1 / 20

Regional Performance of the IPCC-AR4 Models in Simulating Present-Day Mean Climate

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)

zelda
Download Presentation

Regional Performance of the IPCC-AR4 Models in Simulating Present-Day Mean Climate

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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 AR NEU ALA NAS GRL MED EAS NP TIB CAS NA WNA CNA ENA SAH SAS CAM WAF EAF SEA TP TA AMZ TI SAF AUS SSA SP SA SI AN ANT

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

  9. Results

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

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

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

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

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

  15. Multi-Model Mean NCEP/NCAR Reanalysis • 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 (RettoKnutti) 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, 303-311.

  18. CMIP-3

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

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

More Related