The Antarctic sea ice reconstruction (CMST-South) based on the optimized Data Assimilation System for the Southern Ocean

• Main Authors: Luo, Hao, Yang, Qinghua, Mazloff, Matthew, Nerger, Lars, Chen, Dake
• Format: Other/Unknown Material
• Language: English
• Published: Zenodo 2023
• Subjects: Antarctic; sea ice data assimilation; model-dependent parameters; Antarc*; Sea ice; Southern Ocean
• Online Access: https://doi.org/10.5281/zenodo.8214462

The wealth of historical sea ice concentration (SIC) observations, coupled with their extensive spatial coverage, renders them indispensable for the reconstruction of long-term Antarctic sea ice variability. However, recent studies have pointed out the presence of significant uncertainties in certain aspects of Antarctic sea ice reanalyses obtained from assimilating SIC. Notably, while previous studies on ocean data assimilation have already demonstrated the significance of optimizing model-dependent parameters for assimilating oceanic observations, this aspect has received limited attention in current sea ice data assimilation studies. As a result, whether optimizing model-dependent parameters can enhance the effectiveness of assimilating SIC remains an open question. Thus,we address this gap by refining the model-dependent parameters of Data Assimilation System for the Southern Ocean (DASSO), including the development of a latitude-dependent localization scheme and the objective estimation of observation error variance of SIC which takes into account both measurement errors and representation errors. Here, the monthly anomalies in Antarctic sea ice extent and volume (1980 -2018) are uploaded which is produced bythe optimized Data Assimilation System for the Southern Ocean (DASSO) with assimilating SIC. Besides, a 13-month moving mean is applied to monthly anomalies to focus on the low-frequency variability of Antarctic sea ice.