| Summary: | Thesis (Master's)--University of Washington, 2022 The significant role of sea ice in local and global climate and human-environment interactions in a rapidly changing world, necessitates a solid understanding of its recent and future states. Future Arctic sea ice extent decline under anthropogenic climate change is a well-accepted theme within climate science—the Arctic is expected to become seasonally ice-free by 2050 in the majority of models included in CMIP6. As the sea ice pack declines, however, it may become more variable over the next few decades and thus less predictable on shorter timescales. Attempts to improve sea ice forecasting at seasonal-to-subseasonal lead times have been slowed by relatively poor initial conditions and a paucity of observations that capture the full sea ice state. Over the last decade, the first attempts at assimilating sea ice thickness (SIT) data into prognostic sea ice models have demonstrated promise in addressing this initial condition problem. Of the relatively few studies which have attempted such assimilations, only a handful have focused explicitly on the sensitivity of SIT assimilation experiments, and none (to this author’s knowledge) have sought to characterize the full potential of assimilating a single day of SIT observations. This thesis seeks to supply this missing theoretical underpinning and hopes to inform future attempts to assimilate increasingly accurate estimates of SIT into complex sea ice models
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