Ozone (O3) is not emitted directly into the atmosphere but is formed in both the stratosphere and troposphere by photochemical reactions involving natural and anthropogenic precursor species. Ozone is an oxidant as well as a precursor to the formation of hydroxyl and hydroperoxyl radicals that play a critical role in the chemical cycles of many trace gases in the troposphere. The lifetime of ozone in the troposphere is relatively short, being about 22 days, compared to long-lived and globally well-mixed greenhouse gases such as carbon dioxide (CO2). Ozone acts as a greenhouse gas, and adversely impacts human health and plant productivity (Monks et al., 2015). As a greenhouse gas, ozone has the third largest global warming effect after CO2 and methane (CH4), and thus plays an important role in the Earth's climate system. The tropospheric ozone burden is estimated to have increased by about 40 % since preindustrial times as a result of increases in the emissions of ozone precursors (Young et al., 2013). According to the Fifth Assessment Report (AR5) of the United Nations Intergovernmental Panel on Climate Change (IPCC), the anthropogenic component of the radiative forcing due to ozone is estimated to be 0.35±0.2 Wm-2, of which 0.40±0.2 Wm-2 is the tropospheric ozone contribution and -0.05 ± 0.1 Wm-2 is the stratospheric contribution. Ozone represents about 15 % of the total anthropogenic radiative forcing (including aerosols) estimated for the year 2011 (IPCC, 2013, AR5, Chapter 8, Anthropogenic and Natural Radiative Forcing).

Aspects of ozone have been investigated in numerous air quality and climate-related studies, including its sources and sinks (e.g. Vingarzan, 2004; Wild, 2007; Stevenson et al., 2006, 2013; Young et al., 2013; Monks et al., 2015). In the troposphere, the ozone budget is determined by transport from the stratosphere, deposition at the Earth's surface, and chemical production and loss. Dry deposition is an important sink of ozone (Galbally and Roy, 1980), which influences near-surface concentration of ozone as well as its lifetime and long-range transport. The “present-day” (about the year 2000) total global dry deposition of ozone estimated by the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) is of the order of 1094±264 Tgyr-1, which is almost double the flux of ozone from the stratosphere to the troposphere (477±96 Tgyr-1) (IPCC, 2013, AR5, Chapter 8, Anthropogenic and Natural Radiative Forcing, Table 8.1, p. 672; Young et al., 2013). Although the average dry deposition velocity to the ocean is smaller than that to terrestrial surfaces, the larger coverage of the Earth's surface by the oceans leads to considerable dry deposition to water – previous modelling studies estimate that about one-third of total ozone deposition is to water (Ganzeveld et al., 2009; Hardacre et al., 2015). Based on results from a multi-model analysis, Hardacre et al. (2015) observed that ozone deposition to the water surface has the largest uncertainty compared to other surface types. The deposition process needs to be properly accounted for when modelling ozone chemistry in the atmosphere, irrespective of whether the application is related to air quality or climate chemistry.

Similar to the land surface, the commonly used parameterisation for dry deposition of gases to the ocean surface is to express deposition velocity (vd), which is the flux of the gas to the surface divided by its concentration in air near the surface, as the linear sum of three resistances (Wesely, 1989): vd=1ra+rb+rc, where ra, rb, and rc are the resistances exerted on the transfer process in three successive top-to-bottom layers: the aerodynamic resistance ra is the resistance to mixing by turbulent transport in the atmospheric surface layer, the atmospheric viscous (or quasi-laminar) sublayer resistance rb is the resistance to movement across the thin layer (0.1–1 mm) of air that is in direct contact with the surface and not moving with the mean flow of the wind, and the surface resistance rc is the resistance to uptake by the underlying surface that can be controlled by physical, chemical, and/or biological processes depending on the surface type and species of interest.

For ozone deposition to oceanic surfaces, rc is the dominant resistance and several approaches have been proposed to calculate it. A common approach is to use a constant value, normally rc=2000 sm-1, based on Wesely's (1989) deposition parameterisation. Several global chemical transport models use this approach by default, for example, MATCH-MPIC (von Kuhlmann et al., 2003), MESSy (Kerkweg et al., 2006), MOZART-4 (Emmons et al., 2010), CAM-chem (Lamarque et al., 2012), GEOS-Chem (Mao et al., 2013), and UKCA (Abraham et al., 2012).

While a constant rc may be a good first-order estimate of global and long-term average oceanic dry deposition, it does not include any spatial and temporal dependencies of the surface uptake process on any oceanic physical, chemical, or biological properties or processes.

The surface ocean acts as a gateway for molecules to enter the atmosphere or ocean medium and contains complex chemical reactions of inorganic components and dissolved organic matter. Some of these reactions are important sources or sinks of climatically active trace gases. For example, ozone is known to react with a number of dissolved chemical compounds present in the seawater, with the reaction with dissolved iodide being by far the fastest one (Garland et al., 1980; Chang et al., 2004), which needs to be considered. Apart from providing more accurate ozone predictions, a proper treatment of deposition is also important from the point of view of better accounting for feedback cycles; for example, the ozone reaction with iodide in the seawater produces volatile iodine compounds, which may then pass into the atmosphere and participate in catalytic ozone destruction cycles in the marine atmosphere (Carpenter et al., 2013).

For the purpose of clarity we describe the layers of the ocean from the surface down as follows. The top 1 µm–1 mm of the sea surface is termed the sea surface microlayer (SSM) (Carpenter et al., 2015). The physical and chemical properties of the SSM can be different to those of the bulk water. The SSM may consist of various sublayers or scales depending on the physical or chemical properties being considered. The upper part of the SSM is a region where turbulent mixing is drastically reduced and molecular diffusion dominates, resulting in strong gradients in gas concentrations and other properties. A reaction-diffusion length scale can be defined as (D/a)1/2 (Carpenter et al., 2013), where D is the molecular diffusivity (m2s-1) and a is the reaction rate constant (s-1), and it is typically 3 µm (at ∼ 25 ∘C) for the ozone–iodide reaction in seawater. A molecular (or diffusive) sublayer can be defined as the region where ozone molecular diffusivity is greater than turbulent transfer, and is typically 50 µm thick. A viscous (or quasi-laminar) sublayer is where viscous processes effectively dissipate the turbulent energy and is of the order of 1 mm (Fairall et al., 2000). In the surface turbulent layer (∼ 10–50 m) below the viscous sublayer, turbulent processes dominate.

Most early studies on ozone deposition to seawater explored its dependence on dynamical factors related to turbulent transfer without considering any chemical reaction (e.g. Galbally and Roy, 1980). Other studies have considered molecular diffusion coupled with chemical reactions (e.g. with iodide) in the seawater without including any effects of turbulent transfer (e.g. Garland et al., 1980). Using the assumption of Wanninkhof (1992) that the enhancement of deposition due to chemical processes was additive to the turbulent transfer in water, Chang et al. (2004) formulated 1/rc as a linear sum of two independent terms with the correct asymptotic behaviours: the first term representing the influence of molecular diffusion coupled with chemical reaction (Garland et al., 1980) and the second term representing the influence of wind-induced turbulent transfer coupled with a chemical enhancement factor (Liss and Merlivat, 1986).

More recently, by solving a simplified form of the budget equation for mass conservation that includes turbulent and molecular transport and a chemical reaction term, Fairall et al. (2007) derived two formulations for rc that are able to account for the simultaneous effects of oceanic physical and chemical processes (i.e. ozone solubility, molecular diffusion, turbulent transfer, and chemical reaction). Their approach signifies a development based on fundamental conservation laws.

Some of the processes affecting deposition act in opposite directions and some are interlinked (e.g. ozone solubility in water and reaction rate coefficient are a function of sea surface temperature, SST, but in the opposite way). Thus, a proper way to evaluate a mechanistic deposition scheme against field measurements is to couple it to an atmospheric composition model and then analyse the response of the full coupled system that represents the conditions of the measurements. Ganzeveld et al. (2009) explored the response of a free-running global climate–chemistry model to the choice of a constant rc and the one-layer reactivity parameterisation derived by Fairall et al. (2007) (described later in Sect. 5) and compared the modelled ozone deposition velocity with observations reported in the literature. Since then there have been new deposition velocity data reported by Helmig et al. (2012), which together with the inclusion of atmospheric model features such as meteorological nudging, could lead to potential improvements in parameterisation of deposition velocity.

Helmig et al. (2012) presented the first shipborne open-ocean ozone flux measurements and at the same time the most extensive set of these measurements (1700 hourly periods of observations), probably surpassing all previous data. The measurements covered the Gulf of Mexico, the southern and northern Atlantic, the Southern Ocean, and the eastern Pacific Ocean. These experiments gave medians of the oceanic ozone deposition velocity (vd) from the five cruises of 0.009–0.034 cms-1. The measurements cover the range of 45∘ N–50∘ S and show little wind-speed dependence but a marked sea surface temperature dependence. This dataset provides a unique opportunity to both test oceanic ozone deposition schemes and re-evaluate the ozone deposition rate to the world's oceans.

The aims of this paper are (a) to examine schemes of ozone dry deposition to the ocean involving the default constant rc assumption, the one-layer reactivity model suggested by Fairall et al. (2007), and a development of their two-layer reactivity model, within a global climate–chemistry model, ACCESS-UKCA, which incorporates meteorological nudging and monthly-varying reactive-gas emissions, and then compare the results with the ozone deposition data of Helmig et al. (2012). This comparing enables selection of an improved deposition formulation. Then (b) using the best fitting scheme, we re-evaluate the rate of ozone deposition over the ocean and for the combined ocean–land system, and finally (c) with the best fitting scheme, we examine its influence on the comparison of modelled ozone compared with global ozone profile observations covering the troposphere.

The Australian Community Climate and Earth System Simulator (ACCESS, see Bi et al., 2013) has been developed for both climate and numerical weather prediction purposes. The physical atmosphere component of ACCESS is the UK Met Office's Unified Model (MetUM). The UK Chemistry and Aerosol (UKCA, http://www.ukca.ac.uk, described below) atmospheric composition module (at UM vn8.4; see Abraham et al., 2012) is part of ACCESS, and the resulting model is referred to here as ACCESS-UKCA.

Several different chemical schemes are available within UKCA. The configuration used here (at UM vn8.4) combines the tropospheric chemistry scheme described by O'Connor et al. (2014) and the stratospheric chemistry as described by Morgenstern et al. (2009). The resulting chemistry configuration is known as Chemistry of the Stratosphere and Troposphere (CheST). The tropospheric chemistry scheme accounts for Ox, HOx, NOx, CH4, and other volatile organic carbon species (e.g. isoprene). The stratospheric chemistry scheme includes chlorine and bromine chemistry (for ozone-depleting substances, ODS) and heterogeneous polar stratospheric cloud chemistry suitable for simulating stratospheric ozone. The Fast-JX photolysis scheme (Neu et al., 2007) is used in the CheST configuration and is applicable both in the troposphere and stratosphere. For aerosols, the size-resolved aerosol microphysics scheme GLOMAP-mode, which includes sulfur chemistry, is used (Mann et al., 2010). The total number of reactions is 306 across 86 species. Interactive gas-phase dry and wet deposition processes are included. The chemical reactions together with relevant deposition rates are solved within the ASAD (A Self-contained Atmospheric chemistry coDe) framework.

UKCA is coupled to a radiation scheme (via O3, CH4, N2O, and aerosol direct and indirect effects). Changes in atmospheric composition are thus allowed to impact model physics, allowing the representation of feedbacks from chemical composition to atmospheric dynamics when operating in a free-running (un-nudged) climate configuration. ACCESS also has a coupled ocean model, but the present configuration used an atmosphere-only set-up forced by observed sea surface temperature and sea ice fields.

The atmospheric model domain is global, with 85 levels extending from the surface to approximately 85 km, and the horizontal resolution was 1.875∘ in longitude and 1.25∘ in latitude (the so-called N96L85 configuration).

In order for a free-running global climate model to realistically reproduce the state of the atmosphere, especially for process studies at short timescales and subsequent comparison with measured data, meteorological nudging is often used. Nudging is a simple data assimilation technique that uses meteorological reanalysis data to relax dynamical variables of a model towards the observed state of the atmosphere at a given time, thus minimising meteorology as a source of uncertainty in the modelled fields. The variables, horizontal wind components and potential temperature in the free troposphere, are nudged by using the ERA-Interim reanalyses on pressure levels (Telford et al., 2008; Uhe and Thatcher, 2015).

A global monthly-varying emissions database for reactive gases and aerosols that includes both anthropogenic and natural components was compiled for ACCESS-UKCA and is described in detail by Woodhouse et al. (2015), with the exception that here we used GFED4s (including small fires) biomass burning emission data instead of the original ACCMIP database; the former includes interannual variability. For methane, nitrous oxide, and ozone-depleting substances, concentrations are prescribed instead of emissions.

The model has nine surface types (namely broad-leaf trees, needle-leaf trees, C3 and C4 grass, shrub, urban, water, bare soil, and land ice) and for a particular grid box the three resistances are calculated for each surface type and a corresponding deposition velocity is then computed. A first-order loss rate is calculated as the deposition velocity divided by the height of the atmospheric boundary layer (ABL), and this loss rate is applied at each model level contained within the ABL. A grid-box mean loss rate at each model level within the ABL is then obtained by weighting the individual loss rates with the fractions of the surface types present in the grid box. The mean loss rate is used in the species mass conservation equation. Currently there is only one water surface type in the model; thus, the same deposition scheme is used for both seawater and freshwater. In our analysis for the oceanic deposition, we do not consider any coastal grid cells and only consider those grid cells that are 100 % covered by the water surface type.

Garland et al. (1980) introduced the idea that ozone loss in the ocean was dominated by its reaction with dissolved iodide ions (O3+I-→ products) within the seawater. This bimolecular reaction is considered pseudo-first-order in several models of ozone loss in seawater because only a very small proportion of iodide is used and its concentration remains almost constant. The first-order rate coefficient a (s-1) is equal to the pertinent second-order rate coefficient (k) times the iodide concentration [I-], i.e. a=k⋅[I-], where [I-] is in mole per litre (or molar, M) and k is in M-1s-1. If there are other first-order reactions, then the total reaction rate can be determined as a=∑ikiCi, where ki and Ci are the first-order rate coefficient and concentration respectively of the ith species.

Equation (5) represents the integrated reactivity given for the bulk reaction (O3+I-→ products) and is used in this model to calculate the ozone removal by reaction with iodide. However, it is noted that the chemistry of ozone reaction with iodide in aqueous solution is complex (e.g. Sakamoto et al., 2009). There can also be complicating factors, including reactions of ozone with other halogens in seawater (Sarwar et al., 2015), the modification of bulk properties of seawater in the surface microlayer, and the presence of organic compounds in seawater including those preferentially concentrated in the sea surface microlayer (Carpenter et al., 2015). These are not addressed here.

The second-order rate coefficient (k) in Eq. (5) is derived from the data of Magi et al. (1997) (see Sect. “Parameterisation of input quantities”).

Atmospheric ozone in passing into the ocean is transferred by molecular diffusion through the sea surface microlayer (including viscous sublayer) and then by turbulent processes in the surface turbulent layer. At the same time, ozone can be lost by chemical reaction.

Considering current knowledge, assuming that iodide does provide a major sink for ozone in the ocean, the resistance to ozone deposition by the ocean surface will be an unknown function of a number of known variables: rc=f[I-],k,Ts,α,D,u∗w, where α is the dimensionless solubility of ozone in water (which is the ratio of the aqueous-phase ozone concentration to its gas-phase concentration and is related to Henry's law coefficient), Ts is the water temperature, D is the molecular diffusivity of ozone in seawater, and u∗w is the waterside friction velocity. rc is expressed as (Liss and Merlivat, 1986) rc=1αvdw, where vdw is the waterside deposition velocity of ozone. u∗w is calculated as u∗w=ρa/ρw1/2u∗ by assuming that the atmospheric surface stress is equal to the waterside surface stress, where ρa is the air density and ρw is the water density.

Applying horizontal homogeneity and stationarity to the fundamental equation for the conservation of mass of a reactive and diffusive substance in water yields the following equation in the vertical dimension (z, i.e. depth from the water surface) (Fairall et al., 2007): ∂∂zD+Kt∂C∂z-aC=0, where C is concentration and Kt is the turbulent diffusivity. By assuming Kt=κu∗wz, Fairall et al. (2007) solved Eq. (8) to obtain vdw=aD1/2K1ξ0K0ξ0, where Kn(ξ) is the modified Bessel function of the second kind of order n, ξ0=b(aD)1/2 and b=2/(κu∗w). Equation (9) explicitly includes the role of waterside molecular diffusion, turbulent transfer, and first-order chemical reaction. In the limit of fast reaction or negligible turbulence, it reduces to vdw=aD1/2, which is the form proposed by Garland et al. (1980). The scheme above forms part of the COAREG group of gas transfer algorithms (Fairall et al., 2011).

There are four controlling parameters involved in the scheme above for rc (Eqs. 7 and 9): ozone solubility, molecular diffusion, turbulent transfer, and chemical reaction. Deposition velocity increases as these parameters attain larger values. This scheme is termed the one-layer reactivity scheme due to the assumption of uniform reactivity (a) through the ocean depth. We incorporated the scheme above for rc in ACCESS-UKCA. This will be the second model specification for ozone deposition.

Helmig et al. (2012) reported the magnitude and variability of ozone deposition velocity measured over the open ocean from a ship-based eddy-covariance ozone flux system during 2006–2008. The experiments were conducted on five cruises, namely (1) TexAQS06 (7 July–12 September 2006), (2) STRATUS06 (9–27 October 2006), (3) GOMECC07 (11 July–4 August 2007), (4) GasEx08 (29 February–11 April 2008), and (5) AMMA08 (27 April–18 May 2008). The respective areas covered were (1) north-western Gulf of Mexico, (2) the persistent stratus cloud region off Chile in the eastern Pacific Ocean, (3) the Gulf of Mexico and the US east coast, (4) the Southern Ocean, and (5) the southern and northern Atlantic Ocean. Helmig et al. (2012) present a map of cruise tracks together with bin-averaged deposition velocity data as a function of SST and wind speed for each of the five cruises. Information on the actual timings and locations of the observations along the cruise tracks was not available.

Helmig et al. (2012) plotted the observed vd values against SST, but in order to compare the data with the vd values obtained from the offline one-layer reactivity scheme of Fairall et al. (2007), they removed the wind-speed dependence from their data. This resulted in an idealised testing of the model scheme, omitting the joint influences of SST and wind speed that occur at the specific field locations. In the present case, this scheme has been coupled to an atmospheric chemistry model in which all input parameters are supplied and act simultaneously in determining vd for the specific field program locations and durations so that the modelled value and data can be compared directly. We obtained the same vd–SST data but without the wind-speed dependence removed (L. Bariteau, personal communication, 2016) for a consistent comparison with our model results (although there is not a large difference between the data with and without the wind-speed dependence).

The ACCESS-UKCA model was run from January 2005 until the end of 2008. The model output was used for 2006 onwards and consists of monthly averaged values of deposition parameters at each grid point. The simulation months and years were matched with the experimental periods. The model output was extracted at a series of grid-point locations with almost uniform spacings along the tracks of the experimental cruises above, and the modelled values at these points were used for comparison with the measurements. It is noted that the deposition velocity data and modelled values cannot be matched exactly in terms of time and location. The cruise data are averaged with respect to SST or wind-speed bins over a given experimental period. They may vary in both space and time (but information on any such variation was not available). Conversely, the model values are monthly averages extracted along the cruise tracks, with the model months matched to the months corresponding to the experimental periods, which means the binning is dominated by spatial changes. This matching difference is not expected to pose a significant problem for comparison purposes because the deposition velocity variations are mainly examined in terms of SST, which is not expected to vary greatly with time at a given location for a typical experimental period under consideration, and the ozone concentration in marine air has only a minor diurnal variation (Galbally et al., 2000). (Note, however, that in contrast to oceanic deposition, land-based deposition over vegetated surfaces typically has a strong diurnal dependence as a result of the strong diurnal variations in ozone concentrations and in the stomatal uptake.)

The vd–SST plot in Fig. 1 shows that although there are large fluctuations in the data from the various field experiments, a trend of vd increasing with SST is apparent. The lowest deposition velocities are observed in the Southern Ocean (GasEx08) and the largest are observed from the TexAQS06 experiment in the north-western Gulf of Mexico. An interpretation of this observed variation is that despite the stronger wind and wave conditions normally present at Southern Ocean latitudes and the higher ozone solubility due to cooler temperatures there, the deposition velocities are lower because of the lower iodide concentrations at such temperatures, which results in a slower ozone loss rate. Conversely, the observed deposition velocities in the north-western Gulf of Mexico are larger due to warmer waters and higher iodide concentrations, leading to faster ozone loss rate despite relatively weak winds and lower ozone solubility there.

Figure 1 shows the ACCESS-UKCA predictions obtained using the default (i.e. constant rc) deposition scheme for the five experiments (diamonds use the same colours as the data). The range of SSTs from the model for different experiments is in agreement with the measurements, with the lowest values predicted for GasEx08 and the highest for TexAQS06 and GOMECC07. However, the modelled vd is virtually constant at 0.038 cms-1, and the observed variation is progressively overestimated by a factor of 2–4 as water temperatures get cooler. This model behaviour with SST is a consequence of the model using a constant value for the dominant resistance rc, resulting in a near-constant vd with a ceiling value of 1/rc=100/2200=0.0455 cms-1. The difference between the ceiling value and a modelled value corresponds to the contribution made by the airside resistances (ra and rb in Eq. 1) to lower the deposition velocity. The comparison in Fig. 1 demonstrates that the constant rc assumption is not satisfactory with respect to both its absolute value and its lack of variation with SST.

Figure 2a shows the same vd–SST plot as in Fig. 1 but with the modelled values obtained using the one-layer reactivity scheme. Even though the molecular diffusion controlled deposition component (aD)1/2 increases by a factor of 30 from high-latitude cold waters to tropical warm waters, a variation to that extent is not evident in Fig. 2a because turbulent transfer starts to dominate deposition as surface temperatures decrease (this is illustrated in the sensitivity analysis presented later in Sect. 6.1.1). Compared to Fig. 1, the one-layer deposition scheme is able to noticeably better reproduce the observed trend of vd increasing with SST and the large observed fluctuations in vd. However, the model still overestimates vd by a factor of 2–3, with the difference between model and data getting progressively larger as the water temperatures get cooler. In Fig. 2b we plot the behaviour of the one-layer reactivity scheme with the contribution of waterside turbulence removed. Compared to Fig. 2a, the modelled deposition velocities are lower in magnitude and compare better with the data. However, it is clear that for SSTs lower than about 15 ∘C, the model without the waterside turbulence underestimates the deposition velocities compared with the measurements because the influence of waterside turbulence becomes important compared to weaker chemical reaction at lower temperatures. Overall, what this comparison suggests is that the influence of waterside turbulence in the one-layer reactivity scheme is overestimated, and this point will be discussed later on in the paper.

In Fig. 3, the modelled variation of vd with wind speed (at 10 m height) obtained using the default deposition scheme does not agree with the measurements, with the modelled vd values almost constant as in Fig. 1, whereas the data show an increasing vd with wind speed for TexAQS06 and to a lesser extent for GOMECC07. For the other three experiments the variation is almost constant, but it is much lower in magnitude than that estimated by the model. It is apparent that the higher-end range of the modelled wind speed is smaller than that of the data, largely because the wind extremes are averaged out in the modelled monthly averages. This is not as much an issue in the earlier comparison involving SST because this parameter does not change as much temporally as wind speed. Hence, in the present case, the vd–wind speed comparison is perhaps not as robust as the vd–SST comparison.

Figure 4 compares the measured vd variations with wind speed against those computed using the one-layer deposition scheme. The model is able to perform somewhat better than that in Fig. 3 for a constant rc, with a hint of the model curves for the five experiments separating out in a manner that is qualitatively consistent with the data.

It is apparent from the comparison above that the one-layer mechanistic scheme given by Eq. (9) is able to yield trends in deposition velocity in the right direction, but the modelled deposition velocities are still overestimated compared to the observed values overall.

The likely reason for the overestimation of deposition velocity by the one-layer reactivity scheme is that it overestimates the influence of waterside turbulence and, consequently, its interaction with the chemical reaction as a result of the use of a turbulent diffusivity parameterisation Kt=κu∗wzw that is linear in zw, the distance (or depth) from the water surface. This parameterisation is valid in the surface layer with fully developed turbulence, but in the small viscous sublayer (zw<δu) (whose thickness δu=11ν/u∗w, where ν is the kinematic viscosity of seawater) is of the order of 1 mm under typical conditions, the above Kt parameterisation would overestimate turbulent transfer because it ignores the dissipation of small-scale turbulence by viscosity. This is important because the length scale of the ozone–iodide reaction falls within the viscous sublayer. It is suggested that Kt varies as zwm+1 in the viscous sublayer, where m=2 for a smooth surface (Fairall et al., 2000). A form for Kt that is valid for both zw<δu and zw>δu, such as Kt=κu∗wzw/1+δu/zwm, may be considered in Eq. (8) but an analytical solution is currently elusive. Based on this more general form for Kt, we can determine that for zw≪δu the form Kt=κu∗wzw would overestimate Kt by a factor of (δu/zw)m. A consequence of this would be to artificially elevate ozone deposition rates to the ocean, especially in regions where the ozone removal chemistry in the surface layer is slow (i.e. areas with cooler SSTs).

Ozone dry deposition parameterisation schemes are best evaluated within an integrated modelling framework that includes multi-parameter interdependencies of deposition as exist in the field. Using recent measurements, we assessed the performance of three ozone dry deposition schemes for seawater within the global climate–chemistry model ACCESS-UKCA, incorporating meteorological nudging and monthly-varying emissions. The default scheme assumes a constant water surface resistance of 2200 sm-1, which is commonly used in most atmospheric chemistry–composition models. The second scheme is a mechanistic, one-layer reactivity scheme proposed by Fairall et al. (2007) in which the surface resistance formulation includes the simultaneous influence of ozone solubility in water, waterside molecular diffusion and turbulent transfer, and a first-order chemical reaction of ozone with dissolved iodide. The third scheme is a development of the two-layer reactivity scheme from Fairall et al. (2007) in which, unlike the uniform reactivity assumption in the one-layer scheme, the water surface has a high reactivity and the region below has a very small background reactivity.

A comparison of the observed deposition velocity dependencies on sea surface temperature and wind speed with those obtained from ACCESS-UKCA using the three schemes showed that the two-layer scheme is able to describe the absolute magnitude and the sea surface temperature and wind-speed dependence of the field measurements most closely. The two-layer scheme results are sensitive to the value of the thickness of the surface reactive layer. A value between 2 and 3 µm, which is about the same as the typical reaction-diffusion length scale over which the ozone–iodide chemical reaction dominates deposition, provides good estimates of deposition and we chose 2.5 µm. The assumption in the first scheme of a constant water surface resistance overestimates the observed deposition velocity by a factor of 2–4 and does not describe its variability with SST and wind speed. The one-layer scheme performs somewhat better than the default scheme. Although the one-layer scheme includes all the same important processes as the two-layer scheme, it still overestimates the observed deposition velocity by a factor of 2–3 when the reaction is slow under colder SSTs. The likely reason for this problem is an overestimation of the influence of waterside turbulent transfer on chemical reaction via the assumption that turbulent diffusivity is a linear function of depth. This assumption, valid for a fully turbulent flow, overestimates turbulent transfer in the viscous sublayer (within which the bulk of the ozone–iodide reaction takes place) because it does not account for the viscous dissipation of turbulence in this layer. The two-layer reactivity scheme limits the dissolved iodide concentration to a specified depth from the water surface, thereby restricting the ozone–iodide reaction and its interaction with turbulence to that depth. Although this restriction is artificial, it works as surrogate to compensate for the overestimation of turbulent transfer in the viscous sublayer. A better parameterisation of turbulent mixing near the interface in the concentration conservation equation could overcome the issue.

The modelled global distributions show that the two-layer reactivity scheme yields maximum ozone deposition velocities in the tropics, which decrease with increasing latitude and then increase again slightly in the Southern Hemisphere. Significant local variations are also evident. This latitudinal behaviour is the result of the combined effects of chemical reaction, solubility, molecular diffusion, and turbulent transfer, and because all these processes except for turbulent transfer are direct functions of SST, which in turn is a function of latitude, there is a strong latitudinal dependence.

Previous model estimates in the scientific literature suggest that approximately one-third of the total ozone dry deposition is to the ocean. The deposition velocities obtained using the two-layer scheme and also the recent oceanic ozone deposition observations point to a substantially lower ozone deposition to the ocean. A new modelled estimate of ozone deposition to the ocean (which excludes sea ice and coastal grid cells) is almost half of the original oceanic deposition obtained using the default scheme and corresponds to a 10 % decrease in the total global estimate of ozone deposition relative to the original (default) scheme, making a significant relative change to the overall tropospheric ozone budget. Although it does not influence the modelled deposition velocity, the underestimation of tropospheric ozone by ACCESS-UKCA needs to be investigated since it leads to a lower absolute value of annual global deposition.

The effects of the choice of a deposition scheme in the global model were noticeable in the predicted atmospheric ozone mixing ratios, especially in the Southern Hemisphere. For the latitudes 45–70∘ S, the two-layer scheme improved the ozone predictions near the surface by 7 % and those within the altitude range 1–6 km by 5 % compared to the original default scheme.

Based on all the evaluation results presented above, the two-layer reactivity scheme as formulated in this paper is the best performing scheme, compared to the available observations, for describing ozone deposition to seawater in a global modelling framework.

Further observations on deposition velocity with greater temporal and spatial coverage would help constrain oceanic dry deposition schemes better. Such work would also look at potential improvement on the parameterisation of iodide in terms of physical and chemical properties of seawater and an assessment of the relative impact of any additional chemical reactions to ozone uptake at sea surface. Studies involving observations of the sea surface microlayer, the role of wave breaking and bubbles on deposition processes, and the degree of relevant feedbacks in climate–chemistry models (e.g. the ozone reaction with oceanic iodide, which produces iodine compounds that participate in atmospheric ozone destruction cycles in the troposphere) need to be explored.