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An observational constraint to reduce uncertainty on global and regional climate change

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Dr. Aurélien Ribes
February 15, 2023 - 10:00am to 11:00am

This event was held online via Zoom Meetings. 
Watch a recording of the event.

Many studies have sought to constrain climate projections and climate sensitivity based on recent observations. Until recently, these constraints had limited impact, and projected warming ranges were driven primarily by model outputs. Here, I describe a new statistical method to narrow uncertainty on estimates of past and future human-induced warming. Our approach can be viewed as an adaptation of Kalman Filtering (or Kriging) for Climate Change. The definition of what we call "signal" and "noise" are different from those used in typical weather forecasting systems, but then the formalism is pretty similar, and estimation of the "model error" and "observational error" covariance matrices play a central role. This approach allows us to simultaneously attribute historical changes to specific forcings (attribution) and constrain projections. It provides a consistent picture of on-going changes, through merging model simulations and observations in a Bayesian fashion. Cross-validation suggests that our method produces robust results and is not overconfident.

I will describe a few recent applications of this method. Investigation of GSAT changes contributed to the introduction of observational constraints in the IPCC AR6 (one study among others). We find that historical observations narrow uncertainty on projected future warming by about 50%. More recently, the same technique was used to provide constrained local scale projections -- which is a step forward from the AR6 -- which I'll illustrate using a specific application over mainland France. Even at the local scale, we find that observational constraints narrow uncertainty on future warming, and that local observations provide useful information. I will briefly browse a few other applications, including some related to the water cycle. I'll finish with some perspective and implications of this work.


Aurélien Ribes is leading the research team Climstat at CNRM (Météo France, CNRS), Toulouse, France, since 2020. He has been working on detection and attribution of climate change since his PhD, with a particular focus on the statistical methods involved therein. This includes methods designed to infer long-term changes (optimal fingerprinting and others), and methods describing the relationship between one particular extreme event and climate change ("event attribution"). His most recent scientific activity has been focused on observational constraints for climate projections -- the topic of today's talk. He was also an enthusiast visiting scientist at PCIC about 10 years ago.