AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus GmbHGöttingen, Germany10.5194/amt-8-3433-2015Quantifying lower tropospheric methane concentrations using GOSAT near-IR and TES thermal IR measurementsWordenJ. R.john.worden@jpl.nasa.govhttps://orcid.org/0000-0003-0257-9549TurnerA. J.https://orcid.org/0000-0003-1406-7372BloomA.KulawikS. S.LiuJ.LeeM.WeidnerR.BowmanK.FrankenbergC.https://orcid.org/0000-0002-0546-5857ParkerR.https://orcid.org/0000-0002-0801-0831PayneV. H.Earth Sciences Section, Jet Propulsion Laboratory/CalTech, Pasadena, USASchool of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USABay Area Environmental Research Institute, Mountain View, CA, USADept. of Physics and Astronomy, University of Leicester, Leicester, UKJ. R. Worden (john.worden@jpl.nasa.gov)25August2015883433344529March201520April201522July201513August2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/8/3433/2015/amt-8-3433-2015.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/8/3433/2015/amt-8-3433-2015.pdf
Evaluating surface fluxes of CH4 using total column data requires
models to accurately account for the transport and chemistry of methane in
the free troposphere and stratosphere, thus reducing sensitivity to the
underlying fluxes. Vertical profiles of methane have increased sensitivity
to surface fluxes because lower tropospheric methane is more sensitive to
surface fluxes than a total column, and quantifying free-tropospheric
CH4 concentrations helps to evaluate the impact of transport and
chemistry uncertainties on estimated surface fluxes. Here we demonstrate the
potential for estimating lower tropospheric CH4 concentrations through
the combination of free-tropospheric methane measurements from the Aura
Tropospheric Emission Spectrometer (TES) and XCH4 (dry-mole air
fraction of methane) from the Greenhouse gases Observing SATellite – Thermal
And Near-infrared for carbon Observation (GOSAT TANSO, herein GOSAT for
brevity). The calculated precision of these estimates ranges from 10 to 30 ppb
for a monthly average on a 4∘×5∘ latitude/longitude grid making
these data suitable for evaluating lower-tropospheric methane
concentrations. Smoothing error is approximately 10 ppb or less. Comparisons
between these data and the GEOS-Chem model demonstrate that these
lower-tropospheric CH4 estimates can resolve enhanced concentrations
over flux regions that are challenging to resolve with total column
measurements. We also use the GEOS-Chem model and surface measurements in
background regions across a range of latitudes to determine that these
lower-tropospheric estimates are biased low by approximately 65 ppb, with an
accuracy of approximately 6 ppb (after removal of the bias) and an actual
precision of approximately 30 ppb. This 6 ppb accuracy is consistent with
the accuracy of TES and GOSAT methane retrievals.
Introduction
Advances in remote sensing in the last decade have resulted in global
mapping of atmospheric methane concentrations (e.g., Frankenberg et al.,
2005, 2011; Worden et al., 2012) that in turn have
provided new insights into the role of wetlands (e.g., Bloom et al., 2010),
fires (e.g., Worden et al., 2012, 2013), the stratosphere (e.g., Xiong et
al., 2013), and anthropogenic emissions (e.g. Kort et al., 2014) on
tropospheric methane concentrations. However, use of these data to improve
global flux estimates and their trends of either methane or CO2,
relative to measurements from the surface network, is challenging in part
because of their measurement accuracy and sampling (e.g., Bergamaschi et
al., 2013) or because these measurements are primarily sensitive to methane
over the whole column or the free troposphere and stratosphere, which have
long mixing length scales (e.g., Keppel-Aleks et al., 2011, 2012; Wecht et
al., 2012; Worden et al., 2013). For example, Fig. 1 shows a methane
profile derived from Aura Tropospheric Emission Spectrometer (TES) radiances
during July 2009. Because the amount of methane within a sub-column of the
profile scales approximately with the pressure difference of the layer
boundaries, less than 25 % of the total column is typically in the
boundary layer where it is most sensitive to the underlying surface fluxes
with the remaining column amount in the free troposphere or stratosphere.
Figure 2 shows averaged total column measurements derived from GOSAT
radiance measurements (e.g., Parker et al., 2011, and references therein) and
free-tropospheric measurements from the Aura TES instrument (Worden et al.,
2012) for July 2009 (see Appendix B and Sect. 2.3). Although the total
column measurements are more sensitive to near-surface measurements than the
TES measurements, both measurements broadly see similar features because
they are both strongly sensitive to the bulk of the methane column. The
largest methane values occur over the eastern parts of North America and
Asia and moderate values of CH4 over central Asia. Lowest values of the
total column are at high-latitudes because the fractional contribution of
the depleted stratosphere to the total column becomes larger with increasing
latitude for both data sets. Uncertainties in both of these measurements
also increase with latitude because the signal-to-noise ratio of total-column
measurements depends on reflected sunlight and the signal-to-noise ratio of
thermal infrared based measurements depends on temperature, both of which
decrease with increasing latitude. Atmospheric methane concentrations above
the lower troposphere are primarily sensitive to fluxes that are hundreds to
thousands of kilometers away, depending on the latitude (e.g., Keppel-Aleks
et al., 2011, 2012; Worden et al., 2013). Therefore, uncertainties in
transport, both vertical and horizontal, are important to consider when
using these data to investigate underlying fluxes or processes (e.g.,
Stephens et al., 2007; Jiang et al., 2013,
2015; Worden et al., 2013).
A retrieved methane profile from the Aura TES instrument during
July 2009.
Top: XCH4 from the GOSAT instrument. Black indicates no
data.
Bottom: XCH4 from the Aura TES instrument for the free troposphere to
stratosphere (typically 750 hPa to TOA). Black indicates no data.
Methane fluxes used in GEOS-Chem model.
Top: difference in XCH4 between a reference GEOS-Chem run
and another in which the Hudson Bay lowland flux (48 to 66∘ N and 100 to
70∘ W) has been reduced by half. Bottom: same as in top panel but for the lower
troposphere.
We next examine the sensitivity of a total column and lower troposphere
column to changes in the underlying fluxes. Figure 3 shows methane fluxes
used in Version 9.0.2 of the GEOS-Chem global chemical transport model (see
Appendix A as well as Bey et al., 2001; Kaplan, 2002; Pickett-Heaps et
al., 2011; Wecht et al., 2012, 2014; Turner et al., 2015). Fluxes above
50∘ N are primarily due to wetlands whereas those at lower latitudes are
primarily due to a combination of fossil fuels, wetlands, rice farming, and
agriculture. Figure 4 (top panel) shows a comparison between modeled
XCH4 above the Hudson Bay lowlands (∼52∘ N, 85∘ W) to
XCH4 if the modeled southern Hudson Bay lowland (HBL) wetland fluxes
between 48 to 66∘ N and 100 to 70∘ W are reduced by half. The total column
differences in the summer between these two model runs are approximately 10 ppb,
about the same as the precision of a single total column measurement
from the GOSAT TANSO (Greenhouse gases Observing SATellite – Thermal
And Near-infrared for carbon Observation) satellite (Sect. 2). Consequently, substantial
averaging and sampling is required to quantify these high-latitude fluxes
even to within a factor of two using total column data. In contrast, Fig. 4 (bottom panel) shows the effect of this perturbation is much stronger in
the lowermost troposphere (the lowermost 250 hPa of atmosphere or
approximately surface to 750 hPa) with differences of approximately
40 ppb
near the source region. Increasing the sensitivity of remote sensing
measurements to the underlying surface fluxes is therefore our motivation
for this study. We therefore evaluate the capability of estimating lower
tropospheric methane concentrations using GOSAT short-wave infrared (SWIR)
and TES thermal infrared (TIR) measurements because the combination of these
measurements provides greater sensitivity to the underlying fluxes and
reduced sensitivity to transport error (e.g., Jiang et al., 2015, and references
therein) than either the SWIR or the TIR based measurements alone.
We next present a comparison between GOSAT and TES data. We then derive the
instrument operator for lower tropospheric estimates based on the GOSAT/TES
data. We compare these data to lower-tropospheric concentrations from
GEOS-Chem in order to help assess the calculated sensitivity and sampling
errors. We then calculate a total error budget for these estimates followed
by a comparison to surface data.
Sensitivity (or averaging kernel) of the total column with respect
to the retrieved GOSAT and TES methane profile. Both averaging kernels have
been normalized by the sub-column of each layer in the profile.
Estimating lower-tropospheric methane from GOSAT and TES
Recent advances in remote sensing show that combining reflected sunlight and
thermal IR measurements to estimate trace gas profiles can provide improved
vertical resolution compared to measurements from either individual
wavelength region (e.g., Worden et al., 2007; H. M. Worden et al., 2010; Kuai et
al., 2013). In the case where the trace gas varies significantly in the free
troposphere, it is necessary to estimate the trace gas profile from the
radiances when the reflected sunlight and thermal IR measurement observe the
same air parcel (e.g., H. M. Worden et al., 2010). For long-lived trace gases
such as CO2 (e.g., Kuai et al., 2013) we can subtract the
free-tropospheric/stratospheric posterior estimate (based on thermal IR
radiances) from the total column (based on reflected sunlight radiances). In
this case observations that are not exactly co-located in space and time can
be used together to estimate lower-tropospheric concentrations because of
the long mixing length scales of these trace gases in the free troposphere
and stratosphere (Sect. 3). We therefore use the approach described in
Kuai et al. (2013) for estimating lower tropospheric CH4 measurements in
which the thermal IR measurement from TES, which provides information about
atmospheric methane concentrations from approximately 750 hPa through the
stratosphere is subtracted from the total column estimates from the GOSAT
measurement. For example, Fig. 5 shows an example of the sensitivity of
the total column average volume mixing ratio (VMR) of methane from the GOSAT
and TES and retrievals (see Appendix B for a summary of the GOSAT and TES
retrieval characteristics and data source) respectively to the methane
profile (in terms volume mixing ratio or VMR). Both averaging kernels are
normalized by the column of each sub-layer (e.g., Eq. 8 in Connor et
al., 2008, or O'Dell et al., 2012); the GOSAT retrievals are approximately
uniformly sensitive to methane at all levels whereas the TES retrievals have
peak sensitivity in the middle/upper troposphere and declining sensitivity
towards the surface.
Estimation approach
The retrieved column amount is a function of the prior information,
sensitivity, the true state, and uncertainties:
C^=Ca+CairhTA(x-xa)+ΣiCihTδi.
We define Eq. (1) such that C^ is the estimated total column in
units of moleculescm-2 so that we can conveniently subtract the TES
free-tropospheric and stratospheric column amount from the total column
amount measured by GOSAT. The h is the column operator that relates
trace gases given in volume mixing ratio (VMR) to the average column mixing
ratio (typically given in the literature as XCH4 for methane), the
Cair variable is the total dry air column and converts the average
column mixing ratio into the dry air column in units of moleculescm-2, the A is the averaging kernel matrix or
A=∂x^∂x, where x is the true state and x^ is
the estimate of the true state (e.g., Rodgers, 2000). The superscript “a” refers to the a priori used to constrain the retrieval. The summation over
δ refers to all the errors included with this estimate,
mapped to a column amount using the h operator (see Appendix B
for summary of the errors in TES and GOSAT data). Note that the TES data
are reported on a log VMR grid. The GOSAT averaging kernels are already
mapped to a pressure-weighted column relative to x, which is a one-dimensional vector that is linear in VMR. Both sets of averaging kernels
must be converted to the same units prior to comparison.
The GOSAT averaging kernels have been pre-mapped into a “column” averaging
kernel,
a=(hTA)j/hj, (e.g., Connor et al., 2008) where the subscript j refers to the
pressure levels of the GOSAT retrieval grid. The TES averaging kernels are
reported on the forward model pressure levels used in the TES radiative
transfer algorithm. For the next set of equations we find it useful to use
the nomenclature b=hTA which can be
computed from the GOSAT averaging kernels. We next divide up the columns
into a lower-tropospheric component (consisting of the pressure levels for
the lowermost 250 hPa of the atmosphere or typically surface to 750 hPa),
and the rest of the atmosphere. The column amount for the lowermost
troposphere can then be given as
C^L=C^tot-C^U,
where we will use GOSAT to provide the total column and TES to provide the
upper tropospheric column (denoted by subscripts “tot” and “U”
respectively).
Using Eq. (1) we can re-write Eq. (2) as
C^L=CLa+CairbLG(xL-xLa)+CUa+CairbUG(xU-xUa)-(CUa+CUairhUTAUUTES(xU-xUa)+CUairhUT(AULTES)(xL-xLa))+ΣiCihTδi.
Equation (3a) represents the GOSAT contribution to the total tropospheric
column amount estimate in Eqs. (2) and (3b) represents the TES
contribution to the upper tropospheric column. The subscript “L”
refers to the pressure levels that make up the “lower troposphere”, the
subscript “U” refers to the pressure levels that make up the free
troposphere and stratosphere, the superscript “G” refers now to the
GOSAT averaging kernel and the superscript “TES” refers to the TES
averaging kernel. The subscripts “UU” and “LL” indicate the
block diagonal part of the averaging kernel matrix (A) corresponding
to the “U” and “L” levels, respectively. Because
b=hTA,
the vector bU refers to the “u” set of
pressure levels for the vector b and is not the same
as hUAUU. Note that we have
assumed for the sake of simplicity that the a priori constraint vectors (e.g., xa)
are the same for the GOSAT and TES retrievals as we can
always swap one prior with another (e.g., Rodgers and Connor, 2003). The
second part of Eq. (3b) also includes the cross term “UL” which
describes the impact on the upper-tropospheric methane from the lower
tropospheric estimate of methane in the TES retrieval (e.g., Worden et al.,
2004). We drop this term in subsequent equations as we find it is much
smaller than the other terms. The last term in Eq. (3b) describes the
various uncertainties affecting the GOSAT and TES retrievals.
Equation (3) can be re-written as
C^L=CLa+CairbLGxL-xLa+CairbUG-∝hUTAUUTESxU-xUa+∑iCihTδi,
where ∝=CUair/Cair and the variable Ci in the right
side of Eq. (4) refers to either the total column or the upper
tropospheric column, depending on the vertical range of the corresponding
error. Typically, data assimilation or inverse estimates of fluxes involve
applying the averaging kernel from the data to the model, which includes the
averaging kernel terms in Eq. (3a) and (3b). For the comparison discussed
in this paper, we will apply Eq. (2) (equivalent to Eq. 4, but
without uncertainties in the last term of Eq. 4) to the GEOS-Chem model
fields. Because the TES and GOSAT instruments do not typically observe the
same air parcel, we also must use the approach of subtracting a monthly
average of the free-tropospheric CH4 column (based on TES) from the
monthly averaged total column based on GOSAT data. This approach will incur
a “co-location” error that we evaluate in Sect. 3.2 using the GEOS-Chem
model and the TES and GOSAT averaging kernels. A more sophisticated approach
using both data sets could be to assimilate the TES CH4 fields in order
to minimize errors in the model transport and chemistry and then use the
GOSAT data to estimate model fluxes (e.g., Kuai et al., 2013). This approach
is potentially the subject of a future investigation, but is beyond the scope
of this current investigation because of the complexities of the data
assimilation framework.
Top: CH4 lower-tropospheric estimate using GOSAT and TES
data. Black indicates no data. Bottom: lower-tropospheric estimate from
GEOS-Chem model for the same latitudes and longitudes shown in top panel of
Fig. 6.
Top: difference in lower-tropospheric estimate between GOSAT/TES
and the GEOS-Chem model. Black indicates no data. Bottom: difference in
total column estimate between GOSAT and the GEOS-Chem model.
Lower-tropospheric estimates and comparison to GEOS-Chem
We choose to estimate data for July 2009 because (1) both TES and GOSAT have
the best overall sampling during this time period and (2) we want to
evaluate how sensitive these lower-tropospheric estimates are to
high-latitude fluxes. Figure 6 (top panel) shows the July 2009 monthly
estimate of XCH4 for the lower troposphere (lowermost 250 hPa of the
atmosphere) and Fig. 6 (bottom panel) shows the corresponding GEOS-Chem
model values after applying the TES and GOSAT averaging kernels, sampling,
monthly averaging and subtraction used for the TES and GOSAT lower
tropospheric estimate. A global bias of approximately 65 ppb is added to the
GOSAT/TES lower tropospheric values (see Sect. 3.5). The largest
near-surface concentrations are in the northern latitudes, as expected by
the model (Fig. 6), and are a result of summertime fluxes of wetlands
(e.g., Fig. 3). A combination of biogenic and anthropogenic emissions are
responsible for the larger concentrations on the eastern coasts of North
America and Asia with tropical enhancements of methane associated with the
source regions in the western Amazon and Congo regions.
Figure 7 (top panel) shows the difference between the estimated
lower-tropospheric methane with respect to the corresponding GEOS-Chem
values. As discussed in subsequent sections, the precision of these data
ranges from 10 to 30 ppb with an accuracy of approximately 6 ppb (after a
global bias correction). Consequently regions that are higher than 50 ppb or
more (red color) or lower than -50ppb or less (blue colors) are regions
where the modeled fluxes are likely in significant disagreement with the
true fluxes. The largest data/model differences are typically over flux
regions (Fig. 3) and suggest that the high-latitude wetland fluxes are too
large in the GEOS-Chem model and too low in Europe, North America, and Asia.
A large region between the Black and Caspian seas (∼40∘ N, 40∘ E)
is also under-represented in the model. For comparison, Fig. 7 (bottom
panel) shows the total column differences between GOSAT and GEOS-Chem after
a global mean bias of ∼-9.5ppb is removed. As with Fig. 4,
the comparison between the top and bottom panels of Fig. 7 empirically
demonstrates the increased sensitivity of the lower-tropospheric methane to
the underlying methane fluxes as there are significantly larger variations
in the lower-tropospheric methane estimates over the larger flux regions.
This comparison also shows how use of the total column alone can lead to
erroneous conclusions as the total column data is biased high with respect
to the model over South America but the lower-tropospheric estimate
comparison shows much more significant variation, with a positive bias in
northern Amazonia and a negative bias in middle Amazonia and Southern
Brazil. In addition, the data/model difference for the total column shows
very little variation over the Siberian and northern European wetlands
indicating little sensitivity to this important component of the global
methane budget.
Error analysis
We can calculate the “error” statistics of the lower tropospheric methane
estimates by subtracting the “true” lower tropospheric column amount
(hLTxL) from
Eq. (4) and computing the expectation of this difference:
||(C^L-CL)(C^L-CL)T||=Cair2(bLG-hLT)SLL(bLG-hLT)T+Cair2(bUG-∝hUTAUUTES)SUU(bUG-∝hUTAUUTES)T+ΣiCi2hTSih,
where the CL is the “true” lower tropospheric column
amount and the Si term describes the statistics (or
error covariance) of the error terms δ in Eq. (3). The
first two terms on the right-hand side effectively describes the “smoothing
error” (Rodgers, 2000) for the lower-tropospheric estimate. A comparison
between model (e.g., GEOS-Chem) and data (e.g. GOSAT minus TES) does not
need to compare against this smoothing error term as it is removed if the
GOSAT and TES averaging kernels are first applied to the model fields.
However, we will estimate the smoothing error in the next section (Sect. 3.1) for completeness. Note that there is also a cross term in this
expression that we have ignored because it depends on the atmospheric
methane correlations between the upper troposphere and lower troposphere,
which are small, and the term bL-hL, which is also small as discussed in next section.
Uncertainties due to noise and radiative interferences will need to be
calculated for any model/data comparison. These errors are contained in the
TES and GOSAT product files as discussed in Worden et al. (2012), Parker et
al. (2011) and references therein. The error on the lower-tropospheric column
amount will have a much larger percentage error than the total and
free-tropospheric estimates for XCH4 because Eq. (2) subtracts two
large numbers with similar percentage uncertainties to obtain a smaller
number. However, for this comparison we average a month's worth of data over
a 4∘×5∘ lat/lon grid box, which reduces the random component of this
error (e.g., Kuai et al., 2013).
We also need to calculate two additional error sources from the following: (1) the
assumption that we can average GOSAT and TES posterior columns on a chosen
grid box (in this case 4∘×5∘) even though the GOSAT and TES
observations are not necessarily co-located and (2) knowledge error of the
XCO2 distribution used to estimate XCH4 concentrations from the
GOSAT CH4/CO2 “proxy” retrieval.
Smoothing error from the free-troposphere column
The “smoothing error” (Rodgers, 2000) for the lower-tropospheric estimate
is given by the first two terms on the right-hand side of Eq. (5). This
term is composed of the smoothing error corresponding to the
lower-tropospheric levels and the cross state error, which is the impact of
the upper-tropospheric estimate on the lower-tropospheric estimate. Both of
these errors are removed from any model profile/data comparison if the
model is first adjusted with the TES and GOSAT averaging kernels and a priori
constraints (or the instrument operators) prior to comparison. However, if
only the lower-tropospheric component is compared to the model, in order to
mitigate model transport and chemistry errors in a data/model comparison,
then the second term needs to be included in the overall error budget. We
find that the first component of the smoothing error (first term of Eq. 5) is negligible because the expression bL-hL is almost identical to zero. In fact, this term
is approximately 1 ppb even for assumed covariances of up to
200 ppb
(squared) in the lower troposphere. We can evaluate the second term (or
cross-state error) error an a priori methane climatology from the GEOS-Chem model
and the averaging kernels from TES and GOSAT and in general find it to be
less than 15 ppb. Note that the TES and GOSAT averaging kernels must both be
mapped to the same units and dimensions.
Co-location error
As discussed previously, most TES and GOSAT observations do not observe the
same air parcel; consequently, in order to estimate lower-tropospheric
CH4 abundances we subtract monthly averaged free-tropospheric/stratospheric columns (or typically 750 to TOA), derived from the TES
CH4 profile estimates, from monthly averages of the GOSAT total column:
C^LM=C^TOTM-C^UM,
where the superscript “M” refers to the monthly average. An error results
from this assumption because the 750 to TOA column changes over a month due
to transport. For model profile/data comparisons using Eqs. (2) or (5),
this error is not included in the total error budget because the model is
typically sampled at the observations' spatiotemporal coordinates. However,
this error will need to be considered for comparison to monthly averages of
aircraft data, for example.
We evaluate this uncertainty by using the GEOS-Chem model and the TES
averaging kernels. We first calculate the free-tropospheric CH4 column
(750 hPa to TOA) by applying Eq. (3) to the GEOS-Chem model and using the
TES spatiotemporal sampling. We then perform the same operation but with
the GOSAT spatiotemporal sampling and the nearest TES averaging kernels to
these spatiotemporal coordinates. We find that the mean RMS (Root Mean Square) difference in
the monthly averaged 4∘×5∘ binned free-tropospheric sub-column is
approximately 7 ppb or less and is effectively random as a function of
latitude. We add this uncertainty into the total error budget by computing
the RMS of the difference as a function of latitude (Sect. 3.4).
Estimated total precision for the GOSAT/TES lower tropospheric
CH4 estimates.
Total precision
The precision of these estimates can be calculated from the sum of the
observation error covariances (noise and spectral interferences), the
co-location error, and cross-state error. The observation covariances for a
monthly average in each grid box are effectively reduced relative to a
single measurement by the square root of the number of observations. Figure 8
shows the precision as a function of latitude. The precision varies from
10 to 30 ppb and generally varies with latitude likely because the
observation error and sampling becomes poorer for both TES and GOSAT at
higher latitudes. However, this precision is sufficient to resolve, for
example, the high-latitude lower-tropospheric concentrations over the
Siberian wetlands from the adjacent Russian boreal forest as well as the
Canadian wetlands. We next compare these data to surface measurements to
evaluate the actual precision and to estimate the accuracy.
Comparison to surface data and estimate of accuracy
Direct comparison of these lower tropospheric estimates to surface data must
account for variability in surface methane as well as methane in the
boundary layer and residual component of the free troposphere that makes up
the lower tropospheric column. For this reason we use the GEOS-Chem model
shown in Fig. 6 as a way of comparing the GOSAT/TES data to surface sites.
We compare to surface sites available from the World Data Centre for
Greenhouse Gases (WDCGC: http://ds.data.jma.go.jp/gmd/wdcgg/).
These data are typically in background regions which will mitigate
uncertainties in data/model comparisons because of large possible
differences between the fluxes used in GEOS-Chem and the actual fluxes as
demonstrated by the near surface concentration differences shown in the top
panel of Fig. 7. Only sites in continental regions are used because of
sparse sampling by the GOSAT instrument over island sites. Based on these
criteria there are 27 sites available from the WDCGC network that can be
compared to these lower-tropospheric data (Appendix C).
Figure 9 shows a least squares fit between monthly averaged surface
measurements and monthly averaged GEOS-Chem surface values for the sites
discussed in Appendix C (black diamonds). The least squares fit assumes an
uncertainty of 11 ppb for the GEOS-Chem model based on the scatter in the
data. For many sites, only monthly values are provided, and therefore we
assume in an ad hoc manner that the error on the mean for these monthly
values are also 11 ppb. Using these estimates we find that the GEOS-Chem
surface methane concentrations have effectively the same variability as the
surface sites with a slope of 0.94±0.06 and a correlation of 0.94. The
mean difference between GEOS-Chem surface methane and the surface methane
data is 0.46 ppb with an RMS of 20.3 ppb corresponding to an error on the
mean difference of approximately 4 ppb.
(1: Black) Comparison of GEOS-Chem surface values to monthly
surface methane (averaged) measurements from the WDCGC. (2: Blue) GEOS-Chem
lower-tropospheric methane vs. surface measurements. (3: Red) GOSAT/TES
lower tropospheric methane vs. surface measurements. (4: Green) GOSAT/TES
lower tropospheric methane based on CMS XCO2 values vs. surface methane
measurements. The diamonds are the different data sets and the lines are
linear fits to the data. The variable M is the slope of the fitted line and
the variable R is the correlation coefficient.
The blue diamonds (and corresponding line) in Fig. 9 show a comparison
between the lower-tropospheric estimates in GEOS-Chem, corresponding to
those from the GOSAT/TES measurements, and the surface data. This comparison
also demonstrates how methane variability in the lower troposphere is less
than on the surface. The red diamonds are a least squares fit between
the surface data and the GOSAT/TES data.
The RMS difference between the GEOS-Chem and the GOSAT/TES data for these
sites is approximately 22 ppb. The sum (in quadrature) of the RMS
differences between GEOS-Chem and the surface and GEOS-Chem and GOSAT/TES
data is approximately 30 ppb, which is consistent but slightly larger than
the precision shown in Fig. 8. The mean difference between the GOSAT/TES
data and the GEOS-Chem data is approximately 65±4.4ppb and the mean
difference between the GEOS-Chem surface methane and the surface sites is
0.4±3.7ppb. Including the error on the mean between the GEOS-Chem
model and surface data, the bias in the GOSAT/TES data is approximately 65±6ppb. We therefore estimate that the accuracy is approximately
6 ppb
for these measurements after the bias of 65 ppb is removed. This result is
consistent in sign but not quite the magnitude with the positive bias in TES
that is approximately 28±5ppb (Alvarado et al., 2015; note that the
Alvarado et al., 2015, paper computes the RMS of TES minus aircraft data which
is about 30 ppb, whereas the error on the mean is the error of the bias and
is approximately 5 ppb) and the negative bias in GOSAT that is approximately
-17±0.2ppb for the GOSAT proxy retrievals (e.g. Schepers et al.,
2012).
CO2 bias error
As discussed in Frankenberg et al. (2011), Butz et al. (2010), and Parker et
al. (2011) the XCH4 estimates used in this study are derived using the
following approach. First XCH4 and XCO2 are estimated from
radiances in the 1.6 micron band. Then, the XCH4 value is divided by
the XCO2 value in order to mitigate effects from interferences such as
from aerosols and surface albedo. The assumption here is that the
XCO2
and XCH4 values derived from the 1.6 micron radiances are affected in a
nearly identical manner by the interferences in this band. Finally, this
ratio is multiplied by XCO2 derived from the Carbontracker model
(Peters et al., 2007). A potential source of uncertainty in the
XCH4
estimate and consequently these lower tropospheric estimates is from
variable bias error in the total CO2 column from Carbontracker used to
infer the CH4 column. For example, a bias error of 1 % in
XCO2
directly leads to a 4 % bias error in the lower-tropospheric sub-column
between 1000 and 750 hPa, or approximately 80 ppb for CH4 in the
lower troposphere. We test the effects of XCO2 knowledge error on our
estimates of lower-tropospheric CH4 concentrations by first
re-normalizing the XCH4 estimates from the GOSAT data (Parker et al.,
2011), which uses XCO2 from the Carbontracker model (Peters et
al.,
2007), with a preliminary estimate of XCO2 that is derived by
assimilating GOSAT XCO2 estimates into the land/ocean/atmosphere global
carbon models developed for the NASA Carbon Monitoring System or CMS (e.g.,
Liu et al., 2014). A comparison between these revised lower tropospheric
estimates and the GEOS-Chem lower tropospheric values is shown as the green
line in Fig. 9 and shows substantially larger differences between the
comparison shown by the red line, resulting in up to 50 ppb difference in
the lower tropospheric methane derived using these preliminary XCO2
estimates (difference between green and red line). The comparisons shown in
Fig. 9 gives more confidence in the lower tropospheric methane estimates
based on the carbontracker XCO2; however, this comparison highlights the
importance of accurate XCO2 fields for quantifying both XCH4 and
lower tropospheric CH4 concentrations.
Conclusions
This study shows the potential for estimating lower-tropospheric methane
concentrations using a combination of thermal IR and reflected sunlight
measurements. Here we report monthly averaged lower tropospheric methane
concentrations (lowermost 250 hPa of the atmosphere) for July 2009 on a 4∘×5∘ grid. The spatiotemporal resolution is driven by the sampling of the
TES and GOSAT instruments. The smoothing error is approximately 10 ppb or
less and the calculated precision at this spatiotemporal resolution varies
between approximately 10 and 30 ppb. We find that the lower tropospheric
measurements from GOSAT/TES are biased low by approximately 65±6ppb
by comparing these data to those from the GEOS-Chem model and GEOS-Chem to
surface measurements. However, additional comparisons with ground and
aircraft measurements for different seasons are needed to ensure these
estimates of the bias and its errors are robust.
Both the GEOS-Chem model and these new lower tropospheric methane estimates
broadly show the same inter-hemispheric gradient and enhanced concentrations
over regions with large important methane fluxes. However, model/data
differences are larger than the calculated errors (both precision and
accuracy) for northern Canada, Southeast Asia, the tropical wetlands, and a
region between the Black and Caspian seas; these regions should be the
subject of a future study.
The current approach can resolve lower-tropospheric concentrations at
monthly time scales on a 4×5 grid. However, many of the key processes
controlling wetland fluxes such as rainfall, flooding, or the freeze and
thaw of snow and ice occur at time-scales of much less than a month and at
finer spatial scales (e.g., Bloom et al., 2012; Melton et al., 2013; Kort et
al., 2014, and many references therein). Consequently it is desirable for an
instrument designed to characterize the processes controlling methane to
jointly measure the thermal and near-IR radiances for CH4 retrievals at
much finer spatial and temporal resolution. A Geo-orbiting satellite with a
combined thermal and near-IR capability would greatly improve the
spatiotemporal sampling and uncertainty of lower-tropospheric estimates.
Combining IR-based CH4 measurements from the Atmospheric Infrared
Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), or the
Cross-track Infrared Sounder (CrIS) with total column CH4 measurements
from GOSAT or the next-generation Trop-OMI instruments, along with better
estimates of total column CO2 from OCO-2 will also greatly enhance our
ability to resolve near-surface methane concentrations, improving
sensitivity to estimate methane fluxes, especially at higher latitudes.
Description of GEOS-Chem model
We use the v9-01-02 GEOS-Chem methane simulation (http://acmg.seas.harvard.edu/geos/index.html;
Pickett-Heaps et al., 2011; Wecht et al., 2012, 2014; Turner et al., 2015)
driven by Goddard Earth Observing System (GEOS-5) assimilated meteorological
data from the NASA Global Modeling and Assimilation Office (GMAO). The
GEOS-5 data have a native horizontal resolution of 12∘×23∘ with 72 terrain-following pressure levels and
6 h
temporal resolution (3 h for surface variables and mixing depths). Here we
use the global methane simulation at 4∘×5∘
resolution. The main methane sink is tropospheric oxidation by the OH
radical. We use a three-dimensional archive of monthly average OH concentrations from
Park et al. (2004), resulting in an atmospheric lifetime of 8.9 years.
Emissions for the GEOS-Chem methane simulation are from the EDGARv4.2
anthropogenic methane inventory (European Commission, 2011), the wetland
model from Kaplan (2002) as implemented by Pickett-Heaps et al. (2011), the
GFED3 biomass burning inventory (van der Werf et al., 2010), a termite
inventory and soil absorption from Fung et al. (1991), and a biofuel
inventory from Yevich and Logan (2003). Wetland emissions vary with local
temperature, inundation, and snow cover. Open fire emissions are specified
with 8 h temporal resolution. Other emissions are assumed aseasonal.
Turner et al. (2015) lists global emissions for 2009–2011 and their shows
spatial distributions.
Summary of TES and GOSAT retrieval uncertainties
We use Version 6 of the TES CH4 data from the “Lite” product files
(http://tes.jpl.nasa.gov/data/). A full description of the errors for TES
retrievals is provided in Worden et al. (2004) with the basic error analysis
theory described in Bowman et al. (2006) and Worden et al. (2012). These
errors include the effects of noise as well as radiative interferences from
trace gases that absorb and emit in the 8 micron methane band such as
H2O, ozone, and N2O, as well as the effects of temperature and
emissivity.
We use the XCH4 retrievals discussed in Parker et al. (2011). A
description of the errors for GOSAT CH4 retrievals is discussed in Butz
et al. (2010, 2011), Parker et al. (2011), and Schepers et al. (2012) and
references therein and includes the effects of noise, aerosols, and surface
albedo. Uncertainties for both the TES and GOSAT retrievals range from 8 to
20 ppb (or 1 % or less). All TES and GOSAT products include uncertainties,
the a priori and averaging kernel matrices. In this paper we only derive the
uncertainties that result from estimating lower tropospheric methane from
combining TES and GOSAT methane retrievals.
Description of WDCGC data
Lower tropospheric CH4 concentrations are compared against July 2009
CH4 measurements from 25 Global Atmospheric Watch (GAW) sites.
Measurements are obtained from World Data Centre for Greenhouse Gases
(WDCGG, http://ds.data.jma.go.jp/gmd/wdcgg/) website source.
Monthly surface CH4 concentrations are based on stationary platform
continuous and flask air sampling observations. Figure C1 shows the
locations of the measurements used for the comparison. Data are selected if
they corresponded to one of the 5∘lon×4∘lat estimates derived from the
GOSAT/TES measurements, and therefore only land measurements are usable for
this comparison.
Coordinates of the surface data used in the comparison shown in
Fig. 9.
Acknowledgements
Part of this research was carried out at the Jet Propulsion Laboratory,
California Institute of Technology, under a contract with the National
Aeronautics and Space Administration. A. J. Turner was supported by a Computational
Science Graduate Fellowship (CSGF). This research was funded by NASA ROSES
CSS proposal 13-CARBON13_2-0071 and the NASA Carbon
Monitoring System. The GOSAT XCH4 data were generated with funding from
the UK National Centre for Earth Observation and the ESA GHG-CCI project
with the GOSAT L1B data kindly provided by JAXA/NIES/MOE. Methane surface
data were downloaded from the World Data Centre for Greenhouse Gases. We are
very grateful to all the institutions and individuals who provide these
surface data for researchers to use as these efforts are critical for carbon
cycle science research; the following is hopefully an inclusive list
institutions and individuals, based on email response, who provide data that
we use in this research: (1) NOAA, Boulder CO/Ed Dlugokencky, Laboratory
for Earth Observations and Analyses, (2) ENEA, Palermo, Italy/Salvatore
Piacentino, the CSIRO Flask Network/Paul Krummel, (3) Atmospheric
Environment Division, Global Environment and Marine Department Japan
Meteorological Agency/Atsushi Takizawa, and (4) Canadian Greenhouse Gas
Measurement Program, Environment Canada/Doug Worthy.
Edited by: W. R. Simpson
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