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Evaluating the ability of climate models to simulate
extremes
Eric RobinsonNatalie McLean
Christine RadermacherRoss Towe
Yushiang Tung
Project 6
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
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
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
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
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
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
Summary Statistics
CCSM3 vs Reanalysis Extreme Value Biases
Summary Statistics
χ bar: a measure of asymptotic dependence
Asymptotic independence
Asymptotic dependence
Near extremal independence
Summary Statistics
Marginal GEV Return Values (GCM)
Marginal GEV Return Values (Reanalysis)
Cluster analysis
Clustering by return values and the shape parameter
Justification of the Cluster analysis
Results for the GCM Clusters, similar results for the Reanalysis.
Comparison of GCM and Reanalysis Clusters
Pooled GEV Return Levels (Cluster 1)
Pooled GEV Return Levels (Cluster 2)
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
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
Now this is EXTREME!!!!!!!!!
Nobody Canna Cross It!!!!!!!!! http://www.youtube.com/watch?v=hknVoAoyy-k
Any questions?
Thank you