Identifying errors in dust models from data assimilation. Issue 17 (3rd September 2016) - Pope, R. J.; Marsham, J. H.; Knippertz, P.; Brooks, M. E.; Roberts, A. J.
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. Key Points: Increments in AOD from data assimilation reveal sources of dust model error Dust underpredicted/overpredicted in light/strong winds and missed from convection Dust overpredicted/underpredicted in Sahara/Sahel