Representativeness of the climate change impact analysis based on a

subset of available GCMs

Martin DubrovskyCzechGlobe, Brno + Inst. Atmos. Physics, Prague (Czech Rep.)

Miroslav TrnkaCzechGlobe, Brno + Mendel University, Brno (Czech Republic)

EMS conference, Berlin, 12-16 September 2011

www.ufa.cas.cz/dub/crop/crop.htm

Motivation & Aim• Climate projections are loaded by uncertainties from several sources. One of the

sources is the modelling (GCM/RCM) uncertainty.• output from many GCMs are freely available for creating climate change scenarios

(which may be used for CC impact assessments).• The problem arises: to account for uncertainties from various sources, the impact

analysis should optimaly perform their analysis for

(N1 emission scenarios)

x (N2 values of climate sensitivity) x (N3 GCMs)

which implies a large number of simulations with the “impact” models• .... but nobody wants to use all GCMs. Typically impact modellers want to have 3

GCMs (5 in maximum... .... though Mirek accepts up to 7 GCMs!)

• Aims of this presentation:– present a methodologyfor creating a representative GCM subset, &– show how results of the CC impact study differ for various subsets

1

multi-GCM standardized scenarios

(IPCC-AR4 database; Europe)

2

data: 16 GCMs selected from 23 GCMs available in IPCC-AR4(requirement: [PREC,TAVG,PREC] are available for SRES-A2 run)

regions of interest: (1) Czech Republic, (2) whole Europe

GCM-based standardised scenariossummer (JJA) winter (DJF)

∆TA

VG∆

PREC

combining information from n GCMsmotivation: to show the multi-model mean/median + uncertainty in a single map

step1: results obtained with each of n GCMs are re-gridded into 0.5x0.5º grid (~CRU data)step2: median [med(X)] and std [std(X)] from the n values in each grid box are derivedstep3 (map): the median is represented by a colour, the shape of the symbol represents value of

uncertainty factor Q:

Q =

interpreting the uncertainty:- squares and circles [ std(X) < 0.5 * median(X) ] indicate that medX) differs from

0 at significance level higher than 95% (roughly)- 4-point stars indicate high uncertainty [ std(X) > med(X) ]

or: the greater is the proportion of grey (over sea) or black (over land) colour, the lower is the significance, with which the median value differs from 0

std(X)

med(X)

interpreting the uncertainty:- squares and circles [ std(X) < 0.5 * median(X) ] indicate that medX) differs from 0 at

significance level higher than 95% (roughly)- 4-point stars indicate high uncertainty [ std(X) > med(X) ]

or: the greater is the proportion of grey (over sea) or black (over land) colour, the lower is the significance, with which the median value differs from 0

PREC (change in annual sum)

GCM-based standardised scenarios: PREC

autumn (SON) winter (DJF)

spring (MAM) summer (JJA)

!!! STD > 2*median !!!

year

GCM-based standardised scenarios: TAVG

autumn (SON) winter (DJF)

spring (MAM) summer (JJA)

summary on CC scenario for Europe:• TAVG increases everywhere during

the whole year. Mediterranean: largest increase/decrease: summer/winter

• PREC: – significant PREC decrease in spring

and summer– lowest change in winter – note the annual cycle in position of

zero change band

• Mediterranean: TEMP increase + significant PREC decrease drought risk will increase

TAVG PREC

spring

summer

autumn

winter

year

findinga representative subset of GCMs

We have found: The maps of CC scenarios based on individual GCMs show significant differences between GCMs (which is partly related to natural climate variability, but it is mostly due to differences between structure of the models).

Problem: This “modelling” uncertainty should be somehow taken into account in the climate change impact analysis.

Question: Which GCMs should I use for my climate change impact analysis?

Solutions:A. take-them-all

B. stochastic scenario generator / emulator (prototype scenario generator CLIMATESS was developed)

C. choose a subset of GCMs

3

finding a representative subset of GCMs

3 criteria:A. quality of GCM

B. the subset should represent the between-GCM variability (ideally, a subset might include both close-to-mean GCMs and outliersmetrics: X(GCM) = [TMAM*,TJJA*,TSON*,TDJF*, PMAM*, PJJA*, PSON*, PDJF*]

where * implies standardisationTxxx and Pxxx are seasonal means of TAVG and PREC

distance (GCM1,GCM2) = [X(GCM1) − X(GCM2)]2

C. “tradition” (… where is my HadCM? Where is my ECHAM??)

GCM performance in reproducing annual cycle∆

TAVG

∆PR

EC

RMSE RV

“Best” GCM

based on [ RV(Temp), RV(Prec)]

based on RV(Prec)

T+P

based on RV(Temp)

1. “Best” GCM

T+P

2. “Central” GCM ( = closest to Centroid)

best + 2 most distant GCMsstep 1: choose the best GCM

step 2: find 2 other GCMs, which (together with the best GCM) maximise variance

central + 2 most distant GCMsstep 1: choose the central GCM

step 2: find 2 other GCMs, which (together with the central GCM) maximise variance

3 mutually most distant GCMsstep 1: find 3 GCMs, which maximise variance

2 approaches for spatial climate change analysis

1) (shown in previous slides)use grid-specific GCM subsets.......

- n diverse...........................................- Best + n diverse........................- Central + n diverse........................- Best + Central + n diverse....

2) use one subset for whole area…….. will be shown next slides

... in our analysis we will use:3D ( = 3 GCMs)1B + 2D ( = 3 GCMs)1C + 2D ( = 3 GCMs)1B + 1C + 3D ( = 5 GCMs)

best: MPEH5central: CSMK33 most different: CGMR, GFCM21, IPCM4

(BCM2 was excluded due to problems with SRAD data)

HadCM3+NCPCM

1best

1centroid

3centroids

3bests

3 mostdifferent

best:centroid:3 most different:

5(7) GCMs for Czechia

+2 selected by VIP user:

1best

1centroid

3bests

3 mostdifferent

5 GCMsfor Europe

(3799 0.5x0.5 degree land grid boxes)

5 GCMsfor Europe

(1Best+1Central+3Diverse)

Best : ECHAMCentral : CSMK33Diverse: various options...

subsets to be used in following tests• region-specific subsets (applied for whole Europe):

• grid specific subsets:- 1B = 1 best - 3D = most diverse- 3B = 3 best - B+2D = best + 2 diverse- 1C = 1 best - C+2D = central + 2diverse- 3C = 3 central - B+C+3D = best + central + 2 diverse

CLIMSAVE = a subset of 5 GCMs to be used for whole Europe in CLIMSAVE project. The subset was selected by 3 members of the project team (PH, IH, KK) based on a visual assessment of maps of GCM quality & GCM-based scenarios with the aim to represent variability of spatial patterns of climate change (... and accounting for the quality of individual GCMs)"

4

simila

r

PDSI scale:

test of representativenessof GCM subsets

1) climate characteristics derived for each GCM:• standardised seasonal changes in TAVG and

PREC• average values of relative drought indices

(PDSI, Z)

• the maps will show:[AVG, STD] for individual subsets

combining information from n GCMsmotivation: to show the multi-model mean/median + uncertainty in a single map

step1: results obtained with each of n GCMs are re-gridded into 0.5x0.5º grid (~CRU data)step2: median [med(X)] and std [std(X)] from the n values in each grid box are derivedstep3 (map): the median is represented by a colour, the shape of the symbol represents value of

uncertainty factor Q:

Q =

interpreting the uncertainty:- squares and circles [ std(X) < 0.5 * median(X) ] indicate that medX) differs from

0 at significance level higher than 95% (roughly)- 4-point stars indicate high uncertainty [ std(X) > med(X) ]

or: the greater is the proportion of grey (over sea) or black (over land) colour, the lower is the significance, with which the median value differs from 0

std(X)

med(X)

interpreting the uncertainty:- squares and circles [ std(X) < 0.5 * median(X) ] indicate that medX) differs from 0 at

significance level higher than 95% (roughly)- 4-point stars indicate high uncertainty [ std(X) > med(X) ]

or: the greater is the proportion of grey (over sea) or black (over land) colour, the lower is the significance, with which the median value differs from 0

… let’s start with dTEMP (summer)

16

GC

Ms

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

ts dT(JJA)C

Z

!!

!MPEH5, MIMR,

NCPCM, GFCM21,HadGEM

MPEH5, CSMK3, HadGEM, GFCM21,

IPCM4

MPEH5, CSMK3, HadGEM, MRCGCM,

BCM2

MPEH5, CSMK3, CGMR, GFCM21,

IPCM4

MPEH5, CSMK3, CGMR, GFCM21,

IPCM4,HadCM3, NCPCM

!

!

!

“guide”

- no quantitative one-value score for the map.

- 16 GCM is a reference. Other maps should fit it!

compare maps in terms of:- color value of the mean- symbol: between-GCM variability- spatial pattern

indicates a “favourite”

indicates “good performance”

indicates “bad performance”

!

“reference”

*x

*

*

16

GC

Ms

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

ts dT(JJA)C

ZdT(DJF) dT(year)

!!

!MPEH5, MIMR,

NCPCM, GFCM21,HadGEM

MPEH5, CSMK3, HadGEM, GFCM21,

IPCM4

MPEH5, CSMK3, HadGEM, MRCGCM,

BCM2

MPEH5, CSMK3, CGMR, GFCM21,

IPCM4

MPEH5, CSMK3, CGMR, GFCM21,

IPCM4,HadCM3, NCPCM

MPEH5, MIMR,NCPCM, GFCM21,

HadGEM

MPEH5, CSMK3, HadGEM, GFCM21,

IPCM4

MPEH5, CSMK3, HadGEM, MRCGCM,

BCM2

MPEH5, CSMK3, CGMR, GFCM21,

IPCM4

MPEH5, CSMK3, CGMR, GFCM21,

IPCM4,HadCM3, NCPCM

MPEH5, MIMR,NCPCM, GFCM21,

HadGEM

MPEH5, CSMK3, HadGEM, GFCM21,

IPCM4

MPEH5, CSMK3, HadGEM, MRCGCM,

BCM2

MPEH5, CSMK3, CGMR, GFCM21,

IPCM4

MPEH5, CSMK3, CGMR, GFCM21,

IPCM4,HadCM3, NCPCM

!

!

!

“reference” “reference” “reference”

*

x

*

*

*

*

x

16

GC

Ms

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

ts rZ(JJA)C

ZrZ(DJF) rPDSI(year)

!!

!!

!

!

dP(JJA) dP(DJF) dP(year)

*

x

* *

*

*

*

*

*

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

tsdT

EMP

(sum

mer

)

B 3B B+2D 3D

C 3C C+2D B+C+3D

X X

X X !

!

CLIMSAVE EU5a

CZ5 CZ7 16 GCMs

!!

EU5b*x

*

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

tsdT

EMP

(win

ter)

B 3B B+2D 3D

C 3C C+2D B+C+3D

X X

X X !

! !

CLIMSAVE EU5a EU5b

CZ5 CZ7 16 GCMs

!

*

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

tsdT

EMP

(ann

ual)

B 3B B+2D 3D

C 3C C+2D B+C+3D

X X

X X !

! !

CLIMSAVE EU5a EU5b

CZ5 CZ716 GCMs

!

*x

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

tsdP

REC

(ann

ual)

B 3B B+2D 3D

C 3C C+2D B+C+3D

X X

X X !

! !

CLIMSAVE EU5a EU5b

CZ5 CZ7 16 GCMs

!

**

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

tsdP

REC

(sum

mer

)

B 3B B+2D 3D

C 3C C+2D B+C+3D

X X

X X !

! !

CLIMSAVE EU5a EU5b

CZ5 CZ7 16 GCMs

!

*

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

tsdP

REC

(win

ter)

B 3B B+2D 3D

C 3C C+2D B+C+3D

X X

X X !

! !

CLIMSAVE EU5a EU5b

CZ5 CZ7 16 GCMs

!

*

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

tsrZ

(win

ter)

B 3B B+2D 3D

C 3C C+2D B+C+3D

X X

X X !

! !

CLIMSAVE EU5a EU5b

CZ5 CZ7 16 GCMs

!

*

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

tsrZ

(sum

mer

)

B 3B B+2D 3D

C 3C C+2D B+C+3D

X X

X X !

! !

CLIMSAVE EU5a EU5b

CZ5 CZ7 16 GCMs

!

*

regi

on-s

peci

fic G

CM

sub

sets

grid

-spe

cific

GC

M s

ubse

tsrP

DSI

B 3B B+2D 3D

C 3C C+2D B+C+3D

X X

X X !

! !

CLIMSAVE EU5a EU5b

CZ5 CZ7 16 GCMs

!

*

conclusions• 2 aims were followed in this presentation

– present the methodology for defining representative subsets of GCMs– show results applicable in the present European CC impact studies

• the methodology has some optional settings which affect result– “co-ordinates” of GCM / 8 co-ordinates to discriminate 16 GCMs is not good…– definition of the skill score for finding the best GCM– choice of region-specific GCM subset has not strict rules…

• the results show:– using 1 GCMs is not satisfactory– 3-5 GCMs may be acceptable (..not perfect) to represent between-GCM variability

• 1Best + 1Central + 3Diverse seems OK for 16 GCMs in the whole dataset• adding 2 additional GCMs (e.g. based on user’s personal preferences) may improve

representativeness (#GCMs in a subset approaches total number of available GCMs)• 1 GCM may significantly affect the results!!!

– grid-specific vs region-specific subsets:• grid specific subsets should perform optimally for individual grids

– but implies spatial discontinuities• region-specific subsets smoother maps

– it works for smaller regions (CZ)– but non-representative for large areas

…………………………………….AR5 datasets will come soon...

5

end

(…thank you for your attention…)