Subregional inversion of North African dust sources
Author
dc.contributor.author
Escribano, Jerónimo
Author
dc.contributor.author
Boucher, Olivier
Author
dc.contributor.author
Chevallier, Frederic
Author
dc.contributor.author
Huneeus Lagos, Nicolás
Admission date
dc.date.accessioned
2016-12-28T21:18:41Z
Available date
dc.date.available
2016-12-28T21:18:41Z
Publication date
dc.date.issued
2016
Cita de ítem
dc.identifier.citation
Journal of Geophysical Research-Atmospheres. Volumen: 121 Número: 14 Páginas: 8549-8566
es_ES
Identifier
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10.1002/2016JD025020
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/142186
Abstract
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The emission of mineral dust aerosols in arid and semiarid regions is a complex process whose representation in atmospheric models remains crude, due to insufficient knowledge about the aerosol lifting process itself, the lack of global data on soil characteristics, and the impossibility for the models to resolve the fine-scale variability in the wind field that drives some of the dust events. As a result, there are large uncertainties in the total emission flux of mineral dust, its natural variability at various timescales, and the possible contribution from anthropogenic land use changes. This work aims for estimating dust emissions and reduces their uncertainty over the Sahara desert and the Arabian Peninsulathe largest dust source region of the globe. We use a data assimilation approach to constrain dust emission fluxes at a monthly resolution for 18 subregions. The Moderate Resolution Imaging Spectroradiometer satellite-derived aerosol optical depth is assimilated in a regional configuration of a general circulation model coupled to an aerosol model. We describe this data assimilation system and apply it for 1year, resulting in a total mineral dust emissions flux estimate of 2900Tgyr(-1) over the Sahara desert and the Arabian Peninsula for the year 2006. The analysis field of aerosol optical depth shows an improved fit relative to independent Aerosol Robotic Network measurements as compared to the model prior field.