ISSUL · Improving Subseasonal and Seasonal sUmmer forecast over southern Europe through machine Learning

Improving Subseasonal and Seasonal sUmmer forecast over southern Europe through machine Learning

Short- and medium-range weather forecast systems show a predictability limit of about ten days after which the forecast degrades strongly. However, reliable predictions on longer timescales are needed to prepare and better protect citizens and any economic sector sensitive to weather and climate against the occurrence of extreme events. Therefore, in the recent years, the development of forecast model on subseasonal to seasonal (S2S) timescales has become the focus of an intense research work from the scientific community. However, despite the large number of research studies, S2S forecast models still show a limited skill in summer over Europe. This is especially the case for southern Europe, that has generally received less attention, even though it is highly vulnerable to high-impact summer heatwaves, and very sensitive to climate change. Therefore, this project aims at improving and better understanding summer S2S predictability of heatwaves in southern Europe. To do that, a fairly new approach in the field of weather and extremes prediction is employed. Specifically, the S2S forecast model is entirely based on two machine learning algorithms: an optimization algorithm, which aims to select optimal predictors from a pool of candidate drivers, and a regression algorithm (linear or nonlinear) to predict the heatwave occurrence. The overall objectives of this project are: 1. The identification and evaluation of the optimal predictors of heatwave frequency and strength on S2S timescales for the three sub-regions 2. The prediction of the heatwave frequency and strength on S2S timescales over the three areas 3. The evaluation of the S2S forecast system. The main conclusions of the action are the following: 1. The machine learning forecast model based on the coupling of an optimization algorithm with a regression algorithm skilfully predicts regional monthly mean temperature conditions over southern Europe with one month lead time. 2. Regional monthly heatwave intensity in southern Europe is more difficult to predict one month in advance than the mean temperature conditions. 3. Dynamical connections are evidenced between recurrent predictors and the targets, which give confidence in the model architecture.

Data: CORDIS, © European Union