title: Identifying errors in dust models from data assimilation.
abstract: 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.
authors.affiliation: Institute for Atmospheric and Climate Science University of Leeds Leeds UK; National Centre for Earth Observation University of Leeds Leeds UK.
authors.forename: R J
authors.initials: RJ
authors.lastname: Pope
authors.affiliation: Institute for Atmospheric and Climate Science University of Leeds Leeds UK; National Centre for Atmospheric Science University of Leeds Leeds UK.
authors.forename: J H
authors.initials: JH
authors.lastname: Marsham
authors.affiliation: Institute of Meteorology and Climate Research Karlsruhe Institute of Technology Karlsruhe Germany.
authors.forename: P
authors.identifier: 0000-0001-9856-619X
authors.initials: P
authors.lastname: Knippertz
authors.affiliation: Met Office Exeter UK.
authors.forename: M E
authors.initials: ME
authors.lastname: Brooks
authors.affiliation: Institute for Atmospheric and Climate Science University of Leeds Leeds UK.
authors.forename: A J
authors.initials: AJ
authors.lastname: Roberts
keywords: aerosol optical depth; data assimilation increments; dust forecasts; haboobs