physics | climate | python

ExSamples - Generating samples of extreme winters to support climate adaptation

I recently gave a talk about the work I’ve done in collaboration with several co-authors on the ExSamples project. This talk was part of the UKCRP webinar series. The aim of this project is to provide a proof-of-concept of a specific experiment design that could support existing climate projection efforts by more completely sampling the uncertainty surrounding the most extreme future events.


How is climate change affecting our weather?

I recently gave a talk about attribution of extreme weather events as part of the Oxford@home COP Conversations series. In it, I try to briefly cover the current state of the science, as well as going into some detail about my own PhD research into the use of weather forecast models for attribution.



In this post I use a jupyter notebook to demo the package I have created for easily carrying out regression analyses and fitting distributions via L-Moments.



A figure showing the idealised CMIP6 DECK reference experiments carried out using the constrained FaIR probabilistic ensemble.


Historical contributions to warming: what, who, where?

Reasonably recently, I noticed a tweet (it’s always twitter…) asking for an estimate of the contribution of agriculture to global warming. There were a few responses, but they had quite a wide spread and in general seemed inconclusive. I decided, quite some time after seeing the tweet, to see if I could give my own estimate of this using the probabilistic simple climate model ensemble I used in my previous post. As is the way with these “straightforward” projects, it naturally turned into something a bit more comprehensive than intended. This post is the result - my attempt to estimate what (specific forcing drivers), who (sectors) and where (countries) is causing anthropogenic global warming.