Investigating the impact of climate change on ocean circulation and coastal ecosystems
File(s)
Author(s)
Reynard, Nick
Type
Thesis
Abstract
This thesis takes a multi-disciplinary approach to addressing some of ways impacts of climate change are felt throughout the ocean. This is first done through a modelling approach, an investigation into new data-driven methodologies using machine-learning and I lastly bring in policy-focused research that is aimed to connect science and policy.
Turbulent mixing in the ocean is a key driver that ensures the closure of the meridional overturning circulation (MOC), with it acting to upwell abyssal waters across density levels. I begin by defining an effective diffusivity coefficient that achieves the required net water mass transformation rates in a model grid cell. Here I show that this more realistic effective diffusivity leads to significant changes in the sensitivity of ocean circulation under different surface forcings. I go on to look at the impact of a temporally evolving mixing, demonstrating how by allowing varying degrees of freedom turbulent mixing can be considered a mechanism for variability on an impactful scale. Parameterisations in climate models are often informed by observations, however, for mixing these are sparse in comparison to variables such as temperature and salinity. Here I use two supervised machine-learning models, which are informed by physical understanding, to predict key properties of turbulence. Results show promise and often outperform currently used mathematical parameterisations.
I end this thesis by changing themes and present a policy-briefing paper that focuses on the importance blue carbon ecosystems in helping to mitigate and adapt to the effects of climate change. I first look at their global mitigation potential if restored to previous coverage estimates and conclude it to be limited to roughly 0.5% - 2% of current global annual emissions. I discuss how their protection is justified and should be encouraged due their current protection of carbon stores and potential for adaptation and ecosystem services.
Turbulent mixing in the ocean is a key driver that ensures the closure of the meridional overturning circulation (MOC), with it acting to upwell abyssal waters across density levels. I begin by defining an effective diffusivity coefficient that achieves the required net water mass transformation rates in a model grid cell. Here I show that this more realistic effective diffusivity leads to significant changes in the sensitivity of ocean circulation under different surface forcings. I go on to look at the impact of a temporally evolving mixing, demonstrating how by allowing varying degrees of freedom turbulent mixing can be considered a mechanism for variability on an impactful scale. Parameterisations in climate models are often informed by observations, however, for mixing these are sparse in comparison to variables such as temperature and salinity. Here I use two supervised machine-learning models, which are informed by physical understanding, to predict key properties of turbulence. Results show promise and often outperform currently used mathematical parameterisations.
I end this thesis by changing themes and present a policy-briefing paper that focuses on the importance blue carbon ecosystems in helping to mitigate and adapt to the effects of climate change. I first look at their global mitigation potential if restored to previous coverage estimates and conclude it to be limited to roughly 0.5% - 2% of current global annual emissions. I discuss how their protection is justified and should be encouraged due their current protection of carbon stores and potential for adaptation and ecosystem services.
Version
Open Access
Date Issued
2023-07
Date Awarded
2024-01
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Mashayek, Alireza
Publisher Department
Civil and Environmental Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)