CloudEnergyBalance: Simple climate models to quantify impact of large-scale cloudiness & deterministic chaos on climatic variability & tipping points

CloudEnergyBalance：用于量化大规模多云影响的简单气候模型

基本信息

批准号：
EP/Y01653X/1
负责人：
Peter Ashwin
金额：
$ 25.38万
依托单位：
UNIVERSITY OF EXETER
依托单位国家：
英国
项目类别：
Fellowship
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=EP%2FY01653X%2F1
关键词：
CloudEnergyBalance Simple climate models quantify

项目摘要

Climate is one of the most complex natural systems known to humankind. This year's Nobel Prize in Physics highlighted how important it is to quantify how internal climatic variability will change when changing any component of climate, for example greenhouse gases. Energy Balance Models (EBMs) are the simplest physically motivated models of climate and have been used to provide fundamental insight into climate and its variability for decades.However, we are currently lacking EBMs that represent dynamic cloudiness, which is one of the most important and complex parts of the energy balance. In fact, even state-of-the-art large simulation frameworks, Earth System Models, have difficulties correctly capturing changes in cloudiness variability versus warming. This highlights how crucial it is to better understand the interplay of cloudiness and climatic variability. We are also lacking EBMs that allow for deterministic chaos emerging on an energy balance level from the interaction of large-scale climatic components, despite observational data supporting this possibility. Furthermore, lack of representing cloudiness and chaos also means that we currently have no theoretical reasoning on whether (and how) cloudiness or chaos impacts potentially critical climate tipping points. This project will address these gaps, by creating an energy balance model that includes dynamic cloudiness and chaotic time evolution. The model will be used to elucidate how these processes affect climatic variability and climate tipping points. The analysis will be based on the theory of climate physics, nonlinear dynamics, and also comparisons with observational data.

气候是人类已知的最复杂的自然系统之一。今年的诺贝尔物理学奖强调了量化当气候的任何组成部分（例如温室气体）发生变化时内部气候变率将如何变化的重要性。能量平衡模型 (EBM) 是最简单的物理驱动的气候模型，几十年来一直被用来提供对气候及其变化的基本见解。然而，我们目前缺乏代表动态云量的 EBM，而动态云量是最重要和最重要的模型之一。能量平衡的复杂部分。事实上，即使是最先进的大型模拟框架（地球系统模型）也很难正确捕获云量变化与变暖的变化。这凸显了更好地了解云量和气候变化之间的相互作用是多么重要。我们还缺乏允许在大规模气候成分相互作用的能量平衡水平上出现确定性混沌的 EBM，尽管观测数据支持这种可能性。此外，缺乏代表多云和混乱也意味着我们目前没有关于多云或混乱是否（以及如何）影响潜在的关键气候临界点的理论推理。该项目将通过创建一个包含动态云量和混沌时间演化的能量平衡模型来解决这些差距。该模型将用于阐明这些过程如何影响气候变化和气候临界点。分析将基于气候物理学、非线性动力学理论以及与观测数据的比较。