This project explores some open problems at the intersection of statistical physics and climate science, which are the two halves of the 2021 Nobel Prize in Physics.
The coexistence of water and ice is a generic feature of cold regions on planet Earth and possibly other aquaplanets. The spatial patterns and temporal evolutions of meltwater affect the surface albedo, which is a key factor for modelling regional and global climate. Most existing models of meltwater are based on sophisticated multiphysics. Despite their complexity, these models often obscure the essential mechanisms responsible for the observed geometric features such as the shapes and sizes of lakes or ponds.
The core physics of meltwater is the solid-liquid phase transition between ice and water. As such, the discipline of statistical physics, which explains macroscopic phase transitions based on microscopic physical laws, is highly effective in describing meltwater patterns. For example, the Ising model for magnetic materials has recently been used to model the geometry of melt ponds on Arctic sea ice. Such simple models provide frameworks for prescribing subgrid-scale spatial organisations of meltwater in global climate models.
In this PhD, you will build minimal models of meltwater patterns using statistical physics. This involves modifying classical models in statistical physics to incorporate geophysical processes such as ice-albedo feedback. You will investigate these models numerically and analytically and compare your predictions with observational data. You may also explore connections between statistical physics and machine learning in this context.
The essential skills in this project include modeling, computation and analysis, which are readily transferable to other quantitative disciplines such as data science.
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