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Identifying errors in dust models from data assimilation

Pope, R. J.; Marsham, J. H.; Knippertz, P.; Brooks, M. E.; Roberts, A. J.

Abstract (englisch): Airborne mineral dust is an important component of the Earth system and is increasingly predicted prognostically in weather and climate models. The recent development of data assimilation for remotely sensed aerosol optical depths (AODs) into models offers a new opportunity to better understand the characteristics and sources of model error. Here we examine assimilation increments from Moderate Resolution Imaging Spectroradiometer AODs over northern Africa in the Met Office global forecast model. The model underpredicts (overpredicts) dust in light (strong) winds, consistent with (submesoscale) mesoscale processes lifting dust in reality but being missed by the model. Dust is overpredicted in the Sahara and underpredicted in the Sahel. Using observations of lighting and rain, we show that haboobs (cold pool outflows from moist convection) are an important dust source in reality but are badly handled by the model's convection scheme. The approach shows promise to serve as a useful framework for future model development.

Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung - Forschungsbereich Troposphäre (IMK-TRO)
Publikationstyp Zeitschriftenaufsatz
Jahr 2016
Sprache Englisch
Identifikator DOI: 10.1002/2016GL070621
ISSN: 0094-8276
URN: urn:nbn:de:swb:90-593255
KITopen ID: 1000059325
HGF-Programm 12.01.02; LK 01
Erschienen in Geophysical research letters
Band 43
Heft 17
Seiten 9270-9279
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