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Title: Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION

Here, we implement a variance-based distance metric (D n) 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 D n metric is a gamma-distributed statistic that is more general than the χ 2 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 D n 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 D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme tomore » 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.« less
ORCiD logo [1] ; ORCiD logo [1] ;  [2] ;  [2] ; ORCiD logo [1] ;  [3] ;  [4]
  1. T-3 Fluid Dynamics and Solid Mechanics, Theoretical Division, Los Alamos National Laboratory, Los Alamos New Mexico USA
  2. CCS-2 Computational Physics and Methods, Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos New Mexico USA
  3. XCP-8 Verification and Analysis, X Computational Physics Division, Los Alamos National Laboratory, Los Alamos New Mexico USA
  4. Booker Scientific, Fredericksburg Texas USA
Publication Date:
Report Number(s):
Journal ID: ISSN 2169-9275
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Oceans
Additional Journal Information:
Journal Volume: 122; Journal Issue: 4; Journal ID: ISSN 2169-9275
American Geophysical Union
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
58 GEOSCIENCES; Earth Sciences; Mathematics; sea ice model, validation metrics, model uncertainty
OSTI Identifier: