The impacts of optimizing model-dependent parameters on the Antarctic
sea ice data assimilation
Abstract
Given the role played by the historical and extensive coverage of sea
ice concentration (SIC) observations in reconstructing the long-term
variability of Antarctic sea ice, and the limited attention given to
model-dependent parameters in current sea ice data assimilation studies,
this study focuses on enhancing the performance of the Data Assimilation
System for the Southern Ocean (DASSO) in assimilating SIC through
optimizing the localization and observation error estimate, and two
assimilation experiments were conducted from 1979 to 2018. By comparing
the results with the sea ice extent of the Southern Ocean and the sea
ice thickness in the Weddell Sea, it becomes evident that the experiment
with optimizations outperforms that without optimizations due to
achieving more reasonable error estimates. Investigating uncertainties
of the SIV anomaly modeling reveals the nonnegligible role played by the
sea ice-ocean interaction during the SIC assimilation, implying the
necessity of assimilating more oceanic and sea-ice observations.