Effect of marine boundary layer clouds on tropospheric chemistry as ...

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D11, 10.1029/2001JD000468, 2002

Effect of marine boundary layer clouds on tropospheric chemistry as analyzed in a regional chemistry transport model M. C. Barth, P. G. Hess, and S. Madronich National Center for Atmospheric Research, Boulder, Colorado, USA Received 31 January 2001; revised 12 November 2001; accepted 14 November 2001; published 13 June 2002.

[1] A regional chemistry transport model is used to investigate whether clouds affect tropospheric species concentrations, particularly ozone. This study focuses on boundary layer clouds in a 20 by 20 region centered over Hawaii. We find that O3 is depleted by 6% in the cloud-capped boundary layer during a 120-hour integration period. The O3 loss rate is calculated to be 320 pptv d1 for the region, which was dominated by low NOx ( 4.5 the superoxide ion is favored, and therefore reaction of superoxide and ozone in the cloud drops increases. We discuss the importance of including transport and physical processes (e.g., deposition) on the O3 loss rate. We find that O3-rich air is transported into the marine boundary layer via subsidence and diffusion, allowing for more O3 to be depleted in an absolute sense compared to when transport and physical processes do not occur. However, the relative change in O3 is smaller in the boundary layer when transport and physical processes are included because large eddies, which are represented by vertical diffusion in the model, maintain relatively high background O3 levels in the boundary layer. INDEX TERMS: 0320 Atmospheric Composition and Structure: Cloud physics and chemistry; 0365 Atmospheric Composition and Structure: Troposphere—composition and chemistry; 0368 Atmospheric Composition and Structure: Troposphere—constituent transport and chemistry; KEYWORDS: cloud chemistry, tropospheric ozone

1. Introduction [2] During the past decade it has been debated whether clouds affect the oxidation capacity of the troposphere and in particular the gas-phase concentration of ozone (O3). Clouds can influence chemical species by transporting boundary layer constituents to the free troposphere, by precipitating out soluble species, by altering the photodissociation frequencies of several species, by separating soluble and insoluble species such that gas-phase chemistry is altered in clouds, and by providing a medium for aqueous chemistry inside the cloud and rain drops. [3] To understand the relative importance of cloud processes on tropospheric chemistry, a three-dimensional episodic nested regional chemistry transport model (HANK), whose parent meteorology model is the Mesoscale Meteorology Model version 5 (MM5), is used to examine the distributions of trace species over the Pacific Basin in springtime where boundary layer clouds and frontal systems readily occur. The HANK-MM5 system gives us the ability to integrate chemistry for a relatively long period of the order of days coupled with the ability to calculate cloud physics (in particular determining the liquid water content), kinematics, and the feedbacks of cloud physics on the dynamics. We believe that this kind of framework improves Copyright 2002 by the American Geophysical Union. 0148-0227/02/2001JD000468$09.00

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the representation of cloud-chemistry interactions from the previous cloud-chemistry studies. [4] Previous studies that examined how aqueous phase reactions can increase the acidity of rain [e.g., Chang et al., 1987; Hegg et al., 1986] have generally coupled good representations of cloud physics and transport with simple representations of chemistry (primarily sulfur chemistry). On the other hand, modeling studies with a good representation of chemistry have been generally coupled with inadequate representations of cloud physics [e.g., Liang and Jacob, 1997; Walcek et al., 1997; Lelieveld and Crutzen, 1991]. Nevertheless, these studies provide insight as to how tropospheric chemistry can be modified by clouds. [5] Lelieveld and Crutzen [1991] examined how aqueous reactions in clouds can affect trace gas species such as O3, formaldehyde (CH2O), odd nitrogen (NOx = NO + NO2), and odd hydrogen (HOx). By using a box model to calculate the chemical tendency of species in both the gas and aqueous phases, they determined that clouds can reduce the concentration of O3 by either decreasing the O3 production by 40– 50% or increasing O3 destruction by a factor of 2 – 4. This implies that clouds have a large influence on the atmosphere’s ability to oxidize trace species. Liang and Jacob, [1997] contested this result. Their modeling study concluded that clouds have less than a 3% effect on ozone concentrations. Other studies [e.g., Matthijsen et al., 1997; Monod and Carlier, 1999; Walcek et al., 1997] have also

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BARTH ET AL.: INFLUENCE OF BOUNDARY LAYER CLOUDS ON OZONE

shown that the effect of clouds on O3 concentrations is less than 10%. [6] A major shortcoming of these previous studies is that physical processes, such as the determination of the liquid water content, are not consistent with the transport model. To better represent these physical processes, a cloud-scale model is the best tool to use. However, cloud-scale models are not usually integrated for more than a few hours because they do not contain a diurnal cycle of heat fluxes. We examine the effects of clouds on chemical species with a regional chemistry transport model that is linked to, and consistent with, a meteorological model. [7] We focus this study on boundary layer clouds in the remote atmosphere, a 20 20 region centered over Hawaii. Boundary layer clouds are commonly found in nature, especially above oceanic upwelling regions near continents. The total residence time of air parcels in boundary layer clouds can be large compared to other types of clouds, such as convective clouds. We would then expect cloud processing in boundary layer clouds to give a large signal compared to other types of clouds. The sensitivity of the results to the cloud acidity and the relative importance of aqueous chemistry and cloud radiative effects on photolysis rates are discussed.

2. Model Description [ 8 ] The nested regional chemistry transport model HANK [Hess et al., 2000] predicts the concentration of chemical species off-line of the MM5. The chemistry transport model inputs 3-hour averages of winds, temperature, pressure, water vapor mixing ratio, cloud water mixing ratio, and rain mixing ratio from the MM5 simulation. The total concentration (gas and aqueous phases) of the chemical species is advected every 4 min using a positive definite scheme [Smolarkiewicz, 1983]. Convective transport of the total species concentration is accomplished through a modified Arakawa-Schubert scheme [Grell, 1993] for both deep convection and shallow convection. For boundary layer transport of the total chemical species concentration the Holtslag parameterization [Holtslag and Boville, 1993] is used. The washout of chemical species is a first-order loss to the surface without detailed consideration of transport through the intervening layers. For species whose concentration in the rain is not explicitly calculated, the Giorgi and Chameides [1986] parameterization is used to determine the fraction of the species concentration that exists in the rain. For species whose concentration in the rain is predicted (O3, OH, HO2, H2O2, CH3OO, CH3OOH, CH2O, HCOOH, and HNO3), the washout of species is based on this known concentration. Parameterization of the dry deposition of the chemical species uses the resistance method of determining deposition velocity [Wesely, 1989]. [9] Although obtaining meteorological variables from MM5 is an improvement from previous studies, there are still limitations to this approach. The primary limitation is that over a 3-hour period of model integration in HANK clouds are stationary while chemical species are transported in, within, and out of the cloud. Although the total concentration of the species is repartitioned after each model time step (4 min), there can still be an inconsistency between the chemistry transport model and the meteorology model

because the cloud water and rain mixing ratios do not change at each model time step. This limitation also hinders accurate calculation of sedimentation of species in precipitation as it does not explicitly account for the microphysical and consequent chemical transformations of precipitating hydrometeors (e.g., evaporation). [10] The gas-phase chemical mechanism [Hess et al., 2000] is designed for chemical conditions found in rural and remote locations of the world and includes 154 reactions and 51 chemically active species (47 of which are transported). The chemical mechanism has a simplified lumped hydrocarbon chemistry scheme and uses 3-hour time-averaged temperature and water vapor fields. Photolysis rates for cloud-free skies are determined by linear interpolation from a look-up table based on the ozone column, surface elevation, altitude, and solar zenith angle. The photolysis rates are corrected for temperature and clouds [Hess et al., 2000]. The sensitivity of the model results to the cloud correction on photolysis rates is discussed in section 3. [11] The aqueous chemistry (Table 1), which includes 11 reactions and 9 species, is computed for the clouds resolved by MM5. Reaction rate and equilibrium constants either follow Jacob [1986] or are updated from his work. This chemistry includes two photolysis reactions whose rates are 1.25 times the interstitial photolysis frequency. Because sulfur dioxide and sulfate aerosols are not predicted by the model, the pH of the drops is prescribed to a value of 4.5. This assumption is certainly limiting for capturing the variation of pH within a cloud, which for remote marine clouds have a pH range between 4 and 5.5 [Parungo et al., 1982; D. Hegg, personal communication, 2001]. The aqueous chemistry represented in HANK does not capture pH variation within a cloud, but the sensitivity of the model results on the cloud drop pH is discussed based on additional simulations. [12] The chemical mechanism is solved with an Euler backward iterative (EBI) approximation using a GaussSeidel method with variable iterations. A convergence criterion of 0.01% is used for all the species. The time step for the chemistry calculations is 4 min but is reduced every 3 hours to a variable time step (on the order of seconds) for a 28-min period to obtain an accurate aqueous chemistry solution when the mixing ratios of cloud water and rain change. [ 13 ] Nine species (O 3 , OH, HO 2 , H 2 O 2 , CH 3 OO, CH3OOH, CH2O, HCOOH, and HNO3) are allowed to dissolve into the cloud and rain drops. Other oxygenated organic species are not considered to undergo aqueous chemistry in this study, although such chemistry is likely to occur [Herrmann et al., 2000] and should be considered in future studies. Dissolution of the gas-phase species into the cloud water or rain is governed by either Henry’s law or mass transfer limitation depending on the species. HNO3, because of its high solubility, and OH, HO2, and CH3OO, because of their high reactivity in the aqueous phase, are transferred between phases according to mass diffusion processes [Schwartz, 1986]. O3, H2O2, CH2O, HCOOH, and CH3OOH are checked for mass transfer limitation but usually are partitioned between gas and aqueous phases according to Henry’s law equilibrium. Details of the method used can be found in the work of Barth et al. [2001], which


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Table 1. Aqueous-Phase Reactions Included in HANK k298 (A1) (A2) (A3) (A4) (A5) (A6) (A7) (A8) (A9) (A10) (A11)

O3+ hn + H2O H2O2 + hn H2O2 + OH HO2 + O 2 O3 + O 2 + H2 O CH3OO + O2 + H2O CH3OOH + OH CH3OOH + OH CH2(OH)2 + OH + O2 HCOOH + OH + O2 HCOO + OH + O2

(E1) (E2) (E3) (E4) (E5) (E6) (E7) (E8) (E9)

O3 (g) OH (g) HO2 (g) H2O2 (g) CH3OO (g) CH3OOH (g) CH2O HCOOH (g) HNO3 (g)

(E10) (E11) (E12)

HO2 (aq) HCOOH (aq) HNO3 (aq)

Aqueous Chemistry ! H2O2 + O2 ! 2OH ! HO2 + H2O ! HO 2 + O2 ! OH +2O2 + OH ! CH3OOH + OH + O2 ! CH3OO + H2O ! CH2(OH)2 + OH ! HCOOH + HO2 + H2O ! CO2 + HO2 + H2O ! CO2 + HO2 + OH

see see 2.7 1.0 1.5 5.0 2.7 1.9 2.0 1.6 2.5

Henry’s Law Equilibria ] O3 (aq) ] OH (aq) ] HO2 (aq) ] H2O2 (aq) ] CH3OO (aq) ] CH3OOH (aq) ] CH2(OH)2 (aq) ] HCOOH (aq) ] HNO3 (aq)

1.15 102 30. 2. 103 8.33 104 7.47 3.11 102 6.3 103 5.53 103 2.1 105

Acid Dissociation Equilibria ] O2 + H+ ] HCOO + H+ ] NO3 + H+

text text 107 108 109 107 107 107 109 108 109

3.5 105 1.8 104 15.4

ER

1700. 1500. 1500. 1600. 1700. 1900. 1500. 1500. 1500. 2560. 0. 6600. 7379. 5241. 5241. 6425. 5700. 8700. 0. 1510. 0.

are M atm1, and units for Units for the second-order aqueous reactions are M1 s1. Units for solubility constants 1 unless otherwise noted. dissociation constants are M. Reaction rates are of the form k ¼ k298 exp ER T1 298

follows [Schwartz, 1986]. Concentrations of O2 and HCOO are found from the equilibrium constants of their respective acid dissociation reactions [Jacob, 1986]. [14] When aqueous chemistry is included, the wet deposition of the nine species whose concentration in the rain is predicted is a first-order loss rate applied directly to the concentrations of these nine species in rain. The wet deposition of other species is applied to their concentrations as given by the parameterized Henry’s law partitioning of Giorgi and Chameides [1986]. When only gas chemistry is represented, the wet deposition of each species is applied to their concentrations as given by the parameterized Henry’s law partitioning of Giorgi and Chameides [1986]. Thus, when comparing results from model simulations of gas chemistry to results from model simulations of gas and aqueous chemistry, there is an additional difference in the wet deposition schemes to acknowledge because the partitioning of species between the gas and rain reservoirs is calculated differently. When aqueous chemistry is included, the nine species that dissolve into the rain are partitioned by either mass transfer limitation or Henry’s law equilibrium [Barth et al., 2001]. Because mass transfer diffusion limitation is more important for large drops (e.g., rain drops) than small drops, it is likely that the highly soluble species are not in Henry’s law equilibrium between the rain and the gas phases as is assumed by [Giorgi and Chameides, 1986]. The concentration of the highly soluble species in the rain determined in the simulation with no aqueous chemistry (a concentration that is in Henry’s law equilibrium) may be quite different from that determined in the simulation with aqueous chemistry (a concentration that is not in Henry’s law equilibrium). Therefore the wet deposition process, which operates on the concentration of the species in rain,

can produce different results in the simulation with no aqueous chemistry compared to the simulation with aqueous chemistry. [15 ] For the simulations described here, the model domain is the Pacific Basin (8S to 54N, 110E to 107W), which includes eastern Asia and the western United States. In the horizontal the resolution is 243 km, and in the vertical, 23 layers on a hybrid-sigma coordinate system from the surface to 100 mbar are used. The boundary conditions used for the regional model are taken from the global chemistry transport model MOZART [Brasseur et al., 1998]. Surface emissions are obtained from the GEIA emission inventory for biogenic volatile organic compounds and from a revised inventory for nitric oxide, carbon monoxide, methane and other hydrocarbons [Mu¨ller, 1992]. [16] Hess et al. [2000] and Hess [2001] used HANK to simulate the Pacific basin during the springtime Mauna Loa Observatory Photochemistry Experiment 2c (MLOPEX 2c) intensive (15 April to 15 May 1992) and compared these model results to the measurements obtained at the Mauna Loa Observatory (MLO) and from aircraft measurements near MLO. Modeled CO, CH4, C2H6, C3H8, O3, and NOx are within 20% of the measured concentration at MLO. Modeled H2O2 and CH3OOH agree within a standard deviation of measured peroxides. However, modeled NOy , HNO3, and peroxyacetyl nitrate concentrations are high compared to measurements and modeled C2H4 and C3H6 are underpredicted.

3. Results [17] Results from eight simulations, which were integrated from 1200 UTC 18 April 1992 to 1200 UTC 23

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Table 2. Simulations Performed Simulation

Transport and Physical Processes

Aqueous Chemistry

pH

Cloud Correction to Photolysis Frequencies

Length of Simulation

1 2 3 4 5 6 7 8

no no yes yes yes yes yes yes

no yes no yes yes yes yes yes

... 4.5 ... 4.5 4.0 5.0 5.5 4.5

no no yes yes yes yes yes no

60 hours 60 hours 120 hours 120 hours 120 hours 120 hours 120 hours 120 hours

April 1992, are discussed (Table 2). Because the initial conditions were provided from the sixth day of a gas-phase chemistry only simulation [Hess et al., 2000], the gas-phase chemistry is initialized from a quasi-steady state. To discern the effect of just the aqueous chemistry upon the chemical environment, the physical processes, such as advection, vertical diffusion, washout, dry deposition, emissions, and the correction to photolysis frequencies due to clouds, in the regional model are not represented in two simulations. Essentially the regional model is used as thousands of box models for these two simulations: (1) gas chemistry only and (2) gas and aqueous chemistry. The simulations are integrated for only 60 hours. Not only do these simulations isolate the influence of aqueous chemistry, but the accuracy of the EBI solution technique can be verified by comparing the model results with a Gear solver [Gear, 1971]. This comparison shows that the EBI method performed well, which was also verified during the 5th International Cloud Modeling Workshop. Next, all the physical processes (advection, vertical diffusion, washout, dry deposition, emissions, and the correction to photolysis frequencies due to clouds) are represented along with the chemistry. These simulations are integrated for 120 hours. Results from five simulations (Table 2) with aqueous chemistry and one simulation without aqueous chemistry are discussed. The simulations with aqueous chemistry have pH values of 4.0, 4.5, 5.0, and 5.5 prescribed. Finally, to examine the radiative effect of clouds on chemical species, results from a simulation with gas and aqueous chemistry that has all the physical processes represented but no correction to photolysis rates due to clouds are presented (Table 2). [18] Figure 1 shows the cloud water mixing ratio at time 0000 UTC 21 April in the boundary layer (z 0.5 km) and in a north-south vertical cross section through Hawaii. For our aqueous chemistry calculations the cloud water mixing ratio determined in the MM5 model is spread uniformly over each grid cell. Clearly, there are boundary layer clouds at most locations over the Pacific. These boundary layer clouds persist throughout the simulation and there is very little rain associated with them (maximum precipitation rate at surface is 0.2 mm hr1). Hess et al. [2000] evaluated the performance of HANK with ISCCP observations of cloudiness over the Pacific basin. They found that the spatial pattern of total cloudiness agreed fairly well between model and observations while the fractional cloud coverage was overpredicted by the model. At the approximate longitude (156W) of Hawaii the model overpredicted the amount of total cloudiness by 30%. The cloud fraction predicted by HANK is applied to the determination of the photolysis frequencies and the wet deposition, but not to the

aqueous chemistry calculations. Thus the overprediction of cloudiness by the model could overpredict the impact of cloud scattering on photochemistry. Likewise, the assumption that the aqueous chemistry operates on the resolvedscale clouds which have a cloud fraction of 1 could lead to overprediction of the influence of aqueous chemistry on model results. Although it appears that applying cloud fraction for determining photolysis frequencies, but not for aqueous chemistry calculations is contradictory, the consequence for aqueous chemistry is small because the liquid water content generally increases proportionally as the cloud fraction decreases. Because the pH in these

Figure 1. (a) Cloud water mixing ratio over the Pacific Basin for 21 April 0000 UTC at z = 0.5 km. (b) Cloud water mixing ratio in a vertical cross section from south to north through Hawaii (156W). Contour lines are 0.1 and 0.3 g kg1. The dashed box indicates the region for which chemistry results are shown.


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ent chemistry box models, the effect of aqueous chemistry on total (gas + cloud + rain) O3 mixing ratios over the Hawaii subdomain after 60 hours of integration is to deplete O3 by up to 14% in the cloudy layers (Figure 2). The amount of O3 depletion by aqueous chemistry found by comparing the mass of ozone in the Hawaii subdomain below 2 km from each of the two simulations (simulation 2 minus simulation 1, Table 2) indicates that there is a 6.1% depletion of O3 (Table 3) for this region. [22] Aqueous chemistry significantly depleted total mixing ratios of CH2O, hydroxyl radical (OH), hydro peroxy radical (HO2), and methyl hydrogen peroxide (CH3OOH)(Table 3). Recall, that the values listed in Table 3 are integrated in both cloudy and clear air (particularly above and below the boundary layer cloud) and therefore actual in-cloud depletion is greater than what is reported in column one of the table. Hydrogen peroxide (H2O2) and nitric acid (HNO3) are not greatly modified by the aqueous chemistry, however examination of the H2O2 vertical profile indicates 5% depletion near the top of the cloud and 5 – 10% enhancement in lower regions of the cloud. Gas-phase formic acid, which was negligible in the gas chemistry only simulation, reached concentrations of 36 pptv over Hawaii in the gas and aqueous chemistry simulation. Figure 2. Ozone vertical profiles averaged over the Hawaii subdomain at (a) 60 hours after the start of the simulation (0000 UTC 21 April 1992) and (b) 108 hours after the start of the simulation (0000 UTC 23 April 1992). Dashed lines are model results from the simulations that depicted only gas-phase chemistry. Solid lines are model results from the simulations that depicted gas- and aqueousphase chemistry. The x-marked line is the initial ozone vertical profile (1200 UTC 18 April 1992). The solid gray lines denote the liquid water mixing ratio (upper axis) averaged over the Hawaii subdomain. Each simulation is identified according to Table 2.

simulations is prescribed, its value is not affected. However, the diffusion-limited mass transfer rate would be impacted. [19] To examine the influence of aqueous chemistry, chemical species results are presented for a 20 by 20 subdomain centered over Hawaii (dashed box in Figure 1), a remote marine location. This subdomain is near the center of the model domain allowing for analysis away from the imposed boundary conditions. Vertical profiles at t = 60 hours (0000 UTC 21 April) and t = 108 hours (0000 UTC 23 April) are discussed to illustrate the effect of aqueous chemistry at the same time of day (1400 local time). Timevarying results are also shown to describe trends. 3.1. No Physical Processes [20] To isolate the influence of aqueous chemistry on O3 concentrations, we first examine results from two simulations in which dynamical and physical processes operating on chemical species, wet and dry deposition of the species, emissions, and the cloud correction to photolysis rates were not included (sims 1 and 2, Table 2). The photolysis rates are still determined from the look-up table, but they are not modified for the presence of clouds. [21] When all these physical processes in HANK are not represented and the model acts as an ensemble of independ-

3.2. With Transport and Physical Processes [23] Differences between results from the simulations with no physical processes and the simulations with physical processes could arise for several reasons. One example is that for the simulation that includes transport, different chemical concentrations are subject to aqueous chemistry than what is found in the simulation with no transport. Furthermore, the effect of the aqueous chemistry in areas upstream of the region of interest may influence the processes that occur in the region of interest. Likewise, the effect of aqueous chemistry that occurs in the region of interest may be transported out of that region, thus our analysis would partly examine the effects from regions upstream rather than the aqueous chemistry effects in the region of interest. In general, the subtropical high over Hawaii causes subsidence of O3-rich air during the period of integration (18 –23 April 1992). [24] For simulations 3 and 4 where all the physical processes are represented and the pH is set to 4.5, the effect of the physical processes after 60 hours of integration is to alter the shape of the vertical profiles of total ozone (Figure 2). Subsident advection and diffusion of the species Table 3. Percent Change of the Species Total Mass Spatially Integrated Over the Hawaii Subdomain below 2 km Between Simulations With Gas+Aqueous Chemistry Compared With Gas Chemistry Only As Multiple Box Models 100(sim 2 – sim 1)/sim 1 O3 H2O2 CH2O HNO3 OH HO2 CH3OOH

Regional Model 100(sim 4 sim 3)/sim 3

t = 60 h

t = 60 h

t = 108 h

6.1 0.5 30.9 1.4 10.7 41.9 31.2

4.8 36.6 24.8 21.2 4.6 38.8 32.4

6.1 51.4 25.1 39.8 4.5 43.8 35.5

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Figure 3. Ozone vertical profiles averaged over the Hawaii subdomain at 60 hours after the start of the simulation (0000 UTC 21 April 1992). The dashed line is the O3 mixing ratio from the simulation that depicted only gas-phase chemistry with all transport and physical processes included. The solid black line is the O3 mixing ratio from the simulation that depicted gas- and aqueousphase chemistry with all transport and physical processes included. The dotted line is the O3 mixing ratio from the simulation that depicted gas- and aqueous-phase chemistry with all transport and physical processes included except vertical diffusion. The solid gray line denotes the liquid water mixing ratio (upper axis) averaged over the Hawaii subdomain. brings O3-rich air via subsidence into the boundary layer as can be seen by the shift of the O3 profiles to higher mixing ratios. Vertical diffusion, which represents transport by subgrid-scale large eddies, mixes the species in the boundary layer creating a well-mixed boundary layer profile. Vertical diffusion also acts to remove the species in the boundary layer to above the cloud layer in the free troposphere, in which the species is less affected by cloud processing. [25] The effect of aqueous chemistry on total O3 is to deplete O3 by at most 9% over the Hawaii subregion. The spatially integrated absolute loss of O3 mass due to aqueous chemistry (O3 mass from the gas and aqueous chemistry simulation 4 minus the O3 mass from the gas chemistry only simulation 3) for the region is larger than the absolute loss of O3 mass found in the multiple box model simulations (O3 mass from the gas and aqueous chemistry simulation 2 minus the O3 mass from the gas chemistry only simulation 1). With O3-rich air being transported into the region’s boundary layer, more O3 is available to be depleted by the aqueous chemistry and to be altered by the radiative effect of the clouds. However, the relative change in the spatially integrated O3 mass due to aqueous chemistry for the Hawaii region is 5% at t = 60 hours (Table 3), which is slightly smaller than the value found for the multiple box model simulations. This relative change is smaller because large eddies, which are represented by vertical diffusion by the model, maintain relatively high background O3 levels in the boundary layer. Results from a sensitivity simulation in which vertical diffusion did not alter the mixing ratios of the chemical species show that without vertical diffusion the O3 mixing ratio would be smaller in the cloud layer, larger at the surface, and larger above the clouds (Figure 3). Thus

vertical diffusion acts to mix the large O3 depletion occurring in the cloud layer within the boundary layer and with air above the boundary layer. [26] Two days later (t = 108 hours), the depletion of O3 due to aqueous chemistry has increased relative to that at t = 60 hours (Figure 2). The spatially integrated O3 loss below 2 km in the Hawaii subdomain is 6.1% (Table 3). [27] An examination of the O3 depletion as a function of time at each model level below 2 km averaged in the Hawaii subdomain (Figure 4) indicates a diurnal variation and an increase in O3 depletion with time, as these boundary layer clouds persist throughout the period of the simulation. The liquid water content also varies diurnally (peaks in early morning), especially at high liquid water contents (z = 0.74 and 1.08 km). When the physical processes are affecting the chemical species’ mixing ratio (Figure 4a), the O3 deviation below 2 km tends to be the same for most model levels, indicating vertical mixing in the boundary layer. On the other hand, without the physical processes (i.e., multiple box models, Figure 4b), the O3 deviation is larger for the greater liquid water contents and the magnitude of the variations at each model level is distinct. These results show the importance of mixing in the boundary layer. Vertical diffusion, which represents transport by large eddies, acts to spread out the effect of the aqueous chemistry throughout the boundary layer and to remove the species from the boundary layer to higher altitudes. [ 28 ] The total mixing ratios of CH 2O, OH, H 2O 2 , CH3OOH, and HNO3 are also modified by transport, emissions, and deposition processes. The effect of aqueous chemistry on total CH2O, OH, and CH3OOH (Table 3) after 60 hours of integration is similar to, and sometimes smaller than, the effect of aqueous chemistry found by differencing the multiple box model simulations. The percent change of HNO3 and H2O2 from simulation 3 (gas chemistry only) to simulation 4 (gas and aqueous chemistry) includes the effect of aqueous chemistry and the effect of slightly different wet deposition schemes used in the two model simulations as discussed in section 2. The difference in the wet deposition schemes does not affect concentrations of CH2O, OH, HO2, and CH3OOH because most of these species exist in the gas phase rather than in the rain. Thus we believe that the results for HNO3 and H2O2 listed in Table 3 are an artifact of the two wet deposition treatments in the two model simulations. We do not believe that this artifact affects the results of the other species. At t = 108 hours the percent difference between simulations 3 and 4 (without and with aqueous chemistry, respectively) remains about the same for CH2O, OH, HO2, and CH3OOH, while the percent difference for H2O2 and HNO3 is greater than what is found at t = 60 hours. For simulation 4, which included physical processes and aqueous chemistry, formic acid gas-phase concentrations reached 70 pptv over the Hawaii subdomain by t = 60 hours. There is nearly twice the formic acid in simulation 4 at this point of the integration than in simulation 2, which did not include transport, emissions, or deposition. 3.3. Sensitivity to the Acidity of the Cloud [29] The rates of several aqueous reactions are sensitive to the acidity of the cloud drops because the dissociation constant of a species depends on the pH value. For example, the pKa of HO2 is 4.46. Thus, at pH values below 4.46,


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Figure 4. The change in O3 mixing ratio between the simulation with gas and aqueous chemistry and the simulation with only gas chemistry at each model level below 2 km for (a) the simulations (sim 4 sim 3) in which the physical processes affect chemical species and (b) the simulations (sim 2 - sim 1) in which there are no physical processes. (c) Liquid water mixing ratio for each model level below 2 km.

there is less superoxide (O 2 ) than HO2 in the cloud drops, and at pH greater than 4.46 there is more O 2 than HO2. We then expect that reactions (A4) – (A6) (Table 1) would proceed faster at higher pH and that O3 would be more depleted because of its reaction with O 2 (reaction (A5)). [30] We present regional model simulation results with prescribed pH values of 4.0, 4.5, 5.0, and 5.5 (Table 2) at t = 108 hours. The O3 vertical profile averaged over the Hawaii subdomain (Figure 5) shows that more O3 is removed by aqueous chemistry at higher pH values. The spatially integrated depletion of O3 indicates that there is nearly a 17% loss of O3 when pH = 5.5 and a 3% loss when pH = 4.0 (Table 4). The effect of pH on O3 mixing ratios in a higher NOx environment may differ from these results [Walcek et al., 1997]. [31] To allow for pH variations across the model domain, the pH of the cloud water and of the rain should be calculated diagnostically using the charge balance equation. However because we do not represent sulfur species in HANK, we instead prescribe the pH. Nevertheless, the pH values chosen are typical of what has been measured for the remote Pacific [Parungo et al., 1982; D. Hegg, personal

communication, 2001] thus our results should be representative of remote, marine boundary layer clouds. [32] Generally, the pH of the cloud drops vary within a cloud because of the dilution (concentration) of ions in larger (smaller) drops. This variation of pH within a cloud is not represented in these simulations. Results from earlier studies that compared S(IV) conversion to S(VI) from a polydispersed cloud drop population in which pH varied with droplet size to that from a monodispersed cloud drop population in which pH is the same value for each droplet [Roelofs, 1993; Hegg and Larson, 1990] showed that a polydispersed cloud drop population converted more S(IV) than the monodispersed cloud drop population because of the pH dependency of the S(IV) and O3 reaction. Analogously, we can speculate that aqueous chemistry in a pH-varying cloud drop population would reduce O3 concentrations further. However, definite conclusions cannot be made without investigating the effect of a pH-varying cloud drop population on O3 chemistry in more detail. [33] Clouds in remote regions may have reduced O3 more efficiently during pre-industrial times than present day because of changes to the background pH. During the

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Figure 5. Ozone vertical profiles averaged over the Hawaii subdomain at 108 hours after the start of the simulation (00 UTC April 23, 1992) for pH = 4.0 (dotted black line), pH = 4.5 (dash-dotted black line), pH = 5.0 (dashed black line), and pH = 5.5 (solid black line). The gray dashed line represents model results from the simulation that depicted only gas-phase chemistry. The xmarked line is the initial ozone vertical profile (1200 UTC 18 April 1992). The solid gray line denotes the liquid water mixing ratio averaged over the Hawaii subdomain. pre-industrial era when CO2 295 ppmv, SO2 25 pptv, 25 pptv, and the NH4+ to SO42 molar ratio is SO2 4 one (values obtained from Brasseur et al. [1999] and the NCAR CCM with sulfur chemistry model results [Barth et al., 2000]), the pH of cloud drops would be 5.4, while for present-day conditions the pH of cloud drops would be 4.75 with CO2 360 ppmv, SO2 75 pptv, SO2 4 150 pptv, and the NH4+ to SO2 4 molar ratio of one. Thus, according to the results in Table 4 clouds in remote regions during pre-industrial times would decrease O3 by 15% compared to 8% reduction for present-day conditions. [34] The percent change between gas chemistry only and gas and aqueous chemistry simulations of the other species also depend on the pH of the cloud drops. H2O2, CH2O, OH, HO2, and CH3OOH all decrease in concentration as pH increases in this region (Table 4). The percent change of HNO3 mass which was spatially integrated over the Hawaii subdomain below 2 km, has very little variation with pH. 3.4. Cloud Correction on Photolysis Rates [35] Photolysis rate coefficients are corrected for the presence of clouds using two factors, the cloud fraction and the increased (decreased) light above (below) the clouds due to scattering of the light by cloud drops. For the Hawaii subdomain the cloud fraction used for the photolysis rate calculations is 60% in the boundary layer. To correct for clouds the clear sky photolysis rate coefficient ( ji, clear) is adjusted at a model grid point according to the cloud fraction (A) below, above, and within a cloud and the ratio (F) of the photolysis rate with cloud effects to the clear-sky photolysis rate. ji ¼ ji;clear Abelow F below þ Awithin F within þ Aabove F above þ Aclear ; ð1Þ

where Abelow + Awithin + Aabove + Aclear = 1 (an in-depth discussion of the cloud fraction representation can be found in the work of Hess et al. [2000]). In equation (1), ji is the

photolysis rate of species i. The ratio F which is determined following Chang et al. [1987], depends upon the location of the cloud relative to the model grid point, the cloud optical depth, and the zenith angle of the sun. For these simulations the optical depth of the clouds, averaged over the Hawaii subdomain, is 20– 25. The effect of cloud generally shows an enhancement on the photolysis rate of NO2 (Figure 6) beginning either from near cloud base (Figures 6b and 6c) at small zenith angles or from near cloud top (Figures 6a and 6d) at large zenith angles, to above the boundary layer clouds. Below cloud base the NO2 photolysis rate is smaller than the clear-sky photolysis rate. [36] To determine the importance of the cloud correction for photolysis rates on the concentration of various chemical species, we present results from a simulation in which the photolysis rates were not corrected for the presence of cloud (sim 8, Table 2), that is ji = ji, clear, and compare the results to the simulation in which the photolysis rates were corrected for clouds and the pH of the drops was 4.5 (sim 4, Table 2). A comparison (Table 5) of the results at t =108 h between the simulation where the photolysis rates were not corrected for clouds and the simulation where the presence of cloud altered the photolysis rates shows that for all the species except OH and HO2, correcting the photolysis rates due to the presence of cloud (higher photolysis rates above cloud, lower photolysis rates below cloud) has