Abstract

Abstract. The seasonal cycle and optical properties of mineral dust aerosols in northern Africa were simulated for the period from 2006 to 2010 using the numerical atmospheric model ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) coupled to the surface scheme SURFEX (SURFace EXternalisée). The particularity of the simulations is that the major physical processes responsible for dust emission and transport, as well as radiative effects, are taken into account on short timescales and at mesoscale resolution. The aim of these simulations is to quantify the dust emission and deposition, locate the major areas of dust emission and establish a climatology of aerosol optical properties in northern Africa. The mean monthly aerosol optical thickness (AOT) simulated by ALADIN is compared with the AOTs derived from the standard Dark Target (DT) and Deep Blue (DB) algorithms of the Aqua-MODIS (MODerate resolution Imaging Spectroradiometer) products over northern Africa and with a set of sun photometer measurements located at Banizoumbou, Cinzana, Soroa, Mbour and Cape Verde. The vertical distribution of dust aerosol represented by extinction profiles is also analysed using CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations. The annual dust emission simulated by ALADIN over northern Africa is 878 Tg year−1. The Bodélé Depression appears to be the main area of dust emission in northern Africa, with an average estimate of about 21.6 Tg year−1. The simulated AOTs are in good agreement with satellite and sun photometer observations. The positions of the maxima of the modelled AOTs over northern Africa match the observed positions, and the ALADIN simulations satisfactorily reproduce the various dust events over the 2006–2010 period. The AOT climatology proposed in this paper provides a solid database of optical properties and consolidates the existing climatology over this region derived from satellites, the AERONET network and regional climate models. Moreover, the 3-D distribution of the simulated AOTs also provides information about the vertical structure of the dust aerosol extinction.

Highlights

  • Mineral dust aerosol dominates the aerosol mass over some continental regions, with relatively higher concentrations accounting for about 35 % of the total aerosol mass (IPCC, 2013)

  • The aerosol optical thickness (AOT) climatology proposed in this paper provides a solid database of optical properties and consolidates the existing climatology over this region derived from satellites, the Aerosol Robotic Network (AERONET) network and regional climate models

  • Annual dust emissions vary from 843 Tg in 2010 to 924 Tg in 2008

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Summary

Introduction

Mineral dust aerosol dominates the aerosol mass over some continental regions, with relatively higher concentrations accounting for about 35 % of the total aerosol mass (IPCC, 2013). A recent comparative study (Bréon et al, 2011) between AOTs derived from POLDER (Polarization and Directionality of Earth’s Reflectances), MODIS, MERIS (Medium Resolution Imaging Spectrometer), SEVIRI (Spinning Enhanced Visible and Infrared Imager) and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) shows that MODIS has the most reliable estimate of total AOT over ocean and land These data encompass the collective contributions of maritime, continental and desert dust aerosols. Data and results from simulations using the ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) model over northern Africa from 2006 to 2010 are presented This model takes into account the different physical processes responsible for the emission, transport and deposition of dust.

Model description and dust transport
Dust emission model
Ground-based measurement
Satellite data
Annual dust emissions and interannual variability
Seasonality of the dust emissions
Dry deposition
Wet deposition
Monthly variation in aerosol optical thickness
Monthly variation in extinction coefficients
Comparison of simulation outputs to Aqua-MODIS observations
Comparison with AERONET measurements
Comparison to surface dust concentration measurements
Comparison to CALIOP observations
Conclusions

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