Collaborative Research: Linkages in Winter-Time Climate Variability and the Basis for Climate Predictability in the North Atlantic Sector

合作研究：冬季气候变率的联系和北大西洋地区气候可预测性的基础

• 批准号: 0443451
• 负责人: Dara Entekhabi
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0443451&HistoricalAwards=false
• 关键词: Collaborative; Research; Linkages; Winter; Time

This project will pursue the theory that the climate anomaly associated with the North Atlantic and Arctic Oscillations is mostly an artifact of temporal averaging and is the result of the superposition of two distinctive patterns that occur in different winter seasons. These two patterns, referred to as Type A and Type N, evolve as a result of different dynamics involving sub-seasonal climate anomalies across the United States and Europe. The primary objective of the project is to provide a more fundamental understanding of the dynamical framework which may allow for more accurate seasonal predictions with relatively finer spatial and temporal resolution, building upon earlier research by combining analysis of observations with atmospheric general circulation models, supplemented by several simpler modeling schemes. This work is expected to make a significant contribution to the development of the next generation models for seasonal prediction. The project involves unfunded international collaboration with researchers in Canada. One graduate student will be supported by the collaborating investigator at the Massachusetts Institute of Technology.This project is supported under the Climate Variability and Predictability Program (CLIVAR).

该项目将研究与北大西洋和北极涛动相关的气候异常主要是时间平均的人工产物，是在不同冬季发生的两种不同模式叠加的结果。这两种模式被称为A型和N型，它们的演变是美国和欧洲不同季节气候异常的不同动力的结果。该项目的主要目标是提供对动力框架的更基本的理解，这可能允许更准确的季节预测，具有相对更精细的空间和时间分辨率，建立在早期研究的基础上，通过将观测分析与大气环流模式结合起来，辅以几个更简单的建模方案。这项工作预计将对下一代季节预报模式的发展作出重大贡献。该项目涉及与加拿大研究人员的无资助国际合作。一名研究生将由麻省理工学院的合作研究者提供支持。该项目由气候变率和可预测性项目（CLIVAR）支持。