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Climate-model-informed deep learning of global soil moisture distribution

Abstract. We present a deep neural network (DNN) that produces accurate predictions of observed surface soil moisture, applying meteorological data from a
climate model. The network was trained on daily satellite retrievals of soil moisture from the European Space Agency (ESA) Climate Change Initiative
(CCI). The predictors precipitation, temperature and humidity were simulated with the ECHAM/MESSy atmospheric chemistry–climate model (EMAC). Our
evaluation shows that predictions of the trained DNN are highly correlated with the observations, both spatially and temporally, and free of
bias. This offers an alternative for parameterisation schemes in climate models, especially in simulations that use but may not focus on soil
moisture, which we illustrate with the threshold wind speed for mineral dust emissions. Moreover, the DNN can provide proxies for missing values in
satellite observations to produce realistic, comprehensive and high-resolution global datasets. As the approach presented here could be similarly used
for other variables and observations, the study is a proof of concept for basic but expedient machine learning techniques in climate modelling,
which may motivate additional applications.
