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

“全球变暖是公众对气候变化感知的关键指标，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最重要的挑战之一。区域预测中的很大一部分不确定性来自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。该项目的目标是使用机器学习建立一个区域气候变化的数据驱动的数学框架，该框架远远超出简单的全球变暖图片。该框架将：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，这很好地反映在表面温度等变量中。动力机制广义上是指全球大气环流强度的变化或修改，或大气区域之间远程耦合的变化，称为遥相关。由于地球系统内能量的重新分配（热力学）作为环流（动力学）的一部分，这两个组成部分在本质上是相互耦合的。将利用观测数据（如卫星数据）和最先进的气候模型（即用于气候变化预测的复杂计算机模型）的结果对每个驱动因素进行评估，以这一框架为基础，第一个目标是采用新的区域气候变化指标。这些指标应易于可视化，并为非专家所理解，但更好地反映了动态响应的不确定性和重要性，包括对热浪、风暴、干旱和洪水等极端事件的措施。此外，通过重点关注世界某些地区，将确定和测试不确定性的潜在物理驱动因素，以提高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，用于向政府间气候变化专门委员会（IPCC）提供信息的气候模型模拟数据，以及欧洲中-范围天气预报（ECMWF）或NASA。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软件包（scikit-learn，TensorFlow等）将是一个优势，但不是必需的。视学生的兴趣而定，高分辨率数值模型可用于检验在更精细的空间分辨率（如县到城市尺度）上减少较粗糙气候模型预测的不确定性的结果。"

"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)."