Introduction
Carbonyl sulfide (OCS or COS, hereafter OCS) observations have emerged
as a tool for understanding terrestrial carbon uptake and plant
physiology. Some of the enzymes involved in photosynthesis by leaves
also efficiently destroy OCS, so that leaves consume OCS whenever they
are assimilating CO2 (Protoschill-Krebs and Kesselmeier, 1992;
Schenk et al., 2004; Notni et al., 2007). The two molecules diffuse
from the atmosphere to the enzymes along a shared pathway, and the
rates of OCS and CO2 uptake tend to be closely related (Seibt
et al., 2010). Plants do not produce OCS, and consumption in plant
leaves is straightforward to observe. In contrast, CO2 uptake
is difficult to measure by itself. At ecosystem, regional, and global
scales, large respiratory CO2 fluxes from other plant tissues
and other organisms obscure the photosynthetic CO2 signal,
i.e., gross primary productivity (GPP). OCS is not a perfect tracer for
GPP due to the presence of additional sources/sinks of OCS in
ecosystems that complicate this relationship. However, these
sources/sinks are generally small, so measurements of OCS
concentrations and fluxes can still generate useful estimates of
photosynthesis, stomatal conductance, or other leaf parameters at
temporal and spatial scales that are difficult to observe.
Several independent groups have examined OCS and CO2 observations
and come to similar conclusions about links between the plant uptake
processes for the two gases. Goldan et al. (1987) linked OCS plant uptake, FOCS, to uptake of
CO2, FCO2, specifically referring to
GPP. Advancing the global perspective, Chin and Davis (1993) thought
FOCS was connected to net primary productivity, which
includes respiration terms, and this scaling was used in earlier
versions of the OCS budget, e.g., Kettle et al. (2002). Sandoval-Soto
et al. (2005) re-introduced GPP as the link to FOCS, using
available GPP estimates to improve OCS and sulfur budgets, which were
their prime interest. Montzka et al. (2007) first proposed to reverse
the perspective in the literature and suggested that OCS might be able
to supply constraints on gross CO2 fluxes, with Campbell
et al. (2008) directly applying it in this way.
Since then, other applications have been developed, including understanding
of terrestrial plant productivity since the last ice age (Campbell et al.,
2017a), estimating canopy (Yang et al., 2018) and stomatal conductance and
enzyme concentrations on the ecosystem scale (Wehr et al., 2017), assessment
of the current generation of continental-scale carbon models (e.g., Hilton
et al., 2017), and better tracing of large-scale atmospheric processes like
convection and tropospheric–stratospheric mass transfers. Many of these
applications rely on the fact that the largest fluxes of atmospheric OCS are
geographically separated: most atmospheric OCS is generated in surface oceans
and is destroyed by terrestrial plants. In practice, these new applications
often call for refining the terms of the global budget of OCS.
An abundance of new observations have been made possible by technological
innovation. While OCS is the longest-lived and most plentiful
sulfur-containing gas in the atmosphere, its low ambient concentration (∼0.5 ppb) makes measurement challenging. Quantification of OCS in air
used to require time-consuming pre-concentration before injection into a gas
chromatograph (GC) with a mass spectrometer (MS) or other detector.
While extended time series remain scarce, 17 years of observations have been
generated by the National Oceanic and Atmospheric Administration (NOAA)
Global Monitoring Division air monitoring network (Montzka et al., 2007). A system for
measuring flask samples for a range of important low-concentration trace
gases was modified slightly in early 2000 to enable reliable measurements for
OCS. These observations allowed for the first robust evidence of OCS as
a tracer for terrestrial CO2 uptake on continental to global scales
(Campbell et al., 2008). In 2009, a quantum cascade laser instrument was
developed, followed by many improvements in precision and measurement
frequency (Stimler et al., 2010a). Current instruments can measure OCS with
<0.010 ppb precision and a frequency of 10 Hz (Kooijmans
et al., 2016). On larger spatial scales, many Fourier transform infrared
spectroscopy (FTIR) stations and three satellites have recently been used to
retrieve spectral signals for OCS in the atmosphere.
This review seeks to synthesize our collective understanding of
atmospheric OCS, highlight the new questions that these data help
answer, and identify the outstanding knowledge gaps to address moving
forward. First, we present what information is known from surface-level
studies. Then we develop a scaled-up global OCS budget that
suggests where there are considerable uncertainties in the flux of OCS
to the atmosphere. We examine how the existing data have been applied
to estimating GPP and other ecosystem variables. Finally, we describe
where data are available and prioritize topics for further research.
Global atmospheric OCS budget
The sulfur cycle is arguably the most perturbed element cycle on
Earth. Half of sulfur inputs to the atmosphere come from anthropogenic
activity (Rice et al., 1981). OCS is the most abundant and
longest-lived sulfur-containing gas. Ambient concentrations of OCS are
relatively stable over month-long timescales. Observations from flask
(Montzka et al., 2007), FTIR (Toon et al., 2018), and Fourier transform
spectroscopy (FTS) measurements (Kremser et al., 2015) suggest a small (<5 %) increasing trend
in tropospheric OCS for the most recent decade. Over millennia,
concentrations may reflect large-scale changes in global plant cover
(Aydin et al., 2016; Campbell et al., 2017a).
Upscaling ecosystem estimates (Sandoval-Soto et al., 2005) with global
transport models are incompatible with atmospheric measurements (Berry
et al., 2013; Suntharalingam et al., 2008), suggesting that there may be
a large missing source of OCS, sometimes attributed to the tropical oceans;
however, individual observations from ocean vessels do not necessarily
support this hypothesis (Lennartz et al., 2017). The small increase of OCS in
the atmosphere is at least 2 orders of magnitude too small to account for the
missing source. Anthropogenic emissions are an important OCS source to the
atmosphere, but data for the relevant global industries are incomplete
(Zumkehr et al., 2018). Here we analyze our current understanding of global
surface–atmosphere OCS exchange and generate new global flux estimates from
the bottom up, with no attempt at balancing the atmospheric budget (Fig. 1).
We use the convention that positive flux represents emission to the
atmosphere and negative flux represents removal.
A bottom-up budget of atmospheric OCS on the global scale.
Positive values indicate a source to the atmosphere. No attempt has
been made to preserve mass balance. The contribution of lakes and
non-vascular plants is included in the non-wetland ecosystem
estimate. The small increase of OCS in the atmosphere is not
included in this plot.
Global atmosphere
OCS in the atmosphere is primarily generated from ocean and anthropogenic
sources. A portion of these sources are indirect, emitted as CS2
which can be oxidized to OCS (Zeng et al., 2016). Within the atmosphere,
major sinks of OCS are OH oxidation in the troposphere and photolysis in the
stratosphere. Besides large volcanic eruptions, OCS is a significant source
of sulfur to the stratosphere and was briefly entertained as a geoengineering
approach to promote global dimming (Crutzen, 2006). However, the global
warming potential of OCS roughly balances whatever global cooling effect it
might have (Brühl et al., 2012). Abiotic hydrolysis in the atmosphere
plays a small role: while snow and rain were observed to be supersaturated
with OCS (Belviso et al., 1989; Mu et al., 2004), even in the densest
supersaturated clouds the OCS in the air would represent 99.99 % of the
OCS present (Campbell et al., 2017b). Multiple lines of evidence support
uptake by plants as the dominant removal mechanism of atmospheric OCS (e.g.,
Asaf et al., 2013; Berry et al., 2013; Billesbach et al., 2014; Campbell
et al., 2008; Glatthor et al., 2017; Hilton et al., 2017; Launois et al.,
2015b; Mihalopoulos et al., 1989; Montkza et al., 2007; Protoschill-Krebs and
Kesselemeier, 1992; Sandoval-Soto et al., 2005; Stimler, 2010b;
Suntharalingham et al., 2008).
The observed atmospheric OCS distribution suggests that seasonality is
driven by terrestrial uptake in the Northern Hemisphere and oceanic
fluxes in the Southern Hemisphere (Montzka et al., 2007). Improvements
in the OCS budget were derived through inverse modeling of NOAA tower
and airborne observations on a global scale (Berry et al., 2013;
Launois et al., 2015b; Suntharalingam et al., 2008). Lower
concentrations were generally found in the terrestrial atmospheric
boundary layer compared to the free troposphere during the growing
season, and amplitudes of seasonal variability were enhanced at
low-altitude stations, particularly those situated mid-continent.
Total column measurements of OCS from ground-based FTS show trends in OCS concentrations coincident with
the rise and fall of global rayon production, which creates OCS
indirectly (Campbell et al., 2015). Kremser et al. (2015) found an
overall positive tropospheric rise of 0.43–0.73 %yr-1
at three sites in the Southern Hemisphere from 2001 to 2014. The trend
was interrupted by a sharply decreasing interval from 2008 to 2010,
also observed in the global surface flask measurements (Fig. S2;
Campbell et al., 2017a). A similar but smaller dip was observed in the
stratosphere, indicating that the trends are driven by processes
within the troposphere. Over Jungfraujoch, Switzerland, Lejeune
et al. (2017) observed a decrease in tropospheric OCS from 1995 to
2002 and an increase from 2002 to 2008; after 2008 no
significant trend was observed. An increase in OCS concentrations from the
mid-20th-century with a decline around the 1980s was also recorded in
firn air (Montzka et al., 2004), following historic rayon production
trends.
Changes in terrestrial OCS uptake and possibly the ocean OCS source can be
observed from the 54 000-year record from ice cores. Global OCS
concentrations dropped 45 to 50 % between the Last Glacial Maximum and
the start of the Holocene (Aydin et al., 2016). By the late Holocene,
concentrations had risen, and the highest levels were recorded in the 1980s
(Campbell et al., 2017a).
Recommendations. Modern seasonal and annual variability of
OCS can be validated with smaller vertical profile datasets, e.g., Kato
et al. (2011), and data from flights, e.g., Wofsy
et al. (2011). Interhemispheric variability on millennia timescales
requires ice core data from the Northern Hemisphere: all current ice
core data are from the Antarctic (Aydin et al., 2016).
Terrestrial ecosystems
OCS uptake by terrestrial vegetation is governed mechanistically by
the series of diffusive conductances of OCS into the leaf and the
reaction rate coefficient for OCS destruction by carbonic anhydrase
(CA) (Wohlfahrt et al., 2012), though it can also be destroyed by
other photosynthetic enzymes, e.g., RuBisCo (Lorimer and Pierce,
1989). CA is present both in plant leaves and soils, although soil
uptake tends to be proportionally much lower than plant uptake. In
soil systems, OCS uptake provides information about CA activities
within diverse microbial communities. OCS uptake over plants
integrates information about the sequential components of the
diffusive conductance (the leaf boundary layer, stomatal, and
mesophyll conductances) and about CA activity, all important aspects
of plant and ecosystem function. Stomatal conductance in particular is
a prominent research focus in its own right, as it couples the carbon
and water cycles via transpiration and photosynthesis.
Terrestrial plant OCS uptake has typically been derived by scaling
estimates of the plant CO2 uptake with proportionality
coefficients, such as the empirically derived leaf relative uptake
rate ratio (LRU; Sandoval-Soto et al., 2005):
FOCS=FCO2[OCS][CO2]-1LRU,
where FOCS is the uptake of OCS into plant leaves;
FCO2 is CO2 uptake; [OCS] and [CO2] are
the ambient concentrations of OCS and CO2; and LRU is the
ratio of the OCS to CO2 uptake, which is a function of plant
type and water and light conditions. The concept of the LRU is
a simplification of the leaf CO2 and OCS uptake process. The
FCO2-to-FOCS relationship depends on the leaf
conductance to each gas as it changes with the difference between
concentrations inside and outside of the leaf. This requires further
modeling to anticipate within-leaf concentrations of OCS and
CO2, which cannot be observed directly. To keep the
simplicity of the approach, especially when using OCS to evaluate
models with many other built-in assumptions, the data-based LRU
approximation is sufficient in many cases. We have compiled LRU data
(n=53) from an earlier review and merged them with more recent
published studies (Berkelhammer et al., 2014; Stimler et al., 2010b,
2011, 2012). The LRUs compiled in Sandoval-Soto et al. (2005) were
partly re-calculated in Seibt et al. (2010) to account for the lower
gas concentrations in the sample cuvettes. For C3 plants,
OCS uptake behavior is attributed to CA activity (Yonemura et al.,
2005). As shown in Fig. 2, LRU estimates for C3 species
under well-illuminated conditions are positively skewed, with 95 %
of the data between 0.7 to 6.2, which coincides with the theoretically
expected range of 0.6 to 4.3 (Wohlfahrt et al., 2012). The median,
1.68, is quite close to values reported and used in earlier studies
and provides a solid “anchor ratio” for linking C3 plant
OCS uptake and photosynthesis in high light. LRU data are fewer for
C4 species (n=4), converging to a median of 1.21,
reflecting more efficient CO2 uptake rates compared to
C3 species (Stimler et al., 2011).
Frequency distribution (bars) and a lognormal fit (solid
line) to published values (n=53) of the leaf relative uptake rate
of C3 species. The vertical line indicates the median
(1.68). Published data are from Berkelhammer et al. (2014),
Sandoval-Soto et al. (2005), Seibt et al. (2010), and Stimler
et al. (2010b, 2011, 2012).
LRU remains fairly constant with changes in boundary layer and
stomatal conductance but is expected to deviate due to changes in
internal OCS conductance and CA activity (Seibt et al., 2010;
Wohlfahrt et al., 2012). The primary environmental driver of LRU is
light, and an increase in LRU with decreasing photosynthetically
active radiation has been observed at both the leaf (Stimler et al.,
2010b, 2011) and ecosystem scale (Maseyk et al., 2014; Commane et al.,
2015; Wehr et al., 2017; Yang et al., 2018). This behavior arises
because photosynthetic CO2 assimilation is reduced in low
light, whereas OCS uptake continues since the reaction with CA is not
light dependent (Stimler et al., 2011). Note that since low light
reduces CO2 uptake, the flux-weighted effect of the variations
in LRU on estimating FCO2 (or GPP) is also reduced on
daily or longer timescales (Yang et al., 2018).
An additional complication is introduced by soil and non-vascular
plant processes that both emit and consume OCS, with a few studies
reporting net OCS emission under certain conditions comparable in
magnitude to net uptake rates during peak growth. Generally, soil OCS
fluxes are low compared to plant uptake with a few exceptions
(Fig. 3). In non-vascular plants, OCS uptake continues in the dark
even when photosynthesis ceases (Gries et al., 1994; Kuhn et al.,
1999; Kuhn and Kesselmeier, 2000; Gimeno et al., 2017; Rastogi et al.,
2018). Unlike other plants, bryophytes and lichens lack responsive
stomata and protective cuticles to control water losses. OCS emissions
from these organisms seem to be primarily driven by temperature
(Gimeno et al., 2017).
The yearly average land OCS flux rate in recent modeling studies of
global budgets (i.e., plant and soil uptake minus soil emissions)
ranges from -2.5 to -12.9 pmolm-2s-1
(Fig. 3). The only study reporting year-round OCS flux measurements is
from a mixed temperate forest, which was a sink for OCS with a net
flux of -4.7 pmolm-2s-1 during the observation
period (Commane et al., 2015). Daily average OCS fluxes during the
peak growing season are available from a larger selection of studies
and cover the range from -8 to -23 pmolm-2s-1,
excluding that of Xu et al. (2002), which found a surprisingly high uptake
(–97±11.7 pmolm-2s-1) from the relaxed eddy
accumulation method (Fig. 3). Despite the limited temporal and spatial
coverage, these data suggest that some of the larger global land net
sink estimates may be too high (Launois et al., 2015b).
The following subsections explore a few aspects of ecosystem OCS
exchange in greater detail. Observations and conclusions about
forests, grasslands, wetlands, and freshwater ecosystems are
explored. Then we examine OCS interactions reported for components of
ecosystems: soils, microbial communities, and abiotic hydrolysis and
sorption.
Recommendations. Available observations are limited in time
and do not cover tropical ecosystems, which contribute almost 60 %
of global GPP (Beer et al., 2010). More year-round measurements from
a larger number of biomes, in particular those presently
underrepresented, are required to provide reliable bottom-up estimates
of the total net land OCS flux. The causes for the observed
variability in Fig. 2 require more investigation because they hamper
the specification of defensible plant-functional-type-specific LRUs
(Sandoval-Soto et al., 2005) and the development of models with
non-constant LRU (Wohlfahrt et al., 2012). Relatively little is known
regarding using OCS to estimate CA activity (Wehr et al., 2017), which
is a promising new avenue of OCS research. Within this context, plant
physiological and enzymatic adaptations to increasing CO2
and their effects on the exchange of OCS are of special
interest.
Top panel: global average land OCS uptake from modeling
studies. Bottom panel: reported averages and ranges of whole-ecosystem,
site-level OCS observations. Points represent reported
averages; error bars show the uncertainty around the average or the
range of observed fluxes where no meaningful average was reported.
Forests
OCS has the potential to overcome many difficulties in studying the
carbon balance of forest ecosystems. To partition carbon fluxes,
respiration is often quantified at night, when photosynthesis has
ceased and turbulent airflow is reduced (Reichstein et al.,
2005). This method has systematic uncertainties; e.g., less respiration
happens during the day than at night (Wehr et al., 2016). Partitioning
with OCS is based on daytime data and does not rely on modeling
respiration with limited nighttime flux measurements.
Forests are daytime net sinks for atmospheric OCS, when photosynthesis
is occurring in the canopy (Table 1). While the relative uptake of OCS
to CO2 by leaves appears stable in high-light conditions, the
ratio changes in low light when the net CO2 uptake is reduced
(Stimler et al., 2011; Wehr et al., 2017; Rastogi et al.,
2018). Forest soil interaction with OCS has been found to be small
with respect to leaf uptake (Fig. 3) and straightforward to correct
(Belviso et al., 2016; Wehr et al., 2017). Sun et al. (2016) noted
that litter was the most important component of soil OCS fluxes in an
oak woodland. Otherwise, forest ecosystem OCS uptake appears to be
dominated by tree leaves, both during the day and at night (Kooijmans
et al., 2017).
In situ fluxes of forest ecosystems. Some of these data are plotted
in Fig. 2.
Cover; location
Time
Reported fluxes (OCS pmolm-2s-1)
Reference
Quercus, Acer;Harvard Forest,Massachusetts, USA
Jan–Dec 2011,May–Oct 2012,May–Oct 2013
Near 0 in winter and at night to ∼-50 at peak leaf area and light. Anomalous emissions in summer found in the 2015 study were not observed during subsequent summers.
Wehr et al. (2017) andCommane et al. (2015)
Populus, Pinus;Niwot Ridge, Colorado, USA
13–18 Aug 2012
Leaf chamber flux near 0 at night to a peak at ∼-50; soil flux between 0 and -7.
Berkelhammer et al. (2014)
Picea; Solling mountains, Germany
Summer, fall1997–1999
Relaxed eddy accumulation, -93±11.7 uptake; large nighttime emissions.
Xu et al. (2002)
Pinus; 3 sites, Israel
Growing season2012
Eddy flux covariance at 3 pine forests on a precipitation gradient; daylight averages were -22.9±23.5, -33.8±33.1, and -27.8±38.6.
Asaf et al. (2013)
Pinus; boreal forest, Hyytiälä, Finland
Jun–Nov 2015
Nighttime fluxes: -6.8±2.2 (radon-tracer method) and -7.9±3.8 (eddy covariance); daytime fluxes: -20.8 (eddy covariance).
Kooijmans et al. (2017)
Recommendations. Tropical forest OCS fluxes would be
informative for global OCS modeling efforts and are currently absent
from the literature. The OCS tracer approach is particularly useful in
high-humidity or foggy environments like the tropics, where
traditional estimates of carbon uptake variables via water vapor
exchange are ineffective. Additionally, OCS observing towers upstream
and downstream of large forested areas could resolve the synoptic-scale
variability in forest carbon uptake (Campbell et al., 2017b).
Grasslands
OCS observations can address the need for additional studies on
primary productivity in grassland ecosystems. Grasslands generally are
considered to behave as carbon sinks or be carbon-neutral but appear
highly sensitive to drought and heat waves and can rapidly shift from
neutrality to a carbon source (Hoover and Rogers, 2016). Currently OCS
grassland studies are scarce (Fig. 3) but indicate a significant role
for soils. Theoretical deposition velocities for grasses of
0.75 mms-1 were reported by Kuhn et al. (1999), and LRU
values of 2.0 were reported by Seibt et al. (2010). Whelan and Rhew (2016) made chamber-based estimates of ecosystem fluxes from
a California grassland with a distinct growing and non-growing
season. Total ecosystem fluxes averaged
-26 pmolm-2s-1 during the wet season and
-6.1 pmolm-2s-1 during the dry season. During the
wet season, simulated rainfall increased the sink strength. Light and
dark flux estimates yielded similar sinks, suggesting either a large
role for soils in the ecosystem flux or the presence of open stomata
under dark conditions. Yi and Wang (2011) undertook chamber
measurements over a grass lawn in subtropical China. Ecosystem fluxes
of -19.2 pmolm-2s-1 were observed. They noted
average soil fluxes of -9.9 pmolm-2s-1 that were
occasionally greater than 50 % of the total ecosystem flux. The
large contribution of soils to the grassland OCS flux was attributed
to atmospheric water stress on the plants that led to significant
stomatal closure and reduced midday uptake by vegetation. More
recently, Gerdel et al. (2017) reported daily average ecosystem-scale
OCS fluxes of -28.7±9.9 pmolm-2s-1 for
a productive managed temperate grassland.
Solar radiation has been identified recently as a controlling factor
of grassland soil OCS emissions. Kitz et al. (2017) highlighted that,
in grasslands, primary production is devoted to belowground biomass
early in the growing season, leading to a situation where exposed
soils may be emitting photo-produced OCS simultaneously with high
GPP. If unaccounted for, this would lead to an underestimation of the
plant component of the total ecosystem OCS flux (Kitz et al., 2017;
Whelan and Rhew, 2016).
Recommendations. Grassland plants tend to include mixtures of
C3 and C4 species with a relative abundance and
importance to GPP evolving over the season. These different
photosynthetic pathways are known to exhibit different LRU values. On
the one hand, this poses a challenge to direct estimations of GPP from
OCS; on the other hand, observations may provide a unique opportunity
to study C3 and C4 contributions to GPP. Another
pressing research question is the effect of the changing leaf area
index of grasses on radiation and related soil emissions.
Wetlands and peatlands
Much of the early work on OCS terrestrial–atmospheric fluxes was
conducted in wetlands, perhaps because of the large emissions observed
there. Unfortunately, many of these first surveys were conducted with
sulfur-free sweep air, significantly biasing the observed net OCS flux
compared with that under ambient conditions (Castro and Galloway,
1991).
OCS fluxes have been measured in a variety of wetland ecosystems,
including peat bogs, coastal salt marshes, tidal flats, mangrove
swamps, and freshwater marshes. Observed ecosystem emission rates vary
by 2 orders of magnitude and generally increase with salinity
(Fig. 4). OCS emissions in salt marshes usually range from 10 to
300 pmolm-2s-1 (Aneja et al., 1981; DeLaune et al.,
2002; Li et al., 2016; Steudler and Peterson, 1984, 1985; Whelan
et al., 2013), whereas freshwater marshes and bogs have mean emission
rates below 10 pmolm-2s-1 (DeLaune et al., 2002;
Fried et al., 1993) or act as net sinks due to plant uptake (Fried
et al., 1993; Liu and Li, 2008; de Mello and Hines, 1994).
A summary figure for wetland OCS emissions. Lines indicate
minimum to maximum ranges. Studies denoted “S” indicated
a soil-only observation, and “S + V” denotes a soil and
vegetation observation. Points show reported averages, and error bars
show either reported uncertainty or the full range of
observations. Note that some earlier observations using sulfur-free
air as chamber sweep air have been excluded due to overestimation
(Castro and Galloway, 1991).
Although plants are generally OCS sinks, wetland plants may appear as
OCS sources. Emergent stems can act as conduits transmitting OCS
produced in the soil to the atmosphere, or OCS may be a by-product of
processes related to osmotic management by plants in saline
environments. For example, in a Batis maritima coastal marsh,
vegetated plots were found to have up to 4 times more OCS emission
than soil-only plots (Whelan et al., 2013). Growing season OCS
emissions may greatly exceed those in the non-growing season (Li
et al., 2016), but whether this is caused by environmental factors
like temperature and soil saturation or by the developmental stage of
plants is unclear.
Recommendations. Assessing the role of plants in the wetland
OCS budget would require careful investigation of OCS transport via
plant stems and OCS producing capacity of aboveground plant materials
and the rhizosphere. More work needs to be done on the evolution of
OCS in soils with low redox potential. Additional experiments should
aim to help scale up wetland OCS fluxes.
Lakes and rivers
The role of lakes and rivers in the global OCS budget is not well
known. OCS production and consumption have been studied in ocean
waters, and these processes most likely occur similarly in
freshwater. In the ocean, OCS is produced photochemically from
chromophoric dissolved organic matter (CDOM) (Ferek and Andreae, 1984)
and by a light-independent production that has been linked to sulfur
radical formation (Flöck et al., 1997; Zhang et al.,
1998). A mechanism for OCS photoproduction was recently described for
lake water (Du et al., 2017). Dissolved OCS (Fig. 5) is consumed by
abiotic hydrolysis at a rate determined by pH, salinity, and
temperature (Fig. 6; Elliott et al., 1989).
Solubility of OCS in water dependent on ambient OCS
concentration and temperature as calculated in Sun et al. (2015).
Comparison of published hydrolysis rates for OCS based on
laboratory experiments with artificial water (Elliott et al., 1989;
Kamyshny et al., 2003), and under oceanographic conditions using
filtered seawater (Radford-Knȩry et al., 1994). The graph is
replotted using equations from original papers at a pH of 8.2.
OCS is present in freshwaters at much higher concentrations than those
found in the ocean (Table 2). This might be due to more efficient
mixing in the ocean surface waters compared to lakes. However,
Richards et al. (1991) found that the concentration remained the
same throughout the water column and observed a midsummer OCS
concentration minimum in 8 of the 11 studied lakes. This latter point
was surprising because photochemical production should be highest
during the summer months. It has been demonstrated that ocean algae
take up OCS, which might explain the low concentrations when light
levels are high; however, Blezinger et al. (2000) concluded that the
consumption term should be small compared to abiotic hydrolysis and
photoproduction.
OCS concentrations observed in rivers and lakes compared to ocean
observations in Lennartz et al. (2017).
Cover, location
OCS concentration
Reference
Lake, surface, Canada
1.1 nmolL-1
Richards et al. (1991)
Lake, surface, China
910±73 pmolL-1
Du et al. (2017)
River, 0.25 m depth
636±14 pmolL-1
Radford-Knoery and Cutter (1993)
River, 3.84 m depth
415±13 pmolL-1
Radford-Knoery and Cutter (1993)
Lake, whole water column, Canada
90 to 600 pmolL-1
Richards et al. (1991)
Lake, hypolimnion, Antarctica
233 to 316 pmolL-1
Deprez et al. (1986)
Eastern Pacific Ocean
28.3±19.7 pmolL-1
Lennartz et al. (2017)
Indian Ocean
9.1±3.5 pmolL-1
Lennartz et al. (2017)
Lake, hypolimnion, Switzerland
Detected “occasionally”
Fritz and Bachofen (2000)
To our knowledge, there have not yet been any studies on OCS fluxes using
direct flux measurement methods over freshwaters. Richards et al. (1991)
calculated OCS fluxes from different lakes in Ontario, Canada, based on
concentration measurements and wind-speed-dependent gas transfer
coefficients, resulting in fluxes of 2–5 pmol OCS
m-2s-1. In another study, Richards et al. (1994) found fluxes
of 2–34 pmol OCS m-2s-1 in salty lakes. These fluxes
are 5 to 75 times higher than those measured in the oceans (Lennartz et al.,
2017). There is also an indirect atmospheric OCS source from carbon disulfide
(CS2) production (Richards et al., 1991, 1994; Wang et al.,
2001), for which little data exist.
Recommendations. Measurements in lakes are easier than in the
open ocean while generating more information on the processes that may
drive OCS production in both regions. Flux data by eddy covariance
(EC) and floating chamber methods from lakes and rivers are
suggested. Concurrent measurements should target understanding of the
biotic and abiotic factors driving water–air exchange of OCS to
provide the basis for upscaling aquatic OCS fluxes, including
CS2 concentrations.
Other terrestrial OCS flux components
Soils
Measurements show that non-wetland soils are predominantly a sink for
OCS, and wetland (anoxic) soils are typically a source of OCS. OCS
production has also been observed in most non-desert oxic soils when
dry, with particularly large emissions from agricultural soil
(Fig. 7).
Field observations of soil OCS fluxes. Points are reported
averages. Error bars are the reported range or the uncertainty of
the average. Kuhn et al. (1999) represents an upper range due to
under-pressurized soil chambers.
In the field, reported oxic soil OCS fluxes range from near zero up to
-10 pmolm-2s-1, with average uptake rates
typically between 0 and -5 pmolm-2s-1. Higher
uptake fluxes of -10 to -20 pmolm-2s-1 have been
observed in a grassland soil (Whelan and Rhew, 2016), wheat field
soils (Kanda et al., 1995; Maseyk et al., 2014), unplanted rice
paddies (Yi et al., 2008), and bare lawn soil (Yi and Wang,
2011). However, under warm and dry conditions, fluxes approached zero
in grasslands (Berkelhammer et al., 2014; Whelan and Rhew, 2016) and
an oak woodland (Sun et al., 2016). The highest reported uptake rates
are nearly -40 pmolm-2s-1, following simulated
rainfall in a grassland (Whelan and Rhew, 2016). Sun et al. (2016)
also reported a rapid response to re-wetting following a rainstorm in
a dry Mediterranean woodland.
Variations in soil OCS fluxes measured in the field have been linked
to temperature, soil water content, nutrient status, and CO2
fluxes. Uptake rates have been found to increase with temperature
(White et al., 2010; Yi et al., 2008) but also decrease with
temperature such that OCS fluxes approached zero or shifted to
emissions at temperatures around 15–20 ∘C (Maseyk et al.,
2014; Steinbacher et al., 2004; Whelan and Rhew, 2016; Yang et al.,
2018). It can be difficult to separate the effects of temperature and
soil water content in the field, and seasonal decreases in OCS fluxes
may also be associated with lower soil water content (Steinbacher
et al., 2004; Sun et al., 2016). Uptake rates have also been found to
be stimulated by nutrient addition in the form of fertilizer or lime
(Melillo and Steudler, 1989; Simmons, 1999).
Several field studies have found that OCS uptake is positively
correlated with rates of soil respiration, or CO2 production
(Yi et al., 2007), but these relationships also vary with temperature
(Sun et al., 2016, 2017), soil water content (Maseyk et al., 2014), or
high-CO2 conditions (Bunk et al., 2017). The relationship with
respiration is attributed to the role of microbial activity in OCS
consumption, and similar covariance has been seen between OCS and
H2 uptake (Belviso et al., 2013), a microbially driven
process. Berkelhammer et al. (2014) and Sun et al. (2017) have found
that the OCS / CO2 flux ratio has a nonlinear relationship with
temperature, such that the ratio decreases (becomes more negative) at
lower temperatures but is constant at higher temperatures. Kesselmeier
and Hubert (2002) observed both OCS uptake and emission by beech leaf
litter that was related to microbial respiration rates. Sun
et al. (2016) determined that most of the soil OCS uptake in an oak
woodland occurred in the litter layer, composing up to 90 % of the
small surface sink.
Extensive laboratory studies demonstrate that OCS uptake is mainly
governed by biological activity and physical constraints. Kesselmeier
et al. (1999), van Diest and Kesselmeier (2008), and Whelan
et al. (2016) characterized the response of several controlling
variables such as atmospheric OCS mixing ratios, temperature, and soil
water content or water-filled pore space. Clear temperature and soil
water content optima are observed for OCS consumption. These optima
vary with soil type but indicate water limitation at low soil water
content and diffusion resistance at high soil water
content. Additionally, other organism-mediated or abiotic processes in
the soil, such as photo- or thermal degradation of soil organic matter
(Whelan and Rhew, 2015), can play an important role.
The strong activity of sulfate reduction metabolism in anoxic
environments is thought to drive OCS production in anoxic wetland
soils (see Fig. 4) (Aneja et al., 1981; Kanda et al., 1992; Whelan
et al., 2013; Yi et al., 2008). Temperature probably drives the
observed seasonal variation of OCS production, with higher fluxes in
the summer than winter (Whelan et al., 2013). How much OCS escapes to
the atmosphere depends on transport in the soil column. Tidal flooding
may inhibit OCS emission from wetland soils due to decreasing gas
diffusivity with increasing soil saturation rather than changes in OCS
production rates (Whelan et al., 2013).
With high light or temperatures, OCS production in oxic soils can exceed
rates found in wetlands. Substantial OCS production has been observed in
agricultural fields under both wet and dry conditions (Kitz et al., 2017;
Maseyk et al., 2014). OCS fluxes of up to +30 and
+60 pmolm-2s-1 were related strongly to temperature
(Maseyk et al., 2014) and radiation (Kitz et al., 2017), respectively. While
most ecosystems do not experience these conditions, almost all soils produce
OCS abiotically when air-dried and incubated in the laboratory (Whelan
et al., 2016; Liu et al., 2010; Kaisermann et al., 2018; Meredith et al.,
2018a). Whelan and Rhew (2015) compared sterilized and living soil samples
from the agricultural study site originally investigated in Maseyk
et al. (2014), finding that all samples emitted considerable amounts of OCS
under high ambient temperature and radiation, with even higher emissions
after sterilization. Net OCS emissions can occur from agricultural soils at
all water contents (Bunk et al., 2017) develop in summer (Yang et al., 2018),
and OCS production rates do not differ significantly in moist and dry soils
(Kaisermann et al., 2018). Meredith et al. (2018a) found that OCS soil
production rates are higher in low-pH, high-N soils that have relatively
greater levels of microbial biosynthesis of S-containing amino acids and
concentrations of related S compounds.
Two mechanistic models for soil OCS exchange have been developed and can
simulate observed features of soil OCS exchange, such as the responses of OCS
uptake to soil water content, temperature, and the transition from OCS sink
to source at high soil temperature (Ogée et al., 2016; Sun et al., 2015).
Both models resolve the vertical transport and the source and sink
terms of OCS in soil layers. OCS uptake is represented with the
Michaelis–Menten enzyme kinetics, dependent on the OCS concentration
in each soil layer, whereas OCS production is assumed to follow an
exponential relationship with soil temperature, consistent with field
observations (Maseyk et al., 2014). Although diffusion across soil
layers neither produces nor consumes OCS, altering the OCS
concentration profile affects the concentration-dependent uptake of
OCS.
Recommendations. Additional experiments are required to
understand OCS production in oxic soils. The mechanism of soil
production and why some soils are more prone to high production rates
is unknown. In wetlands, the interaction between OCS production and
transport processes remains poorly understood. If OCS produced by
microbes accumulates in isolated soil pore spaces during inundation,
subsequent ventilation can lead to an abrupt release, which may appear
as high variability in surface emissions. Field experiments using
simple transport manipulation (e.g., straight tubes inserted into
sediment) interpreted with soil modeling would clarify matters.
Microbial communities
The mechanism of OCS consumption in ecosystems is thought to be
mediated by CA, a fairly ubiquitous enzyme
present within cyanobacteria, micro-algae, bacteria, and
fungi. Purified from soil environments or from culture collections,
bacteria and fungi show degradation of OCS at atmospheric
concentrations. Mycobacterium spp. purified from soil and
Dietzia maris NBRC15801T and Streptomyces ambofaciens NBRC12836T showed significant OCS degradation
(Kato et al., 2008; Ogawa et al., 2016). Purified saprotrophic fungi
Fusarium solani and Trichoderma spp. were found to decrease
atmospheric OCS (Li et al., 2010; Masaki et al., 2016). Some free-living
saprophyte Sordariomycetes fungi and Actinomycetales bacteria, dominant in
many soils, are also capable of degrading OCS (Harman et al., 2004; Nacke
et al., 2011). Sterilized soil inoculated with Mycobacterium
spp. showed the ability to take up OCS (Kato et al., 2008). In addition, cell-free extract of
Acidianus spp. showed
significant catalyzed destruction of OCS (Smeulders et al., 2011). During OCS
degradation, soil bacteria introduce isotopic fractionation (Kamezaki et al.,
2016; Ogawa et al., 2017). Using different approaches, Bunk et al. (2017),
Sauze et al. (2017), and Meredith et al. (2018b) showed that fungi might be
the dominant player in soil OCS uptake.
In addition, there exist hyperdiverse microbial communities that
colonize the surface of plant leaves or the “phyllosphere” (Vacher
et al., 2016). The phyllosphere is an extremely large habitat
(estimated in 1 billionkm2) hosting microbial population
densities ranging from 105 to 107 cellscm-2 of
leaf surface (Vorholt, 2012). With respect to OCS, it has already been
shown that plant–fungal interactions can cause OCS emissions (Bloem
et al., 2012). It is undetermined if these epiphytic microbes are
capable of consuming and emitting OCS.
Biotic OCS production is a possibility: in bacteria, novel enzymatic
pathways have been described that degrade thiocyanate and
isothiocyanate and render OCS as a byproduct (Bezsudnova et al., 2007;
Hussain et al., 2013; Katayama et al., 1992; Welte et al.,
2016). Evidence for OCS emissions following SCN- degradation has
been observed from a range of environmental samples from aquatic and
terrestrial origins, indicating a wide distribution of OCS-emitting
microorganisms in nature (Yamasaki et al., 2002). Hydrolysis of
isothiocyanate, another breakdown product of glucosinolates (Hanschen
et al., 2014), by the SaxA protein also yields OCS, as shown in
phytopathogenic Pectobacterium sp. (Welte et al., 2016). Some
Actinomycetales bacteria and Mucoromycotina fungi,
both commonly found in soils, are also known to emit OCS, but the
origin and pathway remains unspecified (Masaki et al., 2016; Ogawa
et al., 2016).
Recommendations. Further studies should test the connection
between the microorganisms that degrade OCS and the candidate enzymes
that we assume are performing the degradation. In addition, the
magnitude of biotic OCS production in soils is unknown. While
sterilized soils exhibit higher OCS production than live soils (Whelan
and Rhew, 2015), we have not determined if biotic production is
universally insignificant in bulk soils.
Surface sorption and abiotic hydrolysis
Several abiotic processes can affect surface fluxes of OCS. OCS can be
hydrolyzed in water and adsorb and desorb on solid surfaces. Abiotic
hydrolysis of OCS in water occurs slowly relative to the timescales of
typical flux observations (Fig. 6). This is in contrast to the reaction in
plant leaves, which is also technically a hydrolysis reaction but is
catalyzed by CA. The
temperature dependence of OCS solubility was modeled and described by
Eq. (20) in Sun et al. (2015): for a OCS concentration in air of
500 ppt, in equilibrium at ambient temperatures, the OCS dissolved in
water will be less than 0.5 pmol OCS / mol-1 H2O
(Fig. 5). Some portion of the dissolved OCS is destroyed by hydrolysis,
following data generated by Elliott et al. (1989). For the rate-limiting step
of hydrolysis in near-room-temperature water, the pseudo-first-order rate
constant is around 2×10-5 s-1. The hydrolysis of OCS
gains significance over hours, especially in ice cores (Aydin et al., 2014,
2016).
Under typical environmental conditions, OCS adsorption and desorption
is near steady state. OCS adsorbs onto various mineral surfaces at
ambient temperatures and can be desorbed at higher temperatures (Devai
and DeLaune, 1997). In some ecosystems with large temperature swings,
temperature-regulated sorption cannot be ruled out as playing a small
role in the variability of observed fluxes.
Marine contribution to the atmospheric OCS loading from
direct and indirect (CS2) emissions. The sea surface
concentration determines the magnitude of the oceanic emissions, and
the uncertainty in global emissions decreases with increasing
numbers of measurements. The understanding of processes is important
to extrapolate from small-scale observations to a regional or global
scale and varies between a low level of understanding for
CS2 (i.e., few process studies available) to a medium
level of understanding for OCS (i.e., several process studies
available, but considerable spread in quantifications across
different locations). We recommend reconsidering the contribution of
oceanic DMS emissions.
Recommendations. Abiotic sorption has been overlooked in
studies of OCS exchange. Observing fluxes while abruptly changing OCS
concentrations over a sterile soil or litter substrate could reveal
sorption's role. This information could be used to inform our
mechanistic soil models and explain some of the variability in OCS
soil fluxes we see in the field.
Ocean
The oceans are known to contribute to the atmospheric budget of OCS
directly via OCS and indirectly via CS2 (Fig. 8) (Chin and
Davis, 1993; Watts, 2000; Kettle et al., 2002). Large uncertainties
are still associated with current estimates of marine fluxes (Launois
et al., 2015a; Lennartz et al., 2017, and references therein) and have
led to diverging conclusions regarding the magnitude of their global
role.
The range of observed OCS concentrations in surface waters informs how
the magnitude of direct oceanic emissions is calculated. Observations
of OCS in the surface water of the Atlantic, Pacific, Indian, and
Southern oceans revealed a consistent daily concentration range of ∼10–100 pmolL-1 in the surface mixed layer on average,
across different methods. Largest differences are found
between coastal and estuaries (range: nanomoles per liter) and
open oceans (range: picomoles per liter) (Table 3).
Measurements of OCS water concentration at the ocean surface
(0–5 m) in the open ocean as well as coastal, shelf, and estuary
waters.
Region
Time
Water concentration of OCS mean ± SD (pmolL-1)
No. ofsamples
References
Open ocean
Indian Ocean
Mar/May 1986 Jul 1987
19.9±0.5a 19.9±1.0a
20 14
Mihalopoulos et al. (1992)
Southern Ocean
Nov–Dec 1990
109b
126
Staubes and Georgii (1993)
North Atlantic Ocean
Apr/May 1992 Jan 1994 Sep 1994
14.9±6.9 5.3±1.6 19.0±8.3
118 120 235
Ulshöfer et al. (1995)
Northeastern Atlantic
Jan 1994
6.7 (4–11)
120
Flöck and Andreae (1996)
Western Atlantic
Mar 1995
8.1±7.0
323
Ulshöfer and Andreae (1998)
Northeastern Atlantic Ocean
Jun/Jul 1997
23.6±16.0
940
Von Hobe et al. (1999)
Atlantic (meridional transect)
Aug 1999
21.7±19.1
783
Kettle et al. (2001)
North Atlantic
Aug 1999
8.6±2.8
518
Von Hobe et al. (2001)
Atlantic (meridional transect)
Oct/Nov 1997 May/Jun 1998
14.8±11.4 18.1±16.1
306 440
Xu et al. (2001)
Indian Ocean
Jul/Aug 2014
9.1±3.5
c
Lennartz et al. (2017)
Coastal, shelf, and estuary waters
Western North Atlantic Shelf Estuaries
Jun/Jul 1990 Aug 1990
400 300–12 100
15 Unknown
Cutter and Radford-Knoery (1993)
Indian Ocean, Mediterranean Sea, French Atlantic (coast)
Dec 1989–1990 May 1987
400–70 300
336
Mihalopoulos et al. (1992)
Averages of several cruises (shelf + coast)
Averages of several cruises
112
157
Andreae and Ferek (1992)
Mediterranean Sea (shelf)
Jul 1993
43±24
34
Ulshöfer et al. (1996)
North Sea (shelf)
Sep 1992
49.1±11.7
69
Uher et al. (1997)
Chesapeake Bay (coast)
Oct 1991–May 1994
320.0±351
23
Zhang et al. (1998)
Eastern tropical South Pacific (shelf)
Oct 2015
40.5±16.4
c
Lennartz et al. (2017)
a Converted from ngL-1 with a molar mass of OCS of 60.07 g. b Converted from ngSL-1 with a molar mass of S of 32.1 g. c Continuous measurements.
Marine production and removal processes
The primary sources of OCS in the ocean are divided into photochemical and
light-independent (dark) processes (Von Hobe et al., 2001; Uher and Andreae,
1997). The primary sink is uncatalyzed hydrolysis (Fig. 6; Elliott et al.,
1989). Evidence indicates that these processes can regulate OCS
concentrations in the ocean surface mixed layer, with diverging conclusions
on the magnitude and global significance of marine OCS emissions (Launois
et al., 2015a). We use the Lennartz et al. (2017) budget here because the
emission estimate is based on a model consistent with the
majority of sea surface concentration measurements.
Global estimates of photoproduction for the surface mixed layer can
range by up to a factor of 40 depending on the methodology used
(Fig. 9). The heart of the problem is a limited knowledge of the
magnitude, spectral characteristics, and spatial and temporal
variability of the apparent quantum yield (AQY).
Comparison of OCS photoproduction rates (averages for surface
mixed layer, pmol(OCS) L-1h-1) modeled using
different approaches and demonstrating discrepancies between
methods: (a) Hovmöller (latitude–time) plot of rates
calculated using the approach described in Lennartz
et al. (2017); (b) the same Hovmöller plot generated
with the approach described in Launois et al. (2015a) and two different
formulations for CDOM absorption coefficients from Preiswerk and
Najjar (2000) and Morel and Gentili (2004); and (c) the same
Hovmöller plots generated with the photochemical model of Fichot
and Miller (2010) and the published spectral apparent quantum yields
of Weiss et al. (1995), Zepp and Andreae (1994), and Cutter
et al. (2004).
There is evidence for the role of biological processes (Flöck and
Andreae, 1996) and for the involvement of radicals (Pos et al., 1998)
in OCS production. Independent of a mechanism, only one
parameterization for dark production is currently used in models (Von
Hobe et al., 2001). Neither the direct precursor nor the global
applicability of this parameterization is known. Despite these
unknowns, the current gap in the top-down OCS budget (Sect. 3.1) is
larger than the estimated ocean emissions, including uncertainties
from process parameterization and in situ observations. This suggests
that our estimates of OCS production in oceans will not close the gap
in top-down OCS budgets.
Recommendations. Further studies should focus on generating
a biochemical model for estimating oceanic OCS fluxes. Refining
uncertainty bounds for OCS photoproduction could be facilitated by
a comprehensive study of the variability of AQYs across contrasting
marine environments, the use of a photochemical model that utilizes
AQYs and facilitates calculations on a global scale, and the
cross-validation of the depth-resolved modeled rates with direct in
situ measurements. During nighttime, continuous concentration
measurements from research vessels can be used to calculate dark
production rates assuming an equilibrium between hydrolysis and dark
production.
Indirect marine emissions
Indirect marine emissions from oxidation of the precursor gases
CS2 and possibly dimethyl sulfide (DMS) were hypothesized to be on the same
order of magnitude as or larger than direct ocean emissions of OCS (Chin and Davis,
1993; Watts, 2000; Kettle et al., 2002). Production and loss processes
of CS2 in seawater are less well constrained than OCS
production, and they include photoproduction, evidence for biological
production (Xie et al., 1998, 1999), and a slow chemical sink
(Elliott, 1990).
Measurements of CS2 in the surface ocean comprise several
transects in the Atlantic and Pacific oceans with concentrations in
the lower picomoles-per-liter range. Significantly larger
concentrations have been found in coastal waters (Uher, 2006, and
references therein). In laboratory experiments, Hynes et al. (1988)
found that the OCS yield from CS2 increases with
decreasing temperatures, suggesting larger OCS production from
CS2 at high latitudes.
It is unclear if the ambient yield of OCS from DMS oxidation is
globally important. The production of OCS from the oxidation of DMS by
OH has been observed in several chamber experiments, all of which used
the same technique and experimental chamber (Barnes et al., 1994,
1996; Patroescu et al., 1998; Arsene et al., 1999, 2001) with a molar
yield of 0.7±0.2 %. These studies were carried out at
precursor levels far exceeding those in the atmosphere (ppm), so the
potential exists for radical–radical reactions that do not occur in
nature. In addition, experiments took place in a quartz chamber on
timescales that have potential for wall-mediated surface or
heterogeneous reactions and using only a single total pressure and
temperature (1000 mbar, 298 K). The mechanism and
atmospheric relevance of OCS production from DMS remain highly
uncertain.
Recommendations: To better constrain oceanic CS2
emissions, we suggest expanding surface concentration observations
across various biogeochemical regimes and seasons. Using field
observations, laboratory studies, and process models, we could
characterize production processes and identify drivers and rates when
calculating OCS emission estimates. Elucidating the production pathway
and validating the atmospheric applicability of the reported OCS
yields from DMS would require experiments at lower concentrations in
a system that eliminates (or permits quantification of) wall-induced
reactions.
Anthropogenic sources
Anthropogenic OCS sources include direct emissions of OCS and indirect
sources from emissions of CS2. The dominant source is from rayon
production (Campbell et al., 2015), while other large sources include coal
combustion, aluminum smelting, pigment production, shipping, tire wear,
vehicle emissions, and coke production (Blake et al., 2008; Chin and Davis,
1993; Du et al., 2016; Lee and Brimblecombe, 2016; Watts, 2000; Zumkehr et
al., 2017).
All recent global atmospheric modeling studies have used the low estimate
of 180 GgSyr-1 from Kettle et al. (2002), which did not
capture significant emissions from China. Updated globally gridded
inventories are considerably higher: a bottom-up estimate of
223–586 GgSyr-1 for 2012 (Zumkehr et al., 2018) and
a top-down assessment of 230 to 350 GgSyr-1 for 2011 to
2013 (Campbell et al., 2015). One reason for the gap between the two
recent inventories is that the top-down study used an optimization
approach in which the result was limited to the a priori range, 150 to
364 GgSyr-1. Both datasets indicate that most
anthropogenic sources are in Asia.
Biomass burning is generally accounted for as a category separate from
anthropogenic emissions. Several airborne campaigns have observed
increases in OCS concentrations in air masses from nearby burning
events (Blake et al., 2008). The most recent estimate suggests that
biofuels, open burning, and agriculture residue are 63, 26, and
11 % of the total OCS biomass burning emissions, respectively (Campbell et al.,
2015).
Recommendations. Anthropogenic OCS emissions experience large
year-to-year variation (Campbell et al., 2017a). Ambient OCS
monitoring and on-site industry observations in Asia could observe the
anthropogenic contribution over time. In particular, modern
viscose-rayon factory emissions are necessary to capture the
variability of emissions factors used to scale rayon production to OCS
emissions using economic data.
Volcanic sources
OCS is emitted into the atmosphere by degassing magma, volcanic
fumaroles, and geothermal fluids. OCS can be released at room
temperature by volcanic ash (Rasmussen et al., 1982) and has been
observed to be conservative in the atmospheric plume emitted by the
Mount Erebus volcano up to tens of kilometers downwind of the volcanic
source (Oppenheimer et al., 2010).
Using the linear relationship between the logarithm of the
OCS / CO2 ratio in volcanic gases and temperature, the volcanic
OCS contribution was determined from estimated CO2 emissions
(Belviso et al., 1986). Here we calculate a revised temperature
dependence of log[OCS / CO2] with additional data (Chiodini
et al., 1991; Notsu and Toshiya, 2010; Sawyer et al., 2008; Symonds
et al., 1992), as shown in Fig. 10. The compilation of measurements
from 14 volcanoes shows that the former relationship from Belviso
et al. (1986) overestimated the OCS / CO2 ratio of volcanic
gases with emission temperatures from 110 to 400 ∘C, typical
of extra-eruptive volcanoes. Even with this improved estimate, OCS emissions
of extra-eruptive volcanoes are negligibly small and can
definitely be discarded from the inventory of volcanic OCS emissions.
Eruptive and post-eruptive volcanoes contribute almost all OCS
emissions from volcanism.
Decimal logarithm of the OCS / CO2 ratios plotted
against the reciprocal of the emission temperature of the gases for
volcanoes. The red dots refer to the analytical data published by
Belviso et al. (1986) and the red line corresponds to the linear
model used in that study to evaluate the volcanic contribution to
the atmospheric OCS budget. The blue dots refer to measurements
published by others since 1986 (Chiodini et al., 1991; Notsu and
Toshiya, 2010; Sawyer et al., 2008; Symonds et al., 1992). The
better fit through all measurements is obtained using a polynomial
of the third order (R2=0.89, n=31).
Recommendations. An updated inventory of eruptive volcanoes and a better assessment of
their CO2 emissions will refine our understanding at a regional
scale of the contribution of OCS from volcanoes. Special attention should be paid to the Ring of Fire off the Asian
continent where satellites have observed significant atmospheric OCS
enhancements.
Bottom-up OCS budget
We calculate a “bottom-up” global balance of OCS with several
approaches, as presented in Table 4. Within the atmosphere, the
tropospheric sink owing to oxidation by OH is estimated to be in the
range 82–130 GgSyr-1 (Berry et al., 2013; Kettle
et al., 2002; Watts, 2000), and the stratospheric sink is in the range
30–80 GgSyr-1, or 50±15 GgSyr-1
(Barkley et al., 2008; Chin and Davis, 1995; Crutzen, 1976; Engel and
Schmidt, 1994; Krysztofiak et al., 2015; Turco et al., 1980;
Weisenstein et al., 1997). OCS concentrations are increasing roughly
0.5–1 pptyear-1 averaged over the last 10 years
(Campbell et al., 2017a), suggesting approximately 2 to
5 GgSyr-1 remains in the troposphere.
Total bottom-up atmospheric OCS budget.
Component
OCS global flux (GgSyear-1)
Data source
Forests
-430 to -370
Grasslands
-500 to -200
Deserts
-24
No field data exist for deserts
Agricultural, excluding rice
-150 to +13
Freshwater
+0.8 to +12
Fungus/lichen/mosses
-21 to -8
Wetlands
-150 to +290
Ocean
Total: +265±210 OCS direct: +130±80 OCS from oc. CS2: +135±130 OCS from oc. DMS: 0 (+80)
Lennartz et al. (2017); see Sect. 2.3
Anthropogenic
+400±180
For the year 2012, Zumkehr et al. (2018)
Biomass Burning
+116±52
Campbell et al. (2015)
Volcanoes
+25 to +43
Tropospheric destruction by OH radical
-130 to -82
Berry et al. (2013), Kettle et al. (2002) and Watts (2000)
Stratospheric destruction by photolysis
-80 to -30 or -50±15
Barkley et al. (2008), Chin and Davis (1995), Crutzen (1976), Engel and Schmidt (1994), Krysztofiak et al. (2015), Turco et al. (1980), and Weisenstein et al. (1997)
Remains in the troposphere
+2 to +5
Total range
-1100 to +900
We build a budget for terrestrial biomes that relies on observations where
available, and on estimates of carbon uptake where no data exists, as has
been done previously (Campbell et al., 2008; Kettle et al., 2002;
Suntharalingam et al., 2008). In Table 5, the estimated OCS uptake is first
calculated from a GPP estimate and Eq. (1); then the net OCS flux is
appraised by taking into account observed or estimated soil fluxes for each
biome. The [CO2] and [OCS] are assumed to be 400 ppm and
500 ppt, respectively, and LRU is 1.16±0.2 for C4
plants (Stimler et al., 2010b) and 1.99±1.44 for C3 plants
(Fig. 2). We further assume a 150-day growing season with 12 h of
light per day for the purposes of converting between annual estimates of GPP
and field measurements calculated in per-second units, though this obviously
does not represent the diversity of biomes' carbon assimilation patterns.
Additionally, we assume that plants in desert biomes photosynthesize using
the C4 pathway. Converting annual estimated FCOS
from an annual estimate to a per-second estimate
is sensitive to our growing season assumption. The lack of soil OCS
flux time series datasets makes a more sophisticated upscaling approach
ineffective. Anticipated fluxes from soils and plants are therefore combined
in this purposely simple method, scaled to the area of the biome extent, and
presented in Table 4 as annual contributions to the atmospheric OCS budget.
GPP and OCS exchange estimates by biome.
Biome
GPP estimated by Beer et al. (2010) in PgCyr-1
Biome area(109 ha)
Anticipated FOCS, plants from GPP estimate (pmolm-2s-1)
FOCS, soil(pmolm-2s-1)
FOCS, ecosystem by GPP method (pmolm-2s-1)
FOCS, ecosystem field observations (pmolm-2s-1)
Tropical forests
40.8
1.75
-75
No dataa
-83 to -73
No data
Temperate forests
9.9
1.04
-30
-8 to 1.45b
-38 to -29
∼0 to 93h
Boreal forests
8.3
1.37
-19
1.2 to 3.8c
-18 to -16
0 to -22i
Tropical savannas and grasslands
31.3
2.76
-36
No data
-61 to -29
No data
Temperate grasslands and shrublands
8.5
1.78
-15
-25 to 7.3d
-40 to -8
-26 growingseason; +6.1 non-growing seasonj
Deserts
6.4
2.77
-7
0 (?)e
-7 (?)
No data
Tundra
1.6
0.56
-9
5.27 to 27.6f
-4 to 18
-15 to -1k
Croplands
14.8
1.35
-35
-18 to 40g
-53 to 5
-22 to -16, +18during non-growing seasonl
Total
121.7
13.38
a For the purpose of this estimate, we use the soil
fluxes from temperate forests.b Range of values from Castro and Galloway (1991),
Steinbacher et al. (2004), White et al. (2010), and Yi et al. (2007).c The average reported here is the average and 1 SD
from non-vegetated plots in a boreal forest, defined as plots having
less than 10 % vegetation cover (Simmons, 1999).d Range from Whelan and Rhew (2016). The error estimate
here is different from the one reported because a different LRU was
used. Kitz et al. (2017) found soil-only OCS production of
+60 pmolm-2s-1 in an alpine grassland.e In a laboratory incubation study, Whelan et al. (2016)
found that desert soils exhibit a very small uptake. No field
measurements have been published to our knowledge.f The smaller production is from de Mello and Hines
(1994). The larger production is an average estimate from Fried et al. (1993).g Post-harvest soil exchange estimate from the wheat
field (Billesbach et al., 2014) investigated further in Whelan and
Rhew (2015).h See Table 1.i From Simmons et al. (1999).j Range from Whelan and Rhew (2016), encompassing observations of
a grass field by Yi and Wang (2011).k Range reported in de Mello and Hines (1994), encompassing values
observed by a bog microcosm by Fried et al. (1993). No valid Arctic studies exist. l High value for cotton, low value for wheat in Asaf
et al. (2013). Daily fluxes for a wheat field investigated by Billesbach
et al. (2014) were -21 during the growing season and +18 after harvest.
Agricultural soils have been shown to emit a large portion of OCS compared to
plant uptake under hot and dry conditions (Whelan et al., 2016; Whelan and
Rhew, 2015).
We use a range of OCS flux observations in picomoles of OCS per square meter
per second for fresh and saline wetlands: -15 (de Mello and Hines, 1994) to +27 (Liu and Li,
2008) for freshwater wetlands and -9.5 (Li et al., 2016) to +60 (Whelan
et al., 2013) for saltwater wetlands (Fig. 4). Marine and inland wetlands
cover 660 and 9200×103 km2, respectively (Lehner and Döll, 2004). Performing
a simple scaling exercise results in contributions of -140 to 250 and
-6 to 40 GgSyr-1 for fresh and
saltwater wetlands, respectively, yielding a total range of -150 to
290 GgSyr-1 (Table 4).
To determine the role of non-vascular plant communities to the
atmospheric OCS loading, we leverage Eq. (1) and work that has already
been done on their carbon balance. According to Elbert et al. (2012),
the annual contribution is 3.9 PgCyr-1. A [OCS] of
500 ppt, a [CO2] of 400 ppm, and a LRU of 1.1±0.5 (Gimeno et al., 2017) yield -8 to -21 GgSyr-1.
To estimate the maximum possible source of lakes to the atmospheric
OCS burden, we perform a simple estimation of the global OCS flux
following the approach in MacIntyre et al. (1995) as
FOCS=k(caq-ceq),
where gas transfer coefficient, k, is assumed to be constant at
0.54 md-1 (Read et al., 2012); OCS concentration in the water,
caq, is 90 pmolL-1 to 1.1 nmolL-1
(Richards et al., 1991); and OCS concentration in the
surface water if it was in equilibrium with the above air, ceq,
is calculated using Henry's law at global average temperature of 15 ∘C
and global atmospheric OCS mixing ratio of 500 ppt. Accounting for
the number of ice-free days in a year and total lake surface area per
latitude (Downing et al., 2006), the range of possible COS burden from lakes
to the atmosphere is reported here as 0.8 to 12 GgSyr-1.
Lennartz et al. (2017) generated a direct estimate of direct OCS
emissions from oceans as 130±80 GgSyr-1. A molar
yield of CS2 to OCS of 0.81–0.93 was established by
Stickel et al. (1993) and Chin and Davis (1993), resulting in ocean
OCS emissions from CS2 with an uncertainty of
20–80 GgSyr-1. This uncertainty is from the emissions
of CS2, not the molar yield, for which a globally constant
factor is used. The global DMS oxidation source of OCS was estimated
by Barnes et al. (1994) as 50.1–140.3 GgSyr-1, and
subsequent budgets contain only revisions according to updated DMS
emissions (Kettle et al., 2002; Watts, 2000). We suggest that the
uncertainty in the production of OCS from DMS is underestimated. Until
these issues are resolved, we recommend that this term be removed as
a source from future budgets, but retained as an uncertainty.
Bottom-up analysis of the global anthropogenic inventory indicates
a source of 500±220 GgSyr-1 for the year 2012
(Zumkehr et al., 2018). The large uncertainty is primarily due to
limited observations of emission factors, particularly for the rayon,
pulp, and paper industries. The most recent estimate of the biomass
burning sources is 116±52 GgSyr-1 (Campbell
et al., 2015).
To calculate global volcanic OCS emissions, we first consider the
range of global volcanic CO2 emission estimates of the five
studies reviewed by Gerlach (2011) of 0.15–0.26 Pgyr-1,
or 0.205±0.055 Pgyr-1. Assuming that the mean
OCS / CO2 molar ratio of gases emitted by eruptive and
post-eruptive volcanoes is 2.3×10-4 (for emission
temperatures in the range 525–1130 ∘C, see Fig. 10), the
revised annual volcanic input of OCS into the troposphere is estimated
to be in the range 25–43 GgSyr-1.
Examining Table 4, we find large uncertainties in many global
estimates, and some biome observations are completely absent. It has
been suggested that ocean OCS production has been underestimated
(Berry et al., 2013), and some research points to unaccounted-for
anthropogenic sources (Zumkehr et al., 2018). The uncertainty in our
ocean OCS production and/or the industry inventories does not
necessarily capture the true range of OCS fluxes. Despite the large
uncertainties of the global OCS budget, many applications of the OCS
tracer have been attempted with success.
Recommendations. More observations in the ocean OCS source
region and from industrial processes, particularly in Asia, are needed
to further assess their actual magnitude and variation (Suntharalingam
et al., 2008). Current leaf-based investigations need to be expanded
to include water- or nutrient-stressed plants. Measurements from biomes
with a complete lack of data, such as deserts and the entirety of the
tropics, are desperately needed.
Applications
Top-down global OCS budgets
Top-down estimates use observed spatial and temporal gradients of OCS
in the atmosphere to adjust independent surface fluxes, called the
prior estimate. Constraints can be introduced to the results;
e.g., Launois et al. (2015b) used flask measurement observations to
optimize surface OCS flux components to obtain a closed global OCS
budget. Other top-down estimates without this restriction found
a missing source of about 600–800 GgSyr-1 in the
atmospheric budget of OCS (Berry et al., 2013; Glatthor et al., 2015;
Kuai et al., 2015; Suntharalingam et al., 2008; Wang et al.,
2016). This could be the result of missing oceanic sources, missing
anthropogenic OCS sources from Asia, overestimated plant uptake, or
a combination of factors.
Kuai et al. (2015) implied a large ocean OCS source over the Indo-Pacific
region with the total ocean source budget consistent with the global budget
proposed by Berry et al. (2013). The observations in Kuai et al. (2015) were
estimated OCS surface fluxes from NASA's Tropospheric Emission Spectrometer
(TES) ocean-only observations. A similar conclusion was obtained by Glatthor
et al. (2015), who showed that the OCS global seasonal cycle observed by the
Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) was more
consistent with the seasonal cycles modeled using the Berry et al. (2013)
global budget than using the global budget proposed earlier by Kettle
et al. (2002).
Most of the anthropogenic source is located in China, while most of
the atmospheric OCS monitoring is located in North America (Campbell
et al., 2015). The spatial separation allows regional applications of
OCS to North America to control for most of the anthropogenic
influence through observed boundary conditions (Campbell et al., 2008;
Hilton et al., 2015, 2017). The anthropogenic source has large
interannual variations (Campbell et al., 2015), which suggest that
applications of the OCS tracer to inter-annual carbon cycle analysis
will require careful consideration of anthropogenic variability.
Recommendations. The accuracy of OCS surface flux inversions
can be improved by using simultaneous OCS observations from multiple
satellites, e.g., TES and MIPAS, to provide more constraints on the OCS
distribution in different parts of the atmosphere. Satellite products
need to be compared to observations to determine how well the upper
troposphere can reflect surface fluxes, e.g., long-term tower
measurements, airborne eddy flux covariance, and atmospheric
profiles. This effort is furthered by better estimates of surface
fluxes, in particular observations of OCS emissions from the oceans
where we assume a large source region might exist (Kuai et al., 2015)
and where poorly described anthropogenic sources are located in Asia
(Zumkehr et al., 2018).
Global and regional terrestrial GPP estimates
Here we describe work using OCS observations to assemble more
information about ecosystem functioning on different scales. Estimates
disagree in their diagnoses of global (Piao et al., 2013) and regional
(Parazoo et al., 2015) GPP magnitude and spatial distribution in North
America (Huntzinger et al., 2012), the Amazon (Restrepo-Coupe et al.,
2017), and Southeast Asia (Ichii et al., 2013). Feeding observations
of OCS uptake over land into transport models informs the spatial
distribution and magnitude of GPP. With the suite of OCS flask and
satellite data available, we describe studies that examine OCS fluxes
with the top-down approach. Finally, we examine GPP estimates on very
long temporal scales using the OCS ice core record.
Evaluating biosphere models
There are many uncertainties in evaluating biosphere models using OCS
observations. Hilton et al. (2017) showed that the spatial placement of GPP
dominates other uncertainty sources in the GPP tracer approach on a regional
scale. Land surface models that placed the largest GPP in the Upper Midwest
of the United States produced OCS plant fluxes that matched aircraft
observations well for all estimates of OCS soil flux, OCS anthropogenic flux,
and transport model boundary conditions. OCS plant fluxes derived from GPP
models that place the largest GPP in the southeastern United States were not
able to match aircraft-observed OCS for any combination of secondary OCS
fluxes. Placement of the strongest North American GPP in the Upper Midwest is
consistent with new ecosystem models from the Coupled Model Intercomparison
Project Phase 6 (CMIP6) (Eyring et al., 2016) with space-based estimates from
solar-induced fluorescence (SIF; Guanter et al., 2014; Parazoo et al., 2014). This result is encouraging for
the potential of OCS to provide a directly observable tracer for GPP at
regional scales.
Launois et al. (2015b) analyzed the potential of existing atmospheric
OCS and CO2 mixing ratio measurements to evaluate model GPP
biases. They used the simulated GPP from three global land surface
model simulations from the TRENDY intercomparison (Sitch et al., 2015)
and an atmospheric transport model. The amplitude and phase of the
seasonal variations of atmospheric OCS appear mainly controlled by the
vegetation OCS sink. This allows for bias recognition in the spatial
and temporal patterns of the GPP. For instance, the ORCHIDEE GPP at
high northern latitudes is overestimated, as revealed by a too-large
OCS seasonal cycle at the Alert station (ALT, Canada) (Fig. 11).
These results highlight the potential of current in situ OCS
measurement to reveal model GPP and respiration biases.
Smoothed seasonal cycles of OCS (right) and CO2
(left) monthly mean mixing ratios, simulated at Alert station,
Canada, obtained after removing the annual trends. Simulations are
obtained with the LMDz transport model, using two flux scenarios for
the vegetation uptake of OCS, calculated with the GPP of ORCHIDEE
and CLM4CN models; the other OCS flux components are identical (see
Launois et al. 2015). Observations (red) are from the NOAA/ESRL
global monitoring network (Montzka et al., 2007) averaged from 2007
to 2010.
Recommendations. While current datasets can support or refute
current land surface model GPP data products over North America,
evaluating modeled surface GPP fluxes with OCS observations would
benefit from a broader network of continuous OCS
observations. Unfortunately, satellite data are not currently
sensitive to concentrations at the surface. Maintaining a network of
tall towers with continuous OCS measurements over more than one
continent could, in conjunction with upper-troposphere measurements
from satellites, provide the data needed to refine next-generation
land surface models.
Long-term changes in carbon uptake
Ice core samples from the West Antarctic Ice Sheet Divide were used to
produce a 54 300-year OCS record and an order-of-magnitude estimate
of the change in GPP during the last glacial–interglacial transition
(Aydin et al., 2016). Atmospheric OCS declined by 80 to
100 ppt during the last glacial–interglacial
transition. Interpretation of these measurements with a simple box
model suggests that GPP roughly doubled during the transition. This
order-of-magnitude estimate is consistent with an ecosystem model that
simulates 44 % growth in GPP over the same period (Prentice
et al., 2011).
The ice core OCS record has also been used to explore variation in GPP
over the past 2000 years. Observations show relative maxima at the
peak of the Little Ice Age (Aydin et al., 2008). These data were used
to estimate growth in GPP and were combined with other information to
estimate the temperature sensitivity of pre-industrial CO2
fluxes for the terrestrial biosphere (Rubino et al., 2016).
Given that Earth system model projections have highly uncertain
carbon–climate feedbacks (Friedlingstein et al., 2013), understanding
of GPP in the current industrial era is needed to provide a benchmark
for future model development. Firn air measurements and
one-dimensional firn models have been used to show an increase in
atmospheric OCS during most of the industrial era, with a decadal
period of decline beginning in the 1990s (Montzka et al., 2004,
2007). The trend in the firn record has been interpreted to largely
reflect the increase in industrial emissions, but it also suggests an
increase in GPP during the 20th century of 31±5 %, which is
consistent with some models (Campbell et al., 2017a).
Recommendations. Examining the polar differences in OCS over
glacial–interglacial periods would provide additional evidence for
interpreting changes in GPP. For such an analysis, ice core OCS
observations from the Northern Hemisphere are needed.
OCS to probe variables other than GPP
OCS and CO2 uptake within plant leaves is partly regulated by
the opening of stomata on leaf surfaces. Stomatal conductance is
typically determined from combined estimates of transpiration, water
vapor concentration, and leaf temperature. That approach can be
particularly challenging at the canopy scale, where transpiration is
difficult to distinguish from non-stomatal water fluxes
(i.e., evaporation from soil and canopy surfaces) and to upscale from
sap flux measurements (Wilson et al., 2001). Use of OCS uptake
involves the similar but more tractable challenge of distinguishing
the canopy OCS uptake from soil OCS uptake or emission, as in Wehr
et al. (2017). OCS data can also look at changes in uptake activity
when plants are grown in elevated CO2 environments (White
et al., 2010; Sandoval-Soto et al., 2012). Use of OCS uptake may also
be less sensitive to errors in leaf temperature, which is difficult to
define and quantify at the canopy scale but may be improved by OCS
measurements (Yang et al., 2018). However, leaf temperature may still
enter the problem via estimation of mesophyll conductance and CA
activity.
The use of OCS to study canopy and stomatal conductance is therefore
promising, but it is so far represented mostly by very few studies
(Wehr et al., 2017; Yang et al., 2018). Wehr et al. (2017) used OCS
uptake to derive canopy stomatal conductance and hence transpiration
in a temperate forest. Stomatal conductance was the rate-limiting
diffusive step, and so its diel and seasonal patterns were retrievable
from the canopy OCS uptake to within 6 % of independent estimates
based on sensible and latent heat flux measurements (Fig. 12). OCS
would be especially useful in humid environments or at night, when
transpiration is too small to use other methods that rely on sap flow
or heat flux (Campbell et al., 2017b). However, an independent
estimate of CA activity and mesophyll conductance would be required.
Composite diel cycles of stomatal conductance derived from
the OCS uptake (solid black line with gray bands) and from the
sensible and latent heat fluxes (red dashed line), along with
photosynthetically active radiation (PAR, bottom panel) for context,
including May through October of 2012 and 2013. Lines connect the
mean values of each 2 h bin. The gray bands depict standard
errors in the means as estimated from the variability within each
bin. Adapted from Wehr et al. (2017), which discusses the dawn
storage measurement artifact indicated here by the blue circle.
Recommendations. OCS observations should be used to link
plant physiological variables to one another. OCS fluxes are related to GPP
via all three diffusive conductances, CA activity, transpiration, and
the 18O isotope compositions of CO2 and
H2O. The 18O connection results from the fact that
CA promotes the exchange of oxygen isotopes between CO2 and
liquid water in the leaves. Solar-induced fluorescence measurements
could also be synergistic, as they relate to the photochemical aspect
of photosynthesis, while OCS uptake relates to the gas transport
aspect. So far, few research schemes have taken advantage of these
relationships.
Available datasets
OCS satellite data products
Global OCS concentrations have been retrieved from several satellite
instruments, including NASA's TES (Kuai et al., 2014), the Canadian
Space Agency's Atmospheric Chemistry Experiment–Fourier Transform
Spectrometer (ACE-FTS) (Boone et al., 2005), and the European Space
Agency's MIPAS (von Clarmann et al., 2003; Glatthor et al., 2017) and
Infrared Atmospheric Sounding Interferometer (IASI) (Camy-Peyret
et al., 2017; Vincent and Dudhia, 2017). Among these instruments, TES
and IASI are nadir-viewing instruments (i.e., looking downwards from
space towards the surface), while ACE-FTS and MIPAS are limb scanners
(i.e., looking through the atmosphere tangentially). Nadir measurements
are less prone to cloud interference and provide good horizontal
spatial resolution but coarse vertical resolution. Limb measurements
provide better vertical resolution and higher sensitivity to tracer
concentrations, but they are subject to a higher probability of cloud
interference and poorer line-of-sight spatial resolution. Currently
there are no satellite measurements that are strongly sensitive to OCS
concentrations near the surface, where they are most needed to
evaluate surface fluxes.
The standard TES OCS product is an average between 200 and
900 hPa, with maximum sensitivity to the mid-tropospheric
value (Kuai et al., 2014; Fig. 13a). Currently, the TES OCS retrievals
are available over ocean only for latitudes below 40∘, where
the signal-to-noise ratio is higher (due to larger thermal contrasts)
and the surface spectral emissivity can be easily
specified. Comparisons with collocated airborne and ground
measurements show that the current TES OCS data have an accuracy of
50–80 ppt, and the accuracy is improved to ∼7 ppt when averaged over 1 month (Kuai et al., 2014).
Comparisons of the seasonal horizontal distribution of OCS
retrievals. (a) TES averaged between 200 and 900 hPa,
obtained using TES Level 2 swath OCS retrievals in 2006, averaged
over four seasons (March to May, June to August, September to
November, and December to February). (b) MIPAS
(250 hPa), using MIPAS Level 2 swath retrievals from 2002 to
2011. The data in (a) and (b) have been averaged
to the same 5∘ longitude × 4∘ latitude grid
boxes and have been smoothed to a 20∘×20∘
spatial resolution. (c) Two-month averages of IASI daytime
OCS total column retrievals from 2014 with resolution 0.5∘×0.5∘, extracted from Vincent and Dudhia
(2017). Missing data are represented by white areas in
panels (a) and (b) and by gray areas in
panel (c).
MIPAS retrievals from 7 to 25 km characterize the average OCS
concentration in a thin layer (a few kilometers thick) around the
corresponding tangent height. Currently, the MIPAS OCS product
(Fig. 13b) provides pole-to-pole OCS concentrations at multiple levels
in the upper troposphere and the stratosphere, which show an accuracy
of ∼50 ppt against balloon-borne measurements. Figure 14
shows the summertime (June–August) latitudinal distribution of OCS
observed by MIPAS (Glatthor et al., 2017).
IASI retrieves a single value for the total column OCS
(Fig. 13c). Recently, Vincent and Dudhia (2017) reported the
pole-to-pole global OCS retrieved from the IASI measurements. Their
preliminary test showed that the seasonally averaged IASI OCS data
vary consistently with ground measurements. The IASI OCS observations
over land generally agree with the MIPAS observations, showing large
sinks over South America and Africa. The high spatial resolution also
reveals more clearly the land OCS sources over Asia, which are not
seen in TES or MIPAS observations. Furthermore, the relatively low
OCS abundance over the Intertropical Convergence Zone is only
apparent in IASI data.
The ACE-FTS-reported OCS concentrations in the lower stratosphere are
known to be 15 % lower than the balloon-borne measurements
(Velazco et al., 2011) and ∼100 ppt lower than MIPAS OCS
(Glatthor et al., 2017).
Latitudinal distribution of OCS, observed by MIPAS. Extracted
from Glatthor et al. (2017).
FTIR data
Ground-based FTIR retrievals of OCS are sensitive to the altitudes
between the surface and 30 km, and can therefore more directly
capture the variations near the surface compared to satellite
data. There are two networks of FTIR spectrometers: the Network for
the Detection of Atmospheric Composition Change (NDACC), recording the
mid-infrared spectra including the OCS bands, and the Total Carbon
Column Observing Network (TCCON), mainly focusing on the near infrared
with only some sites including the OCS bands. The FTIR remote-sensing
measurement is an indirect measurement and therefore needs to be
calibrated to in situ observations to have the same scale when
combining the datasets. For example, Wang et al. (2016) added an
offset when comparing FTIR retrievals and HIAPER Pole-to-Pole
Observations (HIPPO) to the same model. Published datasets exist for
the periods 1993–1997 (Griffith et al., 1998), 1978–2002 (Rinsland
et al., 2002), 2001–2014 (Kremser et al., 2015), 2005–2012 (Wang
et al., 2016), and 1995–2015 (Lejeune et al., 2017) and by an
airborne Fourier spectrometer for the period 1978–2005 (Coffey and
Hannigan, 2010). Balloon-borne FTIR data are available starting in
1985 (Toon et al., 2018).
Tower and airborne data
Data are available from two kinds of airborne sampling: survey flights and
atmospheric chemistry projects. OCS measurements from aircraft began in the
late 1980s, using both in situ and flask collection with subsequent analysis
by GC-MS (e.g., Bandy et al., 1992, 1993; Hoell et al., 1993; Thornton
et al., 1996; Blake et al., 2008, etc). The airborne survey flight data are
designed to sample background air at set locations on a regular basis over
long time periods and are part of the NOAA/ESRL/GMD Global Greenhouse Gas
Reference Network's aircraft program
(http://www.esrl.noaa.gov/gmd/ccgg/aircraft/index.html, last access:
6 June 2018; an update of results published in Montzka et al., 2007). This
data collection started in 1999 at a range of locations and has been used
extensively in analysis of the continental US carbon budget (e.g., Campbell
et al., 2008; Hilton et al., 2017). OCS has been measured at 10 globally
distributed sites in the AGAGE network using the MEDUSA GC-MS. The data for
the Jungfraujoch site are presented in Lejeune et al. (2017).
Larger-spatial-scale, shorter-time-interval survey flights include the HIPPO
(2009–2011) and ATom (2016–2018) airborne programs, which predominantly
sample OCS over remote marine locations. Atmospheric chemistry flights are
designed to understand chemical processing and pollution transport and
include sampling as part of pollution transport across the Pacific (e.g.,
Pacific Exploratory Mission–West A (PEM-A); Thornton et al., 1996) or
Transport and Chemical Evolution over the Pacific experiment (TRACE-P), which
sampled Asian outflow dominated by anthropogenic OCS emissions in 2001 (Blake
et al., 2004). Other projects included sampling of OCS over continents (e.g.,
over the US in 2004; Blake et al., 2008).
OCS measurements have been made from tall towers using flasks and subsequent
analysis by GC-MS. Most long-term tall-tower observations have been conducted
as part of the NOAA/ESRL/GMD tower network (Montzka et al., 2007). These data from 11–12 sites include
continuous sampling from 2000 onward at a daily or twice-daily time basis for
most of the record.
Ecosystem-level data
Three approaches have been used to quantify ecosystem fluxes of OCS:
chamber measurements, gradient measurements, and eddy flux covariance
measurements. While researchers have been quantifying OCS
measurements with chambers for decades, most field outings prior to
1990 used dynamic chambers with sulfur-free sweep air, artificially
inducing high emissions (Castro and Galloway, 1991).
Measurements from towers have been made in a variety of ecosystems. An
OCS analyzer capable of determining ambient OCS and CO2
concentrations at 10 Hz is commercially available (Kooijmans
et al., 2016; Commane et al., 2013; Stimler et al., 2010a), allowing
for eddy flux covariance measurements (Asaf et al., 2013; Billesbach
et al., 2014; Commane et al., 2015; Wehr et al., 2017). With this
powerful new tool, traditional methods of partitioning carbon fluxes
over ecosystems can be directly compared to using OCS data as a proxy
for GPP in situ. A few studies have made use of the gradient method
(Berresheim and Vulcan, 1992; Blonquist et al., 2011; Rastogi
et al., 2018).
Oceanic measurements
OCS measurements in the surface ocean comprise about 6000 ship-based
measurements. These samples are usually taken at a depth of
0–5 m below the ocean surface and analyzed by gas
chromatography with various detectors or off-axis integrated cavity
output spectrometry. Table 3 gives an overview on available
measurements. A central database for ship-based OCS measurements is
desired to derive global patterns and facilitate model comparison.
Measurements of the precursor gas CS2 are scarcer than OCS
measurements. Samples for CS2 are taken usually in
a similar way to OCS samples from the same depth range and analyzed
using gas chromatography and mass spectrometry detection.
Components of the OCS budget and data gaps.
Component
Notes
Critical data gaps
Vascular plant leaves
Vascular plant leaves have a well-established exchange of OCS that follows stomatal conductance. OCS is destroyed by both RuBisCO and CA in plant leaves, though it most often encounters CA first. The point of destruction is different for OCS and CO2, though the correlation between their uptakes is consistent under high-light conditions.
Nocturnal uptake and role of phyllosphere is not well characterized, and “mesophyll” conductance to COS is not well constrained.
Non-vascular plantsand lichen
Few studies have addressed non-vascular plants. Bryophytes and lichen have been found to take up OCS depending on their water content, sometimes regardless of light level.
Activities to support scaling up OCS fluxes for non-vascular plants are needed for the assessment of their importance to ecosystem fluxes.
Soil
Most soils are generally small sinks of OCS, making up less than 10 % of the total ecosystem flux. Non-desert soils exhibit large OCS emissions under hot and dry conditions. These OCS-emitting soils include both agricultural soils and some uncultivated soils.
It is unknown what controls the magnitude of the soil source term.
Terrestrial ecosystem
Ecosystem-scale flux measurements are available only from a handful of studies on a limited number of ecosystems and during relatively short periods of time.
No studies from the tropics and only one study in boreal forests have been published.
Regional terrestrial
The highly mechanistic leaf-enzyme kinetic approach to modeling plant–atmospheric OCS exchange yielded similar results to the mechanistically simple LRU approach when focusing on the peak of the North American growing season. However, laboratory studies demonstrate that LRU is not constant.
The minimum spatial and temporal scales at which the constant LRU approximation is viable are unknown. Uncertainties in non-plant OCS fluxes, particularly from soils, remain under-constrained at regional spatial scales.
Surface ocean
While the surface ocean is generally thought to be a source of OCS to the atmosphere, surface measurements of OCS are relatively sparse.
More continuous measurements covering full diurnal cycles are needed especially for the Pacific, Indian, Southern, and Arctic oceans.
Deep ocean
Concentration profiles have been reported from only very few stations in the Atlantic Ocean (e.g., Cutter et al., 2004; Flöck and Andreae, 1996; Von Hobe et al., 2001). Understanding deeper ocean OCS production could allow us to model OCS ocean surface fluxes more accurately.
More data are necessary to make clear predictions of the relationship between deep and surface ocean OCS fluxes.
Regional ocean
Surface measurements comprise different oceanic regimes including several meridional Atlantic transects and oligotrophic and upwelling regions.
Especially, data from the Arctic and Southern oceans are missing.
Freshwaters
There are few quite small datasets of OCS concentrations in lakes and rivers.
No OCS fluxes from freshwater bodies currently exist.
Global, modern
Global satellite products currently lack coverage over the land, and the locations of TCCON sites are purposely chosen to observe atmospheric background.
A new satellite and data product would be necessary to distinguish surface fluxes, e.g., anthropogenic and ocean OCS sources.
Global, paleo
Recent advances have allowed better interpretation of OCS in firn and ice air. There are still only a handful of cores that have been analyzed for OCS.
OCS observations from ice cores in the Northern Hemisphere are critical to GPP interpolar comparisons.
Firn and ice core records
Different hydrolysis rates apply for OCS trapped in bubbly ice vs.
clathrate (bubble-free) ice. Some ice core material is not suitable
for OCS analysis because the environment was too warm for long periods
and OCS was hydrolyzed at high rates for thousands of years. Aydin
et al. (2014, 2016) developed the necessary corrections to take into
account OCS hydrolysis within the ice core bubbles. Corrected data are
published for Taylor Dome, the West Antarctic Ice Sheet Divide, and
Siple Dome (Aydin et al., 2016). Firn data are available for more
recent time periods (Montzka et al., 2004; Sturges et al., 2001).