气候变化领域集成服务门户(CCPortal): Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model

CCPortal

DOI	10.1029/2019MS001937
	Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model
	Mu L.; Nerger L.; Tang Q.; Loza S.N.; Sidorenko D.; Wang Q.; Semmler T.; Zampieri L.; Losch M.; Goessling H.F.
发表日期	2020
ISSN	19422466
卷号	12 期号:4
英文摘要	This paper describes and evaluates the assimilation component of a seamless sea ice prediction system, which is developed based on the fully coupled Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research Climate Model (AWI-CM, v1.1). Its ocean/ice component with unstructured-mesh discretization and smoothly varying spatial resolution enables seamless sea ice prediction across a wide range of space and time scales. The model is complemented with the Parallel Data Assimilation Framework to assimilate observations in the ocean/ice component with an Ensemble Kalman Filter. The focus here is on the data assimilation of the prediction system. First, the performance of the system is tested in a perfect-model setting with synthetic observations. The system exhibits no drift for multivariate assimilation, which is a prerequisite for the robustness of the system. Second, real observational data for sea ice concentration, thickness, drift, and sea surface temperature are assimilated. The analysis results are evaluated against independent in situ observations and reanalysis data. Further experiments that assimilate different combinations of variables are conducted to understand their individual impacts on the model state. In particular, assimilating sea ice drift improves the sea ice thickness estimate, and assimilating sea surface temperature is able to avert a circulation bias of the free-running model in the Arctic Ocean at middepth. Finally, we present preliminary results obtained with an extended system where the atmosphere is constrained by nudging toward reanalysis data, revealing challenges that still need to be overcome to adapt the ocean/ice assimilation. We consider this system a prototype on the way toward strongly coupled data assimilation across all model components. ©2020. The Authors.
语种	英语
scopus关键词	Atmospheric temperature; Forecasting; Ocean currents; Sea ice; Submarine geophysics; Surface properties; Surface waters; Data assimilation systems; Ensemble Kalman Filter; In-situ observations; Prediction systems; Sea ice concentration; Sea surface temperature (SST); Spatial resolution; Unstructured meshes; Climate models; atmosphere-ice-ocean system; climate modeling; data assimilation; environmental modeling; ice drift; ice thickness; prediction; sea ice; sea surface temperature; Arctic Ocean
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156730
作者单位	Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany; Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow, Russian Federation
推荐引用方式 GB/T 7714	Mu L.,Nerger L.,Tang Q.,et al. Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model[J],2020,12(4).
APA	Mu L..,Nerger L..,Tang Q..,Loza S.N..,Sidorenko D..,...&Goessling H.F..(2020).Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model.Journal of Advances in Modeling Earth Systems,12(4).
MLA	Mu L.,et al."Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model".Journal of Advances in Modeling Earth Systems 12.4(2020).