Evaluating the ability of climate models to simulate extremes

1. Evaluating the ability of climate models to simulate extremes Eric Robinson Natalie McLean Christine Radermacher Ross Towe Yushiang Tung Project 6

2. Motivation • Projections depend on accurate modeling of extreme values of severe weather indicators • e.g.: CAPE and shear • Extreme values of CAPE and shear have been shown to be good predictors of severe thunderstorms • Hail > diameter 5cm & wind > 120km/h • Significant thunderstorms (F2 or greater) • Important for the Continental United States • Especially given the increased intensity of extreme weather in recent years • Practical applications: Reinsurance firms • Can use the information to minimise any pay-outs to their customers

3. Background • CAPE – Convective Available Potential Energy • Measure of buoyancy of an air parcel • Indicates atmospheric instability • Summer maximum & winter minimum • Shear (vertical) • Difference in wind speed between 0 and 6 km • Significant wind shear aids the formation of supercells • Winter maximum & late summer minimum

4. Background: Data sets • CCSM3 – Community Climate System Model v3 • 1.4o x 1.4o resolution • Atmospheric, land, ocean and sea-ice models integrated through a coupler • Has positive biases for shear and negative biases for CAPE • Reanalysis – NCEP/NCAR Reanalysis • Observations filled using quality control and data assimilation methods • 1.875o x 1.915o resolution • CAPE and shear are Type-B variables • Determined using both actual observations and modeled data

5. Data Description • GCM data from CCSM3 • Observation data from NCEP/NCAR Reanalysis • 20 years of CAPE and shear data (1980-1999) • Maximum values extracted for the JJA season • Variables analyzed: • CAPE • Shear • CAPE * Shear

6. Previous Research • Bivariate modeling has been shown to have no significant advantages • Modeling CAPE*Shear has statistical advantages • Easier to quantify • Resolves issues of achieving extreme weather with various combinations of CAPE and Shear • Higher values expected over the Appalachians with lower values expected over Minnesota and North Dakota • Analysis on the entire year rather than the summer season • The use of false discovery rate was not beneficial

7. Methodology Summary statistics GEV analysis L Moments GEV analysis Evaluation of return values Cluster analysis QQ Plots to check the clustering Pooled GEV fit Comparison of return values

8. Summary Statistics CCSM3 vs Reanalysis Extreme Value Biases

9. Summary Statistics χ bar: a measure of asymptotic dependence Near extremal independence Asymptotic dependence Asymptotic independence

10. Summary Statistics

11. Marginal GEV Return Values (GCM)

12. Marginal GEV Return Values (Reanalysis)

13. Cluster analysis Clustering by return values and the shape parameter

14. Justification of the Cluster analysis Results for the GCM Clusters, similar results for the Reanalysis.

15. Comparison of GCM and Reanalysis Clusters

16. Pooled GEV Return Levels (Cluster 1)

17. Pooled GEV Return Levels (Cluster 2)

18. Conclusion Differences detected between the GCM and the reanalysis Evidence that the GCM model is tweaked to accurately model the body rather than the tail of the distribution L-moments improved the estimates of the GEV parameters Cluster analysis and spatial pooling improved the parameter estimates but needs to be explored further

19. Future Work • Comparison with a regional climate model as well as ensembles • Investigate further the components of the CCSM3, which leads to the biases • Pooled modal GEV approach • Bootstrapping of data at each site • Introduction of covariates into the analysis • Adjust the model for temporal dependence at sites • Spatial fit to the data • More exploration into a multivariate framework • Statistical downscaling approach

20. Now this is EXTREME!!!!!!!!! • Nobody Canna Cross It!!!!!!!!! • http://www.youtube.com/watch?v=hknVoAoyy-k

21. Thank you Any questions?