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SUMMARY:Multivariate bias corrections of climate simulations: A personal (
 methodological) view  - Dr Mathieu Vrac\,  LSCE - Laboratoire des Science
 s du Climat et de l'Environment
DTSTART:20230626T150000Z
DTEND:20230626T163000Z
UID:TALK202126@talks.cam.ac.uk
CONTACT:Annabelle Scott
DESCRIPTION:Climate models are the major tools to study the climate system
  and its evolutions in the future.  However\, their simulations often pres
 ent statistical biases that have to be corrected against reference  data (
 e.g.\, observations) before being used in impact assessments. Several bias
  correction (BC) methods  have therefore been developed in the last 2 deca
 des\, to adjust simulations according to historical records  and obtain cl
 imate projections with appropriate statistical attributes. Most of the exi
 sting and popular  BC methods are univariate\, i.e.\, correcting one physi
 cal variable and one location at a time and\, thus\,  can fail to reconstr
 uct inter-variable\, spatial or temporal dependencies of the observations.
  These  remaining biases in the correction can then affect the subsequent 
 analyses. This has led to recent  developments of “multivariate bias cor
 rection” (MBC) methods to restore/adjust multidimensional  dependencies.
  However\, these methods often have differences in their technical aspects
 \, assumptions  and applicability\, which can lead to different results. \
 nIn this talk\, I want to give a personal view on bias correction techniqu
 es\, focusing on  multivariate ones\, how they can be categorized\, the ch
 oices required for their configuration\, and some  of their underlying ass
 umptions. For illustration purposes\, a short (non-exhaustive) intercompar
 ison of  four existing MBCs will be provided. The main conclusions will he
 lp to ask still open questions as well  as to outline some perspectives fo
 r future developments and applications of MBC
LOCATION:LT2\, The Computer Lab\, William Gates Building\, 15 JJ Thomson A
 ve\, Cambridge\, CB3 0FD and zoom
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