Abstract
Abstract. We developed a time-dependent dust source map for the NMME Dust Regional Atmospheric Model (DREAM v1.0) based on the satellite MODIS Normalized Difference Vegetation Index (NDVI). Areas with NDVI <0.1 are classified as active dust sources. The updated modeling system is tested for dust emission capabilities over SW Asia using a mesoscale model grid increment of 0.1∘×0.1∘ for a period of 1 year (2016). Our results indicate significant deviations in simulated aerosol optical depths (AODs) compared to the static dust source approach and general increase in dust loads over the selected domain. Comparison with MODIS AOD indicates a more realistic spatial distribution of dust in the dynamic source simulations compared to the static dust sources approach. The modeled AOD bias is improved from −0.140 to 0.083 for the case of dust events (i.e., for AOD >0.25) and from −0.933 to −0.424 for dust episodes with AOD >1. This new development can be easily applied to other time periods, models, and different areas worldwide for a local fine tuning of the parameterization and assessment of its performance.
Highlights
The importance of natural particles, namely desert dust, in the weather and climate has been underlined in a great number of studies
In this study we present the development of a dynamic dust source map for implementation in NMME-DREAM v1.0 over the Arabian Peninsula and the greater areas of the Middle East, SW Asia, and NE Africa
The major dust sources worldwide are located in permanent deserts where the Normalized Difference Vegetation Index (NDVI) is almost always < 0.1 (e.g., Bodélé Depression, Gobi, Arabian Desert), the dynamical scaling of dust emissions presented here can be important for providing up-to-date evidence of active dust sources over nonpermanent deserts
Summary
The importance of natural particles, namely desert dust, in the weather and climate has been underlined in a great number of studies. Dust is a climatic regulator, as it modifies extensively the radiative balance of the atmospheric column (e.g., Torge et al, 2011; Spyrou et al, 2013; Mahowald et al, 2014). At the same time dust aerosols modify the atmospheric water content (Spyrou, 2018), the way clouds are formed by acting as cloud condensation nuclei (CCN) and ice nuclei (IN), and the precipitation process (Kumar et al, 2011; Solomos et al, 2011; Nickovic et al, 2016). Particles injected into the atmosphere from arid soils, under favorable
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