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https://doi.org/10.5194/acp-2020-435
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-435
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  22 Jul 2020

22 Jul 2020

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This preprint is currently under review for the journal ACP.

Source backtracking for dust storm emission inversion using adjoint method: case study of northeast China

Jianbing Jin1,2, Arjo Segers3, Hong Liao1, Arnold Heemink2, Richard Kranenburg3, and Hai Xiang Lin2 Jianbing Jin et al.
  • 1Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center ofAtmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China
  • 2Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
  • 3TNO, Department of Climate, Air and Sustainability, Utrecht, the Netherlands

Abstract. Emission inversion using data assimilation fundamentally relies on having the correct assumptions on the emission background error covariance. A perfect covariance accounts for the uncertainty based on prior knowledge, and is able to explain differences between model simulations and observations. In practice, emission uncertainties are constructed empirically, hence a partially unrepresentative covariance is unavoidable. Concerning its complex parameterization, dust emissions are a typical example where the uncertainty could be induced from many underlying inputs, e.g., information on soil composition and moisture, landcover and erosive wind velocity, and these can hardly be taken into account together. This paper describes how an adjoint model can be used to detect errors in the emission uncertainty assumptions. This adjoint based sensitivity method could serve as a supplement of a data assimilation inverse modeling system to trace back the error sources, in case that large observation-minus-simulation residues remain after assimilation based on empirical background covariance.

The method follows on application of a data assimilation emission inversion for an extreme severe dust storm over East Asia (Jin et al., 2019b). The assimilation system successfully resolved observation-minus-simulation errors using satellite AOD observations in most of the dust-affected regions. However, a large underestimation of dust in northeast China remained despite the fact the assimilated measurements indicated severe dust plumes there. An adjoint implementation of our dust simulation model is then used to detect the most likely source region for these unresolved dust loads. The backward modeling points to the Horqin desert as source region, which was indicated as a non-source region by the existing emission scheme. The reference emission and uncertainty are then reconstructed over the Horqin desert by assuming higher surface erodibility. After the emission reconstruction, the emission inversion is performed again and the posterior dust simulations and reality are now in much closer harmony. Based on our results, it is advised that emission sources in dust transport models include Horqin desert as a more active source region.

Jianbing Jin et al.

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Jianbing Jin et al.

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