Bridging theory to reality in projections of the Asian and West African monsoons (Bridge)

在亚洲和西非季风预测中将理论与现实联系起来（桥梁）

• 批准号: NE/X014827/1
• 负责人: Ruth Geen
• 金额: $ 99.67万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX014827%2F1
• 关键词: Bridging; theory; reality; projections; Asian

In Asia and West Africa the majority of rain falls during the summer monsoon season. Monsoon rain is vital for agriculture, and a late or weak monsoon can mean disaster for crops, to the point where the Indian finance minister once described the monsoon as the country's 'real finance minister'. However, while we have a strong conceptual understanding of climatic changes controlled by thermodynamics (e.g. temperature, sea level), changes controlled by wind patterns (e.g. regional precipitation) are far less intuitive. State-of-the-art models struggle to correctly simulate patterns of monsoon rainfall in the present day, and predict a range of future changes. Without basic understanding of the wind circulations controlling the monsoons it is impossible to judge which predictions we can trust, both seasonally and under global warming.Recently, major advances have been made in our understanding of the mechanisms controlling the monsoons by using very abstract model configurations: aquaplanets (planets covered only in water) and simulations including simple continents. By stripping back the complexity of the real world, these models have at last given us basic theories for the controls on when and where zonal-mean tropical rain falls. However, these successful theories have in general not yet been adapted to the regional scale, and this presents an enormous opportunity for a step-change in our fundamental understanding of regional monsoons, their variability and response to climate change. To address the challenge of connecting theory to reality, we have identified a novel approach combining machine learning methods with a hierarchy of model simulations and data.The model hierarchy will allow us to study how monsoon circulations behave and theory performs as complexity increases. In particular, we will make use of a new, highly-configurable idealised climate model, Isca, which allows us to run simulations ranging from very simple aquaplanets up to a simplified model of Earth within a single, consistent framework. A major challenge in understanding regional monsoons is that the mathematics underpinning theory becomes highly complex at a local scale. However, machine learning has recently been applied to similar problems in oceanic science to identify regions governed by different key processes. We will use these techniques to simplify the mathematics and develop regional theories for monsoon rainfall.Theories appropriate to each region will be used to interpret the behaviour of the latest state-of-the-art climate models. We will use both simulations of past and future climate, and more idealised simulations targeted at identifying differences between models in simulating processes contributing to climate change (e.g. sea ice, plant physiology). This should help in understanding biases in simulations of historical climate and constraining intermodel differences in projections of climate under global warming. By identifying which models can be trusted to simulate the monsoons and the drivers of future changes, it should be possible to produce more robust and useable projections for these key regions.The final phase of the project will test whether different theories are needed to understand monsoon behaviour on different timescales. Do theories for climatological rainfall also explain rainfall variations week-to-week, or decade-to-decade? Do these processes have a lead time which could provide information for subseasonal-to-seasonal or decadal forecasting? Can theoretical insight help us untangle how decadal variations in sea surface temperature modulate the processes governing interannual variability in monsoon rain?By approaching these complex problems from a new perspective, Bridge aims to at last build the same level of confidence in our predictions of circulation-governed monsoon rainfall as we have in thermodynamically controlled climate features.

在亚洲和西非，大部分的雨水福尔斯都是在夏季季风季节。季风雨对农业至关重要，迟来或弱的季风可能意味着农作物的灾难，以至于印度财政部长曾将季风描述为该国的“真实的财政部长”。然而，虽然我们对热力学控制的气候变化（例如温度、海平面）有很强的概念性理解，但风模式控制的变化（例如区域降水）远没有那么直观。最先进的模型很难正确模拟当今季风降雨的模式，并预测未来的一系列变化。如果对控制季风的风环流没有基本的了解，就不可能判断我们可以相信哪些预测，无论是季节性的还是全球变暖的情况。最近，通过使用非常抽象的模型配置，我们对控制季风的机制的理解取得了重大进展：水行星（只被水覆盖的行星）和模拟，包括简单的大陆。通过剥离真实的世界的复杂性，这些模型最终为我们提供了控制纬向平均热带雨福尔斯何时何地降落的基本理论。然而，这些成功的理论一般还没有适应区域尺度，这提供了一个巨大的机会，在我们的基本了解区域季风，其变异性和对气候变化的反应的一步变化。为了解决将理论与现实联系起来的挑战，我们已经确定了一种将机器学习方法与模型模拟和数据层次相结合的新方法。模型层次将使我们能够研究季风环流的行为和理论如何随着复杂性的增加而表现。特别是，我们将使用一个新的，高度可配置的理想化气候模型Isca，它允许我们在一个单一的，一致的框架内运行从非常简单的水行星到地球简化模型的模拟。理解区域季风的一个主要挑战是，数学基础理论在局部尺度上变得非常复杂。然而，机器学习最近被应用于海洋科学中的类似问题，以确定由不同关键过程控制的区域。我们将使用这些技术来简化数学和发展区域理论的季风降雨。适合每个地区的理论将被用来解释最新的国家的最先进的气候模式的行为。我们将使用过去和未来气候的模拟，以及更理想化的模拟，旨在确定模型之间的差异，以模拟气候变化（例如海冰，植物生理学）。这将有助于理解历史气候模拟中的偏差，并限制全球变暖下气候预测的模型间差异。通过确定哪些模型可以信赖来模拟季风和未来变化的驱动因素，应该有可能为这些关键地区提供更可靠和可用的预测，该项目的最后阶段将测试是否需要不同的理论来理解不同时间尺度上的季风行为。气候学上的降雨理论是否也能解释周与周、或十年与十年之间的降雨变化？这些过程是否有一个提前时间，可以提供信息，为亚季节到季节或十年期预测？理论上的洞察力能帮助我们解开海表温度的年代际变化如何调节季风雨年际变化的过程吗？通过从一个新的角度来处理这些复杂的问题，布里奇的目标是最终在我们对环流控制的季风降雨的预测中建立与我们在气候控制的气候特征中相同的信心水平。