3.1 Oceanic fluxes

The oceanic emission of inorganic iodine (HOI and I₂) follows the deposition of O₃ to the surface ocean and reaction with iodide (I⁻) (Carpenter et al., 2013). We use Eqs. (19) and (20) in Carpenter et al. (2013) for the calculation of these emissions. Ocean surface I⁻ is parameterized using MacDonald et al. (2014) (see Fig. S1 in the Supplement). Figure 2 shows the average oceanic emission for inorganic iodine (I₂ in Fig. 2a and HOI in Fig. 2b) during January and February 2012. Higher emissions for inorganic iodine occur in the tropics with HOI being the dominant species.

Figure 2Mean oceanic surface fluxes for inorganic iodine: I₂ (a) and HOI (b). The column-integrated fluxes for inorganic bromine (Br₂, c) from the debromination process during January and February 2012 are also shown. Values are given in 10⁶ molec cm⁻² s⁻¹.

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Two different approaches for the marine emissions of the halocarbons (CHBr₃, CH₂Br₂, CH₃I, CH₂BrCl, CHBrCl₂, CHBr₂Cl, CH₂I₂, CH₂IBr and CH₂ICl) are examined in this model. The first approach uses prescribed monthly average oceanic fluxes from Ziska et al. (2013) and the second computes the oceanic fluxes online. Prescribed monthly average oceanic fluxes from Ziska et al. (2013) were calculated using 6-hourly means of wind speed and sea surface temperature from the ERA-Interim meteorological assimilation database (Dee et al., 2011) for the years 1989–2011 ($\mathrm{1}{}^{\circ }\times \mathrm{1}{}^{\circ }$). Computing the emissions online accounts for an interaction between the modelled atmosphere and the ocean at each time step. Thus, this approach can respond to changes in meteorological parameters, like surface temperature and surface wind speed. A two-layer model (Liss and Slater, 1974) is used to calculate the halocarbons air–sea fluxes:

where K_a is the transfer velocity of the gas (s⁻¹), C_g (ppm) and C_l (nM) are the bulk gas and liquid-phase concentrations, and K_H is Henry's law constant. K_a is parameterized following Johnson (2010) which is mainly a function of wind speed and sea surface temperature (SST) taken from the model at each time step. C_g is also taken from the model. Halocarbon seawater concentrations C_l are taken from Ziska et al. (2013). Figure 3 shows the average air–sea fluxes for CHBr₃, CH₂Br₂ and CH₃I during January and February 2012 for the two approaches. Note that the online calculation could increase, decrease or even reverse the fluxes in comparison with the prescribed emissions. This is the case for the online fluxes of CHBr₃ over the tropics where the model calculates negative fluxes, whereas the prescribed fluxes are positive.

Figure 3Mean oceanic fluxes for halocarbons (CHBr₃, CH₂Br₂ and CH₃I) during January and February 2012. Prescribed fluxes are shown on the top (a, b, c) and online fluxes on the bottom (d, e, f). Values are given in 10⁶ molec cm⁻² s⁻¹.

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Recent studies suggest that the ocean is an important source of OVOCs such as acetaldehyde, ethanol and methanol (Coburn et al., 2014; Lawson et al., 2015; Mahajan et al., 2014; Myriokefalitakis et al., 2008; Sinreich et al., 2010; Volkamer et al., 2015; Yang et al., 2014; Fischer et al., 2012) that models do not generally consider or are not able to capture (Millet et al., 2010; Sherwen et al., 2016a). Oceanic fluxes of several VOCs have been included into WRF-Chem as part if this study. For the three OVOCs (acetaldehyde (CH₃CHO), ethanol (C₂H₆O) and methanol (CH₃OH)), the same online approach as for the VSLHs is used to calculate the marine fluxes where their seawater concentrations are taken from Yang et al. (2014).

Emissions for alkenes and alkanes (C₂H₄, C₃H₆, C₂H₆, C₃H₈) are prescribed and based on the POET (Granier et al., 2005) global inventory.

Deposition over the ocean for the halocarbons and OVOCs is included in the air–sea fluxes described above. For the rest of the species, dry deposition is calculated with the Wesely scheme (Wesely, 1989), which is used over land for several species. Washout of gases by precipitation is simulated using the scheme included in WRF-Chem (Grell and Dévényi, 2002; Zaveri et al., 2008) which was modified to include Henry's law constants for the RHS shown in Table 1.

Table 1Henry's law constant for relevant halogen species implemented in WRF-Chem. INO₂ Henry's law constant is assumed equal to that of BrNO₂. Iodine oxide (I₂O_x) Henry's law constants are assumed to be infinity by analogy with INO₃. Virtually infinity solubility is represented by using a very large number (2.69×10¹⁵).

^* Effective Henry's law of HX is calculated for acid conditions (pH =4.5): $K_{\mathrm{H}}^{*}\left(T\right)=K_{\mathrm{H}}\left(T\right)\times \left(\mathrm{1}+\frac{K_a}{\left[H^{+}\right]}\right)$, where X =Cl, I or Br and $K_{\mathrm{a}}=\mathrm{1}\times {\mathrm{10}}^{\mathrm{9}}$ M is the acid dissociation constant (Bell, 1973).

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The sea-salt aerosol emissions parameterization used in this study is described in Archer-Nicholls et al. (2014). This parameterization is mainly a function of wind speed from the model and uses the emissions scheme from Gong et al. (1997) for particles with dry diameters of 0.45 nm or more, and for smaller particles, it uses Fuentes et al. (2010).

3.2 Gas-phase chemistry scheme

Our reaction mechanism is based on the MOZART-4 mechanism (Emmons et al., 2010; Knote et al., 2014). This mechanism has been extended to include bromine, chlorine and iodine chemistry and has been coupled with the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) four-bin aerosol module (Zaveri et al., 2008). A total of 48 species and 159 halogen reactions have been included (see Tables 2, 3 and 4 for details). Inorganic, organic and inter-halogen reactions come from the 1-D model MISTRA (Sommariva and von Glasow, 2012). Production and loss reactions of the higher order of iodine oxides (I₂O_x, where $x=\mathrm{2},\mathrm{3},\mathrm{4}$) reactions have been included into the model. Photochemistry of I₂O_x species is still an area of high uncertainty in atmospheric iodine chemistry (Sommariva et al., 2012; Saiz-Lopez et al., 2012b). Chemical loss of VSLHs through oxidation by the hydroxyl radical (OH) and by photolysis is included using data from S. P. Sander et al. (2011).

Table 2Bimolecular and thermal decomposition halogen reactions included in WRF-Chem. These reactions are given in the Arrhenius form with the rate equal to $A\times e^{\frac{-Ea}{RT}}$.

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Table 3Termolecular reactions for halogens species included in WRF-Chem. The lower pressure limit rate (K₀) is given by $A_{\mathrm{0}}\times {\left(\frac{T}{\mathrm{300}}\right)}^a$. The high pressure limit (K_∞) is given by $B_{\mathrm{0}}\times {\left(\frac{\mathrm{300}}{T}\right)}^b$. F_c describes the fall of curve of the reaction described by Atkinson et al. (2007). Then the reaction rate (k) is defined as $K_{\mathrm{0}}\left[M\right]/\left(\mathrm{1}+\frac{K_{\mathrm{0}}\left[M\right]}{K_{\mathrm{\infty }}}\right)\times F_c^n$ and n as ${\left(\mathrm{1}+{\left({\log }_{\mathrm{10}}\frac{K_{\mathrm{0}}\left[M\right]}{K_{\mathrm{\infty }}}\right)}^{\mathrm{2}}\right)}^{-\mathrm{1}}$.

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Table 4Photolytic reactions of halogens included in WRF-Chem.

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A schematic representation of the main bromine and iodine chemistry implemented in the model is shown in Fig. 4. Chlorine chemistry is also included into the model; however, since our results are mainly focused on reactive bromine and iodine, we do not include chlorine chemistry in Fig. 4.

Figure 4Schematic representation of the implemented iodine and bromine chemistry in WRF-Chem. Chlorine chemistry has been included into the model; since our results are mainly focused on reactive bromine and iodine, we decided not to include chlorine chemistry in this figure. Red lines represent photolytic reactions, dark blue lines gas-phase pathways, light blue lines fluxes, green lines deposition and purple curved lines heterogeneous pathways.

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Photolysis reactions included in the mechanism are listed in Table 4. To compute the photolysis rates the fast Tropospheric Ultraviolet–Visible (FTUV) online scheme (Tie et al., 2003) is used. The quantum yields and cross section for the photolytic reactions of halogens are from the Jet Propulsion Laboratory (JPL) 10-6 (S. P. Sander et al., 2011) and have been linearly interpolated onto the 17 wavelength bins used by FTUV. For I₂O_x, we use the quantum yield and cross-section data from Gómez Martín et al. (2005).

3.3 Heterogeneous chemistry

Heterogeneous reactions on particle surfaces involving halogens are summarized in Table 5. The heterogeneous chemistry is assumed to take place between a gas-phase species and an adsorbed species. The bulk aqueous-phase chemistry in sea-salt aerosols is not treated. Uptake coefficients are used to calculate first-order rate constants for heterogeneous loss of the gas phase to the adsorbing surface (Jacob, 2000). This follows the approach used by McFiggans et al. (2000), which assumes a free molecular transfer regime approximation. The reaction rate constants, K (s⁻¹), are given by

where γ is the uptake coefficient, S is the root mean square molecular speed (m s⁻¹) and A is the total available aerosol surface area density (cm² cm⁻³). Equation (2) does not take account of any diffusion limitation (i.e. the rate at which gases can diffuse towards the aerosol surface).

Table 5Halogen heterogeneous reactions added to WRF-Chem in this study.

^a Uptake coefficient for moderate temperature.
^b Uptake coefficient for cold temperatures.

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We test the sensitivity of our results by adding the diffusion term, D_g (cm² s⁻¹), following Brasseur and Jacob (2017), where the reaction rate constant, K (s⁻¹), is given by

where n_bin is the number of particle-size bins, r_i is the particle radius for bin i (cm), and A_i is the available aerosol surface area density for bin i (cm² cm⁻³). K is integrated over the aerosol size distribution in order to resolve the dependence of the rate constant for the particle radius. Following Brasseur and Jacob (2017), D_g is given by

where m_g is the mean molecular mass of the gas-phase specie (g mol⁻¹), m_air is the molecular mass of air (g mol⁻¹), n_a is the total air number density (molecules cm⁻³), and T is the temperature (K).

Second-order reaction rate constants are calculated by dividing the first-order rate constant by the concentrations of the adsorbed species. Heterogeneous halogen activation is very efficient under cold or stratospheric conditions as compared to moderate temperatures. For this reason, we have made a distinction between moderate (>243.15 K) and cold temperatures (<243.15 K) in some reactions. Uptake coefficients for reactions in Table 5 are based on literature values where available (Jacob, 2000; Sander et al., 2006; Ordóñez et al., 2012).

There are six reactions implemented for sea-salt particles. The sea-salt surface area is calculated in the following way: (1) using the mass of Na and Cl and the associated H₂O for each bin and the individual dry densities (for Na, Cl and H₂O), the total volume of those particles for each bin is calculated and then, (2) assuming that sea-salt aerosols are spheres, the total surface area is calculated for each bin using this volume and the radius of aerosols in each bin.

It is known that the chemistry involving the release of bromine from the sea-salt aerosol (debromination) is strongly pH dependent, being more efficient for acidified aerosol especially with a pH <5.5 (Keene et al., 1998). Therefore, the pH value of the aerosol particles is calculated in the model for each size bin (see Zaveri et al., 2008 for further description of the pH calculation). We then apply a pH dependence to the heterogeneous reactions that occur on the surface of the sea salt. When the pH <5.5 debromination reactions occur with the release of Br₂ and IBr resulting from the uptake of BrNO₃, BrNO₂, HOBr, INO₃, INO₂ and HOI (Reactions R11–R16). When the pH >5.5 no debromination reactions occur, although uptake of INO₃, INO₂ and HOI on the sea salt still occurs (Reactions R17–R19) leading to a change in iodine speciation but no release of Br. See also Table 5.

If the pH <5.5,

If the pH >5.5,

Due to the high uncertainty in the debromination process, the fraction of Br₂ formed by Reactions (R11)–(R13) was chosen arbitrarily in order to add an extra bromine source in a simple way. A value of 0.6 was chosen. Figure 2 shows the column-integrated fluxes for inorganic bromine (Br₂; Fig. 2c) during January and February 2012.

In addition, the heterogeneous uptake of N₂O₅ onto aerosol particles that contain Cl⁻ to form ClNO₂ is considered in the model. After uptake, N₂O₅ is taken up onto the particle; it reacts reversibly with liquid water to form protonated nitric acid intermediate (H₂ONO⁺²). This then reacts with either liquid water, to form aqueous nitric acid (HNO₃), or with chloride ions to form ClNO₂. See Archer-Nicholls et al. (2014) for further description of this chemistry. In Archer-Nicholls et al. (2014), ClNO₂ was considered as an inert specie; however, in our study, ClNO₂ is not treated as an inert specie but is broken down via photolysis and reaction with OH (see Tables 2 and 4).

4.1 Sensitivity studies

A total of 11 different simulations were performed in this study. Our base simulation, WRF-DEBROM, considered all main processes involving halogen chemistry (sea-salt debromination, heterogeneous chemistry and reactions between halogens and VOCs) and computes the oceanic halocarbon fluxes online. The WRF-ZIS simulation is the same as WRF-DEBROM but uses prescribed oceanic emissions for the halocarbons. To test the sensitivity to the heterogeneous reaction rate constants, two runs were performed: the first one, in which the values for the uptake coefficient (γ from Eq. 2) from Table 5 have been divided by 2, WRF-GAMMADV2, and the second one, where Eq. (3) that has a diffusion term, is used, WRF-DIFF. To account for the importance of the debromination in sea-salt particles, we performed the simulation WRF-NODEBROM which is the same as the WRF-DEBROM simulation but without debromination. The WRF-NOHET simulation is the same as WRF-NODEBROM but without heterogeneous chemistry. A simulation with no halogen chemistry, WRF-NOHAL, is performed to study the effect of halogens on the tropospheric chemistry. All simulations except WRF-NOHAL use ICs/BCs from the GEOS-Chem model that include halogens. The WRF-NOHAL simulation uses ICs/BCs from the GEOS-Chem model with no halogen chemistry. Finally, to study the oxidation of VOCs by halogens, four simulations have been performed: (1) a simulation without the reactions of bromine reactions with alkenes (WRF-NOBRALKE), (2) a simulation without the reactions of bromine with aldehydes (WRF-NOBRALD), (3) a simulation without the reactions of bromine with VOCs, therefore neither alkenes nor aldehydes (WRF-NOBRVOCS), and (4) a simulation without reactions of bromine and chlorine with VOCs (WRF-NOHALVOCS). See Table 7 for a summary of all these simulations.

Table 7Summary of all the simulations to investigate the main processes involving reactions between halogen chemistry.

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5.1 Oceanic emissions: halocarbons

Figure 5 shows the time series of CHBr₃ (Fig. 5a), CH₂Br₂ (Fig. 5b) and CH₃I (Fig. 5c) mixing ratios (in pptv) for the WRF-ZIS (green line) and WRF-DEBROM (black line) runs. In addition, the modelled wind speed (black line) is also shown in Fig. 5 (Fig. 5d). Measurements for the halocarbons and wind speed are represented by the solid red lines. Figure 6 presents the time series of CHBr₃, CH₂Br₂ and CH₃I water concentration (in pmol L⁻¹) from the measurements (dashed red lines) and from the Ziska et al. (2013) climatology (dashed blue lines) used to compute both the prescribed and online fluxes.

Figure 5Time series of CHBr₃ (a), CH₂Br₂ (b) and CH₃I (c) mixing rations (in pptv) for the WRF-ZIS (green line) and WRF-DEBROM (black line) runs during the period of the TORERO campaign in 2012. In panel (d), the wind speed (m s⁻¹) of the model is shown with a black line. Measurements during the TORERO campaign are depicted with red lines.

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Figure 6Time series of measured CHBr₃ (a), CH₂Br₂ (b) and CH₃I (c) water concentration (in pmol L⁻¹) during the TORERO campaign (red dashed line) and from the Ziska et al. (2013) climatology (blue dashed line).

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In general, both simulations reproduce the concentrations of the halocarbons to the right order of magnitude, although there are specific periods with a negative bias. We see a tendency to underestimate CHBr₃ for both model simulations during most of the period. This result is similar to the study of Hossaini et al. (2016), who compared 11 global models using different emissions inventories. The majority of the models do not reproduce the observed concentrations in the tropical marine boundary layer. Over the tropics, high emissions observed are associated with tropical upwelling and active planktonic production (Class and Ballschmiter, 1988; Atlas et al., 1993). One reason for low CHBr₃ concentrations in our model simulations might be that the seawater concentrations are too low in this area (see Fig. 6 for CHBr₃). The fluxes are also low (see Fig. S2). Note that Ziska et al. (2013) used only a very limited amount of data to derive the seawater concentration for the halocarbons in our domain, which leads to uncertainty in the calculated fluxes. The modelled CHBr₃ is underestimated throughout the troposphere when it is compared with aircraft observations (see Fig. S3). Atmospheric concentrations of CH₂Br₂ are in good agreement with the observations although the model underestimates the observed values by ∼0.5 pptv during the periods 6–10 and 22–25 February. Bromocarbon concentrations agree better with the measurements when the oceanic fluxes are calculated online (WRF-DEBROM); in particular, the underestimation is less for specific periods (e.g. 20 February for CHBr₃ and 10 and 22 February for CH₂Br₂) in comparison with WRF-ZIS. Moreover, the correlation coefficients between the observations and the simulations are better for the WRF-DEBROM compared to WRF-ZIS: 0.48 and 0.3 for CH₂Br₂ and CHBr₃, respectively, in the case of WRF-ZIS, and 0.65 and 0.43 for CH₂Br₂ and CHBr₃ in the case of WRF-DEBROM. Modelled CH₃I concentrations show a similar trend to the observations, although, like the bromocarbons, both simulations underestimate the observations during specific periods (6–10 and 18–28 February). This underestimation is more prominent in the WRF-DEBROM simulation. One reason for that could be that the wind speed from WRF-Chem is lower than the wind speed used to calculate the prescribed emissions, producing lower online fluxes. Nevertheless, the correlation coefficients between the observed and simulated CH₃I atmospheric concentrations are better for WRF-DEBROM than for WRF-ZIS: 0.19 is calculated for WRF-ZIS and 0.40 for the WRF-DEBROM simulation.

Specific periods of negative bias for both simulations demand further attention. A possible explanation for the underestimation in halocarbon atmospheric concentrations might be due to the input data (e.g. wind speed, SST, seawater concentration) that we used to compute these fluxes. In the case of the online fluxes, between 6 and 8 February, the model underestimates wind speed and this is directly accompanied by an underestimation for all three halocarbons' atmospheric concentrations. Ziska et al. (2013) demonstrate that changes in the input parameters, especially wind speed and SST, affect the fluxes calculation. The same study suggests that CH₃I emissions are mainly influenced by variations of the wind speed. Moreover, the study of Lennartz et al. (2015), that uses the same seawater concentration as our study, suggests that the negative bias in the modelled atmospheric concentrations could indicate regions where the seawater concentration from the climatologies lacks hotspots; thus, it is missing an oceanic source region. This is clearly seen for the seawater concentrations of CHBr₃ (during most of the period), CH₂Br₂ (peaks around 15 February) and CH₃I (peaks around 20 February) used in this study that seem to be too low in comparison with the observations (see Fig. 6). More data on the seawater concentrations of these halocarbons in this region are required to better constrain the oceanic flux data sets available to models and so to improve the representation of these gases in the atmosphere.

5.2 Gas-phase and heterogeneous chemistry: bromine and iodine partitioning

Figure 7 compares model results sampled along 16 flight tracks with the observations for BrO (pptv) separating tropical from subtropical flights for the five simulations: WRF-NOHET, WRF-NODEBROM, WRF-GAMMADV2, WRF-DIFF and WRF-DEBROM. Results indicate that there is an improvement of the modelled BrO throughout the troposphere in both the tropics and subtropics when the heterogeneous chemistry is included in both tropics and subtropics.

Figure 7Mean vertical profile of BrO (pptv) over the subtropics (a) and tropics (b). An average over 16 flights of the TORERO campaign (red line) are compared to the five different WRF-Chem simulations: WRF-NOHET (blue line), WRF-NODEBROM (green line), WRF-DEBROM (black line), WRF-GAMMADV2 (pink line) and WRF-DIFF (yellow line). Orange and grey horizontal bars indicate the 25th–75th quartile interval for the observations of the TORERO campaign and WRF-DEBROM simulation, respectively. Values are considered in 0.5 km bins and the number of aircraft measurement points for each altitude is given on the right side of each plot.

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In the subtropics, higher values of BrO are found in the altitude range 11–13 km due to the lower altitude of the tropopause. Some data points in this altitude range will be in the lower stratosphere. There is really good agreement with the observations particularly in the middle and upper troposphere where the model is able to capture the higher values of BrO. Within the model, aerosols over the subtropical area tend to be alkaline; thus, BrO does not increase in this area when sea-salt debromination is included. Over the tropics, where the aerosol is more acidic and where the sea-salt aerosols are mostly located (see emissions of Br₂ in Fig. 2), elevated BrO is seen with the inclusion of the debromination (WRF-DEBROM) in the MBL. Debromination improves the simulation of BrO concentrations in the middle troposphere although it excessively increases BrO levels up to 1 pptv in the MBL. Higher values are also seen in other modelling studies that include this process (Schmidt et al., 2016).

In areas, such as the tropics, where debromination dominates, the impact of halving gamma (WRF-GAMMADV2 run) is approximately half of the impact of including heterogeneous chemistry (i.e. the difference between the WRF-DEBROM run and the WRF-NOHET run) at least for the lower troposphere. Very little impact is seen in the upper troposphere (UT), a slight decrease in BrO, when gamma is halved (WRF-GAMMADV2). The simulation in which diffusion limitation is considered in the heterogeneous reactions (WRF-DIFF) gives values of BrO that are generally between the results from the WRF-GAMMADV2 and WRF-DEBROM simulations. They are similar to the WRF-GAMMADV2 values in the MBL, but over the subtropics, where debromination is lower, WRF-DIFF is very close to WRF-DEBROM values. Significant uncertainties still exist in the sea-salt debromination processes and the parameterizations used here might be too simple to represent them.

In addition, the conversion of BrO to HBr is dominated by the reaction between Br and OVOCs, such that the BrO overestimation seen in the MBL could be reduced if the modelled aldehyde concentrations were increased (discussed in Sect. 5.3). However, a reduction in the debromination would also reduce BrO concentrations. Thus, in order to capture the BrO concentrations in the MBL, the right balance between these two chemical processes is needed.

BrO is underestimated in the model by 1 pptv in the upper troposphere over the tropics. The breakdown of bromocarbons, such as CHBr₃, contributes to BrO concentrations in the UT; thus, a good representation of bromocarbons is needed. CHBr₃ is underestimated in the middle and upper troposphere especially over the tropics (see Fig. S3). The reason for that could be a combination of different factors: underestimation of the boundary conditions used in this study for CHBr₃, underestimation in the oceanic fluxes (see Fig. S2) and overestimation of the loss rates. Moreover, an underestimation in the heterogeneous chemistry or uncertainties in the reactions between the halogens and VOCs (discussed in Sect. 5.3) can also contribute to the underestimation of BrO in the UT over the tropics.

Figure 8 shows the vertical profile distribution for inorganic bromine (Br_y in pptv) for the three simulations – WRF-NOHET (Fig. 8a, d), WRF-NODEBROM (Fig. 8b, e) and WRF-DEBROM (Fig. 8c, f) – over the subtropics (Fig. 8a–c) and tropics (Fig. 8d–f). Inorganic bromine concentrations increase with altitude with a maximum of 8 pptv at 14 km in the subtropical area for all three simulations. This reflects the lifetime of the bromocarbon species that breakdown and release Br in the UT and LS. Over the tropical area, inorganic bromine concentrations have a peak in the middle troposphere at 6 km, then decrease until 12 km and then start to increase again. A big impact on the vertical Br_y partitioning is seen between the three simulations. With the inclusion of the heterogeneous chemistry, there is a decrease of HBr and an increase of more reactive species: dihalogens (BrCl, Br₂ and BrI) and BrO. HOBr increases and BrNO₃ decreases in the UT due to BrNO₃ hydrolysis. Over the tropics, Br_y increases in the MBL (∼4 pptv) when debromination is included (WRF-DEBROM). This enhancement is seen for all inorganic species with a maximum in the surface where the concentration of sea-salt aerosols is highest. Over the subtropical area, little difference is seen between WRF-NODEBROM and WRF-DEBROM.

Figure 8Regional average vertical partitioning of inorganic bromine (Br_y) for the three different simulations – WRF-NOHET (a, d), WRF-NODEBROM (b, e) and WRF-DEBROM (c, f) – during January and February 2012. Panels (a, b, c) are over the subtropical area and panels (d, e, f) over the tropical. Units are in pptv.

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Figure 9 compares model results sampled along 16 flight tracks with the observations for IO separating tropical from subtropical flights for the five simulations: WRF-NOHET, WRF-NODEBROM, WRF-GAMMADV2, WRF-DIFF and WRF-DEBROM. No clear impact is seen with the inclusion of the heterogeneous chemistry. At the surface, simulations with heterogeneous chemistry (WRF-DEBROM, WRF-GAMMADV2, WRF-DIFF and WRF-NODEBROM) have slightly lower IO concentrations than the simulation without heterogeneous chemistry (WRF-NOHET). The main reason for that reduction is the sink for the iodine oxides (I₂O_x, where $X=\mathrm{2},\mathrm{3},\mathrm{4}$) included in the heterogeneous chemistry. Over the tropical region, the model overestimated surface IO. This overestimation might be explained by the large modelled inorganic iodine oceanic fluxes in this area. The biggest uncertainty in the inorganic iodine emissions parameterization is the calculation of the iodide concentration in the seawater. Over the subtropics, IO enhancements observed below 4 km are not captured by the model. Some studies suggest that there is abiotic CH₃I production when dust contacts seawater containing iodide (Williams et al., 2007; Puentedura et al., 2012). Implementing this chemistry into the model is out of the scope of this paper and further investigation is needed to explain whether the production of CH₃I enhances the IO concentration or if there are other missing IO precursors. Gómez Martín et al. (2013) presented an analysis of observations of several gas-phase iodine species made during a field campaign in the eastern Pacific marine boundary layer and suggested that the presence of elevated CH₃I does not have a big impact on the IO_x concentrations due to CH₃I in the MBL having a long lifetime (∼2 days at the Equator). An overestimation of modelled IO in the UT needs further investigation. This overestimation is similar to other modelling studies (Sherwen et al., 2016a). Changing the heterogeneous rate constants (difference between the WRF-DEBROM, WRF-GAMMADV2 and WRF-DIFF runs) has very little impact on IO.

Figure 9Mean vertical profile of IO (pptv) over the subtropics (a) and tropics (b). An average over 16 flights of the TORERO campaign (red line) is compared to the five different WRF-Chem simulations: WRF-NOHET (blue line), WRF-NODEBROM (green line), WRF-DEBROM (black line), WRF-GAMMADV2 (pink line) and WRF-DIFF (yellow line). Orange and grey horizontal bars indicate the 25th–75th quartile interval for the observations of the TORERO campaign and WRF-DEBROM simulation, respectively. Values are considered in 0.5 km bins and the number of aircraft measurement points for each altitude is given on the right side of each plot.

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Figure 10 shows the vertical profile distribution for inorganic iodine (I_y) for the three simulations – WRF-NOHET (Fig. 10a, d), WRF-NODEBROM (Fig. 10b, e) and WRF-DEBROM (Fig. 10c, f) – over the subtropics (Fig. 10a–c) and tropics (Fig. 10d–f). I_y is higher in the MBL where it is emitted, especially in the tropical region, with HOI being the dominant species. Concentrations start to decrease above the MBL due to the removal of soluble species by the wet deposition. Unlike Br_y, we do not see a big impact on the vertical profile of I_y partitioning with the inclusion of the heterogeneous chemistry. The only differences are the I_y decreases in the surface with the inclusion of the heterogeneous chemistry, due to the removal of the iodine oxides, and the production of more dihalogens in the MBL, especially when debromination is included. Heterogenous iodine reactions (Reactions R11–R19) compete with the photolysis. Iodine species are more readily photolysed, so less is taken up into the aerosol and the impact of heterogeneous chemistry is lower.

Figure 10Regional average vertical partitioning of inorganic iodine (I_y) for the three different simulations – WRF-NOHET (a, d), WRF-NODEBROM (b, e) and WRF-DEBROM (c, f) – during January and February 2012. Panels (a, b, c) are over the subtropical area and panels (d, e, f) over the tropical. Units are in pptv.

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5.3 Impact on VOCs

Several VOC oceanic fluxes have been included in the model (see Sect. 3.2.1) as well as the oxidation of VOCs by halogens. In order to see the impact of halogen reactions with the VOCs, average loss rates of all organic compounds due to the Cl and Br families are calculated as percent of the total tropospheric losses over the ocean for the WRF-DEBROM simulation. Bromine accounts for 9.2 % of the oxidation of CH₃CHO, 1.4 % of CH₂O, 0.8 % of C₂H₄ and 4.1 % of C₃H₆. Chlorine accounts for 0.6 % of the oxidation of CH₃CHO, 0.3 % of CH₂O, 7.7 % of CH₃OH, 0.8 % of CH₃OOH, 0.6 % of CH₃O₂, 35.5 % of C₂H₆ and 10.5 % of C₃H₈.

A subset of nine flights from the TORERO campaign over the tropics is compared with the WRF-DEBROM, WRF-NOBRVOCS, WRF-NOBRALKE and WRF-NOBRALD simulations for BrO (pptv) in Fig. 11. Comparisons between WRF-DEBROM and WRF-NOBRVOCS simulations show a clear difference (1–4 pptv) throughout the whole troposphere. VOCs play an important role in the MBL regulating the reactive halogens. Without the bromine reactions with the VOCs, BrO concentrations are higher than observed in the MBL. In the middle and upper troposphere, where VOCs emitted from the ocean and forests are transported by convection, the model underestimates the amounts of BrO when these reactions are considered. The results obtained indicate that BrO is highly sensitive to the conversion of reactive bromine into more stable species by these reactions. The partitioning of the products of these reactions (HBr∕Br), and thus the conversion of reactive bromine to more stable species, is highly uncertain (see Sect. 3.2.1) and the results suggest that it might be too effective in these upper layers of the model.

Figure 11(a) Mean vertical profile of BrO (pptv) over the tropics. A subset of nine flights from the TORERO campaign (red line) are compared to the four different WRF-Chem simulations: WRF-NOBRALKE (blue line), WRF-NOBRALD (green line), WRF-NOBRVOCS (blue line) and WRF-DEBROM (black line). (b) Regional average vertical partitioning of inorganic bromine (Br_y) for the WRF-NOBRVOCS run over the tropical area during January and February 2012. (c, d) The WRF-DEBROM (black line) simulation is compared with acetaldehyde and formaldehyde TORERO observations for the same nine flights (red line). Orange and grey horizontal bars indicate the 25th–75th quartile interval for the observations of the TORERO campaign and WRF-DEBROM simulation, respectively. Values are considered in 0.5 km bin and the number of points for each altitude is given on the right side of each plot. Units are in pptv.

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In order to understand which families of VOCs have a higher impact on the BrO concentrations, the oxidation of alkenes and aldehydes by Br has been studied separately in the WRF-NOBRALKE and WRF-NOBRALD simulations. Differences between WRF-DEBROM and WRF-NOBRALD are seen in the whole troposphere with higher differences up to 2 pptv in the MBL, where the concentrations of both bromine and aldehydes are high. The concentrations of the aldehydes are underestimated by the model, especially for CH₃CHO, meaning that BrO-modelled concentrations would be even lower if the modelled concentrations of the aldehydes were reconciled with the observations. The model also seriously underestimates the observed glyoxal mixing ratios. The modelled values are typically ∼1 pptv, whilst the observed values are around 30–40 pptv in the MBL, decreasing to around 5–10 pptv in the upper troposphere (Volkamer et al., 2015; Sinreich et al., 2010). This illustrates that there are large gaps in our understanding of OVOCs in the remote marine atmosphere. Small differences are observed between WRF-DEBROM and WRF-NOBRALKE. However, differences up to 2 pptv between WRF-NOBRVOCS and WRF-NOBRALD are clearly seen especially in the MBL.

These findings suggest that when aldehyde oxidation by Br is included, reactive Br is reduced considerably, thus limiting the amount of alkene oxidation by Br (difference between WRF-DEBROM and WRF-NOBRALKE). However, when the oxidation of aldehydes is included, there is sufficient Br_y present for the oxidation of alkenes by Br to have an impact on the BrO (difference between WRF-NOBRALD and WRF-NOBRVOCS).

Figure 11 also shows the vertical profile distribution for inorganic bromine (Br_y in pptv) for the WRF-NOBRVOCS run over the tropics (Fig. 11b). When reactions of bromine with VOCs are not included, the amount of Br_y increases considerably (difference between WRF-DEBROM and WRF-NOBRVOCS from Figs. 8 and 11), reaching values of 14 pptv in the MBL over the tropics. Moreover, when this chemistry is included, the partitioning of Br_y shifts to more stable bromine species such as HBr.

Figure 12 shows the vertical profile distribution for inorganic chlorine (Cl_y in pptv) for the two simulations – WRF-NOHALVOCS (Fig. 12a, d) and WRF-DEBROM (Fig. 12b, e) – over the subtropics (Fig. 12a–c) and tropics (Fig. 12d–f). Regional average vertical partitioning of reactive chlorine species (Cl^*) is also shown (Fig. 12c, f) where Cl^* is defined as Cl_y gases other than HCl. When the VOCs react with Cl (WRF-DEBROM), almost all the inorganic Cl is in the form of HCl (see Fig. 12). When these reactions are not considered (WRF-NOHALVOCS), Cl_y increases and there is a shift in the partitioning to more reactive chlorine increases, in particular HOCl, but also ClO and the dihalogens.

Figure 12Regional average vertical partitioning of inorganic chlorine (Cl_y) for the two different simulations – WRF-NOHALVOCS (a, d) and WRF-DEBROM (b, e) – during January and February 2012. Regional average vertical partitioning of reactive chlorine species (Cl^*) is also shown (c, f). Cl^* is defined as Cl_y gases other than HCl. Panels (a, b, c) are over the subtropical area and panels (d, e, f) over the tropical. Units are in pptv.

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From this, we concluded that VOCs play an important role in the reactive bromine and chlorine concentrations. Therefore, marine emissions of VOCs as well as halogen reactions with VOCs need to be included in models. However, large uncertainties still exist in some of these reactions (see Sect. 3.2.1).

5.4 Impact on O₃ and O_x

Figure 13a presents a comparison of modelled O₃ from seven simulations (WRF-DEBROM, WRF-GAMMADV2, WRF-DIFF, WRF-NODEBROM, WRF-NOHET, WRF-NOHALVOCS and WRF-NOHAL) sampled along 13 flight tracks with the observed O₃ (ppbv). O₃ is overestimated when halogens are not included (WRF-NOHAL) except in the upper troposphere. When halogens are included, the model (WRF-DEBROM) is in line with the observations, capturing the O₃ gradient and variability of data throughout the troposphere. The average difference between WRF-DEBROM and WRF-NOHAL simulations throughout the troposphere is 7 ppbv. In the MBL, high concentrations of halogens due to ocean emissions destroy O₃ and contribute to a negative bias up to 8 ppbv for WRF-DEBROM run. In the middle troposphere, the model results (WRF-DEBROM) improve with the inclusion of halogens, where the average underestimation is reduced from 4.0 to 2.4 ppbv. In the upper troposphere, where the differences between the simulations (WRF-DEBROM and WRF-NOHAL) are mainly driven by the boundary conditions used for each simulation, both simulations underestimate the ozone concentrations. The heterogeneous halogen chemistry has an impact on O₃ concentrations where a difference of up to 3 ppbv of O₃ is seen between the simulations with and without heterogeneous chemistry (WRF-DEBROM run WRF-NOHET run, respectively) mainly in the MBL. Dividing gamma by 2 (WRF-GAMMADV2) and considering the diffusion limitation (WRF-DIFF) reduces this difference to around 2 ppbv. The modelled O₃ is highly sensitive to the inclusion of the reactions of the halogens with the VOCs (WRF-NOHALVOCS) where O₃ concentrations are much lower (between 12 and 7 ppbv) than in the WRF-DEBROM run.

Figure 13(a) Mean vertical profile of O₃ (ppbv) over the domain area using 13 flights from the TORERO campaign (red line) compared to the seven different WRF-Chem simulations: WRF-NOHAL (purple line), WRF-NOHET (blue line), WRF-NODEBROM (light green line), WRF-DEBROM (black line), WRF-GAMMADV2 (pink line), WRF-DIFF (yellow line) and WRF-NOHALVOCS (dark green line). Orange and grey horizontal bars indicate the 25th–75th quartile interval for the observations of the TORERO campaign and WRF-DEBROM simulation, respectively. Values are considered in 0.5 km bins and the number of aircraft measurement points for each altitude is given on the right side of each plot. (b, c) Mean O₃ difference between the simulation with no halogen chemistry (WRF-NOHAL) and with halogen chemistry (WRF-DEBROM) for January and February 2012. Surface mean bias (ppbv) is shown in panel (b) and surface relative mean bias (%) in panel (c). Relative mean bias (%) is calculated as (WRF-NOHAL − WRF-DEBROM) ∕ WRF-NOHAL ×100.

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Figure 13b–c show the regional effects of halogen chemistry on simulated O₃ concentrations at the surface. Surface mean bias (ppbv) and relative mean bias (%) between the simulation with no halogen chemistry (WRF-NOHAL) and with halogen chemistry (WRF-DEBROM) for the simulation period are presented. We find that the regional O₃ concentrations are reduced by 2–18 ppbv, corresponding to 25 %–70 %, with the inclusion of the halogens. Over the tropics, there is a substantial decrease of O₃ (>8 ppbv, >40 %). As we see in Figs. 2 and 3, there are high iodine and bromocarbon emissions and especially large amounts of bromine produced from debromination over this area. These destroy ozone and contribute to higher difference in O₃ concentrations in this area.

The odd oxygen O_x is defined as

where X is Cl, Br and I; PAN is peroxyacetyl nitrate, MPAN is methacryloyl peroxynitrate; ONIT is organic nitrate; ONITR is lumped isoprene nitrate; ISOPNO₃ is peroxy radical from NO₃+ISOP; PBZNIT is peroxybenzoyl nitrate; MBONO₃O₂ is peroxy radical from NO₃ + 2-methyl-3-buten-2-ol.

The O_x loss resulting from reactions with each of the ozone-depleting families (O_x , HO_x, NO_y, VOCs, Br, Cl and I) is calculated. Note that to calculate the O_x loss due to the O_x depleting family we only consider reactions involving O(³P), O(¹D) and O₃. The average tropospheric vertical profile of O_x loss grouped by ozone-depleting families for the WRF-DEBROM simulation is given in Fig. 14. Figure 15 summarizes the relative contribution of each halogen family averaged at different altitude intervals for the WRF-DEBROM, WRF-GAMMADV2, WRF-DIFF, WRF-NODEBROM, WRF-NOHET and WRF-NOHALVOCS simulations.

Figure 14Regional average percentage contribution of each ozone-depleting family to the total tropospheric vertical odd oxygen loss (O_x) for the WRF-DEBROM simulation.

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Figure 15Integrated odd oxygen loss rates for each O₃ depleting halogen family within the troposphere at different altitude levels: MBL (surface–900 hPa), FT (900–350 hPa), UT (350 hPa–tropopause) and troposphere (surface–tropopause) for the WRF-DEBROM, WRF-GAMMADV2, WRF-DIFF, WRF-NODEBROM, WRF-NOHET and WRF-NOHALVOCS simulations.

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The regional average O_x percentage loss due to the halogens in our model domain is 34 %, 18 % and 40 % in the MBL (p>900 hPa), free troposphere (FT) ($\mathrm{350}<p<\mathrm{900}$ hPa) and UT ($\mathrm{350}\mathrm{hPa}<p<\text{trop}$), respectively, for the WRF-DEBROM simulation. The MBL O_x loss is in good agreement with Sherwen et al. (2016b), who reported 33 % and Prados-Roman et al. (2015) reported 31 %. The tropospheric O_x loss due to the BrO_x, IO_x and ClO_x cycles is 14 %, 16 % and 1 % throughout the troposphere, respectively, for the WRF-DEBROM simulation. The very fast catalytic reactions of iodine species make the iodine loss higher than for bromine and chlorine, especially in the MBL for all simulations that include halogen–VOC reactions (19 %–23 %). With the inclusion of the sea-salt debromination, O_x loss due to the bromine is 14 % in the MBL. In the upper troposphere, iodine contributes 18 %–23 % and bromine 14 %–19 % to the total O_x loss. The impact of halogen chemistry on the tropospheric O_x loss is 31 % for the WRF-DEBROM simulation. This value is comparable with other studies that reported 28 % over the tropics (Saiz-Lopez et al., 2015) and 21.4 % at the global scale (Sherwen et al., 2016b). Moreover, our results are in agreement with Wang et al. (2015), who used a box model and concluded that bromine and iodine are responsible for 34 % of the column-integrated loss of tropospheric O₃. The tropospheric O_x loss due to the iodine is higher than the box model study of Dix et al. (2013), which concluded that the fraction of iodine-induced ozone loss generally is around 10 %. When comparing different simulations with the WRF-DEBROM run, the biggest difference is seen with the WRF-NOHALVOCS simulation, where around 60 % of O_x is removed by halogens. BrO is much higher when the VOC reactions are not included (see Fig. 11), which explains why the amount of O_x loss by BrO_x reactions is much larger (20.5 %). Moreover, the big change though is for the ClO_x, which increases from <1 % to 26 %. Cl is very important in the oxidation of the alkanes. When this chemistry is not included the concentrations of Cl_y increases and there is an impact on the partitioning increasing reactive species (see Fig. 12); hence, the ClO_x cycles play an important role in O_x loss. It should be noted that very little is known about the abundance and distribution of Cl_y, so this is a large uncertainty. Therefore, a large uncertainty in the impact of halogen cycling on the O₃ budget is the reactions of halogens with VOCs. In the model runs performed, excluding these reactions doubled the percentage contribution of halogens to O_x loss (i.e. increase it from 31 % to 60 %) in the troposphere. Heterogeneous chemistry (including debromination) has the effect of increasing the O_x loss by halogen cycling from 25 % to 31 % for the whole troposphere (i.e. comparison between WRF-NOHET and WRF-DEBROM runs). For the UT, the equivalent values are 37 % to 40 %, for the FT 13 % to 18 % and for the MBL 23 % to 34 %. Hence, heterogeneous chemistry increases the percentage of the O_x loss that is attributable to the halogens by about 6 % for the troposphere, ranging from 3 % to 11 % depending on the region of the troposphere. Dividing gamma by 2 (WRF-GAMMADV2) and considering the diffusion limitation (WRF-DIFF) reduces the O_x loss in the troposphere by the halogens to 3 % and 2 %, respectively. Note that the gas-phase halogen chemistry makes a bigger contribution of around 25 % (WRF-NOHET run) to the O_x loss for the troposphere ranging from 13 % to 37 % depending on the region of the troposphere. Therefore, the overall impact of the halogen chemistry on O_x loss appears not to be very sensitive to the treatment of the heterogeneous chemistry.