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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=743997
• 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.

亚季节性到季节性(S2S)时间尺度指的是从一周到一个季节的时间尺度。一周后有用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异性的理解，为大气条件的S2S预报提供基础，包括极端风、降水和气温的概率。在接下来的五年里，我们建议将重点放在S2S气候变化的关键过程上。特别是，我们将研究热带-温带相互作用和大气遥相关。我们的目标是量化温带和高纬度地区的S2S预测技能，确定可预测的限度，并确定熟练预测S2S的机会。利用不同复杂程度的数值模式对过程进行模拟，对物理机制进行诊断分析，并进行预报试验。将利用国际S2S项目数据库，其中包括10多个实际S2S模型的重新预测和预测数据，以评估S2S预测技能，并帮助查明可预测性来源。预计这项工作将澄清与亚季节到季节变化有关的一些重要问题，并有助于加拿大S2S天气预报能力的发展。