Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54

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.

Exploring the uncertainties in the aviation soot-cirrus effect

“…Hence, the contribution of cirrus clouds formed in-situ by heterogeneous and/or homogeneous ice nucleation and cirrus clouds resulting from transport of ice from the mixed phase (e.g., via detrainment from convective clouds) should be further explored. The representation of background aerosol like soot and mineral dust was already demonstrated to be quite good by Kaiser et al (2019), Righi et al (2020) and Beer et al (2020). Nevertheless improvements are still possible, especially with the support of in situ measurements.…”

An aerosol classification scheme for global simulations using the K-means machine learning method

“…As a basis for aerosol classification in the present study, we analysed one of the global model simulations of Beer et al (2020) performed with the global aerosol model EMAC-MADE3. MADE3 simulates nine different aerosol species (sulphate, ammonium, nitrate, the sea salt species sodium and chloride, BC, POM, mineral dust and aerosol water).…”

“…Kaiser et al (2019) further demonstrated the good agreement of MADE3 simulated BC, POM, gaseous species and particle number concentrations with various observations. Beer et al (2020) extended the model setup of Kaiser et al (2019) by including an online parametrization for wind-driven dust emissions (Tegen et al, 2002). The authors performed five model experiments for the time period 2000-2013 applying different horizontal and vertical model resolutions.…”

“…The model results were evaluated by comparison against observational data from the AERONET station network (Holben et al 1998(Holben et al , 2001) and aircraft-based measurements from the SALTRACE field campaign (Weinzierl et al 2017). The comparison in Beer et al (2020) showed that a specific configuration (T63L31Tegen) outperforms the others thanks to its higher resolution and https://doi.org/10.5194/gmd-2021-191 Preprint. Discussion started: 23 July 2021 c Author(s) 2021.…”

“…For the chosen simulation Beer et al (2020) applied EMAC in nudged mode, that is, model dynamics were constrained using ECMWF reanalysis data (Dee et al 2011) including wind divergence and vorticity, temperature, and logarithm of the surface pressure for the years 1999 to 2013. 1999 was the spin-up year and is therefore not included in the analysis.…”