Abstract

Abstract. It was hypothesized that using mineral dust emission climatologies in global chemistry climate models (GCCMs), i.e. prescribed monthly-mean dust emissions representative of a specific year, may lead to misrepresentations of strong dust burst events. This could result in a negative bias of model dust concentrations compared to observations for these episodes. Here, we apply the aerosol microphysics submodel MADE3 (Modal Aerosol Dynamics model for Europe, adapted for global applications, third generation) as part of the ECHAM/MESSy Atmospheric Chemistry (EMAC) general circulation model. We employ two different representations of mineral dust emissions for our model simulations: (i) a prescribed monthly-mean climatology of dust emissions representative of the year 2000 and (ii) an online dust parametrization which calculates wind-driven mineral dust emissions at every model time step. We evaluate model results for these two dust representations by comparison with observations of aerosol optical depth from ground-based station data. The model results show a better agreement with the observations for strong dust burst events when using the online dust representation compared to the prescribed dust emissions setup. Furthermore, we analyse the effect of increasing the vertical and horizontal model resolution on the mineral dust properties in our model. We compare results from simulations with T42L31 and T63L31 model resolution (2.8∘×2.8∘ and 1.9∘×1.9∘ in latitude and longitude, respectively; 31 vertical levels) with the reference setup (T42L19). The different model versions are evaluated against airborne in situ measurements performed during the SALTRACE mineral dust campaign (Saharan Aerosol Long-range Transport and Aerosol-Cloud Interaction Experiment, June–July 2013), i.e. observations of dust transported from the Sahara to the Caribbean. Results show that an increased horizontal and vertical model resolution is able to better represent the spatial distribution of airborne mineral dust, especially in the upper troposphere (above 400 hPa). Additionally, we analyse the effect of varying assumptions for the size distribution of emitted dust but find only a weak sensitivity concerning these changes. The results of this study will help to identify the model setup best suited for future studies and to further improve the representation of mineral dust particles in EMAC-MADE3.

Highlights

  • Mineral dust particles can influence the climate system in various ways

  • There, we first describe model results evaluated against AErosol RObotic NETwork (AERONET) station data, showing an improved representation of the temporal variability in mineral dust when applying the Tegen et al (2002) dust parametrization

  • In addition to the MESSy submodels used in that work, the diagnostic submodel S4D (Sampling in 4 Dimensions; Jöckel et al, 2010) is included here in order to extract model output along aircraft trajectories of the flights conducted during the SALTRACE campaign

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Summary

Introduction

Mineral dust particles can influence the climate system in various ways. Atmospheric dust aerosols interact with solar and terrestrial radiation through absorption and scattering, directly changing the Earth’s radiation budget (Boucher et al, 2013). We compare results from simulations using the AeroCom dust climatology with those applying the online Tegen et al (2002) emission scheme with respect to dust aerosol concentrations near source regions and in target regions of long-range transport. Various meteorological model variables are nudged towards ECMWF reanalyses, and transient aerosol emissions are prescribed for the corresponding time period This enables us to directly compare our model results with the observations. 3. There, we first describe model results evaluated against AERONET station data, showing an improved representation of the temporal variability in mineral dust when applying the Tegen et al (2002) dust parametrization.

EMAC setup
The aerosol submodel MADE3
Emission setup
Observational data
Effects of dust emission scheme
Effects of model resolution
Effects of size distribution assumptions
Findings
Conclusions and outlook
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