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

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.