Preprints
https://doi.org/10.5194/gmd-2020-434
https://doi.org/10.5194/gmd-2020-434

Submitted as: development and technical paper 17 Feb 2021

Submitted as: development and technical paper | 17 Feb 2021

Review status: this preprint is currently under review for the journal GMD.

Climate model-informed deep learning of global soil moisture distribution

Klaus Klingmüller1 and Jos Lelieveld1,2 Klaus Klingmüller and Jos Lelieveld
  • 1Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, Germany
  • 2The Cyprus Institute, P.O. Box 27456, 1645 Nicosia, Cyprus

Abstract. We present a deep neural network (DNN) that produces accurate predictions of observed surface soil moisture, based on 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 parametrisation 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, 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.

Klaus Klingmüller and Jos Lelieveld

Status: open (until 14 Apr 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Klaus Klingmüller and Jos Lelieveld

Klaus Klingmüller and Jos Lelieveld

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Short summary
Soil moisture is of great importance for weather and climate. We present a machine learning model that produces accurate predictions of satellite-observed surface soil moisture, based on meteorological data from a climate model. It can be used as soil moisture parametrisation in climate models and to produce comprehensive global soil moisture datasets. Moreover, it may motivate similar applications of machine learning in climate science.