ABSTRACT

Sea ice thickness (SIT) is an under-represented essential climate variable in most regional and global climate models even today. This paper presents the assimilation of SIT observations from the Soil Moisture and Ocean Salinity (SMOS) mission in the Regional Ice Prediction System in a three-dimensional variational data assimilation system. For the first time, the model uses 10 sea ice categories and SIT redistribution within the model grid. A new SIT redistribution algorithm overcomes difficulties related to grids with partial ice and water, and high SIT values. The assimilation leads to a small improvement in the background state; however, the regions dominated by thick ice did not suggest satisfactory assimilation results attributed to several factors associated with the SIT retrieval techniques from the SMOS observations. The averaged analysis minus the observed root mean square error of all assimilation cycles, 0.11 m, i.e. about 20% of the maximum retrievable SMOS SIT looks reasonable.

摘要

即使在今天，在大多数区域和全球气候模式中，海冰厚度（SIT）仍是代表性不足的基本气候变量。本文介绍了三维变分数据同化系统中来自土壤水分和海洋盐分（SMOS）任务在区域冰预测系统中对SIT观测的同化。该模型首次在模型网格中使用了10种海冰类别和SIT重新分配。一种新的SIT重新分配算法克服了与部分冰和水以及高SIT值的网格有关的难题。吸收导致背景状态的改善很小；然而，以厚冰为主的地区并未显示出令人满意的同化结果，这归因于与SMOS观测中的SIT检索技术相关的几个因素。