邱孙俊杰,刘向文,姚隽琛. 2024. BCC-CSM2模式耦合同化对北极海冰次季节预测的影响. 气象学报,82(3):340-355. DOI: 10.11676/qxxb2024.20230117
引用本文: 邱孙俊杰,刘向文,姚隽琛. 2024. BCC-CSM2模式耦合同化对北极海冰次季节预测的影响. 气象学报,82(3):340-355. DOI: 10.11676/qxxb2024.20230117
Qiu Sunjunjie, Liu Xiangwen, Yao Junchen. 2024. Impact of coupled assimilation on subseasonal prediction of arctic sea ice in the BCC-CSM. Acta Meteorologica Sinica, 82(3):340-355. DOI: 10.11676/qxxb2024.20230117
Citation: Qiu Sunjunjie, Liu Xiangwen, Yao Junchen. 2024. Impact of coupled assimilation on subseasonal prediction of arctic sea ice in the BCC-CSM. Acta Meteorologica Sinica, 82(3):340-355. DOI: 10.11676/qxxb2024.20230117

BCC-CSM2模式耦合同化对北极海冰次季节预测的影响

Impact of coupled assimilation on subseasonal prediction of arctic sea ice in the BCC-CSM

  • 摘要: 近年来北极海冰的加速融化对北半球天气和气候的影响逐渐加剧,开展准确的海冰次季节预测已成为迫切需求。基于北京气候中心气候系统模式(BCC-CSM2),采用多种耦合同化方案(大气同化、海洋-大气同化、海洋-海冰-大气同化)产生模式初值,开展大气(FST-A)、海洋-大气(FST-AO)、海洋-海冰-大气(FST-AOI)预测试验,进而探讨了多圈层耦合同化对北极海冰次季节预测的影响。结果表明,随着耦合同化分量的增加,模式对海冰密集度气候态和变率的预测技巧逐渐提高,其中FST-AOI在所有试验中表现最优。海洋-海冰-大气同化对预测的影响存在明显的季节性差异,其在大多数月份(尤其是少冰期(9—10月))能够明显降低海冰密集度预测误差,但在快速融冰期(6—7月),若干海冰边缘区(波弗特海、东西伯利亚海、拉普捷夫海、喀拉海附近)的密集度误差随预测时间延长而快速增大。因此,多圈层耦合同化在总体上能够有效改善北极海冰次季节预测,但其在快速融冰阶段对预测的影响存在较大不确定性。进一步诊断表明,在快速融冰期,采用海洋-海冰-大气同化初值的试验在预报开始阶段(主要指第1周)低估了海冰边缘区的密集度。在海冰-反照率正反馈机制影响下,这可能导致模拟地表短波辐射吸收增加,引起海水升温和海冰融化加快,进而导致海冰误差快速增长。未来研究将致力于继续提升海洋-海冰-大气多分量耦合同化的协调,以持续改进北极海冰次季节—季节预测能力。

     

    Abstract: In recent years, the accelerated melting of Arctic sea ice has exerted increasing impacts on weather and climate of the Northern Hemisphere. Therefore, accurate prediction of subseasonal sea ice has become an urgent need. In this study, based on the Beijing Climate Center Climate System Model, various coupled data assimilation schemes (atmospheric assimilation, ocean-atmosphere assimilation, ocean-ice-atmosphere assimilation) are used to generate initial conditions for several sets of hindcast experiments (FST-A, FST-AO, and FST-AOI). The impacts of multi-component coupled data assimilation schemes on subseasonal prediction of Arctic sea ice are further investigated based on these hindcast experiments. The results demonstrate that, with the increase in the coupled assimilation components, the prediction skill for sea ice concentration (SIC) and variability is improved. Among all the experiments, FST-AOI shows the highest prediction skill. The impact of ocean-ice-atmosphere assimilation on subseasonal prediction exhibits obvious seasonal differences. Results demonstrate notable reductions in SIC forecast error during most months, particularly within the low-ice season in September and October. However, during the rapid ice-melting process in June and July, the SIC forecast errors in several ice-edge regions (e.g., the Beaufort Sea, the East Siberian Sea, the Laptev Sea, and the Kara Sea) rapidly escalate with increasing forecast time. While multi-component coupled data assimilation effectively improves the subseasonal Arctic sea ice prediction skill, there remains considerable uncertainty regarding its impact during the rapid ice-melt process. Further diagnoses indicate that during the rapid ice-melting process, FST-AOI underestimates SIC near the ice edge in the first week of forecast. Under the influence of the ice-albedo feedback mechanism, this could lead to increased surface shortwave radiation absorption, causing accelerated sea water warming and sea ice melting. As a result, SIC prediction errors rapidly grow in FST-AOI. Future research should focus on enhancing the coordination of coupled data assimilation components to further improve the subseasonal to seasonal prediction skill of Arctic sea ice.

     

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