A quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models was applied to Los Alamos sea ice model (CICE). The study conducted by DOE researchers at Los Alamos National Laboratory quantified the sensitivity of hemispheric sea ice area, extent and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study used a global variance-based approach in which Sobol’ sequences are used to efficiently sample the full 39-dimensional parameter space and account for non-linear and non-additive effects in the model.  A ranking based on variance-based sensitivity indices of the 39 parameters indicated that sea ice model predictions are most sensitive to snow parameters such as conductivity and grain size, and the drainage of melt ponds. The study determined individual parameter effects averaged over all other parameters (main effects), which in some cases hold strong non-linear relationships with sea ice variables of interest. The analysis also identified the most important interactions among input parameters, which act to modify the main effects of individual parameters, depending on what value the other interacting parameter takes. The study also found that in comparison with the southern hemisphere, there are more active parameters driving the sensitivity of the model in the northern hemisphere, and that interactions among parameters are also more important in the northern hemisphere than in the southern hemisphere. It was recommended that research be prioritized towards more accurately determining values for the most influential parameters by observational studies, calibration activities, or by improving parameterizations in the sea ice model.

Reference: Urrego-Blanco, J. R., N. M. Urban, E. C. Hunke, A. K. Turner, and N. Jeffery (2016): Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model. J. Geophys. Res. Oceans, 121, 2709–2732. doi:10.1002/2015JC011558.