AGS-PRF: Understanding Tropical High Cloud Feedbacks via Machine Learning and Super Parameterization

AGS-PRF：通过机器学习和超级参数化了解热带高云反馈

基本信息

批准号：
2020305
负责人：
Zane Martin
金额：
$ 19万
依托单位：
Martin, Zane Karas
依托单位国家：
美国
项目类别：
Fellowship Award
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2022-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020305&HistoricalAwards=false
关键词：
AGS PRF Understanding Tropical Cloud

项目摘要

Convective rainfall often takes the form of deep cumulus clouds, which appear as bright red spots on a radar image, embedded in a larger region of weaker rainfall colored orange or yellow. These broad areas of weaker rainfall typically contain high, relatively flat clouds similar to the "anvils" seen above isolated thunderstorms. The high stratiform clouds are a consequence of the convective cells, just as the anvil forms only after a thunderstorm is fully developed. But the presence of a broad region of upper-level stratus can affect the subsequent development of convective clouds, or in other words it can promote large-scale convective aggregation and organization. Upper-level clouds encourage new cloud formation by blocking outgoing infrared radiation, heating the column below and promoting rising motions. The effect is thought to be important over tropical oceans where the frontal weather systems that organize convection in higher latitudes are largely absent. But understanding of this infrared cloud-radiation feedback and its effects on tropical weather and climate is currently quite limited.Work under this award addresses the role of cloud-radiation feedback in the development of the Madden-Julian Oscillation (MJO), in which a large region of convection organizes over the tropical Indian Ocean and propagates slowly eastward for a period of 30 to 60 days. Cloud-radiation feedback has been invoked as a cause of convective organization in the MJO and as a key factor in determining its propagation speed. A second issue addressed in the proposal is the possible role of convective organization in determining the Earth's climate sensitivity, meaning the amount of global warming or cooling that results from a given increase or decrease in greenhouse gas concentrations.The research is conducted through a combination of observational analysis and numerical simulations. The observational analysis uses artificial neural networks (ANNs) applied to satellite and weather balloon data to determine how stratus cloud development relates to ambient temperature, moisture, and stability in the cloud layer. The relationships identified by the ANN are interpreted using layer-wise relevance propagation (LRP), a technique that identifies the inputs which matter the most for generating an ANN result. The value of machine learning tools like ANN for basic science is often questioned because of their "black box" nature: while machine learning methods can have uncanny predictive power, their results do not come with any explanation for why a particular set of inputs produces a given result. LRP is thus a means to open the black box and gain physical insight and understanding from the empirical relationships identified through the ANN machinery.The work has societal relevance due to the worldwide effects of the MJO, which influences weather and climate phenomena including tropical cyclones (particularly in the Gulf of Mexico), the amount and timing of monsoon rainfall, and the onset of El Nino events. MJO events are predictable in principle given their slow propagation, but prediction skill is limited in current weather and climate models. The possible influence of convective organization on climate sensitivity is also of societal interest given the rapid rise of greenhouse gas concentrations. The project also contributes to workforce development by providing support to an early-career scientist.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

对流降雨通常以深层积云的形式出现，在雷达图像上显示为鲜红色斑点，嵌入在较大的橙色或黄色降雨较弱区域中。这些降雨量较弱的大范围区域通常包含较高且相对平坦的云层，类似于孤立雷暴上方的“砧”。高层状云是对流单元的结果，就像铁砧只有在雷暴完全发展后才会形成一样。但大面积上层层云的存在会影响对流云的后续发展，或者说可以促进大规模的对流聚集和组织。高层云通过阻挡红外辐射、加热下方的云柱并促进上升运动来促进新云的形成。这种效应被认为对热带海洋很重要，因为热带海洋上几乎不存在在高纬度地区组织对流的锋面天气系统。但目前对这种红外云辐射反馈及其对热带天气和气候的影响的了解相当有限。该奖项的工作探讨了云辐射反馈在马登-朱利安振荡 (MJO) 发展中的作用，其中大片对流区域在热带印度洋上空组织起来，缓慢向东传播，持续 30 至 60 天。云辐射反馈被认为是 MJO 中对流组织的原因，也是决定其传播速度的关键因素。该提案讨论的第二个问题是对流组织在确定地球气候敏感性方面可能发挥的作用，即由于温室气体浓度的给定增加或减少而导致的全球变暖或变冷的程度。该研究是通过结合以下方式进行的：观测分析和数值模拟。观测分析使用应用于卫星和气象气球数据的人工神经网络 (ANN) 来确定层云发展与环境温度、湿度和云层稳定性之间的关系。人工神经网络识别的关系使用逐层相关性传播 (LRP) 进行解释，该技术可识别对于生成人工神经网络结果最重要的输入。像人工神经网络这样的机器学习工具对于基础科学的价值经常受到质疑，因为它们具有“黑匣子”性质：虽然机器学习方法可以具有不可思议的预测能力，但它们的结果并不能解释为什么一组特定的输入会产生一个结果。给出的结果。因此，LRP 是打开黑匣子并从通过 ANN 机制识别的经验关系中获得物理洞察力和理解的一种手段。由于 MJO 的全球影响，这项工作具有社会相关性，它影响包括热带气旋在内的天气和气候现象（特别是在墨西哥湾）、季风降雨量和时间以及厄尔尼诺事件的发生。鉴于 MJO 事件传播缓慢，原则上是可以预测的，但当前天气和气候模型的预测能力有限。鉴于温室气体浓度迅速上升，对流组织对气候敏感性的可能影响也引起社会关注。该项目还通过为早期职业科学家提供支持来促进劳动力发展。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The influence of the quasi-biennial oscillation on the Madden–Julian oscillation

准两年期振荡对马登朱利安振荡的影响

DOI：
10.1038/s43017-021-00173-9
发表时间：
2021-06-08
期刊：
Nature Reviews Earth & Environment
影响因子：
42.1
作者：
Z. Martin;S. Son;A. Butler;H. Hendon;Hyemi Kim;A. Sobel;S. Yoden;Chidong Zhang
通讯作者：
Chidong Zhang

The MJO-QBO Relationship in a GCM with Stratospheric Nudging

平流层微推 GCM 中的 MJO-QBO 关系

DOI：
10.1175/jcli-d-20-0636.1
发表时间：
2021-03-02
期刊：
Journal of Climate
影响因子：
4.9
作者：
Z. Martin;C. Orbe;Shuguang Wang;A. Sobel
通讯作者：
A. Sobel

Large-scale state and evolution of the atmosphere and ocean during PISTON 2018

PISTON 2018期间大气和海洋的大尺度状态和演化

DOI：
10.1175/jcli-d-20-0517.1
发表时间：
2021-03-05
期刊：
Journal of Climate
影响因子：
4.9
作者：
A. Sobel;J. Sprintall;E. Maloney;Z. Martin;Shuguang Wang;S. Szoeke;B. C. Trabing;S. Rutledge
通讯作者：
S. Rutledge

Using Simple, Explainable Neural Networks to Predict the Madden‐Julian Oscillation

使用简单、可解释的神经网络来预测 Madden-Julian 振荡