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Seasonal dynamics of threshold friction velocity and dust emission in Central Asia

An improved model representation of mineral dust cycle is critical to reducing the uncertainty of dust-induced environmental and climatic impact. Here we present a mesoscale model study of the seasonal dust activity in the semiarid drylands of Central Asia, focusing on the effects of wind speed, soi...

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Autores principales: Xi, Xin, Sokolik, Irina N
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Ltd 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4672962/
https://www.ncbi.nlm.nih.gov/pubmed/26690836
http://dx.doi.org/10.1002/2014JD022471
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author Xi, Xin
Sokolik, Irina N
author_facet Xi, Xin
Sokolik, Irina N
author_sort Xi, Xin
collection PubMed
description An improved model representation of mineral dust cycle is critical to reducing the uncertainty of dust-induced environmental and climatic impact. Here we present a mesoscale model study of the seasonal dust activity in the semiarid drylands of Central Asia, focusing on the effects of wind speed, soil moisture, surface roughness heterogeneity, and vegetation phenology on the threshold friction velocity (u(*t)) and dust emission during the dust season of 1 March to 31 October 2001. The dust model WRF-Chem-DuMo allows us to examine the uncertainties in seasonal dust emissions due to the selection of dust emission scheme and soil grain size distribution data. To account for the vegetation effects on the u(*t), we use the Moderate Resolution Imaging Spectroradiometer monthly normalized difference vegetation index to derive the dynamic surface roughness parameters required by the physically based dust schemes of Marticorena and Bergametti (1995, hereinafter MB) and Shao et al. (1996, hereinafter Shao). We find the springtime u(*t) is strongly enhanced by the roughness effects of temperate steppe and desert ephemeral plants and, to less extent, the binding effects of increased soil moisture. The u(*t) decreases as the aboveground biomass dies back and soil moisture depletes during summer. The u(*t) dynamics determines the dust seasonality by causing more summer dust emission, despite a higher frequency of strong winds during spring. Due to the presence of more erodible materials in the saltation diameter range of 60–200 µm, the dry-sieved soil size distribution data lead to eight times more season-total dust emission than the soil texture data, but with minor differences in the temporal distribution. On the other hand, the Shao scheme produces almost the same amount of season-total dust emission as the MB scheme, but with a strong shift toward summer due to the strong sensitivity of the u(*t) to vegetation. By simply averaging the MB and Shao model experiments, we obtain a mean estimate (Exp_mean) of season-total dust emission of 255.6 Mt (megaton), of which 26.8%, 50.4%, and 22.8% are produced in spring (March-April-May), summer (June-July-August), and autumn (September-October), respectively. The Exp_mean estimate identifies the Ustyurt Plateau, dried seabed of Aral Sea (called Aralkum), Caspian Sea coast, and loess deserts as the strongest dust source areas in Central Asia. The spatial distribution and seasonality of the Exp_mean estimate are in general agreement with ground station dusty weather observations and satellite aerosol optical depth and absorbing aerosol index products. Compared to Cakmur et al. (2006), the Exp_mean estimate suggests Central Asia contributes 10–17% to the global dust emission in 2001. KEY POINTS: The WRF-Chem-DuMo model is used to study dust seasonality in Central Asia . An accurate representation of u(*t) is critical for dust seasonality . Multiexperiment mean dust emission estimate agrees with observations ;
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spelling pubmed-46729622015-12-16 Seasonal dynamics of threshold friction velocity and dust emission in Central Asia Xi, Xin Sokolik, Irina N J Geophys Res Atmos Research Articles An improved model representation of mineral dust cycle is critical to reducing the uncertainty of dust-induced environmental and climatic impact. Here we present a mesoscale model study of the seasonal dust activity in the semiarid drylands of Central Asia, focusing on the effects of wind speed, soil moisture, surface roughness heterogeneity, and vegetation phenology on the threshold friction velocity (u(*t)) and dust emission during the dust season of 1 March to 31 October 2001. The dust model WRF-Chem-DuMo allows us to examine the uncertainties in seasonal dust emissions due to the selection of dust emission scheme and soil grain size distribution data. To account for the vegetation effects on the u(*t), we use the Moderate Resolution Imaging Spectroradiometer monthly normalized difference vegetation index to derive the dynamic surface roughness parameters required by the physically based dust schemes of Marticorena and Bergametti (1995, hereinafter MB) and Shao et al. (1996, hereinafter Shao). We find the springtime u(*t) is strongly enhanced by the roughness effects of temperate steppe and desert ephemeral plants and, to less extent, the binding effects of increased soil moisture. The u(*t) decreases as the aboveground biomass dies back and soil moisture depletes during summer. The u(*t) dynamics determines the dust seasonality by causing more summer dust emission, despite a higher frequency of strong winds during spring. Due to the presence of more erodible materials in the saltation diameter range of 60–200 µm, the dry-sieved soil size distribution data lead to eight times more season-total dust emission than the soil texture data, but with minor differences in the temporal distribution. On the other hand, the Shao scheme produces almost the same amount of season-total dust emission as the MB scheme, but with a strong shift toward summer due to the strong sensitivity of the u(*t) to vegetation. By simply averaging the MB and Shao model experiments, we obtain a mean estimate (Exp_mean) of season-total dust emission of 255.6 Mt (megaton), of which 26.8%, 50.4%, and 22.8% are produced in spring (March-April-May), summer (June-July-August), and autumn (September-October), respectively. The Exp_mean estimate identifies the Ustyurt Plateau, dried seabed of Aral Sea (called Aralkum), Caspian Sea coast, and loess deserts as the strongest dust source areas in Central Asia. The spatial distribution and seasonality of the Exp_mean estimate are in general agreement with ground station dusty weather observations and satellite aerosol optical depth and absorbing aerosol index products. Compared to Cakmur et al. (2006), the Exp_mean estimate suggests Central Asia contributes 10–17% to the global dust emission in 2001. KEY POINTS: The WRF-Chem-DuMo model is used to study dust seasonality in Central Asia . An accurate representation of u(*t) is critical for dust seasonality . Multiexperiment mean dust emission estimate agrees with observations ; John Wiley & Sons, Ltd 2015-02-27 2015-02-21 /pmc/articles/PMC4672962/ /pubmed/26690836 http://dx.doi.org/10.1002/2014JD022471 Text en ©2015. The Authors. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Xi, Xin
Sokolik, Irina N
Seasonal dynamics of threshold friction velocity and dust emission in Central Asia
title Seasonal dynamics of threshold friction velocity and dust emission in Central Asia
title_full Seasonal dynamics of threshold friction velocity and dust emission in Central Asia
title_fullStr Seasonal dynamics of threshold friction velocity and dust emission in Central Asia
title_full_unstemmed Seasonal dynamics of threshold friction velocity and dust emission in Central Asia
title_short Seasonal dynamics of threshold friction velocity and dust emission in Central Asia
title_sort seasonal dynamics of threshold friction velocity and dust emission in central asia
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4672962/
https://www.ncbi.nlm.nih.gov/pubmed/26690836
http://dx.doi.org/10.1002/2014JD022471
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