Seasonal Ensemble Forecasts 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。pi建议改进他们开发的现有的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。他们的主要目标是：(1)通过改进模式物理和扩大数据同化能力来发展季节集合预报能力。(2)对北极和亚北极海域的海冰厚度、浓度和范围进行每月两次的近实时预报和季节集合预报。(3)通过仔细的预测评价和不确定性分析，确定系统的预测能力，并找出需要进一步改进的地方。(4)通过广泛的外联活动，最大限度地提高季节预报的曝光率和效用。为了实现这些目标，他们将在加强对冰的合成的基础上开发季节性集合预报。海洋模式、强迫数据、同化数据和验证数据。模型物理的改进将针对海冰过程，这一过程在冰盖变薄、变暖的北极地区尤为敏感。利用冰浓度和海温资料同化的近实时预报将提供当前冰和海洋状况的改进近似值，并为预报创造可能的最佳初始条件。最近6至8年的风、温度和云数据将提供将初始条件扩展到未来6至8个实现（集合成员）的强迫数据。广泛的不确定性分析和可预测性评估将揭示如何进一步改进预测系统，这将是北极所有地区海冰季节性预测的重大进展，也是解决有关预调节、大气强迫、太平洋水流入和上层海洋夏季热储存对北极海冰季节性变率和可预测性影响的重要科学问题的有用工具。

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.