Machine learning approaches to constrain and understand the role of clouds in climate change (ML4CLOUDS)

限制和理解云在气候变化中的作用的机器学习方法 (ML4CLOUDS)

• 批准号: NE/V012045/1
• 负责人: Manoj Joshi
• 金额: $ 82.87万
• 依托单位: University of East Anglia
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FV012045%2F1
• 关键词: Machine; learning; approaches; constrain; understand

As a defining challenge of our time, climate change has led to the 2015 Paris Agreement whose central policy goal is to keep global warming well below 2 degrees Celsius. The substantial remaining uncertainty in physical climate change projections, however, means that there is a very wide window of the dates within which this threshold might be passed. Assuming continuous greenhouse gas emissions, it could be within the next decade, or it might not be until well into the second half of this century. To inform their decision-making, policymakers urgently need this uncertainty reduced. Our research proposal, ML4CLOUDS, addresses the leading role of clouds in this uncertainty, and the coupled implications for climate variability.Clouds are ubiquitous phenomena covering around two thirds of Earth's surface at any time and, as such, play key roles in our climate system. Crucially, clouds are the single most important uncertainty factor in global warming projections under increasing atmospheric carbon dioxide (CO2) concentrations. Clouds are also key modulators of the main modes of climate variability, such as the El Niño Southern Oscillation (ENSO), which in turn drive regional climate and weather extremes. A better understanding of the response of clouds and their interactions with the atmospheric circulation and global warming has therefore been highlighted as one of the 7 Grand Challenges by the World Climate Research Programme. Constraining cloud-related uncertainties, and understanding the underlying physical drivers, would consequently be invaluable to society.The fundamental role of clouds primarily arises from their interaction with Earth's energy budget. Low-altitude clouds are highly reflective for sunlight (having a cooling effect on climate), while upper tropospheric clouds trap radiation emitted from the Earth (having a warming effect). Cloud formation itself releases latent heat to the atmosphere. It is the overall impacts of these processes on atmospheric temperature and the hydrological cycle that make clouds so important for the behaviour and evolution of the climate system.ML4CLOUDS aims to provide a better understanding of the complex physical control mechanisms driving cloud formation. This will improve our ability to predict how Earth's cloud cover will change under human influences such as increasing atmospheric CO2 and aerosol pollution, and thus reduce uncertainty in global warming. This reduction in cloud-related uncertainty will also feed back on our ability to model and comprehend present-day climate variability, and on how we expect the main climate modes, such as ENSO, to change in the future.We will achieve these goals through a novel approach incorporating artificial intelligence (or machine learning) methods, paired with targeted climate feedback analyses and state-of-the-art climate model simulations run on supercomputers. Specifically, our project will:1. Use machine learning to derive cloud-controlling relationships from large climate model datasets and from space-based observations. These relationships will provide improved estimates of the cloud response and significantly reduced uncertainty in physical climate change projections. They will further provide new insights into the relative importance of distinct physical mechanisms behind the cloud response. Cloud-controlling relationships learned from observations will also be helpful to inform future climate model development, e.g. of the new UK Earth System Model (UK-ESM).2. Improve our understanding of the role of clouds in modulating the main modes of climate variability. Next to its importance for extreme weather, climate variability is superimposed on long-term trends due to man-made climate change. A better understanding of the role of clouds in climate variability will therefore enhance our ability to detect and attribute historical climate change, and to predict future changes in climate and its extremes.

作为我们这个时代的一个决定性挑战，气候变化导致了2015年的《巴黎协定》，该协定的核心政策目标是将全球变暖控制在远低于2摄氏度的水平。然而，实际气候变化预测仍然存在很大的不确定性，这意味着可能会有一个非常宽的窗口来预测这一门槛可能被超过的日期。假设温室气体持续排放，它可能在未来十年内，也可能要到本世纪下半叶才能实现。为了给他们的决策提供信息，政策制定者迫切需要减少这种不确定性。我们的研究提案ML4CLOUDS解决了云在这种不确定性中的主导作用，以及对气候变化的影响。云是在任何时候覆盖地球表面约三分之二的普遍现象，因此在我们的气候系统中发挥着关键作用。至关重要的是，在大气二氧化碳(CO2)浓度不断增加的情况下，云是全球变暖预测中最重要的不确定因素。云也是气候变化的主要模式的主要调节器，例如厄尔尼诺南方涛动(ENSO)，这反过来又驱动区域气候和天气极端。因此，世界气候研究方案强调，更好地了解云的反应及其与大气环流和全球变暖的相互作用是7大挑战之一。因此，限制与云相关的不确定性，并了解潜在的物理驱动因素，对社会来说将是非常宝贵的。云的基本作用主要来自它们与地球能源收支的相互作用。低海拔的云层对阳光的反射率很高(对气候有降温作用)，而高层对流层云层则捕获从地球发出的辐射(有变暖效应)。云的形成本身会向大气释放潜热。正是这些过程对大气温度和水循环的总体影响，使得云对气候系统的行为和演变如此重要。ML4CLOUDS旨在更好地了解驱动云形成的复杂物理控制机制。这将提高我们预测地球云层在人类影响下如何变化的能力，例如大气二氧化碳和气溶胶污染的增加，从而减少全球变暖的不确定性。云相关不确定性的减少还将反馈到我们建模和理解当前气候变化的能力，以及我们对未来主要气候模式(如ENSO)的预期。我们将通过一种结合人工智能(或机器学习)方法的新方法，以及在超级计算机上运行的有针对性的气候反馈分析和最先进的气候模型模拟来实现这些目标。具体地说，我们的项目将：1.使用机器学习从大型气候模型数据集和基于空间的观测中得出云控制关系。这些关系将改进对云响应的估计，并显著减少实际气候变化预测的不确定性。它们将进一步为云响应背后不同物理机制的相对重要性提供新的见解。从观测中了解到的云控制关系也将有助于为未来气候模型的发展提供信息，例如新的英国地球系统模型(UK-ESM)。提高我们对云在调节气候变化的主要模式中的作用的理解。除了对极端天气的重要性外，由于人为气候变化，气候变化与长期趋势叠加在一起。因此，更好地了解云在气候变化中的作用将提高我们探测和确定历史气候变化的能力，并预测未来气候及其极端情况的变化。

项目成果

Energy budget diagnosis of changing climate feedback.

• DOI: 10.1126/sciadv.adf9302
• 发表时间: 2023-04-21
• 期刊: SCIENCE ADVANCES
• 影响因子: 13.6
• 作者: Cael, B. B.;Bloch-Johnson, Jonah;Ceppi, Paulo;Fredriksen, Hege-Beate;Goodwin, Philip;Gregory, Jonathan M.;Smith, Christopher J.;Williams, Richard G.
• 通讯作者: Williams, Richard G.

Climate feedbacks with latitude derived from climatological data and theory

来自气候数据和理论的纬度气候反馈

• DOI: 10.5194/egusphere-2023-2307
• 发表时间: 2023
• 作者: Goodwin P

Global impacts of recent Southern Ocean cooling.

• DOI: 10.1073/pnas.2300881120
• 发表时间: 2023-07-25
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Kang, Sarah M.;Yu, Yue;Deser, Clara;Zhang, Xiyue;Kang, In- Sik;Lee, Sun- Seon;Rodgers, Keith B.;Ceppi, Paulo
• 通讯作者: Ceppi, Paulo

An observational constraint on the uncertainty in stratospheric water vapour projections

对平流层水汽预测不确定性的观测限制

• DOI: 10.5194/egusphere-egu23-2943
• 发表时间: 2023
• 作者: Nowack P

Recent global climate feedback controlled by Southern Ocean cooling

• DOI: 10.1038/s41561-023-01256-6
• 发表时间: 2023-08
• 期刊: Nature Geoscience
• 影响因子: 18.3
• 作者: Sarah M. Kang;P. Ceppi;Yue Yu;I. Kang