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)

基本信息

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

项目摘要

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《巴黎协定》的签署，其核心政策目标是将全球变暖幅度控制在 2 摄氏度以下，然而，实际气候变化预测仍然存在很大的不确定性。假设温室气体持续排放，可能会在未来十年内达到这一阈值，也可能要到本世纪下半叶才能达到这一目标。迫切需要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。云是一种无处不在的现象，在任何时候都覆盖地球表面的三分之二左右，因此在我们的生活中发挥着关键作用。至关重要的是，在大气二氧化碳（CO2）浓度增加的情况下，云是全球变暖预测中最重要的一个不确定因素，云也是厄尔尼诺等主要气候变化模式的关键调节因素。南方涛动 (ENSO) 进而驱动区域气候和极端天气，因此，世界气候研究将更好地了解云的响应及其与大气环流和全球变暖的相互作用列为七大挑战之一。限制与云相关的不确定性，并了解潜在的物理驱动因素，对社会来说是无价的。云的基本作用主要来自于它们与地球能量预算的相互作用。低空云对阳光具有高度反射性（具有冷却作用）。影响对流层上层云会捕获从地球发出的辐射（具有变暖效应），而云的形成本身会向大气释放潜热，这些过程对大气温度和水文循环的总体影响使云变得如此。对于气候系统的行为和演化非常重要。ML4CLOUDS 旨在更好地理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（例如大气二氧化碳含量增加和二氧化碳浓度增加）下如何变化的能力。气雾剂污染，从而减少全球变暖的不确定性，这种与云相关的不确定性的减少也将反馈给我们模拟和理解当今气候变化的能力，以及我们预期主要气候模式（如 ENSO）如何变化。我们将通过一种新颖的方法来实现这些目标，该方法结合了人工智能（或机器学习）方法，并结合有针对性的气候反馈分析和在超级计算机上运行的最先进的气候模型模拟。具体来说，我们的项目将： 1. 使用机器学习关系导出云控制关系这些关系将提供对云响应的改进估计，并显着减少物理气候变化预测的不确定性，它们将进一步提供关于云响应背后的不同物理机制的相对重要性的新见解。从观测中了解到的云控制关系也将有助于为未来的气候模型开发提供信息，例如新的英国地球系统模型（UK-ESM）。2。其次它的重要性极其重要。由于人为气候变化，气候变化叠加在长期趋势上，因此更好地了解云在气候变化中的作用将增强我们检测和归因历史气候变化以及预测未来气候变化的能力。及其极端。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Global impacts of recent Southern Ocean cooling.

最近南大洋变冷的全球影响。

DOI：
http://dx.10.1073/pnas.2300881120
发表时间：
2023
期刊：
Proceedings of the National Academy of Sciences of the United States of America
影响因子：
11.1
作者：
Kang SM
通讯作者：
Kang SM

Sensitivities of cloud radiative effects to large-scale meteorology and aerosols from global observations

全球观测中云辐射效应对大尺度气象和气溶胶的敏感性