Introduction
The increase in atmospheric methane (CH4) concentrations since 2007
(Rigby et al., 2008) has received
attention due to methane's strong greenhouse effect. The causes of the
renewed increase in CH4 since 2007 and the relative stability of the
atmospheric concentrations for the preceding decade (1996–2006) are not well
understood (Bloom et al., 2010). Improved understanding of the variability of
atmospheric methane can provide more accurate predictions of future
concentrations. Changes in atmospheric CH4 are determined by the balance
between the emissions of CH4 and its loss. The loss is mostly controlled
by the reaction of CH4 with the hydroxyl radical (OH). While the
CH4 loss timescale varies from year to year (Wuebbles and Hayhoe, 2002;
Bousquet et al., 2006) as the OH concentration changes, recent evidence
suggests the interannual variability of OH is small (Montzka et al., 2011).
The primary sources of atmospheric methane include anthropogenic emissions,
natural wetlands, rice paddies, biomass burning, and termites (Denman et al.,
2007; Kirschke et al., 2013). Natural wetlands are the largest single source
of atmospheric CH4 and make a significant contribution to its
variability (Spahni et al., 2011). Using inverse methods, Bousquet et
al. (2006) suggests that 70 % of the global emission anomalies CH4
for the period 1984–2003 are due to the interannual variability in wetland
emissions and furthermore that tropical methane emissions are the dominant
contribution to the global interannual variability. In another methane
inversion, Chen and Prinn (2006) find that the large 1998 increase in
atmospheric CH4 concentration could be attributed to global wetland
emissions.
There are still large uncertainties in global wetland emissions due to (1)
poor understanding of environmental and biological processes that control
methane emissions (Riley et al., 2011; Meng et al., 2012); and (2)
uncertainties in the extent and distribution of wetlands, particularly in
tropical regions (Prigent et al., 2007; Spahni et al., 2011). Process-based
biogeochemical methane models can help improve the understanding of dominant
processes that control methane production, oxidation, and transport. Several
process-based models that incorporate different environmental and biological
processes have been developed. For instance, Wania et al. (2009) developed
the Lund–Potsdam–Jena Wetland Hydrology and Methane (LPJ–WhyMe) model to
simulate peatland hydrology and methane emissions from northern latitudes
using a mechanistic approach. LPJ–WhyMe was recently incorporated into the
Lund–Potsdam–Jena (LPJ) dynamic global vegetation model (DGVM; Spahni et
al., 2011) to simulate methane emissions on a global scale by dividing global
ecosystems into four different types (northern peatland (45–90∘ N),
naturally inundated wetlands (60∘ S–45∘ N), rice
agriculture and wet mineral soils) and using different parameters to
characterize the processes relevant for methane production, oxidation, and
transport in the soil in each of these ecosystems. Zhuang et al. (2004)
couple a methane module to a process-based biogeochemistry model, the
Terrestrial Ecosystem Model (TEM), and explicitly calculated methane
production, oxidation, and transport in the soil. Xu et al. (2010) include a
methane module in the Dynamic Land Ecosystem Model (DLEM) to simulate methane
production, oxidation, and transport (Xu et al., 2010). Riley et al. (2011)
integrate a methane biogeochemical model (CLM4Me) into the Community Land
Model (CLM), the land component of the Community Climate System Models (Gent
et al., 2011) and the Community Earth System Model (CESM). Meng et al. (2012)
add additional features into CLM4Me including an emission dependence on pH
and on redox potential. This revised version of CLM4Me is referred to as
CLM4Me′ (Meng et al., 2012). Detailed description of CLM4Me and
CLM4Me′ can be found in Riley et al. (2011) and Meng et al. (2012).
The large uncertainties in methane fluxes due to parameter uncertainty in
this model are quantified in Riley et al. (2011).
These process-based models are often evaluated against surface CH4 flux
measurements based on chamber techniques (Jauhiainen et al., 2005; Shannon
and White, 1994; Keller, 1990). However, there are only limited observational
data sets available for model evaluation and most of them are in mid- and high
latitudes. The shortage of tropical methane measurements makes it difficult
to evaluate the spatial distribution of modeled surface emissions. This is
especially critical as the tropical wetlands are the largest contribution to
global wetland methane emissions (Meng et al., 2012; Spahni et al., 2011;
Bloom et al., 2010).
The spatial distribution of surface emissions produced by these
biogeochemical models can be used along with other CH4 emission sources
as inputs to atmospheric chemistry and transport models to simulate
atmospheric CH4 concentration. As wetland emissions are the largest
single source, their spatial distribution could significantly affect the
distribution of atmospheric CH4 concentration. The long-term atmospheric
measurement of CH4 can be used to compare with modeled atmospheric
CH4 to further evaluate the spatial distribution of surface emissions.
Recently, a chemistry-transport model (CTM) intercomparison experiment
(TransCom-CH4) quantifies the role of CH4 surface emission
distributions in simulating the global distribution of atmospheric methane
(Patra et al., 2011). In TransCom-CH4, 12 chemistry-transport models
simulations with different surface emissions are evaluated against measured
atmospheric CH4 concentrations. Patra et al. (2011) find that
meteorological conditions and surface emissions from biomass burning and
wetlands can contribute up to 60 % of the interannual variation (IAV) in
the atmospheric CH4 concentrations. However, in Patra et al. (2011), the
methane emissions are specified and do not result from interactions between
simulated meteorology and land-carbon models.
In this study, we explore the temporal and spatial variation of wetland
methane emissions estimated in CLM4Me′. The modeled wetland
emissions are used with other surface emissions (including emissions from
anthropogenic sources, biomass burning, rice paddies, and termites) as inputs
to the Community Atmospheric Model with chemistry (CAM-chem). The CH4
concentration simulated with CAM-chem is compared with a global network of
station measurements. The purposes of this paper are (1) to examine seasonal
and interannual variations in wetland methane emissions simulated by
CLM4Me′ in two different versions of the Community Land Model; (2) to
compare the simulated atmospheric methane concentrations to observations,
including latitudinal gradients and interannual variability so as to
determine the extent to which the atmospheric observations constrain the
emissions; (iii) to understand the drivers of seasonal and interannual
variability in atmospheric methane fluxes. Section 2 describes models,
methods, and data sets. Results and discussions are presented in Sect. 3. We
conclude in Sect. 4 with a summary of major findings.
Models and data sets
Simulations
Methane emissions from 1993 to 2004 are simulated and analyzed in four
different model configurations (see Table 1). All configurations use
Community Atmospheric Model (CAM4) with chemistry (CAM-chem) (Lamarque et
al., 2012) to diagnose atmospheric methane. These configurations differ in
their specification of methane emissions. Other details of the simulations
are identical.
Comparison of the methane sources in the four simulations used in
this study.
Input Data
TransCom
CN_a
CN_b
BGC
Anthropogenic emissions
OB20011
0.72 ⋅ OB2001
OB2001
OB2001
Wetland emissions
Ito and Inatomi (2012)5
CLM4.02
0.64 ⋅ CLM4.02
0.74 ⋅ CLM4.53
Rice paddy emissions
Ito and Inatomi (2012)
CLM4.0
CLM4.0
CLM4.5
Termite emissions
Fung et al. (1991)
Fung et al. (1991)
Fung et al. (1991)
Fung et al. (1991)
Fire emissions
GFED v24
GFED v3
GFED v3
GFED v3
1 OB2001 refers to the anthropogenic methane emissions in
Olivier and Berdowski (2001). The average annual CH4 emissions are
∼ 294 Tg yr-1 over the period of 1993–2004. 2 CLM4.0
refers to the methane emissions estimate in the CLM4.0 model as used in Meng
et al. (2012). The average annual methane emissions were
∼ 228 Tg yr-1. 3 CLM4.5 refers to the methane emissions
estimated in the CLM4.5 model. The estimated annual methane emissions from
CLM4.5 were ∼ 190 Tg yr-1 over the period of 1993–2004.
4 GFED indicates the Global Fire Emission Database. Average annual
CH4 emissions from GFED v2 and v3 are ∼ 20 and
∼ 21 Tg yr-1, respectively. 5 Wetland and rice paddy
emissions from Ito and Inatomi (2012) were downscaled to approximately
183 Tg yr-1 over the period of 1993–2004 for TransCom. CLM4.0 rice
paddy emissions are 37 Tg yr-1. CLM4.5 rice paddy emissions are
42 Tg yr-1. Termite emissions from Fung et al. (1991) are
20 Tg yr-1. Note that the global total averaged emissions for the
study period used in the TransCom, CN_a, and CN_b, BGC are the same (within
1 % variation), but the spatial distribution of methane emissions might
be different.
Comparison of the time series of combined methane emissions from
wetlands and rice paddies used in the TransCom (Patra et al., 2011), CN_a,
CN_b, and BGC experiments. Note that the average methane budget over the
period of 1993–2004 is the same in the TransCom, CN_a, CN_b, and BGC
experiments. CN_b is the reduced CN_a.
The TransCom simulation (Table 1) is reported on as part of the
TransCom-CH4 simulations (Patra et al., 2011). CAM-chem is one
of the 12 models participating in these simulations (Patra et al., 2011).
The methane emissions in the TransCom are specified and included the seasonal
variation of methane emissions from anthropogenic sources (Olivier and
Berdowski, 2001), rice paddies and wetlands (Ito and Inatomi, 2012), biomass
burning (van der Werf et al., 2006), and termites (Fung et al., 1991). The
wetland emissions from Ito and Inatomi (2012) are calculated based on a
process-based terrestrial ecosystem model, the Vegetation Integrative
Simulator for Trace gases (VISIT). In the VISIT, the inundated area is
calculated based on model-derived rainfall and temperature (Mitchell and
Jones, 2005). We select this scenario from the TransCom experiment because it
includes the long-term monthly variations of wetland and rice paddy
emissions.
Differences between the TransCom simulation and the other three simulations
analyzed here (see Table 1) include (1) differences in the specification of
the methane emissions from rice paddies and wetlands: in the TransCom
simulation the emissions are specified while in the remaining three
simulations the methane emissions are obtained from CLM4Me′ a
process-based methane biogeochemical model; (2) differences in the
specification of fire emissions: in the TransCom simulation, fire emissions
are taken from the Global Fire Emission Database (GFED) v2 (on average 20
Tg CH4 yr-1 is emitted) (van der Werf et al., 2006) while in the
remaining three simulations, the fire emissions are from GFED v3 (on average
21.1 Tg CH4 yr-1 is emitted) (Giglio
et al., 2010). The current version, GFED v4, has an average CH4 emission
of 15.7 Tg CH4 yr-1 (van Der Werf et al., 2010) which is much
lower than GFED version 2 and version 3 (Fig. 2). Please note that GFED
version 4 was not used in this study.
Comparison of the interannual variation in methane emissions from
fire in the GFED v2 (van der Werf et al., 1996), GFED v3 (Gilglio et al.,
2010), and GFED v4 (van der Werf et al., 2010). These data sets are obtained
from http://www.globalfiredata.org/.
The interannual anthropogenic methane emissions over the globe,
in the tropics, mid-latitudes, and high latitudes. These data sets are obtained from
TransCom (Patra et al., 2010)
Two of the configurations analyzed (labeled CN_a and CN_b) diagnose
wetlands and rice paddies methane emissions using CLM4Me′ within the
Community Land Model version 4 (CLM4 or CLM–CN) of the Community Earth System
Model (CESM); one configuration (labeled BGC) uses CLM4Me′within the
Community Land Model version 4.5 (CLM4.5 or CLM–BGC) of the CESM.
The wetland emissions simulated by CLM4Me′ model when integrated in
CLM4.0 (228 Tg yr-1) are on the high side of current estimates
(100–284 Tg yr-1) (Denman et al., 2007; Kirschke et al., 2013). In
order to obtain a reasonable overall methane budget
(∼ 517 Tg yr-1, within the range of 492–581 Tg yr-1 shown
in Denman et al. (2014) and Kirschke et al. (2013)), we adjust the emissions
in the simulations using CLM4.0. In simulation CN_a, the anthropogenic
emissions used in the TransCom simulations are multiplied by 0.72; in
simulation CN_b the wetland emissions are multiplied by 0.64, but the
anthropogenic emissions are the same as those in TransCom. Both these
re-scalings retain the temporal and spatial emission distributions from the
original data sets but simulate the approximately correct atmospheric methane
concentrations. In the first case (CN_a), where anthropogenic emissions are
reduced, the total anthropogenic emissions are 211 Tg yr-1. This is at
the low end of estimated anthropogenic emissions, but within the range
(209–273 Tg) of values reported in the literature (see IPCC AR4 Chapter 7)
(Denman et al., 2007; Kirschke et al., 2013) when excluding biomass burning
and rice paddies.
On the other hand, the wetland emissions simulated by CLM4Me′ model
integrated into CLM4.5 (BGC) are higher than CLM4.0 (CN) emissions.
Therefore, we adjust the wetland emissions in the BGC simulation. In
particular, in the BGC simulation the wetland emissions are reduced by
26% to match the total methane
emissions in the other simulations. Reducing the methane emissions is
equivalent to modifying the coefficient for the maximum amount of methane
that can be produced from heterotrophic respiration. The reductions used here
are within the uncertainties of this estimate (e.g., Riley et al., 2011). The
same termite emissions are used in all simulations. The global interannual
average of methane emissions used in CN_a, CN_b, and BGC are similar to
that used in TransCom.
CLM4Me′
CLM4Me′ (Meng et al., 2012) is a process-based methane biogeochemical
model incorporated in CLM version 4 and CLM version 4.5 of the Community
Earth System Model (CESM). The spatial resolution used in this study is
1.8 × 2.5 degree. CLM4Me′ is based on CLM4Me (Riley et al.,
2011) and explicitly calculates methane production, methane oxidation,
methane ebullition, methane diffusion through soils, and methane transport
through aerenchyma. CLM4Me′ is an update of CLM4Me to include pH
and redox functional dependence for methane emissions, and a limitation of
aerenchyma in plants in always-inundated areas (Meng et al., 2012). In
CLM4Me′, methane production (P (mol C m-2 s-1)) is
calculated as follows:
P=RHfCH4Q10′SfpHfpE.
The difference in total emissions used in CN_a, CN_b, BGC
experiments as compared with TransCom. A 12-month smoothed average is also plotted
for the difference of CN_a (CN_a – TransCom), CN_b (CN_b – TransCom),
BGC (BGC – TransCom) with TransCom. Please note that the average of the
difference in the period of 1993–2004 is zero.
Here, RH is heterotrophic respiration from soil and litter
(mol C m-2 s-1), fCH4 is the ratio between CO2
and CH4 production, which is currently set to 0.2 for wetlands and rice
paddies. Q10′ is the control of soil temperature on CH4
production. fpH and fpE are pH and redox
potential function, respectively. A detailed description of CLM4Me and
CLM4Me′ can be found in Meng et al. (2012) and Riley et al. (2011).
Temporal variation of wetland CH4 fluxes estimated in CN_a
(green) and BGC (blue). The globe is divided into three regions: the tropics
(30∘ S to 20∘ N), mid-latitudes (20–50∘N), and high latitudes (> 50∘ N).
Please note that this is the original methane emissions produced by CN_a and
BGC without any multiplication. The smooth green and blue lines indicate the
12-month average wetland CH4 fluxes for CN_a and BGC.
Community Land Model (CLM)
CLM4Me′ is integrated and spun up in two versions of CLM:
CLM4.0 and CLM4.5. CLM4.0 uses the carbon and nitrogen belowground
module from the Carbon-Nitrogen (CN) model (Thornton et al., 2007, 2009). CLM4.5 is updated from CLM4.0 and offers some improvements, with the most
significant change to the belowground carbon cycle (Koven et al., 2013).
CLM4.5 includes an alternate decomposition cascade from the Century soil
model, which is referred to as the biogeochemistry version of the model
(CLM4.5–BGC). This version of the model has increased productivity and carbon
at high latitudes (perhaps an overestimate) and reduced productivity in the
tropics compared to the CN model (see Koven et al. (2013) for more
comparisons). The initial condition in both CLM models is created using
NCEP reanalysis data sets in two steps. First the model is brought close to
equilibrium for 1850 conditions (atmospheric CO2 concentration, aerosol
deposition, nitrogen deposition, and land use change) cycling a 25-year
(1948–1972) subset of transient climate data (1948–2004). Then we use these
equilibrated conditions in a transient simulation from 1850 to 1990 (where
the meteorology is cycled over the period of 1948–2004) to produce the
initial condition used in this study. For the period of this study
(1990–2005), CLM4Me′ is forced with multi-satellite-derived
inundation fraction (Prigent et al., 2007) and NCEP (i.e., the National
Center for Environmental Prediction) reanalysis data sets (Qian et al., 2006;
Kistler et al., 2001). While the simulation period is 1990–2005, satellite-derived
inundation data is only available from 1993 to 2004. We use climatological
monthly average (1993–2004) inundation fraction for years 1990–1992 and
2005.
CAM-chem
We use CAM-chem (Lamarque et al., 2012), driven by the NCEP
reanalysis data set (Kistler et al., 2001; Qian et al., 2006) to predict the
atmospheric concentrations of methane from the methane emissions. In this
study, we conduct simulations with CAM-chem using offline meteorological
forcing, similar to the model setup used in TransCom (Patra et al., 2011).
The simulations are performed at a horizontal resolution of 1.9∘
(latitude) and 2.5∘ (longitude) and at 28 vertical layers. Please
refer to Lamarque et al. (2012) for a detailed description of CAM-chem.
The spatial difference in CH4 fluxes between four high and low
emission years. Please note CH4 fluxes are plotted on a logarithmic
color scale in CN_a (top, a) and BGC (bottom, b). The latitudinal average
CH4 flux is plotted on the right.
In the CAM-chem version used here, the atmospheric chemistry is simplified
compared to Lamarque et al. (2012), and includes only the reactions necessary
to capture the loss of methane. The simulations include the chemical removal
reactions for CH4 including the reaction with OH, the excited atomic
oxygen (O1D), and chlorine (Cl). Specifics of the chemical loss
reactions can be found in Patra et al. (2011). Interannually constant monthly
mean OH is used in the CAM-chem simulations. The optimized OH derived from
CH3CCl3 concentrations scaled from Spivakovsky et al. (2000) is
used where an equal OH abundance is assumed in both the Northern and Southern
hemispheres. The distribution of OH used to compute the loss of atmospheric
methane is identical to that used in TransCom experiments. Stratospheric loss
due to Cl and O1D is also included. Interannually constant monthly Cl
and O1D are used in the simulations. In addition, a soil sink for
CH4 is included using a climatological monthly average derived from LMDZ
(Laboratoire de Météorologie Dynamique Zoom) atmospheric CH4
inversion (Bousquet et al., 2006).
Zonal average monthly methane fluxes with time in CN_a (top, a) and
BGC (bottom, b) experiments.
Atmospheric concentrations of methane are tagged from rice paddy,
wetland, anthropogenic, and biomass burning emission sources. The losses of
tagged methane are identical to those described above.
Observed atmospheric CH4 concentration.
Observational atmospheric CH4 concentration data sets are obtained from
the World Data Centre for Greenhouse Gases (WDCGG) at
http://ds.data.jma.go.jp/gmd/wdcgg/. Monthly concentration data sets
from 14 stations (Table 2) around the world are compared with the simulated
atmospheric CH4 (Butler et al., 2004; Cunnold et al., 2002). Most of the
sites have monthly or weekly measurements and use the flask-sampling method.
Collected samples are analyzed using gas chromatography with flame ionization
detection (Dlugokencky et al., 2005).
A list of stations used in this study.
Station no.
Station name
Lat
Lon
Elevation (m)
Data availability
1
South Pole (USA) (spo)
-89.98
24.80∘ W
2810
Feb 1983 to present
2
Cape Grim (Australia) (cgo)
-40.68
144.68∘ E
94
Jan 1984 to present
3
Tutuila (Cape Matatula) (smo)
-14.24
170.57∘ W
42
Apr 1983 to present
4
Ascension Island (UK) (asc)
-7.92
14.42∘ W
54
May 1983 to present
5
Cape Kumukahi (USA) (kum)
19.52
154.82∘ W
3
Apr 1983 to present
6
Mauna Loa (USA) (mlo)
19.54
155.58∘ W
3397
May 1983 to present
7
Mt. Waliguan (China) (wlg)
36.28
100.90∘ E
3810
May 1991 to present
8
Tae-ahn Peninsula (S. Korea) (tap)
36.72
126.12∘ E
20
Mar 1993 to present
9
Niwot Ridge (USA) (nwr)
40.05
105.59∘ W
3523
Jun 1983 to present
10
Mace Head (Ireland) (mhd)
53.33
9.90∘ W
8
Jun 1991 to present
11
Cold Bay (USA) (cba)
55.2
162.72∘ W
25
May 1983 to present
12
Barrow (USA) (brw)
71.32
156.60∘ W
11
Jan 1986 to present
13
Zeppelinfjellet (Norway) (zep)
78.9
11.88∘ E
475
May 1994 to present
14
Alert (Canada) (alt)
82.45
62.52∘ W
210
Jun 1985 to present
RMS Variability
Seasonal and interannual root mean square (RMS) variability are used to
evaluate the spatial distribution of simulated methane variability. We apply
the method described in Nevison et al. (2008). Here, seasonal RMS variability
is calculated as the RMS of the differences between model climatological
monthly means (1990–2004) and the climatological annual mean. The
interannual variability of RMS is calculated as the RMS of the differences
between each month and the corresponding month from the climatological
seasonal cycle. We calculate RMS separately for methane tagged from each
tagged emission source. This apportions the variability of each source by
calculating the ratio between the variability due to that source's RMS and
the total RMS. Please note that the sum of individual source's contribution
to total RMS is often greater than 1 in cases of cancelation of signals among
individual sources.
Taylor diagrams
Taylor diagrams can provide a concise statistical summary of model
performance in a single polar coordinate plot (Taylor, 2001). In this study,
we use Taylor diagrams to evaluate the model's ability to simulate the
observed interannual variability (IAV) of atmospheric CH4. The Taylor
diagram gives the model–measurement coefficient R reflecting the agreement
in shape and phasing of the model and measurement time series and the ratio
of modeled to measured standard deviation σmodel/σobs, which represents the agreement
between the amplitude of the simulated and observed interannual variability
(IAV) of atmospheric CH4.
RMS variability (in ppb) from 1993 to 2004 of atmospheric CH4
concentration in CN_a experiment. The left panel shows seasonal RMS
variability and the right panel indicates interannual RMS variability. From
the top to the bottom are total RMS variability (a, b), anthropogenic
contribution to total RMS variability (c, d), rice contribution to total RMS
variability (e, f), and wetland contribution to RMS variability (g, h). Note
that proportional RMS variability of anthropogenic sources, rice paddies, and
wetlands add up to > 1 when cancellation among component tracers
occurs in the summing of total CH4.
RMS variability (in ppb) from 1993 to 2004 of atmospheric CH4
concentration in BGC experiment. The left panel shows seasonal RMS
variability and the right panel indicates interannual RMS variability. From
the top to the bottom are total RMS variability (a, b), anthropogenic
contribution to total RMS variability (c, d), rice contribution to total RMS
variability (e, f), and wetland contribution to RMS variability (g, h). Note
that the portions of RMS variability of anthropogenic sources, rice paddies, and
wetlands add up to > 1 when cancellation among component tracers
occurs in the summing of total CH4.
Results and discussions
Comparison of methane fluxes from different sources
A comparison of methane fluxes used in the four experiments shows that
wetlands and rice paddies methane emissions in CN_a are higher than those
used in other three simulations (Fig. 1). Emissions from wetlands and rice
paddies in the CN_b (i.e., the CN_a wetland emissions reduced by
36 %) simulations are comparable with those used in TransCom and BGC
experiments (Fig. 1). There are different magnitudes in the seasonal and
interannual variations among these four experiments. Overall, BGC has the
lowest winter emissions. There is a decreasing trend in the CN_a and CN_b
methane emissions not evident in the TransCom and BGC methane emissions. The
difference in methane emissions in CN_a, CN_b, and BGC experiments will be
discussed in the next session. The fire emissions in the TransCom simulation
(based on GFED v2) and the other simulations (based on GFED v3) (Fig. 2) are
similar in magnitude, but with some distinct seasonal differences.
Overall, the anthropogenic methane emissions tend to stabilize after 1998,
due to the decrease from mid- and high latitudes (Fig. 3). The annual total
methane emissions used in CN_a and CN_b experiments are slightly higher
(lower) than that used in TransCom experiment during the first (second) half
of the study period (Fig. 4). The annual total emissions used in BGC are
slightly lower than those used in TransCom during most years except 1997,
1998, and 2002. There are no statistically significant trends (at
∼ 95 % level) in the difference between the BGC and TransCom total
emissions.
Seasonal and interannual variability in CN_a and BGC methane emissions
CN_a methane emissions
There are strong seasonal and interannual variations in CN_a wetland
methane emissions (Fig. 5). On a seasonal basis, the peak methane emissions
occur in the summer (June, July, and August) and the lowest methane emissions
occur in winter (December, January, and February) as methane emissions are
controlled by both temperatures and inundated area. On an interannual basis,
the summer of 1994 has the highest CN_a methane emissions methane emissions
in the period of 1993–2004. A generally decreasing trend
(-2.1 Tg CH4 yr-1, significant at 95 % level) in CN_a
global wetland emissions occur from 1994 to 2004. This is driven by trends in
tropical wetland emissions (Fig. 5), where tropical wetlands contribute to
∼ 70 % of the global wetland flux. The decreasing rate in tropical
wetland emissions from 1993 to 2004 is approximately
-1.68 Tg CH4 yr-1, statistically significantly different from
0 (no change) at the 95 % confidence level.
We further identify the four highest (1994, 1995, 1996, 1999) and lowest
(2001, 2002, 2003, 2004) annual CN_a methane emissions in the period of
1993–2004 and plot the difference of methane emissions between the average
of these four extreme high and low emission years (Fig. 6a). There are large
differences across much of the globe, but the largest difference occurs in
the tropics (see the latitudinal average on the right side of Fig. 6a). On a regional level,
the largest differences are primarily present in Indonesia and South America
(e.g., the Amazon regions).
The interhemispheric gradients (N–S) in atmospheric CH4
concentration. The N–S gradients are calculated as the difference in
atmospheric CH4 concentration in Northern and Southern hemispheres at
these stations listed in Table 1. The observational CH4 concentration
data set at these stations is from the World Data Centre for Greenhouse Gases
(WDCGG) at http://ds.data.jma.go.jp/gmd/wdcgg/.
BGC methane emissions
The trend in BGC wetland methane emissions is different from that in CN_a
experiment (Fig. 5). In the BGC simulation, the peak emissions occur in 2002
instead of 1994. The wetland emissions do not decrease significantly from
1993 to 2004 with no significant trends in the interannual methane emissions
in the mid- and high latitudes and the tropics in these simulations. There
are several additional differences between CN_a and BGC wetland emissions:
(1) global BGC wetland emissions are approximately 10 % lower than CN_a
wetland emissions; (2) the BGC tropical (-30∘ S to 20∘ N)
wetland emissions of 63 Tg CH4 yr-1 are approximately 60 %
lower than those in CN_a (158 Tg CH4 yr-1); (3) high-latitude
(> 50∘ N) wetland emissions in BGC are
97 Tg CH4 yr-1 while CN_a only produces
12 Tg CH4 yr-1. Such large differences are probably largely due
to the shift of carbon from the tropics to the high latitudes as a result of the
modifications from CN_a to BGC (see Sect. 3.2.3) (Koven et al., 2013). BGC
and CN_a produce similar methane emissions in the mid-latitudes
(20–50∘ N).
The comparison of model-simulated atmospheric CH4 concentration
at the closest grid box vs. observations. The climatological monthly mean is
removed to focus on interannual variability in atmospheric CH4
concentration at these stations. Model simulations are obtained from
TransCom, CN_a, CN_b, and BGC experiments. The observational CH4
concentration data set at these stations is from the World Data Centre for
Greenhouse Gases (WDCGG) at http://ds.data.jma.go.jp/gmd/wdcgg/.
The latitudinal distribution of the methane emissions in CN_a suggests the
largest seasonal variation occurs at approximately 20–30∘ N,
followed by the latitudinal band 50–60∘ N (Fig. 7a). High latitudes
(> 65∘ N) have no clear seasonal cycles due to the low
methane fluxes that CN_a produces in the high latitudes (Meng et al., 2012).
There is a very dampened seasonal variation of CH4 emissions in tropical
wetlands (10∘ S to 10∘ N), although tropical wetlands are
the largest contribution to total wetland emissions. The seasonal cycle in
the different latitudinal bands is consistent with that identified in Spahni
et al. (2011) (see their Fig. 4a).
The latitudinal distribution of methane emissions shows a strong seasonal
variation at high latitudes in the BGC simulation. As clearly shown in
Fig. 7b, peak methane emissions
(> 200 mg CH4 m-2 d-1) occur in summer seasons and low methane emissions
(∼ 10 mg CH4 m-2 d-1) are present in winter. The
maximum emissions occur at approximately 60∘ N as distinct from the
CN_a simulations.
Taylor diagrams comparing the model interannual variability to
methane
observations at 14 stations. In this Taylor diagram, the angle from the
x axis is the correlation coefficient between model and observed time series
of atmospheric CH4 concentration. The value on the radial axis is the
standard deviation ratio: σmodel/σobs. It represents the match between the amplitude of the model
and observed interannual variability. Please refer to Table 2 for the
stations associated with each number.
The peak emissions in BGC from 1993 to 2004 occur in 1998 followed by 2002,
1994, and 2003. The four lowest emission years are 1999, 2000, 2001, and
1996. As shown in Fig. 6b, the increase in methane emissions from the four
lowest to highest years is primarily on the equator, in the Southern
Hemisphere (around 30∘ S) and in the high latitudes
(50–70∘ N). This is distinct from the CN_a simulations, where the
largest change predominantly occurs in the tropics (Fig. 6a).
Comparison of correlations between TransCom, CN_a, CN_b, BGC, and
observations (same as in Fig. 12). The station numbers correspond to those
in Table 2.
Sources of the differences in CLM4.0- and CLM4.5-estimated methane
emissions
The large difference in spatial distribution of methane emissions between
CN_a (CLM4.0) and BGC (CLM4.5) experiment is due to the change in soil
biogeochemistry within the soil C and N models from CLM4.0 to CLM4.5. Koven
et al. (2013) conduct a detailed analysis of the effect of such changes on C
dynamics in the CLM model. Here, we briefly describe the changes that most
affect high-latitude and tropical C dynamics, where the differences are the
largest. The carbon cycle is linked to the nitrogen (N) cycle because N
availability in soils will affect vegetation growth. In CLM4.0, available
mineral N experiences a first-order decay with a time constant of 2 days
that is not subject to environmental limitations. At high latitudes, the long
winters allow most mineral N to decay and only a limited amount of N is
available for vegetative growth during the short growing season. Therefore,
in the high latitudes, CLM4.0 simulates low productivity and low heterotrophic
respiration (HR) available for methane production (in CLM4Me, methane
production is a function of heterotrophic respiration, see methane production
equation in Sect. 2.1). In CLM4.5, an introduction of the dependence of N
losses on temperature and soil moisture and seasonality of N fixation reduce
the unrealistic N limitation in CLM4.0. Thus, CLM4.5 allows for more N to be
used for vegetation growth and produces higher soil carbon, higher
heterotrophic respiration (HR), and thus higher methane fluxes. As shown in
Appendix Fig. A1, HR in CLM4.5 is much higher than that in CLM4.0, particularly in
the Northern Hemisphere summer season when most CH4 is produced. Please note that annual CH4 emissions from northern latitudes are not
affected by winter time HR because CH4 is not produced in winter seasons
due to below-freezing temperatures.
Other changes that affect tropical C dynamics include calculation of
decomposition rates at each model level in CLM4.5 instead of limiting the calculation to top
30 cm in CLM4.0, based on moisture and temperatures and inclusion of oxygen
availability as a limitation factor as well as vertical mixing of soil
organic matter. These changes primarily reduce terrestrial gross primary
productivity (GPP) in tropical forests as a result of reduction in
photosynthesis. The change in nitrogen cycle described above also has an
effect on tropical C dynamics by removing N limitation, which makes the
biosphere more sensitive to the increased temperature and CO2
concentration and leads to a large net uptake of carbon. The overall effect
of the changes in CLM4.5 in the tropics is to reduce heterotrophic
respiration (HR), resulting in a decrease in methane production.
Fig. A1 shows that the HR is much lower in CLM4.5 compared to CLM4.0 in
the tropics. Comparisons of net primary production (NPP) and soil C between CLM4.5 and CLM4.0 are
presented in Figs. B1 and C1.
Contribution of individual sources to seasonal and interannual variability in atmospheric
CH4
In order to determine the relative contribution of each source to total
atmospheric CH4 variability as simulated in CAM-chem, we calculate
the seasonal and interannual root mean square (RMS) variability for the
total CH4 concentration and the relative contribution of the
anthropogenic source, rice paddies, and wetlands to the overall RMS (Figs. 8
and 9). These three sources have the largest contribution to the annual RMS
due to their large magnitudes.
Seasonal and interannual variability in CN_a methane
emissions
Seasonal variability of atmospheric methane concentrations is high in the
tropics and Southern Hemisphere and low in the northern high latitudes in the
CN_a simulations (Fig. 8). The low seasonal variability in the northern high
latitudes is consistent with the relatively low magnitude of northern high-latitude methane fluxes in the CN_a simulations, plus the fact that the
highest emissions occur during the summer, when the vertical mixing is
highest.
Interannual variability (IAV) in RMS is relatively homogeneous across the
globe with slightly higher IAV in the Southern Hemisphere (Fig. 8). Overall,
the IAV RMS of atmospheric methane is generally larger than the seasonal RMS.
Anthropogenic sources and wetlands are the dominant contributors to the
seasonal RMS variability in the Northern Hemisphere (Fig. 8), while wetlands
are the only dominant contributor to the IAV RMS variability. This is in
agreement with Bousquet et al. (2006), who reported that wetland emissions
dominate the interannual variability of methane sources. Rice paddies play a
more important role in seasonal RMS variability than in interannual RMS
variability over Asia and North America. This is consistent with the largest
seasonal variations in rice paddy emissions occur over Asia and North America
(Meng et al., 2012). Similar results are also found in CN_b simulations.
Seasonal and interannual variability in BGC methane
emissions
Compared to CN_a, the BGC methane emissions show higher seasonal and
interannual variability, particularly at high latitudes (Fig. 9). For
instance, Alaska and Siberia have the highest
variability. For both the seasonal and interannual variations, wetlands
dominate the variability, followed by anthropogenic sources. Rice paddies
only play a role in the tropics (0–30∘ N). Both wetlands and
anthropogenic methane emissions in BGC contribute a higher percentage to the
interannual variations than in the CN_a simulations.
Interhemispheric gradients in atmospheric CH4 concentrations
The latitudinal gradient from TransCom, CN_a, CN_b, BGC, and observations
is shown in Fig. 10. The latitudinal gradient is defined as the difference in
averaged CH4 concentration between the Northern and Southern hemispheres
(N–S gradients); stations listed in Table 2. The N–S gradients produced in
all four simulations are highly correlated with observations for the period
of 1993–2004 (Fig. 10). The correlations (r) are 0.83, 0.72, 0.76, and 0.91
(all four correlations are significant at 95 % confidence level) for
TransCom, CN_a, CN_b, and BGC, respectively. It is also clearly shown in
Fig. 10 that the TransCom and CN_a simulations underestimate the N–S
hemisphere gradients. The underestimation of N–S gradients in CN_a might be
due to the high tropical wetland emissions in this case as the high tropical
emissions are likely to increase the CH4 concentration in the Southern
Hemisphere. The BGC simulation significantly overestimates the N–S hemisphere
gradients, by about 70 %, consistent with the large high-latitude methane
emissions in this simulation and the low tropical emissions. The CN_b
simulation, with the same anthropogenic emissions as TransCom, but decreased
wetland methane emissions, compared with CN_a, best reproduces the observed
N–S gradient during the period of 1993–2004. The N–S gradients decrease
between 1993 and 2004 in TransCom, CN_a, and CN_b experiments, although
there is only slight decrease in the measurements (Fig. 10). Dlugokencky et
al. (2011) calculate the inter-polar difference (IPD) (difference
between the northern (53–90∘ N) and southern (53–90∘ S) annual mean
CH4 concentration) from the observations and find a slight decrease in
IPD from 1993 to 2010.
Evaluation of model interannual variability
Atmospheric CH4 growth rate as a function of latitude in
observations, TransCom, CN_a, CN_b, and BGC simulations. 90 %
confidence intervals are also shown.
Taylor diagram comparing model N–S gradients (no. 1), annual growth
rates (no. 2), and interannual variability (no. 3) with observations for the
South Pole, Mauna Loa, Niwot Ridge, and Alert (Canada) stations,
respectively for the four simulations. The four stations are selected to
represent the South Pole, tropics, mid-latitudes, and high latitudes. The
annual growth rate is calculated as the difference between the mean methane concentrations of the current year and
the previous year. The N–S gradients are from
Fig. 10.
Model simulation of the IAV of atmospheric CH4 concentrations is
evaluated against site observations over 14 stations (Table 1) around the
world. The climatological seasonal cycle is removed in order to focus on the
IAV. CN_a simulates the trend in methane from 1993 to approximately 2001 at
all of the stations, but tends to underestimate observations during the later
period (2001–2004) (Fig. 11). Such an underestimation might be due to the
large decrease in CN_a simulated wetland emissions (Fig. 5). The CN_b
simulation with decreased wetland emissions (CN_b in Fig. 11) shows
increased atmospheric methane concentrations during the later period
(2001–2004), allowing for a better match between observations and model
simulations. In the BGC simulation, model-simulated atmospheric
concentrations are relatively flat from 1998 to 2004, which match the
observations well (Fig. 11). In the TransCom experiment, simulated CH4
concentration anomalies are generally in good agreement with observations at
all of the stations.
The Taylor diagram of model–observation comparisons of interannual
variability show that TransCom performed the best among the three cases while
CN_b and BGC simulations performed slightly better than CN_a due to a
better correlation with the measurements (Fig. 12). The performance of BGC
simulations is comparable to (or slightly better than) TransCom in terms of
the correlation (Fig. 13) although the model tends to overpredict the
amplitude of the interannual variability. Decreasing wetland emissions
(CN_b) allows for a better match between model simulations and observations.
This suggests that CN_a simulations might overestimate wetland emissions,
which agrees with the findings in Kirschke et al. (2013).
Methane growth rate
The growth rate refers to the average increase in atmospheric CH4
concentration per year. We calculate the growth rate at each station from the
observations and for each simulation. The observed average growth rate ranges
from 3.2 to 4.6 ppb yr-1 with an average of 4.0 ppb yr-1
(Fig. 14, Table 3). The average growth rate in TransCom, CN_a, CN_b, and
BGC experiments is 4.2, 3.29, 4.05, and 5.68 ppb yr-1, respectively
(Table 3). The growth rate in TransCom, CN_a, CN_b, and BGC simulations has
a large range (from -0.48 to 6.44 ppb yr-1) at all stations
analyzed. As can be seen from Fig. 14, both CN_a and CN_b tend to
underestimate the observed growth rate in the Northern Hemisphere, but
overestimate it in the Southern Hemisphere (Fig. 14) except for the South
Pole station. BGC tends to overestimate the growth rate in the Northern
Hemisphere, particularly in the high latitudes (Fig. 14). TransCom gives
better agreement with the measured growth rates in the Southern Hemisphere
than in the Northern Hemisphere. The largest difference in the growth rate
between the three cases and the observations occur at the Zeppelinfjellet
(zep, Norway) where the average growth rate in TransCom, CN_a, CN_b, BGC,
and observations was -0.92, -0.48, 1.99, 4.2, and 3.4 ppb yr-1,
respectively (Table 3). The largest difference between BGC and observations
is at Barrow, where the growth rate in BGC experiment was approximately 2
times of that in observations. Overall, CN_a and CN_b underestimate the
station growth rate at high latitudes while BGC overestimates it.
Comparison of the interannual variability in wetland CH4
emissions used in this study and in others. A centered 12-month running mean
filter has been applied to smooth monthly output. Data for “LPJ variable
source area”, “LPJ”, and “update of Bousquet et al. (2006) (constant
OH)” are obtained from Spahni et al. (2011). “LPJ variable source area”
indicates emissions anomalies for 1993–2000 calculated by using the observed
monthly inundated area (Prigent et al., 2007). “LPJ” indicates global
CH4 emission anomalies simulated by LPJ (natural ecosystem and rice
agriculture) for scenario SC2 listed on Spahni et al. (2011). “update of
Bousquet et al., 2006 (constant OH)” refers to global wetland emission
anomalies derived from long-term atmospheric synthesis inversion updated from
Bousquet et al. (2006). TransCom refers to emission anomalies derived from
the combined wetland and rice paddy emissions. Methane emissions in Ringeval
et al. (2010) are estimated using the ORCHIDEE global vegetation model with a
process-based wetland CH4 emission model. The wetland area is prescribed
to the observed monthly inundated area (Prigent et al., 2007) in Ringeval et
al. (2010). The mean anomalies over 1993–2000 are adjusted to zero for the
all data plotted on this graph.
12-month smoothed average of the anomalies in wetland areal extent used in
the models that participate in WETCHIMP project (Melton et al., 2013; Wania et
al., 2013) and in this study (satellite-derived inundated area obtained from Prigent
et al. (2007) and Papa et al., 2010). LPJ–Bern_norice and DLEM_norice are
LPJ–Bern and DLEM models that do not include rice paddy simulations,
respectively. These notifications indicate WETCHIMP project also produce
simulations with rice and only no rice simulations are included in this
comparison study. In this figure, the long-term mean (1993–2004) is removed
from each data set.
Comparison of the mean growth rate (ppb yr-1) of atmospheric methane
concentration in each simulation with observations.
Station
Lat
Obs
TransCom
CN_a
CN_b
BGC
spo
-89.98
4.61
6.11
5.05
3.62
6.44
cgo
-40.68
4.64
5.96
4.86
5.67
6.35
smo
-14.24
4.44
5.34
4.21
5.09
5.87
asc
-7.92
4.23
5.72
4.57
5.55
6.29
kum
19.52
3.75
4.21
3.37
4.12
5.46
mlo
19.53
3.95
4.37
3.46
4.28
5.59
wlg
36.28
4.39
4.3
3.27
4.37
6.34
tap
36.72
3.2
3.72
2.92
3.89
4.88
nwr
40.05
4.16
4.4
3.49
4.27
5.56
mhd
53.33
3.92
2.78
1.98
2.98
4.52
cba
55.20
3.77
3.97
3.31
3.81
6.2
brw
71.32
3.27
3.42
2.92
3.35
6.42
zep
78.90
3.42
0.92
-0.48
1.99
4.2
alt
82.45
4.6
3.67
3.08
3.65
5.47
Average
4.03
4.21
3.29
4.05
5.69
Similar to Fig. 16, but showing the models that participate in WETCHIMP
(Melton et al., 2013; Wania et al., 2013). Each model uses a different wetland
extent to estimate methane emissions (see Table 1 in Melton et al. (2013) for
wetland determination scheme in each model). LPJ–WSL prescribes the wetland area
from a monthly inundation data set (Prigent et al., 2007, Papa et al., 2010).
DLEM_norice prescribes the maximum wetland area from the inundation data set
with simulated intra-annual dynamics. Sheffield Dynamic Global Vegetation Model (SDVGM) uses the internal hydrological
model to determine wetland locations. All other models parameterize wetland
areas based on an inundation data set or a land cover data set, which produce
different interannual and intra-annual variability in wetland areas. Please
also refer to Melton et al. (2013) for a detailed description of each model
(SDVGM, LPJ–WSL, ORCHIDEE, LPJ–Bern_norice, DLEM_norice, CLM4Me).
Temporal variation of the anomalies in globally averaged
heterotrophic respiration (HR) and net primary production (NPP) in CN_a and
BGC experiments. Blue dots indicate globally averaged NPP anomalies from
satellites obtained from Zhao and Running (2010). A
12-month smoothing is applied to monthly anomalies in HR and NPP.
In addition, a summary of the comparison of model N–S gradients, annual
growth rates, and interannual variability with observations is presented in
Fig. 15. Four stations are specifically selected to represent the South Pole,
tropical regions, mid-latitudes, and high northern latitudes. Root mean square
errors and biases for the four simulations at these four stations are listed
in Table 4. As seen in Fig. 15, and discussed above, no one model simulation
best matches all the observational metrics.
Model performance statistics including the root mean square error
(RMSE) and bias (ppb yr-1). Bias is calculated as the absolute deviation of the
mean between model simulations and observations.
N–S gradients
Growth rate
Interannual variability
RMSE
South Pole
Mauna Loa
Niwot Ridge
Alert
South Pole
Mauna Loa
Niwot Ridge
Alert
TransCom
25.92
5.41
3.55
2.44
11.65
10.47
8.28
7.35
8.69
CN_a
17.57
0.57
4.22
2.88
5.13
12.65
13.96
14.59
15.06
CN_b
5.39
0.59
5.74
4.19
6.06
11.32
12.1
11.88
12.03
BGC
69.62
0.67
7.78
6.81
30.88
8.88
13.97
10.89
14.18
Bias
South Pole
Mauna Loa
Niwot Ridge
Alert
South Pole
Mauna Loa
Niwot Ridge
Alert
TransCom
0.26
0.58
0.3
0.22
-1.69
0.08
0.01
0.02
0.01
CN_a
0.17
0.03
0.05
0.29
-1.05
0.02
0.02
0.03
0.04
CN_b
0.02
0.07
0.06
0.34
-0.72
0.03
0.02
0.02
0.03
BGC
0.71
0.06
1.36
1.29
-3.13
0.01
0.02
0.02
0.04
Comparison of interannual variability between this study and
others
We also compare the interannual variability in CH4 emission anomalies
in the simulations analyzed here with those given in Spahni et al. (2011), in
an updated long-term atmospheric synthesis inversion from Bousquet et
al. (2006) and from Ringeval et al. (2010). (Fig. 16). As discussed above,
the CN_a emissions reach their maximum in 1994 and decrease thereafter from
1994 to 2004 (Fig. 16). The BGC emissions have the highest emissions in 1998,
the lowest emissions in 1999, and increased emissions from
1999 to 2004. The TransCom emissions increase from 1993 to 1998 and slightly
decrease thereafter. The wetland emissions in Ringeval et al. (2010) decrease
from 1993 to 2000. The Ringeval et al. (2010) averaged annual wetland methane
emissions are ∼ 215 Tg yr-1, similar to the CN_a wetland
emissions. However, the atmospheric synthesis inversions of global wetland
emissions (update of Bousquet et al., 2006 (constant OH)) increase
from 1990 to 2000 followed by a decrease from 2000 to 2005. Thus, there seems
to be little agreement in the interannual variability of the wetland methane
emissions between these various simulations.
We further compare our model-derived wetland emissions with those from the
Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) (Melton et al.,
2013; Wania et al., 2013). We conduct two different comparisons: one
includes all models with their different parameterizations of
wetland extent while the other focuses on models that are driven by satellite-derived
inundation data sets (see Table 1, Melton et al., 2013).
Each model analyzed in Melton et al. (2013) uses a different wetland
parameterization to estimate their wetland extent (see Table 1 in Melton et
al. (2013) for details). Therefore, it is not surprising to see the large
variation in wetland extent among these models from 1993 to 2004
(Fig. 17). Among the models analyzed in Melton et al. (2013), only the
LPJ–WSL model uses prescribed monthly inundation data sets, similar to our
simulations (Fig. 17). DLEM_norice prescribes maximum extent at each
grid cell from satellite-derived inundation data sets but with simulated intra-annual
dynamics. All simulations making use of the satellite measurements (this
study, LPJ–WSL, BGC, and DLEM_norice) show a decrease in wetland extent from
1993 to 2004. The wetland extent anomalies in DLEM_norice simulations differ
as the intra-annual dynamics are simulated (Fig. 17).
The models that do not use a prescribed satellite-derived inundation data set do not
simulate notable decreases in wetland extent during the period of 1993–2004
(Fig. 17). This is not in agreement with the satellite-derived inundation data set:
Papa et al. (2010) find ∼ 5.7 % decrease in mean annual maximum
inundation from 1993 to 2004 with a maximum decrease in the tropics (see
Appendix). In fact, all models (excluding LPJ–WSL, DLEM_norice, and this
study) show large increases in wetland extent in 1998 compared to 1997, which is also documented in Melton et al. (2013).
Melton et al. (2013) demonstrate that the difference in wetland area used in
different models might partially explain the discrepancy in model-estimated
wetland emissions. As shown in Melton et al. (2013), model-derived methane
emissions are strongly correlated with the wetland extent (with an average
correlation of 0.90 on the global scale). All models that produce a peak
methane emission in 1998 have a maximum wetland extent at the same time
(Fig. 18). The difference in model-derived methane emissions can be attributed
partially to the different wetland area used in each model, where the wetland
extent is highly uncertain (Melton et al., 2013). However, it should be noted
that there are several limitations associated with using wetland extent
derived from satellite-derived inundation data sets. As suggested in Prigent et
al. (2007), satellites might underestimate inundated areas due to their
incapability to detect small water bodies. Furthermore, satellite data sets only
include fully inundated areas and excluded unsaturated wet mineral soils,
which might also be an important wetland methane source (Spahni et al., 2011).
The methane emission anomalies in DLEM_norice and LPJ_WSL (the simulations
in Melton et al. (2013) using satellite-derived wetland extent) show similar temporal
variations, as do the methane anomalies in CN_a and CN_b simulations
(Fig. 18). Emissions estimated in CN_a, CN_b, DLEM_norice, and LPJ_WSL
peak in 1993–1994 and showed a decrease afterwards. Such a decreasing trend
is consistent with the decrease in wetland extent used in these models
(Fig. 17). The CN_a and CN_b simulations show a large increase in emissions
from 1993 to 1994, but do not simulate large increases in methane emissions
from 2001 to 2002 even though wetland extent increases during this period. It
should be noted that the BGC simulation uses the same wetland extent as CN_a
and CN_b but does not give the same large decrease in the emissions during
the period of 1993–2004. In fact, the BGC model gives decreasing emissions
from 1993 to 1994 but increasing methane emissions from 2001 to 2002. The BGC
model also shows that the highest emission occurs in 1998 and the lowest in
1999 during the period of 1993–2004.
Both the large increase in the methane emissions from 1993 to 1994 in CN_a
and CN_b and the small increase from 2001 to 2002 are likely due to the
changes in heterotrophic respiration (HR) in CN_a and CN_b simulations (Fig. 19). Please note that the methane production in the models
is a function of HR (see the methane production equation in Sect. 2.1).
Please see Meng et al. (2012) and Riley et al. (2011) for a detailed
description of the processes of methane production, oxidation, and
transport). HR increases dramatically in CN_a from 1993 to 1994 (Fig. 19)
driving higher methane emissions, whereas the decrease in HR from 2001 to
2002 decreases wetland methane production, which might offset the increase in
methane emissions from wetland extent. The HR in the BGC simulation is rather
different, consistent with the different behavior between CN_a and CN_b and
the BGC simulations. Zhao et al. (2005) estimate the net primary production
(NPP) from satellites and find that NPP in 2001 is higher than those in 2002
and 2003. Please note that NPP is related to HR. The correlation of global
wetland emissions with HR and wetland extent is 0.81 and 0.94, respectively,
in CN_a simulations. In BGC simulations, simulated global wetland emissions
are also highly correlated with HR (0.89) and with wetland extent (0.81).
Such high correlations suggest that both HR and wetland extent are dominate
drivers of wetland methane emissions in the CN_a and BGC models. Thus,
although the BGC experiment uses the same satellite-derived inundated area in
CN_a, it does not produce a decreasing trend in methane emissions during the
period, probably due to its different trend in HR estimated in BGC as
compared with that in CN_a (Fig. 19).
Conclusions
In this study, we evaluate the temporal and spatial patterns in wetland
methane emissions simulated in CLM4Me′ from two different
parameterizations of soil carbon–nitrogen dynamics as included in CLM4.0
(CN_a and CN_b) and CLM4.5 (BGC). The subsequent methane distributions are
simulated in CAM-chem using meteorological drivers consistent with those used
to drive the CN and BGC models. Our goals for this study are (i) to
evaluate the wetland methane fluxes simulated in the two versions of CLM
so as determine the sensitivity of methane emissions to the underlying
carbon model; (ii) to compare the simulated atmospheric methane
concentrations to atmospheric measurements, including latitudinal gradients
and interannual variability, so as to determine the extent to which the
atmospheric observations constrain the emissions; (iii) to understand the
drivers of seasonal and interannual variability in atmospheric methane
fluxes.
Even though they are driven by identical meteorological forcing and satellite-derived
wetland area, there are significant differences in the interannual and spatial
variations between the CN and BGC wetland methane emissions as derived by
CLM4Me′. This demonstrates the critical sensitivity in the simulation
of wetland emissions to the underlying model. Compared to the CN_a
simulations, the BGC simulations produce large emissions
(∼ 97 Tg yr-1 on average) in the northern high latitudes
(50–90∘ N) with very strong seasonal variations (from no emissions
in NH winter to more than 300 Tg yr-1 in NH summer) and relatively
small wetland emissions (only ∼ 30 % of global emissions)
in the tropical region. On the other hand, the CN_a simulation has very large
tropical emissions (∼ 70 % of global wetland emissions) so that
changes in the tropics dominate global emissions.
The large difference in their high-latitude emissions can be ascribed to the
different simulation of nitrogen dynamics in the CN and BGC simulations. In
CLM4.0, available mineral N experiences a first-order decay with a time
constant of 2 days, which is not subject to environmental limitations, while
in the BGC simulation the an introduction of the dependence of N losses on
temperature and soil moisture and the seasonality of N fixation reduce the
unrealistic N loss in CLM4.0. The larger nitrogen availability in the BGC
model at high latitudes allows greater carbon pools to develop, thus
increasing the heterotrophic respiration at high latitudes. The large
difference in tropical wetland emissions between the BGC and CN_a
experiments is possibly due to the changes in decomposition rates, carbon
vertical mixing, and the release of nitrogen limitation from the CN_a to the
BGC model (Koven et al., 2013). Overall, these changes reduce NPP and HR in
the tropics, which directly impacts the methane fluxes.
Both the CN and BGC simulations also differ in the relative magnitude of
seasonal vs. interannual variability (IAV) of atmospheric methane
concentrations for the period of 1993–2004. IAV is relatively higher
(average total RMS is approximately 20 ppb) than the seasonal variability
(approximately 10 ppb) across the globe in CN_a (Fig. 8), while in BGC, IAV
is much higher (∼ 25 ppb) than the seasonal variability (∼ 10
ppb) except for the northern latitudes (> 50∘ N)
(Fig. 9). Anthropogenic sources and wetlands contribute significantly to
seasonal variations of atmospheric methane concentrations in CN_a and BGC.
Wetland emissions dominate global interannual
variability when CAM-chem is forced with either the CN_a or BGC methane
emissions, in agreement with findings in Bousquet et al. (2006).
There are also substantial differences in the interannual variability between
the two model versions. CN_a wetland emissions suggest a decreasing trend
from 1994 to 2004, which is similar to those estimates from Ringeval et
al. (2010) and DLEM_norice and LPJ–WSL models (Melton et al., 2013; Wania et
al., 2013). On the other hand, CLM4Me′ methane emissions driven by CLM4.5
(the BGC simulation) are highest in 1999 and do not show significant
decrease during the period. The updated estimate from Bousquet et al. (2006)
gives increasing emissions from 1991 to 2000 and a decrease after 2000. A few
participating wetland emission models in the WETCHIMP project also predict
peak methane emissions in the middle of the period (around 1998–1999).
The methane emissions in all simulations conducted here are input into
CAM-chem so as to constrain the resulting atmospheric methane concentrations
against atmospheric measurements. The meteorological fields driving
atmospheric and land models are consistent. In particular, we compare the
simulations against measured the interhemispheric gradient, interannual
variability, and growth rate. Our results show that CN_b simulations
(with reduced CN_a wetland emissions) are able to better produce observed
atmospheric methane concentrations and the observed N–S gradient in methane
concentrations, suggesting that CN_a might overestimate the current wetland
emissions. In the BGC experiment, modeled atmospheric interannual variability
in concentrations has higher correlations with observations than the CN_a and
CN_b simulations in the majority of stations (Fig. 13). In the TransCom
experiment, the magnitude of the correlation between modeled atmospheric
concentrations and observations is similar to that of the BGC experiment. We
also find that CN_b experiments tend to underestimate the growth rate and
BGC overestimates it at high latitudes. TransCom simulations have an overall
better estimation of the growth rate at all stations than the other three
simulations. In terms of the N–S gradients, CN_b experiments have the
closest match with observations among all experiments. BGC overestimated the
N–S gradients by ∼ 70 % while CN_a and TransCom underestimate it
by ∼ 10 and ∼ 20 %, respectively. Note that BGC predicts much
higher methane emissions from the high latitudes
(> 50∘ N) than CN_a and CN_b experiments. These
simulations generally suggest that the BGC high-latitude fluxes
(∼ 97 Tg yr-1) are unlikely due to its overestimation of the
N–S gradients by ∼ 70 %. The high-latitude methane emissions
should be somewhere in the broad range between those used in CN_b
(∼ 7.7 Tg yr-1) and BGC (∼ 97 Tg yr-1). In general,
however, no one model simulation best matches all the observational metrics.
This study confirms that the large variation in methane emissions exists and
wetland methane emissions play an important role in affecting atmospheric
methane concentrations (Bousquet et al., 2006).
The comparison of the IAV demonstrates large disagreement between different
estimates of the annual wetland emissions. Such a discrepancy in the
variability produced in different models suggests that wetland extent plays
an important role in controlling wetland emissions. We find large
uncertainties exist in wetland extent and wetland methane emissions, in
support of the conclusions of Melton et al. (2013). For instance, all
models (excluding DLEM_norice and LPJ–WSL) that estimate a peak methane
emission in 1998 also produce a peak wetland extent during the same period of
1993–2004. Such a decrease after 2003 is consistent with a decrease in the
tropical inundated area, based on satellite observations.
In addition to wetland extent, the model-simulated carbon pool also has a
significant impact on methane emissions (Riley et al., 2011; Bloom et al.,
2012). Both CN_a and BGC methane simulations are forced with the same
satellite-derived inundated fraction, they produce large differences in both spatial
and temporal variations of methane emissions due to the fact that CN_a and
BGC use different carbon cycle models. Although satellite-derived inundated area
increased from 2001 to 2002, CN_a estimates small increases in methane
emissions from 2001 to 2002 due to decreases in HR. BGC produces different
methane emissions in terms of spatial and temporal trends, probably due to
the shift of carbon uptake and release from the tropics to the northern high
latitudes as a result of multi-level biogeochemistry in BGC (Koven et al.,
2013).
This study suggests that the model-estimated methane budget is sensitive not
only to wetland extent (Melton et al., 2013), but also to the details of the
carbon model from which methane fluxes are estimated. Accurate simulations
of both are necessary to simulate the interannual variation in wetland
methane emissions. Further research should focus on regional wetlands (such
as high-latitude and tropical wetlands) in order to have a better estimate
of the wetland methane budget and its spatial variation.