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 ...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.展开更多
基金supported by the National Key R&D Program of China (2018YFA0605901)the NOAA Climate Program Office (NA15OAR4310163)+1 种基金the National Natural Science Foundation of China (41676185)and the Key Research Program of Frontier Sciences of Chinese Academy of Sciences (QYZDY-SSW-DQC021)
文摘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.
文摘随着全球气候变暖加剧,北极地区的大气海洋环境剧烈变化,导致海冰变化更加不稳定,使得海冰预测的难度增大。本研究选择海表温度、2 m平均气温、二氧化碳浓度为大气海洋变量,海冰范围距平为时序特征参数,将上述参量作为北极海冰范围(Sea Ice Extent,SIE)的预测要素,建立了面向SIE的多变量长短期记忆(Long Short Term Memory,LSTM)神经网络模型,对比分析了2015-2021年不同时间序列预测模型的预测结果。结果显示:本研究所构建模型的RMSE、MAE、MAPE分别为0.353×106 km2、0.261×106 km2和3.191%。相比于其他预测模型,结合大气海洋变量和时序特征参数后的LSTM模型预测结果误差更小,拟合效果更好,可以消除夏季海冰剧烈变化对预测效果的影响,提高海冰范围的预测精度,对北极航道的通航安全保障工作具有重要的研究与应用价值。
