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

该奖项下的研究将审查大西洋海温的可变性和可预测性，特别侧重于“天气噪声”在产生年代际和多年代际海温波动方面的作用。天气噪音是指天气模式的变化，它与海温无关，是由于空气运动的混乱性质而产生的。尽管持续时间短，但天气系统通过海洋混合层长热松弛时间的变红效应产生海气热力通量，这可能是低频海温变率的重要来源。该研究的一个关键工具是在pi先前获得NSF关于该主题的资助期间开发的，它是NCAR社区气候系统模型（CCSM）的修改版本，称为交互式集成CCSM （IE-CCSM）。IE-CCSM通过大幅降低天气噪音来解决天气噪音的作用，因此可以有选择地启用或禁用天气噪音机制。IE-CCSM使用的程序是将耦合气候模式的多个副本的大气分量模式整合起来，并使用所有副本的地表通量平均值来强迫海洋分量模式。每个大气分量模型将有不同的天气噪声，因此天气噪声将通过对所有副本进行平均来去除。海洋分量模式只与大气分量模式的平均值相互作用，因此不能“看到”天气噪声。利用IE-CCSM进行的实验将检验天气噪声对大西洋海温变率的四种主要模式的重要性：北大西洋北部的三极海温异常与北大西洋涛动有关，北大西洋的单极海温异常与大西洋多年代际变率有关，可能与大西洋经向翻转环流的变率有关，热带大西洋的两种变率模式，类似太平洋厄尔尼诺现象的纬向结构，以及以跨赤道海温梯度变化为特征的经向结构。该研究还包括实验，以了解天气噪声对大西洋海温变化的可预测性的影响。通过对三名研究生的支持，这项工作具有更广泛的教育影响，这将有助于培养这一研究领域的下一代科学家。此外，这项研究可能有助于深入了解气候系统在长时间内的变率和可预测性，这将对关注气候变率和变化影响的政策和决策者有意义。

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.