Collaborative Research: Understanding Atlantic Decadal-to-Multidecadal Variability and Predictability

合作研究：了解大西洋十年间到多十年间的变异性和可预测性

• 批准号: 1137902
• 负责人: Edwin Schneider
• 金额: $ 29.46万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1137902&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Atlantic; Decadal

Research under this award will examine the variability and predictability of Atlantic sea surface temperatures (SSTs), with a particular focus on the role of "weather noise" in producing decadal and multidecadal SST fluctuations. Weather noise refers to the variability of weather patterns which is independent of SSTs and occurs owing to the chaotic nature of air movements. Despite their short duration, weather systems produce air-sea thermodynamic fluxes which can be a significant source of low-frequency SST variability, through the reddening effect of the long thermal relaxation time of the ocean mixed layer. A key tool in the research, developed during the PIs' previous NSF grant on this topic, is a modified version of the NCAR Community Climate System Model (CCSM) referred to as the Interactive Ensemble CCSM (IE-CCSM). The IE-CCSM addresses the role of weather noise by dramatically reducing it, so that the weather noise mechanism can be seletively enabled or disabled. The procedure used in IE-CCSM is to integrate multiple copies of the atmospheric component model of the coupled climate model, and use the average of the surface fluxes from all copies to force the ocean component model. Each atmospheric component model will have different weather noise, thus the weather noise will be removed by averaging over all the copies. The ocean component model interacts only with the average of the atmospheric component models, and thus does not "see" the weather noise. Experiments conducted with the IE-CCSM will examine the importance of weather noise for four major patterns of Atlantic SST variability: a tripole SST pattern in the northern North Atlantic which is associated with the North Atlantic Oscillation, a monopole SST anomaly in the North Atlantic associated with Atlantic multidecadal variability, and which may be related to variability of the Atlantic meridional overturning circuation, and two patterns of tropical Atlantic variability, a zonal structure resembling the Pacific El Nino phenomenon, and a meridional structure characterized by changes in the cross-equatorial SST gradient. The research also include experiments to understand the impact of weather noise on the predictability of Atlantic SST variability.The work has eduational broader impacts through its support for three graduate students, which will help to develop the next generation of scientists in this research area. In addition, the research may lead to insights into the variability and predictability of the climate system over long time periods, which would be of interest to policy and decision makers concerned with the impacts of climate variability and change.

该奖项下的研究将研究大西洋海表温度（SST）的变化和可预测性，特别关注“天气噪音”在产生十年和几十年SST波动中的作用。 天气噪音是指天气模式的变化，它与SST无关，是由于空气运动的混乱性质而产生的。 尽管天气系统持续时间很短，但它们产生的海气热力通量可能是低频SST变率的重要来源，这是通过海洋混合层长的热弛豫时间的红化效应实现的。 研究中的一个关键工具，在PI之前的NSF资助期间开发的，是NCAR社区气候系统模型（CCSM）的修改版本，被称为交互式环境CCSM（IE-CCSM）。 IE-CCSM通过显著降低天气噪声来解决天气噪声的作用，以便可以选择性地启用或禁用天气噪声机制。 IE-CCSM中使用的程序是集成耦合气候模式的大气分量模式的多个副本，并使用来自所有副本的表面通量的平均值来强迫海洋分量模式。 每个大气成分模型将具有不同的天气噪声，因此天气噪声将通过对所有副本进行平均来去除。 海洋成分模型仅与大气成分模型的平均值相互作用，因此不会“看到”天气噪声。 利用IE-CCSM进行的实验将研究天气噪声对大西洋SST变率四种主要模式的重要性：北大西洋北方的三极海温型与北大西洋涛动有关，北大西洋的一个极北海温异常与大西洋的年代际变化有关，可能与大西洋纬向翻转环流的变化有关，热带大西洋变化的两种模式，一种是类似太平洋厄尔尼诺现象的纬向结构，另一种是以越赤道SST梯度变化为特征的纬向结构。 该研究还包括实验，以了解天气噪声对大西洋SST变化的可预测性的影响。这项工作通过对三名研究生的支持，将有助于培养该研究领域的下一代科学家，从而产生更广泛的教育影响。 此外，这项研究可能有助于深入了解长期气候系统的可变性和可预测性，这将对关注气候可变性和变化影响的政策制定者和决策者有意义。