Monday, 8 January 2018: 3:15 PM
Ballroom G (ACC) (Austin, Texas)
Knowing Arctic sea ice in the past and predicting it several months in advance are of great significance in various dimensions yet remain challenging. Advanced data assimilation methods can combine real observations with model forecasts to reconstruct sea ice reanalysis, which can provide more accurate initial conditions for sea ice seasonal predictions. This study introduces a sea ice data assimilation (DA) framework, the linked Los Alomas sea ice model version 5 (CICE5) and the Data Assimilation Research Testbed (DART). A series of perfect model Observing System Simulation Experiments (OSSEs) are designed to explore various algorithms and observations of different variables. This study demonstrates that assimilating sea ice concentration (SIC) observations can effectively remove errors of the total Arctic sea ice area by about 60% annually and by about 50% in spring months (April-May). A small localization distance is needed to avoid degradations in the total Arctic sea ice volume (SIV). SIT is proven to be the most important variable to observe as assimilating SIT observations in addition to SIC significantly improve SIT. The ice age observation (fraction of first year ice) is a fairly good approximation of sea ice thickness observation and can improve spring SIT estimates by more than 50%. Estimates of snow depth are not improved as much as SIC and SIT. Assimilation of sea ice surface temperature (TSFC) causes large negative biases in ice volume as it shifts ice from thick categories to thin categories after one year's assimilation.
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