Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Climate Model Dynamics: Offline versus Online
  Performance   Climate models are complicated software systems that approximate atmospheric
and oceanic fluid mechanics at a coarse spatial resolution. Typical climate
forecasts only explicitly resolve processes larger than 100 km and approximate
any process occurring below this scale (e.g. thunderstorms) using so-called
parametrizations. Machine learning could improve upon the accuracy of some
traditional physical parametrizations by learning from so-called global
cloud-resolving models. We compare the performance of two machine learning
models, random forests (RF) and neural networks (NNs), at parametrizing the
aggregate effect of moist physics in a 3 km resolution global simulation with
an atmospheric model. The NN outperforms the RF when evaluated offline on a
testing dataset. However, when the ML models are coupled to an atmospheric
model run at 200 km resolution, the NN-assisted simulation crashes with 7 days,
while the RF-assisted simulations remain stable. Both runs produce more
accurate weather forecasts than a baseline configuration, but globally averaged
climate variables drift over longer timescales.

34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph