Collaborative Research: Revisiting the Low-Frequency Variability of the Extratropical Circulation Using Non-Empirical Orthogonal Function (EOF) Modes and Linear Response Functions
合作研究:使用非经验正交函数 (EOF) 模式和线性响应函数重新审视温带环流的低频变化
基本信息
- 批准号:1921409
- 负责人:
- 金额:$ 36.38万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-08-15 至 2022-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
One dominant pattern of the atmospheric circulation variability in the extratropical troposphere of both hemispheres is the oscillation between stronger and weaker westerly jet streams with a time scale longer than 10 days. Such low-frequency variability of extratropical westerly jet streams is called the annular mode. The annual mode influences the day-to-day weather and extreme events in the middle and high latitude regions and is therefore of great interest to understand. The annular mode in winter hemisphere also influences, and is influenced by, the stratospheric polar vortex. This project seeks a deeper understanding of the factors that control the annular mode. The outcome of this work will potentially transform the understanding of jet stream variability and weather in the middle and high latitudes. The outcome could also lead to advances in predictability past the current weather prediction barrier of about 10 days, and lead to improvements in capabilities of climate models. Furthermore, the new quantitative framework will aid the interpretation of the range of outcomes predicted by comprehensive climate models. These outcomes have the potential to advance the national and global efforts aimed at preparing for climate change and weather-related extremes, resulting in significant socio-economic benefits. This project will support the education and training of two PhD students and a postdoctoral researcher, thus preparing the next generation of atmospheric dynamists and climate scientists. Some of the tools developed during this work will be used by the PIs in their undergraduate and graduate-level teaching to help the students with visualizing and quantifying some aspects of the jet stream variability. Also, the project will engage K-12 students in climate and computer modeling-related research through several STEM outreach programs at Rice and Stanford.In this project, novel analysis methods will be developed, and will be employed along with computer models and observational data to gain a deeper understanding of this variability of the jet streams. Furthermore, a new framework will be developed to quantitively link this variability in the present day to variability in the future. This framework will integrate the response of the jet stream variability and polar vortex to various climate forcings, such as greenhouse gas increase or ozone depletion. The specific tasks of this project are 1) examining the coupling between the barotropic and baroclinic annular modes in observations and in climate models; 2) developing an eddy feedback model for propagating annular modes; 3) understanding stratospheric influence on the tropospheric annular mode in both hemispheres using numerical experiments; 4) studying low-frequency variability modes and fluctuation-dissipation relationship in climate model simulations to better evaluate the fidelity of the climate change projections in these simulations.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.
两半球对流层外大气环流变率的一个主要形式是时间尺度大于10天的强西风急流和弱西风急流的振荡。这种对流层外西风急流的低频变率称为环形模态。年模态影响着中高纬度地区的日常天气和极端事件,因此对了解年模态有很大的兴趣。冬季半球的环形模态也影响平流层极涡,并受平流层极涡的影响。该项目旨在更深入地了解控制环形模式的因素。这项工作的成果将有可能改变对中高纬度急流变化和天气的理解。其结果还可能导致可预测性的进步,超过目前约10天的天气预测障碍,并导致气候模型能力的提高。此外,新的量化框架将有助于解释综合气候模型预测的结果范围。这些成果有可能推动旨在为气候变化和与天气有关的极端事件做好准备的国家和全球努力,从而产生重大的社会经济效益。该项目将支持两名博士生和一名博士后研究员的教育和培训,从而培养下一代大气动力学家和气候科学家。在这项工作中开发的一些工具将用于PI在他们的本科和研究生水平的教学,以帮助学生与可视化和量化的射流变化的某些方面。此外,该项目将通过赖斯和斯坦福大学的多个STEM外展项目吸引K-12学生参与气候和计算机建模相关研究。在该项目中,将开发新型分析方法,并将与计算机模型和观测数据沿着使用,以获得更深入的了解急流的这种变化性。此外,还将制定一个新的框架,将目前的这种可变性与未来的可变性定量联系起来。这一框架将综合急流变率和极涡对各种气候强迫(如温室气体增加或臭氧消耗)的反应。该项目的具体任务是:(1)在观测和气候模式中检验正压和斜压环形模之间的耦合;(2)发展一个传播环形模的涡动反馈模式;(3)通过数值试验了解两半球平流层对对流层环形模的影响; 4)研究低频变异模式和波动-气候模式模拟中的耗散关系,以更好地评估这些模拟中气候变化预测的保真度。该奖项反映了NSF基金会的使命是履行其法定使命,并通过使用基金会的知识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)
Seasonal and Latitudinal Variability of the Gravity Wave Spectrum in the Lower Stratosphere
平流层下部重力波谱的季节和纬度变化
- DOI:10.1029/2020jd032850
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Lindgren, Erik A.;Sheshadri, Aditi;Podglajen, Aurélien;Carver, Robert W.
- 通讯作者:Carver, Robert W.
Frequency‐Dependent Behavior of Zonal Jet Variability
纬向射流变率的频率依赖性行为
- DOI:10.1029/2019gl086585
- 发表时间:2020
- 期刊:
- 影响因子:5.2
- 作者:Lindgren, Erik A.;Sheshadri, Aditi;Plumb, R. Alan
- 通讯作者:Plumb, R. Alan
Tropical Cyclone Frequency Under Varying SSTs in Aquaplanet Simulations
- DOI:10.1029/2020gl091980
- 发表时间:2021-03-16
- 期刊:
- 影响因子:5.2
- 作者:Burnett, Adam C.;Sheshadri, Aditi;Robinson, Thomas
- 通讯作者:Robinson, Thomas
The Atlantic Jet Response to Stratospheric Events: A Regime Perspective
- DOI:10.1029/2020jd033358
- 发表时间:2021-03
- 期刊:
- 影响因子:0
- 作者:M. Goss;E. Lindgren;A. Sheshadri;N. Diffenbaugh
- 通讯作者:M. Goss;E. Lindgren;A. Sheshadri;N. Diffenbaugh
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Aditi Sheshadri其他文献
Gravity Wave Momentum Fluxes from 1 km Global ECMWF Integrated Forecast System
来自 1 公里全球 ECMWF 综合预报系统的重力波动量通量
- DOI:
10.1038/s41597-024-03699-x - 发表时间:
2024-08-21 - 期刊:
- 影响因子:6.900
- 作者:
Aman Gupta;Aditi Sheshadri;Valentine Anantharaj - 通讯作者:
Valentine Anantharaj
Gravity wave momentum fluxes estimated from Project Loon balloon data
根据 Project Loon 气球数据估算的重力波动量通量
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
B. Green;Aditi Sheshadri;M. J. Alexander;M. Bramberger;Fran¸cois Lott - 通讯作者:
Fran¸cois Lott
Bayesian History Matching Applied to the Calibration of a Gravity Wave Parameterization
贝叶斯历史匹配应用于重力波参数化校准
- DOI:
10.1029/2023ms004163 - 发表时间:
2024 - 期刊:
- 影响因子:6.8
- 作者:
Robert C King;Laura A. Mansfield;Aditi Sheshadri - 通讯作者:
Aditi Sheshadri
Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation
非局域重力波传播的机器学习全局模拟
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Aman Gupta;Aditi Sheshadri;Sujit Roy;Vishal Gaur;M. Maskey;Rahul Ramachandran - 通讯作者:
Rahul Ramachandran
Machine Learning Gravity Wave Parameterization 1 Generalizes to Capture the QBO and Response to 2 Increased CO 2 3
机器学习重力波参数化 1 概括为捕获 QBO 和对 2 二氧化碳增加的响应 2 3
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Zachary I. Espinosa;Aditi Sheshadri;G. Cain;E. Gerber;K. DallaSanta - 通讯作者:
K. DallaSanta
Aditi Sheshadri的其他文献
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{{ truncateString('Aditi Sheshadri', 18)}}的其他基金
Collaborative Research: Framework: Improving the Understanding and Representation of Atmospheric Gravity Waves using High-Resolution Observations and Machine Learning
合作研究:框架:利用高分辨率观测和机器学习提高对大气重力波的理解和表示
- 批准号:
2004492 - 财政年份:2020
- 资助金额:
$ 36.38万 - 项目类别:
Standard Grant
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