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

2. 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

3. Error

4. 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)

5. 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

6. Methodology Normalized error variance Regional error index
Overall performance index
<1: Better than average
How capable is a model in simulating regional climate relative to the average performance on the global scale?
Equal weighting
We evaluate
24 CMIP-3 models (excluding BCC-CM1)
average model
multi-model mean
NCEP/NCAR reanalysis

7. Regions Land 22 regions; Giorgi and Francisco (2000) Ocean 10 basins
ALA
WNA
CNA
ENA
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

8. Climate Elements
“Physics” (12)
“Oceans” (9)
“Land” (1)
“Dynamics” (9)

9. Results

10. Average Model Performance
… than average performance over entire globe
As good …
Better …
Worse …
Tropics generally less well (+50%) simulated than extratropics (-20 to -50%)
India and Tibet most problematic (+100%)

11. Breakdown by Quantity Error
Southern Asia (India)
median
Error
individual models
climate elements
most quantities show larger than average errors
v850 and prw are most difficult

12. Average Model Performance
… than average performance over entire globe
As good …
Better …
Worse …
Tropics generally less well (+50%) simulated than extratropics (-20 to -50%)
India and Tibet most problematic (+100%)

13. Breakdown by Quantity Error most quantities well simulated
Mediterranean
Error
most quantities well simulated
Z500 most faithfully

14. Individual Models HADCM HADGM INGV4 INM30 IPSL4 MIROH MRICM PCM11
MIROM
GFD20
GFD21
GISSA
GISSH
GISSR
CSR30
CSR35
ECHM5
ECHOG
FGOAL
BCM21
C3T47
C3T63
CCSM3
CNRM3

15. NCEP/NCAR Reanalysis Multi-Model Mean
Problems over Antarctica, Tropics, Tibet
Oceans better than land
Does well over India (plenty of observations)
Better than NNR for every region

16. Conclusion
Performance index is useful to compare models and to track model changes
Large inter-model differences
Good models do well over all regions and all quantities
Extratropics are generally better simulated than Tropics
Multi-model mean outperforms even the best individual model and even the reanalysis
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

17. 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,

18. CMIP-3

19. Southern Asia (India)
Breakdown by Models
India
Other regions

20. Case Study: Precipitation
NNR
Multi-model mean
GFD21
Average model