|UK Students, EU Students, International Students
|3rd November 2023
|9th January 2024
About the Partnership
This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science.
The cloud controlling factors framework is an empirical approach for expressing meso- and largescale cloudiness in terms of the local environmental “factors”. These factors are simpler quantities (when compared to cloudiness), such as temperature or wind speed, and are generally called cloud controlling factors [1, 2, 3], CCF for short. Existing approaches to CCF focused on expressing cloudiness as a linear regression of the factors. This is sometimes useful, because the factors are much simpler to understand, and understand their response to warming, than cloudiness is. But CCF has some major downsides. For example, the framework cannot highlight causal relationships, only statistical connections. This is a downside because one needs causal (directional) information to build a physical theory. Additionally, because the factors co-vary strongly with each other, this leads to spurious correlations and misleading connections between factors and cloudiness.
Project Aims and Methods
The main goal of the project is to provide a better understanding of how cloudiness affects, and is affected by, related environmental factors. Success of this goal will be of benefit to a wide range of scientific communities, because the interactions of clouds with the mean climate is consistently the most difficult aspect of climate change to estimate correctly. Here, we aim to apply advanced data analysis techniques to provide a more rigorous, and more statistically significant framework for understanding the connections between clouds and climate. The proposed methodology for the project is to use Causal Timeseries Analysis (CTSA) [4, 5] to create a causal graph for large-scale cloudiness, its dependence, and its effect on the related environmental factors. Causal graphs are sketches of the causal connections in a set of dynamic variables and can be created from observed timeseries. CTSA overcomes the aforementioned major downsides of existing approaches to CCFs, and can provide the necessary information to develop a theoretical framework for how large scale cloudiness is connected with the rest of the climate system.
We strongly encourage the student to involve themselves in both the design of the numerical analysis pipeline of CTSA, but more importantly, to also propose alternative ways we may quantify the relation of cloudiness with the environmental factors in more rigor than the existing CCF approaches.
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