Introduction
CH4 fluxes from wetlands play a critical role in global climate
change. CH4 is the second most important long-lived greenhouse gas,
and the radiative forcing of the current atmospheric burden is approximately
26 % of carbon dioxide . Wetlands are possibly the
largest single source of this gas to the atmosphere and account for roughly
30 % of global emissions .
Despite the important role of wetland CH4 fluxes in climate change,
existing estimates of this source differ on the magnitude, seasonality, and
spatial distribution of fluxes, from regional to global scales. In fact,
a recent global model comparison project named WETCHIMP (Wetland and Wetland
CH4 Intercomparison of Models Project) found large differences among
existing CH4 wetland models
Fig. ,. For example,
existing estimates of maximum global wetland coverage differ by about
a factor of 6 – from 4.1×106 to 26.9×106 km2.
Furthermore, estimates of global natural wetland fluxes range from
92 to 264 Tg CH4 yr-1. The relative magnitude of
these uncertainties increases at sub-global spatial scales; CH4 estimates for Canada's Hudson Bay Lowlands (HBL) range
from 0.2 to 11.3 Tg CH4 yr-1. These disagreements
in current CH4 estimates do not bode well for scientists' abilities
to accurately predict future changes in wetland fluxes due to climate change
e.g.,.
Mean of the annual methane fluxes estimated by the WETCHIMP models
(a) and the range of fluxes estimated by the ensemble (b).
Note that the range in estimates is larger than the mean. The fluxes shown
above are the average flux per m2 of land area, not per m2 of wetland
area.
The US and Canadian atmospheric methane observation network for 2007–2008
(14 703 total observations). Small yellow dots indicate observations from
the START08 measurement campaign . Larger dots indicate tower
and aircraft sites with regular observations over the 2-year period
. The grey background delineates the four regions used in
the synthetic data experiments (Sect. ).
A number of studies have used chamber measurements of CH4 to
parameterize or evaluate biogeochemical CH4 models
e.g.,. These measurements usually encompass fluxes
from a relatively small area, and fluxes can often vary greatly with
landscape heterogeneity at these spatial scales
. CH4 data collected in the
atmosphere observe the cumulative effect of CH4 fluxes across a broader
region e.g.,.
Hence, atmospheric data can provide a unique tool for evaluating existing
CH4 flux estimates across different countries or continents.
The present study compares the WETCHIMP CH4 flux estimates against
atmospheric CH4 data and inverse modeling results from 2007–2008
through two sets of analyses. First, we construct a set of synthetic data
experiments to understand whether the atmospheric CH4 observation
network can detect CH4 fluxes from wetlands. We also explore the
factors that might prevent the network from detecting wetland fluxes. To
answer these questions, we utilize a model selection procedure based upon the
Bayesian information criterion (BIC; Sect. , ). This procedure
determines whether wetland fluxes from different regions and seasons are
necessary to describe variability in synthetic atmospheric CH4
observations. Based on these synthetic experiments, we conduct a second set
of analyses using real atmospheric data and inverse modeling results. We use
these data to analyze the magnitude, seasonal cycle, and spatial distribution
of each WETCHIMP CH4 estimate. We investigate these questions over
the US and Canada, using CH4 data collected from towers and regular
aircraft flights operated by NOAA and its partners and from towers operated
by Environment Canada.
Methods
This section first describes the atmospheric CH4 data and the
atmospheric model that allows direct comparison between the data and various
flux estimates. Subsequent sections describe both the synthetic and real data
experiments outlined in the introduction (Sect. ).
Data and atmospheric model
The present study utilizes atmospheric CH4 observations from aircraft
and tower platforms across the US and Canada, a total of 14 703 observations
from 2007–2008. These observation sites include 4 towers operated by
Environment Canada and 10 towers in the US operated by NOAA and its partners.
Observations at the NOAA towers consist of daily (occasionally
weekly) flasks, and observations at the Environment Canada sites are continuous
measurements. As in , we use afternoon averages of these
continuous data. In addition to these towers, we utilize observations from 17
regular NOAA aircraft monitoring locations in the US and Canada
(Fig. ). We incorporate aircraft data up to 2500 m altitude as
was done in . Observations above that height are usually
representative of the free troposphere with limited sensitivity to surface
fluxes. These observations and the associated model runs (described below)
are the same as those used in and .
We then employ an atmospheric transport model to relate CH4 fluxes at
the Earth's surface to atmospheric concentrations at the observation sites.
The modeling approach here combines the Weather Research and Forecasting
(WRF) meteorological model and a particle-following model known as STILT, the
Stochastic Time-Inverted Lagrangian Transport model
e.g.,. WRF-STILT generates a set
of footprints; these footprints quantitatively estimate the sensitivity of
each observation to fluxes at each surface location (with units of ppb per
unit surface flux). We multiply the footprints by a flux model and add this
product to an estimate of the “background” concentration – the CH4
concentration of air entering the North American regional domain. We estimate
this background concentration using CH4 observations collected near
or over the Pacific Ocean and in the high Arctic, a setup described in detail
by and . The resulting modeled
concentrations can be compared directly against atmospheric CH4
observations. The observations, WRF-STILT runs, background concentrations,
and uncertainties in the modeling framework are described in greater detail
in the Supplement, , and .
We can then estimate atmospheric concentrations using fluxes from the
WETCHIMP project (Fig. ) and compare those estimates
against atmospheric observations. The WETCHIMP project was designed to
compare simulated wetland distributions and modeled CH4 fluxes at
multi-year, continental scales . The project
entailed several sets of model runs, but primarily
reported on one set of runs – runs for 1901–2009 that used the same
observed climate and CO2 concentration data sets to force all models. Each
CH4 model utilized its own parameterization for wetland area and
distribution. We use the outputs from this set of model runs in the present
study. Of the WETCHIMP models, seven provide a flux estimate on a suitable
time step for boreal North America and six provide an estimate for temperate
North America. These models include CLM4Me , DLEM
, LPJ-Bern , LPJ-WHyMe ,
LPJ-WSL , ORCHIDEE , and SDGVM
. All flux model outputs used from the WETCHIMP study
have a temporal resolution of 1 month, and we regrid all outputs to a
spatial resolution of 1∘ lat. by 1∘ long. (the resolution of the
WRF-STILT footprints). These models are described in
, , and the Supplement.
Synthetic data experiments
We assess the ability of the CH4 observation network to detect
wetland fluxes using a model selection framework adapted from the BIC. A
model selection framework can sort through a large number of potential
explanatory variables and will choose the smallest set of variables that best
describe the data set of interest e.g.,. In the current
setup, we generate synthetic atmospheric CH4 observations. The model
selection framework then indicates whether a wetland model and/or an
anthropogenic emissions inventory are necessary to describe variability in
these observations. In this way, model selection can indicate the sensitivity
of the observation network to wetland CH4 fluxes.
We use a form of the BIC that has been adapted for use within
a geostatistical inverse modeling framework. This setup has previously been
used to select either bottom-up models or environmental drivers of
CO2 and CH4 fluxes
e.g.,.
The implementation here mirrors that of , ,
and :
BIC=ln|Ψ|+(z-HXβ)TΨ-1(z-HXβ)︸negative log-likelihood+pln(n)︸penalty term.
The first two terms in Eq. () are the negative log-likelihood,
a measure of how well the model fits the data. The last term penalizes
a particular model based upon the number of explanatory variables (p). The
best combination or candidate model has the lowest BIC score.
The variable z (n×1) represents the observations minus
background concentrations and H (n×m) the footprints
(where m refers to the total number of flux or emissions grid boxes in both
space and time). These variables are based upon two existing inverse modeling
studies by (refer to the
Supplement). The matrix X (m×p) contains p explanatory
variables. In the current setup, X can include a wetland flux
estimate and/or individual emissions sources from an anthropogenic inventory.
β (p×1) is a set of coefficients that scale the
variables in X. We set these coefficients to 1 in the
synthetic data experiments. As a result, the model selection framework cannot
reproduce wetland fluxes by simply upscaling anthropogenic emissions sources
that might have a similar distribution to wetlands. Lastly, Ψ (n×n) is a covariance matrix derived from an atmospheric inversion
framework. This covariance matrix represents errors in atmospheric transport
and in the measurements – collectively referred to as model–data mismatch.
This matrix also represents uncertainties in the prior flux estimate. In a
geostatistical inverse model, this prior flux model is given by Xβ (refer to the Supplement for more detail).
The first experiments described here use synthetic atmospheric CH4
data. We generate the synthetic data using one of the WETCHIMP models and the
anthropogenic emissions estimates from . We then multiply these fluxes by the footprints
(H) and add error that is randomly generated from the covariance
matrix (Ψ).
Before generating the synthetic data, we scale the annual HBL CH4
budget in each WETCHIMP model to match the overall magnitude estimated by
several top-down studies . If we did
not downscale the magnitude of the WETCHIMP models, the wetland fluxes would
be a much larger source relative to anthropogenic emissions and
modeling and/or measurement errors. The synthetic data experiments would identify
wetlands too easily, would understate the relative role of model and/or measurement
errors, and would not be representative of the atmospheric methane
observations.
We divide the WETCHIMP wetland fluxes into four regions (Fig. )
and four seasons (DJF, MAM, JJA, and SON). The model selection framework then
chooses variables that are necessary to reproduce the synthetic data,
variables that include EDGAR and the 16 wetland flux maps. The penalty term
in Eq. (1) increases as we add wetland flux maps or add EDGAR to the X
matrix. Each variable added to X will increase the penalty term by
ln(n); an additional variable must improve the log-likelihood by more than
this penalty term to be chosen by model selection.
We then run this framework 1000 times, generating new synthetic data each
time, and calculate the percentage of all trials in which the model selection
chooses a wetland model. The 1000 repeats are needed due to the random or
stochastic nature of the synthetic data experiment; the results of the model
selection can vary slightly, depending on the particular random errors that
we generate based upon the covariance matrix (Ψ). This procedure
ensures that the model selection results are not the output of a single
realization. We then report on how frequently each of the 16 wetland flux
maps is chosen by the BIC-based model selection. If a wetland flux map is
chosen with high frequency, then a wetland flux map is necessary to describe
variability in the synthetic CH4 observations, and the synthetic
observation network can detect aggregate wetland CH4 fluxes from the
given region and season. This setup mirrors that of , who
employed a model selection framework to explore the detectability of
anthropogenic CO2 emissions.
We also explore why the synthetic CH4 observations may not be able to
detect wetland fluxes. We run a series of case studies and in each case
remove a different confounding factor that might prevent the network from
detecting wetland CH4 fluxes. In one case, we remove anthropogenic
emissions. In subsequent cases, we remove model–data mismatch errors and/or
prior flux errors. In each case, we rerun the model selection experiment and
examine whether the results improve when each of these confounding factors is
removed.
Real data experiments
This paper subsequently compares the spatial distribution, magnitude, and
seasonality of each WETCHIMP estimate against real atmospheric CH4
observations, using the synthetic experiments to guide the analysis.
We first explore the spatial distribution of the WETCHIMP flux estimates. We
modify the model selection setup in Sect. to focus on the
spatial distribution of each estimate using a procedure developed by
and . Instead of fixing the coefficients
(β) to 1, we instead estimate the coefficients using real
atmospheric CH4 observations. We also include an intercept term that
can vary by month; the intercept for each month is represented by a vector of
ones in the matrix X, and this intercept is included as part of
each candidate model for X. We then run model selection using real
observations. As a result of this setup, a wetland model is not necessary to
reproduce either the magnitude or seasonality of the atmospheric CH4
data; the model selection framework can simply scale the intercept term or
scale EDGAR to reproduce the magnitude or seasonality of the observations.
The spatial distribution of wetland fluxes, however, can only come from a
wetland model. The model selection procedure will only choose a wetland model
if the spatial distribution of that model describes sufficient additional
variability in the observations e.g.,.
Model selection can therefore indicate which WETCHIMP models have the best
spatial distribution relative to the atmospheric observations; any WETCHIMP
model chosen by model selection has a spatial distribution that improves
model–data fit, and the model improves that fit more than the penalty term in
Eq. (). A negative result does not necessarily indicate that a
WETCHIMP model has a poor spatial distribution. In that case, the
observations may not be very sensitive to the spatial distribution of fluxes
for the given region or given season. Similarly, the spatial distribution in
a WETCHIMP model may improve model–data fit but not by more than the penalty
term in Eq. (). By contrast, a positive result indicates that a
WETCHIMP model likely has a particularly good spatial distribution. As in
Sect. , we divide the wetland fluxes into four
sub-continental regions and four seasons. The Supplement describes this setup
in greater detail.
We then analyze the magnitude and seasonality of the WETCHIMP fluxes using
a number of model–data time series. We model CH4 concentrations at
a number of US and Canadian observation sites using the WRF-STILT model, the
WETCHIMP estimates, and the EDGAR v4.2FT2010 emissions inventory . We average the observations and model output at the monthly scale and
then compare the magnitude of these model estimates for each month against
the averaged observations.
We further compare the seasonality of existing bottom-up models against the
seasonality of a recent inverse modeling estimate by . We
plot the monthly budgets for both the bottom-up models and the inversion
estimate, and we plot the monthly CH4 budget as a fraction of the
annual total.
Note that inter-annual variability in existing CH4 flux models is
small relative to the differences among these models; as a result,
conclusions from the 2-year study period (2007–2008) likely hold for
other years. For example, the inter-annual variability in the total
US and Canadian budget is ±7.3–9.7% (standard deviation),
depending upon the model in question (note that LPJ-Bern has even larger
inter-annual variation due to an issue with model spinup described in
).
Results and discussion: synthetic experiments
The synthetic experiments presented here explore the limits of existing
atmospheric data for constraining wetland fluxes. If atmospheric observations
are to constrain wetland CH4 fluxes, those observations must be able
to detect wetland CH4 fluxes above errors in the transport model and
above other emissions sources such as fossil fuels and agriculture.
The four columns in Fig. a display the results from an
individual season in each of four geographic regions. In this experiment, the
synthetic CH4 observations can detect aggregate wetland CH4
fluxes from eastern Canadian wetlands in greater than 75 % of all
trials for the summer and fall seasons. In the eastern US, the model
selection framework chooses a wetland model in 25–50 % of all
trials in multiple seasons. By contrast, the synthetic CH4
data are least sensitive to wetland fluxes in the western US, and the model
selection framework chooses wetland fluxes from that region in fewer than
25 % of all trials irrespective of the season. That result may be
due, in part, to the scant wetlands and sparse atmospheric observations in
much of the west.
This figure displays the results of the synthetic data experiments. These
experiments examine whether the observation network can detect aggregate
wetland CH4 fluxes. The figure shows the percentage of trials that
are successful. Darker shades indicate that the network is more sensitive to
wetland fluxes in the given region and season. Panel (a) shows the
results for the standard setup while panels (b–e) show the results
of several test cases in which anthropogenic emissions or different errors
are set to zero.
The results also vary by season. Of any region, the atmospheric CH4
network is best able to constrain fluxes across multiple seasons in eastern
Canada. The largest wetland fluxes in the WETCHIMP models are in Ontario and
Quebec (Fig. ). It is therefore unsurprising that the
network is best able to detect wetland fluxes in that region, even though
there are relatively few observation sites in the area. In other regions, the
atmospheric CH4 network is less sensitive to wetlands during the
winter, fall, and spring shoulder seasons.
We run several additional model selection experiments to explore why the
synthetic observations may not always be able to detect wetland CH4
fluxes (Fig. b–e). We remove anthropogenic emissions from the
synthetic data set for the experiment in Fig. b. We remove all
model–data mismatch errors in Fig. c; model–data mismatch
encompasses errors in atmospheric transport and in the measurements.
Subsequently, we remove all errors due to the prior flux estimate in Fig. d. In Fig. e, we remove both types of errors. In
each case, we rerun the model selection experiment to see if the sensitivity
of the atmospheric CH4 network to wetland fluxes improves.
Anthropogenic emissions have only a modest effect on the results in specific
regions and seasons. In experiment b (Fig. b) without anthropogenic emissions, the results
improve by ∼ 25–50 % in the fall and spring shoulder seasons for several
geographic regions.
By contrast, the model–data mismatch and prior flux errors have a much larger
effect on the model selection results. The results improve incrementally
across many regions and seasons when we remove model–data mismatch errors in
experiment c. The results improve across the spring, summer, and fall seasons and
improve across all four geographic regions. However, the magnitude of this
improvement is never more than 25 %. Model–data mismatch errors are likely
dominated by errors in modeled atmospheric transport. These results imply
that transport errors play an incremental yet pervasive role in the utility
of the atmospheric observations.
The prior flux errors have the largest effect on the results, particularly
during the warmest seasons. In experiment d, the results show great improvement
during fall, spring, and summer and show little improvement during winter or
in the western US. In the setup here, the prior flux uncertainties scale with
the seasonal magnitude of the fluxes. When we remove the prior flux errors,
the results concomitantly show the greatest improvement in seasons that have
larger overall CH4 fluxes. These results indicate that the prior
estimate greatly impacts the utility of the atmospheric CH4
observations. A geostatistical inverse model can leverage any combination of
land surface maps, meteorological maps, and/or anthropogenic inventory
estimates in the inversion prior. These maps or estimates are incorporated
into the X matrix in Eq. (). If accurate maps or
estimates are not available, then the prior flux errors will be large, and
the model selection framework will be less likely to choose any particular
variable. If these maps or estimates have high explanatory power, then the
prior flux errors will be small, and the model selection framework will be
more likely to choose any one variable. As a result, the detectability of
wetland CH4 fluxes partly depends on the availability of land surface
or meteorological data that match those fluxes. The atmospheric network can
differentiate wetland CH4 fluxes from other CH4
sources better when accurate prior information can guide that identification.
Experiment e (no model–data mismatch errors and no errors in the prior flux
estimate) shows large, ubiquitous improvements; the model selection chooses a
wetland model 100 % of the time in almost all regions and seasons. The
results for eastern Canada during winter are the exception. In winter, the
wetland model cannot always explain enough variability in the synthetic
observations to overcome the BIC penalty term in Eq. ().
The density of the atmospheric CH4 network may also play a role in
these results. Wetlands in the eastern US are sparse relative to eastern
Canada, but the higher density of observations in the eastern US may
contribute to a moderate success rate (25–50 %) for that region.
Recent and planned network expansions in the eastern US and in Canada could
play a key role in future efforts to constrain wetland fluxes across these
regions.
Overall, the synthetic experiment results indicate that the observation
network cannot detect wetland fluxes from the US (i.e., model selection has a
success rate < 50 %). Across Canada, the results are more promising (i.e.,
near 100 % success rate in some regions and/or seasons), despite the relative
sparsity of the observation network there.
Results and discussion: comparisons with atmospheric data and inverse modeling results
Spatial distribution of the fluxes
We compare the spatial distribution of the WETCHIMP flux estimates against
CH4 data from the atmospheric observation network. To this end, we
use a version of the model selection framework that chooses wetland models
based upon their spatial distribution . WETCHIMP
models that are chosen by the framework have a spatial distribution that is
more consistent with atmospheric observations relative to those that are not
selected.
The results of this model selection analysis are displayed in
Table . This table lists the regions and seasons that had
a success rate >50 % in the synthetic data experiment; the
atmospheric CH4 network is most sensitive to wetland CH4
fluxes in those regions and seasons. Two of the WETCHIMP models were chosen
by the model selection framework – LPJ-Bern (in eastern Canada) and SDGVM
(in eastern and western Canada). The spatial distribution of these models
improve the model–data fit more than the penalty term in Eq. ().
Spatial flux patterns chosen by the model selection framework.
Region
Season
Chosen models
E. Canada
summer
LPJ-Bern, SDGVM
E. Canada
fall
LPJ-Bern
W. Canada
summer
SDGVM
The LPJ-Bern and SDGVM models have several unique spatial characteristics
that could explain these results. Over eastern Canada, LPJ-Bern and SDGVM
concentrate the large fluxes in the HBL. Other models, by contrast, often
distribute the fluxes more broadly across Ontario and Quebec or put the
largest fluxes in Ontario outside of the HBL. In western Canada, SDGVM
distributes fluxes across northern, boreal Saskatchewan and Alberta.
The LPJ-Bern and SDGVM models share another common characteristic: both model
wetland area independently instead of relying solely on remote sensing
inundation data sets. LPJ-WSL, ORCHIDEE, DLEM, and CLM4Me use remote sensing
inundation data sets like GIEMS Global Inundation Extent from
Multi-Satellites, to construct a wetland map. Other models,
like LPJ-Bern and LPJ-WHyMe also use land cover maps and/or land surveys to
estimate wetland (or at least CH4-producing) area. SDGVM estimates
this area dynamically as a function of soil moisture
. Wetland maps generated using these different
approaches show substantial differences. Remote sensing data sets estimate
relatively high levels of inundation in regions of Canada that are not
forested or have many small lakes see further discussion
in. By contrast, modeling approaches that dynamically
generate wetland area or use land cover maps assign more wetlands over
regions with high water tables but little surface water as seen by remote
sensing based inundation data sets. As a result of these differences, models
like LPJ-Bern assign more wetlands and CH4 fluxes in the HBL relative
to other regions of eastern Canada.
Of note, LPJ-Bern and LPJ-WhyMe have many structural model similarities but
predict relatively different spatial distributions of CH4 fluxes. The
latter estimates fluxes that are more broadly distributed across Quebec and
Labrador. LPJ-WhyMe only simulates fluxes from high latitude peatlands and
uses an estimated peatland distribution from ; this
distribution extends across Quebec and Labrador. LPJ-Bern, by contrast,
includes fluxes from non-peatland regions and applies a smaller scaling
factor to peatland fluxes relative to LPJ-WHyMe . As a
result, the fluxes in LPJ-Bern have a spatial distribution that is different
from the peatland map and also different from LPJ-WHyMe.
Flux magnitude
Next, we compare the magnitude of predicted
concentrations using the WETCHIMP models against atmospheric observations at
individual locations. Unlike previous sections that utilized model selection,
this section employs several model–data time series, displayed in
Fig. . The model estimates in Fig.
consist of several components: the background (in green) is the estimated
background concentration of CH4 in clean air before entering the
model domain as in . The estimated contribution
of anthropogenic emissions from EDGAR v4.2FT2010 is added to this background
(in red). The contribution of wetland fluxes from the WETCHIMP models is then
added to the previous inputs, and the sum of all components (blue lines) can
be compared directly against measured concentrations.
These time series compare atmospheric methane measurements at several
observation sites against model estimates using the WETCHIMP ensemble and the
EDGAR v4.2FT2010 anthropogenic emissions inventory. Refer to Fig. S4
for model–data time series at additional sites,
particularly sites that are distant from large wetlands.
The various WETCHIMP flux estimates produce very different modeled
concentrations at the atmospheric observation sites
(Fig. ). Overall, modeled concentrations with the
WETCHIMP fluxes usually exceed the CH4 measurements during summer. At
Chibougamau, Fraserdale, and Park Falls in early summer, all seven WETCHIMP
models predict CH4 concentrations that equal or exceed the
observations. The ORCHIDEE, LPJ-WHyMe, and LPJ-Bern models always exceed the
measurements during summer while DLEM and SDGVM match the observations better
at these sites. Notably, a number of previous studies report that the EDGAR
inventory may underestimate US anthropogenic CH4 emissions
e.g.,. If EDGAR
underestimates emissions, then the WETCHIMP models would be an even larger
overestimate relative to the atmospheric data.
Many models appear to overestimate the magnitude of fluxes across boreal
North America, but this result does not necessarily imply that these models
have underestimated fluxes elsewhere in the world. CH4 models that
estimate the largest fluxes across boreal North America do not always
compensate with smaller fluxes in other regions of the globe. For example,
the ORCHIDEE model not only estimates large fluxes over North America but
also estimates higher fluxes over the tropics than any other model
.
Seasonal cycle
Bottom-up CH4 flux estimates show variable features when compared to
atmospheric observations, and the seasonal cycle of these estimates is no
exception. Figure compares the seasonal cycle of the existing
estimates over Canada's HBL. Eastern Canada is one of the largest wetland
regions in North America (Fig. ), and nearby atmospheric
observation sites see a much larger CH4 enhancement from wetlands relative
to other regions (Fig. and S4).
In this region, the bottom-up estimates diverge on the seasonal cycle of
fluxes. Most estimates predict peak fluxes in July or August, though two
variations of the LPJ model predict seasonal peaks in September and October
– LPJ-WHyMe and LPJ-Bern, respectively. LPJ-WHyMe is a module inside of
LPJ-Bern, a possible explanation for the similar seasonal cycle in these two
models. Differences among models are also notable during the fall and spring
seasons. For example, fluxes in June account for anywhere between 6 and
21 % of the annual CH4 budget, depending upon the model.
Fluxes in October account for between 1 and 23 % of the annual
budget (Fig. b).
The seasonal cycle in methane fluxes estimated for the HBL
(50–60∘ N, 75–96∘ W). We include both the WETCHIMP
estimates and an inverse modeling estimate from . Panel
(a) displays the monthly budget from each estimate while
(b) displays each month as a percentage of the annual budget
estimated by a given model.
Figure also displays the seasonality of an inverse modeling
estimate from for comparison. That estimate incorporates
observations from Chibougamau, Quebec, and Fraserdale, Ontario, atmospheric
measurement sites that are strongly influenced by fluxes from the HBL.
Differences between this inverse modeling estimate and the WETCHIMP models
often exceed the 95 % confidence interval of the inverse model. The
WETCHIMP estimates are often comparable to in magnitude
during fall and spring months but exceed the inverse modeling estimate in
summer months (Fig. a). On whole, the WETCHIMP models have
a narrower relative seasonal cycle than the inverse modeling estimate (Fig. b). That estimate assigns a greater portion of the annual budget
to the fall and spring shoulder seasons.
Additional top-down studies exist for the HBL, but those studies use a
seasonal cycle drawn from an existing bottom-up model and do not estimate the
seasonal cycle independently from CH4 observations
. By comparison, a recent inverse
modeling study of the western Siberian lowlands found parallel results for
that region – existing models also predict a seasonal cycle that is narrower
than the seasonality implied by atmospheric observations
.
Numerous possible explanations could underly differences in the seasonal
cycle of CH4 fluxes. For example, the temperature threshold for
CH4 production may be too high in some models. Relative to summer
months, the bottom-up models predict small fluxes during fall and/or spring months
when air temperatures are near freezing but soils are still unfrozen
(Fig. S3 in the Supplement). According to estimates from the North American Regional Reanalysis
(NARR, ), surface soils in the HBL (0 and 10 cm
depth) begin to thaw in April and are largely unfrozen in May (Fig. S3). In
the fall, surface soils (0 cm depth) begin to freeze in November, but
deeper soils (10 and 40 cm) remain largely unfrozen until December.
Compared to the bottom-up models, the inverse modeling estimate predicts
a wider seasonal window, a result that would be consistent with dates of deep
soil freeze and thaw.
Conclusions
A recent model comparison study revealed wide differences among several
estimates of wetland CH4 fluxes. This study uses atmospheric data
and inverse modeling to evaluate those differences across North America.
In the first component of this study, we use a synthetic data
experiment to understand whether the atmospheric observation network can
detect wetland CH4 fluxes. We find that the network can reliably
identify aggregate wetland fluxes from both eastern and western Canada. The
network can detect wetland fluxes from the eastern US in a smaller fraction
of trials and rarely from the western US. This analysis also accounts for
distracting signals in the atmosphere from anthropogenic sources or simulated
atmospheric transport errors.
In a second component of the study, we analyze each bottom-up CH4
model from the WETCHIMP study using real atmospheric data. We find that the
LPJ-Bern and SDGVM models have spatial distributions that are most consistent
with atmospheric observations, depending upon the region and season of
interest. In addition, almost all models overestimate the magnitude of
wetland CH4 fluxes when compared against atmospheric data at
individual observation sites. The model ensemble may also estimate a seasonal
cycle for eastern Canada that is too narrow (i.e., place too much of the
total annual flux in the summer relative to the fall and spring shoulder
seasons).
The results of this paper suggest possible pathways to improve future
top-down estimates of wetland CH4 fluxes. The ability of the
atmospheric observation network to detect wetland fluxes depends largely
upon the prior flux model. In a geostatistical inverse model, this model
can incorporate land surface maps – wetland maps, estimates of land surface
processes, and maps of anthropogenic emissions sources. This information
plays a large role in whether atmospheric observations can detect wetland
fluxes; the observations can more adeptly identify wetland fluxes when
accurate land surface maps are available to guide that identification. By
contrast, atmospheric transport and measurement errors (i.e., model–data
mismatch errors) have a ubiquitous but smaller effect on the utility of
atmospheric CH4 observations.
The results presented here also hold a number of suggestions for future
bottom-up modeling efforts:
Spatial distribution: bottom-up estimates that use surface water inundation as the only proxy for
wetland area do not perform as well relative to atmospheric observations. Bottom-up models that use satellite
inundation data should incorporate additional tools like wetland mapping or dynamic modeling to capture wetlands covered by vegetation.
Magnitude: existing top-down studies that use a diverse array of in situ and satellite CH4
observations show good agreement on the magnitude of CH4 fluxes from the Hudson Bay Lowlands
region e.g.,. These studies could be used to calibrate
the magnitude of future bottom-up estimates, at least over the HBL where CH4 observations provide a strong constraint on wetland fluxes.
Seasonal cycle: bottom-up models do not show consensus on the seasonal cycle of wetland fluxes across
Canada. Few top-down studies estimate the seasonal cycle independently using atmospheric observations.
Additional top-down studies would indicate the range of seasonal cycle estimates that are consistent with
atmospheric observations, particularly studies that use a diverse set of atmospheric models and/or diverse
observational data sets. These efforts could help reconcile differences in the seasonal cycle among bottom-up
models and between bottom-up models and the few, existing top-down studies.
These steps will hopefully lead to better convergence among wetland
CH4 estimates for North America.