An analogue approach to predicting European climate

Decadal climate prediction is a scientific endeavour of potentially large societal impacts. Yet such predictions remain challenging, as they predict climate skilfully only under certain circumstances or in specific regions. Moreover, decadal climate prediction simulations rely on dedicated coupled climate model simulations that are particularly expensive. In this study, we build upon earlier research by Menary et al. (2021) in search of a method to make skilful and cheap decadal climate predictions by constructing predictions from existing climate model simulations using the so-called analogue method.

The analogue method draws on the idea that there is decadal memory in the climatic state at the start of a prediction. This method identifies the observed state of the climate system at the start of a prediction and then screens the archive of available model simulations for comparable climatic states. It then selects a number of modelled climate states that are similar to the observed situation, and uses the years after the selected simulated climate states as prediction. Using a simple analogue method based on temperature trends in the North Atlantic basin, Menary et al. (2021) demonstrated skilful prediction of North Atlantic SST on par with dynamical decadal prediction simulations. In this study, we refine the original method by using more sophisticated algorithms to select the analogues, and choosing decadal prediction of seasonal European climate as our target. These new selection algorithms include multivariate regression at different time lags as well as non-linear methods.

 

Menary, MB, J Mignot, J Robson (2021) Skilful decadal predictions of subpolar North Atlantic SSTs using CMIP model-analogues. Environ. Res. Lett. 16 064090. https://doi.org/10.1088/1748-9326/ac06fb