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Keywords:

  • hydrogen;
  • modeling;
  • soil

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Implementation
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

[1] A simple process-based model for the consumption of atmospheric hydrogen (H2) has been developed. The model includes a description of diffusion and biological processes which together control H2flux into the soil. The model was incorporated into the LPJ-WHyMe Dynamic Global Vegetation Model, and used to simulate H2 fluxes over the 1988–2006 period. The model results have been confronted with field and laboratory measurements. The model reproduces observed seasonal cycles of H2 uptake at different sites and shows a realistic sensitivity to changes in soil temperature and soil water content in comparisons with field and laboratory measurements. A recent study, based on 3D atmospheric model inversion, found an increase of the global H2 sink from soils, with a trend of −0.77 Tg a−2 for the 1992–2004 period (fluxes are negative as soils act as a sink for atmospheric H2). For the same period, however, our process-based model calculates a trend of only −0.04 Tg a−2. Even when forced with drastic changes in soil water content, soil temperature and snow cover depth, our model is unable to reproduce the trend found in the inversion-based study, questioning the realism of such a large trend.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Implementation
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

[2] Molecular hydrogen (H2) is the second most abundant oxidizable gas in the troposphere after methane (CH4) with an average mixing ratio of 531 ppb [Novelli et al., 1999]. H2has recently attracted interest as hydrogen-based technologies, which are sustainable, clean and transportable, are widely regarded as a future energy alternative to traditional fossil fuels [Larsen et al., 2004]. However, little is currently known about the possible environmental effects of widespread use of hydrogen for fuel. In a hydrogen economy, inevitable H2 leakage into the atmosphere would contribute to an increase in the mixing ratio of H2. On the other hand, switching to hydrogen-based technologies could in principle reduce fossil fuel combustion, one of the main current H2 sources into the atmosphere, thereby contributing to a decrease in atmospheric H2 [Warwick et al., 2004; Schultz et al., 2003]. Hydrogen acts as a sink for hydroxyl radicals (OH); an increase in the atmospheric H2 burden would therefore reduce OH availability, increasing the lifetime of CH4 and thus contributing to the greenhouse effect. An increase in atmospheric H2 would also increase water vapor in the stratosphere through H2 oxidation leading to changes in stratospheric temperature and ozone chemistry [Tromp et al., 2003]. A further complication is the potential impact of climate change on the uptake of H2 by soils, which is the largest sink for atmospheric H2. The magnitude and even the sign of this impact are essentially unknown.

[3] A simplified schematic of the atmospheric H2 cycle is represented in Figure 1. Two main chemical processes produce atmospheric H2. The first is photochemical H2 production through photolysis of formaldehyde (HCHO), which is a product of the oxidation of methane (CH4) and other volatile organic compounds (VOCs). This source represents around 50% of total H2 production. The second is incomplete combustion during biomass burning and fossil fuel combustion. This source accounts for about 40% of total H2 production. Biological N2 fixation on land and in the ocean constitutes a further, minor source of atmospheric H2. Molecular hydrogen is removed from the atmosphere in two main ways. The dominant sink for H2 is soil uptake due to bacterial and/or extracellular enzymatic activity. Soil H2 consumption amounts to 70–80% of the total sink. Oxidation of H2 by OH is the second major loss pathway. A small part of tropospheric H2 manages to reach the stratosphere, where it is destroyed by reactions with OH and O(1D) radicals [Ehhalt and Rohrer, 2009; Constant et al., 2009].

image

Figure 1. Simplified scheme of the H2 cycle. The sources are represented by red arrows and the sinks by green arrows. The numerical ranges come from various authors: Novelli et al. [1999], Simmonds et al. [2000], Hauglustaine and Ehhalt [2002], Sanderson et al. [2003], Rhee et al. [2006]; Price et al. [2007], Xiao et al. [2007], Ehhalt and Rohrer [2009], and Bousquet et al. [2011].

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[4] The detailed mechanisms by which H2 is consumed in soil are still unclear. Guo and Conrad [2008] focus on a family of extracellular hydrogenases of microbial origin. However, more recently Constant et al. [2010] have concluded that Streptomycetes (aerobic bacteria) are the main group responsible for H2 soil uptake. Thus, the mechanisms controlling H2 uptake in the soil are still subject to fairly large uncertainties.

[5] A handful of studies have attempted to estimate the soil H2 sink at a global scale. Three different methods have been used. The first consists in prescribing H2 deposition velocities. Deposition velocities (vd in cm s−1) are defined as the ratio of flux (mol cm−2 s−1) of a gas at a sink surface to its concentration in the atmosphere (in mol cm−3). Hauglustaine and Ehhalt [2002] used previous estimations of CO deposition velocities [Müller, 1992] and applied Yonemura et al. [2000a]'s linear relationship between CO and H2 deposition velocities image = 1.5). NPP (Net Primary Productivity) estimates were used to constrain the seasonal and geographical distribution of CO and H2 deposition velocities. Sanderson et al. [2003] used measured H2deposition velocities and their variation with soil water content and ecosystem type to derive a bottom-up global estimate. Finally,Price et al. [2007] used a simple scheme with a constant H2 deposition velocity of 3.94 × 10−2 s−1over non-snow covered grid cells. To take into account the effect of low soil temperature, H2 deposition velocity was reduced by half below 0°C and again by half below −15°C. The second method consists in using atmospheric model inversions. Xiao et al. [2007] estimated global soil uptake from surface atmospheric H2observations by using a 2-D global transport model and state-space Kalman filter. Recently,Bousquet et al. [2011]used a new approach to estimate the total soil sink using an atmospheric inversion of global and regional fluxes based on a three-dimensional chemistry-transport model, a global network of flask observations of H2 concentration (NOAA/ESRL and CSIRO/CAWS networks) [Novelli et al., 1999; Steele et al., 1992], and prior information on natural and anthropogenic fluxes, in a Bayesian inversion framework. One of the findings of Bousquet et al. [2011]is a long-term trend of −0.77 Tg a−2 in the soil uptake between 1991 and 2004, suggesting that the soil sink has been increasing (fluxes are negative as soils act as a sink for atmospheric H2). The third method involves process-based modeling such that soil H2 uptake is simulated as a function of diffusion and biological oxidation. Smith Downey [2006] modeled soil H2 uptake in this way, with a global estimate ranging between 59.8 and 73.2 Tg a−1. However, her model has not been extensively confronted with laboratory and field measurements.

[6] In this paper we describe a simple, globally applicable process-based submodel representing the diffusion of H2 into the soil and oxidation of H2in the soil and its implementation in the LPJ-WHyMe Dynamic Global Vegetation Model [Wania et al., 2009a, 2009b, 2010]. We present simulations of H2fluxes for the 1988–2006 period, forced by observed variability in climate, and we compare the results with available experimental data. Finally, we re-examineBousquet et al. [2011]'s finding of an increasing trend in soil H2 uptake.

2. Model Implementation

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Implementation
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

[7] We developed a simple diffusion-consumption model using the same approach thatRidgwell et al. [1999] and Curry [2007] used to model the soil consumption of methane. A similar approach has been adopted by Yonemura et al. [2000b] and Smith Downey [2006]. An extended description of the model can be found in the auxiliary material. The model includes a description of diffusion, based on the Fick's first law, and biological processes which together control H2 flux into the soil. Variables controlling diffusion are soil texture, air filled porosity and soil temperature. The effect of snow cover is also taken into account. The biological uptake is primary controlled by soil water content and soil temperature [Smith Downey, 2006; Smith-Downey et al., 2006], and is calculated by modulating a maximal biological uptake, kmax, with functions taking in account the effect of soil water content and soil temperature. A universal empirical value of kmax 0.038 s−1 was calculated by adjusting our global mean uptake calculated for the 1991–2005 period to that obtained by Bousquet et al. [2011]using 3-D atmospheric inversion model. This value is higher than the kmax of 0.012266 s−1 found by Smith Downey [2006]in laboratory experiments (N. Smith-Downey, personal communication, 2008). We thus performed a sensitivity test with akmax of 0.012266 s−1 (Tables 1 and 2). As biological activity requires a minimum of soil organic content to be activated, we used also a Net Primary Productivity (NPP) mask for the desert. We tested the impact of this NPP mask with sensitivity tests described in the auxiliary material and in Tables 1 and 2. As the model doesn't take in account the first few centimeters of the soil, which might be dry enough to inhibit biological uptake [Smith Downey, 2006; Yonemura et al., 2000b], our model might overestimates H2 fluxes under very dry conditions.

Table 1. Description of the Different Sensitivity Tests Done With the Modela
Test LabelDescriptionkmax (s−1)
  • a

    The maximum biological uptake (kmax) has been recalculated so the global mean uptake calculated for the 1991–2005 period matches the one obtained by Bousquet et al. [2011], except for the test with a kmaxfrom N. Smith-Downey (personal communication, 2008).

V_f(NPP)2Test with a progressive NPP mask: inline image0.0317
V_no_f(NPP)Test with no NPP-based mask0.024
V_kmax_NSDTest with kmaxfrom N. Smith-Downey (personal communication, 2008)0.012266
V_f(M)2Test with a new soil water content function: inline image0.0363
Table 2. Calculated Global Soil H2 Uptake Annual Trends Between 1992 and 2004 in Tg a−2 for Model Tests as Described in Table 1 and for the Different Scenarios as Described in Table 4
Scenario TestRefWF − 1WF − 5WF + 1WF + 5T − 1T + 1S − 5
V_f(NPP)2−0.042−0.25−0.29+0.19+1.28+0.003−0.08−0.11
V_no_f(NPP)−0.038−0.23−0.21+0.17+1.2+0.005−0.08−0.1
V_kmax_NSD−0.024−0.14−0.2+0.11+0.76+0.001−0.05−0.07
V_f(M)2−0.04−0.28−0.89+0.22+1.36+0.005−0.083−0.12

[8] In order to estimate the long-term trend and the seasonal variations of the global soil sink of H2, the diffusion-consumption model is applied on the top layer of the Dynamic Global Vegetation Model LPJ, LPJ-WHyMe (LPJWetland Hydrology and Methane) [Wania et al., 2009a, 2009b, 2010]. H2 fluxes were calculated for the 1988–2006 period at a spatial resolution of 1° by 1° for the region 60°S to 90°N,

3. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Implementation
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

3.1. Model Evaluation

[9] The full range of H2 deposition velocities that the model can calculate ranges between zero and 0.12 cm s−1. This range covers most of the typical measurements [Ehhalt and Rohrer, 2009] although a few studies have reported deposition velocities greater than 0.12 cm s−1 [Conrad and Seiler, 1985; Ehhalt and Rohrer, 2009].

[10] Figure 2 shows the model sensitivity to changes in temperature and soil water content for a loamy sand soil (Table S1 in Text S1 in the auxiliary material). The response of the simulated H2 deposition velocity to changes of soil water content is mainly due to its effect on the H2 diffusion into the soil, except for low soil water content when the biological uptake is inhibited in the model. On another hand, the response to changes in temperature is mainly driven by the biological part of the model.

image

Figure 2. Response of the H2 soil model to variation in temperature and volumetric soil water content ( inline image) for a sandy loam soil. (a) Effect of temperature only on H2 diffusion with a maximal constant biological uptake. (b) Addition of the soil water content effect on H2 diffusion. (c) Addition of effect of the soil water content on biological uptake. (d) Full model sensitivity to variation of temperature and soil water content. The temperature range between −15 and 50 C°, the soil water content between 0 and 1, and the deposition velocity between 0 and 0.08 cm s−1.

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[11] We now compare the model against several sets of H2 uptake values in the literature including laboratory and field measurements as well as estimates based on atmospheric measurements.

[12] Lallo et al. [2008] used a soil chamber technique to measure H2 deposition velocity in Finland. The measurements were made at Loppi (60°38′N, 24°21′E) and Helsinki (60°12′N, 25°′3′E) between August 2005 and April 2007. At each site, measurements were done on two different types of soil. Figure 3 shows observed H2 deposition velocities for the period August 2005–December 2006 [Lallo et al., 2008] plotted against the global H2model results for the same period at the grid cell corresponding to the measurement site coordinates. Generally, the model outputs and the data are in good agreement but at the grid cell corresponding to the Loppi site, low levels of soil water content calculated by LPJ-WHyMe between June and September 2006 lead to smaller values of deposition velocities for this period. The model slightly underestimates maximum values of H2 deposition velocities compared to the field measurements (0.054 cm s−1 versus 0.07 (±0.006) cm s−1). Nevertheless, for the Helsinki site, the model attends to well reproduce the seasonal cycle with a correlation (r) of 0.84. For the Loppi site the correlation is low, however, mainly due to the anomalously low values of deposition velocities simulated during the summer of 2006.

image

Figure 3. Digitized H2 deposition velocities in cm s−1 as measured by Lallo et al. [2008] at Loppi (60°38′N, 24°21′E) and Helsinki (60°12′N, 25°′3′E) between August 2005 and December 2006 and H2 deposition velocities simulated by the model for the same period at the corresponding grid cells.

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[13] Figure 4 shows the temperature dependency of H2 deposition velocity as measured by Lallo et al. [2008] during the field measurements and compared to the ones simulated for the same period at the corresponding grid cells. In general, the temperature response is similar between the data and the simulations. The drop in simulated H2deposition velocities at the grid cell corresponding to the Loppi site is due to low soil water content simulated by LPJ-WHyMe during the summer of 2006.

image

Figure 4. Hydrogen deposition velocities as function of soil temperature. Digitized H2 deposition velocities in cm s−1 are from field measurement [Lallo et al., 2008] at Loppi (60°38′N, 24°21′E) and Helsinki (60°12′N, 25°′3′E) between August 2005 and December 2006 and the simulations were made for the same period at the corresponding grid cells.

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[14] Lallo et al. [2008] also performed laboratory chamber measurements on soil collected for the Loppi site to investigate the response of H2 deposition velocity to changes in temperature and soil water content. Figure 5 shows the comparison between the laboratory result and our model results for the mineral soil. The model response to changes in soil water content is in good agreement with the data, with a correlation of 0.74. However, the data suggest that biological uptake starts at lower soil water content. Therefore, we tested our model (test V_f(M)2, Table 1) with a new soil water content function such that biological uptake starts at a soil water fraction of 2% instead of 8%. The new soil water content function becomes:

  • display math

where inline image and ϕwater is the water filled porosity.

image

Figure 5. Comparison between the H2 relative deposition velocities between laboratory experiments on mineral soils from Loppi (60°38′N, 24°21′E) [Lallo et al., 2008] and simulations. The mineral soil used in the experiment is a halpic podsol with a total porosity of 0.75. The model has been forced with loamy sand soil type for the same range of temperatures and soil water contents than in the laboratory experiment. Digitized data from Lallo et al. [2008] are shown as filled symbols, the model simulations as open symbols and solid line. (a) Soil water content ( inline image) dependency of hydrogen relative deposition velocities. The measurements and simulations where performed with a soil temperature of 20°C. (b) Soil water content dependency of hydrogen relative deposition velocities. The simulations were performed with a new soil water content function such as described in Table 1. The measurements and simulations where performed with a soil temperature of 20°C. (c) Temperature dependency of hydrogen relative deposition velocities. The observations were measured in optimum soil water content condition and the simulations were performed with inline image = 0.15.

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[15] Simulations with the new soil water content function (Figure 5) give better results, with a correlation of 0.90. This version of the model is tested further in section 3.3. Figure 5 also shows the model soil temperature response compared with the one measured by Lallo et al. [2008] under laboratory conditions.

[16] Hammer and Levin [2009]'s estimates, based on atmospheric H2 and 222Rn observations in the boundary layer, of H2 deposition velocity at Heidelberg, Germany (49°24′N, 8°42′E) were compared with the global model simulation at the grid cell corresponding to the site coordinates (Figure 6). The measurements are representative of a suburban area influenced by local atmospheric pollution and where a large proportion of soils are covered by asphalt, affecting soil properties. These particular conditions make the comparison between data and simulation difficult. However, the monthly mean seasonal cycle estimated by Hammer and Levin [2009] between January 2005 and July 2007 was compared with the monthly mean seasonal cycle estimated by the model between January 2005 and December 2006, as no simulations have been performed for the year 2007. The H2 deposition velocities simulated by the model are higher than Hammer and Levin [2009]'s estimations with a mean value of 4.6 ± 0.1 10−2 cm s−1 compared to 3 ± 0.7 10−2 cm s−1. The model reproduces the seasonal cycle, with a minimum in February and a maximum in late summer with a correlation of 0.62. Nevertheless, the model has higher peak-to-peak amplitude compared to the estimates from atmospheric H2 and 222Rn observations at the Heidelberg site.

image

Figure 6. Digitized monthly mean H2 deposition velocities in cm s−1 estimated by Hammer and Levin [2009] between January 2005 and July 2007 at the Heidelberg site (49°24′N, 8°42′E) and monthly mean H2 deposition velocities simulated by the model between January 2005 and December 2006 at the corresponding grid cell.

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[17] Yonemura et al. [1999]carried out experimental field measurements in an upland experimental field of the National Institute of Agro-Environmental Sciences in Tsukuba, Japan (36°01′N, 140°07′E), using an open flow chamber method. The soil type in our model that corresponds most closely to the soil particle distribution described byYonemura et al. [1999] is light clay (Table S1 in Text S1 in the auxiliary material). We performed simulations using light clay soil properties over a range of 22–38°C for soil temperature and a range of 35–45% of the volumetric soil water content, corresponding to Yonemura et al. [1999]'s climatic data during the field experiment between 18 and 21 August 1995. The deposition velocities calculated by the model under these conditions ranged between 0.054 cm s−1 and 0.064 cm s−1. These simulations are in good agreement with measured H2 deposition velocities from Yonemura et al. [1999], which range from 0.045 cm s−1 to 0.07 cm s−1.

[18] We compared measurements made by Smith Downey [2006] and Smith-Downey et al. [2008] on sand soil, in a forest ecosystem at San Jacinto Mountain Reserve (33.81°N, 116.79°W). When forced with a loamy sand soil type, which is the closest soil type to sand in our model, for the same range of soil temperature (1–28°C) and the same range of volumetric soil water content (0.05–0.22 cm−3/cm−3), the model simulated H2 deposition velocities ranging from 0.018 to 0.056 cm s−1 which correspond to fluxes ranging from 4 to 12 nmol m−2 s−1 (calculated for an atmospheric concentration of 2.2 × 10−11 mol cm−3: Table S2 in Text S1 in the auxiliary material) with a mean value of 0.037 cm s−1 (8.14 nmol m−2 s−1). These results are in agreement with the values found by Smith Downey [2006] with a range 2–14 nmol m−2 s−1 and a mean value of between 8 ± 4.5 and 8.62 ± 4.8 nmol m−2 s−1 depending on the sampling location.

[19] The same method is applied to compare our simulations with enclosure measurements done at the Grenzhof field site in Germany [49°24′N, 8°36′E] by Schmitt et al. [2009]. We forced the model with sandy clay loamy sand proprieties, for the same range of soil temperature (0–35°C) and volumic soil water content (0.05–0.3 cm−3/cm−3) as in Schmitt et al. [2009]. We obtained a range of H2 deposition velocities between zero and 0.056 cm s−1, compared with the range of 0.008–0.082 cm s−1 in the data. Our model fails to reproduce the higher values of measured deposition velocities but these correspond to only three measurements. The rest of the measurements are in the same range as our modeled values.

[20] Conrad and Seiler [1985] measured H2 deposition velocities at three subtropical sites (Transvaal and Karoo in South Africa, Andalusia in Spain) using static and equilibrium box techniques. The values of 0.033 and 0.01 cms−1 obtained at the Andalusia and Karoo sites at a soil temperature of 30°C can be retrieved by our model with loamy sand type of soil for low soil water content ratio, respectively 10% and 8.5%. None of our simulations is able to simulate the high value of 0.13 cms−1 found for the Transvaal site.

[21] Rahn et al. [2002a] measured H2 soil uptake at the boreal site of Delta Junction in Alaska (63°48′N, 145°06′W), using a new method described in Rahn et al. [2002b], in July 2001. As no information is available on the soil type, or the soil temperature and soil water content, it is hard to make a comparison with the model simulations. Nevertheless, for the latitude band of 63°N the deposition velocities simulated by the model for July 2001 range between 0.031 and 0.059 cms−1, which correspond to the lower values of the range of values observed by Rahn et al. [2002a] (0.044 ± 0.013 cms−1 for the burn site and 0.073 ± 0.015 cms−1).

[22] Finally, Guo and Conrad [2008] analyzed the effect of temperature on hydrogenase activity in soil suspension and soil extract. Soils were extracted from a deciduous forest near Marburg, Germany (51°00′N, 09°50′E). The response to temperature of soil hydrogenase activity measured in laboratory conditions is similar to the function we use to describe the temperature effect on biological oxidation rate, f(Tsoil), described in the auxiliary material, with an activity about one half of the maximum activity at 0°C, an increase between 0°C and the optimum temperature, around 30°C for the measurements, 40°C for the model, followed by a decrease for temperature above the optimum.

[23] In the following section, we discuss a comparison with a global H2 inversion [Bousquet et al., 2011].

3.2. Seasonal and Latitudinal Variations

[24] Figure 7 shows the spatial distribution of H2 deposition velocities simulated for January, April, July and October 2000. The H2 velocities vary from 0 cm s−1 to 0.066 cm s−1. The Northern hemisphere shows a strong seasonal cycle, mainly due to variation in temperature and snow cover, with a minimum simulated H2 soil uptake of 29 Tg a−1 occurring in January–February and a maximum of 50 Tg a−1 occurring in July. Seasonal variations of H2 deposition velocities are weaker in the Southern hemisphere. A slight seasonality is simulated in the tropics due to soil water content variations. On global average, H2 global uptake in the Southern hemisphere is about 22 Tg a−1. Over the period of simulation 1988–2006, the averaged global H2 uptake is 61 Tg a−1 with most of the uptake occurring in the Northern hemisphere, which accounts for 65% of the total soil uptake.

image

Figure 7. Global distribution of H2 deposition velocity in cm s−1 simulated for (a) January 2000, (b) April 2000, (c) July 2000, and (d) October 2000.

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[25] Although we used global estimations of the soil H2 sink from Bousquet et al. [2011] to scale the maximum oxidation rate, kmax (cf. section 2 above), our estimate of seasonality and trends is independent of their techniques and we now compare the two. We calculated the H2 soil uptake fluxes for the same regions of the globe that Bousquet et al. [2011] used for their inversions. The region map (Figure 8) is derived from the region map defined in the TRANSCOM project (http://www.purdue.edu/transcom/). In this paper, we will focus on three big regions, as defined by Bousquet et al. [2011]: the Northern regions (North America, Europe, North Asia), the Tropical regions (Tropical America, Africa, Tropical Asia) and the Southern regions (all other land areas).

image

Figure 8. Map of the 12 regions (11 land regions and 1 ocean region) and of the 62 atmospheric stations used in the inversion. Different symbols represent different networks: circles for NOAA, triangles for CSIRO, and stars when both NOAA and CSIRO operate (ALT, CGO, MLO, and SPO).

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[26] Figure 9 shows the mean seasonal cycle for these three big regions simulated by our model and calculated, with an inversion method, by Bousquet et al. [2011]. Our bottom-up model has a stronger seasonal cycle for the Northern regions with a maximum uptake of 35 Tg a−1 in July and a minimum uptake of 12.9 Tg a−1 in January–February, compared to a maximum uptake of 43 Tg a−1 in July and a minimum uptake of 22.3 Tg a−1 in December for results of Bousquet et al. [2011]. For the Southern and the Tropical regions, our model has a mean uptake of respectively 22.5 Tg a−1 and 10 Tg a−1 and no seasonal cycle is simulated while results of Bousquet et al. [2011] show seasonality for these regions. The lack of seasonality in our model might be due to the difficulty of the model in representing drought effects on the soil. However, no long data series of observed deposition H2 velocity are available to test the seasonality in these regions.

image

Figure 9. Mean seasonal variations of H2 soil uptake in Tg a−1 for the 1991–2004 period. The three regions, defined by Bousquet et al. [2011], are northern regions (North America, Europe, North Asia), tropical regions (Tropical America, Africa, Tropical Asia) and southern regions (all other land areas). The region partition is based on the TRANSCOM map. Data from Bousquet et al. [2011] are in dashed line; H2 model results are in solid line.

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[27] The total seasonal cycle obtained by our bottom-up model and through an inversion method are in good agreement with a maximum uptake of 72.3 Tg a−1 and 78.6 Tg a−1 for our model and for Bousquet et al. [2011]'s results, respectively. In both cases, the maximum uptake occurred in July and the minimum uptake occurred in February for our model (49.5 Tg a−1) and in March for the inversion results (50.8 Tg a−1).

[28] In order to better understand the contribution of each variable controlling H2 uptake in our simulations, we performed a sensitivity test at three grid cells: one in boreal latitudes, one in the midlatitudes and one in the tropics, in a similar way to Smith Downey [2006] (Figure 10). At the grid point 60°N, 25°E, we can see that the maxima are mostly controlled by the effect of soil water content on the diffusion part of the model. The minima are controlled in a minor way by the effect of soil water content and temperature on the biological uptake, but mostly the snow cover effect drives the minimal values calculated at this grid cell. At the midlatitude grid point (42°N, 72°W), the simulated seasonal cycle is mostly controlled by the snow cover effect. We also note that effect of soil water content on biological uptake has no effect, suggesting that the soil water fraction is always higher than 15% for this point. Finally, the simulated seasonal cycle in the tropical grid cell (7°S, 57°W) is controlled in our model by the effect of soil water content on the diffusion of H2.

image

Figure 10. Sensitivity test of the model for grid cells in boreal, temperate and tropical latitudes. Light blue line: effect of temperature only on H2 diffusion with a maximal constant biological uptake. Dark blue line: addition of the soil water content effect on H2 diffusion. Red dashed line: addition of effect of the soil water content on biological uptake. Green line: full model sensitivity to variation of temperature and soil water content, without the effect of snow cover. Black line: full model sensitivity to variation of temperature and soil water content, with the effect of snow cover.

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3.3. Interannual Variability and Long-Term Trends

[29] Our simulated interannual variability of global soil H2 uptake ranges between 60.9 and 62.2 Tg a−1 for the 1991–2005 period. This is lower than estimated in Bousquet et al. [2011]'s atmospheric inversion with a global uptake ranging between 54.3 and 67.9 Tg a−1 for the same period (Table 3). Also, our model simulates a trend in the H2 uptake of −0.04 Tg a−2 over the 1992–2004 period. This trend is much smaller than that inferred by Bousquet et al. [2011] of −0.77 Tg a−2 for the same period. Note that Bousquet et al. [2011] gives a value of −0.12 Tg a−2for a bottom-up approach. This estimate was based on a previous version of our model with an inappropriate parameterization for biological uptake and a simpler model of H2 diffusion through snow. Also, the version used in Bousquet et al. [2011] had not been confronted against observations.

Table 3. Total Annual Uptakes in Tg for the Period 1991–2005 Estimated by Bousquet et al. [2011]and Simulated by the Bottom-Up H2 Soil Model
YearsInversion ModelBottom-Up Model
199154.361.4
199261.661.4
199357.560.9
199455.161.2
199555.362.1
199657.260.9
199761.961.5
199862.461.8
199964.061.8
200063.561.5
200165.161.7
200264.361.7
200366.761.8
200466.961.8
200567.962.2
   
Mean61.661.6

[30] A long-term trend in the H2 soil uptake, if any, should come from trends in the forcing climatic variables (Tsoil, soil water content). As our model shows realistic relationships between H2 uptake and climate drivers at the local scale (see section 3.1), we use the model to investigate what trends in climate variables would be required to reproduce the soil H2 uptake trend found in Bousquet et al. [2011]. We performed four runs over the 1991–2005 period with an artificial increase (decrease) of +1% per year (−1% per year) and +5% per year (−5% per year) in the soil water filled porosity, two runs with an artificial increase (decrease) in soil temperature of 0.07°C per year, and one run with an artificial decrease in the snow depth of 5% per year. Table 4summarizes these runs. The long-term trends of global H2 uptake are shown in Figure 11 and Table 5. None of these cases can reproduce Bousquet et al. [2011]'s global trend of −0.77 Tg a−2 for the H2 soil sink. The strongest negative trend is obtained for the case with a water filled porosity decreased artificially by 5% per year (WF–5), with an H2 uptake trend of −0.35 Tg a−2 still less than half of the mean trend found in Bousquet et al. [2011], although compatible with the lower values of their large range (−0.25 Tg a−2 / −1.30 Tg a−2). However, this scenario is completely unrealistic: 87% of the globe ends up with a water filled porosity under the wilting point by 2004, which means that the soils are too dry for vegetation to grow.

Table 4. Description of the Different Scenarios and Sensitivity Tests Used to Calculate Monthly H2 Soil Uptake Anomalies Shown in Figure 8
Scenarios LabelDescription
B_20113-D inversion model estimationsBousquet et al. [2011]
RefStandard run for H2model forced with climatic data and LPJ-WHyMe outputs
WF−1Soil water filled porosity is artificially decreased by 1% per year for the 1992–2005 period
WF−5Soil water filled porosity is artificially decreased by 5% per year for the 1992–2005 period
WF+1Soil water filled porosity is artificially increased by 1% per year for the 1992–2005 period
WF+5Soil water filled porosity is artificially increased by 5% per year for the 1992–2005 period
T−1Soil temperature is artificially decreased by 1°C between 1992 and 2005 (∼0.07°C per year)
T+5Soil temperature is artificially increased by 1°C between 1992 and 2005 (∼0.07°C per year)
S−5Snow depth is artificially decreased by 5% per year for the 1992–2005 period
image

Figure 11. Interannual variations of H2 soil uptake for the scenarios summarized in Table 4 in Tg a−1 for the 1992–2004 period. Deseasonalized H2 fluxes are plotted for three groups of regions and globally. The three regions, as described by Bousquet et al. [2011], are Northern regions (North America, Europe, North Asia), Tropical regions (Tropical America, Africa, Tropical Asia) and Southern regions (all other land areas). The region partition is based on the TRANSCOM map. A 13-month running mean is applied to the monthly fluxes to calculate the deseasonalized values.

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Table 5. Calculated Soil H2 Uptake Annual Trends Between 1992 and 2004 in Tg a−2 for the Different Scenarios Described in Table 4a
 B_2011RefWF − 1WF − 5WF + 1WF + 5T − 1T + 1S − 5
  • a

    The three regions as defined by Bousquet et al. [2011] are northern regions (North America, Europe, north Asia), tropical regions (tropical America, Africa, Tropical Asia), and southern regions (all other land areas). The region partition is based on TRANSCOM map.

Northern regions−0.68−0.05−0.1−0.3+0.06+0.56−0.02−0.08−0.12
Tropical regions−0.17+0.005−0.12−0.13+0.11+0.58+0.01−0.002+0.005
Southern regions+0.110−0.03+0.08+0.03+0.18+0.006−0.0050
Global−0.77−0.04−0.26−0.35+0.2+1.3+0.003−0.09−0.12

[31] To be sure that this finding is independent of the particular parameterization we chose for the biological response to soil organic content and soil water content, and for maximum biological uptake, we performed the same runs as described in Table 4 for different versions of the model as described in Table 1. The results of these runs and the trends associated are summarized in Table 2. Only one scenario, with a global trend of −0.89 Tg a−2, produces a trend as high as the results from Bousquet et al. [2011]. This scenario corresponds to the version with a new biological soil water content dependency such as described in Table 1 and for the unrealistic scenario where filled porosity decreased artificially by 5% per year (WF–5).

4. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Implementation
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

[32] We have developed a simple global process-based model of atmospheric H2consumption by soils. The model is based on an exact solution of the one-dimensional diffusion-reaction equation in a unique soil layer and includes a parameterization of biological oxidation. The model was incorporated in a Dynamic Global Vegetation Model and simulations have been performed for the 1988–2006 period. The model results were tested against available experimental data and performed satisfactorily.

[33] The global seasonal cycle simulated by our model is due to the Northern hemisphere. No seasonality was shown for the Southern hemisphere. According to our model, 65% of the global H2 uptake by the soil occurs in the Northern hemisphere.

[34] The model outputs were compared with a recent inversion study [Bousquet et al., 2011]. More specifically, we tested our model with different scenarios in order to see if changes in climatic inputs could produce an increase of the simulated H2 soil sink by 0.77 Tg a−2. As pointed out by Bousquet et al. [2011], the large range (−0.25 Tg a−2 to −1.30 Tg a−2) in their long-term trend of H2soil uptake indicates a weak robustness of their results, which appear to be sensitive to the inversion setup and more precisely to the long-term trend of the fossil-fuel and N2 related sources of H2. Only for one scenario is our model able to predict a long-term trend of this magnitude. However, this scenario leads to a desert planet that obviously is not realistic. In general, our process-based model is unable to simulate the long-term trend calculated through the inversion method, even by forcing extremely the different climatic variables controlling H2 uptake by the soils, confirming the lack of robustness of Bousquet et al. [2011]'s long-term trend. Indeed, there is no long-term significant trend observed in atmospheric H2 concentration itself. The increase in H2 soil uptake inferred by the inversion method was compensated by a similar increase of the atmospheric H2source representing fossil-fuel and N2-fixation related emissions. This too is unsubstantiated by data. Even if the economic growth in the mid-1990s to early 2000s could explain a proportion of this trend, the increase in fossil fuel emissions between 1991 and 2004 was only about 23% [Forster et al., 2007] while the increase of the fossil fuel and N2-fixation related H2 source found by Bousquet et al. [2011] is twice as large. In a recent study, Yashiro et al. [2011], using a two-layered soil diffusion/uptake model, could not simulate a long-term trend in the global soil uptake, corroborating our results.

[35] In general, changes in climatic inputs such as soil temperature, soil water content and snow depth do not strongly affect the soil uptake in our model. However, our model does not take in account the effect of the first few centimeters of soil, which can create an inactive layer and decrease the H2soil uptake. This inactive layer can have a non-negligible impact on soil uptake.

[36] In general, and in order to better constrain the H2 soil uptake models, more field measurements for long period are necessary, especially in the tropics where no data are currently available. A better observational constraint on the maximum biological uptake (kmax), and the minimum soil water content needed to activate the biological pump, would be also very useful for modeling.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Implementation
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

[37] We thank Renato Spahni and Rita Wania for their help with LPJ-WHyMe. We thank Nicole Smith-Downey for the explanations she provided us about the implementation in CLM2 of the H2model she developed during her thesis. We also thank T. Aalto and C. Yver for the valuable information they provided concerning their published data. P.N.F. was sponsored by the EU-Project HYMN (Hydrogen, Methane and Nitrous Oxide: Trend variability, budgets, and interactions with the biosphere; GOCE-037048). The research leading to these results (CM, ICP) has received funding from the [European Community's] Seventh Framework Programme (FP7 2007–2013) under grant agreement [238366].

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Implementation
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Implementation
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgments
  8. References
  9. Supporting Information

Auxiliary material for this article explains the H2model implementation based on a diffusion-consumption scheme, similar to the one adopted byYonemura et al. [2000b] and Smith Downey [2006]. The parameterization of factors, such as soil temperature, soil water content or snow depth, controlling H2 diffusion into the soils as well as the H2 biological uptake are described here.

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Additional file information is provided in the readme.txt.

FilenameFormatSizeDescription
gbc1918-sup-0001-readme.txtplain text document0Kreadme.txt
gbc1918-sup-0002-txts01.docWord document0KText S1. Two tables depicting FAO soil categories and model parameters and text describing model implementation, including calculation of soil H2 flux, diffusivity, and oxidation activity; function of soil structure and temperature dependence; H2 response to temperature, soil water content, and soil organic matter; and global application.
gbc1918-sup-0003-t01.txtplain text document1KTab-delimited Table 1.
gbc1918-sup-0004-t02.txtplain text document0KTab-delimited Table 2.
gbc1918-sup-0005-t03.txtplain text document0KTab-delimited Table 3.
gbc1918-sup-0006-t04.txtplain text document1KTab-delimited Table 4.
gbc1918-sup-0007-t05.txtplain text document1KTab-delimited Table 5.

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