Subseasonal to seasonal (S2S) climate variability and predictions

次季节到季节性（S2S）气候变化和预测

• 批准号: RGPIN-2019-04536
• 负责人: Lin, Hai
• 金额: $ 3.13万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714715
• 关键词: Subseasonal; seasonal; S2S; climate; variability

The subseasonal to seasonal (S2S) time scale refers to that ranging from a week to a season. A useful weather forecast beyond a week is of considerable socio-economic value. The long-term objective of this research is to improve the understanding of the atmospheric variability that provides a basis for S2S forecasting of atmospheric conditions including the probability of extreme winds, precipitation, and air temperature. In the next five years we propose to focus on key processes in the S2S climate variability. In particular, we will study the tropical-extratropical interactions, and atmospheric teleconnections. We aim to quantify S2S forecast skill in the extratropical and high-latitude regions, to determine the limits of predictability, and to identify opportunities for skillful S2S forecasts. Numerical models with different complexities are to be utilized to simulate the processes, perform diagnostic analyses for physical mechanisms, and conduct forecast experiments. The database of the international S2S project which includes re-forecast and forecast data of more than 10 operational S2S models will be utilized to assess the S2S forecast skill and to help to identify the sources of predictability. It is expected that this work will clarify some important issues related to subseasonal to seasonal variability, and contribute to the development of S2S weather forecasting capability in Canada.

亚季节到季节（S2 S）时间尺度是指从一周到一个季节的时间尺度。一个星期以上的天气预报具有相当大的社会经济价值。这项研究的长期目标是提高对大气变化的理解，为S2 S预测大气条件提供基础，包括极端风，降水和气温的概率。 在未来的五年里，我们建议把重点放在S2 S气候变率的关键过程。特别是，我们将研究热带-外热带相互作用和大气遥相关。我们的目标是量化的S2 S预测技巧在温带和高纬度地区，以确定可预测性的限制，并确定熟练的S2 S预测的机会。 将利用不同复杂程度的数值模式来模拟过程，进行物理机制的诊断分析，并进行预报试验。国际S2 S项目的数据库包括10多个业务S2 S模式的再预报和预报数据，将用于评估S2 S预报技能，并帮助确定可预报性的来源。预计这项工作将澄清一些重要的问题，有关的季节性变化，并有助于在加拿大的S2 S天气预报能力的发展。