Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION
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- T-3 Fluid Dynamics and Solid Mechanics, Theoretical Division, Los Alamos National Laboratory, Los Alamos New Mexico USA
- CCS-2 Computational Physics and Methods, Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos New Mexico USA
- XCP-8 Verification and Analysis, X Computational Physics Division, Los Alamos National Laboratory, Los Alamos New Mexico USA
- Booker Scientific, Fredericksburg Texas USA
Here, we implement a variance-based distance metric (Dn) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total sea ice extent or volume) by capturing the spatial character of model skill. The Dn metric is a gamma-distributed statistic that is more general than the χ2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased and can only incorporate observational error in the analysis. The Dn statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of sea ice models. The methodology is also useful for model tuning by using the Dn metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos sea ice model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of sea ice concentration and thickness.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1351197
- Report Number(s):
- LA-UR-16-29002
- Journal Information:
- Journal of Geophysical Research. Oceans, Vol. 122, Issue 4; ISSN 2169-9275
- Publisher:
- American Geophysical UnionCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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