Abstract
Abstract. In this study, we describe the development of the aerosol optical depth (AOD) assimilation module in the chemistry transport model (CTM) MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle). Our goal is to assimilate the spatially averaged 2-D column AOD data from the National Aeronautics and Space Administration (NASA) Moderate-resolution Imaging Spectroradiometer (MODIS) instrument, and to estimate improvements in a 3-D CTM assimilation run compared to a direct model run. Our assimilation system uses 3-D-FGAT (first guess at appropriate time) as an assimilation method and the total 3-D aerosol concentration as a control variable. In order to have an extensive validation dataset, we carried out our experiment in the northern summer of 2012 when the pre-ChArMEx (CHemistry and AeRosol MEditerranean EXperiment) field campaign TRAQA (TRAnsport à longue distance et Qualité de l'Air dans le bassin méditerranéen) took place in the western Mediterranean basin. The assimilated model run is evaluated independently against a range of aerosol properties (2-D and 3-D) measured by in situ instruments (the TRAQA size-resolved balloon and aircraft measurements), the satellite Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument and ground-based instruments from the Aerosol Robotic Network (AERONET) network. The evaluation demonstrates that the AOD assimilation greatly improves aerosol representation in the model. For example, the comparison of the direct and the assimilated model run with AERONET data shows that the assimilation increased the correlation (from 0.74 to 0.88), and reduced the bias (from 0.050 to 0.006) and the root mean square error in the AOD (from 0.12 to 0.07). When compared to the 3-D concentration data obtained by the in situ aircraft and balloon measurements, the assimilation consistently improves the model output. The best results as expected occur when the shape of the vertical profile is correctly simulated by the direct model. We also examine how the assimilation can influence the modelled aerosol vertical distribution. The results show that a 2-D continuous AOD assimilation can improve the 3-D vertical profile, as a result of differential horizontal transport of aerosols in the model.
Highlights
In recent years, the role of aerosols in the climate system has been better determined (Boucher et al, 2013)
We describe the development of the aerosol optical depth (AOD) assimilation module in the chemistry transport model (CTM) MOCAGE (e.g. Josse et al, 2004; Sicet al., 2015, Modèle de Chimie Atmosphérique à Grande Echelle)
The scatter plot of all Aerosol Robotic Network (AERONET) observations (Fig. 7) reinforces the conclusion that the assimilation model run reduces the bias in the AOD field of the direct model run and significantly improves the statistical parameters
Summary
The role of aerosols in the climate system has been better determined (Boucher et al, 2013). Efforts to accurately represent aerosols in models increased (for example Kanakidou et al, 2005; Textor et al, 2006; Fuzzi et al, 2006; Vignati et al, 2010; Lee et al, 2011; Boucher et al, 2013; Sicet al., 2015). The development of aerosol modelling enables us to better understand how aerosols affect atmospheric chemistry, air quality, climate, aviation, visibility, radiative budget and clouds. Sicet al.: Aerosol data assimilation in the CTM MOCAGE and chemistry has led to a large diversity of parametrizations which with other uncertainties (e.g. dynamics, emissions, initial conditions) produce large differences in the aerosol model results (Mahowald et al, 2003; Kinne et al, 2006; Textor et al, 2006; Shindell et al, 2013)
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