Introduction
Methane (CH4) emissions from high-latitude wetlands are an important
component of the global climate system. CH4 is an important greenhouse
gas, with approximately 34 times the global warming potential of carbon
dioxide (CO2) over a century time horizon (IPCC, 2013). Globally,
wetlands are the largest natural source of CH4 emissions to the
atmosphere (IPCC, 2013). Because wetland CH4 emissions are highly
sensitive to soil temperature and moisture conditions (Saarnio et al., 1997;
Friborg et al., 2003; Christensen et al., 2003; Moore et al., 2011; Glagolev
et al., 2011; Sabrekov et al., 2014), there is concern that they will provide positive feedback to future climate warming (Gedney et al., 2004; Eliseev
et al., 2008; Ringeval et al., 2011). This risk is particularly important in
the world's high latitudes because they contain nearly half of the world's
wetlands (Lehner and Döll, 2004) and because the high latitudes have been
and are forecast to continue experiencing more rapid warming than elsewhere
(Serreze et al., 2000; IPCC, 2013). Adding to these concerns is the potential
liberation (and possible conversion to CH4) of previously frozen, labile
soil carbon from thawing permafrost over the next century (Christensen et
al., 2004; Schuur et al., 2008; Koven et al., 2011; Schaefer et al., 2011).
Map of the West Siberian Lowland (WSL). (a) Limits of domain
(brown) and peatland distribution (cyan), taken from Sheng et al. (2004);
lakes of area > 1 km2 (blue) taken from Lehner and Döll (2004);
permafrost zone boundaries after Kremenetski et al. (2003); CH4 sampling
sites from Glagolev et al. (2011), denoted by red circles. (b)
Dominant land cover at 25 km derived from MODIS-MOD12Q1 500 m land cover
classification (Friedl et al., 2010).
Process-based models are crucial for increasing our understanding of the
response of wetland CH4 emissions to climate change. Large-scale
biogeochemical models, especially those embedded within earth system models,
are particularly important for estimating the magnitudes of feedbacks to
climate change (e.g., Gedney et al., 2004; Eliseev et al., 2008; Koven et
al., 2011). However, as shown in the global Wetland and Wetland
CH4 Intercomparison of Models Project (WETCHIMP; Melton et al., 2013;
Wania et al., 2013), there was wide disagreement among large-scale models as
to the magnitude of global and regional wetland CH4 emissions, in terms
of both wetland areas and CH4 emissions per unit wetland area. These
discrepancies were due in part to the large variety of schemes used for
representing hydrological and biogeochemical processes, in part to
uncertainties in model parameterizations, and in part to the sparseness of in
situ observations with which to evaluate model performance (Melton et al.,
2013).
In addition to these challenges on the global scale, the unique
characteristics of high-latitude environments pose further problems for
biogeochemical models. For example, much of the northern land surface is
underlain by permafrost, which impedes drainage (Smith et al., 2005) and
stores ancient carbon (Koven et al., 2011) via temperature-dependent
constraints on carbon cycling (Schuur et al., 2008). Similarly, peat soils
and winter snowpack can thermally insulate soils (Zhang, 2005; Lawrence and
Slater, 2008, 2010), dampening their sensitivities to interannual
variability in climate. Several commonly used global biogeochemical models
(e.g., Tian et al., 2010; Hopcroft et al., 2011; Hodson et al., 2011;
Kleinen et al., 2012) lack representations of some or all of these
processes.
The prevalence of peatlands in the high-latitudes poses further challenges to
modeling (Frolking et al., 2009). Peatlands are a type of wetland containing
deep deposits of highly porous, organic-rich soil, formed over thousands of
years under waterlogged and anoxic conditions, which inhibit decomposition
(Gorham, 1991; Frolking et al., 2011). Within the porous soil, the water
table is often only a few centimeters below the surface, leading to anoxic
conditions and CH4 emissions even when no surface water is present
(Saarnio et al., 1997; Friborg et al., 2003; Glagolev et al., 2011). This
condition can lead to an underestimation of wetland area when using satellite
surface water products as inputs to wetland methane emissions models. In
addition, trees and shrubs are found with varying frequency in peatlands
(e.g., Shimoyama et al., 2003; Efremova et al., 2014), interfering with
the detection of inundation. Furthermore, the water table depth within a peatland
is typically heterogeneous, varying on the scale of tens of centimeters as a
function of microtopography (hummocks, hollows, ridges, and pools; Eppinga et
al., 2008). Models vary widely in their representations of wetland soil
moisture conditions, ranging from schemes that do not explicitly consider the
water table position (e.g., Hodson et al., 2011) to a single uniform water
table depth for each grid cell (e.g., Zhuang et al., 2004) to more
sophisticated schemes that allow for sub-grid heterogeneity in the water
table (e.g., Bohn et al., 2007, 2013; Ringeval et al., 2010; Riley et al.,
2011; Kleinen et al., 2012; Stocker et al., 2014; Subin et al., 2014).
Finally, peatland soils can be highly acidic and nutrient-poor, and much of
the available carbon substrate can be recalcitrant (Clymo et al., 1984;
Dorrepaal et al., 2009). While some models attempt to account for the effects
of soil chemical conditions such as pH, redox potential, and nutrient
limitation (e.g., Zhuang et al., 2004; Riley et al., 2011; Sabrekov et al.,
2013; Spahni et al., 2013), not all do.
Given the potential problems of parameter uncertainty and equifinality (Tang
and Zhuang, 2008; van Huissteden et al., 2009) and computational limitations
when wetland components are embedded within global climate models, it is
important to determine which model features are necessary to simulate
high-latitude peatlands accurately and to constrain parameter values with
observations. Until recently, the evaluation of large-scale wetland CH4
emissions models has been difficult, due to the sparseness of in situ and
atmospheric CH4 observations. However, observations from the West
Siberian Lowland (WSL) now offer the opportunity to assess model performance,
thanks to recent intensive field campaigns (Glagolev et al., 2011), aircraft
profiles (Umezawa et al., 2012), tall-tower observations (Sasakawa et al.,
2010; Winderlich et al., 2010), and high-resolution wetland inventories
(Sheng et al., 2004; Peregon et al., 2008, 2009).
Our primary goal in this study is to determine how well current global
large-scale models capture the dynamics of high-latitude wetland CH4
emissions. To this end, we assess the performance of 21 large-scale wetland
CH4 emissions models over West Siberia, relative to in situ and
remotely sensed observations as well as inverse models. We examine both
spatial and temporal accuracy, including seasonal and interannual
variability, and estimate the relative influences of environmental drivers
on model behaviors. We identify the dominant sources of error and the model
features that may have caused them. Finally, we make recommendations as to
which model features are necessary for accurate simulations of high-latitude
wetland CH4 emissions and which types of observations would help
improve future efforts to constrain model behaviors.
Methods
Spatial domain
The West Siberian Lowland (WSL) occupies approximately 2.5 million km2
in northern central Eurasia, spanning from 50 to 75∘ N and 60 to
95∘ E (Fig. 1a). This region is bounded on the west by the Ural
Mountains; on the east by the Yenisei River and the Central Siberian Plateau;
on the north by the Arctic Ocean; and on the south by the Altai Mountains and
the grasslands of the Eurasian Steppe (Sheng et al., 2004). The WSL contains
most of the drainage areas of the Ob' and Irtysh rivers, as well as the
western tributaries of the Yenisei River, all of which drain into the Arctic
Ocean. Permafrost in various forms (continuous, discontinuous, isolated, and
sporadic) covers more than half of the area of the WSL, from the Arctic Ocean
south to approximately 60∘ N, with continuous permafrost occurring
north of 67∘ N (Kremenetski et al., 2003). The region's major biomes
(Fig. 1b) consist of the treeless tundra north of 66∘ N,
approximately coincident with continuous permafrost; the taiga forest belt
between 55 and 66∘ N; and the grasslands of the steppe south of
55∘ N.
Wetlands occupy 600 000 km2, or about 25 % of the land area of the
WSL, primarily in the taiga and tundra zones (Sheng et al., 2004). The vast
majority of these wetlands are peatlands, which have peat depths ranging from
50 cm to over 5 m and which comprise a total soil carbon pool of 70 Pg C (Sheng et
al., 2004). Numerous field studies have documented strong methane emissions
from these peatlands, particularly those south of the southern limit of
permafrost (e.g., Sabrekov et al., 2014; Sasakawa et al., 2012; Glagolev et
al., 2011, 2012; Friborg et al., 2003; Shimoyama et al., 2003; Panikov and
Dedysh, 2000). Permanent water bodies, ranging in size from lakes 100
km2 in area to pools only a few meters across, are comingled with
wetlands throughout the domain (Lehner and Döll, 2004; Repo et al., 2007;
Eppinga et al., 2008). Notable concentrations of lakes are found (a) north
of the Ob' River between 61 and 64∘ N and 68 and 80∘ E; (b)
west of the confluence of the Ob' and Irtysh rivers between 59 and
61∘ N and 64 and 70∘ E; and (c) on the Yamal Peninsula
north of 68∘ N.
Because the vegetative and soil conditions vary substantially across the
domain, we have divided it into two halves of approximately equal size along
61∘ N latitude. The region north of this line contains permafrost,
while the region south of the line is essentially permafrost-free.
Terminology
Estimating wetland CH4 emissions over large scales requires accurately
delineating the wetland area over which CH4 emissions can occur.
Unfortunately, “wetland” definitions vary within the scientific community
(Mitsch and Gosselink, 2000). For the purposes of estimating CH4
emissions, the key characteristics include anoxia and available labile carbon
substrate; therefore, we will adopt the definition proposed by Canada's
National Wetlands Working Group (Tarnocai et al., 1988): land that is
saturated with water for long enough to promote wetland or aquatic processes as
indicated by poorly drained soils, hydrophytic vegetation, and various kinds
of biological activity which are adapted to a wet environment. Because
permanent, deep (> 2m) open-water bodies are subject to additional
processes (e.g., allocthonous carbon inputs, wind-driven mixing of the water
column; Pace et al., 2004), we will exclude them from our definition.
Unfortunately, explicit observations of lake depths are lacking for all but
the deepest lakes; therefore, we will instead use an area threshold
(1 km2) to identify permanent lakes. This definition of wetlands
therefore includes all peatlands (inundated or not), seasonally inundated
non-peatland soils (e.g., river floodplains), and small ponds or lakes but
excludes rivers and large lakes.
We define “surface water” as all freshwater above the soil surface, i.e.,
the superset of inundation, lakes, and rivers. We define “inundation” as
temporary (present for less than 1 year) standing water above the soil
surface; “lakes” as permanent water bodies (present for more than 1 year)
exceeding 1 km2 in area; and “rivers” as channels that carry
turbulent water. Surface water therefore includes areas that do not emit
large amounts of CH4, such as rivers, and also excludes some
CH4-emitting areas such as non-inundated peatlands.
For models, we will use the term “CH4-producing area” to refer to the
area over which CH4 production is simulated, which might not coincide
exactly with the areas of actual or simulated wetlands.
Observations and inversions used in this study.
Name
Reference
Description
Temporaldomain
Temporalresolution
Spatial domain
Spatial resolution
Wetland maps
Sheng2004
Sheng et al. (2004)
Wetland map of WSL based on digitization of regional maps of Markov (1971), Matukhin and Danilov (2000), and Romanova et al. (1977). Supplemented with peat cores.
Second half of20th century
Static map
Western Siberia
1:2 500 000 north of 65∘ N, 1:1 000 000south of 65∘ N
Peregon2008
Peregon et al. (2008)
Wetland map of WSL based on digitization of regional map of Romanova et al. (1977). Wetland types identified by remote sensing and field validation.
Second half of20th century
Static map
Western Siberia
1:2 500 000
Northern CircumpolarSoil Carbon Database(NCSCD)
Tarnocai et al. (2009)
Map of wetlands across the northern circumpolar permafrost region. Over the WSL, based on maps of Fridland (1988) and Naumov (1993).
Second half of20th century
Static map
Northerncircumpolarpermafrostregion
1:2 500 000
Global Lake and WetlandDatabase (GLWD)
Lehner and Döll (2004)
Global lake and wetland map. Wetlands were the union of four global data sets.
Second half of20th century
Static map
Global
1:1 000 000
Surface Water
Global Inundation Extentfrom Multi-Satellites(GIEMS)
Papa et al. (2010)
Remote-sensing inundation product based on visible (AVHRR) and active (SSM/I) and passive (ERS) microwave sensors.
1993–2004
Daily,aggregatedto monthly
Global
25 km equal-area grid, aggregatedto 0.5∘×0.5∘
Surface WaterMicrowave ProductSeries (SWAMPS)
Schroeder et al. (2010)
Remote-sensing inundation product based on active (SeaWinds-on-QuikSCAT, ERS, and ASCAT) and passive (SSM/I, SSMI/S) microwave sensors.
1992–2013
Daily,aggregatedto monthly
Global
25 km equal-area grid, aggregatedto 0.5∘×0.5∘
CH4 Inventory
Glagolev2011
Glagolev et al. (2011)
In situ flux sampling along transect spanning West Siberia, 2006–2010; statistical model of fluxes as function of wetland type applied to map of Peregon et al. (2008).
2006–2010
Monthlyclimatology
Western Siberia
0.5∘×0.5∘
CH4 Inversions
Bloom2010
Bloom et al. (2010)
Global optimization of relationship between SCIAMACHY atmospheric CH4 concentrations (Bovensmann et al., 1999), NCEP/NCAR surface temperatures (Kalnay et al., 1996), and GRACE gravity anomalies (Tapley et al., 2004).
2003–2007
Annual
Global
3∘×3∘
Bousquet2011R
Bousquet et al. (2011),Bousquet et al. (2006)
Global inversion using LMDZ with Matthews and Fung (1987) inventory as the wetland prior.
1993–2009
Monthly
Global
1∘×1∘ resolution for prior, multiplied by single coefficient for all of boreal Asia
Bousquet2011K
Bousquet et al. (2011),Bousquet et al. (2006)
Global inversion using LMDZ with emissions from Kaplan (2002) as the wetland prior.
1993–2009
Monthly
Global
1∘×1∘ resolution for prior, multiplied by single coefficient for all of boreal Asia
Kim2011
Kim et al. (2011)
Global inversion, with Glagolev et al. (2010) as prior in WSL and with Fung et al. (1991) elsewhere.
2002–2007
Monthlyclimatology
Regional
1∘×1∘ resolution for prior, multiplied by single coefficient for all of WSL
Winderlich2012
Winderlich (2012),Schuldt et al. (2013)
Regional inversion over West Siberia, with Kaplan (2002) as the wetland prior.
2009
Monthlyclimatology
Regional
1∘×1∘ resolution for both prior and coefficients over WSL
Observations and inversions
Table 1 lists the various observations and inversions that we used in this
study. We considered four wetland map products over the WSL, all of which
have been used in high-latitude wetland carbon studies. Two of them are
regional maps specific to the WSL: Sheng et al. (2004), denoted by
“Sheng2004”, and Peregon et al. (2008), denoted by “Peregon2008”. Both
Sheng 2004 and Peregon2008 used the 1:2500 000-scale map of
Romanova (1977): Peregon2008 was entirely based on the Romanova map, while
Sheng2004 used the Romanova map north of 65∘ N and used the
1:100 000-scale maps of Markov (1971) and Matukhin and Danilov (2000)
elsewhere. Both of these maps delineate the extents of peatlands, including
ponds and lakes smaller than 1 km2 in area. The Sheng2004 product
additionally includes a separate layer delineating lakes larger than
1 km2. The Peregon2008 product distinguishes between various wetland
subtypes (e.g., sphagnum- or sedge-dominated bogs and high palsa mires).
The third map is the Northern Circumpolar Soil Carbon Database (NCSCD;
Tarnocai et al., 2009), an inventory of carbon-rich soils, including
peatlands, within the Arctic permafrost region. Models that have used this
database have taken the Histel and Histosol delineations to be synonymous
with peatlands. The fourth map is the wetland layer (GLWD-3, excluding the
rivers and lakes of area > 1 km2 of layers GLWD-1 and GLWD-2) of the
Global Lakes and Wetland Database (GLWD; Lehner and Döll, 2004), in
which wetland extents are the union of polygons from four different global
databases.
Two global time-varying surface water products derived from remote-sensing
observations were also examined in this study: the Global Inundation Extent
from Multi-Satellites (GIEMS; Prigent et al., 2007; Papa et al., 2010),
derived from visible and near-infrared (AVHRR) and active (SSM/I) and passive
(ERS) microwave sensors over the period 1993–2004, and the Surface Water
Microwave Product Series (SWAMPS; Schroeder et al., 2010), derived from
active (SeaWinds-on-QuikSCAT, ERS, and ASCAT) and passive (SSM/I, SSMI/S,
AMSR-E) microwave sensors over the period 1992–2013. For both products,
surface water area fractions (Fw) were aggregated from their
native 25 km equal-area grids to a 0.5∘ × 0.5∘
geographic grid and from daily to monthly temporal resolution, for
consistency with model results.
For CH4 emissions, our primary reference for in situ observations was
the estimate of Glagolev et al. (2011), which we will refer to as
“Glagolev2011”. The Glagolev2011 product consists of both a database of
over 2000 individual chamber observations from representative landforms at
each of 36 major sites over the period 2006–2010 (Fig. 1a) and a map of
long-term average emissions created by applying the mean observed emissions
to the wetlands of the Peregon2008 map as a function of wetland type. It is
worth noting that the Glagolev2011 product is currently undergoing a revision
based on higher-resolution maps, which will lead to a substantial increase in
annual emissions from the taiga zone, due to a larger spatial extent of
high-emitting wetland types (Glagolev et al., 2013). Possible changes to
emissions in the tundra zone (in the northern half of the WSL) are not yet
known. We consider this product's large uncertainty in our evaluation of
model predictions.
We also considered emissions estimates from five inversions. Two of them were
regional: “Kim2011” (Kim et al., 2011) and “Winderlich2012” (Winderlich,
2012; Schuldt et al., 2013). Kim et al. (2011) used an earlier version of
Glagolev2011 (Glagolev et al., 2010) at a 1∘ × 1∘
resolution as their prior distribution for wetland emissions within the
atmospheric transport model NIES-TM (Maksyutov et al., 2008) over the period
2002–2007. Kim et al. (2011) derived 12 climatological average monthly
(spatially uniform) coefficients for wetland emissions to optimize
atmospheric CH4 concentrations over the WSL relative to observed
CH4 concentrations obtained by aircraft sampling at two locations in the
WSL. Winderlich (2012) used the Kaplan (2002) wetland inventory for prior
wetland emissions, within the global inversion system TM3-STILT
(Rödenbeck et al., 2009; Trusilova et al., 2010) for the year 2009.
Winderlich (2012) derived 12 monthly coefficients for wetland emissions,
uniquely for each point in a 1∘ × 1∘ grid, to
optimize atmospheric CH4 concentrations over the WSL relative to the
concentrations measured at the Zotino Tall Tower Observatory and three other
CH4 tower observation sites (Demyanskoe, Igrim, and Karasevoe) located
between 58 and 63∘ N.
The other inversions we considered were global: the “Reference” and
“Kaplan” versions of the Bousquet et al. (2011) inversion, denoted by
“Bousquet2011R” and “Bousquet2011K”, respectively, and the estimate of
Bloom et al. (2010), denoted by “Bloom2010”. Bousquet et al. (2011) used
the Laboratoire de Météorologie Dynamique general circulation model
(LMDZ; Hauglustaine et al., 2004) atmospheric transport model on a
3.75∘ × 2.5∘ grid to estimate monthly CH4
emissions at a 1∘ × 1∘ resolution for the period
1993–2009, optimizing atmospheric concentrations of several gases, including
CH4, relative to global surface observation networks, for both
inversions. The Matthews and Fung (1987) emissions inventory was the prior
for wetland emissions in the Bousquet2011R inversion, while the Kaplan (2002)
emissions were the prior for the Bousquet2011K inversion. In both cases, a
single, spatially uniform set of monthly coefficients was derived for each of
11 large regions of the globe. The region containing the WSL was boreal Asia
(in which the WSL makes up the majority of the wetlands). Consequently,
spatial patterns in estimated emissions at the scale of
1∘ × 1∘ were identical to those of the prior
emissions; only the regional total emissions were constrained by the
inversions. The 17-year record length of the Bousquet2011 inversions made
them appealing candidates for investigating the sensitivities of emissions to
interannual variability in environmental drivers. Bloom et al. (2010) did not
use an atmospheric transport model, but rather optimized the parameters in a
simple model relating observed atmospheric CH4 concentrations from the
Scanning Imaging Absorption Spectrometer for Atmospheric Chemistry
(SCIAMACHY; Bovensmann et al., 1999) on the Envisat satellite 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), under
the assumption that gravity anomalies are indicative of large-scale surface
and near-surface water anomalies. The Bloom2010 inversion covered the period
2003–2007, at a 3∘ × 3∘ resolution.
Participating models and their relevant hydrologic
features.
Model
Full name
Reference
Configuration1
Period
Observational constraints on CH4-producing areas
Unsaturatedemissions?6
Water table4
Organicsoil7
Soilfreeze–thaw8
Surfacewater2
Topography3
Maps4
Code5
CLM4Me
Community Land Model v. 4 Methane
Riley et al. (2011)
CLM4Me
1993–2004
GIEMS
–
–
Sa
Yes
Uniform
Yes
Yes
DLEM
Dynamic LandEcosystem Model
Tian et al. (2010, 2011a, b, 2012)
DLEM
1993–2004
GIEMS
–
–
S
Yes
Uniform
No
No
DLEM2
Dynamic LandEcosystem Model v. 2
Tian et al. (2010, 2011a,b, 2012)
DLEM2
1993–2004
GIEMS
–
–
S
Yes
Uniform
Yes
Yes
IAP-RAS
Institute of AppliedPhysics – RussianAcademy of Sciences
Mokhov et al.(2007),Eliseev et al. (2008)
IAP-RAS
1993–2004
–
–
CDIAC NDP017b
M,M+
No
n/a
Yes
Yes
LPJ-Bern
Lund-Potsdam-Jena – Bern
Spahni et al.(2011),Zürcher et al. (2013)
LPJ-Bern
1993–2004
GIEMS
–
NCSCD
M
Yes
Uniform
Yes
Yes
LPJ-MPI
Lund-Potsdam-Jena –Max Planck Institute
Kleinen et al.(2012)
LPJ-MPI
1993–2010
–
Hydro1Kc
–
T
Yes
TOPMODEL
Yes
No
LPJ-WHyMe
Lund-Potsdam-Jena –Wetland Hydrologyand Methane
Wania et al.(2009a, b; 2010)
LPJ-WHyMe
1993–2004
–
–
NCSCD
M
Yes
Microtopography
Yes
Yes
LPJ-WSL
Lund-Potsdam-Jena –Swiss Federal Institute for Forest, Snow,and Landscape (WSL)Research
Hodson et al.(2011)
LPJ-WSL
1993–2004
GIEMS
–
–
I
No
n/a
No
No
LPX-BERN
Land surface Processes and eXchanges – Bern
Spahni et al.(2013),Stocker et al.(2013),
LPX-BERN
1993–2010
GIEMS forinundated non-peatland wetlands
–
Peregon2008 for peatland fraction
M,M+
Yes
Uniform
Yes
Yes
Stocker et al.(2014)
LPX-BERN(DyPTOP)
1993–2010
–
ETOPO1d, Hydro1Kc
–
T
Yes
TOPMODEL
Yes
Yes
LPX-BERN(N)
1993–2010
GIEMS forinundated non-peatland wetlands
–
Peregon2008 for peatland fraction
M,M+
Yes
Uniform
Yes
Yes
LPX-BERN(DyPTOP-N)
1993–2010
–
ETOPO1d, Hydro1Kc
–
T
Yes
TOPMODEL
Yes
Yes
ORCHIDEE
Organizing Carbon andHydrology in DynamicEcosystems
Ringeval et al.(2010)
ORCHIDEE
1993–2004
GIEMS
Hydro1Kc
–
Sa
Yes
TOPMODEL
Yes
Yes
SDGVM
Sheffield DynamicGlobal VegetationModel
Hopcroft et al.(2011)
SDGVM
1993–2004
–
ETOPO 2v2e
–
T
Yes
Uniform
No
No
UW-VIC
Variable InfiltrationCapacity – University ofWashington
Bohn et al.(2013)
UW-VIC(GIEMS)
1993–2004
GIEMS
SRTMf, ASTERg
Sheng2004
M,M+
Yes
Microtopography
Yes
Yes
UW-VIC(SWAMPS)
1993–2010
SWAMPS
SRTMf, ASTERg
Sheng2004
M,M+
Yes
Microtopography
Yes
Yes
VIC-TEM-TOPMODEL
Variable InfiltrationCapacity – TerrestrialEcosystem Model – TOPography basedhydrologic MODEL
Zhu et al. (2014)
VIC-TEM-TOPMODEL
1993–2004
GIEMS
Hydro1Kc
T
Yes
TOPMODEL
No
Yes
VISIT
Vegetation IntegrativeSImulator forTrace gases
Ito andInatomi (2012)
VISIT(GLWD)
1993–2010
–
–
GLWD
M,M+
Yes
Uniform
No
No
VISIT(SHENG)
1993–2010
–
–
Sheng2004
M,M+
Yes
Uniform
No
No
VISIT(GLWD-WH)
1993–2010
–
–
GLWD
M,M+
Yes
Uniform
No
No
VISIT(SHENG-WH)
1993–2010
–
–
Sheng2004
M,M+
Yes
Uniform
No
No
1 Configuration: short name identifying both the model and the
parameter or feature settings for a particular simulation; for models that
contributed only a single simulation, the configuration equals the model
name.2 Surface water: name of time-varying surface water product (if any)
used as a constraint on CH4-contributing area.3 Topography: name of topographic product (if any) used as a constraint
on CH4-contributing area.4 Map: Name of static wetland map product (if any) used as a constraint
on CH4-contributing area.5 Code: single-letter code summarizing the types of
CH4-contributing area constraints used (S: surface water only;
T: topography with or without surface water constraint; M: static wetland map with or without surface water or topography constraints;
M+: subset of M that excludes the NCSCD).6 Water table: approach used to account for water table depths
(uniform: water table depth is the same at all wetland points within
the grid cell; TOPMODEL: water table depth varies spatially within
the grid cell as a function of topography, following a TOPMODEL approach
(Beven and Kirkby, 1979); microtopography: water table depth varies
spatially within the grid cell as a function of assumed microtopography;
n/a: not applicable).7 Soil freeze–thaw: yes or no indicates whether the model
accounts for the freezing and thawing of water within the soil column.a CLM4Me and ORCHIDEE are
listed as S due to tuning or rescaling of inundated areas to match GIEMS,
thus
destroying contribution of topography.b http://cdiac.esd.ornl.gov/ndps/ndp017.html,
c Hydro1K (2013), d Amante and Eakins (2009),
e ETOPO (2006), f Farr et al. (2007),
g NASA (2001).
Models
Among the participating models (Table 2) were those of the WETCHIMP study
(Melton et al., 2013; Wania et al., 2013) that contributed CH4 emissions
estimates: CLM4Me (Riley et al., 2011), DLEM (Tian et al., 2010, 2011a, b,
2012), IAP-RAS (Mokhov et al., 2007; Eliseev et al., 2008), LPJ-Bern (Spahni
et al., 2011, Zürcher et al., 2013), LPJ-WHyMe (Wania et al., 2009a, b, 2010), LPJ-WSL (Hodson et al., 2011), ORCHIDEE (Ringeval et
al., 2010), SDGVM (Hopcroft et al., 2011), and UW-VIC (denoted by “UW-VIC
(GIEMS)”; Bohn et al., 2013). In addition, we analyzed several other models.
“UW-VIC (SWAMPS)” is another instance of UW-VIC with surface water
calibrated to match the SWAMPS product. VISIT (Ito and Inatomi, 2012) contributed four configurations using different combinations of wetland maps
and methane models: “VISIT (GLWD)” and “VISIT (Sheng)” used the Cao
(1996) methane model with the GLWD and Sheng2004 wetland maps, respectively,
and “VISIT (GLWD-WH)” and “VISIT (Sheng-WH)” replaced the Cao model with
the Walter and Heimann (2000) model. LPX-BERN (Spahni et al., 2013; Stocker
et al., 2013, 2014) is a newer version of LPJ-Bern that also contributed four
configurations: “LPX-BERN”, which prescribed peatland extent using
Peregon2008 and inundation extent using GIEMS; “LPX-BERN (DyPTOP)”, which
dynamically predicted the extents of peatlands and inundation; and “LPX-BERN
(N)” and “LPX-BERN (DyPTOP-N)”, which additionally simulated interactions
between the carbon and nitrogen cycles. DLEM2 is a newer version of DLEM that
includes soil thermal physics and lateral matter fluxes (Liu et al., 2013;
Pan et al., 2014). LPJ-MPI (Kleinen et al., 2012) is a version of the LPJ
model that contains a dynamic peatland model with methane transport by the
model of Walter and Heimann (2000). Finally, VIC-TEM-TOPMODEL (Zhu et al.,
2014) is a hybrid of UW-VIC (Liang et al., 1994), TEM (Zhuang et al., 2004),
and TOPMODEL (Beven and Kirkby, 1979).
Participating models and their relevant biogeochemical features.
Model
Ranaerobic/Raerobic1
C substrate source2
pH3
Redoxstate4
Dynamicvegetation5
Nitrogen–carboncycle interaction6
Saturated NPPinhibition7
Parameter selection8
CLM4Me
Variable
Cpool
Yes
Yes
Yes
Yes
No
Optimized to various sites
DLEM
Variable
NPP and Cpool
Yes
Yes
No
No
No
Optimized to various sites
DLEM2
Variable
NPP and Cpool
Yes
Yes
No
No
No
Optimized to various sites
IAP-RAS
n/a
Cpool
No
No
No
No
No
Literature; scaled to global total
LPJ-Bern
Constant
NPP and Cpool
No
No
Yes
No
Yes
Optimized to various sites; scaled to global total
LPJ-MPI
Constant
Cpool
No
No
Yes
No
Yes
Literature
LPJ-WHyMe
Constant
NPP and Cpool
No
No
Yes
No
Yes
Literature; scaled to global total
LPJ-WSL
Constant
Cpool
No
No
Yes
No
No
Literature
LPX-BERN
Constant
NPP and Cpool
No
No
Yes
No
Yes
Optimized to various sites;scaled to global total
LPX-BERN (DyPTOP)
Constant
NPP and Cpool
No
No
Yes
No
Yes
Optimized to various sites;scaled to global total
LPX-BERN (N)
Constant
NPP and Cpool
No
No
Yes
Yes
Yes
Optimized to various sites;scaled to global total
LPX-BERN (DyPTOP-N)
Constant
NPP and Cpool
No
No
Yes
Yes
Yes
Optimized to various sites;scaled to global total
ORCHIDEE
Variable
Cpool
No
No
Yes
No
No
Literature and optimized tovarious sites
SDGVM
Variable
Cpool
No
No
Yes
No
No
Literature
UW-VIC(GIEMS)
Variable
NPP
No
No
No
No
Yes
Optimized to sites inGlagolev2011
UW-VIC(SWAMPS)
Variable
NPP
No
No
No
No
Yes
Optimized to sites inGlagolev2011
VIC-TEM-TOPMODEL
Variable
NPP
Yes
Yes
No
No
No
Optimized to various sites
VISIT(GLWD)
Variable
Cpool
No
No
No
Yes (only affectsupland CH4 oxidation)
No
Literature
VISIT(GLWD-WH)
Variable
NPP
No
No
No
Yes (only affectsupland CH4 oxidation)
No
Literature
VISIT(Sheng)
Variable
Cpool
No
No
No
Yes (only affectsupland CH4 oxidation)
No
Literature
VISIT(Sheng-WH)
Variable
NPP
No
No
No
Yes (only affectsupland CH4 oxidation)
No
Literature
1 Ranaerobic/Raerobic: how the ratio of anaerobic to aerobic
respiration is handled in the model (constant: ratio is held
constant; variable: ratio varies either as an explicit function of
environmental conditions or as the result of separate governing equations
for aerobic and anaerobic respiration; n/a: not applicable).2 Carbon substrate source: Cpool: soil carbon pool; NPP: root exudates, in proportion to net primary
productivity.3 pH: indicates whether soil pH influences CH4 emissions.4 Redox state: indicates whether soil redox state influences CH4
emissions.5 Dynamic vegetation: indicates whether vegetation species abundances
change in response to environmental conditions.6 Nitrogen–carbon cycle interaction: indicates whether interactions
between the nitrogen and carbon cycles influence CH4 emissions.7 Saturated NPP inhibition: indicates whether NPP decreases under wet
soil conditions for any plant species.8 Parameter selection: method of choosing parameter values
(literature: values chosen from ranges reported in literature;
optimized: values chosen to minimize the difference between simulated
and observed values, either of CH4 fluxes at selected sites or of global
atmospheric CH4 concentrations).
The relevant hydrologic and biogeochemical features of these models are
listed in Tables 2 and 3, respectively. The models used a variety of
approaches to define CH4-producing areas. To have some consistency
across models, the original WETCHIMP study asked participating modelers to
use the GIEMS product if their model required wetland extent to be
prescribed. Accordingly, some models (DLEM, DLEM2, and LPJ-WSL) used the
GIEMS surface water product exclusively to prescribe (time-varying)
CH4-producing areas; these are denoted by the code “S” in Table 2.
Several models (CLM4Me, LPJ-MPI, LPX-BERN (DyPTOP), LPX-BERN (DyPTOP-N),
ORCHIDEE, SDGVM, and VIC-TEM-TOPMODEL) predicted surface water and
CH4-producing areas dynamically using topographic information and the
TOPMODEL (Beven and Kirkby, 1979) distributed water table approach (in which
the area over which the water table is at or above the soil surface can be
interpreted to correspond to surface water extent); these models are denoted by a “T” in Table 2. For these models, the CH4-producing area is the
area in which labile soil carbon is sufficiently warm and anoxic for
methanogenesis to occur, including both surface water and any non-inundated
land with sufficiently shallow water table depths. LPJ-MPI and LPX-BERN
(DyPTOP and DyPTOP-N) prognostically determined peatland area as a function
of long-term soil moisture conditions; their CH4-producing areas thus
included peatlands (inundated or not) as well as completely saturated or
inundated mineral soils. Because the other T models' CH4-producing
areas had no explicit limits, those teams reported approximations of the
models' true CH4-producing areas: CLM4Me, ORCHIDEE, and VIC-TEM-TOPMODEL
reported their surface water areas; and SDGVM reported the area for which the
water table was above a threshold depth, with the threshold chosen to
minimize the global rms error between this area and GIEMS. Additionally, both
CLM4Me and ORCHIDEE tied their surface water areas to the long-term mean of
GIEMS: CLM4Me did so by calibration and ORCHIDEE did so by rescaling its
surface water areas. Thus, we have placed these two models in the S
category in Table 2.
Mean annual emissions from the WSL: from inversions (green),
observation-based estimates (red), and forward models (blue). The hatched
portions of the bars indicate the emissions from the southern half of the
domain (latitude < 61∘ N). Error bars on the model results
indicate the interannual standard deviations of the southern and northern
emissions. Error bars on the inversions and observational estimates indicate
the uncertainty given in those studies. Numeric fractions of the total
emissions contributed by the southern and northern halves of the domain are
displayed in the right-hand column.
Finally, the remaining models (IAP-RAS, LPJ-Bern, LPJ-WHyMe, LPX-BERN,
LPX-BERN (N), both UW-VIC configurations, and all four VISIT configurations)
used wetland maps, either alone or in combination with topography and surface
water products, to inform their wetland schemes; these are denoted by “M”
in Table 2. In most cases, the wetland maps were used to determine the
maximum extent of the CH4-producing area, within which inundated area
and water table depths would vary in time. In contrast, LPJ-Bern, LPX-BERN,
and LPX-BERN (N) allowed inundated area (specified by GIEMS) to sometimes
exceed the static map-based peatland area; in such cases, it was assumed that
the excess inundation occurred in mineral soils. Thus, the CH4-producing
area included peatlands and inundated mineral soils. LPJ-Bern additionally
allowed CH4 production in areas of “wet mineral soil” (in which soil
moisture content was greater than 95 % of water-holding capacity) and
included this in the total CH4-producing area.
Models' hydrologic approaches varied in other ways as well. Some (IAP-RAS and
LPJ-WSL) did not include explicit water table depth formulations for
estimating emissions in unsaturated (non-inundated) wetlands; IAP-RAS assumed
all wetlands were completely saturated, and LPJ-WSL only considered
unsaturated wetlands implicitly, using soil moisture as a proxy. Most of the
other models used a TOPMODEL approach to relate the distribution of water
table depths across the grid cell to topography (generally on a 1 km scale).
However, LPJ-WHyMe, UW-VIC (GIEMS), and UW-VIC (SWAMPS) determined water table
depth distributions within peatlands from assumed proportions of
microtopographic landforms (e.g., hummocks and lawns) on the (horizontal)
scale of meters. UW-VIC explicitly handled lakes by treating lakes and
peatlands as a single system, spanning the total area of lakes and peatlands
which was given by the Sheng et al. (2004) data set and within which surface water area
varied dynamically. Areas of permanent surface water over the period
1949–2010 were considered to be lakes and were excluded from methane emissions
estimates.
Models also varied in their soil thermal physics schemes. Most models used a
one-dimensional heat diffusion scheme to determine the vertical profile of
soil temperatures, but VISIT used a linear interpolation between current air
temperature (at the soil surface) and annual average air temperature (at the
bottom of the soil column). Several models (DLEM, LPJ-MPI, LPJ-WSL, and
SDGVM) did not consider the water-ice phase change and therefore did not
model permafrost. While IAP-RAS contained a permafrost scheme, it was driven
by seasonal and annual summaries of meteorological forcings and used simple
analytic functions to estimate the seasonal evolution and vertical profile
of soil temperatures. Additionally, DLEM and LPJ-WSL did not consider the
insulating effects of organic (peat) soil. In contrast, UW-VIC modeled
permafrost, peat soils, and the dynamics of surface water, including lake
ice cover and evaporation, thereby adding another factor that influences
soil temperatures.
Models also varied in their biogeochemical schemes (Table 3). Most
represented methane production as a function of soil temperature, water table
depth (except for IAP-RAS and LPJ-WSL), and the availability of carbon
substrate. Most (except for IAP-RAS and LPJ-WSL) explicitly accounted for
the oxidation of methane above the water table; and most accounted for some
degree of plant-aided transport. Some models (LPJ-Bern, LPJ-MPI, LPJ-WHyMe,
and LPX-BERN) represented methane production as either a constant or
soil-moisture-dependent fraction of aerobic respiration. Some models (DLEM,
DLEM2, and VIC-TEM-TOPMODEL) imposed additional dependences on soil pH and
oxidation state. Models differed in the pathways and availability of carbon
substrate: some models (UW-VIC, VIC-TEM-TOPMODEL, VISIT (GLWD-WH), and VISIT
(Sheng-WH)) related carbon substrate availability to net primary productivity
(NPP) as a proxy for root exudates; some (CLM4Me, IAP-RAS, LPJ-MPI,
LPJ-WSL, ORCHIDEE, SDGVM, VISIT (GLWD), and VISIT (Sheng)) related carbon
substrate to the content and residence times of various soil carbon
reservoirs; and others (DLEM, DLEM2, LPJ-Bern, LPJ-WHyMe, all four LPX-BERN
configurations) drew carbon substrate from a combination of both root
exudates and soil carbon (or dissolved organic carbon, in the case of DLEM
and DLEM2). CLM4Me and two configurations of LPX-BERN simulated interactions
between the carbon and nitrogen cycles. Several models (all versions of LPJ
and LPX, ORCHIDEE, and SDGVM) included dynamic vegetation components. Some
models (LPJ-Bern, LPJ-MPI, LPJ-WHyMe, LPX-BERN, and UW-VIC) accounted for the inhibition of NPP of some plant species under saturated soil moisture
conditions. Finally, models employed a variety of methods, alone or in
combination (Table 3), to select parameter values, including taking the
median of literature values, optimizing emissions to match in situ
observations from representative sites regionally (e.g., UW-VIC optimized
parameter values to match the Glagolev2011 data set in the WSL) or globally,
or optimizing global total emissions to match various estimates from
inversions.
Model simulations
To be consistent with WETCHIMP's transient simulation (“Experiment
2-trans”, Wania et al., 2013), we focused our analysis on the period
1993–2004, although several non-WETCHIMP models provided data from
1993–2010. All models used the CRUNCEP gridded meteorological forcings
(Viovy and Ciais, 2011) as a common input. Model-specific inputs are
described in Wania et al. (2013).
Model outputs (monthly CH4 emissions (average g CH4
month-1 m-2 over the grid cell area) and monthly
CH4-producing area (km2)) were analyzed at a 0.5∘ × 0.5∘ spatial resolution (resampled from native
resolution as necessary).
Observational data sets related to wetland areas. For SWAMPS and
GIEMS, areas shown are the June–July–August (JJA) average surface water area
fraction over the period 1993–2004.
Due to large seasonal variations in CH4-producing areas, our analysis
focused on June–July–August (JJA) averages of area and CH4 emissions,
since it is during these months that the majority of the year's methane is
emitted across all models (areas in other seasons would not be representative
of annual CH4 emissions). Similarly, in analyzing interannual
variability in CH4 emissions, we focused on JJA CH4 emissions,
which dominate the annual total and have stronger correlations with JJA
environmental factors (such as air temperature, precipitation, or inundation)
than annual CH4 emissions have with annual average environmental
factors. We also computed growing season CH4 “intensities” (average
JJA CH4 emissions per unit JJA CH4-producing area).
Observation- and inversion-based estimates of annual CH4
emissions (g CH4 yr-1 m-2 of grid cell area). For
inversions, averages are over the following periods: 2002–2007 (Kim2011),
2003–2007 (Bloom2010), 2009 (Winderlich2012), and 1993–2004 (Bousquet2011K
and R).
Model estimates of JJA CH4 emissions (Tg CH4
month-1) and JJA wetland or CH4-producing area
(103 km2): for the entire WSL (top left) and the southern (bottom
left) and northern (bottom right) halves, for the period 1993–2004. Lines
passing through the origin, with slopes of integer multiples of
1 g CH4 m-2 month-1, allow a comparison of spatial average
intensities (CH4 emissions per unit CH4-producing area). Circles
denote models that used satellite surface water products alone (corresponding
to code S in Table 2) to delineate wetlands. Triangles denote models that
used topographic information, with or without surface water products
(corresponding to code T in Table 2). Squares denote models that used wetland
maps with or without topography or surface water products (corresponding to
code M in Table 2).
Data access
All data used in this study, including observational products, inversions,
and forward model results, are available from WETCHIMP-WSL (2015).
Results
Average annual total emissions
As shown in Fig. 2 and Table S1 in the Supplement, 12-year mean estimates
(±standard error on the mean) of annual total emissions over the WSL from
forward models (5.34 ± 0.54 Tg CH4 yr-1), inversions
(6.06 ± 1.22 Tg CH4 yr-1), and observations
(3.91 ± 1.29 Tg CH4 yr-1) largely agreed, despite large
scatter in individual estimates. Model estimates ranged from 2.42 (LPX-BERN
(DyPTOP-N)) to 11.19 Tg CH4 yr-1 (IAP-RAS). The Glagolev2011
estimate was substantially lower than the mean of the models, corresponding
to the 36th percentile of the distribution of model estimates. However, the
potential upward revision of Glagolev2011 (Sect. 2.2) would move it to a
substantially higher percentile of their distribution. Inversions yielded a
similarly large range of estimates: 3.08 (Kim2011) to 9.80 Tg CH4 yr-1 (Winderlich2012). Despite their large spread, 15 out of
the 17 forward models fell within the range of inversion estimates. Here we
have excluded the “WH” configurations of VISIT and the configurations of
LPX-BERN for which nitrogen–carbon interaction was turned off, due to their
similarities to their counterparts that were included. The wide variety in
the relative proportions of CH4 emitted from the south and north halves
of the domain, with the southern contribution ranging from 13 to 69 %
(right-hand column in Fig. 2), indicates a lack of agreement on which types of
wetlands and climate conditions are producing the bulk of the region's
CH4.
Differences among observational data sets
The large degree of disagreement among observational data sets is worth
addressing before using them to evaluate the models. Important differences
are evident among wetland maps (Fig. 3). Sheng2004 and Peregon2008 are
extremely similar, in part because they both used the map of Romanova (1977)
north of 65∘ N. Both of these data sets show wetlands distributed
across most of the WSL, with large concentrations south of the Ob' River
(55–61∘ N, 70–85∘ E), east of the confluence of the Ob'
and Irtysh rivers (57–62∘ N, 65–70∘ E), and north of the
Ob' River (61–66∘ N, 70–80∘ E). In comparison, the GLWD
map entirely lacks wetlands in the tundra region north of 67∘ N and
shows additional wetland area in the northeast (64–67∘ N,
70–90∘ E). The NCSCD is substantially different from the other
three maps. Owing to its focus on permafrost soils, it completely excludes
the extensive wetlands south of the southern limit of permafrost
(approximately 60∘ N). Given the numerous field studies documenting
these productive southern wetlands (Sect. 2.1), the NCSCD seems to be
inappropriate for studies that extend beyond permafrost.
The two surface water products (GIEMS and SWAMPS) also exhibit large
differences. While they both agree that the surface water area fraction
(Fw) is most extensive in the central region north of the Ob'
River (61–64∘ N), GIEMS gives areal extents that are 3–6 times
those of SWAMPS. Outside of this central peak, GIEMS Fw drops off
rapidly to nearly 0 in most places (particularly in the forested region south
of the Ob' River, which may be due to difficulties in detecting inundation
under vegetative canopy and/or reduced sensitivity where the open-water fraction
is less than 10 %; Prigent et al., 2007), while SWAMPS maintains low
levels of Fw throughout most of the WSL. Along the Arctic
coastline, SWAMPS shows high Fw, which may indicate
contamination of the signal by the ocean. In both data sets, Fw
exhibits some similarity with the distribution of lakes and rivers (Fig. 1),
illustrating the inclusion of non-wetlands in these surface water products.
Among the CH4 data sets (Fig. 4), a clear difference can be seen between
the spatial distributions of Glagolev2011 and Kim2011 (both of which assign
the majority of emissions to the region south of the Ob' River, between 55
and 60∘ N); and Winderlich2012 and Bousquet2011K (both of which
assign the majority of emissions to the central region north of the Ob'
River, between 60 and 65∘ N). We discuss possible reasons for this
discrepancy in Sect. 4.3. The global inversions (Bousquet2011R and K, and
Bloom2010) have coarser spatial resolution than the regional inversions of
Kim2011 and Winderlich2012. Bousquet2011R and K have similar distributions
between 60 and 65∘ N, but Bousquet2011R has relatively stronger
emissions between 57 and 60∘ N and weaker emissions between 65 and
67∘ N; in this respect, Bousquet2011R is intermediate between
Glagolev2011 and Winderlich2012. Finally, Bloom2010 exhibits relatively
little spatial variability in emissions, likely due to its use of GRACE
observations as a proxy for wetland inundation and water table conditions.
Estimates of June–July–August CH4 emissions from subsets of
the participating models, over the entire WSL and its southern (<61∘ N) and northern halves, for the period 1993–2004. Biases were
computed with respect to the Glagolev2011–Peregon2008 estimates.
Subset
Average Jun–Jul–Aug CH4 (TgCH4month-1)
Average Jun–Jul–Aug contributing area (103 km2)
WSL
South
North
WSL
South
North
Mean
Bias
SD
Mean
Bias
SD
Mean
Bias
SD
Mean
Bias
SD
Mean
Bias
SD
Mean
Bias
SD
I
1.10
0.14
0.37
0.22
-0.45
0.16
0.89
0.59
0.24
388
-291
136
66
-270
31
321
-21
112
T
1.42
0.46
0.82
0.81
0.14
0.46
0.61
0.31
0.39
682
4
325
294
-42
173
389
46
153
M
1.32
0.36
1.01
0.69
0.02
0.97
0.64
0.34
0.40
605
-74
113
250
-87
109
355
12
105
M+
1.30
0.34
1.17
0.85
0.18
1.10
0.45
0.16
0.15
633
-46
93
306
-30
34
327
-15
95
Spatial correlations between simulated average annual CH4
emissions and GIEMS surface water area fraction (Fw).
Model
Correlation
Model
Correlation
Model
Correlation
CLM4Me
0.69
LPJ-WHyMe
0.45
UW-VIC (GIEMS)
0.44
DLEM
0.70
LPJ-WSL
0.97
UW-VIC (SWAMPS)
0.11
DLEM2
0.21
LPX-BERN (N)
0.41
VIC-TEM-TOPMODEL
0.41
IAP-RAS
-0.03
LPX-BERN (DyPTOP-N)
0.28
VISIT (GLWD)
0.62
LPJ-Bern
0.56
ORCHIDEE
0.61
VISIT (Sheng)
0.65
LPJ-MPI
0.01
SDGVM
0.09
Mean CH4 emissions from LPX-BERN, 1993–2010, for the entire WSL
and the south and north halves of the domain.
Mean [Tg CH4 yr-1]
Configuration
WSL
South
North
LPX-BERN
3.81
1.98
1.83
LPX-BERN (DyPTOP)
3.17
1.38
1.79
LPX-BERN (N)
3.08
1.92
1.17
LPX-BERN (DyPTOP-N)
2.44
1.37
1.08
Differences
LPX-BERN (N) – LPX-BERN
-0.73
-0.06
-0.66
LPX-BERN (DyPTOP-N) – LPX_BERN (DyPTOP)
-0.73
-0.02
-0.71
LPX-BERN (DyPTOP) – LPX-BERN
-0.64
-0.60
-0.04
LPX-BERN (DyPTOP-N) – LPX-BERN (N)
-0.64
-0.55
-0.09
Primary drivers of model spatial uncertainty
The wide disagreement among models is plainly evident in Fig. 5, which plots
average JJA CH4 emissions versus average JJA CH4-producing areas
for the WSL as a whole (top left), the south (bottom left), and the north
(bottom right). A series of lines (“spokes”) passing through the origin,
with slopes of integer multiples of 1 g CH4 m-2 month-1,
allows comparison of spatial average intensities (CH4 emissions per unit
CH4-producing area). All points along a given line have the same
intensity but different CH4-producing areas. We have included the
Glagolev2011–Peregon2008 CH4–area estimate (denoted by a black star)
and the mean of the inversions (denoted by a grey star) for reference. We set
the area coordinate for the inversions to Peregon2008 because (a) the wetland
area was not available for all inversions and (b) Peregon2008 is a relatively
accurate estimate of wetland area. JJA CH4 emissions, JJA wetland or
CH4-producing areas, and JJA intensities, for all models, observations,
and inversions, are listed in Table S1. Over the entire WSL (Fig. 5, top
left), the scatter in model estimates of CH4 emissions results from
scatter in both area (ranging from 200 000 to 1200 000 km2) and
intensity (ranging from 1 to 8 g CH4 m-2 month-1), with no
clear relationship between the two.
Maps of simulated average annual CH4 emissions (g CH4 m-2 yr-1 of grid cell area).
Maps of average JJA CH4-producing area (fraction of grid cell
area) from participating models.
However, a strong area-driven bias is evident in the south (Fig. 5, bottom
left). Although the mean modeled CH4 emission rate (0.58 Tg CH4
month-1) is fairly close to both Glagolev2011 (0.67 Tg CH4
month-1) and the mean of inversions (0.60 Tg CH4 month-1),
the distribution of model estimates is substantially skewed, with most
models' estimates falling well below both Glagolev2011 and the mean of the
inversions. Glagolev2011's estimate corresponds to the 81st percentile of the
model CH4 distribution; the expected upward revision of Glagolev2011
(Sect. 2.2; exact JJA amount not yet known) would only raise that percentile.
The mean of the inversions corresponds to the 76th percentile. Similarly, the
models substantially underestimate the CH4-producing area, with Peregon2008
occupying the 83rd percentile of the model distribution. On the other hand,
the model intensity distribution is much less biased, with Glagolev2011
corresponding to the 47th percentile. Even a doubling of Glagolev2011's
intensity would place it at only the 69th percentile of the model
distribution, a smaller bias than for area. Thus, the area bias is the major
driver of CH4 bias in the south. In comparison, the north (Fig. 5,
bottom right) is relatively unbiased.
Model inputs and formulations played a key role in determining
CH4-producing area biases. Statistics of model performance relative to
Glagolev2011–Peregon2008, categorized by the wetland codes in Table 2, are
listed in Table 4. The models that used satellite surface water products
alone (denoted by circles in Fig. 5 and the code S in Table 2)
estimated the lowest CH4-producing areas in the south, with a bias of
-270 000 km2 and standard deviation of 31 000 km2.
Additionally, two models (LPJ-Bern and LPJ-WHyMe) from the M group
(denoted by squares in Fig. 5 and the code M in Table 2) also yielded low
areas, due to their use of the NCSCD map, which omitted non-permafrost
wetlands. The “M+” group, consisting of all M models except those
two, exhibited the smallest bias and second-smallest standard deviation
(-31 000 and 34 000 km2, respectively). Models that determined
CH4-producing area dynamically using topographic data but without the
additional input of wetland maps (denoted by triangles in Fig. 5 and the code
T in Table 2) yielded nearly as small a bias as the M+ group
(-42 000 km2) but had the largest scatter (standard deviation of
173 000 km2) of the groups. The fact that two of the S models
(CLM4Me and ORCHIDEE) supplied CH4-producing areas that excluded
non-inundated methane-emitting wetlands had little effect on the results,
since their total CH4 emissions (which included non-inundated emissions)
also suffered from a large negative bias (-0.45 Tg CH4 yr-1, or
-67 %).
Examining the spatial distributions of annual CH4 (Fig. 6) and JJA
CH4-producing areas (Fig. 7) shows why the use of surface water data
alone results in poor model performance. Among the models from the S
group (CLM4Me, DLEM, DLEM2, LPJ-WSL, and ORCHIDEE), the spatial distributions
of both CH4 emissions and CH4-producing area tend to be strongly
correlated with GIEMS (See Table 5 for correlations), which exhibits very low
surface water areas south of the Ob' River, despite the large expanses of
wetlands there (Sect. 3.2). Similarly, the low emissions of LPJ-WHyMe and
LPJ-Bern in the south can be explained by their use of the NCSCD map, which
only considered peatlands (Histels and Histosols) within the circumpolar
permafrost zones (which only occur north of 60∘ N). For LPJ-WHyMe,
these permafrost peatlands were the only type of wetland modeled (i.e., the
model domain only included the circumpolar permafrost zones), so LPJ-WHyMe's
emissions were almost nonexistent in the south. LPJ-Bern also used the
NCSCD's Histels and Histosols to delineate peatlands but additionally
simulated methane dynamics in wet or inundated mineral soils outside the
permafrost zone. While this allowed LPJ-Bern to make emissions estimates in
the south, the much lower porosities of mineral soils resulted in larger
sensitivities of water table depth to evaporative loss than those of peat
soils. These drier soils led to net CH4 oxidation in much of the south.
Aside from area-driven biases, a large degree of intensity-driven scatter is
evident in both the south and north. Indeed, the underestimation of areas in
the south, accompanied by resulting reductions in CH4 emissions,
partially compensated for some of the intensity-driven scatter there.
However, some of the more extreme intensities were arguably the result of
area biases, in that some of the global wetland models (CLM4Me, IAP-RAS,
LPJ-Bern, and LPJ-WHyMe) scaled their intensities to match their global
total emissions with those of global inversions, which could result in local
biases if their wetland maps suffered from either global or local bias
(which was true of these models). Interestingly, several models yielded
estimates similar to those of the two regionally optimized UW-VIC
simulations, implying that the regional optimization did not confer a
distinct advantage on UW-VIC.
Nitrogen limitation influenced intensity in LPX-BERN, the one model that
included it. Although we did not plot results from the two LPX-BERN
configurations that lacked nitrogen–carbon interactions in Fig. 5, we compare
results from all four LPX-BERN configurations in Table 6. In LPX-BERN (N) and
LPX-BERN (DyPTOP-N), the nitrogen limitation imposed by nitrogen–carbon
interactions substantially reduced NPP, relative to LPX-BERN and LPX-BERN
(DyPTOP), leading to a reduction of mean annual CH4 emissions of
approximately 20 % over the entire WSL over the period 1993–2010. This
reduction was slightly larger than the difference in emissions between
simulations using the Sheng2004 map to prescribe peatland area (LPX-BERN and
LPX-BERN (N)) and simulations using the DyPTOP method to determine peatland
extent dynamically (LPX-BERN (DyPTOP) and LPX-BERN (DyPTOP-N)). In addition,
the reduction in emissions due to nitrogen limitation was concentrated in the
northern half of the domain, in contrast to the reduction due to dynamic
peatland extent, which was concentrated in the southern half of the domain.
Nitrogen limitation also reduced trends in CH4 emissions over the entire
WSL over the period 1993–2010, through reductions in soil carbon
accumulation rates. However, both these trends and their reductions were very
small (< 0.5 % per year in most cases) and statistically insignificant
over the study period.
Average whole-domain seasonal cycles (1993–2004) of normalized
monthly CH4 emissions (top), normalized monthly CH4-producing or
surface water areas (lower left), and monthly intensities (g CH4 m-2 of wetland area; lower right), with satellite surface water products
and inversions for reference. CH4 emissions and areas have been
normalized relative to their peak values.
Temporal coefficients of variation (CV) of annual CH4
emissions, 1993–2004.
Model
CV
Model
CV
Model
CV
CLM4Me
0.115
LPJ-WSL
0.208
VIC-TEM-TOPMODEL
0.149
DLEM
0.242
LPX-BERN (N)
0.069
VISIT (GLWD)
0.171
DLEM2
0.140
LPX-BERN (DyPTOP-N)
0.076
VISIT (Sheng)
0.163
IAP-RAS
0.091
ORCHIDEE
0.113
Bousquet2011K
0.160
LPJ-Bern
0.087
SDGVM
0.118
Bousquet2011R
0.446
LPJ-MPI
0.195
UW-VIC (GIEMS)
0.338
LPJ-WHyMe
0.127
UW-VIC (SWAMPS)
0.197
Model temporal uncertainty and major environmental drivers
Average seasonal cycles
Models demonstrated general agreement on the shape of the seasonal cycle of
emissions (Fig. 8, top left) and intensities (Fig. 8, bottom right), despite
wide disagreement on the shape and timing of the seasonal cycle of the CH4-producing area (Fig. 8, bottom left). The regional inversions
(Kim2011 and Winderlich2012) agreed on a July peak for CH4, although
Winderlich2012 suggested a noticeably larger contribution from cold season
months than the others (which is plausible, given reports of non-zero winter
emissions; Rinne et al., 2007; Kim et al., 2007; Panikov and Dedysh, 2000).
In contrast, both Bousquet inversions peaked in August. Unlike the other
three inversions, the Bousquet2011R inversion had negative emissions (net
oxidation) in either May or June of almost every year of its record. These
negative emissions were widespread, throughout not only the WSL but the
entire boreal Asia region, and cast doubt on the accuracy of their seasonal
cycle. Turning to the surface water products (Fig. 8, bottom left), GIEMS and
SWAMPS displayed quite different shapes in their seasonal cycles of surface
water extent: GIEMS exhibited a sharp peak in June and SWAMPS displayed a
broad, flat maximum from June through September. In fact, SWAMPS had a
similar shape to GIEMS south of about 64∘ N; the broad peak for the
WSL as a whole was the result of late-season peaks further north.
Most models' CH4 emissions peaked in July, in agreement with the
regional inversions. A few models peaked in June: CLM4Me, DLEM2, LPJ-MPI,
VISIT (GLWD), and VISIT (Sheng). Correspondingly early peaks in intensity can
explain the early peaks in the DLEM2 and the VISIT simulations, indicating
either early availability of carbon substrate in the soil or rapid soil
warming (the latter is likely for VISIT, given its linearly interpolated
soil temperatures). In contrast, LPJ-MPI's early peak in emissions was the
result of an early (May) peak in CH4-producing area, which, in turn,
was the result of early snowmelt. Two models (LPJ-BERN and UW-VIC (GIEMS))
peaked in August. LPJ-Bern's late peak resulted from a late peak in wet
mineral soil intensity, despite an exceptionally late (October) peak in
CH4-producing area. The late peak of UW-VIC (GIEMS) corresponded to a
late peak in intensity, implying either late availability of carbon
substrate (due to inhibition of NPP under inundation) or delayed warming of
the soil (due to excessive insulation by peat or surface water).
Time series of simulated annual total CH4 emissions (Tg CH4)
from participating models, the Reference and Kaplan inversions from Bousquet
et al. (2011), and the Bloom (2010) inversion.
Aside from the above cases, the relative agreement among models on a July
peak in CH4 emissions comes despite wide variation in seasonal cycles of
the CH4-producing area. For example, DLEM's CH4-producing area held
steady at its maximum extent from April through November, and
VIC-TEM-TOPMODEL's CH4-producing area peaked in August, possibly due to
low evapotranspiration or runoff rates. Some of the discrepancies in
CH4-producing area seasonality arose from several models using static
maps to define some or all wetland areas (Sects. 2.3 and 2.4). These
differences matter little to the seasonal cycle of CH4 emissions, in
part because of the similarity between the seasonal cycles of inundated area
and water table depths within the static CH4-producing areas and in
part because of the nearly universal strong correlation at seasonal timescales between simulated intensities and near-surface air temperature (so
that cold-season CH4-producing areas have little influence over
emissions).
Time series of simulated JJA CH4-producing areas (103 km2), with JJA surface water areas from GIEMS and SWAMPS products for
reference.
Temporal correlations among environmental drivers, 1993–2004.
WSL
CRU T JJA
CRU P JJA
SWAMPS JJA
GIEMS JJA
CRU T JJA
1.00
CRU P JJA
-0.10
1.00
SWAMPS JJA
0.14
0.66
1.00
GIEMS JJA
-0.11
0.44
0.68
1.00
S
CRU T JJA
CRU P JJA
SWAMPS JJA
GIEMS JJA
CRU T JJA
1.00
CRU P JJA
-0.28
1.00
SWAMPS JJA
-0.12
0.44
1.00
GIEMS JJA
-0.10
0.22
0.87
1.00
N
CRU T JJA
CRU P JJA
SWAMPS JJA
GIEMS JJA
CRU T JJA
1.00
CRU P JJA
-0.06
1.00
SWAMPS JJA
0.32
0.60
1.00
GIEMS JJA
-0.05
0.34
0.61
1.00
Interannual variability
At multiyear timescales (shown for the period 1993–2010 in Fig. 9),
models' and inversions' total annual CH4 emissions displayed a wide
range of interannual variability, even after accounting for the effects of
differences in intensity. Values of the coefficient of variation (CV) for
models over the period 1993–2004 ranged from 0.069 (LPX-BERN (N)) to 0.338
(UW-VIC (GIEMS)) with a mean of 0.169 (Table 7). While Bousquet2011K's CV of
0.160 fell near the mean model CV, Bousquet2011R's CV of 0.446 was 25 %
larger than the largest model CV, and over twice the second-largest model CV.
Bousquet2011R's high variability was due in part to a peak in CH4
emissions in 2002 followed by a large drop in emissions between 2002 and
2004, actually becoming negative (net CH4 oxidation) in 2004 before
continuing at a much lower mean value from 2005 to 2009. This peak and
decline coincide with a similar peak and decline in Fw (Fig. 10)
and precipitation (Fig. 11). Several models (notably LPJ-MPI, LPJ-WHyMe,
LPJ-WSL, DLEM, and VIC-TEM-TOPMODEL), as well as Bousquet2011K, mirrored this
drop to varying degrees, but none dropped as much in proportion to their
means or became negative. In contrast, Bloom2010, spanning only the period
2003–2007, exhibited extremely little interannual variability, perhaps due
to its use of GRACE as a proxy for inundated area and water table depth.
Time series of CRU JJA air temperature (∘C) and precipitation
(mm).
To investigate the influence of various climate drivers on CH4
emissions, we computed the individual correlations between the JJA CH4
emissions and the following JJA drivers: CRU air temperature
(Tair), CRU precipitation (P), GIEMS Fw , and SWAMPS
Fw, for forward models and the two Bousquet2011 inversions, over
the period 1993–2004 (Table S2). Here we included four additional model
configurations that we did not show in previous sections: VISIT (GIEMS-WH),
VISIT (SHENG-WH), LPX-BERN, and LPX-BERN-DyPTOP. The two drivers yielding the
highest correlations with JJA CH4 emissions were JJA CRU Tair
and JJA GIEMS Fw. These two drivers also exhibited nearly zero
correlation with each other over the WSL and the south and north halves
(Table 8). Because variations in water table position are driven by the same
hydrologic factors (snowmelt, rainfall, evapotranspiration, and drainage)
that drive variations in Fw, correlation with Fw
should serve as a general measure of the influence of both surface and
subsurface moisture conditions on methane emissions, even for models that
were not explicitly driven by Fw. Therefore, we chose to examine
model behavior in terms of correlations with JJA CRU Tair and JJA
GIEMS Fw. As an aside, this choice was not an endorsement of
GIEMS over SWAMPS (which yielded qualitatively similar results to GIEMS); it
simply resulted in better separation among models.
The relative strengths of the correlations between models' CH4 emissions
and drivers varied widely, as shown in the scatterplots in Fig. 12. Over the
entire WSL (top left) as well as the south and north halves (bottom left and
right), the low correlation between Tair and Fw led to
consistent trade-offs in the correlations between simulated emissions and
Tair (x axis) or Fw (y axis). Some models (all four
LPX-BERN simulations, all four VISIT simulations, IAP-RAS, ORCHIDEE, and
SDGVM) had correlations with Tair that were greater than 0.7 in one
or both halves of the domain; since this means that Tair would
explain the majority of CH4 variance in a linear model, we have denoted
them as “Tair-dominated”. Other models (DLEM, LPJ-WSL, DLEM2, and
LPJ-MPI) were “Fw-dominated” in one or both halves of the
domain. For the other models and inversions, no driver explained the majority
of the variance. A few models had small enough contributions from one or the
other driver for the resulting correlations to be negative, due to the small
negative correlation between Tair and Fw. Neither of
the two Bousquet2011 inversions exhibited strong correlations with either
Fw or Tair, which might imply that models also should
not exhibit strong correlations with one driver.
Indeed, the overarching pattern in the model correlations was that models
that lacked physical and biochemical formulations appropriate to the high
latitudes exhibited stronger correlations with inundation or air temperature
than either the inversions or more sophisticated models. One characteristic
that most of the Fw-dominated models (except for DLEM2) have in
common is that they lack soil thermal formulations that account for soil
freeze–thaw processes; conversely, most of the non-Fw-dominated
models do have such formulations. In addition, inundated fractions of DLEM,
DLEM2, and LPJ-WSL were explicitly driven by GIEMS Fw. Unlike the
other three models, LPJ-MPI does account for the thermal effects of peat
soils, which might explain LPJ-MPI's low (slightly negative) correlation with
air temperature.
Some of the Tair-dominated models also lack sophisticated soil
thermal physics. VISIT's strong correlation with Tair can be
explained by the fact that its soil temperature scheme is a simple linear
interpolation between current air temperature at the surface and annual
average air temperature at the bottom of the soil column; as a result,
VISIT's soil temperature has a 1.0 correlation with air temperature.
Comparing the WH configurations of VISIT to the default configurations,
the model of Walter and Heimann (2000) had a lower correlation with air
temperature than the Cao (1996) model. SDGVM also lacks soil freeze–thaw
dynamics. IAP-RAS assumes all wetlands are completely saturated and holds
their areas constant in time; as a result, its CH4 emissions have no
dependence on soil moisture or Fw but a strong dependence on air
temperature. LPX-BERN's high correlation with air temperature is the result
of a relative insensitivity of CH4 emissions to water table depth, but
at present there are too few sites with multiyear observations in the region
to determine whether this low sensitivity is reasonable. Nitrogen–carbon
interaction (LPX-BERN (N) and LPX-BERN (DyPTOP-N)) appeared to have only a
minor effect on LPX-BERN's interannual variability in the north but led to a
slight reduction in correlation with Tair in the south. Finally,
UW-VIC (GIEMS) had small negative correlations with both Tair and
Fw in the north, likely the result of its surface water
formulation. UW-VIC's surface water dynamics had been initially calibrated
using the SWAMPS product; the much larger surface water extents of GIEMS in
the north resulted in substantially deeper surface water, with corresponding
insulating effects, greater evaporative cooling, and longer residence times,
thus lowering correlations with both observed Fw and
Tair. The large difference in behavior between UW-VIC (GIEMS) and
UW-VIC (SWAMPS) implies that the differences arising from optimizing surface
water dynamics to different products far outweighed the differences between
UW-VIC and other models in their selection of biogeochemical parameters.
Influence of interannual variations in surface water area fraction
(Fw) on model CH4 emissions (expressed as correlation
between JJA GIEMS Fw and JJA CH4) vs. influence of air
temperature (Tair) on model CH4 emissions (expressed as
correlation between JJA CRU Tair and JJA CH4), for the entire
WSL (top) and the southern and northern halves of the domain (bottom).
Fw-Dominated and Tair-Dominated denote
correlation thresholds above which surface water area or air temperature,
respectively, explain more than 50 % of the variance in CH4
emissions. Circles denote models that used satellite surface water products
alone (corresponding to code S in Table 2) to delineate wetlands.
Triangles denote models that used topographic information, with or without
surface water products (corresponding to code T in Table 2). Squares
denote models that used wetland maps with or without topography or surface
water products (corresponding to code M in Table 2).
Discussion
Long-term means and spatial distributions
The most striking finding, in terms of long-term means and spatial
distributions, was the substantial bias in CH4 emissions that resulted
from using satellite surface water products or inaccurate wetland maps to
delineate wetlands. Surface water is an important component of wetland
models, but it clearly is a poor proxy for wetland extent at high latitudes
because it both excludes the large expanses of strongly emitting
non-inundated peatlands that exist there (Sect. 2.1) that were missed by
GIEMS and underrepresented by SWAMPS and erroneously includes the high
concentrations of large lakes there (e.g., Lehner and Döll, 2004), which
do not necessarily emit methane at the same rates or via the same carbon
cycling processes as wetlands (e.g., Walter et al., 2006; Pace et al., 2004).
The practical difficulties in detecting inundation under forest canopies with
visible or high-frequency microwave sensors (e.g., Sippel and Hamilton, 1994)
compound these problems. In the case of the WSL, equating wetlands with
surface water not only caused underestimation of total CH4 emissions
but also led to the attribution of the majority of the region's emissions to the
permafrost zone in the north. This issue is not unique to the WSL, as the
collocation of permafrost, lakes, and inundation is present throughout the
high latitudes (Tarnocai et al., 2009; Lehner and Döll, 2004; Brown et
al., 1998). Indeed, in their analysis of the Hudson Bay Lowland (HBL), Melton
et al. (2013) found that three of the four lowest emissions estimates were
from S models (CLM4Me, DLEM, and LPJ-WSL), although whether this was due
to a bias in area was not examined. Given present concerns over the potential
liberation of labile carbon from thawing permafrost over the next century
(Koven et al., 2011), it is crucial to avoid under- or overestimating
emissions from permafrost wetlands.
It is therefore important for modelers – both forward and inverse – to use
accurate wetland maps such as Peregon et al. (2008), Sheng et al. (2004), or
Lehner and Döll (2004) in their model development, whether as a static
input parameter or as a reference for evaluating prognostically computed
CH4-producing areas, and to account for the existence of non-inundated
portions within these wetlands in which methane emissions have a dependence
on water table depth. Maps such as Tarnocai et al. (2009) may be
inappropriate unless restricting simulations to permafrost wetlands. Ideally,
modelers would be able to draw on a global version of the high-resolution map
of Peregon et al. (2008) that not only delineates wetlands but also
identifies the major subtypes (e.g., sphagnum-dominated or sedge-dominated,
as in Lupascu et al., 2012) to which different methane emissions parameters
could potentially be applied. When using surface water products to constrain
simulated inundated extents, modelers must be sure either to mask out
permanent lakes and large rivers, using a data set such as GLWD (Lehner and
Döll, 2004) or MOD44W (Carroll et al., 2009), or better, to implement
carbon cycling processes that are appropriate to these forms of surface
water.
Temporal variability, environmental drivers, and model features
Another notable finding was that models that lacked physical and biochemical
formulations appropriate to the high latitudes exhibited more extreme
correlations with Fw or air temperature than either inversions or
more sophisticated models. In other words, high-latitude biogeophysical
processes – specifically, soil freeze–thaw, the insulating effects of snow
and peat, and relationships between emissions and water table depth in
peatlands – make a substantial difference to the sensitivities of emissions
to environmental drivers, at least over the 12-year period of this study.
Even if we do not fully trust the Bousquet2011 inversions, it seems
reasonable to assume that the models that simulate high-latitude-specific
processes are more likely to be correct in this regard than the other models.
These sensitivities have a bearing on models' responses to potential future
climate change (e.g., Riley et al., 2011; Koven et al., 2011).
Thus, it appears that the following model features are desirable for reliable
simulations of boreal wetlands:
realistic soil thermal physics, including freeze–thaw dynamics. Most of the
models that were highly correlated with one driver (LPJ-WSL, DLEM, LPJ-MPI, VISIT, and SDGVM)
lacked this feature.
accurate representations of peat soils. Again, many of the models with high correlations with
one driver (LPJ-WSL, DLEM, VISIT, and SDGVM) lacked this feature.
realistic representations of unsaturated (non-inundated) peatlands, including the dependence
of CH4 emissions on water table depth. LPJ-WSL, an
Fw-dominated model, effectively set non-inundated CH4
emissions to 0 because it did not simulate wetlands outside of the
time-varying GIEMS surface water area. At the other extreme, IAP-RAS, a
Tair-dominated model, treated all wetlands in their static map as
if they were saturated, thereby eliminating the contribution of soil moisture
variability. The relative insensitivity of LPX-BERN's emissions to water
table position similarly reduced the contribution of soil moisture
variability, although there are too few observations to say whether this is
unreasonable.
Other model features either made relatively little difference in this study
or were severely underrepresented but warrant further investigation. This is
especially true of biogeochemical processes. For example, whether models
contained dynamic vegetation (phenology and/or community composition) or
dynamic peatland (peat accumulation and loss) components did not affect
performance. However, our 12-year study period was likely too short to see
the effects of these features. Changes in vegetation community composition
may become more important in end-of-century projections (e.g., Alo and Wang,
2008; Kaplan and New, 2006). In particular, recent studies (Koven et al.,
2011; Ringeval et al., 2011; Riley et al., 2011) have found a “wetland
feedback”, in which vegetation growth in response to future climate change
can lower water tables and reduce inundated extents via increased
evapotranspiration. This drying effect reduces end-of-century CH4
emissions from an approximate doubling of current rates without the feedback
to only a 20–30 % increase with the feedback. Similarly, hydrologic and
chemical changes in peat soils, in response to disturbances such as
permafrost thaw or drainage for mining or agricultural purposes, may be
important in end-of-century projections (e.g., Strack et al., 2004). However,
to properly assess the accuracy of dynamic vegetation or peatland schemes and
their effects on CH4 emissions, a longer historical study period, along
with longer observational records (including observations of species
compositions and soil carbon densities) would be necessary.
Other features may warrant further study. Replacing the Cao (1996) model with
the model of Walter and Heimann (2000) modestly lowered VISIT's otherwise
extreme correlation with Tair. It is not clear if this is an
inherent difference between the two formulations or just an artifact of their
parameter values in VISIT, but it might imply that the Walter and Heimann
model is more appropriate for applications at high latitudes. Similarly,
nitrogen–carbon interaction had a substantial latitude-dependent effect on
mean CH4 emissions for LPX-BERN (Table 6). Again, the size of the effect
could be model-dependent, and potential impacts on sensitivities to climate
change might become more apparent over a longer analysis period.
Some of the scatter in model sensitivities to drivers may come from
differences in the values of parameters related to methane production,
methane oxidation, and plant-aided transport, which recent studies (Riley et
al., 2011; Berrittella and van Huissteden, 2011) have found to be
particularly influential over wetland CH4 emissions. The investigation of
these parameters over the WSL in a model intercomparison can be difficult due
to the many large differences among model formulations. As shown in
Sects. 3.3 and 3.4.2, the methods of biogeochemical parameter selection had
far less influence over the model results than the presence or absence of
major features such as sophisticated soil thermal physics. Such a comparison
would require the examination of a subset of the models that have sufficiently
similar snow, soil, and water table formulations in order to isolate the
effects of microbial and vegetative parameters.
Other features that were not investigated here could have potentially large
impacts on the response of high-latitude wetlands to future climate change.
One such feature is acclimatization, in which soil microbial communities
gradually adapt to the long-term mean soil temperature. This feature has been
explored in the ORCHIDEE model (Koven et al., 2011; Ringeval et al., 2010),
where it greatly reduced the response of wetland CH4 emissions to
long-term temperature changes. Unfortunately, the version of ORCHIDEE used in
this study and in the original WETCHIMP study (Melton et al., 2013; Wania et
al., 2013) did not use acclimatization. Acclimatization likely would lower
ORCHIDEE's correlation with Tair over timescales long enough for
changes in the long-term mean to be as large as interannual anomalies.
Another feature explored by Koven et al. (2011) is the liberation of ancient
labile carbon stored in permafrost. As with dynamic vegetation, a robust
evaluation of these effects would require a much longer study period.
Future needs for observations and inversions
The wide disagreement among estimates from observations and inversions
hampers our ability to assess model performance. Given the large influence
that wetland maps can have on emissions estimates (not only in the WSL, but
over larger areas, as shown by Petrescu et al., 2010), care must be taken to
select appropriate maps. Ideally, global satellite or map products such as
the GLWD (which omitted the northernmost wetlands in the WSL) should be
validated against more intensively ground-truthed regional maps, such as
Sheng2004 and Peregon2008, where such maps exist. Similarly, resolving the
discrepancies between the GIEMS and SWAMPS remote-sensing surface water
products would require verification against independent observations.
The large discrepancy between the spatial distributions of emissions from
Glagolev2011 and Kim2011 (concentrated in the south) and Winderlich2012 and
Bousquet2011K (concentrated in the north) may be due to several factors.
First, the inversions' posterior estimates reflect their prior distributions:
Kim2011 used an earlier version of Glagolev2011 (Glagolev et al., 2010) as
its prior, while Winderlich2012 and Bousquet2011K both used the Kaplan (2002)
distribution as their prior. Second, different types and locations of
observations were used: Glagolev2011 was based on in situ chamber
measurements of CH4 fluxes, 80 % of which were obtained south of the
Ob' River, while Winderlich2012 was based on atmospheric CH4
concentrations observed at towers near or north of the Ob' River. Third,
observations were not taken from the same years. Finally, the Winderlich2012
wetland CH4 emissions may have been influenced by assumed emission rates
from fossil fuel extraction and biomass burning, which were not adjusted
during the inversion. Efforts like the revision of Glagolev2011 will
certainly help in resolving some discrepancies, but all estimates would
benefit from incorporating observations over long time periods and wider
areas to reduce uncertainties in their long-term means.
The global inversions were also subject to uncertainties. For example, while
the Bousquet2011 inversions imply that wetland CH4 emissions in the WSL
are not strongly correlated with either Fw or air temperature,
the Bousquet2011 inversions' temporal behaviors must be evaluated with
caution. The reference inversion's coefficient of variability (CV), which
resulted in net negative annual emissions over the WSL in 2004, was
substantially higher than the highest model CV. Bousquet et al. (2006) noted
that their inversions were more sensitive to the interannual variability of
wetland emissions than to their mean; accordingly, it is possible that the
Bousquet2011 inversions underestimated the long-term mean, thereby raising
the CV. Another possibility is that the monthly coefficients that optimized
total emissions over all of boreal Asia were not optimal over the WSL alone,
since the environmental drivers interacting with wetlands elsewhere may not
have been in phase with those in the WSL. A further possibility, given
credence by the reference inversion's consistent net negative emissions over
all of boreal Asia in May and June, is that errors in other components of the
inversion (e.g., atmospheric OH concentrations, methane oxidation rates,
background methane concentrations advected from elsewhere) influenced wetland
emissions. Finally, other methane sources that were not accounted for in the
inversion might have been attributed to wetlands, for example, geological
CH4 seeps (Etiope et al., 2008), leaks from gas pipelines (Ulmishek,
2003), or lakes (Walter et al., 2006).
At the other extreme, the Bloom2010 product exhibited almost no spatial or
temporal variability. This might be an artifact of using GRACE data as a
proxy for wetland inundation and water table levels. The spatiotemporal
accuracy of Bloom2010 must also be questioned, given that it did not use an
atmospheric transport model or account for methane oxidation in the
atmosphere. Thus, while Bloom2010 provided a useful estimate of long-term
mean emissions, it was less helpful in constraining model responses to
climate drivers.
Another general limitation of inversions and observations, distinct from
estimates of long-term mean emissions, is the lack of sufficiently long
periods of record to assess model sensitivities to environmental drivers and
climate change. The Bousquet2011 inversions and the SWAMPS surface water
product are long enough to begin to address this issue on the global scale,
but the Bousquet2011 inversions are not optimized for the WSL. Regional
inversions such as Kim2011 and Winderlich2012, which might offer more
spatially accurate estimates for the WSL than the Bousquet2011 inversions,
only offer a single year of posterior emissions. Long records of in situ
observations of CH4 emissions and the factors that most directly
influence these emissions (e.g., soil temperature and water table depth) only
exist in a handful of locations (e.g., the Bakchar Bog in the WSL; Panikov
and Dedysh, 2000; Friborg et al., 2003; Glagolev et al., 2011). Indeed, the
paucity of long in situ records limited our ability to evaluate LPX-BERN's
relatively low sensitivity to water table depth. Year-round observations
would also be helpful, as winter emissions are sparsely sampled (Rinne et
al., 2007; Kim et al., 2007; Panikov and Dedysh, 2000) and inversions
disagree as to the magnitude of winter emissions (Fig. 8). The recent
implementation of tower networks in the WSL (Sasakawa et al., 2010;
Winderlich et al., 2010) show some promise in this regard, as their
observations are both multiyear and year-round. More comprehensive
observations of emissions from non-wetland methane sources such as seeps,
pipe leaks, and lakes, most of which have so far not been accounted for in
inversions (although pipe leaks are now being considered; Berchet et al.,
2014), would be beneficial in increasing the accuracy of inversions.