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Using machine learning to constrain the atmospheric dynamics contribution to regional climate change

利用机器学习来限制大气动力学对区域气候变化的贡献

基本信息

批准号：
2123640
负责人：
金额：
--
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2123640
关键词：
Using machine learning constrain atmospheric

项目摘要

"Global warming is the key metric in the public perception of climate change but regional changes, for example in weather extremes or rainfall, have a more direct impact on people's lives. These are particularly difficult to predict, however, so increasing confidence in regional impacts is arguably one of the most important challenges in present-day science. A large part of the uncertainty in regional projections arises from the complexity of atmospheric dynamics and its response to increasing atmospheric greenhouse gas concentrations.The goal of this project is to use machine learning to build a data-driven mathematical framework for regional climate change that goes substantially beyond the simple global warming picture.This framework will:1) necessarily include both the thermodynamic and the dynamical response of the Earth system to greenhouse gas forcing. Here, we refer to thermodynamic mechanisms as those primarily driven by local changes in the energy budget, which is well reflected in variables such as surface temperature. Dynamical mechanisms broadly refer to shifts or modifications in the strength of the global atmospheric circulation, or changes in the remote coupling between regions of the atmosphere, which are referred to as teleconnections. Both components are intrinsically coupled due to the redistribution of energy (thermodynamics) within the Earth system as part of the circulation (dynamics).2) put emphasis on an attempt to separate dynamical and thermodynamic drivers of regional change. Each driver will be evaluated using data from observations (e.g. satellite data) and the output of state-of-the-art climate models, i.e. sophisticated computer models used to make climate change projections.Taking this framework as a basis, a first aim is to introduce novel metrics for regional climate change. Such metrics should be easily visualised and understood by non-experts, but better reflect the uncertainty in, and the importance of, the dynamical response including measures for extreme events such as heat waves, storms, droughts and floods. In addition, by focusing on certain world regions, the underlying physical drivers of uncertainty will be identified and tested concerning their potential to increase confidence in regional climate change projections.The project will involve the modification and then application of machine learning algorithms to large climate datasets, for example to data from climate model simulations that is used to inform the Intergovernmental Panel on Climate Change (IPCC) and to data published by the European Centre for Medium-Range Weather Forecasts (ECMWF) or NASA. The ideal candidate should be able to demonstrate a keen interest in the physics of the Earth system and in testing out a number of different supervised and unsupervised machine learning algorithms. All coding will be carried out in Python. Good programming experience and familiarity with some machine learning packages (scikit-learn, TensorFlow etc) would be an advantage, but are not essential. Depending on the student's interests, high-resolution numerical models could be used to test the result of reducing uncertainty in coarser climate model projections on much finer spatial resolutions (e.g. county-to-city scale)."

“全球变暖是公众对气候变化看法的关键指标，但区域变化，例如极端天气或降雨，对人们的生活有更直接的影响。然而，这些特别难以预测，因此增加对区域影响的信心可以说是当今科学中最重要的挑战之一。区域预估的不确定性在很大程度上源于大气动力学的复杂性及其对大气温室气体浓度增加的响应。这个项目的目标是使用机器学习来建立一个数据驱动的区域气候变化数学框架，这远远超出了简单的全球变暖图景。这个框架将：1)必须包括地球系统对温室气体强迫的热力学和动力学响应。在这里，我们将热力学机制称为主要由能量收支的局部变化驱动的机制，这在表面温度等变量中得到了很好的反映。动力机制广义上是指全球大气环流强度的变化或改变，或大气区域之间远程耦合的变化，这被称为遥相关。由于地球系统内能量的再分配（热力学）是环流的一部分（动力学），这两个部分在本质上是耦合的。2)强调分离区域变化的动力和热力学驱动因素的尝试。将利用观测数据（如卫星数据）和最先进的气候模式（即用于预测气候变化的复杂计算机模式）的输出对每个驱动因素进行评估。以这个框架为基础，第一个目标是为区域气候变化引入新的度量标准。这些指标应该很容易被非专家可视化和理解，但更好地反映动态响应的不确定性和重要性，包括对极端事件（如热浪、风暴、干旱和洪水）的措施。此外，通过将重点放在某些世界区域，将确定和检验不确定性的潜在物理驱动因素是否有可能提高对区域气候变化预测的信心。该项目将涉及修改机器学习算法，然后将其应用于大型气候数据集，例如用于政府间气候变化专门委员会（IPCC）的气候模型模拟数据，以及欧洲中期天气预报中心（ECMWF）或美国宇航局发布的数据。理想的候选人应该能够对地球系统的物理学表现出浓厚的兴趣，并且能够测试出许多不同的监督和无监督机器学习算法。所有代码都将在Python中执行。良好的编程经验和熟悉一些机器学习包（scikit-learn， TensorFlow等）将是一个优势，但不是必需的。根据学生的兴趣，高分辨率数值模型可以用来测试在更精细的空间分辨率（例如县到市的尺度）上减少粗糙气候模型预测的不确定性的结果。”