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SUMMARY:Predicting extreme heat waves using rare event simulations and dee
 p neural networks - Freddy Bouchet (ENS - Lyon\, CNRS (Centre national de 
 la recherche scientifique))
DTSTART:20220520T111500Z
DTEND:20220520T121500Z
UID:TALK173675@talks.cam.ac.uk
DESCRIPTION:In the climate system\, extreme events or transitions between 
 climate attractors are of primarily importance for understanding the impac
 t of climate change. Recent extreme heat waves with huge impact are striki
 ng examples. However\, it is very hard to study those events with conventi
 onal approaches\, because of the lack of statistics\, because they are too
  rare for historical data and because realistic models are too complex to 
 be run long enough.\nWe cope with this lack of data issue using rare event
  simulations. Using some of the best climate models\, we oversample extrem
 ely rare events and obtain several hundreds more events than with usual cl
 imate runs\, at a fixed numerical cost. Coupled with deep neural networks 
 this approach improves drastically the prediction of extreme heat waves.\n
 This shed new light on the fluid mechanics processes which lead to extreme
  heat waves. We will describe quasi-stationary patterns of turbulent Rossb
 y waves that lead to global teleconnection patterns in connection with hea
 t waves and analyze their dynamics.\nWe stress the relevance of these patt
 erns for recently observed extreme heat waves and the prediction potential
  of our approach.
LOCATION:Seminar Room 1\, Newton Institute
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