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SUMMARY:Systematic Strategies for Stochastic Climate Modeling - Franzke\, 
 C (University of Cambridge)
DTSTART:20100824T103000Z
DTEND:20100824T113000Z
UID:TALK25895@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:The climate system has a wide range of temporal and spatial sc
 ales for important physical processes. Examples include convective activit
 y with an hourly time scale\, organized synoptic scale weather systems on 
 a daily time scale\, extra-tropical low-frequency variability on a time sc
 ale of 10 days to months\, to decadal time scales of the coupled atmospher
 e-ocean system. An understanding of the processes acting on different spat
 ial and temporal scales is important since all these processes interact wi
 th each other due to the nonlinearities in the governing equations. Most o
 f the current problems in understanding and predicting the climate system 
 stem from the multi-scale nature of the climate system in that all of the 
 above processes interact with each other and the neglect and/or misreprese
 ntation of some of the processes lead to systematic biases of the resolved
  processes and uncertainties in the climate response. A better understandi
 ng of the multi-scale nature of the climate system will be crucial in maki
 ng more accurate and reliable weather and climate predictions. \n\nIn my p
 resentation I will discuss systematic strategies to derive stochastic mode
 ls for climate prediction. The stochastic mode reduction strategy accounts
  systematically for the effect of the unresolved degrees of freedom and pr
 edicts the functional form of the effective reduced equations. These proce
 dures extend beyond simple Langevin equations with additive noise by predi
 cting nonlinear effective equations with  both additive and multiplicative
  (state-dependent) noises. The stochastic mode reduction strategy predicts
   rigorously closed form stochastic models for the slow variables in the l
 imit of infinite separation of time-scales.\n
LOCATION:Seminar Room 1\, Newton Institute
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