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)

Abstract

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.

Assessing the contribution of the ENSO and MJO to Australian dust activity based on satellite- and ground-based observations

“…The deposition of transported dust over ocean affects ocean biogeochemistry through changes to the iron supply (Gabric et al, 2010;Jickells et al, 2005). Australia's arid and semiarid regions provide a key supply of iron to the Southern Ocean and Antarctica by dust, during the present (Li et al, 2010), glacial (Lamy et al, 2014), and interglacial (Revel-Rolland et al, 2006) periods. Since most of the Southern Ocean is iron-limited (Sunda and Huntsman, 1997), the transport and deposition of Australian dust affect its productivity and carbon uptake (Boyd et al, 2004;Gabric et al, 2002).…”

Mineral dust cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) Version 2.0

“…based on the theoretical expression for u * t0 (d i ) from Shao and Lu (2000). In the G01-U implementation, we use a fixed minimal threshold of u t d0 = 5 m s −1 , a typical 10 m wind lower limit for dust emission under favorable land-surface conditions (Kurosaki and Mikami, 2007;Pu et al, 2020). To obtain a more realistic PSD at emission in combination with a dust-particle-size-independent entrainment threshold, we replace the PSD described in Ginoux et al (2001) with that of Kok (2011a) (see Sect.…”

Assessing the contribution of ENSO and MJO to Australian dust activity based on satellite and ground-based observations

“…Following Pu et al (2020), daily DOD is retrieved from collection 6.1, level 2 MODIS Deep Blue aerosol products Sayer et al, 2013), including aerosol optical depth (AOD), single-scattering albedo (ω), and the Ångström exponent (α). All the daily variables are first interpolated to a 0.1˚ x 0.1˚ grid using the algorithm described by Ginoux et al (2010).…”

“…Details about the retrieval process and estimated errors are summarized by Pu and Ginoux (2018a). MODIS DOD products have been widely used for the identification and characterization of dust sources (Baddock et al, 2009(Baddock et al, , 2016Ginoux et al, 2012), as well as examination of variations in regional and global dustiness (Pu et al, 2019(Pu et al, , 2020Ginoux, 2017, 2018a). Following the recommendation from Baddock et al (2016) and previous applications of MODIS DOD (Pu et al, 2019(Pu et al, , 2020Ginoux, 2017, 2018a), here we use DOD with a low-quality flag of QA = 1, under the assumptions that 1) dust sources are better detected using DOD with a low-quality flag, and 2) retrieved aerosol products are poorly flagged over dust source regions.…”