Bivariate sea-ice assimilation for global-ocean analysis–reanalysis

IF 4.1 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Science Pub Date : 2023-09-15 DOI:10.5194/os-19-1375-2023

Andrea Cipollone, Deep Sankar Banerjee, Doroteaciro Iovino, Ali Aydogdu, Simona Masina

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引用次数: 1

Abstract

Abstract. In the last decade, various satellite missions have been monitoring the status of the cryosphere and its evolution. Besides sea-ice concentration data, available since the 1980s, sea-ice thickness retrievals are now ready to be used in global operational prediction and global reanalysis systems. Nevertheless, while univariate algorithms are commonly used to constrain sea-ice area or volume, multivariate approaches have not yet been employed due to the highly non-Gaussian distribution of sea-ice variables together with the low accuracy of thickness observations. This study extends a 3DVar system, called OceanVar, which is routinely employed in the production of global/regional operational/reanalysis products, to process sea-ice variables. The tangent/adjoint versions of an anamorphosis operator are used to locally transform the sea-ice anomalies into Gaussian control variables and back, minimizing in the latter space. The benefit achieved by such a transformation is described. Several sensitivity experiments are carried out using a suite of diverse datasets. The sole assimilation of the CryoSat-2 provides a good spatial representation of thickness distribution but still overestimates the total volume that requires the inclusion of Soil Moisture and Ocean Salinity (SMOS) mission data to converge towards the observation estimates. The intermittent availability of thickness data can lead to potential jumps in the evolution of the volume and requires a dedicated tuning. The use of the merged L4 product CS2SMOS shows the best skill score when validated against independent measurements during the melting season when satellite data are not available. This new sea-ice module is meant to simplify the future coupling with ocean variables.

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全球-海洋分析-再分析的二元海冰同化

摘要在过去十年中，各种卫星任务一直在监测冰冻圈的状况及其演变。除了1980年代以来可获得的海冰浓度数据外，海冰厚度的反演现在已准备好用于全球业务预测和全球再分析系统。然而，虽然单变量算法通常用于约束海冰面积或体积，但由于海冰变量的高度非高斯分布以及厚度观测精度较低，尚未采用多变量方法。这项研究扩展了名为OceanVar的3DVar系统，该系统通常用于生产全球/区域操作/再分析产品，以处理海冰变量。畸变算子的正切/伴随版本用于将海冰异常局部转换为高斯控制变量并返回，在后者空间中最小化。本文描述了这种转换所带来的好处。使用一套不同的数据集进行了几个灵敏度实验。CryoSat-2的单一同化提供了很好的厚度分布的空间表示，但仍然高估了总体积，这需要包含土壤湿度和海洋盐度(SMOS)任务数据来收敛于观测估计。厚度数据的间歇性可用性可能导致体积演变的潜在跳跃，需要专门的调整。合并后的L4产品CS2SMOS的使用在没有卫星数据的融化季节进行独立测量验证时显示出最佳的技能得分。这个新的海冰模块旨在简化未来与海洋变量的耦合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ocean Science 地学-海洋学

CiteScore

5.90

自引率

6.20%

发文量

审稿时长

6-12 weeks

期刊介绍： Ocean Science (OS) is a not-for-profit international open-access scientific journal dedicated to the publication and discussion of research articles, short communications, and review papers on all aspects of ocean science: experimental, theoretical, and laboratory. The primary objective is to publish a very high-quality scientific journal with free Internet-based access for researchers and other interested people throughout the world. Electronic submission of articles is used to keep publication costs to a minimum. The costs will be covered by a moderate per-page charge paid by the authors. The peer-review process also makes use of the Internet. It includes an 8-week online discussion period with the original submitted manuscript and all comments. If accepted, the final revised paper will be published online. Ocean Science covers the following fields: ocean physics (i.e. ocean structure, circulation, tides, and internal waves); ocean chemistry; biological oceanography; air–sea interactions; ocean models – physical, chemical, biological, and biochemical; coastal and shelf edge processes; paleooceanography.