Speaker
Dr
Christian Franzke
Description
FRANZKE: Systematic Strategies for Stochastic Climate Modeling
The climate system has a wide range of temporal and spatial
scales for important physical processes. Examples include
convective activity with an hourly time scale, organized
synoptic scale weather systems on a daily time scale,
extra-tropical low-frequency variability on a time scale of
10 days to months, to decadal time scales of the coupled
atmosphere-ocean system. An understanding of the processes
acting on different spatial and temporal scales is important
since all these processes interact with each other due to
the nonlinearities in the governing equations. Most of 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 misrepresentation of some of
the processes lead to systematic biases of the resolved
processes and uncertainties in the climate response. A
better understanding of the multi-scale nature of the
climate system will be crucial in making more accurate and
reliable weather and climate predictions. In my presentation
I will discuss systematic strategies to derive stochastic
models for climate prediction. The stochastic mode reduction
strategy accounts systematically for the effect of the
unresolved degrees of freedom and predicts the functional
form of the effective reduced equations. These procedures
extend beyond simple Langevin equations with additive noise
by predicting 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
limit of infinite separation of time-scales.
