Extreme heat wave sampling and prediction with analog Markov chain and comparisons with deep learning

Abstract

We present a data-driven emulator, a stochastic weather generator (SWG), suitable for estimating probabilities of prolonged heat waves in France and Scandinavia. This emulator is based on the method of analogs of circulation to which we add temperature and soil moisture as predictor fields. We train the emulator on an intermediate complexity climate model run and show that it is capable of predicting conditional probabilities (forecasting) of heat waves out of sample. Special attention is payed that this prediction is evaluated using a proper score appropriate for rare events. To accelerate the computation of analogs, dimensionality reduction techniques are applied and the performance is evaluated. The probabilistic prediction achieved with SWG is compared with the one achieved with a convolutional neural network (CNN). With the availability of hundreds of years of training data, CNNs perform better at the task of probabilistic prediction. In addition, we show that the SWG emulator trained on 80 years of data is capable of estimating extreme return times of order of thousands of years for heat waves longer than several days more precisely than the fit based on generalized extreme value distribution. Finally, the quality of its synthetic extreme teleconnection patterns obtained with SWG is studied. We showcase two examples of such synthetic teleconnection patterns for heat waves in France and Scandinavia that compare favorably to the very long climate model control run.

Keywords

• convolutional neural network
• heat wave
• Markov chain
• prediction