physics | climate | python
At Climate X, I am responsible for quantifying both the risk from storms in the present, and how it is changing with global warming. I have experience researching windstorms in Europe and tropical cyclones across the globe. This has so far involved adapting a spatially explicit statistical approach for quantifying risk to a non-stationary climate, and applying it to regional climate model simulations (paper in prep.). I’m currently working on how we can make best use of the hierarchy of models available for assessing extreme weather risk. As part of this work, I spend a significant amount of time writing up and optimising code to carry out geospatial and/or statistical analyses on large datasets in python.
The primary focus of my research looks into how climate change is affecting individual extreme weather events. Due to the fact that no directly observable world without human influence on the climate exists, all approaches attempting to answer this question involve some form of modelling, either numerical or statistical. In general, extreme event attribution studies now tend to rely on relatively coarse climate models, and use several different approaches or models to assess the robustness of their conclusions. My work aims to adapt weather forecast models that are demonstrably able to simulate the actual weather extreme being studied to allow them to answer this same question. You can read more about why I am doing this here.
A collaboration with the Met Office and climateprediction.net, the aim of this project is to understand the uncertainty surrounding future extreme winter weather in order to support climate projection efforts such as the UK Climate Projections (UKCP18). I have been heavily involved in analysis of the model output coming out of this project and in writing the accompanying science paper. You can read more about this project here, and the science paper is available here.
Through my masters project exploring the remaining carbon budget under ambitious mitigation scenarios, I became familiar with reduced complexity climate models / emulators, and in particular, the FaIR climate model. Later on, during the early stages of my PhD, I worked on updating FaIR to make it quicker, simpler and more transparent - attempting to identify a minimal level of structural complexity possible. The result was FaIRv2.0.0, a set of six equations that can be used to model the 1-D globally averaged response of the climate system to greenhouse gas and aerosol emissions. Its speed and flexibility lends it to be used for large ensembles of perturbed parameter probabilistic projections or for emulating the response of more complex models (such as those contributing to CMIP6). You can read more about this model update here.
The FABLE consortium aims to understand how countries can transition towards sustainable land-use and food systems, as part of the Food and Land-Use Coalition (FOLU). In particular, it asks how countries can collectively meet associated Sustainable Development Goals (SDGs) and the objectives of the Paris Agreement. I am a member of the UK team, primarily responsible for updating the Excel accounting tool, the FABLE Calculator, that is used to model the potential evolution of food and land-use systems over 2000-2050. You can find out more here.