Preprints
https://doi.org/10.5194/gmd-2022-232
https://doi.org/10.5194/gmd-2022-232
Submitted as: development and technical paper
29 Sep 2022
Submitted as: development and technical paper | 29 Sep 2022
Status: this preprint is currently under review for the journal GMD.

Data-driven aeolian dust emission scheme for climate modelling, evaluated with EMAC 2.54

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. Aeolian dust has significant impacts on climate, public health, infrastructure and ecosystems. Assessing dust concentrations and these impacts is challenging because the emissions depend on many environmental factors and can vary greatly with meteorological conditions. We present a data-driven aeolian dust scheme that combines machine learning components and physical equations to predict atmospheric dust concentrations and quantify the sources. The numerical scheme was trained to reproduce dust aerosol optical depth retrievals by the Infrared Atmospheric Sounding Interferometer on board the MetOp-A satellite. The input parameters included meteorological variables from the fifth generation atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts. The trained dust scheme can be applied as an emission submodel, to be used in climate and Earth system models, which is reproducibly derived from observational data so that a priori assumptions and manual parameter tuning can be largely avoided. We compared the trained emission submodel to a state-of-the-art emission parametrisation, showing that it substantially improves the representation of aeolian dust in the global atmospheric chemistry-climate model EMAC.

Klaus Klingmüller and Jos Lelieveld

Status: open (until 21 Dec 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-232', Anonymous Referee #1, 19 Oct 2022 reply

Klaus Klingmüller and Jos Lelieveld

Klaus Klingmüller and Jos Lelieveld

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Short summary
Desert dust has significant impacts on climate, public health, infrastructure and ecosystems. The impact assessment requires numerical predictions, which are challenging because the dust emissions are not well known. We present a novel approach using satellite observations and machine learning to more accurately estimate the emissions and to improve the model simulations.