Introduction
Methane (CH4) is the second-most-powerful carbon-based greenhouse gas in
the atmosphere behind carbon dioxide (CO2) and also plays a significant
role in the cycles of ozone, hydroxyl radicals (OH), and stratospheric water
vapor (Myhre et al., 2013; Shindell et al., 2009). The atmospheric burden of
CH4 is now more than factor of 2.5 greater than the preindustrial value
of about 700 ppb (Etheridge et al., 1998), mainly due to anthropogenic
emissions. Major sources and sinks of CH4 have been identified (Denman
et al., 2007); however their quantification is still of large uncertainties,
and the annual and interannual variability of atmospheric CH4 are not
well explained. For instance, scientists have not yet agreed on what caused
the leveling-off of atmospheric CH4 since the 1980s (Dlugokencky et al.,
2003; Bousquet et al., 2006; Aydin et al., 2011; Kai et al., 2011; Levin et
al., 2012; Simpson et al., 2012; Kirschke et al., 2013) and the recent
rebounding of its growth since 2007 (Rigby et al., 2008; Dlugokencky et al.,
2009; Nisbet et al., 2014).
To reduce the quantification uncertainty of CH4 sources and sinks, much
effort has been made using Bayesian inference (Bergamaschi et al., 2007,
2009, 2013; Meirink et al., 2008; Cressot et al., 2014; Houweling et al.,
2014; Alexe et al., 2015). In these studies, in situ and/or satellite
observations of CH4 that are representative of large spatial scales were
assimilated into a chemical transport model (CTM) to constrain the initial
estimates of CH4 sources and sinks that are inventoried from field
studies, industrial investigations, and biogeochemical models (Fung et al.,
1991; Zhuang et al., 2004; Walter et al., 2006; Zhu et al., 2013; Tan and
Zhuang, 2015a, b). Spaceborne observations of atmospheric CH4 are
especially useful in inverse modeling because they can deliver dense and
continuous coverage unachievable by surface networks or aircraft campaigns
(Bergamaschi et al., 2007). There are two types of nadir satellite CH4
retrievals: one from solar backscatter in the shortwave infrared (SWIR) and
the other from thermal infrared radiation (TIR). Between them, SWIR
retrievals have been more widely used in atmospheric inversion of CH4
emissions (Bergamaschi et al., 2007, 2009, 2013; Fraser et al., 2013; Cressot
et al., 2014; Houweling et al., 2014; Monteil et al., 2014; Wecht et al.,
2014; Alexe et al., 2015; Turner et al., 2015) because they can provide
column concentrations with near-uniform vertical sensitivity down to the
surface. To date, most of the inversions have been operated at coarse spatial
resolutions over 300 km. However, partly owing to their coarse resolutions,
it is impossible for these inversions to constrain different CH4 sources
that are spatially colocated (Fung et al., 1991; Wecht et al., 2014). To
address this issue, regional inverse models at fine spatial resolutions were
developed (Miller et al., 2013; Wecht et al., 2014; Thompson et al., 2015).
For example, Wecht et al. (2014) and Turner et al. (2015) have used the
1/2∘ × 2/3∘ horizontal resolution Goddard Earth Observing System–Chemistry
(GEOS-Chem) adjoint model to constrain CH4 emissions over North America.
Estimating CH4 emissions from the Arctic is important for understanding
the global carbon cycle because the fast warming of Arctic permafrost, one of
the largest organic carbon reservoirs (Tarnocai et al., 2009), could lead to
a rapid rise of CH4 emissions (Zhuang et al., 2006; Walter et al., 2007;
Koven et al., 2011). Natural sources dominate the Arctic CH4 inventory
(Fisher et al., 2011), e.g., wetlands (McGuire et al., 2012), lakes (Walter et
al., 2006; Bastviken et al., 2011), sea shelves (Berchet et al., 2016; Myhre
et al., 2016), and oceans (Kort et al., 2012). As the factors governing
natural CH4 production (methanogenesis) and oxidation (methanotrophy)
are notoriously heterogeneous, estimates of Arctic CH4 emissions are
still poorly constrained, even with decades of site-level and modeling
studies (Zhuang et al., 2004; Bastviken et al., 2011; Schuur et al., 2015;
Tan and Zhuang, 2015a, b). Previous CH4 inversions over the Arctic only
assimilated surface measurements that were too sparse to constrain fine-scale
CH4 fluxes. Also, possibly important CH4 sources that were newly
identified, e.g., CH4 emissions from Arctic lakes (Walter et al., 2006,
2007; Bastviken et al., 2011; Tan and Zhuang, 2015a) and the East Siberian
Shelf (Berchet et al., 2016; Thornton et al., 2016), have not been included in
these studies. Given the ill-posed nature of trace-gas inversions, realistic
prior fluxes could be important for successful inverse modeling of CH4
emissions from the Arctic (Kaminski and Heimann, 2001).
To address these issues, we used the adjoint of a 3-D CTM at a high spatial
resolution (less than 60 km) to improve the quantification of pan-Arctic
CH4 emissions in 2005. We explored the feasibility of using satellite
CH4 retrievals overpassing the pan-Arctic to further constrain regional
CH4 emissions. For the first time, CH4 emissions from pan-Arctic
lakes were included in high-resolution inverse modeling of CH4
emissions. As wetland emissions are likely the largest pan-Arctic CH4
source, we also investigated the sensitivity of our estimates to the use of
different wetland emission scenarios. Section 2 describes the observation
data of atmospheric CH4 that were used to infer CH4 emissions and
evaluate posterior estimates. Section 3 details the wetland and lake
biogeochemical models that were used in this study (Sect. 3.1), the
pan-Arctic nested-grid CTM (Sect. 3.2), and the adjoint-based inversion
method (Sect. 3.3). Section 4 presents the posterior CH4 emissions,
their evaluation and further discussion.
Observations
Satellite retrievals
SWIR CH4 retrievals are available from SCanning Imaging Absorption
spectroMeter for Atmospheric CHartogrphY (SCIAMACHY) for 2003–2012
(Frankenberg et al., 2006, 2008, 2011) and Greenhouse Gases Observing
SATellite (GOSAT) for 2009 to present (Parker et al., 2011). SCIAMACHY,
aboard the European Space Agency's environmental research satellite Envisat,
retrieves column-averaged CH4 mixing ratios (XCH4) from the SWIR
nadir spectra (channel 6: 1.66–1.67 µm) using the iterative
maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS)
algorithm (Frankenberg et al., 2006, 2008, 2011). The satellite operates in a
near-polar, sun-synchronous orbit at an altitude of 800 km. At channel 6,
the ground pixel size of the retrievals is about 30 km
(along-track) × 60 km (across-track). We use version 6.0 proxy
CH4 retrievals from Frankenberg et al. (2011) that provide a weighted
column-average dry-mole fraction of CH4 with 10-layer averaging kernels
and prior CH4 profiles. The averaging kernels show near-uniform vertical
sensitivity in the troposphere and declining sensitivity above the tropopause
(Butz et al., 2010). Some auxiliary data – e.g., the air mass factor
AF (AF=1/cosθ+1/cosξ, where θ is the solar zenith angle and ξ is the viewing
angle of the satellite), water column density, and dry-air column density –
are also published with the IMAP-DOAS v6.0 XCH4 product.
SCIAMACHY retrievals (n = 37 743) of the weighted
column-average CH4 dry-mole fractions for July–September 2005 in
the pan-Arctic that have passed all quality control tests described in
Sect. 2.1, and the locations of surface flask stations and aircraft
missions used for data assimilation or inversion evaluation.
The estimated single-retrieval precision is scene-dependent and averages
roughly 1.5 %, or 25 ppb (Frankenberg et al., 2011). With this order of
instrument precision, SCIAMACHY cannot resolve day-to-day variability of
emissions but can strongly constrain a multi-year average (Turner et al.,
2015). The retrieving algorithm firstly calculates CH4 total column
density ΩCH4 (molecules cm-2):
ΩCH4=ΩA+aTω-ωA,
where ω is the true 10-layer sub-column densities of
CH4 (molecules cm-2), ωA is the
10-layer prior CH4 sub-column density (molecules cm-2), ΩA is the corresponding a priori CH4 total column density, and
a is an averaging kernel vector that defines the sensitivity of the
retrieved total column to each sub-column in ω. To
account for the impact of aerosol scattering and instrument effects on the
observed light path, Frankenberg et al. (2006) used the CO2 column
density ΩCO2 as a proxy to normalize and convert
ΩCH4 to a column mixing ratio XCH4 (ppb):
XCH4=ΩCH4/ΩCO2XCO2,
where XCO2 is the column-weighted mixing ratio of CO2 from NOAA's
CarbonTracker CO2 measurement and modeling system. CO2 is used as a
proxy because it is retrieved in a spectrally neighboring fitting window and,
relative to CH4, its mixing ratio is known with much higher precision.
The quality of SCIAMACHY observations is controlled by a filtering scheme
that selects only daytime, over-land scenes that are cloud-free or partially
cloudy, and good fitting accuracy
(http://www.temis.nl/climate/docs/TEMIS_SCIA_CH4_IMAPv60_PSD_v2_6.pdf).
Further, a surface elevation filter is applied to filter out observations
that are different from the model grids at surface altitude by more than 250
m (Bergamaschi et al., 2009; Alexe et al., 2015). This filtering process
ensures that the atmospheric columns seen by SCIAMACHY are well represented
by the model columns. To avoid spurious outliers that may have a large impact
on the inversion, XCH4 retrievals of less than 1500 ppb or larger than
2500 ppb are discarded (Alexe et al., 2015). For the pan-Arctic, most of the
qualified XCH4 retrievals were recorded in the summertime, when local
solar zenith angles are higher, surface reflectance is lower, and impact of
Arctic vortex is smaller. Figure 1 shows the SCIAMACHY retrievals
(n = 37 743) of the weighted column-average CH4 dry mixing
ratio for July–September 2005 in the pan-Arctic that have passed all quality
control tests.
Surface observations
The NOAA/ESRL Carbon Cycle Cooperative Global Air Sampling Network provides
high-precision weekly flask measurements of surface atmospheric CH4
dry-air mole fraction (Dlugokencky et al., 2014) that were calibrated against
the WMO X2004 CH4 standard scale maintained at NOAA (Dlugokencky et al.,
2005). Due to the coarse resolution of the GEOS-Chem model, we include only
marine and continental background sites and exclude sites that are strongly
influenced by sub-grid local sources (Alexe et al., 2015), as listed in Table
S1 in the Supplement. The flask-air samples in the NOAA/ESRL network that
were taken from regular ship cruises in the Pacific Ocean serve to evaluate
simulated surface mixing ratios of global inversions over the remote ocean
and downwind the continental sources (Alexe et al., 2015). Figure 1 shows the
Arctic sites that were used for data assimilation and nested-grid inversion
evaluation.
Aircraft campaign observations
To derive the bias of SCIAMACHY CH4 retrievals overpassing the
pan-Arctic and evaluate the modeled CH4 vertical profiles in the
troposphere, we used CH4 measurements that were collected by three
aircraft campaigns: the NOAA/ESRL Carbon Cycle Cooperative Global Air
Sampling Network's aircraft program
(http://www.esrl.noaa.gov/gmd/ccgg/aircraft/data.html; Sweeney et al.,
2015), the National Institute for Environmental Studies (NIES) aircraft
program (Machida et al., 2001; Sasakawa et al., 2013), and NASA's Arctic
Research of the Composition of the Troposphere from Aircraft and Satellite
(ARCTAS) mission (Jacob et al., 2010). For the NOAA/ESRL aircraft mission,
CH4 was routinely collected using 0.7 L silicate glass flasks on
planned flights with maximum altitude limits of 300–350 hPa. The sampling
vertical resolution is up to 400 m in the boundary layer, and all samples
were analyzed by NOAA/ESRL in Boulder, Colorado. For the NIES aircraft
mission, air samples were collected in 550 mL glass flasks over Surgut,
western Siberia (61.5∘ N, 73.0∘ E), at altitude ranging
from 0.5 to 7 km with 0.5–1.5 km intervals. The precision of gas
chromatograph analysis for CH4 measurement was estimated to be 1.7 ppb,
and the NIES-94 scale used in analysis was higher than the NOAA/GMD scale by
3.5–4.6 ppb in a range of 1750–1840 ppb. In ARCTAS, CH4 was measured
over northern Canada by the Differential Absorption CO Measurement (DACOM) tunable diode laser
instrument with an estimated accuracy/precision of 1 %/0.1 %.
Central locations of their flights in the pan-Arctic are shown in Fig. 1.
Table S2 lists the locations and profiles of the NOAA/ESRL aircraft mission
flights used in evaluation.
Prior average CH4 fluxes from wetlands, lakes, and other sources
(i.e., anthropogenic and biomass burning) in 2005 used for the pan-Arctic
nested-grid inversions at 1/2∘ × 2/3∘
resolution. Annual total emission for each pan-Arctic source is presented in
units of Tg CH4 yr-1.
Modeling
Here we describe the prior emissions, the forward model, and the inversion
method used to optimize CH4 emissions in the pan-Arctic on the basis of
SCIAMACHY and NOAA/ESRL observations.
Wetland and lake CH4 emissions
CH4 emissions estimated by the inverse modeling method can be sensitive
to the choice of prior wetland CH4 fluxes (Bergamaschi, 2007). To assess
this sensitivity, we used wetland CH4 emissions simulated by six well-known
wetland biogeochemical models (CLM4Me: the Community Land Model 4 (CLM4) CH4 biogeochemistry model;
DLEM: the Dynamic Land Ecosystem Model; BERN: the Lund-Potsdam-Jena dynamic
global vegetation model – the University of Bern version; WSL: the Lund–Potsdam–Jena
dynamic global vegetation model – the Swiss Federal Research Institute version;
ORCHIDEE: the Organising Carbon and Hydrology in Dynamic Ecosystems model;
SDGVM: the Sheffield Dynamic Global Vegetation Model) to set
up six different inverse modeling experiments. All wetland CH4
simulations follow the same protocol of the WETland and Wetland CH4
Inter-comparison of Models Project (WETCHIMP) as described in Melton et
al. (2013) and Wania et al. (2013). Melton et al. (2013) demonstrated that
the difference of these estimates primarily arises from the model distinction
in CH4 biogeochemistry and wetland hydrology. These models estimated
that the annual global CH4 emissions from wetlands during 2004–2005
were in the range of 121.7–278.1 Tg yr-1 (Fig. S1 in Supplement), and
wetland CH4 emissions are the highest in tropical regions (e.g., the
Amazon, Southeast Asia, and tropical Africa) where extensive floodplains and
warm environment coexist. In the pan-Arctic, the modeled annual wetland
CH4 emissions in 2005 were in the range of 9.1–20.9 Tg yr-1
(Fig. 2), and their spatial distribution was mainly controlled by the modeled
or mapped wetland coverage (Melton et al., 2013). As shown in Fig. 2, because
of some consistency in simulating wetland hydrology, nearly all models
suggest that there are high CH4 fluxes in the west Siberian lowlands,
Finland, and the Canadian Shield.
Lakes, permanent still-water bodies without direct connection to the sea, are
abundant in the pan-Arctic (Lehner and Döll, 2004). Recent studies
indicated that pan-Arctic lakes could contribute a significant amount of
CH4 to the atmosphere (Walter et al., 2006; Tan and Zhuang, 2015a) and
that the emissions could be driven by factors different from wetland
emissions, e.g., the supply of labile yedoma permafrost carbon (Walter et
al., 2006) and deep water mixing (Schubert et al., 2012). Because the
WETCHIMP models cannot account for this source, we used a one-dimension
process-based lake biogeochemical model, bLake4Me, to simulate CH4
emissions from pan-Arctic lakes (Tan et al., 2015; Tan and Zhuang, 2015a).
The bLake4Me model explicitly parameterizes the control of temperature and
carbon substrate availability on methanogenesis, the control of temperature
and oxygen level on methanotrophy, and the transport of gaseous CH4 by
diffusion and ebullition. A detailed model description and evaluation can be
found in Tan et al. (2015). Model quantification of CH4 emissions from
all lakes north of 60∘ N was described by Tan and Zhuang (2015a, b).
On average, the estimated CH4 emissions from pan-Arctic lakes during the
studied period are approximately 11 Tg CH4 yr-1; see Fig. 2.
GEOS-Chem model
Atmospheric CH4 mole fractions are simulated by GEOS-Chem v9-01-03
(http://acmg.seas.harvard.edu/geos/index.html), a global 3-D CTM model
(Bey et al., 2001). For the period of 2004–2005, GEOS-Chem is driven by
GEOS-5 meteorological (hereafter GEOS-5 met) data from NASA's Global Modeling
Assimilation Office (GMAO). The GEOS-5 met data have horizontal resolution of
1/2∘ latitude × 2/3∘ longitude, temporal
resolution of 6 h, and 72 hybrid sigma-pressure levels extending from Earth's
surface to 0.01 hPa. In contrast to the global GEOS-Chem model, the
nested-grid version does not include algorithms for handling advection near
the North and South Pole (Lin and Rood, 1996). To avoid polar grid boxes, we
crop the native 1/2∘ × 2/3∘ resolution GEOS-5
met data to a window region (180∘ W–180∘ E and
80–56∘ N) for the pan-Arctic nested grid. To make it consistent
with the bLake4Me model, only CH4 emissions north of 60∘ N are
analyzed. We expect that the avoidance of the North Pole only has a minor
impact on our inversions because according to Miyazaki et al. (2008) the
Northern Hemisphere (NH) extratropics during summer have a slow mean-meridional
circulation and inactive wave activity but strong vertical transport.
Boundary conditions for nested-grid simulations are produced using the same
period GEOS-Chem 4∘ × 5∘ resolution global-scale
forward runs at 3 h intervals.
The GEOS-Chem CH4 simulation was originally introduced by Wang et
al. (2004) and updated by Pickett-Heaps et al. (2011). As described by Wecht
et al. (2014), the prior anthropogenic sources – including oil/gas production,
coal mining, livestock, waste treatment, rice paddies, biofuel burning, and
other processes – were extracted from the Emission Database for Global Atmospheric
Research v4.2 (EDGAR4.2) with 0.1∘ × 0.1∘ resolution
and no seasonality (European Commission, Joint Research Centre/Netherlands
Environmental Assessment Agency, 2009). CH4 emissions from termites and
biomass burning were obtained from the study of Fung et al. (1991) and the daily
Global Fire Emissions Database Version 3 (GFED3) of van der Werf et
al. (2010), respectively. CH4 emissions from wetlands and lakes were
simulated by biogeochemical models described in Sect. 3.1. Atmospheric
CH4 is mainly removed by tropospheric oxidation initiated by reaction
with tropospheric OH, which was computed using a 3-D OH climatology of
monthly average concentrations from a previous simulation of tropospheric
chemistry (Park et al., 2004). The global mean pressure-weighted tropospheric
OH concentration is 10.8 × 105 molecules cm-3. For minor
sinks, CH4 uptake by upland soils was derived from Fung et al. (1991),
and CH4 oxidation in the stratosphere was calculated from the archived
CH4 loss frequency described by Murray et al. (2012). The resulting
atmospheric lifetime of CH4 is about 8.9 years, consistent with the
observational constraint of 9.1 ± 0.9 years (Prather et al., 2012). We
regridded and cropped the anthropogenic and natural CH4 emissions in
EDGAR4.2, GFED3, and Fung et al. (1991) for our nested pan-Arctic domain using
the Harvard-NASA Emissions Component (HEMCO) software (Keller et al., 2014),
marked as “other” in Fig. 2. Compared to CH4 emissions from natural
sources, these emissions were relatively small in 2005
(∼ 2.1 Tg yr-1).
Inversion method
Atmospheric inversion is a procedure for using observations of atmospheric
gases as constraints to estimate surface gas fluxes. The inverse problem can
be characterized by the solution of
y=Fx+ε.
By applying Bayesian theorem and assuming Gaussian errors, the inverse
problem can be solved by minimizing the cost function, J(x), that
measures the model deviations from both prior assumptions and observations
(Enting et al., 2002; Kopacz et al., 2009):
Jx=Fx-yTCd-1Fx-y+γx-x0TCx0-1x-x0,
where y is a vector of observations from SCIAMACHY and NOAA/ESRL, F
is a model operator that maps emissions to observations, x represents
CH4 emissions to be constrained, x0 is the a priori estimate
of x, Cd is the observational error covariance
matrix that includes contributions from model error, representation error
(sampling mismatch between observations and the model) and measurement error,
and Cx0 is the parameter error covariance matrix
(containing the uncertainties of the parameters and their correlations). The
regularization parameter
γ controls the relative constraints applied by the
observational and a priori parts of J(x) (Kopacz et al., 2009). In the
adjoint method, γ is not fixed at unity but determined by analyzing
its influence on the minimum of J(x) (Henze et al., 2007; Kopacz et al.,
2009).
Bias correction function (left) and standard deviation (right) for
SCIAMACHY retrievals overpassing the pan-Arctic. ΔXCH4 is the
difference between SCIAMACHY and column-average mixing ratios mapped from
aircraft vertical profiles. The red line on the left shows a linear
regression weighted by the number (represented by circle size) of SCIAMACHY
retrievals.
Minimization of J(x) yields the following expression for the maximum a
posteriori solution for the state vector x^ and its associated
error covariance C^x (Rodgers, 2000):
x^=x0+∇xFTCd-1∇xF+γCx0-1-1∇xFTCd-1y-Fx0,C^x-1=∇xFTCd-1∇xF+γCx0-1,
where ∇xF is the Jacobian matrix of the forward model.
J(x) is minimized iteratively through successive forward and backward
simulations with the GEOS-Chem model and its adjoint, developed by Henze et
al. (2007) and previously applied to CO, CO2, and CH4 source
inversions (Jiang et al., 2011; Deng et al., 2014; Wecht et al., 2014). The
GEOS-Chem adjoint model is a 4-dimensional variational data assimilation
(4DVAR) inverse modeling system that allows optimization of a very large
number of parameters using at the same time very large sets of observational
data, such as satellite data. Rather than optimizing CH4 emissions
directly, it optimizes an exponential scale factor ex
(ex=lnx/x0) at each grid cell to avoid
negative emissions. The posterior error covariance C^x
could be approximated by the Davidon–Fletcher–Powell (DFP) or the
limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization
algorithm (Singh et al., 2011; Deng et al., 2014). But the performances of
these deterministic methods are usually not promising, subjecting to the
choice of the initial Hessian, so-called preconditioning (Bousserez et al.,
2015). In contrast, approximating C^x by stochastic
methods, i.e., Monte Carlo sampling and gradient-based randomization, could
help avoid the impact of setting the initial Hessian (Bousserez et al.,
2015). For example, Bousserez et al. (2015) demonstrated that for
high-dimensional inverse problems using a Monte Carlo stochastic approach
that samples ensemble members by perturbing x0 and y in
line with Cx0 and Cd,
respectively, could guarantee a low relative error (10 %) in the variance
with as few as 50 members. In this study, the posterior uncertainty of
nested-grid inversions was estimated using this method.
Summary of bias correction methods and of mean absolute
satellite–model difference (ppb) for 2003–2005 before and after applying
bias correction. ΔBIC is the BIC score increase of a bias correction
method when referring to the latitude-only method.
Bias correction
Mean absolute
ΔBIC
R2
function∗
difference
No correction
9.271
Latitude only
p0+p1φ+p2φ2
6.305
0.62
Air mass factor only
p0+p1AF
7.071
161
0.52
Humidity only
p0+p1HS
6.786
73
0.56
Latitude + humidity
p0+p11φ+p12φ2+p21HS
6.230
-7
0.62
Air mass factor + humidity
p0+p11AF+p21HS
6.396
12
0.60
∗ p0, p1, p2, p11, p12,
and p21 are regression parameters.
For prior emissions, their uncertainties were set as 100 % in each grid
box, and spatial correlation was set as an e-folding function with spatial
correlation lengths of 500 km at the global 4∘ × 5∘
resolution and of 300 km at the nested-grid
1/2∘ × 2/3∘ resolution (Bergamaschi et al., 2009).
Six global coarse-resolution inversions using different wetland emission
scenarios and assimilating both surface CH4 measurements and satellite
CH4 retrievals were performed during the period of
January–December 2005. These inversions provided boundary conditions for the
following nested-grid inversions. For 1/2∘ × 2/3∘
nested-grid inversions, we ran the adjoint model 50 times over the period of
July–September 2005 for each of 12 scenarios: six wetland scenarios by two
data assimilation scenarios. The two data assimilation scenarios include one
scenario assimilating only NOAA/ESRL measurements and another scenario
assimilating both NOAA/ESRL measurements and SCIAMACHY retrievals. As
described above, the 50-member ensemble run is for the calculation of
posterior estimate uncertainty. The steps to construct optimal initial
conditions for global and nested inversions are described in the Supplement.
As in Wecht et al. (2014), observations in the first week were not
assimilated, and each optimization was run iteratively at least 40 times
until the reduction of its cost function became less than 0.5 % with each
successive iteration. In the GEOS-Chem adjoint model, optimization changes
its course automatically if local minimum is reached.
Satellite retrieval bias correction
The importance of bias correction for the assimilation of satellite
retrievals has been discussed in many earlier studies (Bergamaschi et al.,
2007, 2009, 2013; Fraser et al., 2013; Cressot et al., 2014; Houweling et
al., 2014; Wecht et al., 2014; Alexe et al., 2015; Turner et al., 2015).
Usually, these studies represented satellite retrieval bias as a regression
function of one proxy parameter, e.g., latitude, air mass factor, or
specific humidity. The air mass factor was used as a proxy parameter by some
studies due to its correlation with spectroscopic errors and residual aerosol
errors (Cressot et al., 2014; Houweling et al., 2014), and specific humidity
was used because water vapor is the main cause of SCIAMACHY seasonal bias
that lags the variations of solar zenith angle (Houweling et al., 2014).
Relative to the air mass factor and humidity, latitude can represent the
changes in both solar zenith angle and climate variables (Bergamaschi et al.,
2007, 2009, 2013) and thus was used by more studies. Considering that
different proxies can account for different errors, the system bias of
satellites may be better represented by multiple proxy parameters.
To test this hypothesis, we compared the performance of three traditional
one-proxy methods (latitude φ, air mass factor
AF, specific humidity HS) and two new two-proxy
methods (latitude + humidity, air mass factor + humidity), listed in
Table 1. These methods were evaluated using two reference values: the
difference between the satellite-retrieved and the GEOS-Chem-modeled
CH4 column mixing ratios and the Bayesian information criterion (BIC)
score. The BIC criterion is widely used for regression model selection and
aims to award a model that fits measurements with the least model parameters.
In the study, we would select the bias correction method that gives the
smallest difference and the lowest BIC score. In our experiments, all bias
correction functions were updated monthly. As listed in Table 1, the
“latitude-only” correction performs the best among the three single-proxy
correction methods and is only slightly worse than the “latitude + humidity” correction method. The “air-mass-factor-only” method does not
work as well in our experiment. Turner et al. (2015) suggested that it could
be attributed to a potential bias in the GEOS-Chem simulation of CH4 in
the polar stratosphere. As the latitude + humidity method has the
smallest model–data difference and the lowest BIC score, we applied it for
satellite bias correction in all global inversions.
Optimized pan-Arctic CH4 fluxes in 2005 at
1/2∘ × 2/3∘ resolution using both SCIAMACHY and
NOAA/ESRL observations. (a) BERN;
(b) CLM4Me; (c) DLEM; (d) ORCHIDEE;
(e) SDGVM; (f) WSL.
For SCIAMACHY retrievals overpassing the pan-Arctic, because the modeled
atmospheric CH4 could be less reliable, we used another bias correction
method. According to a comparison between SCIAMACHY and the high-precision
Total Carbon Column Observing Network (TCCON) measurements, the system bias
of SCIAMACHY retrievals could be closely correlated with specific humidity
averaged over the lowest 3 km of the atmosphere (Houweling et al., 2014).
And Wecht et al. (2014) has demonstrated that this humidity-proxy method
shows promising performance in debiasing SCIAMACHY retrievals overpassing
North America. In this study, we sought a similar linear regression
relationship between SCIAMACHY bias and specific humidity. First, we detected
the SCIAMACHY bias by comparing SCIAMACHY retrievals with CH4 vertical
profiles measured by the NOAA/ESRL aircraft mission over Alaska, USA; the
NIES aircraft mission over Siberia, Russia; and the NASA/ARCTAS aircraft
mission over Alberta, Canada. Before comparison, these CH4 vertical
profiles had been mapped to the SCIAMACHY retrieval pressure grid using
Eqs. (1) and (2). Figure 3 (left) shows that the retrieved system bias
(ΔXCH4) has a negative relationship with air humidity. Because
the pan-Arctic is normally dry, SCIAMACHY retrievals could be lower than
atmospheric CH4 column-average mixing ratios on most days.
After bias correction, the error variances of SCIAMACHY retrievals were
estimated using the relative residual error (RRE) method described by Heald
et al. (2004). Figure S2 shows the error variances of SCIAMACHY retrievals on
a global scale, and Fig. 3 (right) shows the error variances in the nested
grid. In both global and nested-grid inversions, the total error of
individual SCIAMACHY retrievals is assumed to be at least 1.5 %
(Bergamaschi et al., 2007; Frankenberg et al., 2011). The observational error
of the NOAA/ESRL CH4 mixing ratios is estimated as the sum of
measurement error (∼ 0.2 %) and representation error.
Similar to satellite retrievals, the representation error of surface
measurements is defined as the standard deviation of surface CH4
concentration differences between NOAA/ESRL measurements and GEOS-Chem.
Results and discussion
Optimized global CH4 emissions
As listed in Table 2, when both NOAA/ESRL measurements and SCIAMACHY
retrievals are assimilated, the posterior estimates of total emissions in
2005 show good convergence at a narrow range of
496.4–511.5 Tg CH4 yr-1, although our six prior scenarios span
in a wide range (471.5–627.8 Tg CH4 yr-1). Because the total of
global emissions is constrained by the atmospheric CH4 burden and
lifetime, this convergence probably suggests that surface measurements from
the NOAA/ESRL network are of sufficient density and accuracy to represent the
global CH4 burden if the CH4 lifetime is correct. In contrast, the
posterior CH4 emissions differ largely between different wetland
emission scenarios in the TransCom3 (Atmospheric Tracer Transport Model
Intercomparison Project) land regions. For example, in the DLEM inversion,
the estimated CH4 emissions from the Eurasian temperate region are as
large as 146.1 Tg CH4 yr-1. But in the
CLM4Me inversion, the total of these
emissions is only 84.9 Tg CH4 yr-1. Also, for CH4 emissions
from the South American tropical region, the estimate is
31.4 Tg CH4 yr-1 in the DLEM inversion but nearly 2 times larger
(62.3 Tg CH4 yr-1) in the SDGVM inversion. There are several
possible explanations for the large differences between the scenarios:
high-precision surface measurements could be not of sufficient density in
regional scales, satellite retrievals could be not of sufficient accuracy,
and the GEOS-Chem model and its priors could be not of high enough temporal and spatial
resolutions to resolve satellite retrievals. A detailed comparison between
our estimates and previous inversion studies at the global scale is presented
in the Supplement.
Estimated annual CH4 emissions (units: Tg CH4 yr-1)
for TransCom 3 land regions (NAB: North American boreal; NAT: North American
temperate; SATr: South American tropical; SAT: South American temperate; NAf:
northern Africa; SAf: southern Africa; ErB: Eurasian boreal; ErT: Eurasian
temperate; TrA: tropical Asia; Aus: Australasia; and Eur: Europe). The priors
are the range of the initial CH4 emissions given by the six scenarios.
Region
Priors
Posterior
Fraser et
Alexe et
al. (2013)
al. (2015)
BERN
CLM4Me
DLEM
ORCHIDEE
SDGVM
WSL
NAB
7.9–26.0
24.3
16.2
16.8
27.4
12.0
20.7
5.1 ± 1.1
10.3
NAT
38.5–59.2
33.2
32.8
42.8
49.2
51.2
39.7
62.5 ± 4.4
45.6
SATr
29.6–100.0
43.0
60.8
31.4
61.0
62.3
42.1
49.6 ± 6.4
71.8
SAT
29.1–55.8
31.2
27.1
35.2
39.1
25.6
30.5
55.8 ± 9.5
40.2
NAf
26.8–31.2
34.0
41.3
27.9
28.0
27.7
32.0
46.9 ± 7.3
50.6
SAf
16.0–27.0
18.4
16.2
19.0
24.2
15.6
18.7
36.6 ± 5.8
42.0
ErB
11.5–32.7
19.2
14.3
16.5
18.7
22.2
14.9
16.5 ± 3.8
15.4
ErT
114.9–133.5
97.0
84.9
146.1
92.7
98.3
99.8
115.9 ± 7.3
109.6
TrA
33.1–45.8
47.3
51.4
35.8
33.1
36.4
45.1
43.5 ± 3.2
76.8
Aus
5.8–8.3
7.3
7.7
6.6
7.9
6.3
7.3
17.6 ± 2.7
4.3
Eur
43.6–53.5
54.9
52.2
46.4
43.5
56.5
54.1
39.6 ± 3.7
28.9
Wetlands
121.7–278.1
166.8
164.6
130.0
203.3
161.8
160.7
192.1 ± 16.1
169
Global
471.5–627.8
501.0
497.7
511.5
511.0
496.4
502.9
510.6 ± 18.4
540.5
Optimized pan-Arctic CH4 emissions
Regional CH4 emissions
When using both surface measurements and satellite retrievals, our estimated
CH4 emissions over the pan-Arctic are in the range of
11.9–28.5 Tg CH4 yr-1. The simulation is the largest in the
ORCHIDEE scenario and the smallest in the SDGVM scenario: 24.9 ± 3.6
and 16.1 ± 4.2 Tg CH4 yr-1, respectively. Regionally,
posterior CH4 emissions from Alaska, northern Canada, northern Europe,
and Siberia are 0.3–3.4, 1.3–7.9, 0.8–8.1 and
4.4–14.9 Tg CH4 yr-1, respectively. Same as the global
inversions, the difference of the nested-grid inversions between different
scenarios is much larger than the total uncertainty of priors and
observations of each scenario: 16.6 Tg CH4 yr-1 vs.
5.5 Tg CH4 yr-1. In these regions, CH4 emissions from
Siberia are more uncertain (Fig. 5), a possible indication of the lack of
high-quality measurements in Siberia for assimilation. Our results also
indicate that the assimilation of SCIAMACHY retrievals overpassing the
pan-Arctic can reduce the estimate uncertainty. For example, for the BERN
scenario, the posterior uncertainty is about 18 %, much smaller than the
inversion that only assimilates NOAA/ESRL measurements (27 %). And for
the CLM4Me scenario, the posterior
uncertainty increases from 16 to 23 % when only surface measurements are
assimilated. Our estimates are consistent with other inverse modeling
estimates. For example, Kirschke et al. (2013) reviewed a series of top-down
estimation of CH4 emissions and suggested that CH4 emissions north
of 60∘ N could be in the range of 12–28 Tg CH4 yr-1,
very close to our estimate. This consistency could reflect the robustness of
our nested-grid GEOS-Chem adjoint model and the good constraint of the
NOAA/ESRL sites over the pan-Arctic on the atmospheric CH4 field. Our
estimates also imply that CH4 emission from the pan-Arctic could
constitute a large fraction of CH4 emissions in the northern high
latitudes (> 50∘ N). Based on the estimate
(50 Tg CH4 yr-1) of Monteil et al. (2013), we calculated that
29.2–60.8 % of CH4 emissions in the northern high latitudes could
be emitted from the pan-Arctic (> 60∘ N). For all scenarios, the
inverse modeling adjusts total CH4 emissions downward compared to prior
emissions. It is possible that CH4 emissions are overestimated by the
biogeochemical models or double-counted between the wetland and lake models
or both. This adjustment could also be explained by the underestimate of
CH4 absorption by soils in biogeochemical models due to the lack of
high-affinity methanotrophy (Oh et al., 2016).
CH4 emissions from pan-Arctic lakes
In contrast to CH4 emissions from pan-Arctic wetlands, CH4
emissions from pan-Arctic lakes at large spatial scales are still largely
unknown. Consensus has not been reached yet on how to apply the knowledge
learnt from individual lakes to the pan-Arctic scale, because even lakes in a
small area could have much different transport pathways (ebullition vs.
diffusion), morphology (deep vs. shallow and large vs. small), eutrophication
(eutrophic vs. oligotrophic), and carbon source (thermokarst vs.
non-thermokarst and yedoma vs. non-yedoma). Because wetlands and lakes, both
inundation landscapes, are usually neighboring, it is difficult to use
inverse modeling at coarse spatial scales to detect strong CH4 emissions
that are emitted solely by lakes. To test whether high-resolution inversions
can better represent CH4 emissions from lakes, we conducted a comparison
test (“DLEM only”) over the east Siberian coastal lowlands (Fig. 1) using
the DLEM model and excluding CH4 emissions from lakes. We chose the east
Siberian lowlands to test our hypothesis as lakes there occupy 56 % of
the water-inundated landscapes – i.e., lakes, wetlands, and rivers (Lehner
and Döll, 2004) – and a large fraction of lakes in the region are
high-flux yedoma lakes (Walter et al., 2006). We chose the DLEM model,
considering that the simulated wetland CH4 emissions in this model are
weak for the east Siberian lowlands. This design is also aimed to alleviate
the impact of one major shortcoming: because there are not sufficient
high-quality observations, we optimized the total CH4 emission in each grid cell, and in this manner a fraction
of lake emissions could be attributed incorrectly to wetlands or vice versa.
The inversion of the DLEM-only scenario is shown in Fig. S5. In comparison to
Fig. 4c, CH4 emissions from the east Siberian coastal lowlands are low
in Fig. S5. A further comparison of model–satellite agreement between the
DLEM scenario and this no-lake scenario reveals that the agreement improves
when lake emissions are considered (see Fig. 6; p = 0.0032838 at the
two-sample t test). It implies that CH4 emissions from regional
lakes could be significant. As illustrated above, however, the spatial
neighborhood of wetlands and lakes makes it difficult to conduct similar
experiments in other areas. Thus we are cautious to claim that CH4
emissions from lakes are ubiquitously strong across the pan-Arctic. Rather,
since we used six wetland models that can simulate very different wetland
emission distributions at spatial and temporal scales, our estimates of
2.4–14.2 Tg CH4 yr-1 for lake emissions could be more useful in
explaining the range of this source. The lower bound of our estimate is much
smaller than the estimate of 7.1–17.3 Tg CH4 yr-1 by Bastviken
et al. (2011) in the use of extensive site-level observations. In contrast,
the upper bound of our estimate is within the range. Given the wide span of
this estimate, it is difficult to say whether CH4 emissions from
pan-Arctic lakes can be significant across the region.
Comparison of prior and posterior pan-Arctic CH4 emissions and
their uncertainties. “NOAA only” represents posterior emissions
assimilating only surface measurements. “NOAA + SCIA” represents
posterior emissions assimilating both surface measurements and satellite
retrievals. The uncertainty of prior emissions is 100 %. Scenarios are
represented by their name initials: “B” for BERN, “C” for CLM4Me, “D”
for DLEM, “O” for ORCHIDEE, “S” for SDGVM, and “W” for WSL.
CH4 emissions from pan-Arctic wetlands
Arctic tundra is regarded as an important source of CH4 in the northern
high latitudes. By using process-based models and atmospheric CH4
observations, McGuire et al. (2012) estimated that Arctic tundra was a source
of 25 Tg CH4 yr-1 to the atmosphere during 1990–2006. By using
the Transport Model 5 (TM5)-4DVAR inverse model and assimilating SCIAMACHY
and NOAA/ESRL observations, Alexe et al. (2015) estimated that CH4
emissions from Arctic wetlands were 18.2 Tg CH4 yr-1 for
2010–2011. A similar estimate of 16 ± 5 Tg CH4 yr-1 was
also made by Bruhwiler et al. (2014) using the CarbonTracker-CH4
assimilation system. Our estimate of 5.5–14.2 Tg CH4 yr-1
overlaps with the estimate of Bruhwiler et al. (2014) but is much lower than
the estimates of Alexe et al. (2015) and McGuire et al. (2012). However,
McGuire et al. (2012) did not use complex inverse models, and Alexe et
al. (2015) used the coarse-resolution TM5-4DVAR inverse model. As our global
inversions (Table 2) are consistent with the estimate of Alexe et al. (2015),
this difference is likely introduced by the use of the nested-grid inverse
model. In other words, the nested-grid inverse model reveals some information
that could be missed in global coarse-resolution inversions. For Siberian
wetlands, they could emit much more CH4 (1.6–7.6 Tg yr-1) than
any other areas. But the uncertainty of the Siberian emissions is also the largest. Using the atmospheric CH4
observation data at several sites near Siberian wetlands, Berchet et
al. (2015) estimated that CH4 emissions from Siberian wetlands were in
the range of 1–13 Tg CH4 yr-1, wider than our estimated range.
In addition, our estimate is also much smaller than the estimate of
21.63 ± 5.25 Tg CH4 yr-1 by Kim et al. (2012) for annual
mean CH4 emissions from Siberian wetlands during 2005–2010. According
to our inversions, CH4 emissions from wetlands in Alaska, northern
Canada, and northern Europe are 0–1.2, 0.4–4.8, and
0.7–3.6 Tg CH4 yr-1, respectively. For Alaskan wetlands, the
total of posterior CH4 emissions is much lower than the inferred value
of 4.1 Tg CH4 yr-1 for the Alaskan Yukon River basin during
1986–2005 using the modeling of process-based CH4 biogeochemistry and
large-scale hydrology (Lu and Zhuang, 2012) and also much lower than the
inferred value of 3 Tg CH4 yr-1 for the whole of Alaska (Zhuang
et al., 2007). Our estimate of wetland emissions from northern Europe
compasses a European-scale estimate of 3.6 Tg CH4 yr-1 by Saarnio
et al. (2009), agreeing with the finding that
wetlands in Europe are predominantly located north of 60∘ N.
Distribution of the relative difference between the observed and
simulated posterior SCIAMACHY column-average mixing ratios. The “DLEM + lake” scenario includes CH4 emissions from both wetlands and lakes, and
the “DLEM-only” scenario only includes CH4 emissions from wetlands.
Relative difference is calculated as a percentage of absolute differences
between GEOS-Chem and SCIAMACHY relative to SCIAMACHY retrievals. Two
extending red and blue lines represent the means of the simulation bias
under the DLEM + lake scenario and the DLEM-only scenario,
respectively.
Evaluation of pan-Arctic CH4 inversions
As shown in Fig. 7, in most of scenarios, the nested-grid inversions perform
much better than both the forward simulations and the global inversions at
NOAA/ESRL pan-Arctic flask sites (Fig. 1). For example, for the ORCHIDEE
scenario, the nested-grid inversion reduces the model bias by 44 ppb
relative to the forward run and by 20 ppb relative to the global inversion.
Also, for the SDGVM scenario, it reduces the model bias by 22 ppb relative
to the forward run and by 13 ppb relative to the global inversion. But for
aircraft CH4 measurements, it is more complex. The nested-grid
inversions can reduce the model bias in some scenarios greatly,
e.g., the
CLM4Me scenario and the SDGVM scenario. But in many cases, they do not
perform visibly better than the forward runs and the global inversions. One
possible reason is that the root mean square error (RMSE) of aircraft
CH4 has already been low, and thus the remaining errors, including the
representation error of model diurnal variability, cannot be resolved by our
current inversion system. For example, CH4 emissions from Alaska can be
well constrained by three NOAA/ESRL surface sites in Alaska (BRW, CBA, and
SHM), and the CH4 mixing ratios at the aircraft PFA (Poker Flat, Alaska) site are representative
of the interior of Alaska as pointed out in Sweeney et al. (2015). It is also
possible that the increase of grid cells in the nested-grid inversions
introduced more transport and computation errors.
Evaluation of the posterior GEOS-Chem CH4 mole fractions from
the pan-Arctic nested-grid inversions with independent data sets from the
NOAA flask stations, the NOAA aircraft PFA profiles, and the NIES aircraft
Surgut profiles. Black symbols indicate the RMSE of the forward GEOS-Chem
runs, and red symbols indicate the RMSE of the global inversions.
Further discussion
Both the global and nested-grid inversions indicate that the inverse
modeling is more sensitive to different wetland models than prior emission
error and data error. Thus, to gain better understandings of the global and
pan-Arctic CH4 cycles, it is important to develop more realistic
biogeochemical models. Especially from the perspective of inverse modeling,
focus should be on improving the spatial and temporal representation of the
models rather than emission magnitude.
For the high-resolution inverse modeling, transport and computation errors of
the nested-grid CTMs need to be reduced for better performance. These CTMs
can also benefit the efforts to assimilate aircraft CH4 measurements.
For the purpose of satellite data bias correction, more coordination between
satellite missions and aircraft missions is demanded. The treatment of the
SCIAMACHY bias could be an important uncertainty source for our estimates, as
suggested by Houweling et al. (2014). Future top-down studies could benefit
from a more reasonable bias correction method, even for low bias satellite
products, e.g., GOSAT (Alexe et al., 2015).
The attribution of CH4 fluxes to spatially overlapped sources, e.g.,
wetlands and lakes, could be problematic for even high-resolution
inversions. Carbon isotope measurements (δ13CH4) are
widely used to separate biogenic and geologic CH4 sources (Langenfelds
et al., 2002) but are not useful for two biogenic sources with similar
carbon isotope ratios (Walter et al., 2008; Fisher et al., 2011). In our
study, lake and wetland emissions were simulated separately by different
models. This raised the possibility of double-counting emissions of the two
sources. A possible solution is to simulate them together in one Earth
system model and use a consistent method to identify wetland and lake
pixels.
Our nested-grid adjoint model currently does not cover the regions near the
North Pole. While it could be rare in the summertime, if air mass is
transported across the Arctic Ocean, it may not be represented in the model.
In the following studies, we will adapt the advection algorithm for the polar
region from the global adjoint model to the nested-grid model and validate
the adaptation. These refinements shall reduce the uncertainty of our
estimates. It is also valuable to discuss the integration of other natural
CH4 sources found in the pan-Arctic, such as CH4 emission from
sub-sea permafrost of the East Siberian Shelf (Berchet et al., 2016; Thornton
et al., 2016). As shown in Fig. 1, our inverse modeling assimilated few
high-precision surface CH4 measurements in Siberia and northern Canada.
Since some efforts have already been made by different teams to measure
atmospheric CH4 routinely in Siberia (e.g., the Japan–Russia Siberian
Tall Tower Inland Observation Network(JR-STATION) by NIES, the Zotino Tall Tower
Observatory by the Max Planck Institute for Biogeochemistry (MPI-BGC), and
the Tiksi site by the Finnish Meteorological Institute) and in the North
American Arctic (e.g., the Behchoko site by Environment Canada), we would
like to take advantage of these measurements to further improve our inversion
results and re-evaluate the gains of using satellite data in our future
studies.