Growing season eddy covariance measurements of carbonyl sulfide and CO2 fluxes: COS and CO2 relationships in Southern Great Plains winter wheat

Agricultural and Forest Meteorology 184 (2014) 48–55

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Growing season eddy covariance measurements of carbonyl sulfide and CO2 fluxes: COS and CO2 relationships in Southern Great Plains winter wheat D.P. Billesbach a,∗ , J.A. Berry b , U. Seibt c , K. Maseyk d , M.S. Torn e , M.L. Fischer f , Mohammad Abu-Naser g , J.E. Campbell g a

Department of Biological Systems Engineering, University of Nebraska, Lincoln, NE, United States Department of Global Ecology, Carnegie Institution for Science, Stanford University, Stanford, CA, United States c Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, United States d Bioemco, Universite Pierre et Marie Curie Paris 6, Paris, France e Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States f Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States g Sierra Nevada Research Institute, University of California, Merced, CA, United States b

a r t i c l e

i n f o

Article history: Received 10 December 2012 Received in revised form 28 April 2013 Accepted 10 June 2013 Keywords: Carbonyl sulfide COS GPP Eddy covariance Trace gas flux

a b s t r a c t To test the capabilities of newly available instrumentation and to explore the dynamics of carbonyl sulfide (COS) as a proxy for the measurement of canopy-scale gross primary production (GPP), we conducted an experiment to measure the simultaneous net transfer of COS and CO2 between the atmosphere and a growing wheat canopy, senesced wheat, and the harvested field (located in the Southern Great Plains of the U.S.) using the eddy covariance technique. We found that during the growing season, there was a strong uptake of COS by the canopy (roughly between −10 and −40 pmol m−2 s−1 ) with a strong diel signal that mirrored net CO2 fluxes. After senescence and over the harvested field, we observed a strong source of COS to the atmosphere (up to +40 pmol m−2 s−1 ) that exhibited a weaker diel pattern, again similar to CO2 . These results suggest that the dynamics of COS are more complicated than once thought, but that it may still be possible to independently derive canopy-scale GPP from direct COS measurements and to use them as model constraints on the atmospheric carbon cycle. To demonstrate this, we computed an average value of leaf relative uptake (LRU) (the scaling factor between GPP and ratios of the fluxes of COS and CO2 and ratios of the atmospheric concentrations of COS and CO2 ) that is in good agreement with laboratory results. © 2013 Elsevier B.V. All rights reserved.

1. Introduction An important goal in terrestrial carbon cycle science is partitioning measured carbon fluxes into assimilative (gross primary production, GPP) and respiratory (ecosystem respiration, Reco ) components. The ability to do this is crucial not only to understand the processes contributing to cycling of CO2 between the atmosphere and the Earth’s surface; but it also serves as a means to constrain estimates of the rate of change of carbon in the atmosphere via models and other top–down approaches. There are several methods, commonly used to disaggregate directly measured net CO2 fluxes (net ecosystem exchange, NEE) into GPP and Reco . In the most common method, it is first assumed that radiation-driven assimilative

∗ Corresponding author. Tel.: +1 402 472 7961. E-mail address: [email protected] (D.P. Billesbach). 0168-1923/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agrformet.2013.06.007

processes (i.e. photosynthesis) tend to zero at night and that measured nocturnal CO2 flux values represent Reco . These values are then used to parameterize a model of Reco , based on soil temperature (Lloyd and Taylor, 1994) or in rare cases soil temperature and soil moisture (Luo and Zhou, 2006). Daytime values of Reco are then extrapolated and added to the measured, daytime NEE values to estimate GPP. In a second approach, a semi-physiological, but still empirical model of both Reco and GPP (e.g. Gilmanov et al., 2005) is applied to the entire NEE data set to derive separate values of GPP and Reco . Finally in a third approach, stable isotopic concentration ratios of CO2 are measured and again used to parameterize a model (Zobitz et al., 2008). One feature of these approaches is that they all rely on similar assumptions about and models of the terrestrial carbon cycle and thus cannot provide new information outside the scope of the underlying models or the driving measurements. Methods one and two in particular, use only a single measured quantity (NEE) to derive both GPP and Reco , thus yielding non-independent results. The third method is difficult and

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expensive and in general, gives only limited information about ecosystem processes on a sub-daily temporal scale. This lack of model/measurement independence impedes a robust testing of the models. We would therefore, like to augment these methods by using independent measurements or models of other geochemical trace gas species to constrain our estimates of GPP, Reco , and atmospheric CO2 concentrations. Wohlfahrt et al. (2012) give a very good summary of the problem and relate it to the atmospheric sulfur cycle. Carbonyl sulfide (COS) is the most abundant of the reduced sulfur gases in the atmosphere, and it has been shown that it shares the same diffusive and enzymatic pathways as photosynthetic CO2 (Wohlfahrt et al., 2012). COS will therefore diffuse into the cell along with CO2 , and be hydrolyzed by the photosynthetically important enzyme, carbonic anhydrase (CA) (Protoschill-Krebs et al., 1996), resulting in H2 S and CO2 . The similarity of the processes suggests that measurements of ambient COS concentrations and COS uptake by plants (fluxes) may fill the role of an independent constraint on GPP as described above. Blonquist et al. (2011) describe several methods by which GPP may be estimated using various measurements of COS. In the most direct of these, GPP is obtained from measured values of COS fluxes and molar mixing ratios of CO2 and COS via: GPP = FCOS



CO2

COS

 1  LRU

(1)

where GPP is gross photosynthetic production, FCOS is measured COS flux (plant uptake only),  is the mixing ratio of a species, and LRU is a scaling factor called the leaf relative uptake factor for a single species. In more complex ecosystems, we may replace LRU with CRU or canopy relative uptake which is a population weighted average of individual LRU values. It is assumed that LRU is species independent. Wohlfahrt et al. (2012) examined the behavior and properties of these scaling parameters in more detail and found that this is mostly true, making LRU a universal parameter. One important challenge in this method is to accurately measure FCOS . As an indicator of the difficulty of these measurements, we note that the ambient COS mixing ratio is roughly 500 pptv, or almost 10−6 that of CO2 , 0.16% of N2 O, or 1.5% of O3 . Even though fluctuations of COS are relatively large, this extremely small atmospheric concentration makes flux measurements especially challenging. Surface and airborne measurements of COS mixing ratios (COS ) have been made for quite some time (Montzka et al., 2007) but few researchers have been able to reliably measure canopy fluxes of COS (FCOS ). Mihalopoulos et al. (1989) and Bartell et al. (1993) used the gradient method to measure uptake over an agricultural field, and a wet meadow respectively. Mihalopoulos and Nguyen (2001) again used the gradient method to measure COS fluxes over an Eucalyptus forest. Xu et al. (2002) used the relaxed eddy accumulation method to measure COS exchange over a spruce forest, and White et al. (2010) used the gradient method to observe COS exchange over a loblolly pine forest. Recently however, instrumentation has become available that is capable of making the necessary high frequency COS density measurements required for eddy covariance fluxes. Besides ourselves, we are aware of several other groups (Yakir, Weizmann Institute and Wofsy, Harvard) that are working with similar instrument systems, and there is as yet, only one published account of successful eddy covariance measurements of COS fluxes (Asaf et al., 2013). Apart from local canopy-scale measurements of GPP, flux and concentration measurements of COS can also be used to constrain the carbon dynamics of various biogeochemical models (e.g. SiBCOS or GEOS-Chem) (Montzka et al., 2007; Suntharalingam et al., 2008; Campbell et al., 2008). It has been further suggested (Seibt et al., 2010) that independent measurements of COS can also be useful in constraining measurements of canopy conductance and

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transpiration. This broad range of applications demonstrates the need to better understand the canopy–soil–atmosphere interactions and dynamics of COS. To demonstrate the feasibility of direct, eddy covariance measurements of COS fluxes, to explore the dynamics of the canopy–soil–atmosphere sulfur cycle, and to provide data useful for model constraint, we conducted an experiment to simultaneously measure net fluxes of CO2 and COS using the eddy covariance method over a growing winter wheat field, located in the Southern Great Plains of the United States. We note that a companion experiment was simultaneously conducted to make chamber measurements of soil COS fluxes (Seibt et al., 2013) which we relied on to augment our understanding of the COS cycle. 2. Site description The experiment was performed at the U.S. Department of Energy’s Atmospheric and Radiation Measurement program Central Facility (DOE-ARM CF) located west of Ponca City, OK (36◦ 36 21 N 97◦ 29 20 W 316 m AMS). Soils in the area are characterized as well-drained Kirkland (silt loam; fine mixed thermic Udertic Paleustolls), Renfrow (silty clay loam; a fine mixed thermic Udertic Paleustolls), and Vernon (clay loam; a clayey, mixed, thermic, shallow Typic Ustochrepts) associations, with a sand:silt:clay ratio of 33:22:45 (±3). The dominant land covers are winter wheat and grassland pasture with smaller areas of row-crop agriculture (Fischer et al., 2007). The study field presented a uniform fetch of 300 m in both the east–west and north–south directions and our flux tower was located just south of the ARM 60 m main tower, mid-way between the east and west field boundaries (dominant spring and summer winds are from the south). The field was planted with winter wheat in January of 2012 and was harvested about May 22 (DOY 143). The crop had reached its maximum canopy height of about 30 cm by the time our flux measurements began on April 5 (DOY 96). Data collection was terminated shortly after harvest on June 4 (DOY 156). This period encompassed the maximum growth phase, reproduction, senescence, and post-harvest conditions. There were interruptions due to equipment malfunctions between April 15 (DOY 106) and April 25 (DOY116) and between May 8 (DOY 129) and May 10 (DOY 131). 3. Method The eddy covariance instrumentation consisted of a Gill model R3 sonic anemometer/thermometer (Gill Instruments, Lymington, UK), a LiCor model LI-7500 open-path CO2 /H2 O Infrared Gas Analyzer (IRGA) (LiCor Biosciences, Lincoln, NE) and a model QC-TILDAS-CS Tunable Laser Absorption Spectrometer (TLAS) (Aerodyne Research, Inc., Billerica, MA). This device is a state-ofthe-art trace gas analyzer that uses a quantum cascade laser (QCL) to generate an extremely narrow-bandwidth light beam which is injected into a closed-path gas absorption cell. By scanning the laser in wavelength, the absolute optical absorption from discrete rotational–vibrational molecular transitions of gaseous species in the cell can be measured. The amount of optical absorption is related to the density of that species in the cell by fitting to fundamental physical parameters contained in the HITRAN-2004 database (Rothman et al., 2005) and thus to its absolute mixing ratio. The optical absorption cell is a compact, astigmatic Herriot resonator (McManus et al., 1995) which folds a 76 m absorption path into a 0.5 l volume. By “jump scanning” the QCL laser, the instrument can measure three species simultaneously. In our case, a laser was specified that measured CO2 , H2 O, and COS. The manufacturer specifies an instrument precision of 5 pptv over a 1 s integration time and detectability (2) of 2 pptv for COS. By using a

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high flow-rate pump, the instrument is capable of producing 10 Hz measurements that are suitable for eddy covariance work. This instrument represents a significant performance improvement over previous versions (Stimler et al., 2010). All three instruments were connected to a small laptop computer via RS-232 serial data lines (COM ports). Data were collected and stored on this laptop under the control of the HuskerFlux data collection program (Billesbach et al., 2004). Data were continuously recorded at 10 Hz and were periodically downloaded (every few days) to a server at the University of Nebraska where they were then post-processed to produce high-quality fluxes (Billesbach et al., 2004). The sonic anemometer and the CO2 /H2 O IRGA were mounted on top of a Campbell Scientific (Logan, UT) tripod tower, about 3 m above the soil surface. The TLAS was housed in a temperature controlled shed about 10 m to the north of the instrument tower. Sample air was drawn into the closed-path TLAS by a TriScroll 300 pump (Varian, Inc., Palo Alto, CA) at 6 SLM through a length of Dekabon tubing with an inside diameter of about 6 mm. At this flow rate and at the working (constant) pressure of the absorption cell (40 Torr), we calculate a cell time constant of about 0.166 s. A 1 ␮m Teflon filter and drip inhibitor cup were located at the tower end of the sample tube and positioned just below the sonic anemometer. The exact sample tube transit times (i.e. sample delay time) were determined in the post-processing routine, for each averaging interval using the covariance maximization method of Chahuneau et al. (1989). Because the TLAS had to accommodate both eddy covariance and chamber measurements, a small solenoid valve (located at the inlet to the TLAS) was used to switch between the sampling tubes of the two measurement systems. In addition, this solenoid was also used to direct a stream of dry nitrogen (zero gas) to the TLAS for periodic zero offset corrections. In operation, the TLAS was normally connected to the eddy covariance sampling path except every other hour when it was switched away for 30 min to first check and correct the zero offset, and to then make several chamber measurements. The TLAS reported dry mixing ratios of three gases (CO2 , H2 O, and COS). In post-processing, we converted the dry mixing ratios of CO2 and COS back to ambient densities (using the instantaneous measurements of H2 O mixing ratio and mean values of ambient pressure and temperature. While it is possible to derive fluxes directly from the dry mixing ratios that avoid the so called WPL (Webb et al., 1980) terms (Lee and Massman, 2011), we chose the more traditional approach to avoid any potential errors that could be introduced into the dry mixing ratios from possible zero offsets or drifts in the measured water vapor density. In any case, we believe that any difference between these two approaches will be small. We used, a 15 min flux averaging time to produce the largest number of valid flux measurements possible. Stationarity and integral turbulence tests as well as ogive asymptotic tests (Lee et al., 2004) were performed to assure that the flux integration interval and data collection frequency were appropriate. WPL (Webb et al., 1980) terms were calculated and added to all fluxes as appropriate for closed and open-path gas analyzers (Lee et al., 2004). Both temperature fluctuation and water vapor fluctuation (H and LE) terms were applied to the open-path IRGA (LI-7500) and only the water vapor fluctuation term was applied to the TLAS data, in accordance with the recommendations of Massman (in Lee et al., 2004). This recognizes the fact that sample temperature fluctuations are almost completely damped in closed-path sampling tubes. Spectral corrections (Moore, 1986) were applied to all fluxes, and tube attenuation corrections (Lenschow and Raupach, 1991) were applied to fluxes derived from TLAS data. Readers familiar with the LiCor methane TDLAS (LI-7700 LiCor Biosciences, Lincoln, NE) will note that we did not apply the

Fig. 1. NEE (␮mol m−2 s−1 ) as measured by the open-path, LiCor LI-7500 IRGA (solid circles) and the closed-path, TLAS sensor (crosses).

spectroscopic corrections described for that instrument in its user manual (LiCor, 2011). Although these two instruments use the same basic measurement principle, there are important fundamental differences between them, and the reason for neglecting these corrections lies in the difference between closed and open-path sensors. Finally, we calculated uncertainties in each covariance using the variance of a covariance method (Finkelstein and Sims, 2001, Billesbach, 2011). All uncertainties were then propagated through the correction relationships to yield a total measurement uncertainty for each final flux value. In choosing a set of quality control criteria to screen out bad COS fluxes, we found that simply screening for expected tube delay times (approximately 2 s), sufficient turbulence (u* > 0.15 m s−1 ) and adequate wind speeds (u > 2.0 m s−1 ) yielded a fairly conservative set of flux values. 4. Data In Figs. 1 and 2, we see fluxes of CO2 as measured by both the open-path (LI-7500) and closed-path (TLAS) systems. The time series shown in Fig. 1 reveals a close match between the two independent flux measurements. The agreement is even more evident in Fig. 2 where a direct comparison of the two measurements indicates an agreement of about 94%. We also see in Fig. 1, the typical diel and seasonal variation for the agricultural crop,

Fig. 2. Comparison plot of NEE as measured by the open-path LI-7500 IRGA (x-axis) versus NEE as measured by the closed-path TLAS sensor (y-axis).

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Fig. 4. Time series of the COS fluxes (pmol m−2 s−1 ) measured over the duration of the experiment.

Fig. 3. Cospectral plots of covariances between vertical wind speed and sonic temperature (a) and vertical wind speed and COS density (b). The solid lines represent ideal slopes of −5/3.

consisting of a large daily variation (between daytime assimilation or negative values and nocturnal respiration or positive values) whose amplitude decreases from peak growth to senescence and harvest. Fig. 1 also shows that soil CO2 respiration decreases dramatically after harvest. In Fig. 3, we show cospectral power plots of the covariances between vertical wind and sonic temperature (top) and vertical wind and COS density (bottom). The similar shapes of the curves indicate that the TLAS was making acceptable COS density measurements for eddy covariance analysis. In Fig. 4, we show the time series of net COS fluxes measured over the course of the experiment. We note that there are distinct patterns to the data at both seasonal and diel time scales. The figure shows a strong diel variation, similar to that seen early in NEE (Fig. 1), during peak plant growth. Unlike the NEE trends, this pattern does not show as strong of a daily amplitude decrease at later dates. At the seasonal scale, we note that net COS fluxes during the peak growth phase are exclusively uptake (negative flux values) and evolve to exclusively emissive (positive flux values) after harvest at the end of the experiment. Adding error bars to Figs. 1 and 4 would unnecessarily obscure the actual data. We did however calculate individual uncertainties for each flux value (Billesbach, 2011; Finkelstein and Sims, 2001), for NEE (both the LiCor and the TLAS data) these ranged between 0.5 and 3.0 ␮mol m−2 s−1 during peak growth and between 0.1 and 1.0 ␮mol m−2 s−1 after senescence and post-harvest. For COS fluxes, they ranged between 4 and 12 pmol m−2 s−1 during peak growth and between 2 and 7 pmol m−2 s−1 after senescence and post-harvest. In another context, the uncertainty for individual COS

fluxes was between 25% and 30% of the measured value while the uncertainty of individual NEE values was between 10% and 15% for the duration of the experiment. In Fig. 5, we show the calculated covariance between fluctuations (about the mean) of CO2 and COS dry mixing ratios. This also exhibits a strong seasonal pattern. During peak growth, there is a positive correlation between CO2 and COS fluctuations. The strength of this correlation decreases as the wheat matures and disappears during senescence and after harvest. Fig. 6 (top and bottom) shows this behavior in more detail. To more clearly reveal the diel behavior of CO2 and COS fluxes, we have computed point-by-point, five day averages for portions of the experiment. Fig. 7 (top and bottom) shows the peak growth period (DOY 101–105) fluxes of COS and CO2 respectively while Fig. 8 (top and bottom) shows the averaged fluxes (again of COS and CO2 ) after harvest (DOY 147–151). We note that during peak growth (Fig. 7, top), COS uptake continues even during the night and is about 30% of the peak daytime value. In contrast, after harvest (Fig. 8, top), the field becomes a significant source of COS to the atmosphere, both day and night. The error bars in Figs. 7 and 8 are not the standard deviation of the mean values; rather they are the mean of the individual flux uncertainties that went into the average value calculation. We believe that this procedure gives a better picture of the instrument performance by eliminating natural dayto-day variations.

Fig. 5. Time series of the covariance of fluctuations in COS mixing ratio and CO2 mixing ratio.

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Fig. 6. Time series of COS fluxes (pmol m−2 s−1 ) (crosses) and the covariance of fluctuations in COS mixing ratio and CO2 mixing ratio (solid circles) for the early, peak growth (top) and late post-harvest (bottom) periods.

Fig. 7. Mean COS (top) and NEE (bottom) fluxes for the period between DOY 101 and DOY 105 during the peak growth period. Note that the error bars shown are the averages of the individual flux uncertainties, not the standard deviations of the flux averages.

5. Discussion

(Fig. 5 and Fig. 6, top) also suggests that the two gases are being consumed by similar mechanisms. The night-time assimilation of COS is however in contrast to CO2 but is consistent with behavior observed in a loblolly pine forest (White et al., 2010). Because CO2 is taken up through photosynthesis which requires a radiative energy input (PAR) it necessarily stops at night. COS reactions on the other hand, do not require radiation and can therefore continue during the night, as long as there are pathways into the cells. There is evidence that stomatal conductance does not go strictly to zero at night (Caird et al., 2007) and we are therefore not surprised to observe nocturnal COS assimilation. Bunce (2000) estimates that mid-day stomatal conductance for wheat is between 0.2 and 0.6 mol m−2 s−1 while Caird et al. (2007) estimate that nocturnal stomatal conductance for wheat is around 0.033 mol m−2 s−1 . These values compare with a measured nocturnal conductance of 0.023 ± 0.005 mol m−2 s−1 and a light saturation value of 0.42 ± 0.03 mol m−2 s−1 from our field (K. Maysek, personal communication, 2012). This suggests that stomatally regulated, nocturnal COS uptake may be between 6% and 17% of the peak daytime value or between 2 and 7 pmol m−2 s−1 . Our data (Fig. 7, top) show a night to day ratio of COS fluxes that is closer to 25% or 30% which is roughly 10 pmol m−2 s−1 of uptake, indicating that there is probably another assimilation mechanism involved. There is in fact, evidence that soils contribute to COS uptake since they also contain CA (carbonic anhydrase) (Castro and Galloway, 1991; Kesselmeier et al., 1999; Kuhn et al., 1999; Simmons et al., 1999; Mihalopoulos and Nguyen, 2001; Geng and Mu, 2004; Steinbacher

Since eddy covariance fluxes of COS is such a new subject, and because we believe that this work represents measurements from one of the smallest ambient concentration trace gases yet attempted, we took extra precautions to validate our experiment. Because we had only one COS analyzer, a direct comparison could not be done. We did, however make independent measurements of CO2 fluxes with the TLAS and with a LiCor LI-7500 IRGA. The data shown in Figs. 1 and 2 demonstrate that the two systems agree quite well for CO2 fluxes (within about 6%). This is typical for many AmeriFlux site intercomparisons and the non-unity slope is probably due mostly to differences in instrument calibration. Additionally, Fig. 3 shows that the vertical wind/COS cospectral characteristics of the TLAS compare favorably to the cospectra of vertical wind/sonic temperature, a traditional flux quality standard (Lee et al., 2004). We believe that these factors adequately validate the reality of our COS fluxes while the individual, calculated flux uncertainties (Billesbach, 2011; Finkelstein and Sims, 2001) (25–30% of the COS flux values) define the accuracy of our measurements. In the early (peak growth) season, we observed assimilative COS flux during both day and night (Fig. 4 and Fig. 7, top). Additionally, we observed qualitatively similar diel patterns in both CO2 and COS fluxes (Fig. 7, top and bottom). The latter suggests that the primary mechanism driving both systems is the same and may be closely related to canopy conductance. In addition, the positive correlation between COS and CO2 mixing ratio fluctuations

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Fig. 8. Mean COS (top) and NEE (bottom) fluxes for the period between DOY 147 and DOY 151 during the post-harvest period. Note that the error bars shown are the averages of the individual flux uncertainties, not the standard deviations of the flux averages.

et al., 2004; Van Diest and Kesselmeier, 2008; Seibt et al., 2010; White et al., 2010). Simultaneous chamber measurements of bare soil (Seibt et al., 2013) made during this period do in fact show soil uptake of COS with values of between 2 and 5 pmol m−2 s−1 (Seibt et al., 2013). Like the estimated stomatal uptake, their observed magnitudes alone cannot completely account for the uptake rates seen in the nocturnal eddy covariance data. When combined however, the two mechanisms provide a reasonable explanation for the observed nocturnal eddy covariance values. We should point out that the companion soil chamber flux experiment (Seibt et al., 2013) only used a single flux chamber and caution should be used in extrapolating their measurements to the entire fetch of the eddy covariance flux tower. As noted in the introduction, our principal interest in COS is as an independent means to constrain ecosystem GPP estimates via Eq. (1). As stated, this relationship assumes that the quantity FCOS represents only the component of the daytime flux that is stomatally controlled by the canopy. As we have seen, in our eddy covariance data, there are other source and sink terms for COS, and the quantity actually measured by the eddy covariance instruments is the net sum of all of them defined as: FCOS = Fplant + Fother

(2)

Given this more complex situation, we must re-formulate Eq. (1) as: GPP = Fplant



CO2

COS

 1  LRU

(3)

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Solutions to this relationship require decomposition of the measured net flux. In our particular system, we believe that the Fother component is dominated by soils and that we can use the results from our companion soil chamber flux study to derive Fplant from Eq. (2) (under the assumption that the single chamber represents our entire fetch). This gives us the opportunity to calculate LRU from our measurements if we have knowledge of GPP. Since our experiment lacked the long-term soil temperature measurements that are needed to derive estimates of daytime ecosystem respiration (Reco ) (Lloyd and Taylor, 1994) that are used to calculate GPP, we used Reco estimates from an adjacent AmeriFlux station that shared the same field and fetch as our tower. We combined these data with our mean NEE and other measurements for the period between DOY 97 and DOY 105. The calculation yielded a mean LRU value of 1.57 ± 0.44. This compares very well with leaf-level measurements made in the laboratory of 1.61 ± 0.26 (Stimler et al., 2012) over 22 different species at various light intensities. While the means agree well, the large uncertainties suggest that this method of GPP estimation may not be well suited to short time periods (hourly or daily) and may perform better at weekly, monthly, or annual time scales. The likely reason for this is the relatively large (compared to CO2 ) uncertainties in COS flux measurements. This is an instrumentation issue and will likely improve as better instruments are developed. We should also note, that the good agreement between our value of LRU and literature values is strictly valid for this particular agricultural field. The agreement was possible because of our characterization and understanding of other sources and sinks of COS at this site. To expand the usefulness of COS to larger scales (regional and global) for constraint of GPP will require a better knowledge of non-stomatal sources and sinks of COS over these scales. During the later portion of the year, after senescence and post-harvest, we observed other COS dynamics not associated with plant uptake. In particular, we measured large emissions of COS to the atmosphere. Fig. 1 indicates that net CO2 assimilation and respiration after harvest were minimal and were probably from weedy re-growth and dry soil conditions that resulted from the harvest removal of the wheat canopy. Under these conditions, any COS uptake from this extremely sparse canopy would likely be below the detectability level of our eddy covariance system and we therefore believe that the observed COS flux represents the gross ecosystem emission. Because the soil experiment used only a single chamber, it is difficult to compare them to the eddy covariance fluxes (Seibt et al., 2013). The values obtained however, are similar and tend to suggest that after harvest, both methods (eddy covariance and chambers) measure the same thing, seeing all relevant COS processes. We suspect that the source of these emissions is the soil, roots, and litter. This assumption is supported by the chamber measurements made throughout the experiment (Seibt et al., 2013). The exact mechanism for these emissions is not apparent, and probably cannot be elucidated from our suite of measurements. During this period, we observed a lack of meaningful correlation between mixing ratio fluctuations of COS and CO2 (Fig. 5 and Fig. 6, bottom), but we cannot ascribe much meaning to it since CO2 fluxes (and thus CO2 mixing ratio fluctuations) are much smaller during this time than previously. While other researchers have reported COS emissions from soil, we are somewhat surprised at the magnitude of the observed fluxes. The data in Fig. 4 and Fig. 8, top show a definite diel pattern with the strongest COS emissions during the day. This trend is consistent with a temperature driven production mechanism similar to that seen in soil CO2 respiration (Lloyd and Taylor, 1994). On the other hand, we observe only minimal CO2 respiration during the late season which is probably due to a lack of soil moisture, which does not seem to have affected COS emissions though. We can speculate that one possible source of these emissions might be the decay (oxidation) of sulfur compounds in the fresh litter fall, either though biologic or chemical or physical

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processes. Possible sources could be amino acids in the fresh litter or residual agricultural chemicals. Unfortunately, our experiment can neither confirm nor deny this hypothesis. Laboratory or field studies of litter decay could possibly resolve this question. To summarize, we have shown that it is now possible, using commercially available instrumentation, to make eddy covariance measurements of COS fluxes that are of reasonable accuracy. While the current technology is not yet sufficient for methods detailed in the literature to produce high-accuracy, high-temporal resolution, independent estimates of GPP, we have demonstrated that the methodology can be used to constrain traditional estimates especially at lower temporal resolution. Our experiment has also verified that multiple sources and sinks of COS exist, and we have been able to quantify some them and, for the first time, track their evolution over a growing season. Several questions arise from this work that need further research to answer. One of these pertains to the representativeness of our particular site compared to others. How far can we extrapolate from a Southern Great Plains wheat field? To answer this question will require more field experiments (in a variety of ecosystems) to develop a process-based understanding of the atmosphere–canopy–soil sulfur cycle. Our experiment was conceived and executed as a pilot-scale project and as a demonstration-of-principle. More comprehensive measurements will be needed in future studies. Another question relates to the sources of soil emissions of COS. What fuels these fluxes? We feel that if the source and sink terms that were revealed in this study can be parameterized (especially in terms of commonly measured site variables), COS measurements (both fluxes and mixing ratios) will hold great promise to aid in better understanding the carbon cycle at scales from local to global.

Acknowledgments We would like to thank the staff of the DOE-ARM Central Facility for hosting and assisting us in completing this project. This work was supported by the U.S. Department of Energy, Biological and Environmental Research Division under Award # DE-SC0007094, The Air Liquide Foundation, and through a major equipment grant from the U.S. National Science Foundation.

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