| Summary: | Modern weather and climate models share a
common heritage, and often even components,
however they are used in different ways to answer
fundamentally different questions. As such, attempts
to emulate them using machine learning should
reflect this. While the use of machine learning
to emulate weather forecast models is a relatively
new endeavour there is a rich history of climate
model emulation. This is primarily because while
weather modelling is an initial condition problem
which intimately depends on the current state of the
atmosphere, climate modelling is predominantly a
boundary condition problem. In order to emulate the
response of the climate to different drivers therefore,
representation of the full dynamical evolution of the
atmosphere is neither necessary, or in many cases,
desirable. Climate scientists are typically interested in
different questions also. Indeed emulating the steadystate climate response has been possible for many
years and provides significant speed increases that
allow solving inverse problems for e.g. parameter
estimation. Nevertheless, the large datasets, nonlinear relationships and limited training data make
Climate a domain which is rich in interesting machine
learning challenges.
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