A dynamic-thermodynamic sea ice model is used to illustrate a sensitivity evaluation strategy in which a statistical model is fit to the output of the ice model. The statistical model response, evaluated in terms of certain metrics or integrated features of the ice model output, is a function of a selected set of d (= 13) prescribed parameters of the ice model and is therefore equivalent to a d-dimensional surface. The d parameters of the ice model are varied simultaneously in the sensitivity tests. The strongest sensitivities arise from the minimum lead fraction, the sensible heat exchange coefficient, and the atmospheric and oceanic drag coefficients. The statistical model shows that the interdependencies among these sensitivities are strong and physically plausible. A multidecadal simulation of Arctic sea ice is made using atmospheric forcing fields from 1960 to 1988 and parametric values from the approximate midpoints of the ranges sampled in the sensitivity tests. This simulation produces interannual variations consistent with submarine-derived data on ice thickness from 1976 and 1987 and with ice extent variations obtained from satellite passive microwave data. The ice model results indicate that (1) interannual variability is a major contributor to the differences of ice thickness and extent over timescales of a decade or less, and (2) the timescales of ice thickness anomalies are much longer than those of ice-covered areas. However, the simulated variations of ice coverage have less than 50% of their variance in common with observational data, and the temporal correlations between simulated and observed anomalies of ice coverage vary strongly with longitude.
