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Physics, Atmospheric and Oceanic Physics, physics.ao-ph, Physics, Computational Physics, physics.comp-ph, Physics, Fluid Dynamics, physics.flu-dyn, Physics, Geophysics, physics.geo-ph
Abstract:
The last decade has seen the success of stochastic parameterizations in
short-term, medium-range and seasonal ensembles: operational weather centers
now routinely use stochastic parameterization schemes to better represent model
inadequacy and improve the quantification of forecast uncertainty. Developed
initially for numerical weather prediction, the inclusion of stochastic
parameterizations not only provides more skillful estimates of uncertainty, but
is also extremely promising for reducing longstanding climate biases and
relevant for determining the climate response to forcings such as e.g., an
increase of CO2. This article highlights recent results from different research
groups which show that the stochastic representation of unresolved processes in
the atmosphere, oceans, land surface and cryosphere of comprehensive weather
and climate models a) gives rise to more reliable probabilistic forecasts of
weather and climate and b) reduces systematic model bias. We make a case that
the use of mathematically stringent methods for derivation of stochastic
dynamic equations will lead to substantial improvements in our ability to
accurately simulate weather and climate at all scales. Recent work in
mathematics, statistical mechanics and turbulence is reviewed and its relevance
for the climate problem demonstrated as well as future research directions
outlined.
