Carbonyl sulphide (OCS) is the most abundant, long-lived
sulphur gas in the atmosphere and a major supplier of sulphur to the
stratospheric sulphate aerosol layer. The short-lived gas carbon disulphide
(CS2) is oxidized to OCS and constitutes a major indirect source to the
atmospheric OCS budget. The atmospheric budget of OCS is not well
constrained due to a large missing source needed to compensate for
substantial evidence that was provided for significantly higher sinks.
Oceanic emissions are associated with major uncertainties. Here we provide a
first, monthly resolved ocean emission inventory of both gases for the
period 2000–2019 (available at
10.5281/zenodo.4297010) (Lennartz et al.,
2020a). Emissions are calculated with a numerical box model (2.8∘×2.8∘ resolution at the Equator, T42 grid) for the oceanic
surface mixed layer, driven by ERA5 data from ECMWF and chromophoric dissolved organic matter (CDOM) from
Aqua MODIS. We find that interannual variability in OCS emissions is smaller
than seasonal variability and is mainly driven by variations in CDOM, which influences both
photochemical and light-independent production. A comparison with a global
database of more than 2500 measurements reveals overall good agreement.
Emissions of CS2 constitute a larger sulphur source to the atmosphere
than OCS and equally show interannual variability connected to variability
in CDOM. The emission estimate of CS2 is associated with higher
uncertainties as process understanding of the marine cycling of CS2 is
incomplete. We encourage the use of the data provided here as input for
atmospheric modelling studies to further assess the atmospheric OCS budget
and the role of OCS in climate.
Introduction
The trace gases carbonyl sulphide (OCS) and carbon disulphide (CS2) are
naturally produced in the ocean and emitted to the atmosphere
(Ferek and Andreae, 1983; Kettle et al., 2001; Khalil and Rasmussen, 1984; Watts, 2000). CS2 is oxidized to a large extent to OCS (∼ 82 %
on a molecular basis) within days after emission and thus constitutes a
large indirect source in the atmospheric OCS budget
(Chin and Davis, 1993; Stickel et al., 1993).
OCS is the most abundant sulphur gas in the atmosphere, with an average mixing
ratio of ca. 480 ppt at land-based time series stations
(Montzka et al., 2007) and ca. 550 ppt in the
marine boundary layer (Lennartz et al., 2020b). The
sources and sinks of atmospheric OCS are important in two contexts: first,
OCS is transported to the stratosphere due to its long tropospheric lifetime
of 1.5 to 3 years (Montzka et al., 2007), where
it is a major precursor of sulphate aerosols (Brühl
et al., 2012; Kremser et al., 2016; Turco et al., 1980). The stratospheric
sulphate aerosol layer influences the radiative budget by increasing the
planetary albedo and in addition provides surfaces for ozone-catalysing
reactions (Solomon et al., 2011, 2015). Second,
OCS has been suggested as a promising proxy to constrain the terrestrial
CO2 uptake on a global scale using inverse atmospheric modelling (Berry
et al., 2013; Stimler et al., 2010; Whelan et al., 2018). In order to
understand the dynamics of the sulphate aerosol layer and to apply OCS as a
proxy for gross primary production, the quantification of OCS sources and
sinks to the atmosphere on a global scale is required.
Currently, oceanic emissions are associated with the highest uncertainties
among sources in the atmospheric OCS budget (Kremser et al.,
2016; Whelan et al., 2018). Evidence for increasing the vegetation sink led
to a missing source in the budget (Suntharalingam et al., 2008), and oceanic emissions have been suggested to account for a gap of
600–800 Gg S yr-1 (Berry et al., 2013; Glatthor et al., 2015; Kuai et al., 2015b). Global oceanic emission estimates extrapolated from measurements range from -16 Gg S yr-1 (Weiss et al., 1995b) to
320 Gg S yr-1 (Rasmussen et al., 1982). Surface ocean models that are largely in agreement with observations report direct OCS emissions from the oceans of 41 Gg S yr-1 (Kettle et al., 2002) to 130 Gg S yr-1 (Lennartz et al., 2017). Generally,
surface seawater concentrations of OCS are too low to sustain emissions that
would close the budget (Lennartz
et al., 2017, 2020b). A detailed description of the marine emissions of OCS
and its precursor CS2 can serve as an input to modelling studies and
thus help to identify the missing source.
Models resolving the marine cycling of multiple trace gases are powerful
tools to assess interannual variability in marine emissions through
variations in the factors influencing production and consumption of the gas
in seawater. The processes determining OCS concentration in the surface
ocean are better understood than those of CS2, and model approaches for
marine concentrations and emissions have been developed previously (Kettle,
2000; Kettle et al., 2002; Launois et al., 2015; Lennartz et al., 2017;
Preiswerk and Najjar, 2000). While some show good agreement with
observational data (Kettle et
al., 2002; Lennartz et al., 2017; Preiswerk and Najjar, 2000),
inconsistencies in calculating the hydrolysis rate
(Lennartz, 2016) presumably led to overestimations in
another study (Launois et al.,
2015). All of these models use climatological forcing data. For gases like
OCS and CS2 with a high spatiotemporal variability in their emissions,
refining the temporal resolution of marine emission inventories would help
to further constrain their atmospheric budget. Here we provide such a
monthly resolved model output based on satellite data and reanalysis
products.
The modelled processes include a photochemical-production process, a
light-independent dark-production term, degradation by hydrolysis, and
air–sea exchange. Gas fluxes across the base of the mixed layer, i.e.
diapycnal fluxes, seem to be of minor importance, at least in tropical
waters (Lennartz et al., 2019). The photochemical OCS production involves UV radiation interactions with chromophoric dissolved organic matter (CDOM) (Ferek
and Andreae, 1984; Modiri Gharehveran and Shah, 2018; Pos et al., 1998).
Apparent quantum yields (AQYs) decrease with increasing wavelength but show orders of magnitude differences between locations
(Cutter and Radford-Knoery, 1993; Weiss et al., 1995a; Zepp and Andreae, 1994). Reaction mechanisms involving thiyl radicals have been identified from precursor molecules such as cysteine, cystine, and methionine (Modiri
Gharehveran and Shah, 2018; Pos et al., 1998). However, the complexity of
the natural mixture of dissolved organic sulphur molecules in the ocean (Ksionzek et al., 2016) makes the determination of a photoproduction rate constant on a global scale
difficult. Following an approach initially suggested by von Hobe et al. (2003), the photoproduction rate constant was scaled
according to the CDOM absorption coefficient at 350 nm (a350) in the
global surface ocean box model used in this study
(Lennartz et al., 2017). This approach led
to good agreement of climatological-mean modelled concentration with
measured sea surface OCS concentrations. The mechanism for OCS dark
production is not well understood, and two not mutually exclusive hypotheses
have been suggested, i.e. dark production being connected to abiotic radical
reactions (von Hobe et al., 2001) and microbial
remineralization processes (Cutter et al., 2004). The
dependency of the dark-production rate on CDOM absorption and temperature
shows good agreement across various biogeochemical regimes (Lennartz et
al., 2019). Hydrolysis is the main chemical sink for OCS in the mixed layer.
In both an acid and an alkaline reaction, OCS hydrolysis yields CO2 and
sulphide (Elliott et al., 1987). This reaction is
strongly temperature-dependent, leading to e-folding lifetimes between
several hours in warm waters and several days in cold, high-latitude waters
(Elliott et al., 1989). The temperature dependency of this
reaction has been reasonably well described by independent laboratory and
field studies (Cutter
and Radford-Knoery, 1993; Elliott et al., 1989; Kamyshny et al., 2003).
Schematic overview of processes and forcing included in the box models for (a) OCS and (b) CS2.
CS2 is present in seawater in picomolar concentrations, and
measurements are generally sparse (Lennartz et al., 2020b). A correlation between temperature and CS2 concentration in
surface waters is evident across several datasets (Lennartz
et al., 2019; Xie and Moore, 1999). CS2 is produced by photochemical
reactions as well, following a similar shape of the AQY wavelength spectrum
as OCS (Xie et al., 1998). Precursor
molecules such as cysteine, cystine, methionine, and dimethyl sulphide (DMS) have been
identified, and photochemical CS2 production itself seems to be
temperature-dependent (Modiri Gharehveran and
Shah, 2018). Furthermore, there is evidence for a biological production of
CS2 by phytoplankton species, with varying yield from different species
(Xie et al., 1999), but the exact mechanism
is unknown. Outgassing to the atmosphere is considered the most important
sink process for CS2 in the mixed layer. The only chemical sink
mechanism known so far is hydrolysis, with a lifetime of several years
(Elliott, 1990). However, a chemical sink process in
addition to air–sea gas exchange was needed to explain observations along an
Atlantic transect, with an e-folding lifetime of ca. 10 d
(Kettle et al., 2001).
Here, we use existing models that include parameterizations of processes
known to be relevant for each gas and apply them on a global scale,
accounting for interannual variability in the forcing parameters. We present
the first monthly resolved inventory for marine OCS and CS2 emissions
for the period 2000–2019. The model is driven by diel cycles averaged over
the course of each month or monthly averages of satellite data (Aqua MODIS
for CDOM) and ERA5 reanalysis products for meteorological parameters. We
encourage the community to use these emissions for atmospheric modelling studies in order to elucidate the atmospheric budget of OCS, assess
variability in the supply to the sulphate aerosol layer, and determine gross
primary production on a global scale (available at 10.5281/zenodo.4297010) (Lennartz et al., 2020a).
Model description
A model version as described in Lennartz et al. (2017) is used to model the
interannual variability in oceanic emissions for OCS. A new model is
developed to simulate oceanic emissions of CS2. In both models, the
surface ocean is divided into grid boxes of 2.8∘×2.8∘ at the Equator (T42 grid, Gaussian grid with ∼ 310 km resolution at
Equator; NCAR, 2017) that comprise various depth layers of 1m
thickness depending on the depth of the mixed layer in each grid box. Note
that the model does not resolve physical transport between the boxes (see
Lennartz et al., 2017, for details).
The numerical model simulating OCS seawater concentration and air–sea
exchange (positive for flux from ocean to atmosphere) includes the processes
photochemical production, light-independent production (termed “dark
production”), degradation by hydrolysis, and air–sea exchange across the sea
surface. The process rates are calculated as depicted in Fig. 1 based on
meteorological (global radiation, wind speed, skin temperature) and
physicochemical data (salinity, seawater pH, CDOM absorption, and dry mole
air fraction). The processes photochemical production
d[OCS]photodt, dark production d[OCS]darkdt, hydrolysis dOCShydrolysisdt, and air–sea
exchange d[OCS]asedt are calculated according to Eq. (1), all in pmol (L ⋅ s)-1 (Fig. 1):
d[OCS]dt=+d[OCS]photodt+d[OCS]darkdt-dOCShydrolysisdt-d[OCS]asedt.
Photochemical production is calculated as the product of UV radiation UV (W m-2= J (m2⋅ s)-1), the absorption
coefficient of CDOM at 350 nm a350 (m-1), and the photoproduction
rate constant p integrated over the mixed layer depth (MLD) according to
Eq. (2):
d[OCS]photodt=∫-MLD0UV⋅a350⋅pa350dz.
The photochemical rate constant p (pmol J-1) is scaled
with a350 (m-1), following a rationale suggested by von Hobe et
al. (2003), which reflects that a350 can be regarded as a proxy for
both photosensitizer and sulphur source across large spatial scales. The
linear dependence between a350 and p is calculated based on fits to
observational data from three major ocean basins as described in Lennartz et
al. (2017). This wavelength-integrated approach has been shown to reproduce
both local measurements from several cruises and global OCS
observations (von Hobe et al.,
2003; Lennartz et al., 2017). UV radiation below the sea surface is
calculated according to solar radiation, zenith angle, and wind speed
following von Hobe et al. (2003) as described in Lennartz et al. (2017). The
light field in each 1 m depth layer is calculated by reducing the incoming
short-wave radiation depending on the local absorption coefficient
a350. Photochemical production is then computed for each layer
individually, followed by integration over the entire mixed layer. This
integration inherently assumes a well-mixed surface layer.
Dark production is calculated according to Lennartz et al. (2019). This
reaction rate is an update of the original formulation by von Hobe et al. (2001), resulting in a semi-empirical relationship based
on observations from a wider spatial range of observation than the initial
study. In this formulation, the dark-production rate depends on temperature
and a350 (m-1) (Eq. 3):
d[OCS]darkdt=a350⋅10-6⋅e57.2-16200T.
OCS hydrolysis is determined according to Elliott et al. (1989) and depends
on temperature (T) and salinity (S) as well as the proton activity a (H+) (–),
equivalent to 10-pH, according to Eqs. (4) and (5):
4d[OCS]hydrolysisdt=OCS⋅exp24.3-10459T+exp22.8-6040T⋅KaH+5-log10K=3046.7T+3.7685+0.0035486⋅S.
Air–sea exchange is calculated as the product of the concentration gradient
between water and equilibrium concentration Δc and the transfer
velocity k (m s-1) parametrized according to Nightingale
et al. (2000):
d[OCS]asedt=k⋅Δc.
The equilibrium concentration is calculated according to de Bruyn et al. (1995) based on
the atmospheric dry mole fraction, where here, a fixed value is assumed (Table 1). The transfer velocity is corrected for OCS with the Schmidt number,
calculated based on the molar volume according to Hayduk and Laudie (1974).
Overview of forcing parameters, their resolution, and sources used
for the box model simulations in the 2000–2019 period.
ParameterResolutionSourceAbsorption coefficient of CDOM at 350 nm (a350)Gridded, monthly resolutionAqua MODIS satellite data, monthly composite of absorption due to gelbstoff and detritus at 443, converted to 350 nm with a reference slope of 0.02. Note that years 2000–2002 are the same as 2003 as data are only available from late 2002 onwards (NASA Goddard Space Flight Center, 2019)Surface (skin) temperatureGridded, monthly resolution withmean diurnal cycleERA5 reanalysis (Hersbach et al., 2018), variable name in ERA5: “skin temperature”SalinityGridded, climatological monthlymeanWorld Ocean Atlas 2013 (Levitus et al., 2013)Global radiation (convertedto UV radiation)Gridded, monthly resolution withmean diurnal cycleERA5 reanalysis (Hersbach et al., 2018), variable name in ERA5: “surface solar radiation downwards”Wind speed at surfaceGridded, monthly resolution withmean diurnal cycleERA5 reanalysis (Hersbach et al., 2018), variable name in ERA5: u= “10 m u-component of wind” and v= “10 m v-component of wind” (for this study these were converted into total wind speed = sqrt(u2+v2))pHConstant value (8.1)Mixed layer depthGridded, climatological monthlymeanMonthly Isopycnal and Mixed-layer Ocean Climatology (MIMOC; Schmidtko et al., 2013)Dry air mole fraction of OCSConstant value, 500 pptDry air mole fraction of CS2Constant value, 0 pptSea surface pressureGridded, monthly resolution withmean diurnal cycleERA5 reanalysis (Hersbach et al., 2018), variable name inERA5: “surface pressure”
The model for CS2 includes the processes of photochemical production
and a first-order chemical sink (pmol (L ⋅ s)-1), according to Eq. (7).
d[CS2]dt=+d[CS2]photodt-dCS2chem. sinkdt-d[CS2]asedt
Photochemical production is calculated in the same way as for OCS, with an
additional reduction factor r (–) applied (Eq. 8).
d[CS2]photodt=r⋅∫-MLD0UV⋅a350⋅pa350dz
Xie et al. (1998) approximated that CS2
photoproduction rates are about a factor of 5 smaller than OCS
photoproduction rates by comparing an experimentally derived AQY from
CS2 and OCS (r=0.2 in Eq. 8). The two AQYs were not measured at the
same location but in comparable water properties. Another study with
simultaneous measurements of both gases reported varying factors between 0.2
and 0.014 (5 to 70 times smaller than OCS photoproduction; Lennartz et
al., 2019). Here, we scaled the reduction factor to obtain the best fit in
the average concentration, resulting in a factor r=0.1 in Eq. (8). Thus, the
model reflects the similar shape of the AQY for both gases by assuming a
constant ratio, but the scaling of the overall magnitude of the
photoproduction rate constant is chosen to obtain the best fit to
observations from the database in Lennartz et al. (2020c). A chemical sink according to the model formulation in Kettle (2000), i.e. with an e-folding lifetime of 10 d 1kcs, was implemented according to Eq. (9) (kcs in
units of s-1):
dCS2chem. sinkdt=kcs⋅CS2.
Air–sea exchange was calculated as described for OCS, using the CS2
solubility according to De Bruyn et al. (1995).
As CS2 cycling in the water column is not yet well understood, this
model should be understood as a base model to be extended as soon as
additional process rates and their dependencies become available.
Simulation set-up
Simulations are performed for the period 2000–2019. There are several
changes in the forcing data compared to the climatological run in Lennartz
et al. (2017). Here we use monthly resolved data for the period 2000–2019
for a350, surface short-wave radiation, surface (skin) temperature, wind
speed, and sea level pressure (Table 1). Skin temperature (diagnosed close to
the air–sea interface) is used as forcing data for all temperature-relevant
processes, i.e. air–sea exchange, but also dark production and hydrolysis.
To test the sensitivity of emissions to the choice between skin and sea
surface temperature, we performed a sensitivity test for the year 2000. The
meteorological data were obtained from the ERA5 reanalysis (more
specifically, its product line “ERA5 hourly data on single levels from 1979
to present”; Hersbach et al., 2018)
through the Copernicus Climate Change Service (https://climate.copernicus.eu/, last access: 19 July 2020). One file per year and
parameter, containing hourly data at 0.25∘×0.25∘
resolution, was downloaded.
Spatial variation, averaged over the 2000–2019 period, in (a) mean OCS surface concentration (left panel) and standard deviation of annual mean concentrations (right panel), (b) same for OCS emissions, (c) same for CS2 surface concentration, (d) same for CS2 emissions.
For wind speed, the zonal and meridional components of wind speed at 10 m
altitude (m s-1) (u10 and v10, respectively)
were downloaded separately and converted into wind speed (ws) according to
ws=u102+v102.
The post-processing of the meteorological data was done using CDO (climate data operator) tools
(version 1.9.8) (Schulzweida, 2019) and comprised
the following steps:
The yearly files for each parameter were split into monthly files using
the CDO flag “splityearmon”, resulting in 240 monthly files covering the
20-year period 2000 to 2019 for each parameter.
For each of the 240 months within the period, monthly mean diel cycles of
each meteorological parameter x were calculated using the CDO flag
“dhouravg”, which calculates multi-day averages for every hour of a day asx‾m(h)=1Nm∑d=1Nmx(d,h),where m is the month (1 to 12), h is the hour of the day (1 to 24), d is the
day of the month (1 to 28, 29, 30, or 31), and Nm is the number of days
within month m.
The resulting fields were regridded from the regular 0.25∘×0.25∘ longitude–latitude grid into the spectral T42 grid
(∼2.8∘×2.8∘) using the CDO flag
“remapcon2”, which is a second-order conservative remapping method that
takes into account all source grid points, in both longitude and latitude
directions. The spatial resolution is the same as in Lennartz et al. (2017). Among the remapping methods available
in CDO, “remapcon2” was considered the most appropriate to interpolate the
selected meteorological parameters from a fine grid to a much coarser grid.
Monthly forcing fields for CDOM are derived from Aqua MODIS satellite level
3 product “absorption due to gelbstoff and detritus at 443 nm”
(NASA Goddard Space Flight Center, 2019) and converted to
350 nm with an exponential slope of 0.02 for the wavelength spectrum.
Climatological values are used for salinity and mixed layer depth at a
monthly resolution, which is the same for each month of the year throughout
the simulation period, unchanged compared to Lennartz et al. (2017). The
average diel cycle of each meteorological dataset (wind, pressure, skin
temperature, and solar radiation) is used for the 15th of each month
(one value for every 2 h). In between, data are interpolated separately
for each time of the day, resulting in a continuous change in the amplitude
of the diel cycles. This procedure avoids sharp changes as if a mean monthly
cycle were used for each day of the month while still being computationally
effective. The initial concentration for both gases was taken as a constant
value of 8 pmol L-1 in all grid boxes. The time step in the model is 2 h. The model is spun up for 1 year, repeating the conditions of the year
2000 prior to the simulation period. Maps were created using the
m_map package v1.4k (Pawlowicz, 2020).
Interannual variability in OCS emissions as time series (a) and
mean annual cycle in orange, standard deviation of respective month in the shaded grey area (b). Panels (c) and (d) are the same as (a) and (b) but for CS2. The
model output is saved in 2 h intervals for the 15th of each month
and integrated over 30 d for the monthly emissions shown here.
ResultsSpatial and seasonal variability
Both gases show distinct spatial patterns in their annual concentration and
emission averages, which reflect their marine cycling. For OCS, the highest
concentrations are present in cold, high-latitude waters and shelf areas,
whereas the lowest concentrations prevail in warm, subtropical gyres where CDOM
abundance in the water is low (Fig. 2a). A latitudinal gradient with higher
concentrations in high latitudes and low concentrations in tropical and
subtropical waters reflects the temperature-dependent degradation by
hydrolysis. The degradation is strongest in warm waters, where the lifetime
of OCS is on the order of hours, keeping concentrations low. This general
pattern is in broad agreement with observations of the largest available
database on seaborne OCS measurements (Lennartz et
al., 2020c). Annual mean emissions largely follow the spatial pattern of OCS
seawater concentrations, with sources, i.e. flux from the ocean to the
atmosphere, in shelf areas and high latitudes and sink regions in the
subtropical gyres (Fig. 2b). This general source and sink pattern does not
change in all years covered in this period, but the absolute concentrations
and, hence, the magnitude of the emissions show variability (see Sect. 4.2). The concentration pattern follows the seasonal pattern of radiation
that drives photochemical production, resulting in an annual cycle with
the highest concentrations and emissions in temperate northern latitudes in
boreal summer and the highest concentrations and emissions in the Southern Ocean
in austral summer. The globally integrated monthly emissions are highest in austral summer and lowest in austral winter. The
high emissions in the Southern Ocean outweigh the northern-hemispheric summer emissions due to the Southern Ocean’s large surface area, high wind speeds, and high OCS seawater concentrations. The amplitude of the mean seasonal cycle of OCS
emissions is 21 Gg S yr-1 (Fig. 3b). In July and August, the globally
integrated net emissions are close to zero, similar to a previous budget
using a similar model (Kettle et al., 2002).
Regionally resolved interannual variability in concentrations (a) and emissions (b) for OCS. Same in (c) and (d) for CS2.
CS2 concentrations show a different global pattern than OCS
concentrations. CS2 concentrations and emissions have hotspots in
coastal and shelf regions as well as in tropical and subtropical oceans,
reflecting photoproduction as the main production process in the model. The
tropical and subtropical areas show comparably low CS2 concentrations
(Fig. 4c), and their importance for globally averaged emissions mainly comes
from the large oceanic surface area (Fig. 4d). Notably, CS2 emissions
in the western Pacific, where inverse modelling studies have located the
missing OCS source, are relatively low (Glatthor
et al., 2015; Kuai et al., 2015b). The hotspots being located in the
tropical and subtropical regions with similar intensities of incoming
radiation all year leads to less seasonal variation in globally integrated
emissions, i.e. an amplitude of 3.2 Gg S yr-1. The ocean is a source of
CS2 to the atmosphere over the entire year since emissions are
calculated with an atmospheric mixing ratio of 0 ppt. This assumption is a
simplification, the average of the sparse dataset (less than a thousand
measurements) on CS2 air mixing ratios being 42±24 ppt but
ranging to undetectable in remote ocean regions. The difference can be up
to 30 % in the computed flux, similar to the uncertainty inherent to the
computation of the transfer velocity. In general, the highest emissions occur in
boreal winter and the lowest in boreal summer.
Globally integrated annual emissions of OCS and CS2 for each year
in 2000–2019, together with descriptive statistics and trends.
OCSCS2(Gg S)(Gg S)2000*81.3160.82001*77.3160.02002*78.0161.2200378.8160.32004108.3172.02005100.8169.12006116.3175.32007110.6173.42008114.4175.02009126.3179.72010133.3189.22011109.0179.52012113.3181.22013117.9181.3201497.2170.12015127.6175.02016134.7181.52017142.1189.72018136.9187.82019102.0177.3Mean110.3174.97Standard deviation20.39.3Slope (only 2003–2019)1.7 Gg S yr-10.95 Gg S yr-1p slope (only 2003–2019)0.0280.0067
* CDOM from 2003.
Interannual variability
Surface concentrations of OCS show a similar spatial pattern across the
period of 2000 to 2019, with interannual variability in the absolute
concentration and, hence, emissions. Globally integrated emissions range
from 77.3 Gg S yr-1 in 2001 to 142.1 Gg S yr-1 in 2017, with a
mean of 110.3±20.3 Gg S yr-1 (Table 2). A significant increasing
trend (p=0.028) of about 1.7 g S yr-1 is present in oceanic emissions from the period 2003–2019 (Table 2). This trend is present
also in the area-weighted average sea surface concentration (slope = 0.007 pmol L-1 yr-1, p=8×10-33). Note that for the trend
analysis, we considered only the period 2003–2019 as CDOM seems to be one
of the most important drivers of interannual variability (see below), and
CDOM data are only available from 2003 onwards. Generally, the seasonal
variability in OCS emissions is larger (range of mean annual cycle of 21 Gg S yr-1) than the interannual variability (mean monthly variability of
8.4 Gg S per month) (Fig. 3). Interannual variability in the emissions in
each month is largest during boreal spring (April, May, June) and autumn
(October) (Fig. 3a). These months show the largest difference between minima
and maxima during the whole period (grey area in Fig. 3a). The spatial
pattern of interannual variability in OCS emissions shows the highest
variability, i.e. the highest standard deviation among annual averages in each
grid box, at locations with high OCS concentrations and emissions (Fig. 2).
These regions comprise the northern temperate and polar regions; the
Southern Ocean; and shelf areas; especially those close to coastal upwelling
regions and river plumes (Fig. 2). The standard deviation for OCS
concentrations between annual averages ranges from 0.22 at the oligotrophic
gyres to 143.8 pmol L-1 at the highly dynamic coast off Alaska, USA
(average standard deviation of 3.4 pmol L-1). The interannual variability
also shows latitudinal differences. Polar regions in both Arctic and
Antarctic waters display the largest seasonal cycles in OCS concentration,
i.e. the highest annual variability (Fig. 4), and at the same time also
display the highest interannual variability. Differences in mean concentrations
(area-weighted) in summer range between 72.8 pmol L-1 in June 2011 and
91.6 pmol L-1 in July 2017, i.e. ca. 20 pmol L-1 in the Arctic
Ocean (Fig. 4). Interannual differences in mean monthly OCS concentrations
become smaller with decreasing latitudes and are lowest in tropical oceans,
where they range between 7.0 pmol L-1 in April 2002 and 8.5 pmol L-1 in April 2018 (southern tropical) and between 8.6 pmol L-1 in June 2015
and 9.0 pmol L-1 in June 2018 (northern tropical). Due to their large
surface area and medium surface OCS concentrations, southern temperate
regions (23–66∘ S) have the largest integrated OCS
emissions, followed by northern temperate regions (33–66∘ N) (Fig. 4). In temperate regions, the largest interannual
variability occurs during the months of maximum positive emissions, with a
range from 17.4 to 26.1 Gg S per month in southern temperate regions in
December and from 14.0 to 20.9 Gg S per month in northern temperate regions
in May. In summary, OCS concentrations and emissions show the highest
interannual variability at times and locations where concentrations are high
and in systems that are inherently highly dynamic, such as shelf regions.
Carbon disulphide concentrations are highest in shelf areas in the tropics
and subtropics and generally decrease towards high latitudes (Fig. 2c). The
spatial pattern of the annually integrated emissions mirrors this picture
(Fig. 2d). While the spatial pattern of concentrations and emissions is
similar in each year, the absolute concentration and magnitude of emissions
does show interannual variability (Fig. 3b). Emissions are calculated here
with a boundary layer mixing ratio of zero (maximum possible emission) as is
commonly done for other short-lived gases such as DMS
(Lana et al., 2011), so the ocean is a
CS2 source at every location throughout the year. Globally integrated
emissions range from 160.0 Gg S yr-1 in 2002 to 189.7 Gg S yr-1 in
2017 (Table 2). Similar to OCS, an increasing trend of global CS2
emissions for the period 2003–2019 is significant (p= 0.0067). Emissions
increase with 0.95 Gg S yr-1 on average over the period 2003–2019. For
globally integrated emissions, annual variability (mean range of 3.2 Gg S per month) is comparable to the interannual variability (3.2 Gg S yr-1). This is different to OCS, where annual variability was higher
than interannual variability for globally integrated emissions. This
difference is caused by the location of the respective hotspots of the
produced gases: as OCS has its concentration and emission hotspots mainly
in high latitudes, which experience a very seasonal light regime, its annual
variability is high. The low concentrations of OCS (and corresponding low
emissions) in the tropics result from the fast degradation by hydrolysis. In
contrast, CS2 has its concentration and emission hotspots mainly in low
latitudes with more constant forcing and hence displays smaller annual
variability. The interannual variability in CS2 emissions among single
months has a similar magnitude throughout the year (grey shaded area in Fig. 3d). Maximum monthly mean concentrations of CS2 vary the most in the summer months of the northern temperate regions (23–66∘ N), from 4.3 Gg S per month in June 2011 to 6.0 Gg S per month in June 2018, but show less variability in the winter months,
i.e. between 0.8 and 1.2 Gg S per month in December. Due to their
comparably low surface area and the relatively low concentrations, high-latitude regions do not play a significant role in globally integrated
CS2 emissions (Fig. 4). The dominance of southern temperate emissions
of CS2, despite higher absolute mean concentrations in northern
temperate regions, is explained by the larger surface ocean area in the
southern temperate regions (Fig. 4c and d).
Main drivers of interannual variability
The interannual variability in OCS and CS2 concentrations and emissions
is a result of the interannual variability in their production and
consumption processes, which in turn depend on environmental conditions.
The variability comprises years like 2015 or 2017, in which positive OCS
emissions occur in every month of the year, and years like 2019, where
global net uptake by the ocean is present in 4 of the 12 months (Fig. 3a). Most of the interannual variability in these emissions is driven by
the emissions in the high latitudes. For example, in 2017, emissions in the
Arctic regions are higher than average and lead to an overall increase in
the emissions even in the winter months. El Niño was strong in 2015/2016, and decreased upwelling of cold water with high CDOM content would
expectably lead to low OCS emissions due to decreased photochemical and dark
production and increased hydrolysis due to warmer water temperatures.
However, as fluxes in the tropics are generally small, the global emissions
are not substantially lower compared to other years (for 2015 they are even
higher due to higher emissions in high latitudes). The many negative fluxes
in 2019 seem to result from lower-than-average emissions in the Southern
Ocean.
Explained variance (Pearson's R2) and significance
level p for correlations of globally integrated emissions for OCS and
CS2 with global annual averages of CDOM a350, skin temperature, and
wind speed. Significant results (α=0.01) are indicated in bold
font.
Mean and standard deviation (maps) and interannual variation (right panels) of model input parameters: (a) CDOM a350, (b) skin temperature, (c) wind speed. Data sources listed in Table 1.
Regional correlation of annual OCS (a, c, e) and CS2(b, d, f) emission data with monthly data for temperature (a, b), wind speed (c, d), and CDOM absorption coefficient (e, f). Correlation is shown as Pearson's R2.
Globally integrated annual emissions of OCS correlate significantly with
global annual averages (area-weighted) of CDOM a350, skin temperature,
and wind speed (Table 3). CDOM a350 explains the largest variance, and
sea surface temperature and wind speed explain less of the observed
variance. Thus, CDOM a350 has the strongest influence on the variability
in global-scale OCS concentrations. The influence is not surprising as CDOM
a350 impacts both photochemical and dark production of OCS and modulates
the light field in the water (at higher a350, photoproduction is higher
but also more limited to the surface). The photochemical-production rate has
second-order dependence on CDOM a350, reflecting its double role as
photosensitizer, i.e. those molecules absorbing light energy for
photochemical reactions, and as a proxy for the amount of sulphur molecules
able to form radicals in photochemical reactions. As such, CDOM a350
exerts a strong, non-linear and positive influence on OCS concentration, and
seems to be the main driver of its interannual variability. The overall
strong influence of CDOM a350 on OCS interannual variability is also
underlined by similarity in the spatial pattern of the standard deviation in
annual average concentrations and emissions between OCS and CDOM
a350 (Fig. 5). Sea surface temperature strongly influences OCS
hydrolysis, which leads to low concentrations in warm tropical and
subtropical waters. Temperature also controls the solubility of the gas in
water, i.e. the equilibrium water concentration is higher in colder waters.
Variations in temperature explain a small part of interannual variations in
OCS emissions. However, rising temperature towards the end of the period
(Fig. 5) did not outweigh the increase in CDOM a350,which supports the
above-mentioned result that the observed changes in CDOM a350 had a
stronger influence on overall OCS production than observed temperature
changes had on hydrolysis. Finally, wind speed imposes a non-linear control
on OCS emissions, but the impact is smaller than that of CDOM a350.
Resolving the correlations regionally shows distinct controls on interannual
variability for CDOM and wind speed but not for temperature (Fig. 6).
The highest Pearson's correlation coefficients (R2) for CDOM and
OCS emissions are found globally except in the subtropical gyres (Fig. 6a).
In those gyre regions, CDOM concentration is generally low (Fig. 5a), so
other drivers like wind speed seem to have a higher impact on the
variability (Fig. 6e). Correlations with temperature show no clear spatial
pattern (Fig. 6c).
Globally integrated CS2 emissions correlate significantly with CDOM
a350, with a substantial part of the variance in interannual variability
(67 %) explained by this single factor, although this is less than for
OCS. Photochemical production of CS2 is similarly calculated as that
for OCS and hence depends non-linearly and positively on CDOM a350. The
lesser amount of explained variance compared to OCS may result from the lack
of a CDOM a350-dependent dark-production process.
Interestingly, CS2 emissions correlate with temperature, although
temperature is not part of any production or consumption process in the
model and solely modulates the solubility of CS2. Increasing
temperature decreases the solubility and would lead to a lower surface water
concentration; hence, this effect cannot explain the correlation between
temperature and CS2 surface ocean concentrations in observations
(Lennartz et al., 2020). Potentially, the covariation of temperature with
radiation dose might be responsible for the correlation of CS2
concentration and temperature that is evident across observational datasets
(see Introduction). The spatial variation in the standard deviation of
annual averages of CS2 concentration and emissions resembles that of
CDOM a350, again underlining that this is a major factor for interannual
variability in CS2 (Fig. 5). Regional analysis of correlations of
CS2 emissions with biogeochemical and meteorological data shows that
CDOM is a globally homogeneous driver of emissions, as indicated by the high
Pearson's correlation coefficients globally. Temperature and wind speed show
the highest correlation to CS2 emissions in the tropical West Pacific,
where the assumed source region of the “missing source” of OCS is located.
In these regions, interannual variability in wind speed is highest (Fig. 5),
and temperature shows increased variability there (Fig. 5). This increased
variability might explain the regionally strong correlation with CS2
emissions.
Comparison of model output to observations from the database described in Lennartz et al. (2020). (a) Box plot of OCS reference data from database and subsampled model output at the time and location of measurements (32 cruises), (b) scatter plot of 1 : 1 comparison with the same data as in (a). The black line is the 1 : 1 line, and (c) and (d) are the same as (a) and (b) but for CS2 (three cruises).
Comparison to observations
The model output of the monthly resolved simulation for 2000–2019 is
compared to the database compiled by Lennartz et al. (2020), which contains
2970 fully georeferenced OCS measurements and 501 fully georeferenced
CS2 measurements in the period considered here. The model output is
subsampled at the time (including time of day) and location closest to the
measurements in the respective period for a 1 : 1 comparison.
For OCS, the range of the subsampled model output agrees well with data from
the database (seven cruises, n=2971), with a slight underestimation of
measured concentrations by the model (average of 40.1 pmol L-1 in the
database, 38.4 pmol L-1 in the model; Fig. 7a). The direct comparison
reveals remaining scatter around the 1 : 1 line and a high bias in the model
which grows with increasing OCS concentrations (Fig. 5b). A correction for
this bias was obtained from a linear fit through the 1 : 1 comparison (blue
dots in Fig. 7) and yields the equation [OCS corrected] = 0.83 × [OCS
modelled] - 0.7. Because the bias is still within the scatter of the data,
we did not apply this correction factor in the analysis presented here. The
scatter and high bias in the data likely result from simplifications in the
model. The main simplifications, probably causing these discrepancies
between observations and models, are the missing horizontal transport, the
use of averaged wind speed as forcing, the use of CDOM a350 as a proxy
for photochemical production, and the application of a climatological mean
for the depth of the mixed layer.
Using CDOM a350 as a proxy for OCS photochemical production may
introduce some scatter but likely not a systematic bias. The very complex
nature of the dissolved organic matter pool in the ocean, which comprises
CDOM as the optically active fraction, makes it difficult to assign one
photoproduction rate constant or apparent quantum yield to all the reactions
taking place with different precursors. CDOM a350 has been shown to be a
suitable proxy across three major ocean basins (Atlantic, Pacific, and Indian
oceans), but the rate constant–CDOM a350 relationship showed some
scatter that might be improved when more data become available.
The missing horizontal transport can lead to a systematic model bias,
especially in cold waters, where the OCS lifetime increases to timescales
(days) relevant for physical transport, while environmental conditions might
vary on shorter timescales. But still this process is unlikely to decouple
OCS concentrations from its drivers like CDOM and temperature, which would be
transported accordingly. Due to the short OCS lifetime in water, the effect
of horizontal transport is negligible in warm waters of the tropics,
subtropics, and most of the temperate regions. In regions with deep mixed
layers such as the Southern Ocean, the assumption of a completely well-mixed
surface layer may be violated and cause discrepancies between the modelled
value (average of the mixed layer) and the measured value (close to the surface,
i.e. higher concentration than at the bottom of the mixed layer). Since the
modelled concentration depends on the depth of the mixed layer and its relation
to the photic zone, a climatological average as used here will introduce
biases; however, detailed information on mixed layer depth at monthly
resolution from observations is not available. This simplification mainly
affects OCS concentrations in high latitudes, where concentrations are
relatively high, and thus might be partly responsible for the systematic
bias revealed by the scatter plot in Fig. 5b. Furthermore, averaging wind
speed to a mean monthly cycle will most likely lead to an underestimation of
emissions and, hence, an overestimation of concentrations. Due to the
non-linear relationship of the transfer velocity of the gas exchange with
wind speed, averaging disproportionally reduces the effect of increased
emissions during high wind speeds. Another source of uncertainty are the
forcing data, e.g. the choice of using the skin temperature rather than the
sea surface data. For comparison, we performed a shorter simulation covering
the year 2000 and using the ERA5 sea surface temperature data instead of the
skin temperature. The difference in resulting global emissions was 1.2 %,
i.e. very small compared to other uncertainties. Still, given these
simplifications and assumptions, the overall good agreement with the
measurements underlines the applicability of the model for assessing the
marine cycling of OCS and its emissions to the atmosphere.
The marine cycling of CS2 is less well understood than that of OCS.
This relatively poorer process understanding is reflected by the comparison
of the modelled CS2 concentrations with those of the database (3
cruises, 501 measurements) (R2=0.04). Modelled
concentrations agree with observations on average (average database: 18.0 pmol L-1; average subsampled model output: 18.2 pmol L-1). The
three cruises cover the Mauritanian upwelling (Poseidon 269; blue in Fig. 7d), the Peruvian upwelling (ASTRA-OMZ; yellow in Fig. 7d), and a transect
through the Atlantic (Transpegaso; green in Fig. 7d). As such, they cover a
broad range of different biogeochemical regimes, but regions such as
oligotrophic gyres or high-latitude waters are not covered; i.e. a
substantial part of the global variability might be missing in the reference
dataset. While the cruises Poseidon 269 and ASTRA-OMZ are relatively well
represented by the model (colour code in Fig. 7d), the variability in the
measurements during Transpegaso is not well captured. The model used here
has some underlying assumptions and simplifications that call for refinement
in the future when detailed process understanding is available. For example,
the model is based on the assumption of a constant ratio between the
apparent quantum yields of OCS and CS2. It has been shown that this
ratio is not always constant (Kettle,
2000; Lennartz et al., 2019), but as the production pathways of both gases
show some similarities (Modiri Gharehveran and
Shah, 2018), the model formulation with a constant ratio is a first
approximation. Second, the presence of a chemical sink is rationalized by
its necessity to explain observed concentrations along an Atlantic transect (Kettle, 2000; Lennartz et al., 2019) but has no mechanistic foundation so far.
Dedicated laboratory experiments disentangling the source and sink processes
in the water column are needed to further resolve this issue and to improve
modelling efforts. Finally, this model does not consider any biological
production of CS2. This assumption is justified for a first
approximation as CDOM and primary production (photosynthesis) show similar
global-scale patterns. High CDOM will thus lead to high production of
CS2 in the water, even though the scaling of the photoproduction rate
constant (AQY) might inherently include biological production due to the
covariation of photosynthesis patterns with CDOM and radiation. The
calculated CS2 emission estimate is not sensitive to the choice of
the temperature forcing data; the resulting differences in global emissions when
using the sea surface temperature instead of the skin temperature for the
year 2000 resulted in a negligible deviation of 0.12 %. Overall, the
presented CS2 concentration and emissions are a first approximation,
and more detailed process understanding is important to improve emission
estimates. Assuming that the presented oceanic emissions are in a realistic
range, the calculated emissions would not be enough to close the gap in the
atmospheric budget of OCS on the order of 600 to 800 Tg S yr-1 (Berry
et al., 2013; Glatthor et al., 2015; Kuai et al., 2015a) given that only a
little more than half of the sulphur in CS2 is converted to sulphur in
OCS.
The emission estimate of the gases OCS and CS2 includes further
uncertainties introduced by the parameterizations of the transfer velocity
used for calculation of air–sea exchange, which carry large uncertainties,
especially at high wind speeds (Wanninkhof, 2014).
Furthermore, emissions here are calculated based on the concentration
gradient between surface water and the equilibrium concentration dictated by
the atmospheric mixing ratio without taking into account any potential
effect of the sea surface microlayer. Whether and how the enrichment of
surfactants in the sea surface microlayer affects emissions of these gases
has not been sufficiently assessed to date.
Code and data availability
The code is available on GitHub under
https://github.com/Sinikka-L/OCS_CS2_boxmodel (Lennartz, 2020).
The simulation output is available at Zenodo (10.5281/zenodo.4297010) (Lennartz et al., 2020a). The output consists of one netCDF file for each gas, each of a size of ca. 444 MB, with monthly averages of sea surface concentrations and
emissions to the atmosphere as well as a mean diel cycle for each month.
Summary and conclusions
OCS and CS2 are climate-relevant trace gases, and OCS can also be used
as a proxy to infer terrestrial gross primary production. A missing source
in the atmospheric OCS budget currently makes conclusions on the future
impact on both gases and the application of this proxy on a global scale
difficult. Since both gases contribute to the atmospheric OCS budget, their
oceanic emissions have been suggested previously to account for that missing
source. We provide monthly resolved OCS and CS2 concentration and
marine emission data for the period 2000–2019 based on a mechanistic ocean
box model. We show that interannual variability in OCS is smaller than its
seasonal variability in globally integrated emissions but that a
significant positive trend is evident across the period 2000–2019. The main
driver for interannual variabilities is variation in CDOM a350. The
comparison of our data to a database with more than 2500 measurements
reveals an overall good agreement. The CS2 model presented here for the
first time is a first approximation and reveals stronger interannual
variability than seasonal variability in emissions. Again, CDOM (or,
indirectly, biological production) seems to strongly influence
concentration and emission patterns of CS2. Similarly, an increasing
trend in CS2 emissions is significant for the period 2000–2019. Based
on the data presented here, it seems unlikely that the missing atmospheric
source of 600–800 Gg S yr-1 (Berry
et al., 2013; Glatthor et al., 2015; Kuai et al., 2015a) might be balanced
by tropical marine emissions of OCS or CS2. We encourage the use of the
data provided here as input for atmospheric modelling studies to further
assess the atmospheric OCS budget and the role of OCS in climate.
Author contributions
STL and MG conceived the study with input from MvH and CAM. MG prepared meteorological forcing data for the model simulations. STL performed the simulations and evaluated data jointly with MG, MvH, and CAM. STL wrote the manuscript with contributions from all coauthors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Surface emissions for atmospheric chemistry and air quality modelling”. It is not associated with a conference.
Acknowledgements
The authors thank the Ocean Biology and Processing Group at NASA Goddard
Space Flight Center for access to the Aqua MODIS data as well as ECMWF and the Copernicus Climate Change Service for access to the ERA5 data.
Review statement
This paper was edited by Nellie Elguindi and reviewed by two anonymous referees.
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