Methane (CH4) emissions from wetlands are likely increasing and
important in global climate change assessments. However, contemporary
terrestrial biogeochemical model predictions of CH4 emissions are very
uncertain, at least in part due to prescribed temperature sensitivity of
CH4 production and emission. While statistically consistent apparent
CH4 emission temperature dependencies have been inferred from
meta-analyses across microbial to ecosystem scales, year-round
ecosystem-scale observations have contradicted that finding. Here, we show
that apparent CH4 emission temperature dependencies inferred from
year-round chamber measurements exhibit substantial intra-seasonal
variability, suggesting that using static temperature relations to predict
CH4 emissions is mechanistically flawed. Our model results indicate
that such intra-seasonal variability is driven by substrate-mediated
microbial and abiotic interactions: seasonal cycles in substrate
availability favors CH4 production later in the season, leading to
hysteretic temperature sensitivity of CH4 production and emission.
Our findings demonstrate the uncertainty of inferring CH4 emission or
production rates from temperature alone and highlight the need to represent
microbial and abiotic interactions in wetland biogeochemical models.
Introduction
Methane (CH4) is the second most important climate forcing gas with at
least a 28-fold higher global warming potential (GWP) than carbon dioxide
(CO2) over a 100-year horizon (Myhre et al., 2013). Atmospheric
CH4 concentrations have more than doubled since 1750
(Saunois et
al., 2016) and have contributed about 20 % of the additional radiative
forcing accumulated in the lower atmosphere
(Ciais et al.,
2013). Recent assessments have found that CH4 emissions from wetland
and other inland waters are the largest and most uncertain sources affecting
the global CH4 budget (Kirschke
et al., 2013; Poulter et al., 2017; Saunois et al., 2016). Such CH4
emissions account for 25 % to 32 % of current global total CH4 emissions (Saunois et
al., 2016) and contribute substantially to the renewed and sustained
atmospheric CH4 growth after 2006 (Saunois et al., 2017).
Increasing CH4 emissions could offset mitigation efforts and accelerate
climate change (Bastviken et
al., 2011; Kirschke et al., 2013) due to their strong influence on the
global radiative energy budget (Neubauer and
Megonigal, 2015). However, CH4 emission estimates are poorly
constrained due to insufficient quality-controlled measurements
(Bastviken
et al., 2011; Kirschke et al., 2013; Saunois et al., 2016) and uncertain
model structures and parameterizations
(Melton
et al., 2013; Wania et al., 2013; Xu et al., 2016). In fact, simulations in
the ongoing Coupled Model Intercomparison Project Phase 6 (CMIP6;
Eyring et al., 2016) do
not even request wetland CH4 emission predictions for the historical or
21st century periods. A number of knowledge gaps
(Xu et al., 2016) need to be addressed
to improve CH4 model representations and thereby CH4 climate
feedback predictions (Dean et al.,
2018). Such efforts are imperative because, among other reasons, permafrost
degradation resulting from observed global-scale permafrost warming
(Biskaborn et al., 2019) can
stimulate organic matter decomposition
(Schuur et al., 2015) that
could augment global warming with a strong contribution from CH4 (Knoblauch et al., 2018).
Many contemporary terrestrial biogeochemical models parameterize CH4 production (or even CH4 emissions) as a static temperature function
of net primary production or heterotrophic respiration
(Melton
et al., 2013; Wania et al., 2013; Xu et al., 2016). Such parameterization is
supported by recent meta-analyses that indicate a static and consistent
apparent CH4 production and emission temperature dependence across
microbial to ecosystem scales (Yvon-Durocher et al., 2014).
However, measurements collected across sites with similar wetland
climate, hydrology, and plant community compositions suggest large spatial
and temporal variability in the ratio between ecosystem productivity and
CH4 emissions (Hemes et al., 2018).
Further, ecosystem-scale CH4 emissions have hysteretic responses to
seasonal changes in gross primary productivity (GPP), water table depth
(WTD), and temperature
(Brown
et al., 2014; Goodrich et al., 2015; Rinne et al., 2018; Zona et al., 2016),
suggesting that CH4 biogeochemistry may not be accurately represented
by static relationships. Consequently, a mechanistic understanding of
factors modulating CH4 production and emission rates is urgently
needed to improve the currently uncertain CH4 biogeochemistry
parameterization.
Although observations of changes in CH4 production, oxidation, and
emission rates; spatial heterogeneity; and seasonal dynamics following
permafrost degradation have been discussed (Hodgkins
et al., 2014; McCalley et al., 2014; Olefeldt et al., 2013; Perryman et al.,
2020), an understanding of mechanisms regulating intra-seasonally varying
CH4 emissions and their response to temperature is still lacking. We
therefore investigated the impacts of soil thermal and hydrological history
on CH4 emissions to improve understanding of apparent CH4 emission
temperature dependence and inform CH4 model structure and
parameterization. We hypothesized that a static apparent CH4 emission
temperature dependence is not sufficient for modeling CH4 emissions
due to substrate-mediated hysteretic microbial and abiotic interactions
(Tang and Riley, 2014) over seasonal timescales. We
used a comprehensive biogeochemistry model (ecosys) to investigate observed
intra-seasonal changes in apparent CH4 emission temperature
dependence at two high-latitude sites: Stordalen Mire (68.2∘ N,
19.0∘ E) and Utqiaġvik (formerly Barrow; 71.3∘ N,
156.5∘ W). We focus most of the detailed analysis at Stordalen
Mire, where we recently validated the modeled CH4 production pathways
using acetoclastic and hydrogenotrophic methanogen relative abundance
inferred from 16S rRNA gene amplicon sequencing data (Chang
et al., 2019b). We also evaluated the uncertainty of ignoring
substrate-mediated hysteretic microbial and abiotic interactions.
MethodsStudy site description
The Stordalen Mire sites are about 10 km east of the Abisko Scientific
Research Station in the discontinuous permafrost zone of northern Sweden and
include intact permafrost palsa, partly thawed bog, and fen
(Hodgkins et al., 2014).
The mean annual air temperatures and precipitation rates at the Stordalen Mire are
around 0.6 ∘C and 336 mm yr-1, respectively. The measured
CH4 emissions are near zero in the palsa due to its deeper WTD and
shallower active layer depth (ALD) (Bäckstrand
et al., 2008a, b, 2010); we therefore did not include this site in our
analysis. The bog is ombrotrophic (pH ∼ 4.2) with WTD
fluctuating from the peat surface to 35 cm below the peat surface
(Bäckstrand
et al., 2008a, b; Olefeldt and Roulet, 2012) and is dominated by
Sphagnum spp. mosses with a moderate abundance of short sedges such as Eriophorum vaginatum and Carex bigelowii (Bäckstrand
et al., 2008a, b; Malmer et al., 2005; Olefeldt and Roulet, 2012). The
fen is minerotrophic (pH ∼ 5.7), has WTD near or above the peat
surface throughout the growing season, and is dominated by tall sedges such
as E. angustifolium, C. rostrata, and Esquisetum spp. (Bäckstrand
et al., 2008a, b; Olefeldt and Roulet, 2012). The Stordalen Mire bog and
fen both have a peat layer ranging from 0.5 to 1 m
(Rydén and Kostov, 1980) and an ALD greater
than 0.9 m (Bäckstrand et
al., 2008b).
The Utqiaġvik site is located at the Barrow Experimental Observatory at
the northern tip of Alaska's Arctic coastal plain and is characterized by
polygonal landforms caused by seasonal freezing and thawing of tundra soil
(Hinkel et al., 2005). These
polygonal landforms were categorized into separate features based on
moisture variation determined by surface elevations
(Wainwright et al., 2015). We analyzed
CH4 emissions modeled in the low-centered polygonal landform that was
represented as a connected combination of trough, rim, and center structures
(Grant et al.,
2017b). The mean annual air temperature and precipitation at Utqiaġvik
are around -12∘C and 106 mm yr-1, respectively. The ALD
varies spatially from approximately 20 to 60 cm, which is influenced by soil
texture, vegetation, soil moisture, and interannual variability
(Shiklomanov et al., 2010).
Field measurements
A system of six automated gas-sampling chambers made of transparent Lexan
was installed at the Stordalen Mire in 2001 (three in the bog and three in
the fen). Each chamber covered an area of 0.14 m2 (38 cm × 38 cm) with a height of 25–45 cm, depending on the vegetation and the depth of
insertion, and was closed for 5 min every 3 h. In addition, each
chamber is instrumented with thermocouples measuring air and ground surface
temperatures, and WTD is measured manually 3 to 5 times per week from
June to October each year (McCalley et al., 2014).
The system was updated with a new chamber design similar to that described
in Bubier et al. (2003) in 2011. The new chambers
each cover an area of 0.2 m2 (45 cm × 45 cm), with a height
ranging from 15 to 75 cm depending on habitat vegetation.
Apparent temperature dependence calculation
We quantify the apparent temperature dependencies of daily CH4 emission
and CH4 production by fitting Boltzmann–Arrhenius functions of the following
form:
lnFiT=Ea,i‾⋅-1kT+εFi,
where FiT is the rate of CH4 emission (i=1) and
CH4 production (i=2) at absolute temperature T; Ea,i‾ (in
electronvolt, eV) and εFi correspond to the fitted apparent activation
energy (slope) and base reaction rate (intercept), respectively. k is the
Boltzmann constant (8.62×10-5 eV K-1).
We defined earlier and later periods as the times before and after the
highest daily temperature analyzed in a given thawed season, respectively,
to quantify intra-seasonal changes in apparent CH4 emission or
production temperature dependencies. Thawed seasons were defined as the time
period when measured or modeled temperatures are at least 1 ∘C to
avoid low CH4 emissions in the 0–1 ∘C temperature window
that can alter the base reaction rate of our Boltzmann–Arrhenius functions.
Four types of temperature were used in our analysis: (1) measured soil
surface temperature (e.g., Fig. 1), (2) modeled vertical mean 0–20 cm
soil temperature (e.g., Fig. 2), (3) measured air temperature (e.g.,
Fig. S1 in the Supplement), and (4) modeled air temperature (e.g., Fig. S2). The vertical mean 0–20 cm soil temperature was chosen for our
analysis because CH4 production at our study site is concentrated in
the top 20 cm of soil (Chang et al., 2019b). Consistent
hysteretic temperature responses were derived with above-zero vertical mean
0–20 cm soil temperatures (i.e., include the modeled 0–1 ∘C
temperature window), e.g., Fig. 2 vs. Fig. S3.
CH4 emissions are hysteretic to soil surface temperature
measured in individual automated chambers at the Stordalen Mire bog (top
three panels) and fen (bottom three panels) sites from 2012 to 2017 thawed
seasons (left to right). Open circles and lines represent the daily data
points and the fitted apparent CH4 emission temperature dependence,
respectively. The earlier, later, and full-season periods are colored in
red, blue, and black, respectively. Earlier and later periods are defined as
the time before and after the seasonal maximum soil surface temperature
denoted by black cross signs. Start date and end dates represent the
beginning and ending of a thawed season defined as the period when measured
daily soil surface temperature is above 1 ∘C, respectively.
CH4 emissions are hysteretic to soil temperature modeled in
the Stordalen Mire bog (a–c) and fen (d–f) and the Utqiaġvik
low-centered polygon (g–i) from 2011 to 2013 thawed seasons. Dots and lines represent the daily data points and the fitted apparent
temperature dependence, respectively. Earlier, later, and full-season
period lines are colored in red, blue, and black, respectively. Earlier and later
periods are defined as the time before and after the seasonal maximum 0–20 cm soil temperature, denoted by black cross signs. Start date and end dates
represent the beginning and ending of a thawed season defined as the period
when modeled daily 0–20 cm soil temperature is above 1 ∘C,
respectively.
Model description
The ecosys model is a comprehensive biogeochemistry model that explicitly
represents interactions among biogeophysical (i.e., hydrological and
thermal), biogeochemical (including carbon, nitrogen, and phosphorus), plant,
and microbial processes. The aboveground processes are represented in
multi-specific multilayer plant canopies, and the belowground processes
are represented in multiple soil layers with multiphase subsurface reactive
transport. CH4 production (i.e., acetoclastic and hydrogenotrophic
methanogenesis), CH4 oxidation, and CH4 transport (i.e.,
diffusion, aerenchyma, and ebullition) are explicitly represented in
ecosys. The ecosys model operates at variable time steps (seconds to 1 h) determined by convergence criteria, and it can be applied at patch
scale (spatially homogenous one-dimensional scale; e.g., Chang et al., 2019a) and landscape
scale (spatially variable two- or three-dimensional scale; e.g., Grant
et al., 2017a, b). The ecosys model has been extensively examined against
field measurements made in 2002–2007 (Chang et al., 2019a) and 2011–2013
(Chang et al., 2019b) at our study sites at the Stordalen
Mire and in 2013 at our study sites at Utqiaġvik (Grant
et al., 2017a, b, 2019). A qualitative summary of the ecosys model is provided
in the Supplement to this article, and detailed descriptions are
available in the supplements of Grant
et al. (2017a, b). The ecosys model structure remains unchanged from that in
earlier studies.
Experimental design
The primary purpose of this study is to explore the implications of the
observed CH4 emission hysteresis (Fig. 1) and highlight the need to
recognize factors other than temperature that control ecosystem-scale
CH4 emissions. We develop a mechanistic explanation for such hysteresis
by investigating how the modeled environmental drivers modulate CH4
emission hysteresis. The modeled data used in this study are extracted from
our earlier simulations that can be downloaded from the IsoGenie database
(https://isogenie-db.asc.ohio-state.edu/, last access: 19 November 2020; Chang et al., 2019a,
b) and the NGEE-Arctic database (https://ngee-arctic.ornl.gov/, last access: 19 November 2020; Chang
and Riley, 2020; Grant et al., 2017a, b). Our analysis focuses on
modeled data because some factors (e.g., root exudates, substrate
availability, and methanogenic population and activity) modulating CH4
production, oxidation, and emission rates are not continuously measured at
our study sites. Our recently published model results at the Stordalen Mire
and Utqiaġvik sites indicate good comparisons with observations,
including for thaw depth (R2=0.75 to 0.90), WTD (mean bias =-4.3
to 4.0 cm), and CO2 (R2=0.43 to 0.88) and CH4 (R2=0.31 to 0.93) surface fluxes (Chang
et al., 2019a, 2019b; Grant et al., 2017a, b, 2019). In particular, the
CH4 production pathway modeled at our Stordalen Mire sites has been
validated by the relative abundances of acetoclastic and hydrogenotrophic
methanogenic lineages reported in McCalley et al. (2014), suggesting that substrate and microbial dynamics are reasonably
represented. For conciseness, we focus the discussion in the remainder of the paper on the Stordalen Mire fen site, since it exhibits strong apparent hysteresis, and the underlying mechanisms leading to hysteretic CH4 emissions are similar across all study sites.
We note the relevant point that the ecosys model itself represents temperature
dependence of soil metabolic activity and gas production through locally
simulated soil temperature profiles with a modified Arrhenius function that
includes terms for low- and high-temperature inactivation
(Grant, 2015). Besides temperature
effects, the ecosys model also represents substrate controls (through
Michaelis–Menten kinetics) on microbial biomass and activity
(e.g., Chang et al., 2019b), which is not explicitly
characterized by inferring an apparent whole system temperature dependence
(e.g., Eq. 1). These representations allow the model to simulate overall
CH4 emission patterns with more complex dynamics than represented in
the apparent temperature dependence function alone, making it a suitable
tool for investigating the relative importance of temperature dependence
versus other factors.
Results and discussionObserved patterns of apparent CH4
emission hysteresis
The CH4 emissions measured in the Stordalen Mire bog and fen exhibit
hysteretic responses to soil surface temperature; that is, at the same soil
surface temperature, greater CH4 emissions occur during the later compared to the
earlier periods of the thawed season (Fig. 1). At both sites, plotting time-
and chamber-specific CH4 emission and soil surface temperature
measurements from the beginning to end of the thawed season result in a
counterclockwise hysteresis loop at each site and year (2012 to 2017). Such
hysteretic responses lead to intra-seasonally varying apparent CH4 emission temperature dependencies, suggesting that a proper representation
of temporal variability is needed to recognize factors modulating CH4 emissions. For example, three distinct apparent CH4 emission
temperature dependencies can be derived from the same chamber sampling at
different periods within the same thawed season (i.e., earlier period, later
period, and full season). Despite the high spatial heterogeneity, the
observed patterns of CH4 emission hysteresis are consistent across
chambers within and between the bog and fen habitats. Our results thus
demonstrate that CH4 emissions are generally more sensitive to
temperature changes during the later part of the thawed season and that
CH4 emission strength and temperature dependence vary substantially
among sites and years. Consistent hysteretic responses can be found in CH4
emission and air temperature measurements (Fig. S1), suggesting
that the apparent CH4 emission hysteresis is not dependent on time
lags between air and soil temperatures (Wohlfahrt
and Galvagno, 2017). The observed CH4 emission hysteresis indicates
that models cannot accurately represent CH4 dynamics without
representing the large spatial and temporal variability in apparent CH4
emission temperature dependencies.
Modeled patterns of apparent CH4 emission
hysteresis
The CH4 emissions modeled by ecosys, extracted from our recently published
results at the Stordalen Mire and the Utqiaġvik sites (Chang
et al., 2019b; Grant et al., 2017b), have hysteretic responses to mean 0–20 cm soil temperature (Fig. 2) and air temperature (Fig. S2). The
apparent CH4 emission temperature dependence inferred from the
modeled results varies substantially from the beginning to the end of the
thawed season, suggesting that CH4 emissions may not be accurately
represented as a single function of temperature. For each site and year,
CH4 emissions modeled in the later period are greater than those in the
earlier period at the same temperature (e.g., Fig. 2), consistent with
observations (e.g., Fig. 1). The apparent CH4 emission hysteresis is
larger and clearer in the Stordalen Mire fen compared to the bog and the
Utqiaġvik low-centered polygon, likely from its warmer soil
temperatures, shallower WTD, and higher CH4 emissions
(Chang et al., 2019b). Consistent hysteresis patterns are
found at weekly timescales (Fig. S4), suggesting that the
apparent CH4 emission hysteresis is not sensitive to temporal
resolution nor the timing of maximum seasonal temperature. In addition to
temporal variability, changes in biogeophysical conditions driven by
fine-scale hydrology and vegetation differences can also alter the apparent
functional relationship between CH4 emission and temperature. For
example, apparent CH4 emission temperature dependencies inferred for
individual topographic features (i.e., troughs, rims, and centers) vary
substantially within the same wetland ecosystem at Utqiaġvik
(Fig. S5).
We evaluate the effects of intra-seasonal variability on ecosystem-scale
CH4 emissions by estimating apparent CH4 emission temperature
dependencies during different parts of the thawed season. By fitting the
Boltzmann–Arrhenius function (Eq. 1) to the CH4 emissions and 0–20 cm
soil temperatures modeled during different time frames (i.e., earlier
period, later period, and full season), we developed and evaluated three
temperature dependence models for each thawed season. Our results show that
CH4 emission estimates improve when apparent CH4 emission
temperature dependencies were separately represented in the earlier and
later periods compared to those assuming a seasonally invariant apparent
CH4 emission temperature dependence (Tables S1, S2 in the Supplement). In
the Stordalen Mire, neglecting intra-seasonal variability in apparent
CH4 emission temperature dependence leads to overestimated (10 % to
81 %) and underestimated (-21 % to -40 %) CH4 emissions during
the earlier and later periods, respectively (Table S1).
Consistent prediction biases were found in the Utqiaġvik low-centered
polygon, except in the rims where drier conditions limit CH4 emissions
(Table S2).
These results demonstrate that models based on a seasonally invariant
apparent CH4 emission temperature dependence may introduce errors by
improperly prescribing the seasonal dynamics of CH4 biogeochemistry
with a static function of temperature. The substantial intra-seasonal
variability, potentially led by site-specific thermal and hydrological
history (Updegraff et al., 1998), could be an
important and overlooked property of natural wetlands that currently account
for 25 % to 32 % of global total CH4 emissions
(Saunois et
al., 2016). Representing intra-seasonally variable apparent CH4 emission or production temperature dependencies in large-scale wetland
biogeochemical models may thus reduce CH4 emission prediction biases
and model structural uncertainty.
Microbial substrate-mediated CH4
production hysteresis
For conciseness, we focus our discussion on the potential drivers causing
the hysteretic relationship between CH4 emission and soil temperature
modeled at the Stordalen Mire fen at 2011, as the underlying mechanisms are
consistent across all sites and years. The temporal evolution of CH4 emissions modeled by ecosys follows that of CH4 production, with limited
offsets from CH4 oxidation (Fig. 3a). Modeled CH4 emission (e.g.,
Fig. 2d) and production (Fig. 3b) rates both exhibit intra-seasonal
variations in their apparent temperature dependencies during the thawed
season, consistent with the varying temperature responses to microbial
thermal history reported in laboratory incubations
(Updegraff et al., 1998). The relatively low
CH4 oxidation suggests that hysteretic responses of modeled CH4 emissions to temperature (Fig. 2) primarily result from hysteretic
CH4 production (Fig. 3b) associated with asymmetric methanogen biomass
(Fig. 3c) and activity (Fig. 3d) between the earlier and later periods.
Further, the consistent seasonal cycles in CH4 production, oxidation,
and emission rates modeled from 2011 to 2013 (Fig. S6) indicate
that the CH4 emission hysteresis modeled in that period (Fig. 2d, e, f)
is not caused by relatively low CH4 oxidation modeled in a particular
site and year. This result is consistent with isotopic measurements which also
indicated that changes in CH4 production, not CH4 oxidation,
determine the CH4 emissions observed in the Stordalen Mire sites (McCalley
et al., 2014).
Intra-seasonal variations in apparent CH4 production
temperature dependence result from asymmetric microbial biomass and activity
modeled between the earlier and later periods. Daily CH4
emissions, CH4 production, CH4 oxidation, and 0–20 cm soil
temperature modeled in the Stordalen Mire fen during the 2011 thawed season (a). The corresponding apparent temperature dependence of the modeled
CH4 production (b), methanogen biomass (c), and methanogen growth
respiration (d) during the 2011 thawed season. Earlier, later, and
full-season periods are colored in red, blue, and black, respectively.
Earlier and later periods are defined as the time before and after the
seasonal maximum 0–20 cm soil temperature denoted by black cross signs.
Start date and end dates represent the beginning and ending of a thawed
season defined as the period when modeled daily 0–20 cm soil temperature is
above 1 ∘C, respectively.
Although CH4 oxidation has been proposed to be an important control
regulating wetland CH4 emissions, e.g., Perryman et al. (2020) and Singleton et al. (2018), the competitive dynamics between
methanogens and methanotrophs throughout the year has not been included in
such studies. The modeled CH4 oxidation rate is relatively low during
the thawed season when CH4 production is strongest, and relatively high
during the shoulder season when CH4 production is weakest
(Fig. S6). These strong seasonal variations suggest that the
relative importance of CH4 production and oxidation on regulating
CH4 emissions may fluctuate throughout the year, highlighting the need
to properly represent the underlying dynamics controlling CH4
biogeochemistry.
Increased soil temperatures elevate oxygen demands for aerobic heterotrophs
while reducing oxygen solubility, which favors fermenter and methanogens and
thereby enhance CH4 production. Our model results indicate that the
elevated methanogen biomass and activity during the later period are driven
by the increased substrate availability for methanogenesis later in the
thawed season. Specifically, modeled substrate concentrations remain
relatively high after peak substrate production rate at maximum seasonal
soil temperature for both acetoclastic methanogenesis (AM; Fig. 4a) and hydrogenotrophic
methanogenesis (HM; Fig. 5a). Relatively high AM (Fig. 4b) and HM (Fig. 5b)
substrate availability during the later period elevates AM and HM energy
yields at a given soil temperature, resulting in higher methanogen growth
(Fig. 3d) and biomass (Fig. 3c) later in the thawed season. Therefore,
CH4 production rates during the later period become higher than those
during the earlier period at the same soil temperature (Fig. 3b), which
drives higher CH4 emissions with increased aqueous CH4
concentrations. Although AM and HM each exhibit microbial substrate-mediated
hysteretic temperature responses, AM appears to be more hysteretic to soil
temperature than HM (Fig. 6). The stronger AM hysteresis is consistent with
the larger and clearer CH4 emission hysteresis found in the Stordalen
Mire fen (Fig. 2), where the fractional contribution of AM to total CH4
production is higher than in the Stordalen Mire bog
(Chang et al., 2019b; McCalley et al.,
2014). A schematic summarizing the abovementioned mechanisms for microbial
substrate-mediated CH4 production hysteresis is presented in Fig. 7.
Daily acetate concentration and acetate production modeled in the
Stordalen Mire fen during the 2011 thawed season (a). The corresponding
apparent temperature dependence of the modeled acetate concentration (b) and
acetate production (c) during the 2011 thawed season. Dots and lines
represent the daily data points and the fitted apparent temperature
dependence, respectively. The earlier, later, and full-season periods are
colored in red, blue, and black, respectively. Earlier and later periods are
defined as the time before and after the seasonal maximum 0–20 cm soil
temperature denoted by black cross signs. Start date and end dates represent
the beginning and ending of a thawed season defined as the period when
modeled daily 0–20 cm soil temperature is above 1 ∘C,
respectively.
Daily hydrogen concentration and hydrogen production modeled in
the Stordalen Mire fen during the 2011 thawed season (a). The corresponding
apparent temperature dependence of the modeled hydrogen concentration (b)
and hydrogen production (c) during the 2011 thawed season. Dots and lines
represent the daily data points and the fitted apparent temperature
dependence, respectively. The earlier, later, and full-season periods are
colored in red, blue, and black, respectively. Earlier and later periods are
defined as the time before and after the seasonal maximum 0–20 cm soil
temperature denoted by black cross signs. Start date and end dates represent
the beginning and ending of a thawed season defined as the period when
modeled daily 0–20 cm soil temperature is above 1 ∘C,
respectively.
Apparent temperature dependence of daily CH4 production for
acetoclastic (a) and hydrogenotrophic (b) methanogenesis and daily growth
respiration for acetoclastic (c) and hydrogenotrophic (d) methanogens
modeled in the Stordalen Mire fen during the 2011 thawed season. Dots and
lines represent the daily data points and the fitted apparent temperature
dependence, respectively. The earlier, later, and full-season periods are
colored in red, blue, and black, respectively. Earlier and later periods are
defined as the time before and after the seasonal maximum 0–20 cm soil
temperature denoted by black cross signs. Start date and end dates represent
the beginning and ending of a thawed season defined as the period when
modeled daily 0–20 cm soil temperature is above 1 ∘C,
respectively.
Schematic of the microbial substrate-mediated CH4 production
hysteresis proposed in this study. Higher substrate (i.e., acetate and
hydrogen) availability stimulates higher methanogen biomass during the later
period, which leads to intra-seasonal differences in CH4 production
between the earlier and later periods.
Although the CH4 emission rates and CH4 production pathways
modeled in the Stordalen Mire fen have been examined
(Chang et al., 2019b), continuous substrate concentration
measurements are lacking for validating the substrate-mediated hysteretic
temperature responses proposed here. Wide ranges of acetate and hydrogen
concentrations have been reported from incubation experiments studying
methanogenesis (e.g., Hines
et al., 2008; Tveit et al., 2015; Zhang et al., 2020); however, those
values may not be used to validate the time- and space-specific substrate
concentrations modeled at our study sites. Therefore, further studies and
additional field measurements are needed to test our proposed hypothesis of
the causes of observed CH4 emission hysteresis.
Other factors regulating intra-seasonal
CH4 emissions
To evaluate whether microbial substrate-mediated CH4 production
hysteresis is the primary cause of the observed hysteretic relationship
between CH4 emission and temperature, we evaluated four alternative
hypotheses: interactions with (1) water table depth, (2) GPP (via exudation,
root litter inputs, and aerenchyma development), (3) thaw depth, and (4) residual pore-water CH4 concentrations at the end of the earlier part
of the thawed season.
First, studies have found that seasonal variations of WTD determine CH4
cycling dynamics by regulating the temperature response of CH4
emissions, leading to hysteretic CH4 emissions when drought-induced WTD
drawdown below the critical zone for CH4 production
(Brown et al., 2014; Goodrich et al.,
2015). The substantial CH4 emission hysteresis observed in the
Stordalen Mire fen is unlikely caused by seasonal variations in WTD, because
the observed WTDs are around or above the peat surface throughout the thawed
season with limited effects on CH4 emissions
(Bäckstrand et al.,
2008b).
Second, Rinne et al. (2018)
reported that the temporal variations of CH4 emissions are strongly
regulated by GPP, and the time required to convert GPP to methanogenesis
substrates may cause the observed apparent hysteresis found between GPP and
CH4 emissions. Such apparent hysteresis was also modeled at our study
sites (e.g., Fig. 8a), which shows higher CH4 emissions later in the
thawed season at a given GPP. We further analyzed factors linking GPP and
CH4 emissions modeled at the Stordalen Mire fen to explore whether an
apparent hysteretic relationship between CH4 emissions and GPP is
causally connected. We examined three primary pathways by which GPP could
lead to a delayed effect on CH4 emissions and thereby apparent
hysteresis: increases in (1) fresh carbon inputs from root exudation (Fig. 8b), (2) belowground litter inputs (Fig. 8c), and (3) aerenchyma transport
caused by GPP-induced growth of porous sedge roots (Fig. 8d). In contrast to
the apparent hysteresis with GPP, all three of these mechanisms exhibit
reversed hysteresis cycles compared to those between CH4 emissions and
temperature. Therefore, these three primary mechanisms are inconsistent with
a causal hysteretic relationship between GPP and CH4 emissions.
Daily CH4 emissions have hysteretic responses to gross
primary productivity (a), carbon released from root exudation (b), carbon
released from belowground litter decomposition (c), the amount of root
biomass for sedges (d), and thaw depth (e) modeled in the Stordalen Mire fen
during the 2011 thawed season. Dots and lines represent the daily data
points and the fitted apparent temperature dependence, respectively. Black
cross signs represent the seasonal maximum 0–20 cm soil temperature. Start
date and end dates represent the beginning and ending of a thawed season
defined as the period when modeled daily 0–20 cm soil temperature is above 1 ∘C, respectively.
Third, studies have suggested that soil temperature increases can expand the
volume of unfrozen soil and thereby stimulate deep carbon decomposition,
which can also contribute to higher carbon emissions later in the thawed
season, as has been observed for upland CO2 emissions
(Goulden et al., 1998) and wetland CH4 emissions
(Iwata et al., 2015). Our results
show a weak correlation between thaw depth and CH4 emissions during the
later part of the thawed season, although CH4 emissions appear to
increase with deeper thaw during the earlier period (Fig. 8e). Therefore,
the hysteretic relationship between CH4 emission and soil temperature
found at our study sites is not causally connected with the greater volume
of unfrozen soil later in the thawed season. This result may be explained by
the relatively shallow zone (mostly within the top 20 cm of soil) of
CH4 production (Chang et al., 2019b) compared with
the much deeper thaw depth (>90 cm) measured and modeled during
the peak CH4 emission period (i.e., July to August)
(Chang et al., 2019a).
Fourth, we conducted a sensitivity test to examine the amount of lagged
CH4 emissions resulting from CH4 residual stored in the soil
profile at the end of the earlier part of the thawed season. In the
sensitivity test, we turned off CH4 production during the later part of
the thawed season so the later-period CH4 emissions modeled in this run
are driven by lagged releases of earlier-period CH4 production. At the
Stordalen Mire fen, later-period CH4 emissions resulting from
earlier-period CH4 residual concentrations decreased approximately
exponentially and contributed about 25 % of the CH4 emissions
during the later period (Fig. 9). The timing and magnitude of later-period
CH4 emissions attributed to lagged CH4 emissions do not match
with the relatively high CH4 emissions modeled during the later
period. Therefore, our results suggest that lagged CH4 emissions from
residual CH4 produced in the earlier period are not a dominant factor
leading to the observed CH4 emission hysteresis, although lagged
CH4 emissions may amplify the apparent CH4 emission hysteresis
detected in the system.
Daily CH4 emissions (black line, left axis) and 0–20 cm mean
soil temperature (green line, right axis) modeled at the Stordalen Mire fen
during the 2011 thawed season. Black solid and dashed lines represent the
modeled CH4 emissions with and without CH4 production during the
later period, respectively. Earlier and later periods are defined as the
time before and after the modeled seasonal maximum 0–20 cm soil temperature.
Collectively, our results suggest that microbial substrate-mediated CH4 production hysteresis is likely to be the primary control of the observed
apparent CH4 emission hysteresis. The physical controls on CH4 production and emission (and potentially their hysteresis patterns) in the
sediments of terrestrial freshwater systems may differ from those we derived
from vegetated peat surfaces (Wik et al., 2016),
and further investigation is needed to assess their apparent temperature
dependence. To better understand factors controlling CH4 production and
emission, continuous measurements of seasonal development of methanogenesis
substrates and soil temperature at the depth where CH4 production is
prevalent are needed.
Conclusions
Many contemporary CH4 models parameterize wetland CH4 production
(or emission) as a fixed fraction of net primary productivity or
heterotrophic respiration regulated by a single static function of
temperature (Melton
et al., 2013; Wania et al., 2013). Our results suggest that such a
parameterization is not accurate because it oversimplifies microbial
responses to changing thermal and hydrological conditions that modulate
wetland CH4 production and emission rates. More continuous observations
across sites are required to assess model prediction uncertainty and the
broader extent to which our mechanistic explanations apply. In summary, we
found that apparent CH4 emission temperature dependencies vary from the
earlier to later part of the thawed season due to substrate-mediated
CH4 production hysteresis caused by intra-seasonal changes in
methanogen biomass and activity. We examined four alternative mechanisms
that may contribute to the observed CH4 emission hysteresis with
temperature and found that none of them can exclusively explain the underlying
dynamics. Our findings motivate explicit model representations of microbial
dynamics that physiologically link microbial and abiotic interactions, as
only 3 of 40 recently reviewed CH4 models mechanistically represent
CH4 biogeochemistry (Xu et al.,
2016).
Code availability
The ecosys source code is available at Zenodo (10.5281/zenodo.3906642; Tang, 2020).
Data availability
The data presented in this study are available at the NGEE Arctic Database
(10.5440/1635534, Chang and Riley, 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-5849-2020-supplement.
Author contributions
KYC and WJR designed the study. PMC synthesized field measurements and RFG developed the ecosys model. KYC performed the analyses and led the writing of the paper. All authors contributed thoughtful discussions and insights to the study, and all authors contributed to the editing of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This study was funded by the Genomic Science Program of the United States
Department of Energy Office of Biological and Environmental Research under
the ISOGENIE (DE-SC0016440) and NGEE-Arctic projects under contract
DE-AC02-05CH11231 to Lawrence Berkeley National Laboratory and grants from
Swedish VR (Vetenskaprådet) and Swedish FORMAS to Patrick M. Crill. We acknowledge the US
National Science Foundation MacroSystems program (NSF EF 1241037) support
for autochamber measurements between 2013 and 2017. We thank the Abisko
Scientific Research Station of the Swedish Polar Research Secretariat for
providing the meteorological data.
Financial support
This research has been supported by the United States Department of Energy (grant nos. DE-SC0016440 and DE-AC02-05CH11231).
Review statement
This paper was edited by Tina Treude and reviewed by two anonymous referees.
ReferencesBäckstrand, K., Crill, P. M., Mastepanov, M., Christensen, T. R., and
Bastviken, D.: Non-methane volatile organic compound flux from a subarctic
mire in Northern Sweden, Tellus B, 60, 226–237,
10.1111/j.1600-0889.2007.00331.x, 2008a.Bäckstrand, K., Crill, P. M., Mastepanov, M., Christensen, T. R., and Bastviken, D.: Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden, J. Geophys. Res., 113, G03026, 10.1029/2008JG000703, 2008b.Bäckstrand, K., Crill, P. M., Jackowicz-Korczyñski, M., Mastepanov, M., Christensen, T. R., and Bastviken, D.: Annual carbon gas budget for a subarctic peatland, Northern Sweden, Biogeosciences, 7, 95–108, 10.5194/bg-7-95-2010, 2010.Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M., and
Enrich-Prast, A.: Freshwater methane emissions offset the continental carbon
sink, Science, 331, 50 pp., 10.1126/science.1196808, 2011.Biskaborn, B. K., Smith, S. L., Noetzli, J., Matthes, H., Vieira, G.,
Streletskiy, D. A., Schoeneich, P., Romanovsky, V. E., Lewkowicz, A. G.,
Abramov, A., Allard, M., Boike, J., Cable, W. L., Christiansen, H. H.,
Delaloye, R., Diekmann, B., Drozdov, D., Etzelmüller, B., Grosse, G.,
Guglielmin, M., Ingeman-Nielsen, T., Isaksen, K., Ishikawa, M., Johansson,
M., Johannsson, H., Joo, A., Kaverin, D., Kholodov, A., Konstantinov, P.,
Kröger, T., Lambiel, C., Lanckman, J. P., Luo, D., Malkova, G.,
Meiklejohn, I., Moskalenko, N., Oliva, M., Phillips, M., Ramos, M., Sannel,
A. B. K., Sergeev, D., Seybold, C., Skryabin, P., Vasiliev, A., Wu, Q.,
Yoshikawa, K., Zheleznyak, M., and Lantuit, H.: Permafrost is warming at a
global scale, Nat. Commun., 10, 1–11, 10.1038/s41467-018-08240-4,
2019.Brown, M. G., Humphreys, E. R., Moore, T. R., Roulet, N. T., and Lafleur, P.
M.: Evidence for a nonmonotonic relationship between ecosystem-scale
peatland methane emissions and water table depth, J. Geophys. Res.-Biogeo., 119, 826–835, 10.1002/2013JG002576, 2014.Bubier, J., Crill, P., Mosedale, A., Frolking, S., and Linder, E.: Peatland responses to varying interannual moisture conditions as measured by automatic CO2 chambers, Global Biogeochem. Cy., 17, 1–15, 10.1029/2002GB001946, 2003.Chang, K.-Y. and Riley, W.: Hysteretic temperature sensitivity of wetland
CH4 fluxes explained by substrate availability and microbial activity:
Model Archive, Next Gener. Ecosyst. Exp. Arct. Data Collect. Oak Ridge Natl. Lab., U.S. Dep. Energy, Oak Ridge, Tennessee, USA, 10.5440/1635534, 2020.Chang, K.-Y., Riley, W. J., Crill, P. M., Grant, R. F., Rich, V. I., and Saleska, S. R.: Large carbon cycle sensitivities to climate across a permafrost thaw gradient in subarctic Sweden, The Cryosphere, 13, 647–663, 10.5194/tc-13-647-2019, 2019a.Chang, K.-Y., Riley, W. J., Brodie, E. L., McCalley, C. K., Crill, P. M., and
Grant, R. F.: Methane Production Pathway Regulated Proximally by Substrate
Availability and Distally by Temperature in a High-Latitude Mire Complex, J.
Geophys. Res.-Biogeo., 124, 3057–3074, 10.1029/2019JG005355, 2019b.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J.,
Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C.,
Le Quéré, C., Myneni, R. B., Piao, S., and Thornton, P.: Carbon and
Other Biogeochemical Cycles, in: Climate Change 2013 – The Physical Science
Basis, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom, 465–570, 2013.Dean, J. F., Middelburg, J. J., Röckmann, T., Aerts, R., Blauw, L. G.,
Egger, M., Jetten, M. S. M., de Jong, A. E. E., Meisel, O. H., Rasigraf, O.,
Slomp, C. P., in't Zandt, M. H., and Dolman, A. J.: Methane Feedbacks to the
Global Climate System in a Warmer World, Rev. Geophys., 56, 207–250,
10.1002/2017RG000559, 2018.Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, 10.5194/gmd-9-1937-2016, 2016.Goodrich, J. P., Campbell, D. I., Roulet, N. T., Clearwater, M. J., and
Schipper, L. A.: Overriding control of methane flux temporal variability by
water table dynamics in a Southern Hemisphere, raised bog, J. Geophys. Res.-Biogeo., 120, 819–831, 10.1002/2014JG002844, 2015.Goulden, M. L., Wofsy, S. C., Harden, J. W., Trumbore, S. E., Crill, P. M.,
Gower, S. T., Fries, T., Daube, B. C., Fan, S.-M., Sutton, D. J., Bazzaz, A.,
and Munger, J. W.: Sensitivity of Boreal Forest Carbon Balance to Soil Thaw,
Science 279, 214–217, 10.1126/science.279.5348.214,
1998.Grant, R. F.: Ecosystem CO2 and CH4 exchange in a mixed tundra and a fen within a hydrologically diverse Arctic landscape: 2. Modeled impacts of
climate change, J. Geophys. Res.-Biogeo., 120, 1388–1406,
10.1002/2014JG002889, 2015.Grant, R. F., Mekonnen, Z. A., Riley, W. J., Wainwright, H. M., Graham, D.,
and Torn, M. S.: Mathematical Modelling of Arctic Polygonal Tundra with
Ecosys: 1. Microtopography Determines How Active Layer Depths Respond to
Changes in Temperature and Precipitation, J. Geophys. Res.-Biogeo.,
122, 3161–3173, 10.1002/2017JG004035, 2017a.Grant, R. F., Mekonnen, Z. A., Riley, W. J., Arora, B., and Torn, M. S.:
Mathematical Modelling of Arctic Polygonal Tundra with Ecosys: 2.
Microtopography Determines How CO2 and CH4 Exchange Responds to Changes in Temperature and Precipitation, J. Geophys. Res.-Biogeo., 122,
3174–3187, 10.1002/2017JG004037, 2017b.Grant, R. F., Mekonnen, Z. A., Riley, W. J., Arora, B., and Torn, M. S.:
Modelling climate change impacts on an Arctic polygonal tundra. Part 2:
Changes in CO 2 and CH 4 exchange depend on rates of permafrost
thaw as affected by changes in vegetation and drainage, J. Geophys. Res.-Biogeo., 124, 1323–1341, 10.1029/2018JG004645, 2019.Hemes, K. S., Chamberlain, S. D., Eichelmann, E., Knox, S. H., and Baldocchi,
D. D.: A Biogeochemical Compromise: The High Methane Cost of Sequestering
Carbon in Restored Wetlands, Geophys. Res. Lett., 45, 6081–6091,
10.1029/2018GL077747, 2018.Hines, M. E., Duddleston, K. N., Rooney-Varga, J. N., Fields, D., and
Chanton, J. P.: Uncoupling of acetate degradation from methane formation in
Alaskan wetlands: Connections to vegetation distribution, Global Biogeochem.
Cycles, 22, 1–12, 10.1029/2006GB002903, 2008.Hinkel, K. M., Frohn, R. C., Nelson, F. E., Eisner, W. R., and Beck, R. A.:
Morphometric and spatial analysis of thaw lakes and drained thaw lake basins
in the western Arctic Coastal Plain, Alaska, Permafr. Periglac. Process., 16, 327–341, 10.1002/ppp.532, 2005.Hodgkins, S. B., Tfaily, M. M., McCalley, C. K., Logan, T. A., Crill, P. M.,
Saleska, S. R., Rich, V. I., and Chanton, J. P.: Changes in peat chemistry
associated with permafrost thaw increase greenhouse gas production, P.
Natl. Acad. Sci., 111, 5819–5824, 10.1073/pnas.1314641111, 2014.Iwata, H., Harazono, Y., Ueyama, M., Sakabe, A., Nagano, H., Kosugi, Y.,
Takahashi, K., and Kim, Y.: Methane exchange in a poorly-drained black spruce
forest over permafrost observed using the eddy covariance technique, Agric.
For. Meteorol., 214/215, 157–168, 10.1016/j.agrformet.2015.08.252,
2015.Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G.,
Dlugokencky, E. J., Bergamaschi, P., Bergmann, D., Blake, D. R., Bruhwiler,
L., Cameron-Smith, P., Castaldi, S., Chevallier, F., Feng, L., Fraser, A.,
Heimann, M., Hodson, E. L., Houweling, S., Josse, B., Fraser, P. J.,
Krummel, P. B., Lamarque, J.-F., Langenfelds, R. L., Le Quéré, C.,
Naik, V., O'Doherty, S., Palmer, P. I., Pison, I., Plummer, D., Poulter, B.,
Prinn, R. G., Rigby, M., Ringeval, B., Santini, M., Schmidt, M., Shindell,
D. T., Simpson, I. J., Spahni, R., Steele, L. P., Strode, S. A., Sudo, K.,
Szopa, S., van der Werf, G. R., Voulgarakis, A., van Weele, M., Weiss, R.
F., Williams, J. E., and Zeng, G.: Three decades of global methane sources
and sinks, Nat. Geosci., 6, 813–823, 10.1038/ngeo1955, 2013.Knoblauch, C., Beer, C., Liebner, S., Grigoriev, M. N., and Pfeiffer, E. M.:
Methane production as key to the greenhouse gas budget of thawing
permafrost, Nat. Clim. Chang., 8, 309–312, 10.1038/s41558-018-0095-z, 2018.Malmer, N., Johansson, T., Olsrud, M., and Christensen, T. R.: Vegetation,
climatic changes and net carbon sequestration in a North-Scandinavian
subarctic mire over 30 years, Glob. Chang. Biol.,
11, 1895–1909, 10.1111/j.1365-2486.2005.01042.x, 2005.McCalley, C. K., Woodcroft, B. J., Hodgkins, S. B., Wehr, R. A., Kim, E.-H.,
Mondav, R., Crill, P. M., Chanton, J. P., Rich, V. I., Tyson, G. W., and
Saleska, S. R.: Methane dynamics regulated by microbial community response
to permafrost thaw, Nature, 514, 478–481, 10.1038/nature13798,
2014.Melton, J. R., Wania, R., Hodson, E. L., Poulter, B., Ringeval, B., Spahni, R., Bohn, T., Avis, C. A., Beerling, D. J., Chen, G., Eliseev, A. V., Denisov, S. N., Hopcroft, P. O., Lettenmaier, D. P., Riley, W. J., Singarayer, J. S., Subin, Z. M., Tian, H., Zürcher, S., Brovkin, V., van Bodegom, P. M., Kleinen, T., Yu, Z. C., and Kaplan, J. O.: Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP), Biogeosciences, 10, 753–788, 10.5194/bg-10-753-2013, 2013.
Myhre, G., D., Shindell, F.-M., Bréon, F.-M., Collins, W., Fuglestvedt, J.,
Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and Natural Radiative Forcing, in: Climate Change 2013 - The Physical Science Basis, vol. 23, edited by: Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 659–740, 2013.Neubauer, S. C. and Megonigal, J. P.: Moving Beyond Global Warming
Potentials to Quantify the Climatic Role of Ecosystems, Ecosystems, 18,
1000–1013, 10.1007/s10021-015-9879-4, 2015.Olefeldt, D. and Roulet, N. T.: Effects of permafrost and hydrology on the
composition and transport of dissolved organic carbon in a subarctic
peatland complex, J. Geophys. Res.-Biogeo., 117, 1–15,
10.1029/2011JG001819, 2012.Olefeldt, D., Turetsky, M. R., Crill, P. M., and Mcguire, A. D.:
Environmental and physical controls on northern terrestrial methane
emissions across permafrost zones, Glob. Change Biol., 19, 589–603,
10.1111/gcb.12071, 2013.Perryman, C. R., McCalley, C. K., Malhotra, A., Fahnestock, M. F., Kashi, N.
N., Bryce, J. G., Giesler, R., and Varner, R. K.: Thaw Transitions and Redox
Conditions Drive Methane Oxidation in a Permafrost Peatland, J. Geophys.
Res.-Biogeo., 124, e2019JG005526, 10.1029/2019JG005526, 2020.Poulter, B., Bousquet, P., Canadell, J. G., Ciais, P., Peregon, A., Saunois,
M., Arora, V. K., Beerling, D. J., Brovkin, V., Jones, C. D., Joos, F.,
Gedney, N., Ito, A., Kleinen, T., Koven, C. D., McDonald, K., Melton, J. R.,
Peng, C., Peng, S., Prigent, C., Schroeder, R., Riley, W. J., Saito, M.,
Spahni, R., Tian, H., Taylor, L., Viovy, N., Wilton, D., Wiltshire, A., Xu,
X., Zhang, B., Zhang, Z., and Zhu, Q.: Global wetland contribution to
2000–2012 atmospheric methane growth rate dynamics, Environ. Res. Lett.,
12, 094013, 10.1088/1748-9326/aa8391, 2017.Rinne, J., Tuittila, E. S., Peltola, O., Li, X., Raivonen, M., Alekseychik,
P., Haapanala, S., Pihlatie, M., Aurela, M., Mammarella, I., and Vesala, T.:
Temporal Variation of Ecosystem Scale Methane Emission From a Boreal Fen in
Relation to Temperature, Water Table Position, and Carbon Dioxide Fluxes,
Global Biogeochem. Cy., 32, 1087–1106, 10.1029/2017GB005747,
2018.
Rydén, B. E. and Kostov, L.: Thawing and Freezing in Tundra Soils, Ecol.
Bull., 30, 251–281, 1980.Saunois, M., Bousquet, P., Poulter, B., Peregon, A., Ciais, P., Canadell, J. G., Dlugokencky, E. J., Etiope, G., Bastviken, D., Houweling, S., Janssens-Maenhout, G., Tubiello, F. N., Castaldi, S., Jackson, R. B., Alexe, M., Arora, V. K., Beerling, D. J., Bergamaschi, P., Blake, D. R., Brailsford, G., Brovkin, V., Bruhwiler, L., Crevoisier, C., Crill, P., Covey, K., Curry, C., Frankenberg, C., Gedney, N., Höglund-Isaksson, L., Ishizawa, M., Ito, A., Joos, F., Kim, H.-S., Kleinen, T., Krummel, P., Lamarque, J.-F., Langenfelds, R., Locatelli, R., Machida, T., Maksyutov, S., McDonald, K. C., Marshall, J., Melton, J. R., Morino, I., Naik, V., O'Doherty, S., Parmentier, F.-J. W., Patra, P. K., Peng, C., Peng, S., Peters, G. P., Pison, I., Prigent, C., Prinn, R., Ramonet, M., Riley, W. J., Saito, M., Santini, M., Schroeder, R., Simpson, I. J., Spahni, R., Steele, P., Takizawa, A., Thornton, B. F., Tian, H., Tohjima, Y., Viovy, N., Voulgarakis, A., van Weele, M., van der Werf, G. R., Weiss, R., Wiedinmyer, C., Wilton, D. J., Wiltshire, A., Worthy, D., Wunch, D., Xu, X., Yoshida, Y., Zhang, B., Zhang, Z., and Zhu, Q.: The global methane budget 2000–2012, Earth Syst. Sci. Data, 8, 697–751, 10.5194/essd-8-697-2016, 2016.Saunois, M., Bousquet, P., Poulter, B., Peregon, A., Ciais, P., Canadell, J. G., Dlugokencky, E. J., Etiope, G., Bastviken, D., Houweling, S., Janssens-Maenhout, G., Tubiello, F. N., Castaldi, S., Jackson, R. B., Alexe, M., Arora, V. K., Beerling, D. J., Bergamaschi, P., Blake, D. R., Brailsford, G., Bruhwiler, L., Crevoisier, C., Crill, P., Covey, K., Frankenberg, C., Gedney, N., Höglund-Isaksson, L., Ishizawa, M., Ito, A., Joos, F., Kim, H.-S., Kleinen, T., Krummel, P., Lamarque, J.-F., Langenfelds, R., Locatelli, R., Machida, T., Maksyutov, S., Melton, J. R., Morino, I., Naik, V., O'Doherty, S., Parmentier, F.-J. W., Patra, P. K., Peng, C., Peng, S., Peters, G. P., Pison, I., Prinn, R., Ramonet, M., Riley, W. J., Saito, M., Santini, M., Schroeder, R., Simpson, I. J., Spahni, R., Takizawa, A., Thornton, B. F., Tian, H., Tohjima, Y., Viovy, N., Voulgarakis, A., Weiss, R., Wilton, D. J., Wiltshire, A., Worthy, D., Wunch, D., Xu, X., Yoshida, Y., Zhang, B., Zhang, Z., and Zhu, Q.: Variability and quasi-decadal changes in the methane budget over the period 2000–2012, Atmos. Chem. Phys., 17, 11135–11161, 10.5194/acp-17-11135-2017, 2017.Schuur, E. A. G., McGuire, A. D., Schädel, C., Grosse, G., Harden, J.
W., Hayes, D. J., Hugelius, G., Koven, C. D., Kuhry, P., Lawrence, D. M.,
Natali, S. M., Olefeldt, D., Romanovsky, V. E., Schaefer, K., Turetsky, M.
R., Treat, C. C., and Vonk, J. E.: Climate change and the permafrost carbon
feedback, Nature, 520, 171–179, 10.1038/nature14338, 2015.Shiklomanov, N. I., Streletskiy, D. A., Nelson, F. E., Hollister, R. D.,
Romanovsky, V. E., Tweedie, C. E., Bockheim, J. G., and Brown, J.: Decadal
variations of active-layer thickness in moisture-controlled landscapes,
Barrow, Alaska, J. Geophys. Res.-Biogeo., 115, G00I04, 10.1029/2009JG001248,
2010.Singleton, C. M., McCalley, C. K., Woodcroft, B. J., Boyd, J. A., Evans, P.
N., Hodgkins, S. B., Chanton, J. P., Frolking, S., Crill, P. M., Saleska, S.
R., Rich, V. I., and Tyson, G. W.: Methanotrophy across a natural permafrost
thaw environment, ISME J., 12, 2544–2558,
10.1038/s41396-018-0065-5, 2018.Tang, J.: Ecosys v1.0 release (Version v1.0), Zenodo, 10.5281/zenodo.3906642, 2020.Tang, J. and Riley, W. J.: Weaker soil carbon-climate feedbacks resulting
from microbial and abiotic interactions, Nat. Clim. Chang., 5, 56–60, 10.1038/nclimate2438, 2014.Tveit, A. T., Urich, T., Frenzel, P., and Svenning, M. M.: Metabolic and trophic interactions modulate methane production by Arctic peat microbiota in response to warming, P. Natl. Acad. Sci. USA, 112, E2507–E2516,
10.1073/pnas.1420797112, 2015.Updegraff, K., Bridgham, S. D., Pastor, J., and Weishampel, P.: Hysteresis in
the temperature response of carbon dioxide and methane production in peat
soils, Biogeochemistry, 43, 253–272, 10.1023/A:1006097808262, 1998.Wainwright, H. M., Dafflon, B., Smith, L. J., Hahn, M. S., Curtis, J. B.,
Wu, Y., Ulrich, C., Peterson, J. E., Torn, M. S., and Hubbard, S. S.:
Identifying multiscale zonation and assessing the relative importance of
polygon geomorphology on carbon fluxes in an Arctic tundra ecosystem, J.
Geophys. Res.-Biogeo., 120, 788–808, 10.1002/2014JG002799, 2015.Wania, R., Melton, J. R., Hodson, E. L., Poulter, B., Ringeval, B., Spahni, R., Bohn, T., Avis, C. A., Chen, G., Eliseev, A. V., Hopcroft, P. O., Riley, W. J., Subin, Z. M., Tian, H., van Bodegom, P. M., Kleinen, T., Yu, Z. C., Singarayer, J. S., Zürcher, S., Lettenmaier, D. P., Beerling, D. J., Denisov, S. N., Prigent, C., Papa, F., and Kaplan, J. O.: Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP), Geosci. Model Dev., 6, 617–641, 10.5194/gmd-6-617-2013, 2013.Wik, M., Varner, R. K., Anthony, K. W., MacIntyre, S., and Bastviken, D.:
Climate-sensitive northern lakes and ponds are critical components of
methane release, Nat. Geosci., 9, 99–105, 10.1038/ngeo2578, 2016.
Wohlfahrt, G. and Galvagno, M.: Revisiting the choice of the driving
temperature for eddy covariance CO2 flux partitioning, Agr. Forest Meteorol., 237/238, 135–142, 10.1016/j.agrformet.2017.02.012, 2017.Xu, X., Yuan, F., Hanson, P. J., Wullschleger, S. D., Thornton, P. E., Riley, W. J., Song, X., Graham, D. E., Song, C., and Tian, H.: Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems, Biogeosciences, 13, 3735–3755, 10.5194/bg-13-3735-2016, 2016.Yvon-Durocher, G., Allen, A. P., Bastviken, D., Conrad, R., Gudasz, C.,
St-Pierre, A., Thanh-Duc, N., and Del Giorgio, P. A.: Methane fluxes show
consistent temperature dependence across microbial to ecosystem scales,
Nature, 507, 488–491, 10.1038/nature13164, 2014.Zhang, L., Liu, X., Duddleston, K., and Hines, M. E.: The Effects of pH,
Temperature, and Humic-Like Substances on Anaerobic Carbon Degradation and
Methanogenesis in Ombrotrophic and Minerotrophic Alaskan Peatlands, Aquat.
Geochem., 26, 221–244, 10.1007/s10498-020-09372-0, 2020.Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., and Miller, C.
E.: Cold season emissions dominate the Arctic tundra methane budget, P.
Natl. Acad. Sci., 113, 40–45, 10.1073/pnas.1516017113, 2016.