Introduction
Methane (CH4) is an important greenhouse gas, with a greenhouse warming
potential about 25 times that of CO2 (IPCC, 2013). Globally, wetlands
are the largest natural CH4 source (Fung et al., 1991; Hein et al.,
1997; IPCC, 2013). The strong sensitivity of wetland CH4 emissions to
ambient soil conditions has led to concerns about possible feedbacks to
climate change (Gedney et al., 2004; Eliseev et al., 2008). The northern high
latitudes contain about one half of the world's wetlands (Lehner and
Döll, 2004) and are experiencing more rapid climate change than elsewhere
globally (Serreze et al., 2000; Diffenbaugh and Giorgi, 2012). The potential
liberation of vast quantities of carbon from thawing permafrost provides
additional impetus to efforts to understand the sensitivity of northern
wetland CH4 emissions to climate change (Schaefer et al., 2011; Koven et
al., 2011).
CH4 emission rates in northern wetlands (which are predominantly
peatlands) depend on a number of environmental and climate controls,
including soil temperature, water table depth, labile carbon substrate, soil
pH, oxidation state, nutrient concentrations, and vegetation composition
(Saarnio et al., 1997; Christensen et al., 2003; Zhuang et al., 2004; Riley
et al., 2011; Spahni et al., 2011; Glagolev et al., 2011; Lupascu et al.,
2012; Levy et al., 2012; Olefeldt et al., 2013; Sabrekov et al., 2014). Many
of these factors can interact and compete. For example, Bohn et al. (2007)
showed via a process-based model that air temperature and precipitation exert
competing influences on (a) water table depth, through winter snow
accumulation, spring snowmelt, and summer precipitation and
evapotranspiration, and (b) metabolic rates, through soil temperature,
leading to trade-offs in their influences on emissions. Extreme (limiting)
values of one factor can raise the sensitivity of emissions to that factor
(Olefeldt et al., 2013). As a result of these interactions, different factors
exert dominant controls at different sites (Olefeldt et al., 2013) or
timescales (Sabrekov et al., 2014), hindering efforts to constrain model
behaviors in the face of sparse observations (Melton et al., 2013).
Therefore, isolating those conditions under which different factors dominate
or limit the response of wetland methane emissions to climate change would
benefit future field campaigns and modeling studies.
Previous attempts to characterize the sensitivities of northern wetland
CH4 emissions to environmental factors have included both data-driven
(Bloom et al., 2010; Olefeldt et
al., 2013) and process-based modeling (Bohn et al., 2007; Ringeval et al.,
2010) approaches. Data-driven studies have the potential advantages of
relative accuracy and simplicity but can have limited predictive power. For
example, Olefeldt et al. (2013) found clear relationships between observed
emissions from over 300 high-latitude sites and soil temperature, water table
depth, and vegetation composition. However, while these relationships are a
crucial step forward in our understanding, they must be embedded within a
process-based model to estimate the aggregate response of northern wetland
emissions to a given change in climate or characterize how these
relationships may change with changing climate. Bloom et al. (2010) fit a
regression model to observed atmospheric CH4 concentrations from the
Scanning Imaging Absorption Spectrometer for Atmospheric Chemistry
(SCIAMACHY; Bovensmann et al., 1999) to observed surface temperatures from
the National Center for Environmental Prediction/National Center for
Atmospheric Research (NCEP/NCAR) weather analyses (Kalnay et al., 1996) and
gravity anomalies from the Gravity Recovery and Climate Experiment satellite
(GRACE; Tapley et al., 2004) and found that air temperature exerted the
dominant control over high-latitude emissions. Unfortunately, the short (4
years) record length and the use of GRACE data as a proxy for near-surface
wetland soil moisture suggest that these findings are highly uncertain and
limited to the time span of the satellite data sets used.
Process-based studies potentially have greater predictive power, but their
relative complexity may involve highly uncertain parameterizations. For
example, Ringeval et al. (2010) found that variations in inundated area
contributed 30 % to the interannual variability in CH4 emissions
over the latitudes north of 50∘ N. However, despite the strong
emissions observed at non-inundated peatlands throughout the high latitudes
(e.g., Saarnio et al., 1997; Panikov and Dedysh, 2000; Friborg et al., 2003;
Glagolev et al., 2011), they only considered emissions from inundated
wetlands, thus potentially inflating the contribution attributed to
inundation. Bohn et al. (2007) accounted for non-inundated emissions, but
their study was restricted to a small area in West Siberia. Numerous other
process-based studies (using both forward and inverse models) have
investigated the response of northern CH4 emissions to historical or
future climate variations (e.g., Chen and Prinn, 2006; Bousquet et al.,
2011; Riley et al., 2011; Spahni et al., 2011; Bohn et al., 2013, 2015; Zhu et
al., 2014), but none have attempted to characterize the
sensitivities of emissions to climate factors as a function of geographic
location, wetland type, or climate conditions.
CH4 emissions are not the only biogeochemical process for which
environmental controls have been investigated. Nemani et al. (2003) found
that annual net primary productivity (NPP) is limited by temperature and
radiation at high latitudes but by moisture-related factors at lower
latitudes. Teuling et al. (2009) and Seneviratne et al. (2010) investigated
global climate controls on annual evapotranspiration (ET), and found that
temperature is the dominant control over northern Eurasia, while
precipitation is the dominant control at mid- to low latitudes and in
northern Canada. These data-driven studies all produced maps of the regions
in which various climate factors dominate the flux in question. Such maps are
useful in understanding how climate factors interact, which processes are
most important, and how these fluxes might evolve under future climate
change, particularly in cases where observations are sparse (as is true for
CH4 emissions).
In this study, we use a process-based model to characterize the dominant
climate drivers of northern high-latitude wetland CH4 emissions and
how they will change with changing climate. We address three questions:
What have been the aggregate long-term CH4 emissions from the pan-Arctic wetland area over the last 50 years, and how have they changed?
What have been the dominant factor(s) controlling changes in the space–time variability of CH4 emissions over that time period?
How will these conditions be affected by a changing climate over the remainder of the 21st century?
To investigate these questions, we use an enhanced version of the Variable
Infiltration Capacity (VIC) large-scale hydrology model (Liang et al., 1994;
Bohn et al., 2013) and the wetland CH4 emissions model of Walter and
Heimann (2000). In answering questions (2) and (3), we develop (a) maps of
the sensitivities of simulated pan-Arctic wetland CH4 emissions to
various environmental factors, (b) maps of correlations between these factors
and CH4 emissions, and (c) empirical estimates of how these
sensitivities and correlations depend on climate. These sensitivity maps and
climate dependencies provide a basis for projecting future emissions in the
region, which we then compare with our VIC model projections to evaluate
their ability to capture the effects of underlying processes.
Methods
Spatial domain
Our study domain is the global land area north of 45∘ N (Fig. 1a)
with slight modifications. Because this region contains all the river basins
that drain into the Arctic Ocean, we will refer to it as the “pan-Arctic”
hereafter, as in Slater et al. (2007). Our domain boundaries are as in the
TransCom project (Gurney et al., 2000), except that we exclude Greenland. We
also include southern Russia and the permafrost part of Tibet. We divided the
domain into 3775 100 km EASE (Equal-Area Scalable Earth) grid cells (Brodzik
and Knowles, 2002).
Our domain includes three major wetland areas (Lehner and Döll, 2004;
Tarnocai et al., 2009; Fig. 1b): the West Siberian Lowland (WSL), which we
define as the region from 55 to 75∘ N and 60
to 90∘ E; Scandinavia (55–75∘ N and
15–45∘ E); and the Hudson's Bay Lowland (HBL), which
we define as the region from 45 to 60∘ N and
75 to 100∘ W. There are also many smaller wetlands
distributed over the domain. The vast majority of the domain's wetlands are
peatlands, which are reservoirs of organic carbon (Tarnocai et al., 2009),
and have the potential to produce huge fluxes of carbon (CO2 or
CH4) to the atmosphere. Forests cover about 23 % of the total land
area of our study domain, as evidenced by the belt of high values of leaf
area index (LAI) between about 55 and 65∘ N (Myneni et
al., 2002; Fig. 1c).
Relevant characteristics of study domain: (a) spatial extent of
the domain; (b) lake or wetland area fractions (taken from Lehner and Döll,
2004, and Tarnocai et al., 2009; see text for details); (c) July LAI (taken
from Myneni et al., 2002); (d) permafrost distribution (taken from Brown et
al., 2014).
![]()
Our domain includes essentially the entire Northern Hemisphere permafrost
land area, aside from a few high-altitude areas (Fig. 1d; see also Brown et
al., 2014). Within the permafrost areas, deep soil temperatures are generally
below 0 ∘C for successive years, which restricts biological
methanogens. However, during summer, the active layer (seasonally thawed)
provides a suitable environment for CH4 production.
Model framework
We used a modified version of the Variable Infiltration Capacity (VIC)
version 4.1.2 (Liang et al., 1994; Bohn et al., 2013) that simulates carbon
fluxes as well as the hydrologic processes represented in the standard
version of the VIC model. The VIC model resolves the soil moisture and
temperature profiles through a coupled water–energy balance scheme that
accounts for cold-climate processes such as soil freeze–thaw and the
insulating effects of organic soils. We provide here a brief description of
the model features related to wetland process. The main enhancement in the
version of VIC we used is a module for calculating the carbon inputs into the
ecosystem, which is the substrate source of biogeochemical processes that
produce CH4. Within each grid cell the model represents multiple land
cover “tiles”. This modified version of VIC also represents lakes and
wetlands as described in Bohn et al. (2013). Each grid cell in the study
domain is assumed to be composed of a lake–wetland tile and an upland
portion (that may contain several different land cover tiles). The
lake–wetland tile contains peatlands of fixed area, within which a
time-varying portion may be seasonally inundated and which may contain a
permanent lake. Peatlands, which are modeled as a mix of moss and shrubs, are
allowed to emit CH4, subject to oxidation above the water table, but
lake CH4 emissions are set to 0. The water table depth within peatlands
follows a distribution derived from assumed microtopography. Net primary
productivity (NPP) within peatlands experiences inhibition when the water
table is above the soil surface. More details of the lake–wetland continuum
are included in Bohn et al. (2013).
Permanent lakes were prescribed using the Global Lakes and Wetlands data set
(GLWD) of Lehner and Döll (2004). Wetland areas were taken in most cases
from the union of wetland classes from the GLWD and wetland pixels from the
MODIS plant functional type data set MCD12Q1 (Friedl et al., 2010). However,
in regions where the GLWD delineated wetland classes as 25–50 and
50–100 % (occurring in Alaska and Canada), we defined wetlands as pixels
with soil organic carbon content above 70 % from the Northern
Circumpolar Soil Carbon Database (Tarnocai et al., 2009). Of the domain's 3775 cells, 2049 contain wetlands (lake–wetland fractions shown in
Fig. 1b).
The enhanced VIC model is linked to the Walter and Heimann wetland CH4 emissions model (Walter and Heimann, 2000),
as described in Bohn et al. (2013). The Walter and Heimann CH4 model takes the water table depth
distribution, soil temperature profile and net primary productivity (NPP)
generated by VIC to calculate a distribution of CH4 emissions rates.
The model assumes that labile carbon leaks into the soil through plant roots
in proportion to NPP and is converted to CH4 through anaerobic
respiration of methanogens as a function of the soil thermal and moisture
conditions.
The combined VIC and CH4 models were calibrated over West Siberia in
Bohn et al. (2013), and we adopted the median parameter values from the
distributions from that study (Table 1) for our primary simulations. In Bohn
et al. (2013), two parameter sets were optimized for the West Siberian
Lowland: “south” (primarily within the forest belt, or taiga) and “north”
(primarily tundra). These parameter sets only corresponded to broad
geographic regions, rather than to specific types of wetlands such as bogs or
fens. To extend these parameter sets across our entire domain, we assigned
the “south” parameter set to grid cells with July LAI higher than 4 and
the “north” parameter set to all other grid cells.
LAI data were taken from the MODIS MCD15A2 data set (Myneni et al., 2002)
for the period 2002–2010. We used the mean seasonal cycle for this period
repeatedly for every year in our simulation period. Soil parameters were
taken from Su et al. (2006).
The primary meteorological forcings used to drive the VIC include 3 h
precipitation, air temperature, wind speed, downward shortwave and longwave
radiation. These data were obtained from Sheffield et al. (2006) at
0.25 × 0.25∘ spatial resolution, which we regridded to a 100 km EASE grid. Atmospheric CO2 concentration data were taken from
Bohn et al. (2013).
Parameter distributions used in the Walter and Heimann (2000)
CH4 model.
Region
Parameter
Units
Percentile
1st
50th
99th
North
r0*
µmol L-1 h-1
0.015
0.020
0.026
(g C m-2 d-1)-1
xvmax
µmol L-1 h-1
0.06
0.14
0.32
rkm
µmol L-1
4.2
11.0
13.9
rq10
–
2.5
3.4
5.2
oxq10
–
1.3
4.9
5.9
tveg
–
6
11
15
South
r0*
µmol L-1 h-1
0.016
0.019
0.022
(g C m-2 d-1)-1
xvmax
µmol L-1 h-1
0.16
0.24
0.27
rkm
µmol L-1
13.0
16.1
17.1
rq10
–
9.7
10.7
11.7
oxq10
–
1.6
2.1
3.4
tveg
–
7
12
15
Note: r0* is the reference CH4 production rate per unit
annual average LAI (r0* is related to the original r0 parameter
from Walter and Heimann (2000) by r0*=r0/LAIavg as
described in Bohn et al., 2013); xvmax is the maximum CH4 oxidation
rate; rkm is the Michaelis–Menten constant for CH4 oxidation; rq10
is the Q10 value for the CH4 production rate; oxq10 is the
Q10 value for the CH4 oxidation rate; and tveg is a dimensionless
integer value ranging from 0 to 15 that indicates the strength of plant-aided
transport.
Simulations
Our historical simulation period was 1948–2006. Model spin-up consisted of
two stages: (1) initialization of carbon pool storages and (2) a 50-year
spin-up to stabilize moisture and carbon pools. We initialized soil carbon
pools via an iterative procedure in which we identified the initial storage
that would result in zero net change in carbon storage over the period
1948–1957. Then, to account for the pools' not yet having reached
equilibrium with recent Holocene climate, we rescaled all three pool storages
by the ratio of observed to simulated total carbon storage across West
Siberia, using observations from Sheng et al. (2004). Then we ran the model
for 50 years (5 × the decade 1948–1957) to stabilize its moisture
and carbon storages. Starting from the model state at the end of this 50-year
spin-up, we then performed simulations for 1948–2006.
To isolate the effects of various climate factors that drive the variability
in CH4 emissions, we performed five control experiments in which we
removed trends (at each grid cell) in one or more variables (air temperature
and longwave radiation; precipitation; air temperature, longwave radiation
and precipitation; atmospheric CO2 concentration; and solar radiation)
during the period 1960–2006. Air temperature and longwave radiation were
considered together, since downward longwave radiation can be expressed as a
function of near-surface air temperature (e.g., Brutsaert, 1975). For air
temperature and longwave radiation, we linearly regressed the annual values
over time and removed cumulative changes due to the trend since 1960 from
each subsequent year. For annual total precipitation and annual average
shortwave radiation, we linearly regressed the annual values, computed each
year's ratio of detrended to original annual values, and multiplied all
original daily values by that ratio for each day within the year. For
detrended atmospheric CO2, we used the 1960 concentration level for the
entire period 1960–2006. Trends in the forcing variables were removed in
cases when the trend was significant at the 0.05 level. At the 0.05
significance level, the entire domain experienced increasing trends in air
temperature (0.0322 K yr-1), precipitation (0.5183 mm yr-1),
[CO2] (1.4009 ppm yr-1), and downward longwave radiation
(0.0670 W m-2 yr-1), and a decreasing trend in downward
shortwave radiation (-0.0385 W m-2 yr-1), which is consistent
with Fang and Yihui (2009; Table 2).
Using these historical and control forcings, we designed six experiments to
investigate the impact of historical climate change on the wetland CH4
emissions:
R01: historical simulation, driven by historical forcings;
R02: air temperature and longwave radiation (TLW) control run, using detrended air temperature and longwave radiation;
R03: CO2 control run, using the 1960 CO2 level;
R04: TLW and precipitation (TLWP) control run, using detrended air temperature, detrended longwave radiation, and detrended precipitation;
R05: precipitation (P) control run, using detrended precipitation;
R06: shortwave radiation (SW) control run, using detrended shortwave radiation.
Trends in spatial average climate factors from 1960 to 2006.
Factor
Trend
Mean annual air temperature (T)
0.0322 K yr-1
Annual precipitation (P)
0.5183 mm yr-1
Mean annual [CO2]
1.4009 ppm yr-1
Mean annual shortwave radiation (SW)
-0.0385 W m-2 yr-1
Mean annual longwave radiation (LW)
0.0670 W m-2 yr-1
Sensitivities to climate drivers as a function of climate
We defined the sensitivity coefficients (α) of CH4 emissions
to long-term changes in the driver variables as the following partial derivatives:
αp=dCH4dP(g CH4⋅m-2⋅yr-1⋅mm-1)αTLW=dCH4dTair(g CH4⋅m-2⋅yr-1⋅K-1)αCO2=dCH4d[CO2](g CH4⋅m-2⋅yr-1⋅ppm-1)αSW=dCH4dSW(gCH4⋅m-2⋅yr-1⋅(Wm-2)-1),
where the total change in annual methane emissions due to climate change
ΔCH4=αP×dP+αTLW×dTair+αCO2×dCO2+αSW×dSW+interaction. The CH4, T,
[CO2] and SW values in this relationship were annual average values,
while P was annual total precipitation.
We computed the sensitivity coefficients at each grid cell by first computing
the time series of differences between the historical and control emissions
and then performing a linear regression between the differences in CH4
and the differences between historical and detrended values of the driver
variable. We then created maps of these sensitivities. To characterize the
dependence of these sensitivities on climate, we divided the domain's grid
cells into groups by their 46-year (1961–2006) average historical JJA T
and JJA P, in increments of 2 ∘C and 20 mm, respectively (JJA T
and P were chosen as independent variables for purposes of characterizing
sensitivities rather than annual average T and P because the majority of
annual CH4 emissions occur during the growing season). Then, we computed
the average sensitivities in each group and plotted them as a function of
JJA T and P. This gave us two-dimensional matrices of sensitivities. Grid
cells with the same JJA T and P conditions could come from quite
different locations in the study domain; thus the resulting averaged
sensitivities were not overly influenced by the characteristics of a single
region.
Identifying the dominant emission controls
We calculated the correlation coefficients between the time series of
CH4 emissions and the various drivers at each grid cell, giving us a map
of dominant controls (those with the highest correlations) across the domain.
Similar to the sensitivities in Sect. 2.4, we created two-dimensional
matrices of correlations as a function of JJA T and JJA P.
Future projections
We generated two future projections of CH4 emissions over the period
2007–2106: a process-based projection, in which we ran our modeling
framework with future meteorological forcings, and a sensitivity-based
projection, in which we applied the four sensitivity coefficients computed
in Sect. 2.4 to projected future forcings. To generate meteorological
forcings for the future projections, we computed the monthly changes in
meteorological forcings from the 4. CCSM4 (Community Climate System Model version 4) RCP4.5 (+4.5 W m-2 Representative Concentration Pathway) projection (which falls near
the middle of the set of all CMIP5 (Coupled Model Intercomparison Project Phase 5) RCP4.5 projections) over the period
2007–2106, relative to the period 1996–2005, and applied these changes to
the Sheffield et al. (2006) meteorology.
Based on the sensitivity matrices, and given a reference climate condition
and corresponding CH4 emission rate, we can derive the projected
emission rate via
CH4(t+1)=CH4‾(t)+αP(T‾(t),P‾(t))⋅(P(t+1)-P‾(t))+αTLW(T‾(t),P‾(t))⋅(T(t+1)-T‾(t))+αCO2(T‾(t),P‾(t))⋅CO2(t+1)-CO2‾(t),
where t is the year; CH4‾(t),
T‾(t), P‾(t), and
CO2‾(t) are the average values of annual
CH4, JJA T, JJA P, and [CO2] for
the current grid cell over the last 10 years; and the coefficients are those
defined in Eq. (1). Here we assume that interactions among the
individual climate forcings are negligible. We check this assumption in
Sect. 3.2.1. We also assume that, as a grid cell's average T and P change,
its sensitivities to drivers will evolve to resemble the current
sensitivities of cells at the new (T, P) coordinates. We discuss the
validity of this assumption in Sect. 4.2.
Parameter uncertainty
Our baseline simulations used the median parameter values of Bohn et al. (2013) as described in Sect. 2.2. However, to assess the effects of
parameter uncertainty on our results, we also generated an ensemble of 18
simulations using randomly sampled parameter values from the posterior
distributions of Bohn et al. (2013; Table 1). The parameters that we
examined included r0* (the reference CH4 production rate per unit
annual average LAI), xvmax (the maximum CH4 oxidation rate), rkm (the
Michaelis–Menten constant), rq10 and oxq10 (the Q10 values
for the temperature dependencies of the CH4 production and oxidation
rates, respectively), and tveg, a dimensionless integer value ranging from 0
to 15 that indicates the strength of plant-aided transport. The posterior
distribution of tveg, which was held constant at a value of 12 in Bohn et al. (2013), was determined via Bayesian estimation from an ensemble of 3000
simulations that randomly sampled values of tveg across the range 0 to 15
and sampled values of all other parameters from their posterior
distributions, in comparison with the observations of Glagolev et al. (2011). We did not vary the parameter pox, which represents the fraction of
CH4 oxidized in the root zone, as variations in tveg can compensate for
variations in pox. Instead, we held pox constant at a value of 0.5, as in
Walter and Heimann (2000) and Bohn et al. (2013). To account for uncertainty
in our estimate of the border between the “south” and “north” regions,
we performed two additional simulations, in which the entire domain used
either the median “south” parameter set or the median “north” parameter
set (“all-south” and “all-north”, respectively). Adding these two
simulations to our ensemble resulted in a total of 20 simulations. For each
of these ensemble members, we constructed a distinct set of sensitivity
matrices and created a sensitivity-based projection.
Results
Historical simulation
Before examining simulated CH4 emissions, we first evaluated model
performance in simulating the environmental factors that are relevant to
CH4 emissions. The spatial distribution of simulated inundation
extents was similar to that of the Surface Water Microwave Product Series
(“SWAMPS”) remote-sensing inundation product of Schroeder et al. (2010),
with high concentrations in the WSL, Scandinavia, the HBL, and western
Canada (Fig. 2a, b). VIC's inundated extent was biased low in western
Canada, at about half the area given by SWAMPS.
To evaluate our simulated soil temperatures, we compared the distribution of
continuous and discontinuous permafrost from the Circum-Arctic Map of
Permafrost and Ground Ice Condition (CAMPGIC) map (Brown et al., 2014; Fig. 2c) with the VIC-simulated active layer depth (ALD) in the permafrost area
(Fig. 2d). The spatial distribution of VIC's ALD was similar to the
distribution of permafrost. An ALD of 1 m is an approximate threshold for
“continuous permafrost” in the Brown et al. (2014) map.
We compared the simulated NPP distribution (Fig. 2e) with the MODIS
MOD17A3 NPP product (Running et al., 2004; Fig. 2f). Model results and
MODIS patterns matched reasonably well (spatial correlation 0.87), with
a slight (about 6 %) overestimation of NPP in the boreal forest band between
55 and 65∘ N latitude.
Observed and simulated factors relevant to wetland methane emissions
over the study domain: (a) observed June–July–August average (JJA)
inundated area fraction over 2002–2010 from SWAMPS (Schroeder et al., 2010);
(b) simulated JJA inundated area fraction over 1948–2006;
(c) observed permafrost distribution from CAMPGIC (Brown et al.,
2014; dark blue: continuous permafrost; light blue: discontinuous
permafrost); (d) simulated maximum active layer depth (ALD) over
1948–2006; (e) observed JJA net primary productivity (NPP) over
2002–2010 (Running et al., 2004); (f) simulated JJA LAI over
1948–2006.
![]()
The spatial distribution of simulated average annual CH4 emissions
over the period 1960–2006 (Fig. 3) was similar to the distribution of
wetlands (Fig. 1b), with notable concentrations in the WSL, Scandinavia,
the HBL, and southern Canada. However, emissions were strongest in the
boreal forest belt between 55 and 65∘ N latitude, as a
consequence of warmer temperatures, greater inputs of labile carbon (due to
the higher rates of NPP there; see Fig. 2e, f), and the more productive
“south” CH4 parameter set that we used there. As an aside, the higher
NPP values in the boreal forest belt do not necessarily imply that the
peatlands there are forested, although some peatlands there do contain
substantial numbers of trees (the VIC model does not distinguish between
forested and non-forested peatlands).
We evaluated our simulated CH4 emissions over three subdomains: the WSL,
the HBL, and the high latitudes of the western hemisphere. Over the WSL, we
compared our simulations with the estimate of Glagolev et al. (2011), which
is based on in situ observations of mire landscape CH4 emissions during
2007–2010 (Fig. 4). While our model tended to overestimate emissions in the
middle of the domain, it captured the general north–south gradient in
emissions. As to the total emission from the WSL area, Glagolev et al. (2011) estimated 3.91 ± 1.29 Tg
CH4 yr-1, as compared with our estimate of 7.12 Tg
CH4 yr-1. Our result here is also considerably higher than the
estimate of Bohn et al. (2013) of 3.65 Tg CH4 yr-1, primarily
because we (a) replaced that study's WSL-specific peatland maps (Sheng et
al., 2004; Peregon et al., 2008) with the GLWD wetland map (Lehner and
Döll, 2004), which attributes substantially higher wetland fractions to
the region between 63 and 66∘ N latitude than the WSL-specific maps
do; (b) we replaced the WSL-specific assignment of “north” and “south”
CH4 parameter sets by the bioclimatic zone with the more general criterion
of July LAI > 4 (Sect. 2.2), which extended the region of more
productive wetlands (“south” parameters) slightly further northward; and
(c) used the meteorological forcings of Sheffield et al. (2006) instead of
those of Adam et al. (2006). However,
our estimate is within the range of estimates from inversions over the WSL,
which range from 3.08 Tg CH4 yr-1 (Kim et al., 2011) to 9.80 Tg
CH4 yr-1 (Schuldt et al., 2013; Winderlich, 2012).
Average annual CH4 emissions over the study domain for 1960–2006.
![]()
CH4 emissions over the HBL have been estimated by Pickett-Heaps et
al. (2011) as 2.3 ± 0.3 Tg CH4 yr-1 during 2004–2008. Our
estimate for the same region is 3.11 ± 0.45 Tg CH4 yr-1.
Although larger than the Pickett-Heaps estimate, it is almost identical to
the estimate of 3.1 ± 0.5 Tg CH4 yr-1 by Zhu et al. (2014).
Several studies have estimated total CH4 emissions from all northern
wetlands (Table 3), giving a range of 20–55 Tg CH4 yr-1 over
similar domains. Our model gives an estimate of 35.0 Tg CH4 yr-1
during 1997–2006. This result is within the range of estimates from studies
since the 1990s and is closer to some of the more recent results, e.g.,
34 ± 13 Tg CH4 yr-1 from Chen and Prinn (2006) and
38.1–55.4 Tg CH4 yr-1 from Zhu et al. (2014). The difference is
well within the uncertainty range ascribed to most previous estimates.
Estimates of total CH4 emissions over the study domain.
Method
Estimate
Area
Reference
Period
(Tg CH4 yr-1)
VIC+Walter CH4
35.0 ± 6.7
Pan-Arctic wetlands
This study
1997–2006
VIC+TEMa
38.1–55.4
Pan-Arctic area
Zhu et al. (2014)
1993–2004
MATCHb (inversion)
34 ± 13
N. Hemisphere high-latitude wetlands
Chen and Prinn (2006)
1996–2001
Walter CH4 model
56
Wetlands north of 45∘ N
Walter et al. (2001)
1982–1993
Inversion
48
Wetlands north of 45∘ N
Hein et al. (1997)
1983–1989
Process-based model
20 ± 13
Northern wetlands and tundra
Christensen et al. (1996)
1990s
WMEMc
23.3
Wetlands north of 40∘ N
Cao et al. (1996)
–
Literature review
35
N. Hemisphere wetlands
IPCC (1996)
1980s–1990s
Literature review
38
Wetlands north of 45∘ N
Bartlett and Harris (1993)
1980s
a Variable Infiltration Capacity plus Terrestrial Ecosystem Model, b Model for Atmospheric Transport and Chemistry, c Wetland Methane Emission Model.
Comparison of simulated CH4 emission rate and
field-campaign-based data over WSL. (a) VIC simulated fluxes;
(b) field-campaign-based flux data from Glagolev et
al. (2011).
![]()
Sensitivity to climate factors
Historical trends
Over the entire pan-Arctic domain, CH4 emissions increased substantially
over the period 1960–2006, with a trend of 0.158 Tg CH4 yr-1
(Fig. 5a and Table 4, 4th column). Emissions from the control runs are shown
in Fig. 5b–f. Defining the net impact of a driver as the difference between
the historical trend in CH4 emissions and the trend of the corresponding
control run (Fig. 5g and Table 2, 4th column), we can see that air
temperature and longwave radiation (TLW) had the largest impact on emissions
(0.104 Tg CH4 yr-1, or 66 % of the historical trend),
followed by CO2 (0.030 Tg CH4 yr-1, or 19 %) and
precipitation (0.015 Tg CH4 yr-1, or 10 %). The combined
impact of TLW and P (TLWP), at 0.115 Tg CH4 yr-1, is slightly
less than the sum of the impacts of TLW and P separately (0.119 Tg
CH4 yr-1), implying that these two drivers acted in opposition to
each other to some extent but also indicating that the interaction between
T and P was a relatively small effect. Locally, the effects of
precipitation were often larger than those of CO2, but these effects
largely canceled over the domain.
Sensitivity as a function of climate
The sensitivities of wetland CH4 emissions to the climate factors we
investigated varied in space or time and were strongly influenced by climate
conditions. In Fig. 6a, which shows the distribution of spatial average
annual CH4 emissions as a function of 10-year average JJA T and P,
maximum CH4 emissions occur along a “ridge” of slope 13 mm K-1
for JJA T values above 285 K and JJA P values above 120 mm.
Consequently, increasing one factor (P or T) while holding the other
factor constant may cause CH4 emissions to increase or decrease,
depending on the current climate state of the wetland. Under relatively cold
or dry conditions, emissions tend to increase with increasing T and P.
However, at high P values, emissions decrease with increasing P, due to
the inhibition of NPP under inundated conditions in the VIC model (Bohn et
al., 2013). At high T values, emissions decrease with increasing T, due
to increased oxidation of CH4 as higher evaporation rates draw down the
water table (Bohn et al., 2007).
Trends in CH4 emissions from historical and control
simulations from 1960 to 2006. All values are in units of Tg CH4 yr-1.
Simulation
Trend
95 % confidence bound
Driver impact
(historical trend – control trend)
R01 (historical)
0.158
(0.107, 0.207)
–
R02 (TLW control)
0.054
(0.006, 0.103)
0.104
R03 (CO2 control)
0.128
(0.079, 0.176)
0.030
R04 (TLWP control)
0.043
(-0.007, 0.093)
0.115
R05 (P control)
0.143
(0.093, 0.194)
0.015
R06 (SW control)
0.154
(0.104, 0.204)
0.004
Time series of domain-averaged annual methane fluxes from
(a) the historical simulation; (b–f) the five climate
control runs, in each of which one climate driver was detrended starting in
1960; (g) differences between historical simulation in
(a) and the control runs (b–f). “TLW” and “Tair LW”
denote detrending of air temperature and associated downward longwave
radiation; “CO2” denotes detrending of atmospheric CO2
concentrations; “TLW+P” denotes detrending of both air temperature (and
associated longwave radiation) and precipitation; “P” denotes detrending
of precipitation; “SW” denotes detrending of downward shortwave radiation;
and “inter” denotes the difference between “TLW” and “TLW+P”.
![]()
Temporal correlations between historical annual CH4 emissions and the
three most important climate drivers (JJA T, JJA P, and JJA CO2)
were fairly consistent with this pattern (Fig. 6b–d). Correlations between
annual CH4 emissions and JJA T (Fig. 6b) were highest when JJA T is
to the left of (colder than) the ridge of maximal emissions in Fig. 6a, and
lowest (negative, in fact) to the right of (warmer than) the ridge.
Similarly, correlations with JJA P were highest below (drier than) the
Fig. 6a ridge and lowest (negative) above (wetter than) the ridge, although
this pattern broke down for JJA T below 285 K, where temperature
limitation dominated the response and correlations with JJA P were only
weakly positive or negative. Correlations with JJA CO2 were moderately
positive at all but the most extreme JJA T and P conditions, implying
that CH4 emissions generally benefit from CO2 fertilization, via
an increased input of carbon substrate into the soil.
These differing responses of wetland CH4 emissions to climate factors
displayed strong geographic patterns, as a function of local climate
(Fig. 7). In Fig. 7a, the ensemble median correlations between CH4 and
JJA T are represented on a blue (positive) to yellow (negative) color
gradient. Similarly, correlations between CH4 and JJA P are
represented on a red (positive) to green (negative) color gradient.
Therefore, blue indicates a strong positive temperature control on CH4 emissions
(T+), and this can be thought of as too cold for maximum emissions; yellow
indicates a strong negative temperature (T-) control (too warm); green
indicates a strong negative precipitation (“P-“) control (too wet); and
red indicates a strong positive precipitation (“P+”) control (too dry).
In general, northern cells were T+ dominated (blue), due to the low summer
air temperatures that they experience. These blue regions corresponded
approximately to the distribution of permafrost (Fig. 1d). Moving southward,
emissions became P+ dominated (red). Southern West Siberia is relatively
dry and warm, thus showing both P+ and T+ controls (orange). However, in
the northernmost regions of Alaska and Canada (where inundation fractions
were high, see Fig. 2b), we saw predominantly P- control (green).
Comparison of this figure with Fig. 2b also shows that P+ and T+ (orange)
areas were associated with smaller inundated area fractions and warmer
temperatures, due to deeper water tables and greater oxidation rates.
Panel (a): the 1960–2006 average annual CH4 emission over JJA
(June–July–August) T and JJA P space; panels (b–d): correlation between 1960–2006
annual CH4 emission and JJA drivers in the same T-P space.
![]()
Spatial distributions of ensemble median (left) and range at
95 % confidence level (right) of correlations between annual CH4
emissions and JJA T and P. The green-red and yellow-blue axes depict the strength of correlation (-1 to 1) with JJA P and JJA T, respectively.
![]()
Parameter-based uncertainties in the correlations (Fig. 7b), expressed as
the range of correlations across the ensemble, were generally small
(< 0.3) in both the T and P dimensions, except for P--limited
(green) regions in northeastern Canada and central Tibet and the northern
portion of the T+-limited region in north-central Canada. The general
pattern of P+ limitation in the southern reaches of the domain and T+
limitation in much of the northern reaches of the domain appeared in all
ensemble members.
Correlations between emissions and drivers tell us which driver is most
influential at a given location. However, the sizes of the correlations are
affected by both the relative sensitivities of emissions to the drivers and
the relative amplitudes of the drivers' signals. It is therefore useful to
consider the sensitivities alone. Sensitivities of annual emissions to the
three main drivers (JJA T, JJA P, and JJA CO2) were markedly higher
outside the continuous permafrost zone than within it (Fig. 8). To first
order, the explanation for this pattern is the general insensitivity of
CH4 emissions to all drivers at low temperatures, evident in Fig. 6a.
Nevertheless, there were important differences among the distributions; for example,
emissions in eastern Canada and eastern Siberia showed strong sensitivity to
T but weak sensitivity to P and CO2. Spatial correlations between
these sensitivities and various hydrologic and ecological terms, listed in
Table 5, give some indication of which processes were most influential. The
sensitivity of CH4 emissions to JJA T (Fig. 8a) was most highly
correlated (r= 0.30) with April–May snow water equivalent (AM SWE),
which is consistent with a lack of water limitation, due to larger spring
snowpacks leading to wetter summer conditions. Similarly, the sensitivity of
emissions to P (Fig. 8b) was larger in absolute magnitude (positive or
negative) where temperatures were warm, allowing for a higher
(temperature-dependent) CH4 production rate to be affected more
dramatically by oxidation under drier conditions and reduced carbon input
under wetter, more inundated conditions. The lack of strong correlations
between the sensitivity to P and the various environmental factors in
Table 5 may be the result of relatively high spatial heterogeneity in P and
wetland moisture conditions (e.g., inundation), in comparison with those of
T, leading to more “noise” in the relationships between them. Finally,
the sensitivity of emissions to CO2 (Fig. 8c) was most strongly
correlated (r= 0.45) with NPP (Fig. 2f), which is consistent with the
relationship between rates of carbon input into the soil and NPP in the model
of Walter and Heimann (2000). Because relatively warm conditions and high NPP
are associated with boreal forests, the geographic distributions of
sensitivities to all factors also bore a strong similarity to the distribution
of boreal forest.
Spatial distributions of sensitivities of CH4 to climate
drivers. Panel (a): sensitivity to air temperature; panel (b): sensitivity to
precipitation; panel (c): sensitivity to [CO2].
![]()
Spatial correlation coefficients between sensitivities and
environmental factors.
Environmental factor
Sensitivity of
JJA Ta
JJA Pb
JJA Finundc
AM SWEd
JJA LAIe
ALDf
Annual NPPg
JJA Tsoilh
annual CH4
(K)
(mm)
(m)
(g C m-2 yr-1)
(K)
(g CH4 m-2 yr-1) to
JJA T(K)
0.1928
0.1827
0.0438
0.2990
0.1735
0.1813
0.2658
0.1682
|JJA P| (mm)i
0.2231
0.0309
-0.1068
-0.0530
0.1570
0.0797
0.1013
0.0462
[CO2] (ppm)
0.3856
0.3209
0.0887
0.2951
0.3364
0.3096
0.4541
0.3064
aJJA T: June–July–August average air temperature;
bJJA P: June–July–August total precipitation; cJJA
Finund: June–July–August inundated area fraction; dAM
SWE: April–May average snow water equivalent; eJJA LAI:
June–July–August average leaf area index; fALD: maximum annual
active layer depth; gAnnual NPP: annual net primary productivity;
hJJA Tsoil: June–July–August average temperature in the top
10 cm of the soil column. i Extreme values of sensitivity
(> 0.005 g CH4 m-2 yr-1 per mm change in JJA
P) were ignored; these occurred in 164 cells, out of 2049 cells containing
wetlands.
Process- and sensitivity-based projections
To create a projection of future CH4 emissions based on the climate
sensitivities, we computed matrices of the sensitivity of aggregate emissions
to each driver as a function of JJA T and P (Fig. 9a, c, e), similarly to
the earlier correlation matrices (Fig. 6). To ensure that sensitivities exist
for all possible future combinations of JJA T and P in the projection, we
filled gaps in the matrices via a 3 row × 3 column window with a
Gaussian kernel with σ= 1. Similar to the correlation matrices
discussed in Sect. 3.2.2, the sensitivities to JJA T, JJA P, and
[CO2] all exhibited maximum values along a diagonal “ridge” for T
> 285 K and P > 120 mm (which correspond to the
climate conditions in which boreal forest is found). For the sensitivities to
JJA T and [CO2], the ridges had similar slopes of approximately
30 mm K-1. Sensitivities to JJA T were negative for
P < 50 mm and 285 K < T < 291 K, due to
increasing CH4 oxidation above the water table with increasing
temperature. In contrast, the ridge of maximum sensitivities to JJA P had a
lower slope of about 12.5 mm K-1, with a region of negative
sensitivities for P > 190 mm and 287 K
< T < 293 K, due to reduced productivity under
inundated conditions. Again, sensitivities to all drivers were nearly 0 for JJA T < 285 K, due to the nonlinear temperature dependence of
CH4 production as well as the tendency for wetlands in that temperature
range to be less productive (and therefore use the less productive “north”
parameter set). Uncertainty in the methane model parameters (across the
ensemble of random parameter combinations) led to a wide range of sensitivity
values (Fig. 9b, d, and f). However, the contours of the matrices of the
individual ensemble members had similar shapes, so that regions of higher or
lower sensitivities occurred in similar locations in climate space. The
ensemble of sensitivity-based projections, created by applying these
sensitivity matrices to meteorological forcings based on the CCSM4 RCP4.5
projection over the period 2006–2106, yielded a similar trajectory of
CH4 emissions to the projection from our process-based model (Fig. 10).
Both the process-based (black) and median sensitivity-based (blue)
projections agreed that emissions will initially remain relatively constant
from 2007 to 2026 (in response to relatively little trend in air temperatures
over the period; Fig. 10b) and then resume their increase. For the period
2056–2065, the process- and median sensitivity-based projections reached
46.1 and 43.4 Tg CH4 yr-1, respectively (132 and 124 %,
respectively, of the 1997–2006 level). By the end of the century
(2096–2105), they reached 50.1 and 48.3 Tg CH4 yr-1 (142 and
138 %, respectively, of their 1997–2006 levels). Uncertainty in the
methane model parameters led to a range of 39 to 57 Tg CH4 yr-1
in sensitivity-based end-of-century emissions at the 95 % confidence
level. However, the other members of the uncertainty ensemble followed
trajectories that were similar to the median sensitivity-based projection
over the course of the century, resulting in increases of 38 to 53 % over
their 1997–2006 levels.
The 1960–2006 average T, P and CO2 sensitivities (a, c, and e, respectively) of
CH4 emissions in JJA T and JJA P space using the median methane
model parameter set and their ranges at the 95 % confidence level across randomly sampled
methane model parameter sets (b, d, and f,
respectively).
![]()
While the two projections agreed on long-term behavior, their year-to-year
variability disagreed at times, with the median sensitivity-based projection
sometimes anticorrelated with the process-based projection. This is likely
due to our construction of average sensitivities over all grid cells having
similar climate conditions, which ignored the influence of local land cover,
topography, and soils. Thus, during some years in some grid cells, our
sensitivity matrices may have indicated a sensitivity of opposite sign to
that of the process-based model, due to the grid cell's “ridge” of maximum
emissions occurring in a different location in T-P space than in the
domain-average matrix. Nevertheless, the general agreement in the long-term,
domain-wide behavior implies that the sensitivity-based method captured the
aggregate response of wetland CH4 emissions to climate reasonably well.
Geographically, the regions of largest increases in emissions during the next
century were in the boreal forest belt (Fig. 11a, c). This behavior was
fairly consistent across the ensemble of methane parameter sets, with the
exception of uncertainties > 30 % of the median in southern
Canada and northwestern Siberia (Fig. 11b). These increases in emissions
began at the southern edge of the domain and spread northward over time,
corresponding to a northward shift in the types of controls exerted by
climate factors, as shown in Fig. 12. Between 1997–2006 and 2096–2105,
areas of P+ control (red and pink) migrated northward by 10–20∘ of
latitude, into territory that was previously under T+ control (blue;
Fig. 12, left). In other words, wetlands between 55 and 65∘ N
latitude that were previously colder than optimal experienced warming without
a sufficient corresponding increase in precipitation, leading to their
becoming drier than optimal and increasing their positive response to
increases in precipitation. Other regions of historically T+ control with
large lake areas (e.g., Finland and northern Canada) were replaced by P-
control (green) as they warmed. These patterns were robust across the
parameter uncertainty ensemble, with large uncertainties primarily confined
to northeastern Canada (Fig. 12 right).
Historical and projected annual methane emissions and climate
drivers over the pan-Arctic from 2007 to 2106. Panel (a): sensitivity- and process-based
projections (blue and black, respectively) of methane emissions from
northern wetlands during 2007–2106, with historical simulation (red)
1948–2006. Parameter-based uncertainties in the sensitivity-based projection
are plotted as the yellow and green envelopes (50 and 95 % confidence
bounds, respectively); panels (b–d): climate conditions for projections. The
end-of-century window for time slice analysis (2096–2105) is denoted with
vertical solid and dashed lines in (a).
![]()
Ensemble median (a) and range (b) of average annual
end-of-century (2096–2105) CH4 emissions for the sensitivity-based
projection and the difference between the median and the annual emissions of
year 2006 (c).
![]()
To investigate the role of inundated area in the interannual variation of
methane emissions, we calculated the changes in inundated fraction (Fig. 13a–c) and mean water table levels (Fig. 13d–f) of the process-based
projection between the periods 1960–2006 and 2081–2100. The large areas of
drying (reduced inundated area and falling water tables) in southern Canada
and Alaska in Fig. 13c and f are consistent with the increase in the
extent of P+-limited (red) wetlands in those same places over the
21st century, shown in Fig. 12.
Spatial distributions of ensemble median (left) and range at
95 % confidence level (right) of correlations between annual CH4
emissions and JJA T and P for the period 2081–2100 of the sensitivity-based
projection. The green-red and yellow-blue axes depict the strength of correlation (-1 to 1) with JJA P and JJA T, respectively.
![]()
Discussion
Historical climate controls on CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> emissions
Our analysis indicates that summer air temperature increases explain almost
two thirds of the long-term trend in CH4 emissions over the last half
century over the pan-Arctic domain. Precipitation had a smaller net effect
(it explains only 10 % of the long-term trend), but this is due in part
to spatial heterogeneity in the historical trends of P and their effects on
CH4, leading to partial cancellation over the pan-Arctic domain.
Nevertheless, the dominant role of air temperature in the pan-Arctic is not
entirely surprising, given that the region is generally cold, leading to
temperature limitation on metabolic rates. Our map of the historical controls
on emissions (Fig. 7) corroborates this notion, since most of the region has
historically been T+ limited. This finding is largely consistent with Bloom
et al. (2010), who also found that air temperature was the dominant factor
controlling CH4 emissions at high latitudes. However, our finding of
strong P+ limitation in the band between 50 and 60∘ N (Fig. 7) is
at odds with Bloom et al. (2010). This discrepancy may be due to a lack of
variability in GRACE observations there (Bohn et al., 2015) or the inability
of the global linear regression used by Bloom et al. (2010) to capture the location- and
climate-dependent sensitivities accounted for by process-based models and the
sensitivity-based approach that we have used here.
Within the pan-Arctic domain, we found strong geographic patterns in climate
controls on CH4 emissions. Similar (observation- rather than
model-based) analyses have been performed on NPP (Nemani et al., 2003)
and ET (Teuling et al., 2009). Our study shares some similarity in
conclusions. For example, these studies show that CH4, NPP and ET are
all T+ controlled around Hudson Bay and in Scandinavia, and P+
controlled in the wetlands of southwestern Canada. This is not surprising,
because NPP and ET are both tightly linked with CH4 production: NPP
determines how much carbon can be converted to CH4, while ET is
positively correlated with soil moisture content, as is the CH4
emission rate. In the WSL, the wetlands in the south are P+ and T-
controlled, suggesting that this area is much drier than the north, with
more CH4 emitted as the water tables are drawn down during summer
(Bohn et al., 2007). NPP in this southern area is in transition from T
limited to P limited (Nemani et al., 2003), which is consistent with
CH4. In a recent process-based study, Liu et al. (2015) also found that
ET in southern Siberia is P+ limited.
Changes in inundated area fraction and water table position during
the historical period (1960–2006) and projection period (2081–2100) for the
process-based projection. Panel (a) is the average inundation fraction
during 1960–2006, (b) is the average of 2081–2100, (c) is
the difference between these two averages (b–a).
Panels (d–f) are similar for water table positions.
![]()
Despite their similarities, there are some differences in the spatial
distributions of controls between our and previous studies. In Nemani et
al. (2003), NPP over northern Europe and West Siberia is almost entirely
limited by temperature and radiation, while in our results, CH4 is P+
limited over a considerable area. This is due in part to the nearly
negligible role shortwave radiation plays in CH4 emissions (Fig. 5), in
part to the drier optimal soil moisture conditions for upland vegetation
(included in the Nemani et al. NPP analysis), relative to wetland plants
(which we focus on here), and in part to the rapid drop in CH4 emissions
as the water table is drawn down beyond a few centimeters. Similarly, the area of P+
limitation of ET in western Canada in Teuling et al. (2009) is smaller than
the area of P+ limitation of CH4 emissions in our study. This can also
be explained by the presence of forested uplands in this area, where the
moisture deficit in upper soil layers from low precipitation is partly
compensated for by water extracted from deeper soils. Thus, only those places
with a considerable shortage of water will show up as P+ in the Teuling et
al. ET map.
The validity of our results depends on our model's temporal behavior, which
is subject to both model uncertainty and parameter uncertainty. Verification
opportunities include in situ observations and atmospheric model inversions.
Both are problematic, due to the paucity of long in situ observational
records in the first case and the errors to which inversions are subject in
the second (see Bohn et al., 2015). Nevertheless, in Bohn et al. (2015), the
interannual variability of our modeling framework (called “UW-VIC” therein)
was assessed over the West Siberian Lowland over the period 1993–2004,
relative to observations, several atmospheric inversions (including those of
Bousquet et al., 2011), and many other process-based models. While there was
little agreement across these data sets in terms of interannual variability,
the process-based models (including UW-VIC) that employed soil physics
formulations appropriate to high latitudes and realistic relationships
between CH4 emissions and water table depth tended to be more similar to
the inversions than those that did not. Our investigation of parameter
uncertainty (Figs. 9 and 10) revealed a substantial range in sensitivities
and end-of-century CH4 emissions but made little difference to the shape
of the trajectory over the next century or the spatial distribution of
climate controls. Thus, we believe our findings here are robust with respect
to parameter uncertainty. However, investigation of the impacts of model
uncertainty on climate controls on CH4 emissions using other model
formulations would be useful.
Sensitivity-based future projections
Our sensitivity estimates provide a simplified description of wetland
behavior and is in effect, a linearization of our process-based model.
Nevertheless, the similarity between our process-based and sensitivity-based
projections suggests that our domain-averaged sensitivities capture most of
the dependence of CH4 emissions on climate conditions, as represented
within our modeling framework. Our projected emissions are comparable to
those of other process-based studies. Our estimate of a 24–32 % increase
in pan-Arctic CH4 emissions by mid-century is comparable to the 25 %
increase estimated by Anisimov (2007). Over northern
Eurasia, our estimate of end-of-century emissions is 21.5 Tg
CH4 yr-1, similar to the estimate of 25.1 ± 3.7 Tg
CH4 yr-1 by Zhu et al. (2011). The widespread warming and drying
of wetlands and consequent reduced sensitivity of emissions to warming in our
projections are consistent with similar findings in other studies (Koven et
al., 2011; Riley et al., 2011; Ringeval et al., 2011; Lawrence et al., 2015).
Our characterization of the sensitivities of emissions to climate requires
the assumption that, as a grid cell's climate changes, its future
sensitivities will come to resemble those of cells with similar climate
today, in essence attributing climate sensitivities completely to current
climate state. Several studies have, however, found associations between
vegetation and CH4 emissions (Glagolev et al., 2011; Lupascu et al.,
2012; Levy et al., 2012; Olefeldt et al., 2013). In particular, Olefeldt et al. (2013) found that emission rates from sedge-dominated wetlands are not
only higher but also more sensitive to changes in both soil temperature and
water table depth than are emission rates from non-sedge-dominated wetlands.
On the other hand, dynamic vegetation models suggest that vegetation
communities will migrate northward with future climate change (e.g., Kaplan
and New, 2006; Alo and Wang, 2008), potentially bringing with them any
characteristics (e.g., aerenchyma) that enhance CH4 emissions. To the
extent that vegetation communities can migrate in step with climate change,
our sensitivity matrices would still be applicable. Nonetheless, this
suggests an interesting avenue for future research.
Future changes in the dominant controls
In our future projections, we found that much of the region will shift from
T+ limitation (colder than optimal) to T- and P+ limitation (warmer and
drier than optimal). This large-scale shift towards the warm and dry side of
the “ridge” of maximum emissions implies that air temperature will play a
smaller role in end-of-century emissions than at present, for two reasons:
first, the positive response to an increase in temperature in the northern
portion of the domain will be partially or completely canceled by the
negative response from the southern portion, and, second, the response to
precipitation will increase due to the widespread drier-than-optimal
conditions. This suggests that, beyond the year 2100, emissions may level off
or even decrease under further climate change, unless precipitation can
increase sufficiently to compensate for the increases in air temperature. The
larger future role of P in controlling pan-Arctic CH4 emissions may lead
to greater uncertainty in future projections beyond 2100, due to the poorer
performance and greater lack of agreement of global climate models in
projecting future precipitation than temperature (Hawkins and Sutton, 2011;
IPCC, 2013).
There are additional reasons to think that T will play a reduced role in
the future. There is some indication that the metabolic impacts of higher
temperatures have been overestimated by most models, as most studies neglect
acclimatization. Koven et al. (2011), for instance, found that soil microbial
communities essentially adapt to warmer soil temperatures and CH4
emissions rates return to their previous levels. Koven et al. (2011) showed
that acclimatization could eliminate over 50 % of the increase in
emissions over the pan-Arctic by the end of the century that would otherwise
occur. Under such conditions, the primary effects of increased T would then
be on drying out the wetlands through increased ET. In addition, because our
model did not simulate dynamic vegetation phenology, we did not account for
increased transpiration arising from CO2 fertilization, which also would
have a drying effect on the wetlands (the wetland–climate CH4 feedback
as discussed by Ringeval et al., 2011; Koven et al., 2011; and Stocker et
al., 2013). Including these effects in the model on which our sensitivities
were based would likely reduce the sensitivity of future emissions to further
increases in T and perhaps even change the sign of the sensitivity to
negative in some water-limited locations. Thus, our estimates of the
expansion of the water-limited zone and the reduction of the role of T may
be considered a lower bound.