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Identifying Errors in Dust Models from Data Assimilation over Northern Africa

Pope, R.; Marsham, J. H.; Knippertz, P. ORCID iD icon; Brooks, M.; Roberts, A. J.

Abstract (englisch):

Airborne mineral dust is an important component of the Earth system (e.g. radiation balance, cloud microphysics). It is also relevant for many forecast applications (e.g. air quality) and is increasingly being 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 data assimilation increments (DAI) from MODIS AODs over northern Africa in the Met Office global forecast model. Comparisons with unassimilated AERONET data from the region show that assimilation improves dust forecasts. We find that the model underestimates dust AOD under light winds and overestimates under strong winds. This is consistent with (sub-) mesoscale processes lifting dust in reality, but being missed by the model. Dust is over-predicted in the Sahara and under-predicted in the Sahel, which is potentially linked to the model's land surface. Using satellite observations of lighting and rainfall as a proxy for moist convection, we show that haboobs (cold pool outflows from moist convection) are an important dust source in reality, but the associated dust is missing in the model, since the parameterized convection in the model fails to represent haboobs. ... mehr

Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung Troposphärenforschung (IMKTRO)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Vortrag
Publikationsjahr 2016
Sprache Englisch
Identifikator KITopen-ID: 1000064934
HGF-Programm 12.01.02 (POF III, LK 01) Proc.res.f.multisc.predictab.of weather
Veranstaltung EGU General Assembly, Vienna, Austria, 17–22 April 2016
Bemerkung zur Veröffentlichung EGU2016-5527
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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