An Observational Constraint to Reduce Uncertainty on Global and Regional Climate Change
February 15, 2023 @ 10:00 am - 11:00 am
Wednesday, February 15
10:00 – 11:00am PST
Zoom
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, the presenter describes a new statistical method to narrow uncertainty on estimates of past and future human-induced warming. The 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 this method produces robust results and is not overconfident.
The presenter 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). 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 the presenter will illustrate using a specific application over mainland France. Even at the local scale, observational constraints narrow uncertainty on future warming, and that local observations provide useful information. The presenter will briefly browse a few other applications, including some related to the water cycle. The presenter will finish with some perspective and implications of this work.
