Mineral dust modelling with MADE3 in EMAC v2.54

Mineral dust particles play an important role in the climate system, by e.g. interacting with solar and terrestrial radiation or facilitating the formation of cloud droplets. Additionally, dust particles can act as very efficient ice nuclei in cirrus clouds. Many Global Chemistry Climate Models (GCCMs) use prescribed monthly mean mineral dust emissions representative of a specific year, based on a climatology. It was hypothesized that using dust emission climatologies may lead to misrepresentations of strong dust burst episodes, resulting in a negative bias of model dust concentrations compared to observations for these 5 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 for our model simulations: i) a prescribed monthly-mean climatology of dust emissions representative of the year 2000; 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 10 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 mineral dust properties in our model. The model is 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. ob15 servations 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. 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. 20


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, thus directly changing the Earth's radiation budget (Boucher et al., 2013).
Estimates of direct radiative forcings by mineral dust are subject to large uncertainties, with global annual net (shortwave + longwave) radiative forcings at the surface having a cooling effect in the range of (−0.5 to −2.0 ) Wm −2 (Choobari et al., 2014). Additionally, mineral dust particles can act as cloud condensation nuclei and ice nuclei, consequently influencing the formation of cloud droplets and ice crystals, resulting in additional climate modifications (e.g., Hendricks et al., 2011;Boucher et al., 2013;Mülmenstädt and Feingold, 2018). These indirect effects of mineral dust on the Earth's radiation budget are even 5 more uncertain than direct radiative forcings and are subject of ongoing research activities (Choobari et al., 2014;Tang et al., 2016;Mülmenstädt and Feingold, 2018). Dust storms also pose significant hazards for global air traffic (e.g., De Villiers and Heerden, 2007) and influence energy production of solar energy power plants (e.g., Rieger et al., 2017). Furthermore, dust particles may have negative implications for human health, e.g. by causing respiratory diseases (Chan et al., 2008;Sajani et al., 2011;Giannadaki et al., 2014). On the other hand, mineral dust provides nutrients such as iron or phosphorus that are essential 10 for the growth of tropical rainforests, as well as oceanic life (Chadwick et al., 1999;Jickells et al., 2005;Nenes et al., 2011;Yu et al., 2015).
To correctly simulate mineral dust in global models, a reliable representation of the particle numbers, the size distribution and the global distribution of dust particles is necessary. As mineral dust is a primary aerosol, dust abundance and distribution in the atmosphere are strongly related to its emissions. In many GCCMs, mineral dust emissions are represented by climatologies, 15 i.e. prescribed monthly mean dust emissions for a specific year (e.g., de Meij et al., 2006;Liu et al., 2007). The AeroCom project (Aerosol Comparison between Observations and Models) led to the development of a global dust emission climatology (Ginoux et al., 2001Dentener et al., 2006), that has been widely used in global modelling studies (e.g. Huneeus et al., 2011). To simplify the description of dust emissions in global models, the climatology prescribes monthly mean emission rates, neglecting the variation of emission fluxes on shorter time scales. However, dust emissions are strongly influenced by 20 meteorology resulting in high temporal variability from day to day (e.g. dust storms). Dust emissions also show large longterm (e.g. year-to-year) variations Banks et al., 2017). The AeroCom dust climatology, however, is representative of the year 2000, which was characterized by relatively low dust emissions . It has been argued that using monthly mean dust climatologies in GCCMs could lead to a misrepresentation of strong dust outbreaks, resulting in a negative bias of model dust concentrations during these episodes compared to observations (Aquila et al., 2011; In this study, we aim to improve the representation of atmospheric mineral dust in the atmospheric chemistry general circulation model EMAC (ECHAM/MESSy Atmospheric Chemistry model; Jöckel et al., 2010Jöckel et al., , 2016 including the MESSy (Modular Earth Submodel System; Jöckel et al., 2010) aerosol microphysics submodel MADE3 (Modal Aerosol Dynamics model for Europe, adapted for global applications, 3rd generation; Kaiser et al., 2014). In previous model studies with MADE3 (or its predecessors) in EMAC, dust emissions were represented by the offline AeroCom dust climatology (Aquila et al., 2011;Righi 5 et al., 2013;Kaiser et al., 2019). We now apply the online dust emission scheme developed by Tegen et al. (2002) to account for highly variable wind-driven dust emissions and strong emission episodes. 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. Additionally, we analyse the effect of different vertical and horizontal model resolutions, as well as the effect of varying the dust size distribution upon emission for the Tegen 10 et al. (2002) dust setup. We analyse the capabilities of these different model setups with special focus on the representation of dust emissions as well as the resulting atmospheric dust distribution and properties. The objective is to improve the representation of mineral dust in the model and to optimize the model setup for future studies concerning, for instance, the effect of heterogeneous ice nucleation induced by ice nucleating particles such as mineral dust. As shown in many laboratory studies, dust particles have indeed the ability to serve as very efficient ice nuclei (e.g. Kanji et al., 2017). The resulting potential of dust 15 to influence ice clouds on the global scale has also been demonstrated by modelling studies (Lohmann and Diehl, 2006;Hoose et al., 2010;Hendricks et al., 2011). As future applications of our model are intended to focus on aerosol effects on ice cloud properties (Righi et al., 2020), the present study is a necessary step towards an improved model setup suitable for this kind of model investigations.
The model results obtained here are evaluated by comparison with different observations, i.e. ground-based remote sensing 20 and airborne in situ measurements. In Kaiser et al. (2019) a thorough evaluation of different aerosol properties simulated with MADE3 as part of EMAC was performed. Here the model evaluation concentrates on measurements specifically related to mineral dust since it is the major target of the model improvements in this study. As a special focus, we compare the model results with data from the SALTRACE campaign, performed during June/July 2013 with observations in Barbados, Puerto Rico and Cabo Verde . SALTRACE aimed to explore the relevant processes associated with the transport of 25 Saharan mineral dust across the Atlantic Ocean and its impacts on clouds and radiation. The Sahara Desert is the largest dust source on Earth providing at least half of the globally emitted dust (Huneeus et al., 2011). Data from the SALTRACE campaign is particularly extensive, including different measurement techniques and instruments. Foci were on dust source regions in the Sahara, dispersion and transformation processes, and long range dust transport towards the Caribbean, making the campaign exceptionally valuable for our model evaluation. We simulate specific episodes of the SALTRACE campaign. For this episodic In our previous studies (Aquila et al., 2011;Righi et al., 2013;Kaiser et al., 2019), a climatological simulation concept was applied instead of modelling a specific episode. There the comparison of long-term model means with short-term measurement episodes led to discrepancies, due to different meteorological situations and emissions. The episodic comparison performed in 35 this study aims to reduce these uncertainties. In addition to the SALTRACE data, we apply long-term observations of aerosol optical depth from AERONET stations (Holben et al., 1998(Holben et al., , 2001 at dust-dominated locations, covering also the SALTRACE episode, in order to evaluate the model's capability to reproduce the temporal variability of airborne mineral dust. The paper is organized as follows. In Sect. 2 we describe the EMAC model, including the different model setups used in this work, as well as the observational data used for model evaluation. Results of the model evaluation are presented in Sect.  The EMAC model is a global numerical chemistry and climate simulation system including various submodels that describe tropospheric and middle atmosphere processes. It uses the second version of MESSy to connect multi-institutional computer codes. The core atmospheric model is the ECHAM5 (5th generation European Centre Hamburg) general circulation model 15 (Roeckner et al., 2006).
In this work we apply EMAC (ECHAM5 version 5.3.02, MESSy version 2.54) in three different resolutions, namely T42L19, T42L31, and T63L31 with spherical truncations of T42 (corresponding to a quadratic Gaussian grid of approx. 2.8 by 2.8 degrees in latitude and longitude) and T63 (approx. 1.9 by 1.9 degrees), respectively, and with 19 or 31 vertical hybrid pressure levels up to 10 hPa. Model timesteps for these resolutions are 30 minutes, 20 minutes, and 12 minutes respectively and the 20 temporal resolution for most simulation output is chosen as 12 hours. The model output for aerosol optical depth (AOD) is generated every hour for comparisons with observations on a daily mean basis.
The EMAC-MADE3 setup used in this work is largely based on the setup described in Kaiser et al. (2019). In addition to the MESSy submodels used in their 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 25 campaign. The S4D submodel interpolates the model output along the track of a moving platform (here an aircraft) online, i.e. during the model simulation, thus facilitating a direct and more accurate comparison of model output and aircraft observations. All simulations discussed in this paper cover the years 1999 to 2013 and were performed in nudged mode, i.e. wind divergence and vorticity, sea surface and land temperature, as well as the logarithm of the surface pressure were relaxed towards ECMWF reanalyses (ERA-Interim) for the corresponding years. The first simulated year (1999) is regarded as a spin-up phase 30 and only the subsequent time period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) is used for model evaluation. A summary and short description of the different simulation setups applied in this study is shown in Table 1.

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These aerosol components are distributed into nine log-normal modes that represent different particle sizes and mixing states. Each of the MADE3 Aitken-, accumulation-and coarse-mode size ranges incorporates three modes for different particle mixing states: particles fully composed of water-soluble components, particles mainly composed of insoluble material (i.e. insoluble particles with only very thin coatings of soluble material), and mixed particles (i.e. soluble material with inclusions of insoluble particles).

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MADE3 simulates the following aerosol processes: gas-particle partitioning of semi-volatile species, particle coagulation, condensation of sulfuric acid and low-volatile secondary organic aerosol species, and new particle formation. MADE3 calculates changes of particle number concentration, size distribution, and particle composition induced by these processes and solves the aerosol dynamics equations by applying analytical approximations and process-specific numerical solvers. A detailed description of this approach can be found in Kaiser et al. (2014).  10 with the density of air ρ air , the gravitational constant g, the relative surface area coverage for each size class s i , the wind friction velocity u, which is calculated from the prognostic 10 m wind speed, and the threshold friction velocity u thr (i). Only for velocities exceeding this threshold, dust emissions can occur. The vertical emission fluxes VF(i) are calculated from the horizontal fluxes according to:

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where α accounts for the soil texture characteristics, β considers relative soil humidity and is 0 for soil humidities higher than 0.99 and 1 otherwise, and f is a function of the Leaf Area Index (LAI) describing the vegetation cover.
To account for the log-normal representation of the aerosol size distribution in modal aerosol models like MADE3, the mass emission fluxes of the single size classes are summed up and distributed in two size modes that are here assigned to the MADE3 insoluble accumulation and coarse mode. As MADE3 also requires the corresponding number emissions, these are derived from mass emissions assuming a log-normal size distribution with count median diameter D = 0.42 µm and mode width σ = 1.59 for the accumulation mode, and D = 1.3 µm, σ = 2.0 for the coarse mode, respectively, following the AeroCom 5 recommendations (Dentener et al., 2006). The corresponding conversion function (M2N) for log-normal distributions is given as (e.g. Seinfeld and Pandis, 2016): with the median diameter D i and mode width σ i of the log-normal size distribution for mode i, and the density ρ = 2500 kg/m 3 of mineral dust.

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In a sensitivity experiment (T42L31TegenS) we tested the effect of using a different assumption for the dust size distribution upon emission (results in Sect. 3.3), i.e. by varying the parameters for converting dust mass to number emissions. To this purpose, we use the dust size distribution measured during the SAMUM-1 dust campaign (Weinzierl et al., 2009(Weinzierl et al., , 2011. This campaign took place in 2006, in southern Morocco, close to the Sahara desert. It is therefore especially suited for this sensitivity study, as it focuses on dust near the source regions in the Sahara. In Weinzierl et al. (2011) the dust size distribution 15 is represented by four modes with D i , σ i , and the number concentration N i , i = 1, . . . , 4. The mass concentration m i of each of these four modes can be calculated using the factor M2N −1 i . For the online dust emission scheme a bimodal distribution is required. Therefore, the two smaller sized modes and the two larger ones are combined, in order to calculate the conversion factors for the accumulation (M2N acc ) and the coarse mode (M2N coa ) of the required bimodal distribution, 20 An overview of the mass-to-number conversion factors (M2N) for the different online dust model setups is shown in Table 2.
Wind-driven online dust emissions need to be tuned for each applied model setup. The tuning procedure is described in the following section.

Dust emission tuning
In order to keep total wind-driven dust emissions comparable between different model simulations, dust emissions were tuned 25 in the following way. As a reference for dust emissions we use the AeroCom climatology (Dentener et al., 2006), as this dataset is well evaluated and widely used in global modelling studies. We apply a global correction for online dust emissions by adjusting the wind friction velocity threshold for dust emissions by multiplication with the scaling factor t wind , as described in Tegen et al. (2004). Only for velocities exceeding this scaled threshold, dust emissions can occur. A higher (lower) threshold therefore results in lower (higher)   because the SALTRACE dust campaign focuses on dust transport from North Africa to the Caribbean, which is a central point for model evaluation in this study. The resulting values for the wind stress threshold tuning parameter (t wind ) are shown in Table 2.
Furthermore, an additional correction to dust emissions was necessary in our model, since it simulates unrealistically high emissions in a few model grid boxes close to the Himalaya region. These artefacts dominate global dust emissions and are -5 e.g. for the T42L19 resolution -up to 100 times higher than emission peaks in the Sahara. In this critical region, dust sources, namely the Taklamakan desert, and areas of high surface winds (resulting from pronounced orographic gradients at the northern slope of the Himalayas) are located within the same model grid box. Hence, due to the relatively low spatial resolution, these areas overlap in the model, although they are spatially disjunct in reality. This conflict results in unrealistically high dust emissions in the corresponding grid boxes and was also reported by Gläser et al. (2012) in a model study with EMAC using the 10 Tegen et al. (2002) dust scheme. They further showed that these artefacts vanish for horizontal grid resolutions of and above T85 (approx. 1.4 by 1.4 degrees in latitude and longitude). As such a high resolution would be computationally too expensive and time consuming for our simulations and planned applications of this model setup, we choose a different solution.
In order to remove these high emission artefacts in the Himalaya region prior to the tuning procedure described above, we exclude the corresponding grid boxes from the calculation of dust emissions by setting an upper threshold for orography. Above  Table 2. This procedure affects also some other grid boxes that show no high emission artefacts, mainly in the T42L19 and T63L31 setups, due to the somewhat lower t orogr compared with T42L31. However, these boxes are few and they correspond only to minor dust sources, mostly in the Tibetan Plateau. The numbers of dust emitting 20 grid boxes that are excluded by setting t orogr are 35, 12, 80, for the T42L19, T42L31, and T63L31 model setup, respectively.
This procedure for tuning online dust emissions was also described and applied in Righi et al. (2020).  Table 2.

Observational data
Aircraft measurements provide valuable insights in the vertical distribution of aerosol particles by measurements of particle concentrations along the aircraft flight trajectory. Here, we use observational data from the SALTRACE campaign . During this campaign (June, July 2013), aircraft measurements of various parameters, including size-resolved particle number and black carbon mass concentrations, were performed mainly in the regions around Cabo Verde, Puerto Rico, and temporal interpolation, to ensure direct comparability between observation and model data. Additionally, we use groundbased Lidar observations also collected during the SALTRACE campaign. In particular dust extinction coefficients at 532 nm, measured with a stationary Lidar system located on Barbados, provide valuable information directly related to mineral dust (Groß et al., 2015(Groß et al., , 2016. In addition to SALTRACE observations, we use sun photometer measurements of aerosol optical depth (AOD) at 440 nm 20 from the ground-based AErosol RObotic NETwork (AERONET; Holben et al., 1998Holben et al., , 2001. AOD provides an integral measure of radiation extinction by the vertical aerosol column. In the EMAC model, AOD is computed from simulated aerosol properties (in the submodel AEROPT) and compared with daily mean AOD values from AERONET radiometers (at 440 nm). To compare with the model data, we use a nearest-neighbour approach by selecting the model grid box covering the station coordinates.
The observational data used in this study are summarized in Table 3.  To quantitatively compare model simulations with observational data, we use the skill score (S), defined by Taylor (2001):

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where R is the correlation coefficient, σ m and σ o are the standard deviations of model and observational data, respectively, and R 0 is the maximum attainable correlation. This skill score is commonly used for model comparisons with observations (e.g. Klingmüller et al., 2018;Parajuli et al., 2019). For simplicity, we use R 0 = 1, as we are mainly interested in the relative changes of the skill score for different model simulations. Skill score values range from 0 to 1, with higher values indicating a better agreement between model and observations. 20 Fig. 4b shows the comparison of skill scores for the two model setups T42L31AeroCom and T42L31Tegen, respectively.
In general nearly all selected AERONET stations show an improved agreement with model results for the Tegen et al. (2002) online dust setup compared to the offline dust setup. The average skill score over all stations is nearly twice as high for the T42L31Tegen setup (0.22) as for the T42L31Aerocom setup (0.14). Especially the Dakar station shows a nearly five times higher skill score for the T42L31Tegen setup compared to T42L31AeroCom (0.38 versus 0.08, respectively). Remaining un-25 certainties and deviations from observed values can be attributed to spatial sampling issues when comparing grid-box averages to localized observations (Schutgens et al., 2016). Additional deviations may result from uncertainties in prescribed soil surface properties and modelled winds, as well as from assumptions on the specific optical properties of the single aerosol types in the AEROPT submodel, which are used to calculate AOD. Furthermore, the assumption on the dust size distribution upon emission may lead to differences; this is analysed in Sect. 3.3 with a sensitivity experiment (T42L31TegenS).

Effects of model resolution
Previous EMAC studies employing the aerosol submodel MADE3 or its predecessors (Aquila et al., 2011;Righi et al., 2013Righi et al., , 2015Righi et al., , 2016Kaiser et al., 2019) were mainly based on a relatively low model resolution of T42L19 (i.e. approx. 2.8 by 2.8 We compare the simulated vertical aerosol distribution with vertical aerosol concentration profiles measured during the SALTRACE campaign . In general, comparing climatological 3-D model output with aircraft measurements is difficult and prone to large uncertainties due to the limited spatial and temporal data coverage of aircraft observations. In order to improve the climatological comparison method used in Kaiser et al. (2019), we constrained the model as described Additionally, we compare our model results with ground-based Lidar observations also collected during the SALTRACE 10 campaign. In particular, we consider vertical profiles of dust extinction coefficients at 532 nm, measured with a stationary Lidar system located on Barbados (Groß et al., 2015(Groß et al., , 2016. Simulation and Lidar measurement data were binned into 500 m intervals for this comparison.
In Fig. 5 vertical aerosol profiles of total particle number concentrations in two different size ranges, as well as vertical profiles of the Lidar dust extinction coefficient are shown for the observations and the three different model setups, respectively.

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Only data from the SALTRACE-West regions (around Puerto Rico and Barbados) are presented here because of better data coverage due to a larger number of measurement flights compared to SALTRACE-East (around Cabo Verde). Number concentrations are shown for aerosol particles with diameters in the size range of 0.3 µm < D < 1.0 µm and 0.7 µm < D < 50 µm, respectively. These size ranges represent the detection size limits of the particle counters used in the aircraft measurements and serve as rough estimates for aerosol numbers in the accumulation and coarse mode, respectively. The size cutoff values of the 20 particle counters are also subject to uncertainties and may change slightly during a flight.
In general, the low resolution T42L19 setup shows a reasonably good agreement with both aircraft and Lidar observations in the lower troposphere (up to around 600 hPa) but overestimates number concentrations and extinction coefficients at higher altitudes significantly, up to a factor of 10 for the number concentration above 400 hPa. This large positive bias is slightly reduced for the T42L31 setup with higher vertical resolution. When increasing both the horizontal and the vertical model 25 resolution (T63L31 setup) the bias at higher altitudes vanishes almost completely in the comparisons with number concentration measurements (Fig. 5a,b). Number concentrations are reduced by up to a factor of 10 compared to the T42L19 setup above 400 hPa, so that they now correspond to observed values within the uncertainty ranges. Also, the steep gradient in the Lidar observations around 600 hPa (Fig. 5c) is reproduced better by the T63L31 setup, with again up to 10 times lower values compared to the T42L19 setup. This steep decrease in the Lidar observations is representative of the vertical extent of the 30 Saharan Air Layer (SAL), a warm, dry, elevated air layer (reaching up to approx. 4 km in the Caribbean) in which the main dust transport from the Sahara to the Caribbean takes place Haarig et al., 2019).
The comparison with Lidar observations is of special importance, as here the dust extinction coefficient provides a measure directly related to mineral dust, whereas in the total particle number concentrations also non-dust particles are included. Nevertheless, these size ranges comprising relatively large particles are probably dominated by mineral dust (Kaiser et al., 2019). The as was also argued in Kaiser et al. (2019).
A similar evaluation of the vertical aerosol total particle number distribution as presented in Fig. 5a,b (SALTRACE-West region) was performed for SALTRACE-East (region around Cabo Verde; see Figure S1 in the Supplement). Those results show a similar behaviour as seen in Fig. 5a,b (SALTRACE-West), i.e. a large positive bias for the T42L19Tegen setup in the upper troposphere, which is reduced in the model configurations with higher spatial resolution (T42L31Tegen, T63L31Tegen). 10 However, as only a few measurement flights were performed in that region, the data set is limited, which complicates the analysis and results in larger uncertainties.
In addition to measurements focusing on mineral dust, black carbon (BC) mass mixing ratios were measured during the SALTRACE campaign, likely representing aerosol particles originating from biomass burning events in Central Africa . Hence, a similar comparison as for aerosol particle numbers can be performed for BC mass mixing  ratios (in units of ng kg −1 ) for the three different model setups (Fig. 6). Again, the high bias in the upper troposphere is significantly reduced for the T63L31Tegen setup with respect to T42L19Tegen, corroborating the findings described in the previous paragraphs.
Additionally, modelled BC mass mixing ratios, as well as number concentrations of particles in different size regimes were evaluated against additional aircraft measurements from several campaigns, as done in Kaiser et al. (2019)

Effects of size distribution assumptions
As described in Sect. 2.3, a typical mineral dust size distribution has to be assumed in the model in order to assign the emitted dust particles to the respective lognormal size modes of the MADE3 aerosol submodel and also to convert mass 15 emissions to number emissions. This assumption controls key properties of the freshly emitted particles, such as the dust particle number concentration in the specific modes or the ratio of fine to coarse mode dust particle number concentration.
Hence, it also has a large importance for modelling subsequent interactions of the particles with clouds and radiation. In order to analyse the sensitivity of the modelled atmospheric distribution and properties of mineral dust aerosols to an alternative size distribution assumption, we performed an additional sensitivity simulation (T42L31TegenS). In this experiment we apply the dust size distribution calculated from aircraft-based in situ measurements during the SAMUM campaign (Saharan Mineral Dust Experiment) instead of the AeroCom size distribution (Dentener et al., 2006) used in the T42L31Tegen simulation.  (Weinzierl et al., 2009). There, the particle number 10 size distribution of mineral dust aerosol measured during that field campaign is represented by a lognormal distribution with four modes. As a bimodal size distribution is required as input for the dust emission scheme in EMAC/MADE3, the two smaller sized modes of the measured distribution are combined, as well as the two modes with larger particles, to match the accumulation and coarse mode of MADE3, respectively.
We compare the simulation output from the T42L31Tegen and T42L31TegenS experiments with measurements from the 15 SALTRACE campaign, similar to the evaluation in Sect. 3.2. Fig. 7 shows again aerosol number concentration profiles as well as vertical profiles of the Lidar extinction coefficient (as seen in Fig. 5), but comparing the T42L31Tegen and T42L31TegenS model setups. For the sensitivity simulation (T42L31TegenS), number concentrations of smaller sized particles are slightly shifted to larger values (Fig. 7a), whereas concentrations of larger particles are slightly decreased (Fig. 7b). This is in line with the SAMUM-1 size distribution showing a larger (smaller) fraction of particles in the accumulation (coarse) mode, compared 20 with the reference distribution (see also M2N values in Table 2). However, comparison of observed and simulated particle numbers is difficult, as the measured particle size ranges do not correspond directly to model accumulation and coarse mode.
In the comparisons of dust extinction coefficients in Fig. 7c, the T42L31TegenS simulation shows smaller values. This is due to lower simulated dust mass concentrations compared with the reference simulation, resulting from stronger removal processes.
The lower coarse mode numbers of the SAMUM-1 distribution lead to larger simulated particle diameters, as the emitted dust 25 mass remains constant. These larger particles are more efficiently removed by sedimentation and dry deposition processes in the model, with approximately 10 percent larger sedimentation and dry deposition fluxes in North Africa and the Caribbean.
However, sedimentation of coarse particles is generally problematic for modal schemes, as size distributions may develop and deviate from the assumption of lognormal modes. Additionally, recent observations, in particular also during SALTRACE, found coarse and giant particles large distances downwind of their sources Ryder et al., 2019). This 30 could also hint to possibly missing processes in the model that keep large dust particles airborne over that long distances .
In general, the differences between the two setups in Fig. 7 are small, with no notable improvement for the comparison with observations. Johnson et al. (2012) and Nabat et al. (2012) found improved agreement of simulated AOD with observations when using a dust representation with a larger fraction of the dust mass emitted in the coarse mode. However, the SAMUM- Finally, we tested the effect of varying the assumptions for the size distribution of emitted dust using the Tegen et al. (2002) dust parametrization, by adopting the size distribution measured during the SAMUM-1 dust campaign (setup T42L31TegenS). 10 However, we find no clear improvement with respect to the reference setup (T42L31-Tegen). Applying a size distribution with a larger fraction of dust particles in the coarse mode may improve the model results and could be a subject for future studies.
In general, we achieved an improved representation of atmospheric mineral dust in our model, especially due to an enhanced representation of dust emissions, compared with previous model setups. This provides an important foundation for future model studies on the role of dust particles in the climate system including, for instance, simulations of the climatic impact of 15 dust-induced modifications of mixed-phase and cirrus clouds.
Code and data availability. MESSy is continuously developed and applied by a consortium of institutions. The usage of MESSy, including MADE3, and access to the source code is licensed to all affiliates of institutions which are members of the MESSy Consortium. Institutions can become members of the MESSy Consortium by signing the MESSy Memorandum of Understanding. More information can be found on the MESSy Consortium Website (http://www.messy-interface.org). The model configuration discussed in this paper has been developed 20 based on version 2.54 and will be part of the next EMAC release (version 2.55).
The model simulation data analyzed in this work is available upon request and will be published via doi together with the final version of this manuscript.
Author contributions. CB conceived the study, implemented the method for tuning online dust emissions at low model resolutions, designed and performed the simulations, analysed the data, evaluated and interpreted the results, and wrote the paper. JH contributed to conceiving 25 the study, to the model evaluation, the interpretation of the results and to the text. MR assisted in preparing the simulation setup, helped designing the evaluation methods, and contributed to the interpretation of the results and to the text. BH and IT assisted in implementing the method for tuning online dust emissions at low model resolutions. DS, AW, and BW provided data from aircraft-based observations and assisted in the corresponding model evaluation. SG provided data from ground-based Lidar observations and assisted in the corresponding model evaluation.