Differentiable Programming for Earth System Modeling
- URL: http://arxiv.org/abs/2208.13825v2
- Date: Fri, 2 Jun 2023 08:56:26 GMT
- Title: Differentiable Programming for Earth System Modeling
- Authors: Maximilian Gelbrecht and Alistair White and Sebastian Bathiany and
Niklas Boers
- Abstract summary: State-of-the-art Earth System Models (ESMs) can reproduce the observational global mean temperature anomalies of the last 150 years.
We argue that making ESMs automatically differentiable has huge potential to advance ESMs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Earth System Models (ESMs) are the primary tools for investigating future
Earth system states at time scales from decades to centuries, especially in
response to anthropogenic greenhouse gas release. State-of-the-art ESMs can
reproduce the observational global mean temperature anomalies of the last 150
years. Nevertheless, ESMs need further improvements, most importantly regarding
(i) the large spread in their estimates of climate sensitivity, i.e., the
temperature response to increases in atmospheric greenhouse gases, (ii) the
modeled spatial patterns of key variables such as temperature and
precipitation, (iii) their representation of extreme weather events, and (iv)
their representation of multistable Earth system components and their ability
to predict associated abrupt transitions. Here, we argue that making ESMs
automatically differentiable has huge potential to advance ESMs, especially
with respect to these key shortcomings. First, automatic differentiability
would allow objective calibration of ESMs, i.e., the selection of optimal
values with respect to a cost function for a large number of free parameters,
which are currently tuned mostly manually. Second, recent advances in Machine
Learning (ML) and in the amount, accuracy, and resolution of observational data
promise to be helpful with at least some of the above aspects because ML may be
used to incorporate additional information from observations into ESMs.
Automatic differentiability is an essential ingredient in the construction of
such hybrid models, combining process-based ESMs with ML components. We
document recent work showcasing the potential of automatic differentiation for
a new generation of substantially improved, data-informed ESMs.
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