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

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 <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

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

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

Results

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%)

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

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%)

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

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

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

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

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

Southern Asia (India) Breakdown by Models India Other regions

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