Funds are provided to solidify knowledge of arctic sea ice thickness through analyses of observations, which will, in particular, elucidate interannual variations, and through a critical assessment of discrepancies between observed and simulated ice thickness. By the spring of 2005, submarine ice draft data should be publicly available from some 35 cruises that took place during three decades - 1969 to 1999. Two tasks are proposed to analyze these data. First, the data will be fit to a statistical representation using multiple regression analysis. This is meant to isolate interannual variability from spatial and seasonal variability. Second, optimally interpolated fields of thickness quantities will be produced, primarily the mean, but also the mode, the probability density function of thickness, and the open water fraction. Thickness data will be separated into winter and summer groups and analyzed to produce optimal fields for the entire 31-year data set, for several years at a thickness maximum, and for several years at a thickness minimum. Both representations of the analyzed data- the multiple regressions and the optimally interpolated fields- will be placed in a national archive for community use by the end of the project.
An in-house, state-of-the-art, coupled ice-ocean model of the Arctic Ocean, treating the combined momentum, heat, and mass balances of sea ice, including a detailed treatment of sea ice thickness with snow cover, has been shown to simulate successfully observed mean ice thickness averaged over the Arctic Ocean and the general decline in ice thickness from the late 1980s to the present. However, the model is unsatisfactory at reproducing the spatial field of thickness, i.e. the variations across the Arctic Ocean. Substantial improvement in the model should be possible through focused attention on the spatial structure of the discrepancies between model and observations. It is proposed to analyze how these discrepancies are structured in space, time, and with thickness itself and to investigate what aspects of the ice-ocean model and its forcing data are most likely causing these discrepancies. Forcing data, such as cloudiness, radiation, and snowfall, and model components, such as rafting and ridging, are candidates for detailed investigation.
Improved sea ice models are necessary to understand the interactions of the Arctic with the global climate system. If the timing and location of abnormally thin sea ice were reliably predictable, it would have implications for topics as varied as the safety of indigenous hunters and the routing of shipping through the Arctic.
