Seasonal Ensemble Forecasts of Arctic Sea Ice

北极海冰的季节性集合预报

• 批准号: 0908769
• 负责人: Jinlun Zhang
• 金额: $ 41.31万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0908769&HistoricalAwards=false
• 关键词: Seasonal; Ensemble; Forecasts; Arctic; Sea

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The PIs propose to improve an existing seasonal ensemble forecasting system that they developed and use the system to predict sea ice conditions in the arctic and subarctic seas with lead times ranging from two weeks to three seasons. Their primary objectives are:(1) To develop seasonal ensemble forecasting capabilities by improving model physics and expanding data assimilation capabilities.(2) To conduct and distribute twice monthly near real-time hindcasts and seasonal ensemble forecasts of sea ice thickness, concentration, and extent in the arctic and subarctic seas.(3) To determine the predictive skill of the system through careful forecast evaluation and uncertainty analysis and identify areas for further improvement.(4) To maximize the exposure and utility of the seasonal forecasts through extensive outreach activities.To achieve these goals they will develop seasonal ensemble forecasts based on an enhanced synthesis of an ice?ocean model, forcing data, assimilation data, and validation data. Improvement of model physics will target sea ice processes that are particularly sensitive in a warming Arctic with a thinning ice cover. Near real-time hindcasts with the assimilation of ice concentration and SST data will be developed to provide an improved approximation of the current ice and ocean conditions and to create the best possible initial conditions for the forecasts. Wind, temperature, and cloud data from six to eight recent years will provide the forcing data to extend the initial conditions into the future in six to eight realizations (ensemble members). Extensive uncertainty analyses and predictability assessments will shed light on how to further improve the forecasting system, which will be a significant advance in seasonal forecasts of sea ice for all regions of the Arctic and a useful tool to address important science questions about the impact of preconditioning, atmospheric forcing, Pacific water inflow, and summer heat storage in the upper ocean on the seasonal variability and predictability of arctic sea ice.

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)资助的。私营部门建议改进他们开发的现有季节性集合预报系统，并使用该系统预测北极和亚北极海域的海冰状况，提前时间从两周到三个季节不等。它们的主要目标是：(1)通过改进模式物理和扩展数据同化能力来发展季节集合预报能力。(2)每月进行和分发两次北极和亚北极海冰厚度、密度和范围的近实时后预报和季节集合预报。(3)通过仔细的预报评估和不确定性分析来确定系统的预报技能，并确定需要进一步改进的区域。(4)通过广泛的外展活动，最大限度地提高季节预报的曝光度和实用性。为了实现这些目标，他们将基于加强综合冰海模式、强迫数据、同化数据、和验证数据。模型物理的改进将针对在北极变暖和冰盖变薄时特别敏感的海冰过程。将开发具有同化冰浓度和SST数据的近实时后预报，以提供对当前冰和海洋状况的更好的近似，并为预报创造可能的最佳初始条件。最近六到八年的风、温度和云数据将提供强迫数据，以在六到八个实现(集合成员)中将初始条件扩展到未来。广泛的不确定性分析和可预测性评估将阐明如何进一步改进预报系统，这将是北极所有区域海冰季节预报方面的重大进步，也是解决有关预处理、大气强迫、太平洋水流入和上层海洋夏季热储存对北冰洋海冰季节性变异性和可预测性影响的重要科学问题的有用工具。