and launch ofclean technologies (B1) and a world with focus on lo-cal solutions for economic and ecological sustainabil-ity (B2) The variables in each model include popu-lation growth economic development energy use ef-ficiency of energy use and mix of energy technolo-gies respectively The TYN SC 203 dataset consid-ers the scenarios A1FI (fossil fuel intensive) A2 B1and B2 Five general circulation models were used tosimulate climatic changes The Hadley Centre CoupledModel Version 3 (HadCM3) the National Center forAtmospheric Research-Parallel Climate Model (NCAR-PCM) the Second Generation Coupled Global ClimateModel (CGCM2) the Industrial Research Organizationndash Climate Model Version 2 (CSIRO2) and the EuropeanCentre Model Hamburg Version 4 (ECHam4) Since

eschweizerbartxxx_ingenta

Meteorol Z 19 2010 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 139

the GCMs had a substantially coarser spatial resolutionthan 05 degree MITCHELL et al (2004) interpolatedthe GCM-patterns to the higher resolution by applyingan Delaunay triangulation and by relating them to theobserved temperatures 1961ndash1990 To estimate futureKoppen-Geiger climate classifications ensemble-meansfrom the 5 GCM runs for temperature and precipitationhave been calculated for each of the 4 emission scenar-ios

The calculation scheme for the Koppen-Geiger classesapplied here was briefly described by KOTTEK et al(2006) It comprises a total of 31 climate classes de-scribed by a code of three letters The first letter de-scribes the main classes namely equatorial climates (A)arid climates (B) warm temperate climates (C) snowclimates (D) and polar climates (E) The second letteraccounts for precipitation and the third letter for temper-ature classes (see also the legends of Fig 1 and Fig 2)

In the past Wladimir Koppen and Rudolf Geiger re-garded the climate as constant and used all of the sparseclimate information available at their time to compile asingle map1 To consider climate change the questionabout the shortest period to be selected for a representa-tive world map of climate classification arises More pre-cisely one has to define the lower limit of the length ofan averaging interval to get a representative climate mapFRAEDRICH et al (2001) solved this by statistically in-vestigating the climate shift during the observational pe-riod 1901ndash1995 for 15 Koppen-Geiger climate classesTheir result was an optimal averaging interval of above15 years and they stated further that longer time inter-vals do not lead to a significant change in the trend Ac-cording to this considerations the Koppen-Geiger cli-mate classification maps presented here were calculatedfrom temperature and precipitation data averaged overperiods of 25 years

3 Results

Applying a 25 year running average to the observedand projected monthly temperature and precipitationdata a total of 176 Koppen-Geiger climate classificationmaps was compiled for each of the 4 emission scenar-ios covering the period 1901ndash2100 They are archivedas computer animations and available via the inter-net (httpkoeppen-geigervu-wienacat) Two of theseworld maps one for the observational period 1901ndash1925and one for the projection period 2076ndash2100 are de-picted in Fig 1 and Fig 2 The map representing theIPCC A1FI emission scenario was selected for Fig 2 toshow the scenario with maximum climate shift For afurther printed map we refer to KOTTEK et al (2006)who published a map representative for the current cli-mate period 1951ndash2000

1The first map of climate classification was published by KOPPEN (1900)

the latest version by GEIGER (1961)

1515

-411 plusmn 114

1104

2914

268 plusmn 254

3182

2162

-214 plusmn 466

1948

1467

053 plusmn 337

1520

1942

304 plusmn 144

2246

1515

-294 plusmn 133

1221

2914

093 plusmn 127

3007

2162

013 plusmn 125

2175

1467

-038 plusmn 097

1429

1942

227 plusmn 101

2169

Figure 3 Shifts between main Koppen-Geiger climate classes of

the periods 1976ndash2000 vs 2076ndash2100 for IPCC A1FI (top panel)

and B1 (bottom panel) emission scenario Mean values and range of

5 GCM projections are given in percentage of the global land area

covered by climate data Note that 1 corresponds to an area of

143 middot 106 km2

Most visible climate change may be found in theNorthern hemispheric 30ndash80 belt where B C D andE climates successively shifts to the north Regardingthe whole globe the shift of climate zones within the

21st century exactly for the 100 years period 1976ndash2000 vs 2076ndash2100 is depicted in Fig 3 Again theextreme scenarios have been selected to demonstrate cli-mate change The A1FI scenario shows the largest shiftsthe B1 scenario the smallest shifts between the mainKoppen-Geiger climate classes Values for A2 and B2scenarios (not shown) are within this range

In the observational period 1976ndash2000 a total of 2914 of the global land area is covered by climates of typeB followed by 2162 D climates 1942 A climates1515 E climates and 1467 C climates respec-tively Assuming an A1FI emission scenario for the pe-riod 2076ndash2100 (Fig 3 top panel) the ensemble meanof the 5 GCM projections results in an increased cover-age of 2246 of A climates (+304 ) 3182 of Bclimates (+268 ) 1520 of C climates (+053 ) aswell as a decreased coverage of 1104 of E climates(ndash411 ) and 1948 of D climates (ndash214 ) Sig-nificantly smaller shifts of climate zones were projectedfor the B1 emission scenario (Fig 3 lower panel) Ad-

eschweizerbartxxx_ingenta

Meteorol Z 19 2010 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 139

the GCMs had a substantially coarser spatial resolutionthan 05 degree MITCHELL et al (2004) interpolatedthe GCM-patterns to the higher resolution by applyingan Delaunay triangulation and by relating them to theobserved temperatures 1961ndash1990 To estimate futureKoppen-Geiger climate classifications ensemble-meansfrom the 5 GCM runs for temperature and precipitationhave been calculated for each of the 4 emission scenar-ios

The calculation scheme for the Koppen-Geiger classesapplied here was briefly described by KOTTEK et al(2006) It comprises a total of 31 climate classes de-scribed by a code of three letters The first letter de-scribes the main classes namely equatorial climates (A)arid climates (B) warm temperate climates (C) snowclimates (D) and polar climates (E) The second letteraccounts for precipitation and the third letter for temper-ature classes (see also the legends of Fig 1 and Fig 2)

In the past Wladimir Koppen and Rudolf Geiger re-garded the climate as constant and used all of the sparseclimate information available at their time to compile asingle map1 To consider climate change the questionabout the shortest period to be selected for a representa-tive world map of climate classification arises More pre-cisely one has to define the lower limit of the length ofan averaging interval to get a representative climate mapFRAEDRICH et al (2001) solved this by statistically in-vestigating the climate shift during the observational pe-riod 1901ndash1995 for 15 Koppen-Geiger climate classesTheir result was an optimal averaging interval of above15 years and they stated further that longer time inter-vals do not lead to a significant change in the trend Ac-cording to this considerations the Koppen-Geiger cli-mate classification maps presented here were calculatedfrom temperature and precipitation data averaged overperiods of 25 years

3 Results

Applying a 25 year running average to the observedand projected monthly temperature and precipitationdata a total of 176 Koppen-Geiger climate classificationmaps was compiled for each of the 4 emission scenar-ios covering the period 1901ndash2100 They are archivedas computer animations and available via the inter-net (httpkoeppen-geigervu-wienacat) Two of theseworld maps one for the observational period 1901ndash1925and one for the projection period 2076ndash2100 are de-picted in Fig 1 and Fig 2 The map representing theIPCC A1FI emission scenario was selected for Fig 2 toshow the scenario with maximum climate shift For afurther printed map we refer to KOTTEK et al (2006)who published a map representative for the current cli-mate period 1951ndash2000

1The first map of climate classification was published by KOPPEN (1900)

the latest version by GEIGER (1961)

1515

-411 plusmn 114

1104

2914

268 plusmn 254

3182

2162

-214 plusmn 466

1948

1467

053 plusmn 337

1520

1942

304 plusmn 144

2246

1515

-294 plusmn 133

1221

2914

093 plusmn 127

3007

2162

013 plusmn 125

2175

1467

-038 plusmn 097

1429

1942

227 plusmn 101

2169

Figure 3 Shifts between main Koppen-Geiger climate classes of

the periods 1976ndash2000 vs 2076ndash2100 for IPCC A1FI (top panel)

and B1 (bottom panel) emission scenario Mean values and range of

5 GCM projections are given in percentage of the global land area

covered by climate data Note that 1 corresponds to an area of

143 middot 106 km2

Most visible climate change may be found in theNorthern hemispheric 30ndash80 belt where B C D andE climates successively shifts to the north Regardingthe whole globe the shift of climate zones within the

21st century exactly for the 100 years period 1976ndash2000 vs 2076ndash2100 is depicted in Fig 3 Again theextreme scenarios have been selected to demonstrate cli-mate change The A1FI scenario shows the largest shiftsthe B1 scenario the smallest shifts between the mainKoppen-Geiger climate classes Values for A2 and B2scenarios (not shown) are within this range

In the observational period 1976ndash2000 a total of 2914 of the global land area is covered by climates of typeB followed by 2162 D climates 1942 A climates1515 E climates and 1467 C climates respec-tively Assuming an A1FI emission scenario for the pe-riod 2076ndash2100 (Fig 3 top panel) the ensemble meanof the 5 GCM projections results in an increased cover-age of 2246 of A climates (+304 ) 3182 of Bclimates (+268 ) 1520 of C climates (+053 ) aswell as a decreased coverage of 1104 of E climates(ndash411 ) and 1948 of D climates (ndash214 ) Sig-nificantly smaller shifts of climate zones were projectedfor the B1 emission scenario (Fig 3 lower panel) Ad-

eschweizerbartxxx_ingenta

140 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 Meteorol Z 19 2010

ditionally in contrast to an increased coverage projectedfor the A1FI scenario a decreased coverage of C cli-mates was projected for the B1 scenarios

The shifts between the main climate classes (greyarrows in Fig 3) are maximal around the D climatesFor example concerning the A1FI emission scenariothe maximum shift was projected from D to C climates(467 plusmn 387 ) and for the B1 emission scenario fromE to D (260 plusmn 125 ) Not only the climate shifts butalso the uncertainties between the 5 GCM projectionsare generally smaller for the B1 scenario The spatialshift between the main climates and the sub-climates isassessed by visually comparing Fig 1 and Fig 2 Asfor the main climates the change is maximal for D sub-climates Dfb Dfc and Dfd Further climate shifts arevisible for example in the tropics where areas coveredby Af and Am climates increase The latter visualizesthe widening of the tropical belt as recently explainedby SEIDEL et al (2007)

4 Conclusion

A series of new world maps of Koppen-Geiger climateclassification depicting spatial and temporal trends ispresented here and as supplemental material on the inter-net at httpkoeppen-geigervu-wienacat It comprisesthe first projected maps of global climate classificationcalculated on a 05 degree grid Other projected mapsof global climate classification were calculated onto themuch coarser grid resolution of the involved GCMs(KALVOVA et al 2003) On a regional scale howeverfor several studies high resolution maps were compiledRODERFELD et al (2008) for example described thepotential impact of climate change on ecosystems ofthe Barents Sea region by Koppen climate classifica-tions evolved from 05 degree resolution Regional Cli-mate Model (REMO) simulations Their Koppen cli-mate classification employed to northern Europe rep-resented by REMO and the underlying B2 scenario cor-responds quite well with our maps A further regionalstudy was presented by BAKER et al (2010) to investi-gate climate change in China Instead of regional climatemodel projections the authors applied the GCM datafrom HadCM3 and PCM which were downscaled us-ing the additional information of observed climate dataanalyzed on a grid of 4 km2 As in our study the GCMdata are taken from the TYN SC 20 dataset calculatedfor A1FI and B1 SRES scenarios

The regional studies discussed above demonstratepossible applications of our global climate maps There-fore our paper provides only a brief description of cli-mate trends depicted by series of Koppen-Geiger mapsUsers are invited to draw conclusions for their regions ofinterest and apply the digital datasets for their own stud-ies Particular advantage is expected for studies whichrelate the abundance of plants animals or pathogens notexclusively to current climates but also to projections of

past and future climates Many of these studies may alsobenefit from the printed world maps avoiding the moreexpensive application of the digital dataset

Acknowledgments

The Tyndall Centre for Climatic Change Research atthe University of East Anglia UK and the Global Pre-cipitation Climatology Centre (GPCC) in OffenbachGermany provided the data We thank Peter FINGER

(GPCC) for assisting the data transfer as well as TobiasFUCHS and Bruno RUDOLF for discussing the applica-tion

References

ARNELL NW MJL LIVERMORE S KOVATS P ELEVY R NICHOLLS M L PARRY S R GAFFIN2004 Climate and socio-economic scenarios for global-scale climate change impacts assessments Characterisingthe SRES storylines ndash Glob Environ Change 14 3ndash20

BAKER B H DIAZ W HARGROVE F HOFFMAN 2010Use of the Koppen-Trewartha climate classification to eval-uate climatic refugia in statistically derived ecoregions forthe Peoplersquos Republic of China ndash Climate Change 98 113ndash131

DE CASTRO M C GALLARDO K JYLHA H TUOMEN-VIRTA 2007 The use of a climate-type classification forassessing climate change effects in Europe from an ensem-ble of nine regional climate models ndash Climate Change 81329ndash341

DIAZ HF JK EISCHEID 2007 Disappearinglsquoalpine tundrarsquo Koppen climatic type in the westernUnited States ndash Geophys Res Lett 34 L18707doi1010292007GL031253

DIAZ P A PAZ-SILVA R SANCHEZ-ANDRADE J LSUAREZ J PEDREIRA M ARIAS P DIEZ-BANOSP MORRONDO 2007 Assessment of climatic and oro-graphic conditions on the infection by Calicophoron daub-neyi and Dicrocoelium dendriticum in grazing beef cattle(NW Spain) ndash Vet Parasitol 149 285ndash289

FRAEDRICH K F-W GERSTENGARBE PC WERNER2001 Climate shift during the last century ndash ClimateChange 50 405ndash417

FUCHS T 2008 GPCCrsquos Full Data Re-analysis Version 4 for 1901-2007 PersonalCommunication data available at ftpftp-anondwddepubdatagpcchtmlfulldata downloadhtml

GAO X F GIORGI 2008 Increased aridity in the mediter-ranean region under greenhouse gas forcing estimated fromhigh resolution simulations with a regional climate modelndash Glob Planet Change 62 195ndash209

GEIGER R 1961 Uberarbeitete Neuausgabe von GeigerR Koppen-Geiger Klima der Erde (Wandkarte 116Mill) ndash Klett-Perthes Gotha

GERSTENGARBE F-W P C WERNER 2009 A shortupdate on Koeppen climate shifts in Europe between 1901and 2003 ndash Clim Change 92 99ndash107

KALVOVA J T HALENKA K BEZPALCOVAI NEMESOVA 2003 Koppen climate types in ob-served and simulated climates ndash Stud Geophys Geod47 185ndash202

eschweizerbartxxx_ingenta

Meteorol Z 19 2010 F Rubel amp M Kottek Observed and projected climate shifts 1901ndash2100 141

KOPPEN W 1900 Versuch einer Klassifikation der Kli-mate vorzugsweise nach ihren Beziehungen zur Pflanzen-welt ndash Geographische Zeitschrift 6 593ndash611 657ndash679

KOTTEK M F RUBEL 2007 Global daily precipitationfields from bias-corrected rain gauge and satellite observa-tions Part I Design and development ndash Meteorol Z 16525ndash539

KOTTEK M J GRIESER C BECK B RUDOLFF RUBEL 2006 World map of the Koppen-Geiger cli-mate classification updated ndash Meteorol Z 15 259ndash263

KUTTLER W 2009 Grundriss Allgemeine GeographieKlimatologie ndash Schoning Paderborn 260 S

LLOYD SJ RS KOVATS BG ARMSTRONG 2007Global diarrhoea morbidity weather and climate ndash Cli-mate Res 34 119ndash127

MANABE S J L HOLLOWAY 1975 The seasonal varia-tion of the hydrologic cycle as simulated by a global modelof the atmosphere ndash J Geophys Res 80 1617ndash1649

MITCHELL T D P D JONES 2005 An improved methodof constructing a database of monthly climate observationsand associated high-resolution grids ndash Int J Climatol 25693ndash712

MITCHELL T D T R CARTER P D JONES M HULMEM NEW 2004 A comprehensive set of high-resolutiongrids of monthly climate for Europe and the globe the ob-served records (1901ndash2000) and 16 scenarios (2001ndash2100)

Tyndall Centre of Climate Change Research Working Pa-per 55

MIZOGUCHI Y A MIYATA Y OHTANI R HIRATAS YUTA 2009 A review of tower flux observation sites inAsia ndash J For Res 14 1ndash9

RAW M 2008 OCR AS Geography ndash Hodder EducationDeddington 370

RODERFELD H E BLYTH R DANKERS G HUSED SLAGSTAD I ELLINGSEN A WOLF M A LANGE2008 Potential impact of climate change on ecosystems ofthe Barents Sea Region ndash Climate Change 87 283ndash303

SEIDEL DJ Q FU WJ RANDEL TJ REICHLER 2007Widening of the tropical belt in a changing climate ndashNature Geoscience DOI101038ngeo200738

UTESCHER T V MOSBRUGGER D IVANOV D LDILCHER 2009 Present-day climatic equivalents of Eu-ropean Cenozoic climates ndash Earth Planet Sci Lett 284544ndash552

WANG M JE OVERLAND 2004 Detecting arctic climatechange using Koppen climate classification ndash ClimateChange 67 43ndash62

ZANOBETTI A J SCHWARTZ 2009 The effect of fineand coarse particulate air pollution on mortality A nationalanalysis ndash Environ Health Perspect 117 898ndash903