Emma Boland
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About
Research Area
I study the Polar Oceans using a combination of computer models and observations. The polar oceans are hugely important to our climate, being the only place on the planet where old deep waters come to the surface, exchange heat and carbon with the atmosphere, and then return to the global ocean interior, locking away that heat and carbon for hundreds to thousands of years. Most of the heat and carbon we have added to the climate system has ended up in the ocean, so it’s crucial to understand what controls these processes now and how it might change in the future.
I use a variety of techniques to carry out my research, including adjoint modelling techniques, analysing climate models and observations, and machine learning techniques. Adjoint modelling is an exciting tool for examining how climate models behave that can find connections that traditional approaches miss. I’m enthusiastic about using machine learning to help do more with the sparse observations we have in the polar regions - I currently use unsupervised clustering techniques to uncover hidden structures that can help us track the impact of climate change.
Project Interests
I’d be happy to develop projects in investigating polar ocean dynamics using ocean models; particularly using adjoint modelling techniques in traditional models like MITgcm, or investigating new possibilities using high-resolution GPU-based models like Oceananigans. Possible research questions include: how will Southern Ocean heat and carbon storage change under climate change? How would an ice-free Arctic change the dynamics of the Arctic ocean?
I’m also happy to develop projects using machine learning techniques to study ocean dynamics. Possible research questions include: how will the structure of the polar oceans change in future climate change scenarios? How robust are unsupervised clustering techniques based on sparse observations, and what can different methods tell us about the observations we do have?