Abstract

I have long been frustrated with the run-of-the-mill data analyses we tend to go through in the “mainstream” of atmospheric or climate science. Many common approaches do not exploit our measurements or modelling tools fully, which means we could be missing out on insights, over or under-estimating uncertainty, and potentially be damaging our credibility. To try and do better, I’ve sought collaborations with statisticians and computer scientists over the last few years…although the real work is now mostly done by students and postdocs!

In this talk, I will give a flavour of the varied environmental problems we are addressing and data science methods we are using, drawing on the work within the Data Science of the Natural Environment (DSNE) project and our new Centre of Excellence for Environmental Data Science (CEEDS), both joint initiatives between Lancaster University and the UK Centre for Ecology and Hydrology. I will also reflect on what I’ve learned about working on interdisciplinary and transdisciplinary projects, including with critical social sciences, humanities and large businesses