期刊
SCIENCE BULLETIN
卷 64, 期 1, 页码 63-72出版社
ELSEVIER
DOI: 10.1016/j.scib.2018.11.018
关键词
Arctic sea ice prediction; Remote sensing; Data assimilation
资金
- National Key R&D Program of China [2018YFA0605901]
- NOAA Climate Program Office [NA15OAR4310163]
- National Natural Science Foundation of China [41676185]
- Key Research Program of Frontier Sciences of Chinese Academy of Sciences [QYZDY-SSW-DQC021]
Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that (1) have been utilized for improved model initialization, including concentration, thickness and drift, and (2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the best estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models. (C) 2018 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.
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