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Tuesday, November 27
Metrics for Climate Model ValidationCharles Jackson, The University of Texas, Austin
Computer Science and Mathematics Division Seminar
10:00 AM — 11:00 AM, Research Office Building (5700), Room L-204
Contact: Clayton Webster (email@example.com), 865.574.3649
AbstractA "valid" model is a model that has been tested for its intended purpose. In the Bayesian formulation, the "log-likelihood" is a test statistic for selecting, weeding, or weighting climate model ensembles with observational data. Thisstatistic has the potential to synthesize the physical and data constraints on quantities of interest. One of the thorny issues in formulating the log-likelihood is how one should account for biases because not all biases affect predictions of quantities of interest. Dr. Jackson makes use of a 165-member ensemble CAM3.1/slab ocean climate models with different parameter settings to think through the issues that are involved with predicting eachmodel's sensitivity to greenhouse gas forcing given what can be observed from the base state. In particular, Dr. Jackson uses multivariate empirical orthogonal functions to decompose the differences that exist among this ensemble to discover what fields and regions matter to the model's sensitivity. What is found is that the differences that matter can be a small fraction of the total discrepancy. Moreover, weighting members of the ensemble using this knowledge does a relatively poor job of adjusting the ensemble mean toward the known answer. Dr. Jackson will discuss the implications of this result.