Recent field studies have documented a surprisingly strong and consistent methane sink in arctic mineral soils, thought to be due to high-affinity methanotrophy. However, the distinctive physiology of these methanotrophs is poorly represented in mechanistic methane models. We developed a new model, constrained by microcosm experiments, to simulate the activity of high-affinity methanotrophs. The model was tested against soil core-thawing experiments and field-based measurements of methane fluxes and was compared to conventional mechanistic methane models. Our simulations show that high-affinity methanotrophy can be an important component of the net methane flux from arctic mineral soils. Simulations without this process overestimate methane emissions. Furthermore, simulations of methane flux seasonality are improved by dynamic simulation of active microbial biomass. Because a large fraction of the Arctic is characterized by mineral soils, high-affinity methanotrophy will likely have a strong effect on its net methane flux.
- We developed a methane model that represents high-affinity methanotrophy and active microbial biomass changes in arctic mineral soils
- High-affinity methanotrophy facilitated accurate simulation of methane consumption in arctic mineral soils
- Active microbial biomass changes strongly influenced seasonal methane fluxes
Arctic soils constitute an important methane source to the atmosphere. Mechanistic methane models and observation-based atmospheric inversions indicate a net arctic methane source in the range +15–30 Tg CH4 yr−1 [Tan et al., 2015]. Furthermore, mechanistic methane models indicate that methane emissions from the Arctic may increase with temperatures and permafrost thaw [McGuire et al., 2009; Schaefer et al., 2011; Koven et al., 2013; Schuur et al., 2013].
However, not all arctic locations are methane sources. Recent field studies have identified multiple sites that are net methane sinks [Zhu et al., 2012; Brummell et al., 2014; Emmerton et al., 2014; Martineau et al., 2014; Jørgensen et al., 2015; Lau et al., 2015; Stackhouse et al., 2015]. The cryosols associated with methane sinks are drier, more oxic, and contain less soil organic carbon (SOC) than methane-emitting, SOC-rich wetlands. SOC-poor cryosols span 87% of the Arctic [Hugelius et al., 2014]. Because mechanistic methane models have been parameterized using observations of SOC-rich soils and anaerobic conditions [Melton et al., 2013; Wania et al., 2013; Zhuang et al., 2013], it is not obvious that they can accurately simulate the net methane uptake associated with dry cryosols or predict how methane fluxes associated with such cryosols will evolve with further warming.
The net methane flux in arctic soils can potentially be explained by biogeographic differences in methanotroph community composition [Christiansen et al., 2015]. In wet, SOC-rich soils, methane production by methanogenic archaea (hereafter, methanogens) is typically larger than methane oxidation by methanotrophic bacteria (hereafter, methanotrophs), with the surplus methane released into the atmosphere [Le Mer and Roger, 2001]. The dominant methanotrophs in SOC-rich soils are classified as “low affinity” and require methane concentrations in excess of 600 ppmv for growth and maintenance [Conrad, 2009]. But in drier mineral arctic cryosols, the dominant (“high-affinity”) methanotrophs can survive at atmospheric methane concentrations [Lau et al., 2015]. Soil incubation experiments have shown that high-affinity methanotrophs are about 2–3 times as sensitive to temperature as low-affinity methanotrophs [Christiansen et al., 2015; Jørgensen et al., 2015; Lau et al., 2015] and that high-affinity methanotrophs have surprisingly high activity under saturated soil moisture conditions [Stackhouse et al., 2015]. These characteristics may allow high-affinity methanotrophs to oxidize more methane as temperatures increase. Some recent mechanistic models have included parameterizations of high-affinity methanotrophs, but model parameters have not been constrained by observations from arctic mineral soils [Riley et al., 2011; Ringeval et al., 2010].
Changes in active biomass may explain why seasonal variations in methane fluxes lag behind seasonal variations in air temperature [King, 1994] and have been shown to be important for understanding methanotrophy [Shukla et al., 2013]. But growth rates in natural arctic settings are generally slow, and it is unknown whether high-affinity methanotrophs can grow at atmospheric methane concentrations. Thus, we chose to model the changes in active biomass that would come about during thaw by the repair of critical enzymes in microorganisms that had been dormant during the winter rather than a net increase in total cellular abundance. A recent methane model has included microbial dynamics [Xu et al., 2015]; however, the model dynamics were not tested at field scales.
We test two hypotheses: (1) the representation of high-affinity methanotrophs facilitates accurate simulation of atmospheric methane consumption in arctic mineral cryosols; and (2) explicit consideration of the active methanotroph biomass improves simulated seasonal methane fluxes. To evaluate these hypotheses, we present a new model and evaluate it against laboratory and field measurements of methane fluxes.
2 Materials and Methods
2.1 Model Description
We developed a methane model (eXplicit High-Affinity Methanotroph model; “XHAM”) that includes both high-affinity methantrophs and active microbial biomass dynamics (Figure 1a). To isolate the effect of active microbial biomass changes, we compared simulations from XHAM to simulations from a simplified model that did not include active microbial biomass dynamics (High-Affinity Methanotroph model; “HAM”) (Figure 1b). We compared our XHAM and HAM results to simulations from three established mechanistic methane models (CLM4Me, ORCHIDEE, and LPJ-WHyMe) (Figure 1c and Method S1 in the supporting information) [Riley et al., 2011; Ringeval et al., 2010; Wania et al., 2010].
2.2 Data Sets
Our models were parameterized and tested against measurements from microcosm and core-thawing experiments and field measurements [Emmerton et al., 2014; Jørgensen et al., 2015; Lau et al., 2015; Stackhouse et al., 2015; D'Imperio, 2016] (Table 1, Method S2, and Figure S1). All sites had SOC lower than 5%.
|Experiment||Purpose||Data Description||Soil Forcing||Ensemble Design|
|Microcosm experiment [Lau et al., 2015]||Parameterized high-affinity methanotrophs||Axel Heiberg Island, Canada||31 days||Six different temperature and soil moisture treatments||Prescribed from observations||Prescribed from observations||20 simulations parameterized with a random draw from the joint posterior probability density function derived from Markov chain Monte Carlo (MCMC) for methanotrophs|
|Core-thawing experiment [Stackhouse et al., 2015]||Test methane fluxes at laboratory scales||Axel Heiberg Island, Canada||16 weeks||Control and moistened treatments||Prescribed from observations||Simulated using evaporation, precipitation, and redistribution||20 simulations parameterized with (1) same methanotroph parameters as microcosm experiment; and (2) methanogen parameters sampled from literature values|
|Field measurement [Emmerton et al., 2014; Jørgensen et al., 2015; D'Imperio, 2016]||Test methane fluxes at field scales||Canadian High arctic, Northeast and West Greenland||Jun–Sep||In situ observations at three dry tundra sites and one moist tundra site||Prescribed from observations (top 5 cm) and simulated using thermal conduction (bottom 95 cm)||Prescribed (top 5 cm) and simulated using redistribution (bottom 95 cm)||20 simulations initialized with same methanogen and methanotroph parameters as core-thawing experiment|
In a microcosm experiment, three replicates of 8–10 g of turbic cryosols from the Canadian High Arctic were incubated at different temperatures (4 and 10°C) and volumetric soil moistures (33, 66, and 100%) for 31 days [Lau et al., 2015]. Headspace methane concentrations were monitored throughout the experiment to determine methane oxidation rates. These experiments were used to parameterize our model of high-affinity methanotrophy.
In a core-thawing experiment [Stackhouse et al., 2015], a 16 week thaw was carried out mimicking in situ conditions using turbic cryosol cores of 1 m length and 7.5 cm diameter from the Canadian High Arctic. The cores were frozen at −4°C during transfer and storage. The cores thawed from the top down, finally reaching a uniform temperature of +4.5°C. Four cores received 40 mL of artificial rainwater each week (hereafter, “moistened” cores), whereas eight other cores did not receive any water (hereafter, “control” cores). The headspace of each core was flushed with a gas mixture at the start of each week for 16 weeks. For the first 14 weeks, the gas mixture was methane free and consisted of 79.0% N2 and 20.5% O2. For weeks 15 and 16, the gas mixture included 2 ppmv of methane.
We also used methane fluxes measured at four field sites: sparsely vegetated dwarf shrub on Ellesmere Island in the Canadian High Arctic instrumented during July 2011 [Emmerton et al., 2014], dry and moist dwarf-shrub tundra sites in Northeast Greenland instrumented during July–September 2012 [Jørgensen et al., 2015], and dry dwarf-shrub tundra in West Greenland instrumented during June–August 2013 [D'Imperio, 2016]. All sites had a mean air temperature of ~10°C during the study period. The dry tundra sites had a mean soil moisture of 15% (0–5 cm depth), and the moist tundra site had a mean soil moisture of 40%. The core-thawing experiments and the field measurements were used to evaluate our model at laboratory and field scales, respectively.
2.3 Model Experiments
We used the microcosm experiments to parameterize methanotrophy in our XHAM and HAM models. Simulations were forced with the observed temperature and soil water content data, and the simulated methane fluxes were compared to observations. Markov chain Monte Carlo was used to obtain the joint probability distribution of four parameters related to the soil temperature and moisture sensitivities of high-affinity methanotrophy (Method S3). We sampled the resulting probability distribution 20 times and used these samples to define 20 different model ensemble members (Method S3 and Tables S5 and S6). Full listings of parameter values are given in Tables S1 and S2. Simulations under microcosm conditions were repeated for each ensemble member.
We simulated the core-thawing experiments with each ensemble member. In each simulation, initial conditions for methane, acetate concentrations, and temporal changes of soil temperature were based on observed values [Stackhouse et al., 2015]. Because soil moisture was not measured, we simulated it using a simple model (Method S2.2).
We also carried out ensemble simulations of the four sites in the Canadian High Arctic and Greenland. Time series of soil temperature and moisture of the top 5 cm during the study period were prescribed from observations. Because soil temperature and moisture dynamics below 5 cm depth were not measured, we simulated them using thermal conduction and redistribution models (Method S2.3).
3.1 Microcosm Experiment
The simulated methane fluxes from XHAM and HAM are generally consistent with observational data (mean R2 values of 0.86 and 0.72, respectively) (Figure 2). This result was expected because XHAM and HAM were parameterized using these data. The largest model biases correspond to water-saturated conditions (Figure 2e). Overall, the conventional models (CLM4Me, ORCHIDEE, and LPJ-WhyMe) show larger biases than XHAM or HAM. Under unsaturated conditions, they mostly simulate complete consumption of methane within 2 days, except 10°C 33% conditions for LPJ-WhyMe (Figures 2a–2d); under saturated conditions, all conventional models simulate strong net methane emission (Figures 2e and 2f).
To investigate the effect of microbial biomass changes, we compared the daily methane oxidation rate simulated by HAM and XHAM to the observed rates (Figure S2). Both observations and XHAM show bell-shaped curves. In XHAM, the initial increase corresponds to a transient increase in active methanotrophic biomass by 4 (10°C 66, 100%, and 4°C, 33% conditions) to 10 times (10°C, 33% conditions), and the decline corresponds to the gradual reduction in methane concentration. In contrast, HAM simulates its maximum daily methane oxidation at the beginning of the experiment and oxidation monotonically declines as methane concentration decreases. Residual error in the XHAM simulations may be due to errors in model parameters that were taken from the literature, including those relevant to microbial biomass accumulation and turnover (equation (S3-2)). Furthermore, the model does not account for the possibility of methanotrophs consuming substrates other than methane under low-methane conditions.
3.2 Soil Core-Thawing Experiment
XHAM simulations are broadly consistent (mean R2 of 0.73) with the observed methane fluxes from soil core-thawing experiments (Figure 3). Emissions are initially large but decrease throughout the first 6 weeks of the experiment. Supplementary simulations with microbial activity deactivated show a similar signal in weeks 1–6 (Figure S3), indicating that the week 1–6 response corresponds to the release of methane gas trapped during the last freeze-in and wintertime periods [Stackhouse et al., 2015]. Compared to the control cores, less methane is released from the moistened cores during the initial 6 weeks (Figure 3b), which the model attributes to slower diffusion of methane through water-filled pore space. Additionally, the XHAM simulations capture the rapid methane uptake that occurs during weeks 15 through 16, when 2 ppmv of methane was introduced to the headspace.
HAM simulates methane emission from the core in weeks 1–6, but simulates negligible methane oxidation in weeks 15–16 (Figure 3). The difference between XHAM and HAM simulations occurs because only XHAM includes a buildup of active methanotroph biomass. These biomass increases coincide with increases in temperature and rates of methanogenesis. We found that the differences between the XHAM and HAM simulations were due to the dynamics of active biomass changes in XHAM rather than differences in model parameters (Figure S4).
CLM4Me and ORCHIDEE simulate methane consumption under control conditions (Figure 3a). In these models, methane production is restricted if the soil is not fully saturated. For saturated soils (Figure 3b), CLM4Me and ORCHIDEE simulate large rates of methane emission. LPJ-WHyMe simulates methane emission for both control and moistened conditions because its soil moisture sensitivity function is more permitting of methanogenesis under subsaturated conditions (Table S3).
3.3 Field Measurements
XHAM captures the large methane uptake from the four field sites throughout the summer (−6.52 ± 2.61 µmol m−2 h−1, 1 SD) (Figure 4). The HAM model is nearly neutral (−0.21 ± 0.39 µmol m−2 h−1, 1 SD). The three conventional models typically simulate emission of methane from the soil or are nearly neutral (+1.49 ± 1.27 µmol m−2 h−1, 1 SD). These results are of same magnitude as those from methane model intercomparison studies [Tan et al., 2015; Melton et al., 2013; Wania et al., 2013]. LPJ-WhyMe simulates the highest methane emission rate in moist tundra because of its soil moisture sensitivity function (Table S3). In contrast, CLM4Me simulates less emission from moist tundra than from dry tundra because its optimal methane oxidation occurs at 60–80% of water saturation (Figure S5). The methane fluxes from ORCHIDEE range from small negative to large positive fluxes during the growing season because of its high temperature sensitivity of methane production (Q10 of 6) relative to methane oxidation (Q10 of 2) (Table S3).
Our results emphasize the importance of simulating high-affinity methanotrophy and active microbial biomass changes. For different experimental scales, site locations, and soil moisture conditions, the XHAM model accurately simulates methane uptake from arctic mineral soils. Models that do not include microbial biomass dynamics or do not parameterize high-affinity methanotrophy exhibit significant biases. Incorporation of high-affinity methanotrophy into mechanistic methane models may improve the comparison between models and atmospheric inversions [Aronson et al., 2013; Kirschke et al., 2013].
Our parameterization of high-affinity methanotrophs reflects their unique physiology. We infer a temperature sensitivity for high-affinity methanotrophy (117 kJ mol−1, Table S2) that is higher than the temperature sensitivity of methanogenesis (106 kJ mol−1) [Allen et al., 2005; Yvon-Durocher et al., 2014] and 2–3 times higher than the temperature sensitivity of low-affinity methanotrophy [Jang et al., 2006]. In contrast, current methane models parameterize the temperature sensitivity of methanotrophy to be 1–3 times smaller than the temperature sensitivity of methanogenesis (Figure S5). Moreover, the microcosm and core-thawing experiments reveal that methane oxidation was possible under saturated conditions; however, some of the conventional mechanistic models require aerobic conditions for methanotrophs to oxidize methane.
Taken together, these results have implications for our understanding and modeling of arctic methane fluxes (Figure 5). Previous studies predicted a positive feedback between temperature and methane emission, whereby increased temperature triggers permafrost thaw, which increases soil moisture, further increasing methane emission by methanogenesis (Figure 5, circles 1–2) [Hinzman et al., 2013]. However, this framework does not account for the physiology of high-affinity methanotrophs. Because high-affinity methanotrophs may respond more strongly to temperature and less strongly to soil moisture than low-affinity methanotrophs (Figure 5, circles 3–4), this feedback loop may be partially suppressed.
Accounting for seasonal biomass changes improved our simulations of core-thawing experiments and field observations. Such seasonal biomass changes are likely important for annual methane budgets. For example, increases in the thaw season length will likely have a nonlinear effect on microbial productivity [Roy Chowdhury et al., 2015]. Moreover, explicit modeling of microbial dynamics (Figure 5, circle 5) will facilitate future model developments that include effects of microbial adaptation [Graham et al., 2012].
Circumpolar application of our XHAM model will require additional work. First, the current model was parameterized using data from a limited set of microcosm experiments. Additional data, corresponding to a wider range of soil temperatures and moisture contents, would be helpful to generalize the model. Second, SOC varies throughout the Arctic and exerts a first-order control on arctic methane fluxes; a future priority should be to evaluate models at sites having a broader range of SOC. Such work will better allow us to test the interplay between high-affinity methanotrophs, low-affinity methanotrophs, and different classes of methanogens. Currently, our model is perhaps best suited for dry, mineral cryosols, where high-affinity methanotrophy has been shown to be important [Lau et al., 2015]. The role of methane-oxidizing archaea should also be explored [Hu et al., 2014; Segarra et al., 2015; Shelley et al., 2015]. Lastly, experiments should be done to better constrain microbial dynamics in models to facilitate better prediction and understanding of arctic methane budgets.
We acknowledge support from the Danish National Research Foundation (CENPERM DNRF100) and Natural Sciences and Engineering Research Council of Canada (NSERC). We thank William Riley, Shuhei Ono, Jaya Khanna, and two anonymous reviewers for helpful comments. The data and model code are available upon request to the authors.
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