New ways for dynamical prediction of extreme heat waves: rare event simulations and stochastic process-based machine learning.

In the climate system, extreme events or transitions between climate attractors are of primarily importance for understanding the impact of climate change. Recent extreme heat waves with huge impact are striking examples. However, they cannot be studied with conventional approaches, because they are too rare and realistic models are too complex. 

We will discuss several new algorithms and theoretical approaches, based on large deviation theory, rare event simulations, and machine learning for stochastic processes, which we have specifically designed for the prediction of the committor function (the probability of the extreme event to occur). We will discuss results for the study of midlatitude extreme heat waves and demonstrate the performance of these tools.

Using the best available climate models, our approach shed new light on the fluid mechanics processes which lead to extreme heat waves. We will describe quasi-stationary patterns of turbulent Rossby waves that lead to global teleconnection pattern in connection with heat waves and analyze their dynamics.  

We stress the relevance of these patterns for recently observed extreme heat waves with huge impact and the prediction potential of our approach.