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Collaborative Research: EaSM 2: Stochastic Simulation and Decadal Prediction of Large-Scale Climate

Collaborative Research: EaSM 2: Stochastic Simulation and Decadal Prediction of Large-Scale Climate
合作研究:EaSM 2:大尺度气候的随机模拟和年代际预测
批准号:
1243175
负责人:
Dmitri Kondrashov
金额:
$39.86万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-02-15 至 2017-06-30

项目摘要

项目成果

Dmitri Kondrashov的其他基金

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中文摘要
翻译
在这个项目下,将在观测和模拟最先进的地球气候环流模式(GCMs)中研究大尺度、低频模式(LFMs)。该项目将建立在先前由美国国家科学基金会和美国能源部资助的LFMs研究的基础上,LFMs是由大西洋和太平洋地区的海风驱动和翻转环流以及它们与大气和热带气候变化的相互作用引起的。该项目将侧重于:(a)改进目前对低通量模型和它们之间相互作用的理解;(b)发展、修订和检验基于这些模式的概率年代际预测的统计方法;(c)将这些年代际预测与观测和GCM模拟结合起来,在年代际和更长时间尺度上深入了解气候变化和气候变率的动力原因。该项目将利用先进的数据自适应光谱方法,利用最近两个国际气候相互比较模式项目的观测和模拟,继续确定大西洋和太平洋的年代际模态。这些方法包括多通道奇异谱分析(MSSA)、谐波库普曼分析(HKA)以及基于经验模型约简(EMR)的随机建模。研究人员将进一步应用UCLA和UWM开发的新方法来研究同步混沌振荡器,以进一步了解太平洋内部和大西洋与太平洋之间的十年尺度遥相关,以及它们与厄尔尼诺-南方涛动(ENSO)的关系。利用项目第一部分研究的LFMs,研究者将研究它们的年代际可预测性,并评估我们基于EMR、MSSA和HKA的新经验预测模型所做的回溯年代际预测的技能。这些经验性预测将与作为IPCC第五次评估报告一部分的初始气候预测进行比较。这种比较将有助于量化由于内在年代际模式及其与ENSO和气候变化的相互作用而产生的潜在技能。这些活动的结合将产生先进的随机模拟工具,用于根据观测数据和近期气候变化和气候变率的最可靠和最可预测的要素的组合,创建未来气候的基准情景,直至大约2050年。这个项目的智力价值在于为气候预测和动态气候模型的验证开发了通用的统计工具和方法。该项目还将促进我们对突出的大尺度低频模式之间的全球耦合的理解。该项目的一个预期结果是通过开发和测试与全球多个lfm之间的耦合和同步相关的新指标来验证气候模式预测,从而改进对气候预测可靠性的估计。该项目更广泛的影响在于解决一个对社会至关重要的问题,即了解气候变异和变化。该项目的研究人员致力于使他们的随机模拟易于气候学界使用。这项工作将促进加州大学洛杉矶分校和华盛顿大学之间的科学伙伴关系,并进一步发展两所大学的气候动力学研究生课程。主要研究人员将通过专门的网站、经审阅的出版物、研讨会和在国家和国际会议上的发言广泛传播研究结果。
英文摘要
Under this project, large-scale, low-frequency modes (LFMs) will be studied in observations and simulations of state-of-the-art general circulation models (GCMs) of Earth's climate. This project will build on previous NSF- and DOE-funded work on LFMs arising from the ocean's wind-driven and overturning circulations in the Atlantic and Pacific sectors, as well as from their interactions with the atmosphere and tropical climate variability. This project will focus on: (a) improving current understanding of LFMs and interactions between them; (b) developing, revising and testing statistical methods for probabilistic decadal prediction based on these modes; and (c) using these decadal predictions in conjunction with observations and GCM simulations to gain insight into the dynamical causes of climate change and climate variability at decadal and longer time scales. This project will continue the identification of decadal modes in the Atlantic and Pacific, from both observations and the simulations from the two most recent international climate intercomparison modeling projects, using advanced, data-adaptive spectral methods. These methods will include multi-channel singular spectrum analysis (MSSA), as well as harmonic Koopman analysis (HKA), along with stochastic modeling based on empirical model reduction (EMR). The investigators will further apply novel methods for the study of synchronized chaotic oscillators developed at UCLA and UWM, to gain additional understanding of decadal-scale teleconnections within the Pacific and between the Atlantic and the Pacific, as well as their relationships with the El Nino-Southern Oscillation (ENSO). Using the LFMs studied in the first part of the project, the investigators will examine their decadal predictability and assess the skill of retrospective decadal forecasts made with our new empirical forecast models based on EMR, MSSA and HKA. These empirical forecasts will be compared against the initialized climate predictions being made as part of the IPCC's 5th Assessment Report. This comparison will help quantify potential skill due to intrinsic decadal modes and their interactions with ENSO, as well as with climate change. The combination of these activities will lead to advanced stochastic simulation tools for creating benchmark scenarios of future climate, based on a combination of observed data and the most robust and predictable elements of near-term climate change and climate variability, out to about 2050.The intellectual merit of this project is in developing versatile statistical tools and methodologies for climate prediction and the validation of dynamical climate models. The project will also advance our understanding of global coupling between prominent large-scale low-frequency modes. An expected outcome of the project is improved estimates of reliability of climate projections, by developing and testing novel metrics - associated with coupling and synchronization between multiple LFMs across the globe - for validating climate model predictions.The project's broader impacts lie in addressing a problem of utmost societal importance, i.e., understanding climate variability and change. The project's investigators are committed to making their stochastic simulations easily available to the climate community. This work will foster scientific partnerships between UCLA and UWM, and further the development of graduate curricula in climate dynamics at both institutions. The principal investigators will broadly disseminate the results by means of a dedicated web site, refereed publications, seminars and presentations at national and international meetings.
期刊论文(3)
专著(0)
科研奖励(0)
会议论文
Data-adaptive harmonic decomposition and prediction of Arctic sea ice extent
北极海冰范围的数据自适应调和分解与预测
DOI: 10.1093/climsys/dzy001
发表时间: 2018
期刊: Dynamics and Statistics of the Climate System
影响因子: --
作者: [Kondrashov, Dmitri, Chekroun, Mickaël D, Ghil, Michael]
通讯作者: Ghil, Michael
Data-adaptive harmonic analysis and modeling of solar wind-magnetosphere coupling
太阳风-磁层耦合的数据自适应谐波分析和建模
DOI: 10.1016/j.jastp.2017.12.021
发表时间: 2018
期刊: Journal of Atmospheric and Solar-Terrestrial Physics
影响因子: 1.9
作者: [Kondrashov, Dmitri, Chekroun, Mickaël D.]
通讯作者: Chekroun, Mickaël D.
DOI: 10.3390/fluids3010021
发表时间: 2018-03-01
期刊: FLUIDS
影响因子: 1.9
作者: [Kondrashov, Dmitri, Chekroun, Mickael D., Berloff, Pavel]
通讯作者: Berloff, Pavel
Collaborative Research: GEM--Towards Developing Physics-informed Subgrid Models for Geospace MagnetoHydroDynamics (MHD) Simulations
  • 批准号:
    2247677
  • 项目类别:
    Standard Grant
  • 资助金额:
    $34.81万
  • 财政年份:
    2023
  • 负责人:
    Dmitri Kondrashov
  • 依托单位:
EAGER: Machine Learning and Data Assimilation for Discovery of Generalized Fokker-Planck Equation for Radiation Belt Modeling
  • 批准号:
    2211345
  • 项目类别:
    Standard Grant
  • 资助金额:
    $25.01万
  • 财政年份:
    2022
  • 负责人:
    Dmitri Kondrashov
  • 依托单位:
NSFGEO-NERC: Multiscale Stochastic Modeling and Analysis of the Ocean Circulation
  • 批准号:
    1658357
  • 项目类别:
    Standard Grant
  • 资助金额:
    $60.0万
  • 财政年份:
    2017
  • 负责人:
    Dmitri Kondrashov
  • 依托单位:
Gap Filling of Solar Wind Data by Singular Spectrum Analysis
  • 批准号:
    1102009
  • 项目类别:
    Standard Grant
  • 资助金额:
    $9.76万
  • 财政年份:
    2011
  • 负责人:
    Dmitri Kondrashov
  • 依托单位:
国内基金
海外基金
Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    SATOSHI NAWATA
  • 依托单位:
Cell Research
Cell Research
Cell Research (细胞研究)