Employment of an Arctic sea-ice data assimilation scheme in the coupled climate system model FGOALS-f3-L and its preliminary results

Abstract

Arctic sea ice is an important component of the global climate system and has experienced rapid changes during in the past few decades, the prediction of which is a significant application for climate models. In this study, a Localized Error Subspace Transform Kalman Filter is employed in a coupled climate system model (the Flexible Global Ocean–Atmosphere–Land System Model, version f3-L (FGOALS-f3-L)) to assimilate sea-ice concentration (SIC) and sea-ice thickness (SIT) data for melting-season ice predictions. The scheme is applied through the following steps: (1) initialization for generating initial ensembles; (2) analysis for assimilating observed data; (3) adoption for dividing ice states into five thickness categories; (4) forecast for evolving the model; (5) resampling for updating model uncertainties. Several experiments were conducted to examine its results and impacts. Compared with the control experiment, the continuous assimilation experiments (CTNs) indicate assimilations improve model SICs and SITs persistently and generate realistic initials. Assimilating SIC+SIT data better corrects overestimated model SITs spatially than when only assimilating SIC data. The continuous assimilation restart experiments indicate the initials from the CTNs correct the overestimated marginal SICs and overall SITs remarkably well, as well as the cold biases in the oceanic and atmospheric models. The initials with SIC+SIT assimilated show more reasonable spatial improvements. Nevertheless, the SICs in the central Arctic undergo abnormal summer reductions, which is probably because overestimated SITs are reduced in the initials but the strong seasonal cycle (summer melting) biases are unchanged. Therefore, since systematic biases are complicated in a coupled system, for FGOALS-f3-L to make better ice predictions, oceanic and atmospheric assimilations are expected required.

摘要

当前, 快速变化的北极海冰对全球气候有重要影响, 海冰的预报是气候模式的重要应用方向之一. 本研究基于PDAF同化框架, 使用LESTKF方法将北极海冰密集度 (SIC) 和厚度 (SIT) 观测数据同化到气候系统模式FGOALS-f3-L中开展融化季节的海冰预测. 同化的引入共分为集合初始化, 同化分析, 分析场引入, 模式预报, 集合重采样等五个步骤. 试验表明, 连续同化可以持续改进模式模拟的SIC和SIT并生成接近真实的初始场, 同时同化SIC和SIT比只同化SIC能更好地纠正SIT的空间偏差. 利用连续同化生成的初始场进行预报, 能够显著减少海冰边缘的SIC多偏差, 整体的SIT厚偏差以及海洋和大气中的冷偏差, 使用同化了SIC和SIT的初始场能带来更合理的空间改进. 但受模式中海冰季节循环偏强的影响, 预报的夏季海冰会出现偏少, 这表明在耦合系统中准确预报海冰还需纳入海洋和大气同化.