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SUMMARY:Probabilistic causal network modelling of Southern Hemisphere eddy
 -driven jet long-range predictability in spring-to-summer  - Elena Saggior
 o\, University of Reading
DTSTART:20230213T130000Z
DTEND:20230213T140000Z
UID:TALK196057@talks.cam.ac.uk
CONTACT:Prof. John R. Taylor
DESCRIPTION:Causal networks are increasingly being adopted as a framework 
 to guide robust statistical analysis in climate science\, as well as a sta
 tistical modelling approach. Causal networks represent a system of variabl
 es as nodes and their causal relationships as directed links\, which can b
 e parametrized with functions or conditional probabilities. Their structur
 e implies a set of conditional independence relationships\, which provide 
 testable implications given data. After a brief introduction to the subjec
 t\, in this talk I will show an application of probabilistic causal networ
 ks to study long-range predictability of the Southern Hemisphere eddy-driv
 en jet variability.\n\nIn the spring-to-summer months\, the jet is influen
 ced by the stratospheric polar vortex\, whose variability is in turn affec
 ted by long-lead drivers in both the stratosphere and the troposphere. How
 ever\, a quantification of the predictability arising from these drivers a
 nd their combination has been lacking. Here a probabilistic causal network
  model of the coupled stratospheric-tropospheric monthly variability is co
 nstructed to generate synthetic predictions of the jet and quantify the sk
 ill arising from the above-mentioned drivers. The vortex state is confirme
 d to be determinant for skilful predictions of jet’s variability. Howeve
 r\, the vortex long-lead drivers provide only moderate skill. This suggest
 s that long-range jet predictability may be ultimately constrained by how 
 well models capture internal stratospheric variability on sub-monthly time
  scales.
LOCATION:MR5\, CMS
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