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SUMMARY:Stochastic dynamical systems and seasonal climate dynamics - Wooso
 k Moon (DAMTP)
DTSTART:20160208T130000Z
DTEND:20160208T140000Z
UID:TALK63806@talks.cam.ac.uk
CONTACT:Doris Allen
DESCRIPTION:One of the key aspects of our climate is the seasonal cycle\; 
 often the largest signal in most of climate time series. Climate time-seri
 es reveal the combination of the seasonal cycle\, short-time processes rel
 ated to weather\, and slowly-varying signals caused by ocean circulation o
 r global warming. Despite the myriad of processes involved in shaping clim
 ate time-series\, the most parsimonious physical framework to describe the
 m is that of Brownian particles flowing through a slowly-varying seasonal 
 cycle\, which is described with a periodic non-autonomous stochastic dynam
 ical system. This mathematical model consists of the deterministic contrib
 ution\, generating a reliable seasonal cycle and a stochastic forcing\, ca
 pturing the impact of short-time scale processes.  The generality of the m
 ethod affords applicability to a wide range of systems/subsystems.  First\
 , I will show how this model can be used to understand the seasonal variab
 ility of Arctic sea ice. Analytic solutions constructed from a stochastic 
 perturbation method reveal the basic physical processes controlling the se
 asonal variability. Second\, I will use this formalism to construct a stoc
 hastic model to regenerate the statistics of monthly-average surface tempe
 rature data\, which spans around 133 years from 1880 to present. I will us
 e this framework to discuss issues such as climate sensitivity and predict
 ability.\n\n
LOCATION:MR5\, Centre for Mathematical Sciences
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