HR: 1340h
AN: GC43A-0937    [Abstracts]
TI: Towards an Objective Characterization of Climate Model Performance
AU: * Pennell, C J
EM: chis.pennell@utah.edu
AF: Department of Meteorolgy, University of Utah, 135 S 1460 E RM 819, Salt Lake City, UT 84112-0110, United States
AU: Kim, J
EM: junsu.kim@utah.edu
AF: Department of Meteorolgy, University of Utah, 135 S 1460 E RM 819, Salt Lake City, UT 84112-0110, United States
AU: Reichler, T
EM: thomas.reichler@utah.edu
AF: Department of Meteorolgy, University of Utah, 135 S 1460 E RM 819, Salt Lake City, UT 84112-0110, United States
AB: This study is based on previous work where we measured the performance of models in terms of their ability to simulate the observed climate mean state for a wide range of quantities. We have shown that the mean of a multi-model ensemble consistently outperforms any individual simulation. Now an important question is how to construct an optimally weighted multi-model mean which maximizes the strengths while minimizing the weaknesses discovered in the model conglomerate. Consequently, we explore ways to reduce our rather comprehensive choice of climate quantities into a much smaller subset. Our goal is to derive an unbiased description of model performance retaining a significant proportion of information while neglecting a considerable amount of data redundancy. Statistical methods as diverse as cluster analysis and principal component analysis are shown to be successful in producing a minimal collection of climate quantities which are distinctly useful in model evaluation. To the first order, this subset consists of two variables: one primarily representing model physics, while the second mainly represents model dynamics. We apply these results to the IPCC-AR4 ensemble and demonstrate how it can be used to construct an optimally weighted average of many models.
DE: 1600 GLOBAL CHANGE
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting