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SUMMARY:Causal networks for climate model evaluation - Peer Nowack\, Unive
 rsity of East Anglia
DTSTART:20220308T110000Z
DTEND:20220308T123000Z
UID:TALK171470@talks.cam.ac.uk
CONTACT:Herbie Bradley
DESCRIPTION:Global climate models are central tools for understanding past
  and future climate change. The assessment of model skill\, in turn\, can 
 benefit from modern data science approaches. Here I will present recent wo
 rk on causal discovery algorithms as a novel approach for process-oriented
  climate model evaluation [1\,2].\n\nFollowing an introduction to the conc
 ept of causal discovery\, I will move on to key scientific implications of
  this new approach when applied to global sea level pressure datasets. Usi
 ng causal networks learned from meteorological reanalysis data (as a proxy
  for observations) and from CMIP5 climate model output\, I demonstrate tha
 t climate models which better reproduce the observed causal information fl
 ow also better reproduce important precipitation patterns over highly popu
 lated areas such as the Indian subcontinent\, Africa\, East Asia\, Europe\
 , and North America. In addition\, the method identifies expected model in
 terdependencies due to shared development backgrounds of many climate mode
 ls. Finally\, I find that causal network metrics provide stronger relation
 ships for constraining precipitation projections under climate change than
  traditional model evaluation metrics. Such emergent relationships highlig
 ht the potential of causal discovery approaches to constrain longstanding 
 uncertainties in climate change projections.\n\n[1] Nowack P\, Runge J\, E
 yring V\, Haigh JD. Causal networks for climate model evaluation and const
 rained projections. Nature Communications 11\, 1415 (2020).\n[2] Runge J\,
  Nowack P\, Kretschmer M\, Flaxman S\, Sejdinovic D. Detecting and quantif
 ying causal associations in large nonlinear time series datasets. Science 
 Advances 5\, eaau4996 (2019).
LOCATION:https://zoom.us/j/6708259482?pwd=Qk03U3hxZWNJZUZpT2pVZnFtU2RRUT09
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