Clear analogies between carbonyl sulfide (OCS) and carbon dioxide (CO
The amplitude of variations in atmospheric OCS mixing ratios is mainly dictated by the vegetation sink over the Northern Hemisphere. This allows for bias recognition in the GPP representations of the three selected models. The main bias patterns are (i) the terrestrial GPP of ORCHIDEE at high northern latitudes is currently overestimated, (ii) the seasonal variations of the GPP are out of phase in the NCAR-CLM4 model, showing a maximum carbon uptake too early in spring in the northernmost ecosystems, (iii) the overall amplitude of the seasonal variations of GPP in NCAR-CLM4 is too small, and (iv) for the LPJ model, the GPP is slightly out of phase for the northernmost ecosystems and the respiration fluxes might be too large in summer in the Northern Hemisphere. These results rely on the robustness of the OCS modeling framework and, in particular, the choice of the LRU values (assumed constant in time) and the parameterization of soil OCS uptake with small seasonal variations. Refined optimization with regional-scale and seasonally varying coefficients might help to test some of these hypothesis.
The continental biosphere is an integral component of the climate system,
and of the carbon and water cycles: it has absorbed about a quarter of the
CO
The net ecosystem exchange (NEE) flux can be measured continuously with
the eddy-correlation technique at site level. However, GPP is not directly
measurable. Indirect approaches have been proposed to estimate the
biospheric gross fluxes (GPP and respiration), for instance, by using
differences between nighttime and daytime NEE measurements (Reichstein et
al., 2005; Lasslop et al., 2010) or combining different tracers including
stable isotopologues of CO
Carbonyl sulfide (OCS) is now measured at several atmospheric monitoring
stations, and its use as a tracer promises to bring new constraints on the
gross fluxes of CO
Atmospheric records of OCS mixing ratios exhibit clear seasonal variations.
Maximal and minimal values for OCS concentrations are observed in winter and
late summer, respectively, and the seasonal variations of OCS are highly
correlated with those of CO
Here, we use OCS to constrain the annual, seasonal and spatial variations of
GPP of three dynamic global vegetation models (DGVMs): Lund–Potsdam–Jena (LPJ; Sitch et al.,
2003), National Center for Atmospheric Research – Community Land Model 4 (NCAR-CLM4, hereafter referred to as CLM4CN; Thornton et al., 2007),
and Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE, hereafter referred to as ORC; Krinner et al., 2005). These
DGVMs exhibit contrasting global photosynthetic carbon fluxes (120, 130 and
160 Pg C yr
With the results of these simulations, we successively investigate the following questions:
How does our revised parameterization of surface fluxes (oceanic emissions, soil and leaf uptakes) compare with the temporal and spatial variations of atmospheric OCS? What is the sensitivity of the phase and amplitude of the simulated seasonal cycles and the sensitivity of the latitudinal gradient of OCS concentrations to changes in surface fluxes? Given the current uncertainties in the surface fluxes, how well would optimized fluxes compare with the observed time series of atmospheric OCS? Can we use the OCS atmospheric observations to benchmark the GPP simulated by current DGVMs, given the uncertainties in OCS surface processes?
In the first section, we describe our new set of tropospheric global sources
and sinks of OCS and discuss the spatial and temporal distribution of the
fluxes. In the second section, we investigate the resulting OCS atmospheric
concentration using a forward modeling approach. We then analyze the results
of the inverse approach in terms of model–data fit and impact on the fluxes.
We finally discuss the potential constraint from these results on the GPP of
each DGVM.
Atmospheric OCS and CO
Monthly mean direct oceanic emissions (first row, from the
standard run of Launois et al., 2014) for January (left column) and July
(right column), monthly mean uptake of OCS by soils (second row, using
H
Samples were analyzed using gas chromatography and mass spectrometry. OCS
data are available for the scientific community at
OCS is emitted from the oceans to the atmosphere either directly, because
surface waters are generally supersaturated in OCS, or indirectly through
oxidation of atmospheric dimethylsulfide (DMS) and carbon disulfide
(CS
Here, the direct emissions are based on parameterizations of ocean
production and removal processes of OCS implemented in the NEMO-PISCES
oceanic general circulation and biogeochemistry model (Launois et al.,
2015). These parameterizations lead to a direct ocean emission of 813 Gg S yr
OCS and CO
Since HCOOS
Different approaches can be used to model leaf uptake of OCS, from
process-based formulations with an explicit representation of diffusion and
hydration of OCS as in Berry et al. (2013) to more simple parameterizations
where the uptake of OCS is expressed as a linear function of GPP:
Summary of forward and inverse simulations performed using the LMDZ
transport model and specific setups of surface fluxes. We compared three
dynamic global vegetation models (DGVMs), carried out a series of sensitivity
tests and optimized major fluxes (the allowed range of variations is
expressed in percent). ORC stands for ORCHIDEE. CLM4CN stands for NCAR-CLM4.
Overview of global budgets of carbonyl sulfide. Units are Gg S yr
OCS surface fluxes before and after their optimization. Units are
Gg S yr
* Surface fluxes which, after optimization, reached the set upper or
lower limits of variation. Computed plant and soil uptakes after optimization are on average 714 and
396 Gg S yr
The general picture is that oxic soils are a sink of OCS while anoxic soils are a source (Whelan et al., 2013).
OCS uptake by oxic soils is believed to be essentially a microbial and
enzymatically driven process with carbonic anhydrase and OCS hydrolases
playing central roles (Chin and Davis, 1993a, b; Seibt et al., 2006;
Wingate et al., 2008; Ogawa et al., 2013). There are also clear indications
that OCS soil uptake varies according to soil type, temperature and soil
water content (Kesselmeier et al., 1999; Van Diest and Kesselmeier, 2008).
Previous studies used different approaches based either on temperature and
water content (Kettle et al., 2002) or on soil heterotrophic respiration
(which tracks microbial activity) and the fraction of water filled pore
space (Berry et al., 2013). Here we propose a new approach, based on observed
co-variations of OCS and dihydrogen (H
The role of soils in the OCS budget was recently reviewed by Whelan et al. (2013), with special attention to anoxic soils. The authors underlined the
major influence of soil temperature and flooding on OCS emissions from
anoxic soils and wetlands. Therefore, we followed their approach but used a
model simulation for the spatial and temporal distributions of anoxic soils
(from Wania et al., 2010). More details on this OCS source can be found in
Appendix A4. For optimization purposes, we defined a scaling parameter,
Other sources are related to biomass burning and anthropogenic emissions.
OCS emissions from biomass burning were simulated from the gridded CO
The removal of atmospheric OCS by OH radicals is also a significant sink of
OCS. We used monthly maps of OH radicals concentration (integrated
vertically up to the tropopause) from Hauglustaine et al. (1998), to
distribute both horizontally and temporally a total annual atmospheric sink
of 100 Gg S yr
For the purpose of this study, three independent DGVMs have been used: LPJ, ORCHIDEE (referred as ORC) and CLM4CN.
We used the simulated GPP from each model that was performed for the TRENDY
inter-comparison experiment (
The simulated mixing ratios were obtained using the global atmospheric
circulation model (GCM) of the Laboratoire de Météorologie Dynamique
(LMDZ, version 3; Hourdin et al., 2006). The OCS surface fluxes described
above are transported in offline mode using the LMDZ transport model,
nudged with wind from the European Centre for Medium-Range Weather Forecasts
(ECMWF) reanalysis. The transport model uses a 3.75
An optimization algorithm was used to correct the surface OCS fluxes in
order to improve the simulation of atmospheric OCS temporal and spatial
gradients. The optimization scheme relies on a Bayesian framework that
accounts for prior knowledge of the surface fluxes (Tarantola, 1987). Each
flux has been assigned a scalar coefficient
The optimization is based on a 5-year-long simulation covering the 2004–2009 period, long enough to characterize broad atmospheric OCS concentration features (trends and mean seasonal cycles). OCS monthly mean concentrations are used as the observational constraint in the optimization.
Assuming a Gaussian probability density function (PDF) distribution for the
measurement errors, model structure errors (including flux and transport
models) and model parameter errors (flux scalars), the optimal set of
parameters under the Bayesian framework corresponds to the minimum of the
following cost function
Details about the optimization scheme (gradient-based algorithm with imposed range of variation) as well as the setup of the inversions (uncertainties on the observations and parameters) are presented in Appendix A6. We performed standard optimizations with the OCS leaf uptake derived from each DGVMs and a large range of variation for the scaling parameters but also a few additional sensitivity optimizations summarized in Sect. 2.5.
A series of simulations was performed, for which the setups are summarized
in Table 1. We carried out three major runs using the three different DGVMs
(STD_ORC, STD_LPJ, STD_CLM4CN, see Table 1 for details). We made four
sensitivity experiments to the representation of soil OCS uptake (with ORC
for plant uptake) varying the H
An additional series of simulations was performed to calculate CO
Optimization experiments of the surface fluxes (optimization of a scaling
coefficient for each OCS flux component; see Sect. 2.4) were conducted,
based on the three different vegetation models. For each model, we tested
five scenarios (see Table 1):
OPTIM_H-Er: marine, soil and vegetation fluxes are
allowed to vary over a large range (up to 50 %) OPTIM_L-Er: marine, soil and vegetation fluxes are
allowed to vary over a narrow range ( OPTIM_Leaf_ONLY: only leaf fluxes are
optimized with a large range of variation, OPTIM_Soil_ONLY: only soil fluxes are
optimized with a large range of variation, OPTIM_Ocean_ONLY: only ocean fluxes are
optimized with a large range of variation.
All other fluxes (OCS oxidation by OH radicals,
emissions from anoxic soils and wetlands, direct and indirect anthropogenic
emissions, and emissions from biomass burning) were kept unchanged.
Observed and simulated monthly OCS and CO
The meaning of the squared data bias is obvious. The second term indicates
differences in the fast variability: the lack of correlation (
Figure 1 presents the monthly mean emissions and uptakes of OCS by the oceans and the terrestrial biosphere (soils and vegetation) for the months of January and July, as calculated from the new parameterizations presented above. Table 2 describes the corresponding annual fluxes, spatially averaged over oceans and continents.
Following the standard run defined by Launois et al. (2015), oceans emit a yearly total of 813 Gg S (Table 2). The spatial distribution indicates a large tropical ocean source (45 % of total OCS emissions). Overall, our simulation provides direct oceanic emissions that are about 20 times larger than those from Kettle et al. (2002) and that are roughly comparable to the estimates from Berry et al. (2013), obtained using an optimization procedure (Table 3). Details about the regional and seasonal distribution of these emissions can be found in Appendix A7.
On a yearly and global basis, the oceans are also a net source of DMS and
CS
As described in Sect. 2.2, the standard run for oxic soil uptake of OCS is
obtained using the H
The emissions from anoxic soils, as described in Sect. 2.2, mainly take
place in the northernmost regions (above 60
Overall, at a global scale, soils constitute a net sink of OCS. In the Northern Hemisphere, our estimated sink is lower than that of Kettle et al. (2002) and that of Berry et al. (2013) where the OCS emissions by anoxic soils were not taken into consideration. Details of the regional and spatial variations of the net OCS soil fluxes can be found in Appendix A7.
Global maps of OCS mean uptake by plants for the months of January and July
constructed from the GPP of the ORC model are shown in Fig. 1 (bottom).
Using ORC, plants take up 1335 Gg S yr
An OCS sink of about 100 Gg S yr
The direct and indirect anthropogenic fluxes were taken from Kettle et al. (2002), who estimated that 180 Gg S are emitted on an annual basis, without strong seasonal variations. Eastern Asia, eastern Europe and the eastern parts of Canada and the United States concentrate most of the emissions.
As described in Sect. 2.2, the OCS emissions from biomass burning are
proportional to the emissions of CO
Table 2 provides an overview of the global sources and sinks of OCS. Only
Kettle et al. (2002) and Berry et al. (2013) have provided balanced budgets
between sources and sinks, but it is worth remembering that Berry et al. (2013) artificially increased the marine emissions of Kettle et al. (2002)
by 600 Gg S yr
We transported the simulated OCS surface fluxes with LMDZ using a forward approach: the resulting global monthly 3-D fields of atmospheric OCS mixing ratios have been compared with in situ observations from the NOAA atmospheric network. Special attention was paid to the annual, seasonal and latitudinal variations of this gas.
Annual variations of OCS monthly mean mixing ratios (in ppt), simulated and monitored at Mauna Loa. Simulations with the LMDZ model use the STD_ORC, STD_LPJ and STD_CLM4CN configurations described in Table 1. Data derived solely from the Kettle et al. (2002) surface fluxes are shown by the black solid line. Observations (red crosses) are from the NOAA/ESRL global monitoring network (Montzka et al., 2007).
Figure 2 compares the simulated monthly mean atmospheric OCS concentrations
with the observations at Mauna Loa (MLO), a mid-latitudinal background
station in the middle of the tropical Pacific Ocean (20
Sensitivity tests performed using the TEST_Ocean_
Results of sensitivity tests on the ocean OCS source (see Sect. 2.5) are
displayed in Fig. 3. Changing the ocean source
significantly (by
Sensitivity tests performed using the TEST_Soil_MORF_0.5:1, TEST_Soil_MORF_1:1, TEST_Soil_BOUSQ_0.5:1 and TEST_Soil_BOUSQ_1:1 setups of surface fluxes (Table 1) to simulate annual variations of OCS monthly mean mixing ratios (left panel) and smoothed seasonal variations obtained after removing the annual trends (right panel), at Mauna Loa. The simulations based solely on the Kettle et al. (2002) surface fluxes are shown by the black solid line. Observations (red crosses) are from the NOAA/ESRL global monitoring network (Montzka et al., 2007).
Figure 4 (left panel) shows the impact on the annual trend at MLO of four
sensitivity tests on the calculation of oxic soils OCS uptake (see Sect. 2.5).
The annual trend is more affected by changes in
Smoothed seasonal cycles of OCS (left column) and CO
Figure 5 (right panels) compares the smooth seasonal cycle of OCS
concentrations of three different simulations, the concentrations deduced
from Kettle inventory and the observations at the South Pole (SPO),
Alert (ALT) and MLO stations. The ALT data help in exploring the influence
of boreal and temperate ecosystems of the Northern Hemisphere on the
biogeochemical cycle of OCS, while SPO station combines the southern ocean
and land influences. The simulation based on Kettle et al. (2002) fluxes
exhibits amplitudes which are unrealistically low and not in phase with the
observations at ALT and MLO. At SPO, on the contrary, Kettle et al. (2002)
fluxes produce a good fit to the observations, both in terms of seasonal
amplitude and phase. At SPO, our three models simulate slightly larger
amplitudes (
The ORC model displays the highest seasonal amplitudes both at ALT and MLO where the northern vegetation influence dominates seasonal variations (around 250 and 80 ppt, respectively). These variations are unrealistically high compared to the observations (100 and 55 ppt, respectively). However, ORC shows seasonal OCS variations more in phase with the observations than when using the two other DGVMs' GPP, especially at ALT. Using CLM4CN leads to the right amplitude of the seasonal variations in OCS concentrations at ALT, but the phase is incorrectly represented (earlier OCS build-up and draw-down). This model provides a better representation of the phase of the OCS cycle at MLO but leads to a 10 % underestimation of the OCS seasonal amplitude at this station. With the data shown in the right panels of Figs. 3 and 4, we aim to characterize the sensitivity of seasonal variations to changes solely in marine emissions and in the soil sink, respectively (following sensitivity experiments described in Sect. 2.5). At MLO, both the phase and the amplitude of the seasonal variations are unaffected by changes in marine emissions (Fig. 3) or oxic soils uptake (Fig. 4). At SPO, a 30 % increase of the ocean flux leads to about 10 % increase in amplitude of the seasonal variations (Fig. 3). Similar experiments were conducted to evaluate the contribution of plant uptake on the overall seasonality of atmospheric OCS (runs where only the plant uptake is transported). Figure 6 shows that the amplitude and the phase of the seasonal variations at ALT and MLO are both primarily determined by the uptake of OCS by vegetation. On the other hand, plant uptake plays a minor role on the seasonality at SPO.
Average smoothed seasonal cycles of OCS (left column) and CO
Annual mean mixing ratios for the 10 stations of the NOAA monitoring network, plotted as a function of latitude, are shown in Fig. 7. Note that the simulated global mean OCS concentration (across all sites) has been rescaled to the observed global mean, so that only the gradients between stations should be investigated. The main results from this hypothesis are
Differences in OCS annual mean mixing ratios between 10 stations
of the NOAA monitoring network, plotted as a function of latitude, for
observations (red crosses surmounted by station acronyms) and simulations
(no symbol). Note that the global mean for each mixing ratio series has been
set to the global mean of the observations. Simulations obtained with the
LMDZ model using the STD_ORC, STD_CLM4CN and STD_LPJ setups (Table 1). Data derived
solely from the Kettle et al. (2002) surface fluxes are shown by the black solid
line. The sensitivity of latitudinal gradients to changes in soil uptake and
ocean emissions (dashed colored lines) was investigated using the
TEST_Soil_MORF_1:1
Our new OCS surface flux scenarios capture the main differences in annual mean
concentration between stations with lower concentrations at continental stations in the
Northern Hemisphere (LEF, BRW, ALT) than at background stations as in the Southern Hemisphere (around 50 ppt
lower). Observed differences between southern extratropical marine stations and tropical
marine stations (higher concentration over the tropics by 10 ppt) are also represented
by the different scenarios. Significant discrepancies still affect all scenarios, such as for instance, the difference
between NWR and LEF, with simulated values around 25 to 30 ppt compared to observed ones around
60 ppt. There are small but significant differences between the three scenarios based on three
different ecosystem models. For instance, between Cape Grim (CGO) and American Samoa (SMO),
although all models largely overestimate the mean concentration gradient, using CLM4CN reduces
it by nearly 20 ppt compared to ORC. Similarly, CLM4CN gives a lower annual mean concentration at Point Barrow
(BRW) than at Alert (ALT) while the two others models give higher concentrations at BRW, in line with the
observations. The simulation based on the Kettle et al. (2002) fluxes shows much smaller annual mean
gradients across stations than our three scenarios. The better match between the observed gradients
and our new flux scenarios partly arises due to the re-estimated high oceanic emissions in the tropical regions.
The two sensitivity tests, where ocean emission and soil uptake were
increased by 30 % (TEST_OCEAN_
Each surface flux component has been scaled with an optimization procedure (see Sects. 2.4 and 2.5) in order to obtain the best fit to the atmospheric OCS concentrations (raw data). We investigate whether the observed temporal and spatial OCS variations can be matched through the optimization and highlight corrections on the GPP and other fluxes that would be needed. Table 3 summarizes the initial and the optimized values of the surface fluxes for the different optimization configurations.
Annual variations of OCS monthly mean mixing ratios (in ppt), optimized and monitored at Mauna Loa. Simulations obtained with the LMDZ model using the OPTIM_H-Er setup (Table 1) applied to ORC, NCAR-CLM4 and LPJ models. Observations (red crosses) are from the NOAA/ESRL global monitoring network (Montzka et al., 2007). A sensitivity test was carried out using ORC and the OPTIM_L-Er setup (dashed blue line).
The OCS monthly mean concentrations simulated with the optimized surface
fluxes of the OPTIM_H-Er scenario (large range of
variation for the optimized parameters) are shown in Fig. 8. Allowing a
50 % range of variation on the surface fluxes is sufficient to yield
equilibrated global budgets after optimization, in agreement with the
observations (see the last line of Table 3). Overall, the total sink is
decreased in all optimization results, from an average value (all three
DGVMs included) of 1721 Gg S yr
The soil uptake of OCS is reduced as much as 45 % even if the maximum
range of variation allowed is 50 %. The leaf uptake of OCS is also not
reduced by the maximum amount when given a variation limit of 50 %. On
average, vegetation and soil optimized uptakes are respectively converging
around 714 and 396 Gg S yr
In the low variation range scenario (OPTIM_L-Er), a
negative trend of about 30 ppt yr
the vegetation plays a determining role in the OCS global atmospheric budget the leaf uptake of OCS is too large when using ORC, highlighting a too large global annual GPP flux.
In reality, the LRU values (taken from Seibt et al., 2006) are likely to be
too large so that the conclusion on the GPP of ORC cannot be inferred yet
with the OCS budget only.
Smoothed seasonal cycles of OCS monthly mean mixing ratios, simulated at 10 stations of the NOAA monitoring network, and obtained after removing the annual trends. Forward simulations with the LMDZ model use surface fluxes from the STD_ORC, STD_CLM4CN and STD_LPJ setups (dashed lines). The OPTIM_H-Er setup (Table 1) was used in the optimizations (solid lines). Observations (red crosses) are from the NOAA/ESRL global monitoring network (Montzka et al., 2007). Global root mean square errors (RMSEs) are given in the legend.
Figure 9 shows the OCS mean seasonal cycles before and after optimization
using the three DGVMs. At most NOAA stations, the optimization of the
surface fluxes significantly improves the simulated seasonal amplitude of
the atmospheric OCS concentrations, with a global reduction of the MSE (in
ppt
With the ORC model, the standard OPTIM_H-Er configuration strongly reduces the
amplitude of the simulated OCS seasonal cycle, especially at high latitudes (e.g.,
from 225 to 140 ppt at ALT). The resulting amplitudes are more consistent with the observations
but are still too large at high latitudes (140 versus 100 ppt at ALT). At MLO, the amplitude of
the OCS levels is reduced from 80 to 45 ppt, a value slightly lower than the observations (50 ppt).
Finally, we also note that scaling the surface fluxes through the optimization leads to
negligible modifications of the phase of the simulated OCS concentrations. With LPJ, the optimization also leads to a reduction of the sources and sinks (Table 3), which
decreases the amplitude of the OCS seasonal cycle. Among the three DGVMs, LPJ displays the best fit
of the amplitude of the OCS annual cycle at temperate latitudes with the observations. However, the
optimization does not improve the phase of the atmospheric OCS signal with, therefore, the same
2-month-early shift of the model at northern stations (ALT and BRW) as for ORC. With CLM4CN, the optimization does not significantly improve the mean seasonal cycle, with too small prior and posterior amplitudes at high northern sites compared to the observations
(60 and 75 % of the observed cycle amplitude at ALT and MLO, respectively). The phase is
also not changed and most discrepancies noted in Sect. 3.2.2 remain (e.g., 2-month phase advance at BRW). The total sink is always reduced, mainly through a decrease of soil uptake and plant uptake. The large direct emissions of OCS by the tropical oceans are decreased by only 15 %, with
global annual mean around 691 Gg S yr New vegetation and soil uptakes, respectively, around 714 and 396 Gg S yr
Overall the three final sets of optimized fluxes (Table 3) confirm that
Differences in OCS annual mean mixing ratios between 10 stations of the NOAA monitoring network, plotted as a function of latitude, for observations (red crosses surmounted by station acronyms) and simulations (no symbol, forward approach (colored dashed lines), inverse approach (colored solid lines)). Forward simulations with the LMDZ model use the STD_ORC, STD_CLM4CN and STD_LPJ setups (dashed lines). The OPTIM_ H-Er setup (Table 1) was used in the optimizations (solid lines). A sensitivity test was carried out using ORC and the OPTIM_ L-Er setup (blue dotted line). Note that the global mean for each simulation ensemble has been set to the global mean of the observations.
Figure 10 presents annual mean OCS mixing ratios at all stations as a function of the latitude before and after optimization. The overall improvements from
the optimization are summarized with the mean of the MSE for all sites (see
the legend). Posterior MSEs are similar between the three scenarios (around
24 ppt
Although our revised OCS budgets agree relatively well with the observed
temporal and spatial gradients recorded at NOAA stations (using the LMDZ
transport model), other biases still exist. These biases will be first
discussed to highlight potential errors in the OCS leaf, soil and ocean
surface fluxes. In a second step, we will review and discuss the constraint
brought by OCS on the GPP of the three tested DGVMs, when the information
from both OCS and CO
The standard optimizations (OPTIM_H-Er) using the three
DGVMs provide an equilibrated atmospheric budget, with fluxes for the three
most important OCS surface processes converging to similar values (Table 3)
across all simulations: leaf and soil mean annual uptake are 714 and 396 Gg S yr
Upper row: differences in annual trends (in ppt yr
Below, we summarize the performances of different optimization scenarios (based on the three DGVMs) and highlight the remaining discrepancies in terms of simulated trend, amplitude and phase of the seasonal cycle. Figure 11 displays the observed minus modeled trend at MLO (first row), the mean square error (MSE) decomposition (phase, bias and variance; see Sect. 2.5, Eq. 3) obtained from the detrended concentrations at ALT and MLO (second and third row) and the amplitude of the seasonal cycle at ALT and MLO (last two rows). The results from several optimization scenarios (based on the three DGVMs) are displayed including prior fluxes (Pri), optimized fluxes with high and low uncertainties (OPTIM_H-Er and OPTIM_L-Er), and three tests where only the leaf, soil or ocean component are optimized (the other components being fixed).
As shown in Fig. 11, the optimization successfully corrects the annual
trends, for most scenarios. For ORC, the global budget is closed only if
the leaf uptake is decreased by 45 %, which is not possible in the low
error test. This suggests that LRU values provided by Seibt et al. (2010) are
likely too large. Future studies would benefit from using lower LRU values, such
as those published in other studies (Sandoval-Soto et al., 2005; Stimler et
al., 2012; Berkelhammer et al., 2014). Lower LRU values also correspond to a
test in Seibt et al. (2010) where the internal mesophyll conductance is set
as the major limitation in the diffusional pathway of OCS (average LRU would
be 2.08 with this assumption, instead of 2.8 as used in the present paper).
Moreover, several studies have shown that OCS-to-CO
Optimizing only one flux component is usually enough to correct the trend, except for ORC, pointing out again the likely too high leaf uptake, which can be due to overestimated LRU or too large
GPP
Looking at the phase component of the MSE decomposition, a few general features can be drawn:
On average, only small changes are observed at most sites between prior and posterior estimates (only shown for MLO
and ALT, Fig. 11, second and third rows). A 35 % reduction of the phase error is observed at MLO for ORC and also a 25 % improvement for LPJ. These small phase changes result from the optimization of only one global annual scalar for each flux component. On average, ORC provides, after optimization, the best phase agreement with the observations at high northern latitude
stations (see ALT). At MLO the optimized results are closer between the three models, although LPJ and CLM4CN provide slightly
better matches with the observations (72 and 70 ppt Most changes are due to the optimization of the leaf uptake, while OPTIM_SOIL_ONLY and OPTIM_OCEAN_ONLY
configurations do not allow for significant phase improvement. Further improvement of the phase should account for potential variations of LRU throughout the season, or possible important
soil deposition velocity changes throughout the season, as mentioned in the recent paper by Maseyk et al. (2014). This could be achieved with an optimization of the monthly flux of each component.
The analysis of the amplitude of the simulated seasonal cycle corresponds
to the last two rows of Fig. 11 and also partly to the variance term of the
MSE decomposition. The main features are
The improvement compared with the prior main results from the optimization of the OCS leaf uptake. Smaller or negligible changes are observed at ALT and MLO stations when only the ocean fluxes are optimized
(OPTIM_OCEAN_ONLY configuration), but significant improvements can be seen at southernmost stations (10 % variance error correction, not shown). When only the soil uptake is optimized (OPTIM_SOIL_ONLY configuration), no improvement on the simulated
amplitude is obtained. The amplitude is too large in the prior for ORC at both ALT and MLO and remains too large at ALT after optimization,
suggesting again that either LRU values or GPP fluxes are too large for high-latitude ecosystems. LPJ provides the best
compromise in terms of amplitude when we consider all stations. However, the optimization of only one global coefficient for each
flux does not allow for corrections of local flux biases, which leads to over- and underestimated amplitudes at different sites for both LPJ and ORC. For CLM4CN, the simulated amplitude is too small at most stations, and cannot be corrected through the optimization
of a global scaling factor because of the initial phase mismatch. Finally, one should note that the amplitude of the atmospheric signal also depends on the transport model and potential
vertical mixing errors. The version of the LMDZ model used here is believed to have too large mixing in the planetary boundary layer
(PBL) (Patra et al., 2011; Locatelli et al., 2013), which would thus dampened the amplitude of the seasonal cycle.
We use the bias estimates from the MSE decomposition, which also accounts for any remaining trend mismatch, to analyze the annual mean gradients. As demonstrated by the results of the optimization of only one component (OPTIM_XXX_ONLY tests), all processes make a similar impact on the annual mean OCS concentrations. However, the optimization scheme leads to a degradation of the bias at MLO for the three models and the bias remains highly variable at other sites. The constraint imposed by the annual mean gradients cannot be significantly improved through optimization. The overall fit at some stations can be decreased (see for instance CLM4CN at MLO, Fig. 11) because of compensation by improvements at other sites. When testing the impact of the observation errors on the optimization, including a test with equal observation errors (18 ppt), the results were not substantially modified.
We now analyze and discuss potential constraints on the GPP of each
ecosystem model that could be derived from the results of the OCS
simulations (direct and inverse) and of additional CO
Scatter plots of normalized amplitudes of smoothed seasonal
cycles of OCS versus those of CO
We now discuss the implications of the simulated OCS and CO
The analysis of the concentrations at boreal stations provides a first hint
on northern high-latitude ecosystems. Both OCS and CO
The signal at MLO integrates the contribution from the land (and ocean)
fluxes of the whole Northern Hemisphere. In this case, there is relatively
good agreement for the phase and amplitude of the CO
The seasonal cycle at the remote SPO station is more difficult to interpret
as (i) the amplitude of the cycle is 8 times smaller than at MLO for CO
Overall, the joint OCS/CO
The amplitude of the seasonal cycle simulated with CLM4CN is underestimated
at nearly all stations for both OCS and CO
The combined OCS and CO
Increasing CLM4CN GPP by 20 % and shifting its seasonal course by 2 months for high latitudes would create a significant improvement of both
OCS and CO
Using the GPP from LPJ leads to intermediary results for the seasonal
amplitude, for both tracers, with no systematic biases across stations (Fig. 12). If we consider boreal stations, the modeled seasonal amplitude for
CO
At MLO, the phase shift becomes much lower for OCS. The too low amplitude
for CO
Overall, the above joint OCS and CO
the spatial and temporal variations of the OCS-to-CO the seasonality of soil OCS uptake. Our modeling strategy, based on similarities between H the impact of potential atmospheric transport errors. Indeed the mixing within the atmospheric transport
model is still subject to significant uncertainties, which in turn may impact the conclusions that are
directly linked to the amplitude of the seasonal cycle. Nonetheless, the LMDZ model has been used in many tracer transport studies with no strong known biases (Peylin et al., 2014).
Several studies have proposed a relationship between GPP and a concomitant
OCS uptake by the vegetation, which would partly explain the atmospheric OCS
concentration variations, yet the observed atmospheric measurements of OCS
concentrations have never been used in a quantitative way to obtain
information about the GPP of current global vegetation models. In this
context, this study proposed a new set of global sources and sinks of OCS,
using the GPP from three different global vegetation models to compute the
leaf uptake of OCS. We further used the LMDZ atmospheric transport model to
compute the temporal and spatial gradients of OCS concentration (as well as
of CO
We proposed a global OCS budget fully based on parameterized processes that include large emissions by the ocean and important uptake by soil and vegetation. After the optimization of all flux components (within given ranges), we obtained a new flux scenario that (i) matches the observed OCS trend in the atmosphere (close to zero) and (ii) provides good agreement with the atmospheric concentrations (in terms of amplitude and phase of the seasonal cycle and annual mean gradients). Our modeling framework suggests that the GPP-related uptake of OCS mainly controls the seasonal cycle of atmospheric OCS concentrations, with much smaller influence from ocean and soil fluxes.
More importantly, combining the information from OCS and CO
For the first time, our study quantifies the potential of OCS measurements to benchmark gross carbon fluxes from current DGVMs. It also highlights the need to better characterize the different processes that control the surface OCS fluxes and in particular the seasonality of soil uptake. From such a preliminary study, we foresee additional and complementary experiments that would
improve the inversion framework in order to optimize the temporal pattern of each flux component, using for
instance a monthly time step optimization. This would provide further information on the potential biases associated
to the seasonal variations of the GPP of each model. combine the different models for the GPP-related uptake of OCS within a single inversion framework, where we
would optimize a unique set of LRU coefficients (for each plant functional
type (PFT)) together with the GPP fluxes of all DGVMs simultaneously. optimize multi-data streams, based on both atmospheric OCS and CO
The main production pathway of OCS is photochemical, hence light dependent
and favored by UV-absorbing chromophoric dissolved organic matter (CDOM).
The second pathway, the so-called “dark production”, is temperature and
organic-matter dependent. The two removal processes are hydrolysis
(pH dependent) and ventilation (dependent on temperature and wind speed). In
the standard run defined by Launois et al. (2015), the direct emissions of
OCS were equal to 813 Gg S yr
As suggested by Barnes et al. (1997), OCS accounts for 0.7 % of the oxidation products of DMS. Since DMS exhibits a short residence time (Koch et al., 1999; Chin et al., 2000; Kloster et al., 2006), here we assumed that 0.7 % of the marine emissions of DMS were instantaneously converted into OCS. For that, we used a new version of the prognostic module developed by Belviso et al. (2012) to compute seawater DMS concentrations and DMS air–sea fluxes. This module, embedded within NEMO-PISCES similar to that of OCS, improves the representation of DMS dynamics in subtropical waters (Masotti et al., 2015).
CS
Atmospheric OCS follows the same path as CO
Here, we used the results of the study from Seibt et al. (2010), who
estimated a global average value for
A map combining Köppen–Geiger climate zones with phenology-type from
satellite land-cover data provided by the MODIS instrument was used to
determine the major plant functional type for each region (Poulter et al.,
2011; Kottek et al., 2006). Each species was assigned to a plant functional
type (PFT) on the previously described map and then assigned the
corresponding
The approach relies on atmospheric observations that suggest that OCS uptake
by oxic soils is proportional to H
Two different approaches for estimating
OCS emissions by anoxic soils are largely based on the recent inventory by
Whelan et al. (2013). Anoxic soil types were mapped accordingly to the
representation used in the work by Wania et al. (2010) to represent seasonal
methane emissions, as simulated using the LPJ–WHyME model. This way, anoxic
soils activity were located via methane emissions and translated into hotspots of OCS emissions from anoxic soils, with similar temporal and spatial
patterns. Each anoxic soil grid cell was associated the mean value for the
anoxic soil OCS emission found in Whelan et al. (2013). However, because of
the large uncertainties associated with the OCS flux inventories (see Fig. 3
in Whelan et al., 2013, “soil only” case), we finally assigned zero
emission of OCS to rice paddies and 25 pmol m
We used the GPP simulated by three different models (ORCHIDEE, LPJ, CLM4CN)
for a specific inter-comparison exercise, TRENDY, in order to derive the
leaf uptake of OCS. We use monthly mean outputs from the so-called “S2”
simulations, which indicates that the DGVMs were run with the same
meteorological forcings (CRU-NCEP data set, see Ahlström et al., 2015) and
changes in the atmospheric CO
The DGVM runs have been executed with a constant land use mask and
disturbance turned off, which is supposed to represent the impact of climate
and CO
The optimization relies on the use of the LMDZ transport model that relates the surface fluxes to be optimized to the observed atmospheric concentrations. We used pre-calculated transport fields as in Peylin et al. (2005), where the outputs from the LMDZ transport model were only saved on a monthly time step. For each monthly mean observation, we selected the closest monthly mean simulated concentration to compare with. The optimized fluxes correspond to all sources and sinks of Table 2, to which the scaling coefficients are applied for each corresponding flux component.
For each parameter (scaling coefficient of a flux), we assigned a possible
range of variation as well as a prior error (1
These relatively large errors, combined with the range of variations defined
for each flux component (Sect. 2.2), account for current uncertainties on
the OCS processes that control the different sources and sinks. We also
performed sensitivity tests for the optimization (see Table 3), using a
limited 10 % error and restricted ranges of variation for all scaling
factors (
The different observations are assigned different weights in the
optimization algorithm, represented as observation monthly errors. The
choice of this so-called “observation error” is however difficult. It
should gather the measurement error as well as the model error including the
flux model error, the transport model error and the representation error
(scale mismatch between the observed concentration at a given location and
the model concentration at coarse scale). Usually the measurement error is
relatively small compared to the modeling error. A proper assessment of
model error could be done with the use of different models with different
parameterizations. However, for transport modeling studies this is usually
not feasible and simpler approaches are used. As a first approximation, we
used the RMSE of the prior model–observation concentration differences at
each station. We choose this simple approach and further averaged the RMSE
by latitudinal bands to avoid the complexity of longitudinal differences in
model skills. In this case, high-latitude stations such as ALT were
displaying large prior MSE (nearly 2000 ppt
The first term of
Given that we optimize scalars of the OCS surface fluxes and that the OCS
destruction by OH in the atmosphere is fixed (i.e., prescribed and
independent of the atmospheric OCS concentrations), the optimization problem
is linear (i.e., the atmospheric concentrations linearly depend on the
surface fluxes and their scaling factors).
Note that in order to account for bounds on each flux parameter (to limit the optimal value in the prescribed range of variation), we iterated the scheme seven times. At each of the iterations, the optimized value for each parameter may be outside its range of variation. In this case, we fixed the parameter value (flux scalar) to its boundary and re-optimized excluding the parameter from the optimization. We then repeated the process until all parameters were fixed or within their range of variations. Note finally that assuming Gaussian errors allows us to estimate the posterior error covariance matrix on the parameter from a matrix formulation (see Tarantola, 1987) and thus to compute error correlations.
Photoproduction and hydrolysis are the main drivers of the mid- and high-latitude flux seasonality of both hemispheres: oceans take up OCS from the
atmosphere in winter (Fig. 1, top), whereas summer fluxes are largely
positive, between 3 and 10 pmol m
The tropical regions (30
Global maps of OCS emissions from DMS atmospheric oxidation for the months of January and July are provided in the Supplement (Fig. S1). Most of the OCS indirect emissions occur at high latitudes in the Southern Hemisphere, regions where the amplitude of the seasonal cycle is also the most important, with seasonal emissions varying between 4 and 7 Gg S per month (Fig. S1).
The OCS emissions based on the CS
The sensitivity of monthly soil OCS uptake rates to the different
parametrizations (H
Whatever the magnitude of the ratio between deposition velocities, the
seasonal variations are more important in the extratropical areas of the
Northern Hemisphere than elsewhere, and they differ between models of
H
At the global scale, the monthly fluxes of OCS vary between 0 and
Using the TEST_SOIL_BOUSQ_1:1 configuration, the simulated fluxes vary between
Because OCS uptake by plants is represented in our models as a linear function of GPP (Eq. 2), the phase and amplitude of the seasonal variations in OCS plant uptake and GPP have the same patterns.
The ORC model displays stronger OCS uptake than the other models, throughout
the year and especially during summer months (Fig. S3) due to its larger
GPP. In ORC, the extratropical regions of the Northern Hemisphere are
responsible for this summer uptake and account for about a third of the
total plant uptake. The uptake of OCS in tropical regions is roughly
constant and accounts for 45 % of the total uptake. The remaining 20 %
is contributed by the extratropical regions of the Southern Hemisphere
where the intensity of the summer maximum (about 35 Gg S month
Special thanks are due to the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Division (GMD) in Boulder, CO, for providing the observational data used in this study. We are equally grateful to Elliott Campbell for sharing the flux estimates from Kettle et al. (2002) and to Samuel Levis for letting us use their global dynamic vegetation model NCAR-CLM4 to compare with the two other models. The salary of Thomas Launois was partly supported by the European Research Council project SOLCA (grant agreement no. 338264).Edited by: S. Kloster