Assessing Paleoclimate Uncertainty

This was the title of a recent workshop held by the Past Earth Network (PEN) in Cambridge. PEN is one of 4 EPSRC-funded networks aiming to bring together mathematicians/statisticians and environmental scientists to help address problems in environmental change. While I wasn’t part of the PEN bid, I am acting as one of the co-leaders of one of the working groups (primarily interested in translating paleoclimate research to forecasting climate change).

The workshop was organised by another two of the working groups but they were kind enough to invite me as one of the speakers. But before it kicked off on Wednesday morning, I had the chance to go for a row on Tuesday night as a sub in William’s boat, which was fun. Haven’t done a whole lot of rowing in the past 20 years but it’s apparently not something you forget too easily, at least not after doing it in my sleep at 7am for several years…


The sessions were arranged with two speakers (nominally one climate scientist and one statistician) talking around a specific theme, such as parameter estimation/model tuning, spatio-temporal modelling, and time series analysis/tipping points. Some of these worked particularly well when both speakers were able to approach the same topic from different angles. There was quite a lot of overlap in scope between sessions with similar issues coming up in discussion throughout the meeting. For example, almost everyone was doing some sort of model-data comparison or synthesis, and it’s not always easy to decide how to do this. The first session on parameter estimation and uncertainty quantification was particularly good – it highlighted some practical and theoretical considerations that weren’t immediately obvious and which could materially affect research outputs.

The session I was talking in was about data assimilation, which is of course very much focussed on methods for model-data synthesis. Perhaps a limitation from the statistics point of view is that it tends to take a very model-centric view of the world, in that we are fundamentally trying to construct a model-like state that resembles reality (born out of its basis in model initialisation and weather forecasting), rather than just using models as one tool in reconstructing reality. Some state of the art particle filtering ideas discussed by the other speaker are likely to be useful to me, so I found that a particularly interesting part.

Cambridge was in the middle of a heatwave so it wasn’t ideal weather for sitting in a lecture room but as well as the rowing we had an afternoon of punting so it wasn’t all work and no play.


Corpus Christi, where we didn’t stay, looking resplendent in the sunshine.

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