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SUMMARY:Biases and uncertainty in multi-model climate projections - Kunsch
 \, H (ETH Zrich)
DTSTART:20100825T143000Z
DTEND:20100825T153000Z
UID:TALK25900@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:The ensemble approach has originally been derived in probabili
 stic medium-range weather forecasting\, and is now broadly used in numeric
 al weather prediction\, seasonal forecasting and climate research on a wid
 e range of time scales. Applications geared towards climate projections ar
 e usually based on a heterogeneous ensemble with typically a mere handful 
 of ensemble members\, stemming from different models in an only partly coo
 rdinated framework. \n\nAn important feature of ensemble approaches in cli
 mate research is the inability to rigorously quantify climate model biases
 . While biases of climate models are monitored for the control period\, th
 e lack of long-term comprehensive observations (on the centennial time-sca
 les considered) implies that it is difficult to decide how the model biase
 s will change with the climate state. In contrast to other studies\, we lo
 ok not only at 20 or 30 year averages\, but also at the interannual variab
 ility. This allows us to consider additive and multiplicative biases. In t
 he talk\, I will discuss two plausible assumptions about the extrapolation
  of additive biases\, referred to as the ``constant bias'' and ``constant 
 relation'' assumptions. The former is used implicitly in most studies of c
 limate change. The latter asserts that over-/underestimation of the intera
 nnual variability in the control period leads also to over-/underestimatio
 n of climate change\, and this assumption is closely related to the statis
 tical post-processing of seasonal climate predictions. In addition we expl
 icitly allow the additive and multiplicative model biases to change betwee
 n control and scenario periods\, resolving the resulting lack of identifia
 bility by the use of informative priors. \n\nAn analysis of of GCM/RCM sim
 ulations from the ENSEMBLES project shows that bias assumptions critically
  affect the results for several regions and seasons. \n
LOCATION:Seminar Room 1\, Newton Institute
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