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

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，在改变气候的任何组成部分（例如温室气体）时，量化内部气候可变性将如何变化是多么重要。能量平衡模式（EBMs）是最简单的物理驱动气候模式，几十年来一直用于提供对气候及其变异的基本见解。然而，我们目前缺乏表示动态云量的EBMs，而动态云量是能量平衡中最重要和最复杂的部分之一。事实上，即使是最先进的大型模拟框架，即地球系统模型，也很难正确地捕捉到云量变率与变暖之间的变化。这凸显了更好地理解云量和气候变化之间的相互作用是多么重要。尽管观测数据支持这种可能性，但我们也缺乏允许大规模气候成分相互作用在能量平衡水平上出现确定性混乱的EBMs。此外，缺乏对云和混沌的描述也意味着我们目前没有关于云和混沌是否（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括动态云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率和气候临界点。分析将基于气候物理理论、非线性动力学以及与观测数据的比较。

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.