On the effect of the wet deposition scheme on the simulated aerosol life-cycle in a global climate model

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On the effect of the wet deposition scheme

on the simulated aerosol life-cycle in a global climate model

Eemeli Holopainen Master’s Thesis Aerosol physics research group University of Eastern Finland, Department of Applied Physics February 20, 2019

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UNIVERSITY OF EASTERN FINLAND, Faculty of Science and Forestry, Applied Physics

Tatu Eemeli Holopainen: On the effect of the wet deposition scheme on the simulated aerosol life-cycle in a global climate model

Master’s Thesis, 58 pages

Instructors: Thomas Kühn (PhD, senior researcher), Kari Lehtinen (Dr. Tech, Pro- fessor)

August 2017

Key words: aerosol, climate model, ECHAM-HAMMOZ, M7, SALSA, wet deposition

Abstract

The transportation of atmospheric aerosols to the Arctic areas is usually underestimated in global climate models. The removal of aerosol species through wet deposition is proposed to be one of the possible sources for this underestimation. This study presents the implementation and evaluation of a new wet deposition scheme for the aerosol microphysics module SALSA, which is coupled to the global climate model ECHAM-HAMMOZ. The effect of the new wet deposition module within SALSA is evaluated comparing the simulated aerosol wet deposition fluxes, mass concentrations, burdens of different aerosol species, and optical properties to those simulated with the old wet deposition scheme. The new and the old versions are also compared against observations of aerosol optical properties and mass concentrations. The old and the new method are different in their treatment of in-cloud nucleation and impaction scavenging execution for water and ice clouds. This difference is reflected in the results so that the largest differences can be seen around the highly polluted regions as a decrease in the wet deposition flux in the new method compared to the old method. This results in an increase in the transportation of aerosol species in the Northern Hemisphere concentrations when the new method is used. The new wet deposition module reproduces the observed aerosol optical properties and the Arctic black carbon concentrations better than the old method, but slightly overestimates the concentrations near the source regions when evaluated against the observations.

Overall, the new SALSA wet deposition module shows promising results in improving the wet deposition of aerosol species of ECHAM-HAMMOZ and needs to be further improved to reproduce the aerosol concentrations more realistically also near source regions.

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Tiivistelmä

Globaaleissa ilmastomalleissa aerosolien kulkeutumista ilmakehässä Arktiselle alueelle yleensä aliarvioidaan. On esitetty, että yksi tähän vaikuttavista prosesseista on aerosolikomponenttien poistuminen märkäpoistumalla. Tässä Pro-gradu tutkielmassa esitetään uuden märkäpoistumamallin kytkeminen aerosolien mikrofysiikkamalliin SALSA, joka on puolestaan kytketty globaaliin ilmastomalliin ECHAM-HAMMOZ.

Uuden märkäpoistumamallin toimintaa arvioidaan vertaamalla uudella mallilla simu- loituja aerosolien märkäpoistumavoita, massa pitoisuuksia, eri aerosolikomponenttien kokonaismääriä sekä optisten ominaisuuksien arvoja vanhaan märkäpoistuma- malliin. Uuden ja vanhan märkäpoistumamallin arvoja verrataan myös havaittui- hin aerosolien optisien ominaisuuksien ja massa pitoisuuksien arvoihin. Uuden ja vanhan version eroavuudet ovat pilven sisäisen nukleaation ja impaktion keräyso- suuksien toteutuksessa vesi- ja jääpilville. Eroavuus näkyy tuloksissa siten, että su- urin ero havaitaan kaikkein saastuneimmilla alueilla, joilla märkäpoistumavuo on pienempi uudessa metodissa. Tämä heijastuu aerosolien kulkeutumiseen niin, että pohjoisen pallonpuoliskon pitoisuudet kasvavat. Uuden märkäpoistumamallin simu- loimat aerosolien optiset ominaisuudet ja Arktisen alueen mustahiilipitoisuudet vas- taavat paremmin havaittuja arvoja kuin vanha malli. Toisaalta uusi malli yliarvioi hieman pitoisuuksia lähellä lähdealueita, kun niitä verrataan havaintoihin. Kaiken kaikkiaan uusi SALSA märkäpoistumamalli antaa lupaavia tuloksia aerosolien märkä- poistumalle ECHAM-HAMMOZ mallissa, mutta sitä pitää edelleen parantaa, jotta myös aerosolien pitoisuuksille saataisiin parempia tuloksia saastuneilla alueilla.

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1 Introduction

Atmospheric aerosols affect the climate directly by reflecting and absorbing radiation and indirectly through aerosol-cloud interactions (Twomey, 1991; Albrecht, 1989).

Thus, in order to estimate the radiation budget of the Earth correctly, aerosols have to be modelled correctly.

One of the compounds which can affect the radiation budget of the Earth is black carbon (BC). BC can absorb solar radiation, influence cloud processes and accelerate the melting of snow and ice cover. BC is emitted through incomplete combustion processes, of which a large fraction is through anthropogenic activities (Bond et al., 2013). At high latitudes, BC, in particular, has been shown to be a major warming agent and its mitigation has been proposed as a possible means to slow Arctic warming. (Stone et al., 2014). Other chemical compounds included in the study presented here are: organic carbon (OC), sulphate (SU), mineral dust (DU) and sea salt (SS).

The aerosol size distribution and composition is, among others, affected by aerosol transport, which is particularly noticeable in regions where no or only small amounts of aerosols are emitted (Rasch et al., 2000; Croft et al., 2010). In remote areas, like for instance the Arctic, global models often underestimate aerosol (especially BC) concentrations, because the transport of aerosols into these regions is modelled poorly. One of the possible causes of this problem is how wet deposition is modelled (Bourgeois and Bey, 2011). The removal of aerosol species through wet deposition processes is modelled very differently in different global models (Textor et al., 2006;

Kipling et al., 2016). More research is therefore needed to better parameterise and constrain wet deposition in models.

Wet deposition is a process in the atmosphere where aerosol particles are scavenged by hydrometeors and then carried to the surface by precipitation (Seinfeld and Pandis, 2006). Wet deposition processes are usually divided into two categories: in-cloud and below-cloud scavenging. In in-cloud scavenging the aerosol species can act as cloud condensation or ice nuclei, thus entering the cloud droplets or ice crystals through a nucleation process. Additionally aerosols can be scavenged by the ice crystals or cloud droplets through collision. As the cloud droplets or ice particles grow to precipitating sizes, the collected aerosol particles are removed from the atmosphere through rain (Pruppacher and Klett, 1997; Croft et al., 2010). Below-cloud scavenging is an event where the aerosol species below the cloud are collected through the collision with precipitating rain droplets or snow crystals (Seinfeld and Pandis, 2006). Below-cloud scavenging by rain strongly depends on the aerosol and raindrop size-distributions,

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which has been shown by observational studies (Andronache, 2003; Andronache et al., 2006).

This study will introduce the aerosol-climate model ECHAM-HAMMOZ and its two different aerosol microphysics packages, the modal description M7 and the sectional description SALSA, including their current wet deposition modules. It will also be explained how the ECHAM-HAMMOZ wet deposition module was implemented as a stand-alone (or box) model, and how the mapping between the two aerosol representations was performed. The pre-existing methods to compute the in-cloud nucleation scavenging of aerosols are explained for bot microphysics models, as well as the method developed in this study. In the results section the study compares the pre-existing and the here developed wet deposition modules for the sectional representation, including a comparison of the wet deposition flux, mass concentrations from vertical profiles at different latitudes, mass burdens, aerosol optical properties as well as an evaluation of the model results against satellite and aircraft measurements.

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2 Materials

The global climate model ECHAM-HAMMOZ and its aerosol microphysics packages are described in the following section. The original wet deposition methods of the climate model for both aerosol microphysics packages are also described in this section.

2.1 ECHAM-HAMMOZ

ECHAM-HAMMOZ is a 3-dimensional aerosol-chemistry-climate model and it con- tains comprehensive parameterizations for modal and sectional aerosol schemes as well as a detailed stratospheric and tropospheric reactive chemistry representation (Schultz et al., 2018).

ECHAM-HAMMOZ is developed by a consortium composed of ETH Zürich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford, the Finnish Meteorological Institute and the Leibniz Institute for Tropospheric Re- search, and managed by the Center for Climate Systems Modeling (C2SM) at ETH Zürich.

This study uses the newest version of ECHAM-HAMMOZ (ECHAM6.3-HAM2.3- MOZ1.0; hereafter referred to as ECHAM6) which is a general circulation model (GCM) and it is based on solving the equations for vorticity, divergence, surface pressure and temperature. These equations are prognostic and they are solved with spheric harmonics which include a triangular truncation. The model solves the equations for cloud liquid water and ice and also has a prognostic-statistical scheme for solving cloud cover predictions. Convective clouds and transport follow a mass-flux scheme (Stier et al., 2005). ECHAM6 has different resolutions with different trunca- tions and vertical levels. The center of the top level of the atmosphere is approxi- mately at 0.01 hPa which is about 80 km from the surface, and the top pressure in the model is 0 hPa (Stevens et al., 2013; Roeckner et al., 2004). In this study we used the resolution T63L47 which states that the grid boxes span about 1.9^◦x1.9^◦ longitude and latitude and the amount of vertical levels is 47. The large-scale meteorology can be nudged which in the model is implemented by relaxing the prognostic variables (such as vorticity, divergence and surface pressure) towards data from operational weather forecast models (Stier et al., 2005; Kokkola et al., 2018).

ECHAM6 includes the Hamburg Aerosol Model (HAM) which was developed to ex- amine the interactions between climate and aerosols. HAM is an aerosol model which predicts the evolution of the size-distribution and composition of different aerosol

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populations. HAM calculates all of the aerosol properties within ECHAM6 which includes emissions, deposition, radiation and microphysics (Stier et al., 2005; Tegen et al., 2018).

ECHAM6 is coupled with the Model of Ozone and Related Chemical Tracers (MOZART) which is a three-dimensional chemical transport model. MOZART has a detailed chemistry scheme for nitrogen oxides, tropospheric ozone and hydrocarbon chemistry with 63 chemical species. It uses a flux-form semi-Lagrangian scheme with a pressure fixer to evaluate the tracer advection (Schultz et al., 2018; Horowitz et al., 2003).

In this study MOZART is not included in model evaluations, but the chemistry for sulfur is calculated in HAM.

The model provides two different aerosol microphysics packages which can be selected in each evaluation. These packages are introduced in the following subsections.

2.1.1 The modal aerosol microphysics package M7

The schematic representation of the M7 size distribution is presented in Fig. 1

Figure 1: Schematic representation of M7 size distribution.

M7 is a modal microphysics module which represents the aerosol spectrum using a superposition of seven lognormal modes. The main modes are nucleation, Aitken,

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accumulation and coarse, which are, apart from the nucleation mode, divided into soluble and insoluble populations. Thus the soluble modes are nucleation (NS), Aitken (KS), accumulation (AS) and coarse (CS) and the insoluble are Aitken (KI), accumulation (AI) and coarse (CI). The geometric standard deviation is fixed and set 2.00 for the coarse mode and to 1.59 for all other modes (Wilson et al., 2001). The median radius of each mode, which is needed for most model calculations, can be calculated from the corresponding aerosol number and mass concentrations. The chemical compounds which are included in the different modes are shown in Fig. 1. The transfer of aerosol number and mass concentrations between modes are dominated by aerosol growth due to condensation of sulfuric acid and coagulation of aerosol particles (Stier et al., 2005).

2.1.2 The section aerosol microphysics package SALSA

The schematic representation of the SALSA size distribution is presented in Fig. 2

Figure 2: Schematic representation of SALSA size distribution.

The Sectional Aerosol module for Large Scale Applications (SALSA) microphysics package uses a hybrid bin sectional approach to describe the aerosol population (Chen and Lamb, 1994). The aerosol population is divided into 2 subregions. The first subregion spans aerosol diameters from 3 nm to 50 nm and the second spans diameters from 50 nm to 10 µm. Subregion 2 has two parallel populations: soluble (a) and insoluble (b). The subregions are divided into sections (size bins) which define a minimum and maximum diameter of the particles inside the section. Subregion 1 has

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3 sections and subregion 2 has 7 sections. As an example, if an aerosol particle belongs to subregion 1, section 3 and it is soluble then it is referred to as bin "1a3". In each size bin the aerosol population is assumed to be monodisperse and different microphysical processes, new particle formations and chemistry are calculated separately in each bin.

The chemical compounds which are included in different subranges and populations are presented in Fig. 2 (Kokkola et al., 2008a, 2018; Bergman et al., 2012).

2.1.3 Current wet deposition module for SALSA

The current wet deposition scheme for SALSA includes in-cloud and below cloud scavenging and, if the precipitation evaporates below the cloud, the subsequent re- lease of aerosols back into the atmosphere (Bergman et al., 2012).

In-cloud scavenging is implemented by calculating the removal of aerosol species from the cloud following Stier et al. (2005), where the change in mixing ratios of the particles C_i during time ∆t are

∆C_i

∆t = F_iC_if^cl Cwat

(Q^liq

f^liq + Q^ice f^ice

)

, (1)

whereF_i is the scavenging parameter for aerosols depending on size and composition, C_wat is a mixing ratio of total cloud water, f^cl is the cloud fraction, f^liq and f^ice are cloud water and ice fractions, andQ^liqandQ^ice are the total conversion rates of water and ice into precipitation through auto-conversion, aggregation and accretion (Stier et al., 2005; Bergman et al., 2012). The fixed scavenging coefficientsFi, for stratiform and convective clouds with different phases (liquid, mixed and ice) and solubilities, adapted for SALSA following Stier et al. (2005), are presented in Table 1 (Stier et al., 2005; Bergman et al., 2012).

Subrange and section Strati. liq. Strat. mix. Strat. ice Conv. mix.

1a1 0.10 0.10 0.10 0.20

1a2-1a3 0.25 0.40 0.10 0.60

2a1-2a4 0.85 0.75 0.10 0.99

2a5-2a7 0.99 0.75 0.10 0.99

2b1-2b4 0.20 0.10 0.10 0.20

2b5-2b7 0.40 0.40 0.10 0.40

Table 1: Fixed in-cloud scavenging coefficients for different subranges and sections for current SALSA in-cloud scavenging scheme.

Below cloud scavengingis a function of aerosol concentration, collection efficiency

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and area of precipitation (Bergman et al., 2012). The size dependent collection efficiency for rain and snow uses the parameterization described by Croft et al. (2009) and is described in more detail in the end of section 2.1.4.

2.1.4 Wet deposition module for M7

The M7 wet deposition module in ECHAM6 has two coupled schemes: in-cloud and below-cloud scavenging. The in-cloud scavenging scheme considers two kinds of scavenging: cloud nucleation scavenging and size-dependent in-cloud impaction scavenging.

In-cloud nucleation scavengingcomputes the number of removed aerosol particles using the pre-calculated cloud droplet number concentration (CDNC) and ice crystal number concentration (ICNC) as input following (Ghan et al., 1993). The sum of the newly formed cloud droplets and ice crystals is assumed to be the total number of aerosols which are scavenged by droplet nucleation in each time step in stratiform clouds (Croft et al., 2010). To obtain the total number of aerosols scavenged from the j-th mode, the sum is multiplied by the number of aerosols with radii greater than 35 nm, in mode j, divided by the total number of aerosols with radii greater than 35 nm (Croft et al., 2010). Using this information the in-cloud nucleation scavenging fractions for aerosol number mixing ratio, for the soluble modes, are obtained with

F_j,nuc = N_j,scav

N_j , (2)

where N_j,scav is now the total number of aerosols scavenged from j-th mode and N_j is the total number of aerosols in j-th mode (Croft et al., 2010). CDNC and ICNC are prognostic variables in ECHAM6 (Lohmann et al., 2007).

For convective clouds only a mixed cloud phase case is considered, with different scavenging ratios for different modes which are described in detail in Stier et al. (2005).

For insoluble modes the nucleation scavenging ratios are assumed to be zero.

The nucleation scavenging for the mass distribution uses the Köhler theory. The Köhler theory, in principle, describes the process of water vapor condensing on a particle and form cloud droplets. The Köhler theory is described in detail by Seinfeld and Pandis (2006). The Köhler curve describes the equilibrium water vapor pressure over a particle with a given dry diameter as function of its wet diameter. When following the curve, a maximum occurs at a certain droplet diameter, which is called the critical diameter (Seinfeld and Pandis, 2006). When a particle grows beyond

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this critical diameter, it is considered to have activated into a cloud droplet. Hence, all particles which grow to sizes larger than their critical diameter are scavenged by nucleation, which means for the mass distribution, the nucleation scavenging fraction is calculated as Croft et al. (2010)

F_i,nuc =

∫∞

rj,critm_i,j(r_p)dr_p

∫∞

0 m_i,j(r_p)dr_p (3)

where r_j,crit is the critical radius of the j-th mode, m_i,j(r_p) is the lognormal mass distribution of the i-th species of the j-th mode and r_p is the aerosol radius (Croft et al., 2010).

In-cloud impaction scavenging fractions for aerosol-cloud droplet collisions are computed for species i as

F_i,imp,liq= Λ_m(r_pg,m)∆t , (4)

where

Λ_m(r_pg,m) =

∫∞

0 Λ(r_pg,m)r³_pn(r_p)dr_p

∫∞

0 r³_pn(r_p)dr_p , (5)

which is the mean mass scavenging coefficient in units of inverse time and Λ(r_pg,m) =

∞

∫

0

πR²_liqU_t(R_liq)E(R_liq, r_pg)n(R_liq)dR_liq, (6) which is called the scavenging coefficient in inverse time. HereR_liqis the cloud droplet radius,U_t(R_liq) is the cloud droplet terminal velocity,E(R_liq, r_pg) is the collision efficiency of aerosol particles and cloud droplets, andn(R_liq) is the cloud droplet number distribution (Croft et al., 2010).

For number mixing ratios the evaluation is the same but using Λ_n(r_pg) =

∫∞

0 Λ(r_pg)n(r_p)dr_p

∫∞

0 n(r_p)dr_p , (7)

which is the mean number scavenging coefficient in inverse time (Croft et al., 2010).

For aerosol-ice crystal collisions the scavenging fraction is calculated as

F_i,imp,ice =K(R_ice, r_pg)·ICNC·∆t , (8)

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where R_ice is the maximum extent of the ice crystal and

K(R_ice, r_pg) =πR²_iceU_t(R_ice)E(R_ice, r_pg) (9) is the collection kernel where U_t(R_ice) is the terminal velocity of the ice crystals and E(R_ice, r_pg) is the collision efficiency of aerosol particles by ice crystals (Croft et al., 2010).

Below-cloud scavengingscheme is based on Croft et al. (2009) and it has different below-cloud scavenging parameterizations for rain and for snow. The parameterization for rain assumes that raindrops, which are acting as the collectors, follow a distribution and the scavenging coefficients are a function of aerosol size (rp) and are calculated as

Λ^r(rp) =

∞

∫

0

πR²_pUt(Rp)E(Rp, rp)N(Rp)dRp (10) whereR_p is the raindrop radius,r_p is the aerosol radius,U_t(R_p) is the raindrop’s terminal velocity, N(R_p) = n₀exp−4.1R^−0.21R_p is the assumed raindrop distribution, where n₀ = 8·10³ m⁻³mm⁻¹ andR is the rainfall rate (Marshall and Palmer, 1948), and E(R_p, r_p) is the collection efficiency as a function of raindrop and aerosol radii.

The collection efficiencies are described in detail in Croft et al. (2009) and are parameters which depend on both aerosol and collector drop size (Croft et al., 2009). The parameterization for snow uses a size-dependent collection efficiency for snow and it is calculated as

E = mU_t

6πr_pηR + 4P e⁻¹(1 + 0.4Re^1/6P e^1/3), (11) where m is the mass of aerosol particle mass, U_t is the terminal velocity of the snow crystals, rp is the radius of aerosol particles, R is the radius of snow crystals, ηis the absolute viscosity of air, Reis the Reynold’s number and P e is the Peclect number.

The snow crystals are assumed to have a radius of 0.5 mm, a U_t of 80 cm s⁻¹ and a mass of 30 µg (Croft et al., 2009). The collection efficiency is then used to calculate the precipitation flux normalized scavenging coefficients as

Λ^s(r_p) = πR²

M E, (12)

where M is the mass of the snow crystal.

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3 Methods

To improve the wet deposition module for SALSA we used the M7 wet deposition module, described in section 2.1.4, as a starting point. The wet deposition module was extracted from the host model ECHAM6 and implemented to work as a box model. A box model is a zero-dimensional, offline tool which allows for evaluation and modification of a part of a larger, multi-dimensional program more efficiently.

The box model developed here includes the wet deposition module itself and further supporting subroutines and modules. The ambient conditions, as an input for the box model, were obtained from host model simulation using M7. The box model input variables were remapped from M7 modes to SALSA bins and the wet deposition box model was then adjusted to work with SALSA input variables. The wet deposition module was then adjusted to calculate the in-cloud nucleation scavenging fractions for water clouds according to Croft et al. (2010) using the number fraction of activated particles from the Abdul-Razzak and Ghan (2002) cloud activation scheme directly.

For ice clouds the scavenging fractions were calculated according to Tabazadeh et al.

(2002), assuming that the nucleation rate is a surface based process. This means that the scavenging fraction can be approximated using the ratio of surface area of the activated particles in each bin divided by the total surface area of all particles.

After testing with the box model the now functional new wet deposition module was then ported back to the host model ECHAM6. A schematic representation of the box model and the built supporting modules are described in this section. In addition, the mapping of aerosol representations between M7 and SALSA, the calculation of the old and new versions of the SALSA in-cloud nucleation scavenging fractions and the implementation of SALSA in-cloud scavenging fractions to the wet deposition module are presented in this section.

3.1 Box model

The box model approach has two main advantages. Firstly it takes a lot less compu- tation time to run than the entire climate model, which is due to the fact that the box model is run in only one grid box. Secondly the wet deposition module can be literally identical to the version implemented in ECHAM6, which gives the opportu- nity for easier modifications and also for easier evaluation of the results. A schematic representation of the boxmodel is presented in Fig. 3.

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Figure 3: Schematic representation of transfer from ECHAM6 to box model.

The main idea of a box model is that we have a fully operational climate model with a variety of modules from which we extract the module of interest (in this case the wet deposition module) and design further assisting modules which are needed run the extracted module. The values used as input for the box model are the same as for the climate model (usually taken from an additional simulation with the full climate model), which makes it easier to assure consistency between box and climate model.

3.1.1 Building the box model

After extracting the wet deposition module from the climate model the module struc- ture necessary to run the wet deposition module was designed. The box model needs a driver program, which provides the basic input parameters to the module. These parameters include control parameters, like number of levels, variable indices and in- cloud and below-cloud scheme flags, as well as ambient conditions, like temperature, rain rate, snow rate, etc. The latter set of parameters and the species mixing ratios, which serve as input to the module, were sampled from a short ECHAM6 simulation (1 month plus spin-up).

For the physical calculations, additional supporting modules were needed: one of which calculates the critical radius for one M7 mode with a log-normal aerosol distribution and the second of which uses this critical radius to calculate the number or mass fraction larger than the critical radius of the given mode using a cumulative normal distribution function.

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M7 uses up to 7 different mixing ratios per mode: total aerosol number (NUM) (_kg^#) as well as mass mixing ratios for SU (^kg_kg), BC (^kg_kg), OC (^kg_kg), SS (^kg_kg) and DU (^kg_kg). In addition, aerosol water (WAT) (^kg_kg) is included. For these variables, a definition module was implemented, which includes all settings for the individual variables. Note that the aerosol number, expressed in _kg^#, is not strictly speaking a mixing ratio as defined e.g. in Seinfeld and Pandis (2006), but for simplicity we will use here the terms “number mixing ratio” (_kg^#) and “mass mixing ratio” (^kg_kg) for aerosol number and aerosol mass, respectively, or, when referring to both, just

“mixing ratio”. Based on the input parameters provided by the driver program, the wet deposition module calculated the wet deposition flux for both in-cloud and below- cloud scavenging, described in section 2.1.4, which were then evaluated against the fluxes calculated in ECHAM6.

3.1.2 Mapping of aerosol representation from M7 to SALSA

For the comparison of M7 and SALSA microphysics packages in the box model, we discretized the modal aerosol representation into the SALSA size bins. This includes a conversion of both cloud-free and cloudy mixing ratios for all variables. The aerosol population in a M7 mode is lognormally distributed

n_N(lnD_p) = dN

dlnD_p = N_tot

√2πlnσg

exp

(

−(lnD_p −ln ¯D_pg)² 2 ln²σ_g

)

, (13) where σ_g is the geometric standard deviation of a mode, ¯D_pg is the geometric mean diameter of a mode andN_tis the total aerosol number concentration in a mode (Sein- feld and Pandis, 2006). In SALSA, size biniis defined using the lower diameter bound D_p,i,lo and the upper diameter boundD_p,i,hi. Integrating a log-normal mode between these bounds, we find the contribution of the mode to the number concentration in bin i,

N_i = N_tot 2

⎡

⎣erf

⎛

⎝

ln^D_D^p,i,hi_¯

√ pg

2 lnσ

⎞

⎠−erf

⎛

⎝

ln^D_D^p,i,lo_¯

√ pg

2 lnσ

⎞

⎠

⎤

⎦. (14)

The according contribution to the mass of species j in bin i can be calculated using Equation 13, when the density and the mass fraction of the species are known, as

M_i,j =w_jρjπNtot

12 exp

(

3 ln ¯D_pg+ 9 ln²σg

2

)

·

⎡

⎢

⎣erf

⎛

⎜

⎝

ln⁽^D_D^p,i,hi_¯

pg

)−3 ln²σ_g

√2 lnσ_g

⎞

⎟

⎠−erf

⎛

⎜

⎝

ln⁽^D_D^p,i,lo_¯

pg

)−3 ln²σ_g

√2 lnσ_g

⎞

⎟

⎠

⎤

⎥

⎦ (15)

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A detailed derivation of Equations 14 and 15 is presented in appendix A.

3.2 SALSA in-cloud nucleation scavenging fractions

The new in-cloud nucleation scavenging for SALSA was implemented following the method of Croft et al. (2010) described in section 2.1.3. However, instead of comput- ing the amount of scavenged aerosols from the total amount of formed CDNC, the fractions of activated particles for each size bin were directly obtained from the cloud activation scheme by Abdul-Razzak and Ghan (2002). In this method, the fraction of activated particles in each size bin depends on the maximum supersaturation of a rising air parcel, S_max, which is calculated as

S_max= Se

[

0.5⁽^ζ_η⁾^3/2+⁽_η+3ζ^S²^e ⁾^3/4

]1/2 , (16)

where

η= (αv_ud/G)^3/2

2πρ_wγN , (17)

ζ = 2A 3

(αv_ud G

)1/2

, (18)

v_ud is the updraft velocity, G is a size dependent growth coefficient (Abdul-Razzak et al., 1998), N is the total aerosol number concentration and α and γ are size- invariant coefficients in the supersaturation balance equation (Abdul-Razzak et al., 1998). In Equation 16,Se is the effective critical supersaturation which is calculated as

Se=

( ∑I i=1N_i

∑I

i=1N_i/S_i^2/3

)3/2

, (19)

where I is the number of bins,S_i is the critical supersaturation in the middle of the bini andNi is the number of particles in bini(Abdul-Razzak and Ghan, 2002). The critical supersaturation for each bin is calculated as

S_i = exp

⎛

⎝





√

4A³ρwπ 27n_s,iM_w6

⎞

⎠−1, (20)

where ρ_w is the water density, M_w is the mole weight of water, and n_s,i = νρ_s

M_sV_s,i, (21)

which is the number of moles of solute in one particle in bin i. Here ν is the number

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of ions,ρ_s is the density of solute,M_s is the mole weight of solute and V_s,iis the total volume concentration in a size bin i. Also in Equation 20

A= 4M_wσ_w

RρwT , (22)

describes the increase in water vapour pressure due to the curvature of the particle surface (Seinfeld and Pandis, 2006). Finally the number fraction of activated particles for size bin i, n_i, can be calculated as

ni = 0, ifS_max < Si,l (23)

n_i = logS_max/S_i,l

logS_i,u/S_i,l , ifS_i,l ≤S_max ≤S_i,u (24) n_i = 1, ifS_max > S_i,u (25) whereS_i,landS_i,urepresent the critical supersaturation of the lower and upper bound- ary of size bini, respectively, and they are calculated with Equation 20 (Abdul-Razzak and Ghan, 2002).

The original description assumed that almost all the mass in a particle is soluble, neglecting any possibly insoluble material. Usually this assumption is good for ap- proximating the amount of cloud droplets formed, especially when the soluble fraction is large. However the assumption may lead to an underestimation of scavenged particles if the fraction of insoluble material is very large (>0.99). This occurs because fairly large particles with a thin soluble coating are implicitly assumed to be fairly small and may thus fail to activate into cloud droplets. An illustration of a situation where the original description of critical supersaturation calculation is problematic is presented in Fig. 4.

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Figure 4: Schematic representation of the implicit reduction of particle size due to the assumptions made in the original cloud activation calculations in ECHAM6.

3.3 SALSA scavenging fractions for particles containing an insoluble core

The critical supersaturation for particles containing an insoluble core can be calculated by starting out with the Köhler curve, after Equation 17.38 in Seinfeld and Pandis (2006):

ln

(p_w(D_p) p^◦

)

= A

D_p − B

D³_p−D³_p,0 , (26) where A is obtained from Equation 22 and

B = 6n_sM_w

πρ_w , (27)

which is called the solute effect term. In Equation 26 Dp is the particle diameter and Dp,0 is the diameter of the insoluble core (Seinfeld and Pandis, 2006). The critical supersaturation, S_c, is defined as ln(p_w/p^◦) at the critical diameter, D_pc. D_pc is the position of the maximum of ln(p_w/p^◦), which can be calculated as the root of derivative of Equation 26 with respect to D_p. The calculations for the particle containing an insoluble core are based on the technical report from Kokkola et al.

(2008b) where the critical supersaturation is obtained as

S_c A =

2⁽₃^b⁾²+⁽^b₃⁾

[(_γ

+

2

)1/3

+⁽^γ₂⁻⁾^1/3

]

+

[(_γ

+

2

)2/3

+⁽^γ₂⁻⁾^2/3

]

9⁽₃^b⁾³+ 6⁽₃^b⁾²

[(_γ

+

2

)1/3

+⁽^γ₂⁻⁾^1/3

]

+ 3⁽₃^b⁾

[(_γ

+

2

)2/3

+⁽^γ₂⁻⁾^2/3

]

+d

(28)

(20)

where

γ± =

⎡

⎣2

(b 3

)3

+d

⎤

⎦±





√4

(b 3

)3

d+d² (29)

and

b=

√3B

A (30)

d=D_p,0³ (31)

Using this new expression for the critical supersaturation, the effective critical supersaturation, maximum supersaturation, and the number fraction of activated particles for each size bin can be calculated using Equations 19, 16 and 23-25. To illustrate the difference between Equations 20 and 28, the Köhler curves of different particles using only the soluble fraction, and accounting for the insoluble core are shown in Fig. 5. The left figure shows the Köhler curves plotted with a fixed dry diameter of 3·10⁻⁷ m with an increasing insoluble core size, and the right figure shows the Köhler curves plotted with fixed insoluble diameter of 1·10⁻⁶ m with an increasing amount of soluble substance. The bottom figure shows a schematic representation of how the insoluble or soluble parts are increased in the particles.

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Figure 5: Equilibrium supersaturation as a function of particle size with fixed dry radius and fixed insoluble core radius assuming that the entire particle is soluble (dashed lines) and accounting for the insoluble core (solid lines.

From Fig. 5 one can see that the critical supersaturation is smaller but occurs at a larger wet diameter when the insoluble core of the particle is being accounted for.

Even though accounting for the insoluble core of the aerosol particles in the supersaturation calculations has a big effect on the amount of large insoluble particles, there is nearly no notable change in modelled CDNC (not shown here). This is due to two restrictions imposed by other parts of the model. Firstly, ECHAM6 enforces a minimum CDNC of 40 _cm^#3, which is rarely exceeded in the Abdul-Razzak and Ghan (2002) calculations. Therefore most of the computed increase in CDNC is simply cut off by this restriction. Secondly, the cloud fraction, which is used to express how much of a model grid cell is covered by cloud, has a very high variability in ECHAM6.

Because the CDNC burden calculations are directly affected by the variability of the cloud fraction, most of the rest of the changes in CDNC are smeared out and thus hard or impossible to detect. However the strongest effect is seen in the number of activated particles, in the largest insoluble bins, as it should increase, because these

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particles often contain large fractions of mineral dust. The strongest effect can be seen in Fig. 6.

Figure 6: Number of activated particles in largest insoluble bin (2b7) taken as a vertical sum over all altitudes, 2010 yearly mean, simulated with the Abdul-Razzak and Ghan (2002) cloud activation method without and with the insoluble core correction.

In Fig. 6 the top panel shows the number of activated particles in the largest insoluble bin, 2b7, simulated with Abdul-Razzak and Ghan (2002) cloud activation method without the insoluble core correction, calculated as a vertical sum over all

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altitudes and a yearly mean from the year 2010. The bottom panel shows the same but taking into account the insoluble core in the particles. The number of activated particles increase massively when taking into account the insoluble core in the activation calculations. The strongest effect is seen especially around the Sahara area, since dust particles, although insoluble, can activate after they are aged by condensation of sulfate onto their surface.

3.4 SALSA scavenging fractions in wet deposition module

Before implementation in ECHAM6, the new in-cloud nucleation scavenging scheme was tested extensively in the box model. For water containing clouds the in-cloud nucleation scavenging for SALSA was implemented using the new Abdul-Razzak and Ghan (2002) method. The new method takes into account the insoluble core of the particles as explained in the previous section.

The in-cloud nucleation scavenging for ice containing clouds was implemented according to Tabazadeh et al. (2002). In a unit volume of air during a unit of time, the total number of ice crystals formed can be derived as

J_T =J_VV_t+J_SS_t, (32) where J_V is the sum of the nucleation rate in a unit volume of liquid solution, V_t is the collective volume, J_S is the nucleation rate on a unit surface area of the liquid solution and S_t is the collective surface area (Tabazadeh et al., 2002).

However, experimental studies and intuitive thermodynamic calculations for the ice- water-air system suggest that the total number of ice nuclei formed may be dominated by surface-based processes, so that J_SS_t ≫J_VV_t Tabazadeh et al. (2002). Therefore Equation 32 reduces to

J_T ≈J_SS_t. (33)

Now the scavenging fraction in the ice containing clouds in bin i is proportional to the ratio between nucleation rate in the bin and the total nucleation rate. Thus we get for the scavenging fraction for the ice containing clouds in each bin

F_i = S_in_i

S_tot = S_in_i

∑S_in_i, (34)

where S_i are the surface area of one particle in bin i and n_i the number of particles in bin i. The surface area for a particle is derived using the wet diameter in each

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monodisperse size bin.

The in-cloud impaction scavenging for SALSA was implemented the same way as was done by Croft et al. (2010) for the M7 microphysics package. Since the impaction scavenging uses a look-up table, compiled from the calculations in section 2.1.4, to get the impaction scavenging coefficients for different size cloud droplets, or ice crystals, and aerosol particles, the evaluation can be used similarly for SALSA as for M7.

The below-cloud scheme was left untouched as it uses the M7 parameterization described in section 2.1.4, but assuming that the SALSA bins are log-normal modes. The modification of below-cloud scheme to better suit the sectional approach of SALSA is left for later studies.

As a conclusion for the new SALSA wet deposition scheme, the version follows Croft et al. (2010) but using the Abdul-Razzak and Ghan (2002), for the cloud activation, in in-cloud nucleation scavenging taking into account the insoluble core of the particles in the calculations. The new scheme also calculates the nucleation scavenging for ice containing clouds following Tabazadeh et al. (2002). The in-cloud impaction follows Croft et al. (2010) method the similar way as for M7. The old version of SALSA wet deposition scheme is the original description of the wet deposition where the method only uses fixed scavenging coefficients for in-cloud calculations, which is described in section 2.1.3.

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4 Results

The comparison of ECHAM6 simulations between the old and new wet deposition scheme and the comparison of the results to data from aircraft and satellite measurements are presented in this section. The simulations were done with ECHAM6 for the year 2010 using 3-hourly data output, after a 6 month spin-up. The emissions were obtained from the ACCMIP (Emissions for Atmospheric Chemistry and Climate Model Intercomparison Project) emission inventories. In particular, RCP4.5 (Representa- tive Concentration Pathways) was selected. The model meteorology was nudged towards meteorological observations of ECMWF (European Centre for Medium-Range Weather Forecasts), and the sea surface temperature (SST) and sea ice cover (SIC) were also prescribed. SST and SIC were obtained from monthly mean climatolo- gies from AMIP (Atmospheric Model Intercomparison Project). A summary of the differences between the used schemes is presented at the end of section 3.4.

4.1 Comparison of the old and the new SALSA wet deposi- tion schemes

The wet deposition flux computed by the model provides information about the amount of aerosol deposited by wet removal in units of kilograms per square me- ter per second. Here we compared the simulated wet deposition flux of total aerosol number and BC mass between the old and the new SALSA wet deposition scheme.

The wet deposition fluxes of total aerosol number from the year 2010 as an annual mean for the old and the new methods as well as the difference between these methods and the descriptive statistical data are presented in Fig. 7.

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(a) (b)

(c)

Total aerosol number flux (_m^#2s) Old (a) New (b) Diff. (c)

min 3.63e3 2.83 -4.42e8

max 7.49e8 6.40e8 9.21e6 mean 4.29e7 1.94e7 -2.35e7

Figure 7: Annual mean wet deposition flux of total aerosol number for the year 2010 retrieved by (a) the old method (b) the new method and (c) the difference between the methods and the descriptive statistical data of the figures.

We can see that the wet deposition flux of total aerosol number decreases globally in the new method especially around the main aerosol source regions, including parts in China and around the main sea salt emitting areas such as the southern parts of the Indian and Pacific Oceans. The same decrease in the new method, around the main aerosol source regions, can also be seen in the wet deposition flux of BC mass.

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(a) (b)

(c)

Black carbon (_m^kg2s)

Old (a) New (b) Diff. (c) min 1.26e-18 7.80e-21 -1.23e-11 max 2.18e-11 1.09e-11 9.93e-13 mean 4.66e-13 4.65e-13 -1.13e-15

Figure 8: Annual mean wet deposition flux of black carbon for the year 2010 retrieved by (a) the old method (b) the new method and (c) the difference between the methods and the descriptive statistical data of the figures.

Fig. 8 shows the wet deposition flux of BC as an annual mean for the year 2010 simulated with the old and the new method and the difference between the methods as well as the descriptive statistical data from the figures. The main decrease in BC wet deposition flux can be seen around the most polluted area in China.

A vertical profile visualises the concentration of aerosol species at different altitudes.

Here we compared the vertical profiles of black carbon, simulated with the old and

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new SALSA wet deposition schemes for the year 2010 taken as a monthly mean values.

Fig. 9 shows the vertical profiles of BC mass for the different latitude bands (0^◦-30^◦, 30^◦-60^◦, 60^◦-90^◦). The left column shows the results for the old method and the right shows the results for the new method.

Figure 9: Vertical profiles of BC mass for the different latitude bands (0^◦-30^◦, 30^◦-60^◦, 60^◦-90^◦).

The new method shows an increase in the concentrations especially during winter

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seasons for the lower latitudes but also for all months in the Arctic. Between 0^◦ and 30^◦-60^◦ north, the concentrations seem to increase closer to the surface than in the Arctic, but the Arctic latitudes show a massive increase at higher altitudes. Espe- cially around the altitudes corresponding vertical levels 19-37 the concentrations in the new method are more than doubled compared to the old method. As the surface concentrations are mainly controlled by local sources of aerosols the increase the near-surface concentrations in the new method is most noticeable at the latitudes where the sources are situated. In the Arctic, the lack of primary sources leads to only slight differences near the surface. The concentrations at the higher altitudes are dominated by transport of aerosol species. Therefore the increase at the higher altitudes in the Arctic implies that BC transport into the Arctic is increased in the new method.

The atmospheric burden of aerosol species provides the information of the total mass of aerosol in a vertical column of air. Here we compared the atmospheric burden of black carbon and mineral dust simulated with the old and the new method of the SALSA wet deposition scheme. The annual mean of atmospheric BC burden for the year 2010 with the old and new method, their difference, and the descriptive statistical data are presented in Fig. 10.

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(a) (b)

(c)

Black carbon (_m^kg2)

Old (a) New (b) Diff. (c) min 1.63e-8 5.89e-8 -1.43e-8 max 4.17e-6 5.55e-6 1.69e-6 mean 3.11e-7 5.47e-7 2.36e-7

Figure 10: Annual mean atmospheric burden of black carbon for the year 2010 retrieved by (a) the old method (b) the new method and (c) the difference between the methods and the descriptive statistical data of the figures.

For BC we can see that the atmospheric burden increases in the new method, not only around the aerosol source areas, such as central Africa, Northern India and China, but also throughout the Northern Hemisphere. The annual mean of atmospheric burden of mineral dust from the year 2010 with the old and new method and the difference as well as the descriptive statistical data are presented in Fig. 11. Note also that the scale in the difference between the methods is lowered for better visualisation.

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(a) (b)

(c)

Mineral dust (_m^kg2)

Figure 11: Annual mean atmospheric burden of mineral dust for the year 2010 retrieved by (a) the old method (b) the new method and (c) the difference between the methods and the descriptive statistical data of the figures. NOTE: Scale in difference is lowered, compared to different method visualisations, for better indication of the difference.

Figure 11 shows a clear decrease in the atmospheric DU burden around the Sahara region, when simulated with the new method. This occurs because the insoluble mineral dust particles in the largest size bins are more effectively activated when the insoluble core of these particles is taken into account in the cloud activation calculations. Thus the wet deposition flux of these particles increases.

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Aerosol optical thickness (AOT) describes the amount of visible light attenuated by aerosol species from the top of the atmosphere to the surface. The AOT for light of 550 nm wavelength gives a good representation of the amount of aerosol in different regions. Here we compared the 550 nm wavelength AOT of the total aerosol, simulated with the old and the new method of the SALSA wet deposition scheme. The annual mean AOT from the year 2010 with the old and new method and the difference as well as the descriptive statistical data are presented in Fig. 12.

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(a) (b)

(c)

AOT total 550nm

Figure 12: Annual mean AOT of 550 nm light for the total aerosol for the year 2010 retrieved by (a) the old method (b) the new method and (c) the difference between the methods and the descriptive statistical data of the figures.

We can see that the AOT of total aerosol is increased in the new method not only at the source areas (China) but also all across the globe. There is also some increase around the high northern latitudes, which implies that the amount of total aerosol is also increased in the Arctic. These Arctic aerosols usually consist mostly of sulfates, particulate organic matter, black carbon and dust when the concentrations are at maximum (von Hardenberg et al., 2012). This implies that the transport of these aerosol species to the Arctic increases in the new method.

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4.2 Evaluations against MODIS

Evaluation of the model against satellite observations usually is a good indicator of how well the model reproduces the aerosol characteristics. Here we compared the simulated AOTs from the old and the new method against satellite observations retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) which is an instrument on board the satellites Aqua and Terra (King et al., 1999; Kokkola et al., 2018). The evaluations were made for the year 2010 and for MODIS the combined product of Deep Blue and Dark Target retrievals for 550 nm wavelength AOT was used (Sayer et al., 2014; Kokkola et al., 2018). The modeled AOT was sampled from locations and times where satellite observations were made using CIS (Community Intercomparison Suite) (Watson-Parris et al., 2016). MODIS measurements and the comparison between the old and the new method against MODIS observations as well as the descriptive statistical data of the measurements and the differences are presented in Fig. 13.

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(a)

Deep Blue and Dark AOT 550nm

Abs Bias

MODIS (a) Old (b) New (c)

min -1.60e-2 -3.71 -3.66

max 3.72 0.42 0.41

mean 1.70e-1 -5.24e-2 1.01e-2

(b) (c)

Figure 13: Annual mean AOT of 550 nm light for the total aerosol for the year 2010 retrieved by (a) MODIS, (b) the difference between the old method and MODIS, (c) the difference between the new method and MODIS, and the descriptive statistical data of the figures.

Here we can see that there are still quite big differences in the observations and model simulations especially around the highly polluted areas such as India, China, South America and central Africa, which are areas with a strong primary aerosol emissions.

The formation of secondary organic aerosol (SOA) is a dominant effect in producing aerosols in central Africa and South America. This causes a strong bias around these areas, because SOA formation is not included in the model simulations. This can

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also be seen in the mean difference in the AOT of the new method which has a lower bias towards MODIS compared to the old method. The bias in the new method over the ocean areas around the equator is lower as well. In addition, the Sahara region, where the new method alters the DU burdens most noticeably, shows a slight decrease in AOT and therefore a lower bias towards MODIS. The surroundings of the highly polluted areas also show a lower bias in the new method.

Zonal mean AOT gives a good indicator of how well the simulated AOT represents the measurements at different latitudes. Fig. 14 shows the zonal mean for the annual mean of AOT simulated with the old, the new SALSA wet deposition scheme and for MODIS measurements.

Figure 14: Zonal mean of aerosol optical thickness (AOT) as an annual mean for year 2010 simulated with the old (red curve) and the new (blue curve) method of SALSA wet deposition scheme and observed by MODIS (black curve).

From Fig. 14 can be seen that, compared to the MODIS obervations, the AOT decreases faster from Equator to the poles in the simulations, for both the old and the new method. The trend over the Southern Hemisphere is different in the model simulations compared to the satellite observations. Overall, the zonal average AOT of the new method has a better agreement to the observations than the old method except over the latitude bands 20^◦-35^◦ N and 15^◦-30^◦ S. At these latitude bands the AOT is overestimated by the new method. On the other hand, the old method underestimates the AOT between 20^◦-35^◦ N compared to MODIS. The new wet deposition scheme increases the AOT towards the Arctic, showing a better agreement with MODIS, as the zonal gradient of AOT is strongly dependent on the wet deposition over the

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Northern Hemisphere (Kokkola et al., 2018).

4.3 Evaluation against aircraft observations

The vertical profiles of the modeled BC mass mixing ratios of the old and the new method were compared against the mass mixing ratios from different aircraft measurements campaigns. The data for the aircraft measurements were obtained from the following campaigns: ARCPAC (Brock et al., 2011), ARCTAS (Jacob et al., 2010), ARCTAS-CARB (Jacob et al., 2010), TC4 (Toon et al., 2010), CR- AVE (https://espo.nasa.gov/ave-costarica2/, last access: 8 January 2019), and AVE- Houston (https://espo.nasa.gov/ave-houston, last access: 8 January 2019). The year 2010 monthly mean vertical profiles of BC, corresponding with the month of the performed campaign, simulated with the old and the new method and the observed values are presented in Fig. 15. Note that the mass mixing ratios are shown on a logarithmic scale.

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(a)

(b)

Figure 15: Monthly mean vertical profiles of BC for the year 2010 for the observed aircraft measurement campaigns (black curves) and modeled with the old (blue curves) and the new (red curves) method. Figures (a) represents the areas near the primary sources and (b) represents the areas near the Arctic.

In Fig. 15 the aircraft campaigns are divided into two categories: tropics and midlat- itudes (near primary sources: AVE Houston, CR-AVE, TC4 and CARB), and high latitudes (near the Arctic: ARCTAS and ARCPAC). A detailed description of the aircraft campaigns is given in Koch et al. (2009). From Fig. 15 we can see that near the sources the model simulations tend to overestimate the BC mass mixing ratios, except for the CARB campaign, where the old method falls between the standard deviation limits of the measurements, but the new method overestimates the mass mixing ratios at higher altitudes. When looking at the high latitudes the mass mixing ratios are underestimated in both simulations except the Summer ARCTAS NASA DC-8 campaign, where the new method overestimates the mass mixing ratios at higher al-

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titudes. Overall the new method overestimates the BC mass mixing ratios more than the old method near the source areas, but near the Arctic the new method tends to represent the measured mass mixing ratios better than the old method. This is visible in all vertical profiles in the arctic campaigns with exception of the Summer ARCTAS NASA DC-8 campaign. In addition, the new method shows higher mass mixing ratios at higher altitudes compared to the old method. The trends in ARCTAS Spring and ARCPAC seem to also be better in line with the observations in the new method.

The vertical profiles of SU and OC simulated with the old and the new method were also compared against 17 aircraft measurement campaigns. The data for the aircraft measurements were obtained from the following campaigns: ACE-Asia (Hue- bert et al., 2003; Maria et al., 2003; Gilardoni et al., 2007), ADIENT (Morgan et al., 2010), ADRIEX (Highwood et al., 2007; Crosier et al., 2007), AMMA (Redelsperger et al., 2006; Capes et al., 2009), ARCTAS (Jacob et al., 2010; Cubison et al., 2011), DABEX (Haywood et al., 2008; Capes et al., 2008), DODO (Capes et al., 2008), EUCAARI (Kulmala et al., 2009; Morgan et al., 2010), IMPEX (Dunlea et al., 2009), ITCT-2K4 (Heald et al., 2006; Sullivan et al., 2006), ITOP (Fehsenfeld et al., 2006;

Lewis et al., 2007), OP3 (Hewitt et al., 2010; Robinson et al., 2011), TexAQS (Parrish et al., 2009; Bahreini et al., 2009), TROMPEX (Heald et al., 2011) and VOCALS-UK (Wood et al., 2011; Allen et al., 2011).

The vertical profiles for different aircraft campaigns and the old and new method modelled results for SU and OC are presented in Fig. 16 and Fig. 17 respectively.

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Figure 16: Monthly mean vertical profiles of SU for the year 2010 for the observed aircraft measurement campaigns (black curves) and modeled with the old (blue curves) and the new (red curves) method.

On the effect of the wet deposition scheme on the simulated aerosol life-cycle in a global climate model