Regional Performance of the

IPCC-AR4 Models

in Simulating Present-Day Mean Climate

Junsu Kim and Thomas Reichler University of Utah, Salt Lake City, USA

Introduction• Previous work

– “How well do coupled models simulate today’s climate?” (Reichler and Kim 2008, BAMS, JGR)

– 3 model generations: CMIP-1 to CMIP-3– Focus: Global performance skill

Error

Introduction• Previous work

– “How well do coupled models simulate today’s climate?” (Reichler and Kim 2008, BAMS, JGR)

– 3 model generations: CMIP-1 to CMIP-3– Focus: Global scale

• Basic idea of this model intercomparison work– Realistic simulation of current climate is a necessary condition

for confidence in simulation of future

• This work– Regional variations in model performance– CMIP-3 models (IPCC-AR4)

How to Evaluate Model Performance?

• Problem of objectiveness– measure of error (or goodness)– choice of quantities/processes– relative weights

• Method– current (79-99) mean climate and seasonal cycle– multivariate approach: aggregate errors from many climate

quantities into a single index– rational

• complex interrelationship amongst individual components of climate• it is not enough to focus on just one particular quantity of interest• to have confidence in a model, it must simulate every aspect of climate

well

– moments of climate– timescale– observational uncertainty– spatial domain

Methodology

• Normalized error variance

• Regional error index

• Overall performance index

2

2

1 ,

Nn n

nn n o

s oE w

22

2

rr m

g

EI

E <1: Better than average

2v

I Equal weighting

How capable is a model in simulating regional climate relative to the average performance on the global scale?

• We evaluate– 24 CMIP-3 models (excluding BCC-CM1)– average model– multi-model mean– NCEP/NCAR reanalysis

Regions

ALA

WNA CNAENA

GRL

CAM

AMZ

SSA

NEU

MED

SAH

WAF

EAF

SAF

CAS

NAS

TIB

SAS

EAS

SEA

AUS

ANT

AR

NP

TP

SP

NA

TA

SA SI

TI

AN

Land 22 regions; Giorgi and Francisco (2000)Ocean 10 basins

Climate Elements

8

“Physics” (12)

“Dynamics” (9)

“Oceans” (9)

“Land” (1)

dy

na

mi

cs

zonal/meridional wind 200 hPa global U200, V200 m/s ERA

stream function 200 hPa global χ200 106 m2s-1 ERA

velocity potential 200 hPa global ψ200 106 m2s-1 ERA

temperature 200 hPa global T200 K ERA

geopotential 500 hPa global Z500 gpm ERA

stationary waves 500 hPa global SW500 gpm ERA

zonal/meridional wind 850 hPa global U850, V850 m/s ERA

Quantity v Domain Acronym Units Validating observations O1-5

ph

ys

ic

s

surface air temperature global TAS K CRU, ICOADS, NOAA

total cloudiness global CLT % CERES, ISCCP

surface radiation (up/down, short-/longwave)

global RSDS, RSUS, RLDS, RLUS

Wm-2 BSRN, CERES, GEBA, ISCCP

TOA outgoing shortwave radiation global RSUT Wm-2 CERES, ERBE, ISCCP

TOA outgoing longwave radiation global RLUT Wm-2 CERES, ERBE, ISCCP, NOAA

TOA cloud radiative forcing global CFLT, CFST Wm-2 CERES, ERBE, ISCCP

precipitation global PR mm/day CMAP, GPCP

precipitable water global PRW mm HOAPS2, NVAP

o c

e a

n s

sea surface temperature ocean TOS K GISST

zonal/meridional surface wind stress ocean TAUU, TAUV 10-2 Nm-2 GSSTF2, ICOADS

sea level pressure ocean PSL hPa ERSLP, HADSLP, ICOADS

surface sensible/latent heat fluxes ocean HFSS, HFLS Wm-2 GSSTF2, HOAPS2, ICOADS, JOFURO, OAFLUX

sea surface height ocean ZOS m GRACE-DOT

sea ice content ocean SIC % GICE

sea surface salinity ocean SO ‰ NODC

lan

d

surface skin temperature land TS K ISCCP

Results

Average Model Performance

Tropics generally less well (+50%) simulated than extratropics (-20 to -50%)

India and Tibet most problematic (+100%)

… than average performance over entire globe

As good …

Better …

Worse …

Breakdown by Quantity

individual models

median

• most quantities show larger than average errors• v850 and prw are most difficult

Southern Asia (India)

climate elements

Err

or

Average Model Performance

Tropics generally less well (+50%) simulated than extratropics (-20 to -50%)

India and Tibet most problematic (+100%)

… than average performance over entire globe

As good …

Better …

Worse …

Breakdown by Quantity

• most quantities well simulated• Z500 most faithfully

Mediterranean

Err

or

HADCM HADGM INGV4 INM30 IPSL4

MIROH MRICM PCM11MIROM

GFD20 GFD21 GISSA GISSH GISSR

CSR30 CSR35 ECHM5 ECHOG FGOAL

BCM21 C3T47 C3T63 CCSM3 CNRM3

Individual Models

NCEP/NCAR Reanalysis

• Problems over Antarctica, Tropics, Tibet

• Oceans better than land• Does well over India

(plenty of observations)

Multi-Model Mean

• Better than NNR for every region

Conclusion1. Performance index is useful to compare models and to

track model changes2. Large inter-model differences3. Good models do well over all regions and all quantities4. Extratropics are generally better simulated than Tropics5. Multi-model mean outperforms even the best individual

model and even the reanalysis6.

Important to keep in mind (Retto Knutti)Good performance in current climate increases credibility of a model simulation but it is not a guarantee for a reliable prediction of future climate

Thank You

Reichler, T., and J. Kim (2008): Uncertainties in the climate mean state of global observations, reanalyses, and the GFDL climate model, J. Geophys. Res., 113

Reichler, T., and J. Kim (2008): How Well do Coupled Models Simulate Today's Climate? Bull. Amer. Meteor. Soc, 89, 303-311.

CMIP-3 ,

2v s

mI

Southern Asia (India)Breakdown by Models

Other regions

India

Case Study: PrecipitationNNR Multi-model mean

Average modelGFD21