In NEMO-PISCES, surface irradiance received at each grid point is a function of cloud coverage and deduced surface UV irradiance is taken equal to 4.4 % of the total light received at sea surface. UV penetration at depth in marine waters in NEMO-PISCES was taken equal to the penetration calculated with the deep blue wavelength for visible light attenuation coefficient. As this is a rough approximation and might lead to overestimating maximum depth penetration for UV irradiance, we set the UV value to zero for layers deeper than 30 m, which corresponds to the average depth at which less than 10 % of surface UV irradiance penetrates for marine waters containing less than 1 mg m-3 of chlorophyll (Bricaud et al., 1995; Tedetti and Sempéré, 2006).

Chromophoric (or colored) dissolved organic matter (CDOM) is the fraction of the dissolved organic matter that absorbs light, ranging from ultraviolet to visible wavelengths. CDOM has been identified as one of the most influential factors controlling UV attenuation in waters. Its concentration increases in seawater with elevated biological production rates and terrestrial inputs. CDOM distribution is also controlled by the deep ocean circulation, upwelling and/or vertical mixing (Para et al., 2010, and references therein). Its concentration decreases with photochemical degradation and microbial consumption. CDOM absorbs part of available light, therefore negatively impacting primary productivity of aquatic ecosystems. However, as provider of a substitute for microbial respiration, photo-degraded CDOM positively impacts the secondary productivity of the oceanic ecosystems.

As no reliable parameterization is currently available to calculate CDOM concentrations, due to insufficient knowledge on the controlling processes of CDOM formation and destruction, we chose to follow the study of Para et al. (2010) and assumed that the CDOM absorption at 350 nm (a350) was a good indicator of CDOM concentrations. The wavelength of 350 nm was chosen because it corresponds to the maximum sensitivity of the Eppley UV light sensors used during the marine campaigns where links between CDOM concentration, CDOM absorption and OCS production were established (Uher and Andreae, 1997; Preiswerk and Najjar, 2000). This wavelength has also been proven to be the most efficient for the photochemical excitation of dissolved organic matter (Farmer et al., 1993). OCS production is either dependent on irradiance in the UV domain (photo-production) or on CDOM and organic matter concentration (dark production). As a350 allows for a link with both variables, it is a key parameter in our parameterizations of OCS production. Sensitivity tests were performed using NEMO-PISCES and three different formulations of a350.

The first two formulations of CDOM absorption coefficients were proposed by Morel and Gentili (2009) and Preiswerk and Najjar (2000), who deduced them at a given wavelength from in situ measurements, and then extrapolated the absorption coefficient of CDOM at 350 nm by using the following standard exponential relationship: aCDOMλ=aCDOMref×e-S×ref-λ, where S is the spectral slope coefficient of CDOM between λ and the reference wavelength (ref).

The parameterization of a350 from Morel and Gentili (2009) is based on spectral reflectances of the ocean over case 1 waters. Case 1 waters are those for which the optical properties of CDOM closely follow the optical properties of phytoplankton, as defined in Morel (1988). Spectral reflectances were derived from ocean color remote sensing data at several wavelengths to allow separation between CDOM and chlorophyll reflectance signatures. Products from SeaWiFS monthly global composites for the 2002–2007 period were used, and led to the following relationship between CDOM absorption coefficient and chlorophyll concentration: aCDOM(400)=0.065[Chl]0.63.

The second parameterization was taken from Preiswerk and Najjar (2000) who deduced a350 from modeled CDOM absorption coefficient at 440 nm. To model a440, satellite ocean color data were used as a proxy for chlorophyll concentration and combined with the relationship of Garver and Siegel (1998), Eq. (2): per(a440)=-26logchl+26 aPH,440=0.0448chl, pera440=a440aPH,440+a440×100%, where aPH,440 is the absorption coefficient of the phytoplankton at 440 nm, and per(a440) is the percent of the total non-seawater absorption coefficient at 440 nm (due to CDOM).

A relationship between a350 and Chlorophyll a was established independently, using MODIS Aqua ocean color data collected continuously between July 2002 and July 2010. Monthly climatologies of MODIS Aqua Chlorophyll a surface concentrations were used, and MODIS Aqua remote-sensing reflectances were used to derive corresponding monthly climatologies of a350 for the global surface ocean. The SeaUV algorithm developed by Fichot et al. (2008) was used to estimate the diffuse attenuation coefficient at 320 nm, Kd(320), from the remote-sensing reflectances. A ratio aCDOM(320) / Kd(320) = 0.68 derived from an extensive set of in situ measurements was then used to calculate the absorption coefficient of CDOM at 320 nm, a320, from Kd(320) (Fichot and Miller, 2010). A spectral slope coefficient of 0.0198 derived from the same in situ data set was then used to calculate a350 from a320 using Eq. (1).

The a350 data from the twelve monthly climatologies were regressed on the corresponding MODIS Aqua Chlorophyll a concentrations using the fourth-order polynomial shown in Eq. (6). ln(a350)=0.5346C-0.0263C2-0.0036C3+0.0012C4-1.6340, where C is the chlorophyll concentration in mg m-3, and a350 has units of m-1.

The Eq. (6) was then added in NEMO-PISCES to complete the sensitivity tests of the OCS concentrations on the different a350 expressions tested.

OCS photo-production is primarily induced by the interaction of UV radiation and natural photosensitizers in CDOM (Ferek and Andreae, 1984; Flöck et al., 1997). Therefore, the Uher and Andreae (1997) photo-production parameterization takes into account both the incident UV irradiance and OCS production efficiency (apparent quantum yield, AQY). An AQY represents the spectral efficiency of a photochemical process (e.g., photochemical production of OCS), and is generally determined in the laboratory by normalizing the quantity of OCS produced during solar exposure to the amount of photons absorbed by CDOM during that same solar exposure. The resulting expression for photoproduction rate proposed is P=pUV, where P is the OCS photo-production rate, p a zeroth-order photoproduction constant (fmol L-1 s-1 W-1 m-2) and UV the solar UV light density.

This expression was established using strong assumptions, such as considering that no other source or sink of OCS affects OCS concentration in seawaters. In their study, Uher and Andreae (1997) measured mean values for the photoproduction constant p around 1.3 ± 0.3 fmol L-1 s-1 W-1 m-2 on offshore samples and values twice as high in inshore waters, around 2.8 ± 0.3 fmol L-1 s-1 W-1 m-2 (all measurements done in April 1993, in the North Sea).

A few AQY for OCS have been published, but they exhibit considerable variability, with values varying by a factor of > 7 depending on the environment considered (quantum yields ranging from 9.3 × 10-8 to 6.4 × 10-7 in the Sargasso Sea for Weiss et al., 1995a, and Zepp and Andreae, 1994, respectively). The quantum yields depend both on the location and the season of the measurement, especially considering that CDOM quality and its absorption coefficient might vary through time (Kettle et al., 2002; Weiss, 1995b; Cutter et al., 2004). To compensate for part of this natural variability, Uher and Andreae (1997) normalized the measured AQY by the absorption coefficient of CDOM available for the reaction at the same location. Therefore, the new relationship, implemented in NEMO-PISCES, is the following: P=a350UVpa350=ka350UV where P is the OCS photo-production rate (pmol m-3 s-1), and UV is the incident irradiance integrated from 295 to 385 nm (W m-2). The k coefficient is retrieved from the normalization of measured photoproduction constants to measured CDOM absorption coefficient values at 350 nm. For offshore waters (the majority of global waters), k was found in the Uher and Andreae (1997) study to be close to a value of 2.1 fmol L-1 s-1 W-1 m3. Note that the k coefficient deduced from inshore water samples was found to be 2.8 fmol L-1 s-1 W-1 m3 on average. The smaller difference between the two k values justified the choice of using this normalized expression rather than Eq. (7a) which showed more sample dependence.

Measurements of large OCS concentrations well below the photic zones have proven that OCS can be produced when no light is available. The so-called dark production pathway was shown to largely depend on available organic matter. The pool of organic matter is quantified by the a350 parameter, following Para et al. (2010), as explained in Sect. 2.2.2. Microbial activities are suggested as main precursors for the OCS dark production pathway, but their exact nature and the mechanisms underlying this process are poorly known. Von Hobe et al. (2001, 2003) calculated dark production rates assuming that after-dawn OCS concentrations were reaching a steady-state when dark production was compensating for the parallel hydrolysis. Equation (8) was established using measurements from a campaign in the Sargasso Sea and hydrolysis rates were calculated following the Elliott et al. (1989) formulation.

The formulation from Von Hobe et al. (2001) relating OCS dark production rates to the CDOM absorption coefficient was implemented in NEMO-PISCES as follows: Q=a350e(55.8-16200T)(TinK), where Q is the dark production rate in pmol m-3 s-1, and a350 is the CDOM absorption coefficient which is used here to describe the CDOM/organic matter concentration.

OCS is chemically removed in seawater through reaction with H2O and OH-: H2O+OCS↔H2S+CO2, OH-+OCS↔HS-+CO2. OCS hydrolysis rate measurements were done in the dark, using filtered water, therefore canceling the potential impact of parallel dark production. Reactions (R1a) and (R1b) are actually composites of complex mechanisms involving several intermediates, and concentrations that have been used to calculate hydrolysis rates are much larger than observed in seawater, which may lead to some errors.

We performed sensitivity tests in NEMO-PISCES by using two different hydrolysis parameterizations to study the impact of the choice of the hydrolysis constant formulation. Both Kamyshny et al. (2003) and Elliott et al. (1989) relate the value of OCS hydrolysis constant to the marine water pH and its temperature, respectively, as follows: khydr_Elliott=e(24.3-10450T)+kw[H+]e(22.8-6040T)(TinK), khydr_Kamyshny=4.19E-12e-12110T+kwOH-1.41E18e--11580T(TinK), with kw the ion product of marine water, and [OH-] and [H+] the OH- and H+ activities.

Both hydrolysis constant rates, as function of temperature, are represented in the case of pH = 8.2 in Fig. 2.

OCS exchange between the ocean and the atmosphere can be described in an analogous way to Fick's diffusion law. The sea–air OCS flux depends on the OCS concentration in seawater and the partial pressure of OCS in air: FOCS=kwaterOCSaq-OCSatmH, where FOCS is the sea–air flux (pmol m-2 s-1), [OCS]aq and [OCS]atm are the OCS concentration at sea surface and in the atmosphere, respectively (in pmol m-3). The atmospheric OCS concentration [OCS]atm over the sea surface was constantly imposed when running NEMO-PISCES, assuming an atmospheric mixing ratio of 500 ppt. Through H, the Henry's law constant, the sea–air OCS flux also depends on the air temperature, and was implemented in NEMO-PISCES following the expression established by Johnson and Harrison (1986): H=e(12722-3496T)(T,airtemperatureinK), where kwater is the piston velocity (in m s-1) for OCS. The coefficient is deduced from the Schmidt number of OCS, depends on surface wind speed and is calculated with the relationship of Wanninkhof (1992): kwater=0.3u2+2.5×0.5246+0.016256T+0.00049946T2×660SOCS(T,airtemperaturein∘C), where u is the wind speed (in m s-1).

Note that Kettle et al. (2002) used similar parameterizations for the sea-surface exchange coefficient and the same relationship from Wanninkhof et al. (1992) to model the global OCS flux at sea surface to the one presented in this work.

The Schmidt number for OCS, SOCS (dimensionless), was implemented in NEMO-PISCES following the suggestion by Ulshöfer (1995) to deduce it from kinetic viscosity (ν) and diffusion coefficient (D) (both in m2 s-1), respectively derived from SOCS=νD, with ν=(1.792747-5.126103E-2T+5.918645E-4T2)×1E-6(T,airtemperaturein∘C) and D=10-1010T-1.3246×1E-4(T,airtemperaturein∘C).

Our MODIS Aqua based extrapolation (Eq. 6) resulted in the highest values of simulated a350 (up to 0.15 m-1, both in January and in August), while the parameterization from Preiswerk and Najjar (2000) resulted in a350 values that were about half as much (Fig. 5), consistent with the difference in the respective a350–chlorophyll formulations (Fig. 3). Values for a350 deduced from Morel and Gentili (2009) (Eq. 2) gave an intermediary result. The pronounced August maximum around 80 m depth (Fig. 5b) reflected a chlorophyll content maximum at this depth (a350 is monotonically increasing for low levels of chlorophyll). In contrast, low a350 values near the surface translated to a local minimum in the chlorophyll content. Note also the abrupt decrease of chlorophyll concentrations, and therefore the decreasing a350, for depths below 80 m in August. In January the mixed layer was 120 m thick in NEMO-PISCES at the BATS site (Fig. 5a). Chlorophyll content (thus a350) remained high and constant over the first 120 m of the ocean before an abrupt decrease in the pycnocline. For both January and August, chlorophyll concentrations and a350 values became negligible below 200 m, with the exception of a350 calculated with the relationship proposed in this work.

Differences in a350 estimations using the relationships in Eqs. (2)–(6) led to three-fold difference between the most extreme near-surface OCS maximum concentrations simulated by NEMO-PISCES (from 100 to 300 pmol L-1 in August and from 30 to 85 pmol L-1 in January). In the photic zone (the first 30 m below the surface, as implemented in NEMO-PISCES), August subsurface OCS concentrations (Fig. 5d) were clearly driven by photo-production (vertical profile of photo-production not shown here). Where the influence of UV-light irradiance is smaller or negligible (below 30 m in August or in the entire water column in January), OCS concentration profiles are driven by the predominant dark production (vertical profile of the dark production contribution not shown here). Therefore, in these layers, OCS concentrations mostly followed the chlorophyll content profiles. Thus, OCS concentration profiles simulated with NEMO-PISCES in January showed a drop below the mixed layer (below 120 m), and became negligible below 200 m. In August, the highest concentrations were found at the surface. A second peak of OCS levels was found around 80 m depth, where chlorophyll content peaks. Deeper, the OCS concentrations decreased, down to negligible values below 200 m.

OCS concentrations simulated with NEMO-PISCES showed very large values in the few first meters under the surface, averaging 70, 90 or even 270 pmol L-1 in August at the BATS site, depending on the a350–chlorophyll relationship used. Some OCS levels measured with buoys during a field campaign in August 1999 at the BATS site peaked at 150 pmol L-1 in the first 3 m (Cutter et al. 2004), showing a potential to reach such high values. When using the a350 formulas derived from the studies of Morel and Gentili (2009) or Preiswerk and Najjar (2000), the simulated vertical profiles of OCS concentrations in the Sargasso Sea in August (Fig. 5d) fall into the range of measured OCS concentrations reported by Cutter et al. (2004). This is however not the case when using the a350 based on MODIS Aqua data which lead to the highest simulated OCS concentrations (270 pmol L-1 at the sea surface) and seem to overestimate the natural variability of the OCS concentrations as measured in these waters.

The lower OCS concentrations in deeper layers reflected the quick removal of OCS by hydrolysis in the model (vertical profile of the hydrolysis contribution not shown here). This behavior fit well with the estimated short lifetime of the OCS molecule in marine waters, ranging between 4 and 13.4 h, according to the models of Elliot et al. (1989) and Radford-Knoery and Cutter (1993), respectively.

Absorption coefficients of CDOM at 350 nm (a350) simulated using NEMO-PISCES were evaluated for the different formulations of a350 against the annual climatology of a350 derived from MODIS Aqua ocean color data as in Fichot and Miller (2010). The MODIS Aqua-derived a350 data (Fig. 6a) showed mini mal values in the subtropical gyres, and maximum values in coastal regions and at high latitudes (higher than 45∘ N and 45∘ S). Note that the MODIS Aqua-derived values should not be considered as direct observations but only as an independent estimate relying on a generic relationship (i.e., a statistical model).

Regions where a350 was not accurately modeled also suffered from biases in simulated chlorophyll values. Therefore, the highest a350 values observed near the coasts were not represented in NEMO-PISCES due to its limited spatial resolution. Additionally, the simulated chlorophyll maps (thus those of a350 as well) showed a higher contrast between low and high latitudes than the SeaWiFS-derived observations (Fig. 4). In tropical regions (30∘ S–30∘ N), especially in the Atlantic Ocean, the Indian Ocean and in the Western Pacific Warm Pool, chlorophyll levels simulated by NEMO-PISCES were underestimated by a factor of 2 compared to the SeaWiFS chlorophyll observations (Fig. 4). As these are regions of warm ocean waters favorable to OCS dark production, the consequence might be an underestimation of OCS production in these regions. In regions showing low chlorophyll concentrations, this underestimation translates to an approximate 30 % underestimation of the a350 value (depending on the a350 formulation used), which directly translates to an equivalent underestimation of OCS dark and photo-production, since both parameterization linearly depend on a350.

Finally, NEMO-PISCES-simulated chlorophyll levels at mid- and high latitudes were similar for northern and southern oceans, with average values around 0.5 mg m-3. However, chlorophyll concentrations deduced from satellite observations showed average mid- and high-latitude values around 0.2 mg m-3 in the Southern Hemisphere and 0.5 to 1 mg m-3 in the Northern Hemisphere. Thus, the NEMO-PISCES model overestimated the chlorophyll concentrations by a factor of 2 over most of the mid- and high latitudes of the Southern Hemisphere – especially in the Pacific Ocean and south of Australia (Fig. 4). Therefore, our modeled OCS production in the Southern Hemisphere is likely overestimated.

The different a350–chlorophyll relationships used in the present work (Eqs. 2, 3, 6) led to simulated values of a350 differing by as much as a factor of 3. The CDOM absorption coefficient values obtained with the formulations of Preiswerk and Najjar (2000) and Morel and Gentili (2009) were similar to the MODIS-derived estimates for low and mid-latitudes (below 60∘ S and 60∘ N), but largely underestimated at high latitudes in the Northern Hemisphere, with values 2 to 3 times smaller than the MODIS-derived estimates (Fig. 6). Conversely, the formulation presented in this work (Eq. 6) correctly reproduced the observed levels of a350 in the northern high latitudes, but clearly overestimated the values for CDOM absorption coefficient at low latitudes and in the Southern Hemisphere: simulated a350 values in some subtropical oligotrophic regions reached values 3 to 4 times higher than the MODIS-derived values.

In the present study, the a350-dependent NEMO-PISCES model and the AQY-dependent photochemical model from Fichot and Miller (2010) were used to provide two independent estimates of OCS photo-production rates. Sensitivity tests were performed on the annual global OCS photo-production over the entire water column (from the sea surface to the ocean floor). Both models were run with different formulations of a350 (NEMO-PISCES model) or using different AQY (Fichot and Miller photochemical model) from the literature.

The AQY estimates used were collected in open ocean environments (Weiss et al., 1995a) and coastal environments (Zepp and Andreae, 1994), respectively. Large uncertainties around AQY estimations depending on the measurement location led to large differences in the estimates of global OCS photo-production. Global OCS photo-production modeled with the Fichot and Miller (2010) model thus ranged from 876 to 5500 GgS yr-1 (Table 1). Extremely high AQY values have been measured on the continental shelf (Cutter et al., 2004), but were not considered appropriate for representing the average global ocean. Using this last value would have led to 37 700 GgS yr-1 of OCS global photoproduction, far above observed photo-production levels and other model estimates.

Both the photochemical model from Fichot and Miller (2010) and the NEMO-PISCES model led to similar spatial distributions of OCS photo-production (Fig. 7). Indeed, subtropical regions are the major contributors in terms of yearly total photo-production of OCS because the photo-production rates were roughly constant through the entire year, regardless of model used. However, the highest monthly photo-production rates were found in mid-latitude regions (40–60∘ N and 40–60∘ S) during the period of maximum irradiance, with rates twice as large as the nearly constant rates obtained in tropical regions, as can be seen in the time–latitude diagram in Fig. 8a. Depending on the value of the driving parameter for the two models used (AQY or a350, respectively), large uncertainties existed over the total quantities of OCS photo-produced. Global photo-production of OCS for the NEMO-PISCES model and the photochemical model from Fichot and Miller (2010) are compared in Table 1. When using the a350-based model NEMO-PISCES, the range of the global OCS photo-production was reduced but still large, with estimates between 1390 and 4540 GgS yr-1 depending on which formulation was chosen to calculate a350. These values were in rather good agreement with the range obtained with the AQY-based photochemical model from Fichot and Miller (2010).

The photochemical model from Fichot and Miller and NEMO-PISCES showed lower OCS photo-production rates than in situ measurements, irrespective of the a350 formulation. For instance, Cutter et al. (2004) estimated August photo-production rates of up to 10 or 15 pmol L-1 h-1 in the Sargasso Sea, which is above the values of 4 to 9 pmol L-1 h-1 obtained by running the NEMO-PISCES model at the same location (with implemented Eqs. (3) and (6), respectively) (Fig. 7).

Dark production is a linear function of a350 (Eq. 8). However, temperature is the main driver of global OCS dark production as simulated by NEMO-PISCES. The time–latitude representation of dark production rates (Fig. 8b) show that the maximum values were located at low latitudes, in warm marine waters, despite the fact that these regions correspond to the lowest a350 values (Fig. 6). The dark production rates in these regions remained relatively constant throughout the year. On the contrary, chlorophyll-rich waters at higher latitudes, leading to higher a350 values (Fig. 6), corresponding to colder marine waters and thus limited dark production rates (due to the temperature dependency in Eq. 8).

Measurements from Von Hobe et al. (2001) at the BATS site showed dark production rates of 1 to 1.5 pmol L-1 h-1. NEMO-PISCES results showed a very good agreement with this data, with rates of 0.8 pmol L-1 h-1 in August at the BATS site (not shown). In the study of Cutter et al. (2004), calculated dark production rates reached 4 pmol L-1 h-1 in August, significantly above the simulated range by NEMO-PISCES. Von Hobe et al. (2001) estimated that dark production produces around 50 % of OCS at these low latitudes. In the NEMO-PISCES model, dark production only represented 34 % of the OCS produced at low latitudes, and 66 % of OCS is photo-produced.

Figure 2 presents the hydrolysis reaction constant as a function of temperature for a given pH, as given by the Kamyshny et al. (2003) and Elliott et al. (1989) formulations. Both formulations relate the OCS hydrolysis to the OCS concentration and to the seawater pH (Reactions R1a and R1b). At a given pH, the difference between the two formulations led to a 50 % difference in the hydrolysis constant for seawater temperatures above 12 ∘C (Fig. 2). A comparison between time–latitude maps of the hydrolysis rate (Fig. 8c) and the OCS concentration (in Fig. S1 in the Supplement) suggests that OCS hydrolysis rates in NEMO-PISCES are largely driven by OCS concentrations. These spatiotemporal variation patterns only slightly differ around the Equator, where marine waters are somewhat less alkaline, which leads to a limitation of the OCS hydrolysis rate through pH influence. Simulations run with two different hydrolysis parameterizations (based on Reactions R1a and R1b) provide global OCS emissions diverging by a factor of 2.5 (see Fig. 9).

Maps of annual mean surface OCS concentration patterns at sea surface simulated with NEMO-PISCES are presented in Fig. 10 (right column). NEMO-PISCES simulations produced maximum annual mean OCS levels in equatorial and sub-tropical regions, where dark production was maximal and photo-production was constantly active. In low-latitude marine waters, OCS concentrations remained nearly constant throughout the year (Fig. S1 and Fig. 10). However, the model showed a strong seasonal variability of OCS concentrations for mid- and high latitudes, with roughly a factor of 10 difference between maximal and minimal OCS concentration levels reached throughout the year. These spatial distributions and the intra-annual variation amplitudes were relatively independent of the formulation used in NEMO-PISCES to calculate a350.

Modeled OCS concentrations were evaluated against observational data available in the literature. OCS concentrations measured near European shores and estuarine regions over the year showed large spatial and temporal variability (Uher, 2006). The few measured OCS concentrations in estuarine regions were close to 250 pmol L-1 in winter and 430 pmol L-1 in summer, while smaller values were measured near shores from 40 pmol L-1 in winter to 100 pmol L-1 in summer. Von Hobe et al. (2003) also measured summer OCS surface maximum levels of 120 pmol L-1 in an upwelling region near the Portuguese coast. When using the MODIS Aqua-based a350 formulation (Eq. 6) which gives the best representation of a350 in the region (Fig. 6), simulated OCS nearshore concentrations only reached values from 30 pmol L-1 in winter to 100 pmol L-1 in summer (Fig. 10). NEMO-PISCES matches correctly the seasonal amplitude of OCS concentrations measured in these areas and represents quite accurately the absolute values measured near the shores. However, as expected, the lack of resolution of the model translates into an underestimation of the estuarine concentrations.

As shown in the comparison done in the study of von Hobe et al. (2003), the reproduction of the OCS depth profiles by their models was generally less accurate than that of surface data because the models were tuned to fit the surface concentrations. In our study, the model was not tuned to fit surface or depth concentrations. As NEMO-PISCES provides gridded monthly mean concentrations of OCS on the entire water column, monthly mean concentrations of OCS data series should, ideally, be used to evaluate the global simulations.

Unfortunately, a global database of sea surface OCS measurements and a procedure to calculate sea surface OCS as a function of latitude, longitude and month are not available in the literature as, for example, for DMS (e.g., Kettle et al., 1999; Lana et al., 2011). The assemblage of a global OCS database was not achievable in the framework of this project. The evaluation of the modeled oceanic OCS concentrations that had been carried out is not fully satisfactory because we implicitly chose to compare modeled monthly mean concentrations and discrete measurements.

With these caveats in mind, 150 OCS measurements classified according to location, date and depth were gathered from the literature (Weiss et al., 1995a; Ulshöfer et al., 1996; Cutter et al., 2004; Von Hobe et al., 2001, 2003) and compared with the outputs from the model run with its “standard” parameterization, as described in the discussion section. The results are displayed in Fig. S2 and show that the outputs of the model generally overestimate the measured concentrations by a factor of 2 to 4 at the sea surface (first 10 m, A), especially at sites where low concentrations were measured. In seawaters with high OCS concentration measurements (higher than 100 pmol L-1), the corresponding simulated concentrations were generally underestimated, up to a factor of 2. A better agreement between modeled and observed concentrations is found with the subsurface data (below 10 m, B).

This model–data comparison suggests that simulated OCS concentrations might be overestimated in a significant way in surface waters, which might lead to an overestimation of the simulated OCS outgassing fluxes (up to a factor of 2 to 4). However, the limited spatial (many measurements were done around 40∘ N) and temporal (many measurements in July and August) distribution of the measurements severely reduced the possibility for an exhaustive model validation and for the identification of concentration biases in the model. Furthermore, a large range of concentrations were measured even for sites close in latitude and for measurements realized around the same period of the year. Finally, this overestimation might also compensate for the underestimation in the OCS production in shallow water, since the model is lacking an exhaustive representation of the estuarine regions.

The formulation used to calculate a350 values did not affect the global spatial distribution of OCS concentrations, but it largely influenced the absolute value of the simulated OCS concentrations. For instance, maximal OCS concentrations in tropical sub-surface waters were estimated close to 300 pmol L-1 with the MODIS Aqua-based a350 formulation (Eq. 6), while estimates based on Morel and Gentili (2009) and Preiswerk and Najjar (2000) only reached one-half (one-third, respectively) of these modeled maximal concentrations (Fig. 10). Note that the formulation of Morel and Gentili (2009) led to results that were in better agreement with the campaign measurements described in Sect. 3.2.2 (Cutter et al., 2004; von Hobe et al., 2001).