In-cloud scavenging scheme for aerosol modules

In this study we introduce an in-cloud wet deposition scheme for liquid and ice phase clouds for global aerosol-climate models which use a size-segregated aerosol description. For in-cloud nucleation scavenging, the scheme uses cloud droplet activation and ice nucleation rates obtained from the host model. For in-cloud impaction scavenging, we used a method where the removal rate depends on the aerosol size and cloud droplet radii. The scheme was compared to a scheme that uses fixed 5 scavenging coefficients. The comparison included vertical profiles and mass and number distributions of wet deposition fluxes of different aerosol compounds and for different latitude bands. Using the scheme presented here, mass concentrations for black carbon, organic carbon, sulfate, and the number concentration of particles with diameters larger than 100 nm are higher than using fixed scavenging coefficients, with the largest differences in the vertical profiles in the Arctic. On the other hand, the number concentrations of small particles show a decrease, especially in the Arctic region. These results indicate that, compared 10 to using fixed scavenging coefficients, nucleation scavenging is less efficient and impaction scavenging is increased in the scheme introduced here. Without further adjustments in the host model, our wet deposition scheme produced unrealistically high aerosol concentrations, especially at high altitudes. This also leads to a spuriously long lifetime of black carbon aerosol. To find a better setup for simulating aerosol vertical profiles and transport, sensitivity simulations were conducted where aerosol emission distribution and hygroscopicity were altered. The simulated vertical profiles of aerosol in these sensitivity studies 15 were evaluated against aircraft observations. The lifetimes of different aerosol compounds were also evaluated against the ensemble mean of models involved in the Aerosol Comparisons between Observations and Models (AEROCOM) project. The best comparison between the observations and the model was achieved with the new wet deposition scheme when black carbon was emitted internally mixed with soluble compounds instead of keeping it externally mixed. This also produced atmospheric lifetimes for the other species which were comparable to the AEROCOM model means. 20

Here, we describe a new in-cloud scheme for wet deposition using physical parameterisations for nucleation and impaction scavenging in liquid and ice clouds. We further tested the sensitivity of our new scheme to assumptions in aerosol emissions distribution and hygroscopicity. The structure of the paper is as follows. In Sect. 2 we present details on in-cloud nucleation and impaction scavenging in general and introduce our new in-cloud nucleation scavenging scheme for liquid and ice clouds.
In addition, we present details on the aerosol module SALSA and its components, which we used to test and evaluate our new scheme and its sensitivity. In Sect. 2 we present the modifications performed for SALSA to include in-cloud impaction scavenging. In the same section, we also present the aerosol-chemistry-climate model setup which is used for testing the scheme on a global scale. In Sect. 3 we present the evaluation of our new scheme against a fixed scavenging coefficient scheme in terms of vertical profiles and wet deposition fluxes of different aerosol compounds. In addition, in the same section, we evaluate the vertical profiles of different aerosol compounds from the sensitivity simulations against those from ATom aircraft 70 campaigns (Wofsy et al., 2018). We also compare the wet deposition fluxes, of different aerosol compounds, from different sensitivity simulations to each other. Finally, we compare the lifetimes from all of the simulations to mean from several models in the Aerosol Comparisons between Observations and Models (AEROCOM) project.

In-cloud wet deposition scheme
In this section we will describe the in-cloud nucleation and impaction scavenging, for both liquid and ice phase clouds. For 75 both of these cloud phases, the removal of aerosol particles is expressed in terms of a scavenging coefficient. The rate of change in the concentration of compound l in size class i, C l i , due to in-cloud nucleation and impaction scavenging, for both liquid and ice clouds, is of the form where F i,nuc,liq and F i,nuc,ice are the fractions of activated particles due to nucleation scavenging in liquid and ice clouds, 80 respectively, and F i,imp,liq and F i,imp,ice are the scavenging coefficients due to impaction scavenging in liquid and ice clouds, respectively (Croft et al., 2010). Furthermore, f cl is the cloud fraction, f liq is the liquid fraction of the total cloud water, Q liq is the sum of conversion rate of cloud liquid water to precipitation by autoconversion, accretion and aggregation processes, C liq is the cloud liquid water content and f ice , Q ice and C ice are the equivalent variables for ice (Croft et al., 2010).

In-cloud scavenging scheme for liquid clouds 85
The in-cloud process of nucleation scavenging refers to activation and growth of aerosol particles into cloud droplets (Köhler, 1936). When water vapor reaches supersaturation, a fraction of the aerosol population is activated to cloud droplets. After these cloud droplets have grown to precipitation size, the particles can be removed from the cloud through precipitation (Wang et al., 1978). The ability of an aerosol particle to activate to a cloud droplet depends on its size, chemical composition and the ambient supersaturation (Köhler, 1936). 90 In aerosol modules of global climate models, the aerosol size distribution can be approximated by, for example, a modal or sectional discretisation, which effectively separates the size distribution into different size classes (Stier et al., 2005;Kokkola et al., 2018a). In each size class the fraction of activated particles can be calculated as the portion of particles that exceed the critical diameter of activation in that size class (Köhler, 1936;Croft et al., 2010). However, many models describe the nucleation scavenging by assuming a constant scavenging coefficient for different aerosol size classes (Stier et al., 2005;de Bruine et al., 95 2018; Seland et al., 2008).
The new in-cloud nucleation scavenging scheme for liquid clouds introduced here, calculates the scavenging coefficients of aerosol based on the fraction of activated particles in each size class, i.e. F i,nuc,liq in Eq. (1). Thus, using the scheme requires that the atmospheric model incorporates a cloud activation parameterisation that calculates size segregated cloud activation.

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In-cloud impaction scavenging, for liquid clouds, is a process where aerosol particles collide with existing cloud droplets and are thereby removed from the interstitial air of the cloud (Chate et al., 2003). This aerosol scavenging by cloud droplets is based on coagulation theory, which quantifies the rate of removal. This is further used to define the scavenging coefficients by impaction (Seinfeld and Pandis, 2006). Commonly, these scavenging coefficients, for the full aerosol particle distribution, can be calculated as where d p is the diameter of the aerosol particle, D liq is the cloud droplet diameter, K(d p , D liq ) is the collection efficiency between aerosol particles and cloud droplets and n(D liq , t) is the cloud droplet number distribution (Seinfeld and Pandis, 2006).

2.2
In-cloud scavenging scheme for ice clouds coefficient in ice-containing clouds in size class i is proportional to the ratio between nucleation rate in the size class and the total nucleation rate. Thus, we get for the scavenging coefficient, for the ice-containing clouds, in each size class where S i are the surface area concentration of size class i, ∆ICNC is the ice crystal number concentration obtained from the ice cloud activation scheme and n i the number concentration in size class i. The total surface area in each size class is derived 130 using the associated number or mass median wet aerosol radius.

SALSA
To test our new in-cloud wet deposition scheme and its sensitivity, we used the Sectional Aerosol module for Large Scale Application (SALSA) in our model simulations. SALSA is a very versatile aerosol microphysics module which has been implemented in several models of very different spatial resolution (Kokkola et al., 2018a;Tonttila et al., 2017;Andersson et al., 135 2015; Kurppa et al., 2019). To describe the aerosol population, SALSA uses a hybrid bin sectional approach for calculating the evolution of the size distribution (Chen and Lamb, 1994;Kokkola et al., 2018a). In SALSA the aerosol population is divided into two subregions regarding their size. The first subregion is from 3 nm to 50 nm and the second is from 50 nm to 10 µm. These subregions are further divided into size sections defining the minimum and maximum diameter of the particles. In each size section the aerosol particles are assumed to be monodisperse, and chemistry and different microphysical processes 140 are calculated for each size section separately. In addition, the second subregion is divided into externally mixed soluble and insoluble populations. A more detailed description of the newest SALSA version, SALSA2.0, is presented in Kokkola et al. (2018a).
Originally, SALSA uses fixed scavenging coefficients, F i , for different size classes i, in its wet deposition calculations. These coefficients include all the processes for in-cloud and below cloud scavenging (Bergman et al., 2012). The fixed coefficients, 145 for stratiform and convective clouds with different phases (liquid, mixed and ice) and solubilities, are adapted for SALSA from the calculations presented by Stier et al. (2005), and they are presented in detail in Bergman et al. (2012). Here we refine the entire scavenging scheme by calculating the scavenging coefficients online.
We used the Abdul-Razzak and Ghan (2002) cloud activation scheme to derive the fraction of activated particles in each size class for our in-cloud nucleation scavenging calculations. However, the original activation scheme considers only the soluble 150 Ghan (2002) activation calculations to account for the insoluble core in particles. The calculations are otherwise the same, but the critical supersaturation for each size class is calculated using Eq. (17.38) in Seinfeld and Pandis (2006). The supersaturation calculations, used in the Abdul-Razzak and Ghan (2002) cloud activation, for particles containing an insoluble core are presented in appendix A. As input for the in-cloud nucleation scavenging coefficients in ice clouds we used the ice crystal nucleation scheme described in Lohmann (2002).
As the in-cloud nucleation scavenging was changed into a more functional method we also needed to alter the calculation of the in-cloud impaction scavenging. We calculate the in-cloud impaction scavenging in SALSA, for liquid clouds, using the same method as described in Croft et al. (2010). This method computes in-cloud impaction as a function of aerosol particle size (r p ), median aerosol particle radius (r pg ) and cloud droplet radii (R liq ). Using this same information from our monodisperse size classes for aerosol particles, we can assume that each size class is a log-normal mode and the in-cloud impaction 165 scavenging coefficients, for liquid clouds, are then obtained as where Λ m (r pg ) is the mean mass scavenging coefficient, and it is defined as and which is called the scavenging coefficient in inverse time (Croft et al., 2010). In Eq. (6) and Eq. (7) n(r p ) is the aerosol number, R liq is the cloud droplet radius, U t (R liq ) is the terminal velocity of cloud droplets, E(R liq , r pg ) is the collision efficiency between the aerosol particles and cloud droplets, and n(R liq ) is the cloud droplet number (Croft et al., 2010).
The in-cloud impaction scavenging, for ice clouds, is calculated following Croft et al. (2010), but as our model assumes that 175 the ice crystals are monodisperse, there is no need to integrate over ice crystal number distribution (Croft et al., 2010). Thus, the in-cloud impaction scavenging coefficients are 6 https://doi.org/10.5194/gmd-2020-220 Preprint. Discussion started: 17 July 2020 c Author(s) 2020. CC BY 4.0 License.
where R ice is the radius of the ice crystal in its maximum extent, U t (R ice ) is the terminal velocity of the ice crystals and E(R ice , r pg ) is the collection efficiency of the collisions between aerosol particles and ice crystals (Croft et al., 2010). HAM has a comprehensive parametrisation for both modal and sectional microphysics representations of aerosol populations.
In addition to BC, the aerosol compounds included in this study are: organic carbon (OC), organic aerosol (OA) (here assumed 190 to be 1.4 times the modelled OC mass), sulfate (SO 4 ), mineral dust (DU) and sea salt (SS). ECHAM6.3 is further coupled to the chemistry model MOZ (not used here) which contains a detailed stratospheric and tropospheric reactive chemistry representation for 63 chemical species, including nitrogen oxides, tropospheric ozone and hydrocarbons (Schultz et al., 2018;Horowitz et al., 2003).

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We used a total of 6 different simulations to investigate the performance of the new wet deposition scheme. The first two simulations were done with default wet deposition scheme of SALSA (hereafter referred to as "old") and the wet deposition scheme introduced in this study (hereafter referred to as "new"). As will be shown later, in the default model configuration the new scheme resulted in spurious BC vertical profiles. To investigate the reasons for this, we carried out 4 additional sensitivity simulations where we changed the assumptions of emission size distribution, as well as internal mixing and ageing of BC. An  Table 1.
In the model simulations, the runs "baserun_new" and "baserun_old" are used to compare the new and old in-cloud scavenging schemes. The simulations "BC_small", "BC_large", "BC_soluble", and "insol2sol" were conducted to evaluate the sensitivity of the new in-cloud scavenging scheme. These sensitivity studies were chosen based on the findings of Kipling et al. 205 (2016) who studied how model processes affect the simulated aerosol vertical profiles. Their study indicated that the processes which have the strongest effect on aerosol vertical profiles in the HadGEM model are emission distribution, hygroscopicity, deposition and microphysical processes (Kipling et al., 2016).
In the first two sensitivity runs, we altered the BC emission distribution for SALSA. This was done so that all of the BC emissions were directed to either size class of small or large insoluble particles, respectively. In the default configuration the 210 BC emission size distributions are log-normal mass fraction distributions following AEROCOM emission recommendations Table 1. Overview of the simulations used in this study.

Setup Description Illustration
baserun_old Old ECHAM-SALSA in-cloud scavenging scheme with fixed scavenging coefficients.
baserun_new New in-cloud nucleation scavenging using Abdul-Razzak and Ghan (2002) for liquid clouds and Lohmann (2002) for ice clouds. In-cloud impaction for liquid and ice clouds according to Croft et al. (2010) BC_small All BC emissions directed to small insoluble size class.

BC_large
All BC emissions directed to large insoluble size class.

BC_soluble
All BC emissions directed to soluble population with the same mass distribution as for baseruns. insol2sol Simulating ageing of insoluble particles by moving them to soluble aerosol population after they activate at 0.5 % supersaturation. (Stier et al., 2005;Dentener et al., 2006), which are remapped to the SALSA size classes. The mode radii (r m ) and standard deviations σ for the original BC emission size distributions are r m = 0.015 µm and σ = 1.8, for fossil fuel emissions, and r m = 0.04 µm and σ = 1.8, for wild-fire emissions (Dentener et al., 2006). In the BC_small simulation, we directed all BC emissions to an insoluble size class where particle diameter spans from 50 nm to 96.7 nm. In the BC_large simulation, we 215 directed all BC emissions to an insoluble size class where particle diameter spans from 0.7 µm to 1.7 µm.
To study the sensitivity of the wet deposition scheme to BC hygroscopicity, we conducted a simulation where all BC emissions were directed to soluble size classes. The size distribution for the emissions was the same as for the baserun simulations when they are directed to the insoluble classes. This simulation is referred to as BC_soluble in the model simulations. In the fourth sensitivity study, called insol2sol, insoluble particles are transferred to parallel size classes of soluble particles. This 220 allows for separation of fresh and aged particles and is a method to simulate aerosol ageing used also in other global aerosol models (e.g. Stier et al., 2005). The criterion for transfer is that particles activate at a supersaturation of 0.5 %. A schematic of the aerosol emission distribution for the different simulations is presented in Fig. 1.

Experimental setup
The simulations were performed with ECHAM-HAMMOZ for the year 2010, with the SALSA aerosol module, using 3-hourly prescribed. SST and SIC were obtained from monthly mean climatologies from AMIP (Atmospheric Model Intercomparison Project). The analysis is made between the old and the new wet deposition scheme using SALSA. In addition, the sensitivity of the new scheme to emission sizes, aging, and hygroscopicity of BC-containing aerosol, is tested using ECHAM-HAMMOZ with SALSA.

ATom aircraft measurements 235
To see how the new scheme and the sensitivity studies reproduces the vertical properties of different aerosol compounds, we compared the model simulations against aircraft measurements. The aircraft data was obtained from all NASA's Atmospheric Tomography (ATom) missions (1, 2, 3, and 4), and the dataset was merged data from all instruments which measure atmospheric chemistry, trace gases, and aerosols (Wofsy et al., 2018).
To get the best representative comparison between the ATom aircraft measurements and model data, the model data was 3 Results

Differences between simulated values of old and new wet deposition schemes
First, we compared how aerosol properties differ when using the old and the new wet deposition schemes. In order to assess, how the two schemes affect aerosol transport and vertical profiles, we compared the modelled aerosol vertical profiles over the tropics (0-30 • N), the mid-latitudes (30-60 • N) and the Arctic (60-90 • N). Here we focused on SO 4 , OC (or OA), and BC as they are readily available from the ATom aircraft campaign measurements. illustrates that all three of the compounds show similar differences in the vertical profiles in all three latitude bands, between the two runs. The concentrations for each compound are higher for the new scheme compared to the old scheme for almost the entire vertical domain. The differences between the different wet deposition schemes are greatest at higher altitudes starting from approx. 900 hPa upwards. In the tropics, these differences in the profiles are lowest with a maximum relative difference of approx. 200 % for BC and OC and slightly exceeding 150 % for SO 4 . These maxima occur at approx. 200 hPa altitude.

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In the mid-latitudes, the differences are slightly higher than at the tropics and the maximum difference in the values are at ∼300 hPa altitude. The new method shows ∼350 % higher concentrations at maximum for BC and SO 4 and ∼400 % for OC. The Arctic shows the greatest differences in the compound profiles. The difference is largest at ∼500 hPa altitude where the concentrations in the new scheme outweigh the concentrations in the old scheme by ∼600 % for BC, 650 % for OC and 800 % for SO 4 . As emissions of these aerosol particles in the Arctic are low, most aerosol is transported into the Arctic from 265 emission regions outside the Arctic. It is thus evident that the wet removal of these aerosol particles is reduced in the new scheme, which allows for the particles to be transported to higher altitudes and longer distances. In addition, we found that the model accumulates BC at the higher altitudes in simulations spanning several years (not shown), which can be considered spurious behaviour.   There is a modest change in the mass fluxes between the old and the new schemes. As in steady state the total emissions of a compound must match its total removal, these differences mostly stem from changes in the interplay between dry and 12 https://doi.org/10.5194/gmd-2020-220 Preprint. Discussion started: 17 July 2020 c Author(s) 2020. CC BY 4.0 License. indicate that the BC lifetime should be less than 5.5 days (Lund et al., 2018). This is a very interesting result: the more physical wet deposition scheme produces more spurious atmospheric lifetimes for BC. Consequently, also the ability of ECHAM-HAMMOZ to reliably simulate aerosol vertical profiles and long range transport of aerosol is decreased when using the more physical scheme with the default model setup. This may be due to the fact that a more physical treatment of the wet deposi-300 tion processes makes the model more sensitive to influences outside of the parameterisation. We therefore performed further sensitivity simulations and compared their results to observational data.

Sensitivity simulations
As reported in the previous section, ECHAM-HAMMOZ with the new, more physical scheme, in its default setup, produced spurious BC vertical profiles. With the sensitivity simulations we aimed to explore different possibilities to improve the BC 305 vertical profiles and long-range transport in the model. In order to increase nucleation scavenging of BC, we considered three different possibilities to make BC-containing particles more susceptible to cloud droplet activation. One way to achieve this is to emit BC into larger particles, which require less aging to be activated at a given supersaturation. This was tested in simulation BC_large. Another way is to mix BC with soluble compounds in order to enhance hygroscopicity of BC-containing particles and thus their cloud activation susceptibility. This can be done in two ways, either by emitting BC directly to soluble size 310 classes (simulation BC_soluble) or by emitting BC to insoluble size classes and transferring particles to soluble classes after aging (simulation insol2sol). A third way is to emit BC into smaller size classes in order to facilitate transfer of BC into larger, more easily activated particles by coagulation (simulation BC_small). represent the BC concentrations slightly better than BC_large between 500 hPa and 300 hPa. BC_small and baserun_new 320 overestimate the BC concentrations at all latitudes, except in the tropics at lower altitudes starting from ∼700 hPa downwards, where they represent the BC concentrations slightly better than the other sensitivity simulations. As we saw in the previous chapter, the reduced efficiency in the wet deposition increases BC concentrations at higher altitudes which causes baserun_new 15 https://doi.org/10.5194/gmd-2020-220 Preprint. Discussion started: 17 July 2020 c Author(s) 2020. CC BY 4.0 License.
to overestimate the BC concentrations. This is because the default emission sizes of BC particles are not very susceptible to cloud activation. In addition, although BC_small aimed at increasing BC wet removal by emitting BC to small particle sizes 325 and thus enhancing their collection by coagulation to large particles, it is apparent that coagulation is not very efficient in doing so.   upwards. This is also due to more efficient activation compared to baserun_new for medium-sized particles which reduces the transport to higher altitudes.
The N tot profiles are, all in all, fairly similar for all of the sensitivity simulations, with only modest differences. In the tropics the trend of the profiles varies between simulations and measurements. The sensitivity simulations tend to overestimate the N tot concentrations at the surface and at the highest altitudes by over 50 percent, but underestimate them at approx. 400-700 365 hPa. In the mid-latitudes, all of the simulations represent N tot concentrations fairly well when compared to the measurements.
However, in the Arctic, all of the simulations underestimate the N tot profiles. At higher altitudes, starting from approx. 600 hPa upwards, insol2sol underestimates N tot least, showing quite a good agreement with the measurements.
One of the reasons for the differences in the N tot and N 100 surface concentrations may be due to a misrepresentation of the emitted particle size distribution. In ECHAM-HAMMOZ the same aerosol emission size distribution per compound and 370 emission sector is assumed throughout the whole world, which is not very realistic for every aerosol particle source (Paasonen et al., 2016). At higher altitudes, the aerosol microphysical processes correct the aerosol size distribution towards more realistic profiles.
To investigate the effects of the different sensitivity studies further, we computed the size and mass distribution of the wet deposition flux (Fig. 7). The mass fluxes in the soluble population do not change much between baserun_new and the different 375 sensitivity studies, except for the insol2sol simulation which allows for sufficiently hygroscopic particles of the insoluble population to be repartitioned to the soluble population. This leads to an increase in DU mass in the soluble population and a decrease in the insoluble population. In addition to more efficient wet removal of DU due to this process, this also increases dry deposition and sedimentation (not shown) of DU in insol2sol. For the mass fluxes in the insoluble population, BC_large and BC_soluble show an increase in the largest size class for DU. This effect is due to more efficient removal of BC-containing 380 particles, which allows for more SO 4 to condense on larger, DU-containing particles, which enhances the activation of these particles.
The number fluxes for different sensitivity simulations in soluble population show most change in the two smallest size classes, which increases by a factor of about 1.3 in the insol2sol simulation and about 1.1 for BC_large and BC_soluble when compared to baserun_new. These differences stem from changes in medium-sized and large particle concentrations, which 385 act as condensation sink for SO 4 and thereby regulate the amount of SO 4 available for new particle formation. In addition, there is a slight increase of OC in the insol2sol simulated number distribution, which is being transferred from the insoluble population. Otherwise, there is no notable change in other compounds as the SO 4 dominates the number distribution in the soluble population. The relative BC mass contribution to the wet deposition number flux of the insoluble aerosol population very well reflects the assumptions made in the different sensitivity studies. While for BC_large and BC_soluble the BC mass 390 fraction in the medium-sized insoluble particles disappears, in BC_small the BC fraction in the 50 to 100 nm insoluble particles is about 3 times larger than in baserun_new. This shows that coagulation is not effective in moving BC from these small insoluble particles to large soluble particles. In insol2sol, most of the BC is moved to the insoluble aerosol population before removal, which can be seen in a strong decrease in removed insoluble aerosol number for that simulation.  With the assumption that the AEROCOM mean atmospheric lifetimes are the current best guess, we can use Table 2 to select a simulation that best reproduces these mean lifetimes and therefore could be considered as the best solution to address the overestimated BC lifetimes in baserun_new. While baserun_old, baserun_new and BC_small overestimate the BC lifetime by factors of 1.6, 2.5 and 2.8, respectively, BC_large, insol2sol and BC_soluble all produce BC lifetimes within one day of the AEROCOM mean. In addition, the BC lifetimes should be less than 5.5 days according to Lund et al. (2018). However, of 2). This has already been discussed by Kokkola et al. (2018a) and Tegen et al. (2019).

Conclusions
We developed a new in-cloud nucleation wet deposition scheme for liquid and ice clouds. For liquid clouds, the scavenging coefficients are calculated using the size-segregated fraction of activated particles from a cloud activation scheme. For ice 420 20 https://doi.org/10.5194/gmd-2020-220 Preprint. providing the ice nucleation rates for the nucleation scavenging scheme (see Tabazadeh et al., 2002). The in-cloud impaction scavenging for SALSA was adapted from the method for modal scheme by Croft et al. (2010).
Compared to using fixed scavenging coefficients, the new scheme showed an increase in BC, OA, and SO 4 vertical profiles almost throughout the entire vertical domain for all latitude bands. In the Arctic region this increase was most pronounced, 430 with a maximum increase of up to 800 %. The differences in vertical profiles had similar functional shapes in all latitude bands and for all three compounds. The increase was mainly due to a decrease in the nucleation scavenging of aerosol particles in the new scheme, which increased aerosol transport into the upper atmosphere and subsequently to the Arctic region. The new scheme also showed a significant increase in large particle concentrations which was similar in shape to the change in aerosol compound mass. However, the small particle concentrations decreased everywhere, with a maximum decrease of 90 435 % in the Arctic. This implies that new particle formation was reduced in the new scheme due the increased concentration of large particles, which increased the condensation sink for SO 4 . In addition, impaction scavenging in the new scheme was faster which increased the removal rate of small particles even more. concentrations of activation-sized particles at the highest altitudes in the tropics, which was strongly tied to the underestimation of OC at these altitudes. Furthermore, the atmospheric lifetime of atmospheric mineral dust (DU) was strongly underestimated in the simulation using insoluble-to-soluble transfer of aged particles. The atmospheric lifetimes of seasalt (SS) did not change between the different sensitivity studies. All in all, while reasonable BC vertical profiles and atmospheric lifetimes could be achieved with the new wet deposition scheme in three of the sensitivity studies, namely emitting BC to more hygroscopic 460 or to larger particles or transferring insoluble, BC-containing particles, to soluble size classes, only the first option is really suitable. Emitting BC to large particles is quite unrealistic, because the emission size of BC-containing particles is fairly well established (Tissari et al., 2008;Krecl et al., 2017;Corbin et al., 2018;Zhang et al., 2019) and insoluble-to-soluble transfer, on the other hand, lead to too small atmospheric lifetimes of DU.
To conclude, even though the new in-cloud wet deposition scheme is more physically sound than using fixed scavenging 465 coefficients, it failed to reproduces global aerosol fields adequately in the default setup of the host model. In particular, the BC atmospheric lifetime was almost 3 times as large as what observations indicate (Lund et al., 2018). Based on the results of our sensitivity simulations, model produces the best vertical profiles and aerosol lifetimes with the new scheme if BC is mixed with more soluble compounds at emission time. The settings for the simulations are given in the same folder, in folder "gmd-2020-220". Alternatively, the data and codes for figures can be obtained directly from authors or from https://etsin.fairdata.fi/dataset/f3cb5807-66fe-4a0d-a20a-ac208d3aab5a (last access: 29 June 2020, Holopainen et al., 2020). All other input files are ECHAM-HAMMOZ standard and are available from the HAMMOZ repository (see https://redmine.hammoz.ethz.ch/projects/hammoz, HAMMOZ consortium, 2019).

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ATom aircraft data can be obtained through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1581 (last access: 25 November 2019, Wofsy et al., 2018) Appendix A: Calculations for particles containing an insoluble core The calculations for the particle containing an insoluble core are based on the technical report by Kokkola et al. (2008) where the critical supersaturation is obtained as In Eq. (A3) A and B are obtained from Seinfeld and Pandis (2006). A describes the increase in water vapour pressure due to the curvature of the particle surface and is denoted as 495 and B is called the solute effect term and is denoted as Using this new expression for the critical supersaturation, the effective critical supersaturation, maximum supersaturation, and the number fraction of activated particles for each size size class can be calculated using Eq. (8), (9) and (12-15) from the Abdul-Razzak and Ghan (2002).