Retrieving the global distribution of the threshold of wind erosion from satellite data and implementing it into the Geophysical Fluid Dynamics Laboratory land-atmosphere model (GFDL AM4.0/LM4.0)

Bing Pu, Paul Ginoux, Huan Guo, N. Christina Hsu, John Kimball, Beatrice Marticorena, Sergey Malyshev, Vaishali Naik, Norman T. O'Neill, Carlos Pérez García-Pando, Juliette Paireau, Joseph M. Prospero, Elena Shevliakova, Ming Zhao

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Dust emission is initiated when surface wind velocities exceed the threshold of wind erosion. Many dust models used constant threshold values globally. Here we use satellite products to characterize the frequency of dust events and land surface properties. By matching this frequency derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products with surface winds, we are able to retrieve a climatological monthly global distribution of the wind erosion threshold (Vthreshold) over dry and sparsely vegetated surfaces. This monthly two-dimensional threshold velocity is then implemented into the Geophysical Fluid Dynamics Laboratory coupled land-atmosphere model (AM4.0/LM4.0). It is found that the climatology of dust optical depth (DOD) and total aerosol optical depth, surface PM10 dust concentrations, and the seasonal cycle of DOD are better captured over the "dust belt" (i.e., northern Africa and the Middle East) by simulations with the new wind erosion threshold than those using the default globally constant threshold. The most significant improvement is the frequency distribution of dust events, which is generally ignored in model evaluation. By using monthly rather than annual mean Vthreshold, all comparisons with observations are further improved. The monthly global threshold of wind erosion can be retrieved under different spatial resolutions to match the resolution of dust models and thus can help improve the simulations of dust climatology and seasonal cycles as well as dust forecasting.

Original languageEnglish
Pages (from-to)55-81
Number of pages27
JournalAtmospheric Chemistry and Physics
Volume20
Issue number1
DOIs
StatePublished - Jan 3 2020

Funding

This research has been supported by NASA (grants no. NNH14ZDA001N-ACMAP and NNH16ZDA001NMAP). Acknowledgements. This research is supported by NOAA and Princeton University’s Cooperative Institute for Climate Science and NASA. The authors thank Veronica Chan and Hyeyum Shin for their helpful comments on the early version of this paper and Sophie Vandenbussche for her valuable suggestions. The helpful comments from two anonymous reviewers and the co-editor improved the paper. We also thank the AERONET program for establishing and maintaining the sun photometer sites used in this study and the IMPROVE network for the data. IMPROVE is a collaborative association of state, tribal, and federal agencies and international partners. The US Environmental Protection Agency is the primary funding source, with contracting and research support from the National Park Service. The Air Quality Group at the University of California, Davis is the central analytical laboratory, with ion analysis provided by the Research Triangle Institute and carbon analysis provided by the Desert Research Institute.

FundersFunder number
National Aeronautics and Space AdministrationNNH14ZDA001N-ACMAP, NNH16ZDA001NMAP
National Oceanic and Atmospheric Administration
Princeton University
773051

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