AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus GmbHGöttingen, Germany10.5194/amt-8-4785-2015Assessing 5 years of GOSAT Proxy XCH4 data and associated uncertaintiesParkerR. J.rjp23@le.ac.ukhttps://orcid.org/0000-0002-0801-0831BoeschH.BycklingK.WebbA. J.PalmerP. I.https://orcid.org/0000-0002-1487-0969FengL.BergamaschiP.https://orcid.org/0000-0003-4555-1829ChevallierF.https://orcid.org/0000-0002-4327-3813NotholtJ.DeutscherN.https://orcid.org/0000-0002-2906-2577WarnekeT.HaseF.SussmannR.KawakamiS.KiviR.https://orcid.org/0000-0001-8828-2759GriffithD. W. T.https://orcid.org/0000-0002-7986-1924VelazcoV.Earth Observation Science, Department of Physics and Astronomy, University of Leicester, Leicester, UKNational Centre for Earth Observation, Department of Physics and Astronomy, University of Leicester, Leicester, UKSchool of GeoSciences, University of Edinburgh, Edinburgh, UKNational Centre for Earth Observation, School of GeoSciences, University of Edinburgh, Edinburgh, UKEuropean Commission Joint Research Centre, Institute for Environment and Sustainability, Ispra, ItalyLab. des Sciences du Climat et de l'Environnement, CNRS, Gif-sur-Yvette, FranceInstitute of Environmental Physics, University of Bremen, Bremen, GermanyKarlsruhe Institut für Technologie, Karlsruhe, GermanyJapan Aerospace Exploration Agency (JAXA), Tsukuba, JapanFinnish Meteorological Institute, Arctic Research, Sodankylä, FinlandSchool of Chemistry, University of Wollongong, Wollongong, AustraliaR. J. Parker (rjp23@le.ac.uk)17November2015811478548015May201517June201520October201529October2015This 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/8/4785/2015/amt-8-4785-2015.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/8/4785/2015/amt-8-4785-2015.pdf
We present 5 years of GOSAT XCH4 retrieved using the
“proxy” approach. The Proxy XCH4 data are validated against
ground-based TCCON observations and are found to be of high quality
with a small bias of 4.8 ppb (∼0.27 %) and
a single-sounding precision of 13.4 ppb (∼ 0.74 %). The station-to-station bias (a measure of the relative
accuracy) is found to be 4.2 ppb. For the first time the
XCH4/XCO2 ratio component of the Proxy
retrieval is
validated (bias of 0.014 ppbppm-1 (∼0.30 %),
single-sounding precision of 0.033 ppbppm-1 (∼0.72 %)).
The uncertainty relating to the model XCO2 component of the
Proxy XCH4 is assessed through the use of an ensemble of
XCO2 models. While each individual XCO2 model is found to
agree well with the TCCON validation data (r=0.94–0.97), it is
not possible to select one model as the best from our
comparisons. The median XCO2 value of the ensemble has
a smaller scatter against TCCON (a standard deviation of
0.92 ppm) than any of the individual models whilst
maintaining a small bias (0.15 ppm). This model median
XCO2 is used to calculate the Proxy XCH4 with the maximum
deviation of the ensemble from the median used as an estimate of the
uncertainty.
We compare this uncertainty to the a posteriori retrieval error (which is assumed to reduce with sqrt(N)) and
find typically that the model XCO2 uncertainty becomes
significant during summer months when the a posteriori error is at
its lowest due to the increase in signal related to increased
summertime reflected sunlight.
We assess the significance of these model and retrieval
uncertainties on flux inversion by comparing the GOSAT XCH4
against modelled XCH4 from TM5-4DVAR constrained by NOAA
surface observations (MACC reanalysis scenario S1-NOAA). We find
that for the majority of regions the differences are much larger
than the estimated uncertainties. Our findings show that useful
information will be provided to the inversions for the majority of
regions in addition to that already provided by the assimilated surface
measurements.
Introduction
Atmospheric methane (CH4) contributes significantly to the
Earth's radiative forcing budget , making it the
second most important anthropogenic greenhouse gas after carbon
dioxide (CO2). The major sources of atmospheric methane
include wetland emission, rice production, enteric fermentation
(cattle), termites, biomass burning, fossil fuel production, and waste
. There remains, however, a large degree of
uncertainty on the magnitude of these individual sources
.
The lifetime of CH4 in the atmosphere is mainly controlled by
its reaction with the hydroxyl free radical (OH), resulting in an
atmospheric lifetime of approximately 9 years. Given its long atmospheric lifetime, there is
a need for long-term global measurements to fully understand how the
atmospheric distribution of CH4 is evolving with time. Indeed,
recent unexpected variability in the atmospheric growth rate of
methane has emphasised gaps in our current understanding
.
In order to begin to understand the spatio-temporal distribution of
atmospheric methane, regular global satellite observations of
CH4 can be coupled with highly precise but geographically
sparse surface concentration data. Through the combination of both
data sources, the large uncertainties related to the upscaling of
surface concentration data can be minimised whilst also obtaining
information in remote regions where surface measurements are not
available.
Various studies have demonstrated the utility of such space-borne
measurements in determining the regional surface fluxes of methane
using data from the SCIAMACHY
and Greenhouse gases Observing SATellite (GOSAT)
instruments.
The SCIAMACHY instrument operated onboard ENVISAT and provided
a 9-year record (2003–2012) of global methane total column
observations . The
continuation of this time series of space-based observations was
ensured by the launch of the first dedicated greenhouse gas measuring
satellite, the Japanese GOSAT,
in 2009 . GOSAT provides global coverage with
a 3-day repeat cycle and was designed with the intention of
characterising continental-scale sources and sinks.
In a previous work we presented the first year of
our global short-wave infrared (SWIR) measurements of the dry-air
column-averaged mole fraction of CH4 (XCH4) from the
GOSAT mission using the “proxy” retrieval approach. This data
product has subsequently been developed and
validated as part of the ESA Climate Change
Initiative Greenhouse Gas project and we now report an assessment of
the full 5-year data set for version 5.0 of the University of
Leicester GOSAT Proxy XCH4 data product.
This work is motivated by the desire to better understand the
uncertainty characteristics of the Proxy XCH4 data for use
within flux inversion systems, especially relating to uncertainties
introduced by the model XCO2.
In Sect. we describe the retrieval approach,
including details of the updates since the original version of the
University of Leicester GOSAT Proxy XCH4 data
. In Sect. we compare both the
Proxy XCH4 and the XCH4/XCO2 ratio against
the ground-based validation data. In Sect. we
assess the CO2 model component of the Proxy XCH4 for
the first time, with Sect. then
discussing the associated uncertainty of the final Proxy XCH4
product and its utility in constraining surface fluxes within an
inversion framework. Finally, we conclude the paper in
Sect. and provide recommendations for data
users.
University of Leicester GOSAT Proxy XCH4 retrieval
updates
The University of Leicester GOSAT Proxy XCH4 retrieval
utilises the Orbiting Carbon Observatory (OCO) “full physics” retrieval algorithm, developed for
the original NASA OCO mission to
retrieve XCO2 (dry-air column-averaged mole fraction of
CO2) from a simultaneous fit of SWIR O2 and
CO2 bands and has subsequently been modified to operate on
GOSAT spectral data.
Full details of the OCO retrieval algorithm can be found in
. In short, the retrieval algorithm utilises an
iterative retrieval scheme based on Bayesian optimal estimation to
estimate a set of atmospheric, surface, and instrument parameters from
the measured spectral radiances, referred to as the state vector. The
state vector of our retrieval consists of 20-level profiles for
CH4 and CO2 volume mixing ratios (vmr), profile
scaling factors for H2O vmr, and temperature, surface albedo, and
spectral dispersion.
Rather than perform the “full physics” retrieval as typically used
for CO2, an alternative
approach is possible for CH4, the so-called “proxy”
method. First used for the retrieval of XCH4 from SCIAMACHY
, this approach uses the fact that there
exists CO2 and CH4 spectral signatures located close
together at around 1.6 µm and hence the majority of
atmospheric scattering and instrument effects will be similar between
the two bands. The ratio of the retrieved XCH4/XCO2
should cancel modifications to the length of the light path that are
experienced due to scattering , with the CO2
effectively acting as a “proxy” for the unknown light-path
enhancements. As CO2 is known to vary much less than
CH4, the final XCH4 product can be obtained by
multiplying this XCH4/XCO2 ratio by a model
CO2 value, typically taken from a global chemistry transport
model (Eq. ).
ProxyXCH4=[XCH4][XCO2]×ModelXCO2
The “proxy” retrieval approach has various advantages over the full
physics approach . Because there is no reliance
on an explicit a priori knowledge of the aerosol distribution, the
proxy approach is more robust in the presence of aerosols and also far
less sensitive to instrumental issues or inconsistent radiometric
calibration between the spectral bands than is the case for the full
physics approach. Additionally, as moderate scattering from aerosols
will be cancelled out and still result in an accurate retrieval of
XCH4, the number of successful soundings for the proxy
approach is typically much higher than for the full physics approach
which requires far stricter post-filtering. This leads not only to
more soundings in general but also to more soundings over regions where
very little full physics data may be available, such as in the
tropics.
The main disadvantage with the proxy approach is that it is reliant on
an accurate, unbiased model XCO2 data set to convert the
XCH4/XCO2 ratio back into an XCH4 quantity;
otherwise errors relating to the model XCO2 may be folded into
the final XCH4 result. Here we present assessments of the
different uncertainties to determine the importance of this aspect of
the Proxy XCH4 data.
We process the latest versions of the GOSAT Level 1B files (version
161.160) acquired directly from the NIES Large Volume Data Server and
apply the recommended radiometric calibration and radiometric
degradation correction as per .
For the spectroscopic inputs we use v4.2.0 of the OCO line lists with
CH4 taken from the Total Carbon Column Observing Network
(TCCON) line lists (version “20120409”). The
a priori pressure, temperature, and water vapour is taken from the
ECMWF ERA-Interim data . For the CO2 a priori
we use the MACC-II CO2 inversion (v13r1) and for the
CH4 we use the MACC-II CH4 inversion (v10-S1NOAA,
using 2012 data for 2013) but here we adjust the stratospheric methane
using a specialised full chemistry run (run ID 563) of the TOMCAT
stratospheric chemistry model from the University of Leeds
. This TOMCAT model run has been validated against ACE-FTS observations and was found to provide a more accurate representation of the stratosphere.
The spectral noise is estimated from the standard deviation of the
out-of-band signal. Spectra over ocean or with a signal-to-noise ratio (SNR)
below 50 are removed. Cloud-contaminated scenes are removed by the
comparison of a clear-sky surface pressure retrieval from the
O2 A-band to the ECMWF surface pressure for the relevant
measurement time and location. A scene is determined to be cloudy when
the retrieved surface pressure differs by more than 30 hPa
from the estimated ECMWF surface pressure. This relatively
loose threshold is allowed as the proxy retrieval approach
remains relatively robust in the presence of near-surface clouds. The average difference between our retrieved surface pressure
and ECMWF after filtering for cloud is approximately 3 hPa
with a standard deviation of below 10 hPa, with the offset
from 0 hPa being attributed to spectroscopic uncertainties in
the O2 cross-sections. The Proxy XCH4 retrieval is
performed for all scenes that are deemed to be sufficiently
cloud free.
After filtering for signal-to-noise, cloud, and data quality we are
left with 1 032 760 XCH4 retrievals over land between
April 2009 and December 2013. Figure shows
global maps of the Proxy XCH4 for each season and compares it
to the MACC-II model XCH4 data. Both model and observation
show the XCH4 variability in time and space, in particular
with the large emissions of methane from wetland and rice cultivation
over India and S.E. Asia.
Seasonal global maps of the University of Leicester GOSAT
Proxy XCH4 (top) and the MACC-II (bottom) model XCH4
data (v10-S1NOAA). Both model and observation show the XCH4
variability in time and space, in particular with the large
emissions of methane from wetland and rice cultivation over India
and S.E. Asia. Note that GOSAT changed their pointing pattern in August 2010 from five across-track points to three across-track points, resulting in a change in spatial coverage.
Validation of the Proxy XCH4 and
XCH4/XCO2 ratio
This section presents the validation of the University of Leicester
GOSAT Proxy XCH4 v5.0 data through comparison to observations
from the ground-based TCCON. In addition, for the first time the
XCH4/XCO2 ratio itself, the core component of the
Proxy XCH4 data, is validated against the corresponding TCCON
data.
TCCON is a global network of ground-based high-resolution Fourier
transform spectrometers recording direct solar spectra in the
near-infrared spectral region . The TCCON data are
calibrated to World Meteorological Organization (WMO) standards by
calibration against aircraft measurements . Although
it should be noted that this aircraft calibration does not measure the
whole column, the TCCON data are the standard against which current
satellite observations of greenhouse gases are validated
.
To date, all previous validation of satellite greenhouse gas
observations against TCCON has used TCCON data that were affected by
instrumental biases relating to a laser sampling error which resulted
in an XCO2 error of approximately 0.26 % (1 ppm)
. Although the corresponding XCH4
error was not quantified, it is expected that it would be of similar
magnitude (i.e. 1 part in 400). The latest, recently released, version
of the TCCON data (GGG2014) incorporates a correction for the laser
sampling errors and any remaining bias is expected to be small.
GGG2014 TCCON XCH4
data and the Proxy XCH4 plotted as time series for each TCCON site.
The mean GOSAT-TCCON difference, the standard deviation of the GOSAT-TCCON
difference, the correlation coefficient, and the number of soundings are all provided for each site.
Figure shows the GGG2014 TCCON XCH4
data and the Proxy XCH4 plotted as time series for each TCCON site.
The mean GOSAT-TCCON difference, the standard deviation of the GOSAT-TCCON
difference, the correlation coefficient, and the number of soundings are all provided for each site.
Figure (top) shows the correlation between
the GGG2014 TCCON XCH4 data and the Proxy XCH4 values
within ± 5∘ of each TCCON site and a temporal coincidence
of ± 2 h. It should also be noted that for all TCCON comparisons,
the difference inherent in the data due to using different a priori
has been compensated for (as discussed in , by
replacing the a priori used in the GOSAT retrievals with the TCCON
a priori after the retrieval has been performed) which typically
increases the GOSAT XCH4 data by an average of between
0 and 5 ppb with the larger effect seen at more northernly TCCON
stations. We use all TCCON sites where version GGG2014 has been
processed at the time of writing that contain data during the GOSAT
time period (2009–2014). This results in 11 TCCON stations ranging
from Sodankylä, Finland, at 67.4∘ N to Lauder, New Zealand,
at 45.0∘ S. The correlation between the GOSAT and TCCON data
is reasonable/good across all sites, ranging from 0.54 at Karlsruhe to 0.79 at
Lauder with an overall correlation coefficient of 0.87 between 22 619
points. The overall bias is found to be 4.8 ppb with an
overall single measurement precision of 13.4 ppb (ranging from
8.3 ppb at Darwin to 14.9 ppb at Garmisch). The
station-to-station bias, which is an indication of the relative
accuracy, is calculated as the standard deviation of the individual
site biases and is found to be just 4.2 ppb.
In addition to the validation of the Proxy XCH4 data, we also
present for the first time the validation of the
XCH4/XCO2 ratio. This ratio is the quantity directly
retrieved from the satellite measurement, is independent of any model
XCO2, and has recently itself been used directly within a flux
inversion study . The correlation coefficient
across all stations is found to be 0.88 (ranging from 0.6 at
Wollongong to 0.88 at Sodankylä) with a mean bias of
0.014 ppbppm-1 and a single-sounding precision of
0.033 ppbppm-1 (ranging from 0.20 ppbppm-1
at Darwin to 0.037 ppbppm-1 at Garmisch). The statistics
for the XCH4/XCO2 ratio are therefore comparable to
those of the Proxy XCH4 itself, suggesting that the majority
of the variation is from the satellite retrieval itself and not
introduced by the model XCO2. The next section investigates
this aspect in more detail.
Correlation plot of the Proxy XCH4 (top) and the
XCH4/XCO2 ratio (bottom) data against TCCON
ground-based FTS data at 11 TCCON sites. The overall bias, standard
deviation (single-sounding precision), correlation coefficient, and
total number of soundings are provided. Note that the Lauder TCCON
station upgraded the instrument from a Bruker 120 to a Bruker 125 in
February 2010 and these two data sets are displayed separately.
Assessing the CO2 model ensemble component
In Sect. the final Proxy XCH4 and the
XCH4/XCO2 component were both validated against the
TCCON data. In this section we validate the remaining component of the
proxy product from Eq. (), namely the model
XCO2.
As discussed in Sect. , this update to the
University of Leicester GOSAT Proxy XCH4 data uses an ensemble
of model XCO2 data to act as the model XCO2
component. We utilise the XCO2 from three state-of-the-art
global transport models which all assimilate surface in situ
measurements; GEOS-Chem University of Edinburgh –
v1.50, MACC-II (, v14r1) and
CarbonTracker NOAA –vCT2013B. These model runs
have assimilated similar surface measurements but not necessarily from
all of the same data sets or the same locations. The models also have
different spatial resolutions and different temporal coverage
(GEOS-Chem: 2009–2011, 5∘× 4∘; CarbonTracker: 2009–2012, 3∘× 2∘; MACC-II: 2009–2012, 3.75∘× 1.89∘).
Where the model does not cover the full GOSAT time period studied here,
the data from the previous year are used and adjusted by the NOAA annual growth rate.
Correlation plot of the model XCO2 data for
GEOS-Chem, MACC-II, CarbonTracker, and the ensemble median against
TCCON ground-based FTS data at 11 TCCON sites. The overall bias,
standard deviation (single-measurement precision), correlation
coefficient, and total number of soundings are provided separately.
The main concern with using modelled XCO2 data for the proxy
method is that the additional uncertainty added to the final proxy
data product is difficult to determine. Where the model XCO2
data are constrained by surface data there can be a high degree of
confidence that the model data are close to representing the true value of
CO2; however, it is away from such regions where there is
a possibility of adding additional biases into the Proxy XCH4
data. The TCCON stations are mostly in regions that are also
well constrained by surface in situ measurements and hence the model
CO2 data should be well constrained, at least at the surface
level, and it is therefore expected to reasonably reproduce the TCCON
column data. Figure confirms that this
is the case. As the model XCO2 is used as a component in the
proxy retrievals, the models are treated as “pseudo-measurements” and validated in the same
way as the satellite data in order to maintain consistency with the satellite validation. The model XCO2 data sampled at each GOSAT
measurement point within ± 2∘ of each TCCON station are found to
agree well with the TCCON data, with the correlation coefficients ranging
from 0.94 (GEOS-Chem) to 0.97 (MACC-II and CarbonTracker). Similarly the
precision and bias to TCCON are both found to be small (ranging from 0.97 to
1.3 and 0.07 to 0.27 ppm respectively). The relative accuracies (the
standard deviation of the individual site biases) are similar at around
0.5 ppm, with CarbonTracker and GEOS-Chem performing slightly better than
MACC-II. Another metric to assess the models is how often they provide the
median value of the ensemble. CarbonTracker (41 %) and MACC-II (36 %)
tend to provide the median value more often than GEOS-Chem (22 %) but this
can vary per site with the contribution from MACC-II as low as 27 % at
Darwin (and CarbonTracker at 60 %) and conversely as high as 44 % at
Wollongong (with CarbonTracker only 21 %). This provides further indication
that no one model can be determined to be the “best”.
For a more detailed analysis of the performance of the different XCO2
models please see Table in Appendix A. In short,
none of the models are found to consistently be superior over the other
models. GEOS-Chem typically has the highest scatter against TCCON but also
has the smallest bias at 5 out of 12 of the sites. MACC-II has the smallest
bias at seven sites but the highest bias at four of the sites. CarbonTracker has the
highest bias at seven of the sites but also has the smallest scatter at eight of the
sites. Whilst the absolute bias in the calculated median XCO2 is
typically not quite as small as the best of the individual models, the
scatter in the median is better than (or the same as) the best scatter from
any of the individual models at every site except Lauder_120 (where the
time series is the shortest) and even there it is only worse than the best
model by less than 0.1 ppm.
The above has demonstrated that it is not a simple decision to
determine which model most accurately represents the true atmosphere,
even in locations where all of the models have been constrained by
(often the same) surface measurements and high-quality validation data
are available. In more remote regions where we neither have validation
data nor surface measurements to constrain the models, this
inconsistency between the models becomes more pronounced. It is this
uncertainty in model XCO2 in regions away from the available
validation data that we attempt to address through the use of the
XCO2 model ensemble. Each of the three XCO2 models are
sampled at every GOSAT time and location and convolved with the
scene-specific GOSAT averaging kernels. The median value of the three
model values is used as the model XCO2 in calculating the
final Proxy XCH4. However, we also define the uncertainty on
this median XCO2 as the maximum of the absolute differences of
each individual model to the median value.
We have already demonstrated that the models all well reproduce the
validation data at TCCON sites without any one model identified as
being better than the others from our comparisons. Where the models
all agree well with each other away from the validation sites, the
assumption is that the models are accurately representing the true
atmosphere. Where the models disagree with each other, we do not know
which model is correct in the absence of further validation data and
in some cases the discrepancy between models can be very large
(i.e. >4ppm). In such cases where no validation is
possible, the best estimate of the uncertainty in the model
XCO2 data is obtained by examining the difference of the model
data around the median value. Figure
shows global maps of this estimated model uncertainty for each
season. There are clear spatial/temporal patterns in the distribution
of this model uncertainty. During March–May (boreal “spring”),
there is a large uncertainty (>2ppm) over India and the
African regions typically associated with biomass burning. There is
also a moderate level of uncertainty (>1ppm) over Europe,
South America, and for the latter years over North America and
Australia. For the summer months (June–August) it is the Eurasian
region, extending from the Ural mountains eastwards through Siberia
and northern China, where the model uncertainty is largest at over
2 ppm. This is to be expected as in the Northern Hemisphere it
is the period of greatest photosynthetic activity and the model
sensitivity to the underlying mechanisms is likely to be
largest. During boreal autumn (September–November), the uncertainty
in the Northern Hemisphere is vastly reduced again, with India being
the major region of uncertainty along with South America and regions
of biomass burning in Africa. Winter is similar to autumn, with all
three models in very good agreement with each other in the Northern
Hemisphere, with only S.E. Asia showing a moderate level of
uncertainty. In the Southern Hemisphere, again South America and
southern Africa show moderate uncertainty which appears to be linked
to emissions from biomass burning.
This section has shown that the estimated uncertainty of the model
XCO2 can vary greatly in time and space. When considering the
implication of this uncertainty on flux inversions of the Proxy
XCH4 data, the relative importance of the different
uncertainties must be considered. The following section investigates
the distribution of the model XCO2 uncertainty and judges its
relative importance against the a posteriori error from the retrieval
itself. Finally, both of these uncertainties are assessed against the
difference to modelled XCH4 already constrained by surface
observations to determine the utility of the satellite data despite
the presence of these uncertainties.
Seasonal maps of the model difference, defined as the maximum
absolute difference of the three-model ensemble from the median. All
individual soundings have been averaged into
2∘×2∘ grid boxes over each season. The
largest uncertainties occur in regions where the CO2
variability is expected to be highest and the models are
unconstrained by surface measurements.
Assessing the relative uncertainties
In order to assess the importance of the uncertainty of the model
XCO2, we bin the three model fields into 4∘×5∘
grid boxes over 8-day time steps and calculate the maximum difference of the
three-model ensemble from the median value to use as an estimate of the
uncertainty in the model values. We convert this uncertainty in model
XCO2 into an uncertainty in XCH4 by multiplying each point by
its respective retrieved XCH4/XCO2 amount. We also
calculate the average a posteriori error for the same data. Unlike the more
systematic XCO2 model uncertainty, the a posteriori error should be
close to random and hence reduce approximately with the square root of the
number of soundings being averaged. If the error does not reduce as much, the
model XCO2 component would then contribute even less to the total,
leading to this assumption being a “worst case” scenario for the effect of
the model XCO2 uncertainty. These 4∘×5∘ grid
boxes are then themselves averaged over the Transcom regions
as defined in Fig. .
The Transcom regions over which the
4∘×5∘ gridded data are then averaged in
Fig. .
In Fig. , the red line shows the mean of the Proxy XCH4
random (a posteriori) error from each 4∘×5∘ box
averaged over each Transcom region with the green line representing
the estimated uncertainty related to the model XCO2. The
majority of regions exhibit a similar trend over time. The
a posteriori error peaks in the winter months when the SNR of the measurement is at its lowest and is at a minimum
during the summer months when the SNR is at a maximum. This seasonal
effect is more pronounced at higher latitudes which experience
a greater degree of variability of sunlight throughout the
year. Conversely, the XCO2 model uncertainty follows
biospheric activity with the uncertainty largest during the summer
months when the XCO2 variability is at a maximum and reduces
to a minimum in the winter months when biospheric activity is
lower. This leads to the situation where the a posteriori error
dominates the model uncertainty in winter months but during summer
months the model uncertainty can be comparable to, or even exceed, the
a posteriori error. Taking the North America Temperate region as an
example, during winter the a posteriori error can reach up to
8 ppb with the error from the model XCO2 significantly
lower with values less than 2 ppb. In contrast, during the
summer months, the a posteriori error reduces to around 5 ppb
but the error for the model XCO2 increases to 5 ppb,
meaning that both become significant components of the overall
uncertainty.
We have shown that the uncertainty related to the XCO2 model
can, particularly in the Northern Hemisphere during summer months, be
of comparable magnitude to the a posteriori retrieval error. However,
that in itself does not preclude the data from adding useful
information to a CH4 flux inversion.
The MACC-II model XCH4 (v10-S1NOAA) data have assimilated NOAA
surface measurements at background sites and hence are well
constrained in the remote atmosphere . Here we
calculate the difference between the MACC XCH4 model field and
the GOSAT Proxy XCH4 data for each GOSAT measurement (referred
to from here as ΔXCH4). We then aggregate these differences in the
same way as the model XCO2 uncertainties. Note that the MACC XCH4 model data are currently only available until the end of 2012. As some inversion
systems will perform a simple (e.g. latitudinal) bias correction, the
calculated retrieval a posteriori and model XCO2 uncertainties
can potentially be much lower than the ΔXCH4 value
but still not provide information to the inversion. For this reason,
it is also important to consider both the mean
(μΔXCH4) and the standard deviation
(σΔXCH4) of the ΔXCH4. To
determine whether the GOSAT data are capable of providing information
to the inversion, we compare the a posteriori and model XCO2
uncertainties to the μΔXCH4 and
σΔXCH4 values as shown in
Fig. , with the seasonal averages for all of these
values presented in Table .
Time series for each Transcom region showing the a posteriori
retrieval error (red), the estimated uncertainty from the model
XCO2 (green), and the mean (navy) and standard deviation
(purple) of the difference between the GOSAT and MACC-II
XCH4. The a posteriori error is assumed to be a random error and
hence reduces with the square root of the number of measurements
whilst the XCO2 model uncertainty is expected to be
a systematic error and hence does not reduce.
It should be noted here that the absolute values are not necessarily
quantitatively comparable when taking into account how an inversion
system will use the two different quantities. The a posteriori error
of the retrieved XCH4 is an indication of the weighting that
the inversion will give to an observation over the a priori, with
a smaller value indicating that the inversion will “trust” the
observation more. The ΔXCH4 is an indication of how
much the inversion needs to adjust the fluxes in order to match the
observation. However, if the estimated uncertainties are significantly
less than the μΔXCH4
and σΔXCH4 values it is expected that the
observations should provide value to the inversion. It should also be
noted that this bias term (μΔXCH4) may also
reflect systematic biases in the XCH4 model due to, for
example, errors in the vertical model profile whilst the sigma term
(σΔXCH4) may also relate to subgrid-scale
variations which are unresolved at the model resolution.
For the North American Boreal region, both the
μΔXCH4 and σΔXCH4
values are very similar in terms of phase and magnitude to the
a posteriori uncertainty with the σΔXCH4
ranging from an average of 9.6 ppb in summer to
14.5 ppb in winter compared to the a posteriori uncertainty
that ranges from 6.6 ppb in summer to 10.8 ppb in
winter. This suggests that regardless of the contribution to the
uncertainty from the XCO2 model, it would be difficult for the
satellite data to inform the inversion any further than the in situ
data already do. However, this is not the case for the North
American Temperate region where the μΔXCH4
(7.4–11.6 ppb) and σΔXCH4
(8.4–10.0 ppb) are far larger than the total uncertainty
(6.0–6.9 ppb) for much of the year. Both South American
regions exhibit more complicated behaviour with far less of an
apparent seasonality in the μΔXCH4. Instead,
for most years μΔXCH4 is much higher than the
uncertainties (which themselves do not exhibit much seasonality in
these regions). However, the year 2010 seems to be an anomalous year
where the μΔXCH4 data are much more in
agreement and in this year the difference is of comparable magnitude
to the uncertainties with values between 4 and 8 ppb. The
σΔXCH4 does exhibit more seasonality than
the μΔXCH4 and is again considerably higher
than the estimated uncertainties (7–20 ppb
vs. 5–6 ppb). In combination, this suggests that the GOSAT
observations over South America should add considerable information to
the inversion.
For Northern Africa, both the a posteriori error and the uncertainty
related to the XCO2 model are small due to the high SNR over
the Sahara and the low CO2 variability respectively (with
seasonal average values ranging from 3.3 to 3.6 ppb for the
a posteriori error and from 2.8 to 4.6 ppb for the model XCO2
error). In contrast, the μΔXCH4
(7.2–12.3 ppb) and σΔXCH4
(4.9–8.4 ppb) values over this region are relatively large
with a high degree of temporal variability, suggesting that the
satellite data should add considerable value in constraining the
inversion over this region. One complication is that GOSAT operates in
a “medium gain” mode over the desert and hence may exhibit different
instrumental biases over such regions but, due to the proxy method, any
such differences in instrumental biases that relate to
light-path modification should be minimised. Southern
Africa shows similar behaviour with the total uncertainty being low
(seasonal averages of 5.1–7.3 ppb) compared to the much
larger μΔXCH4 (12.6–19.8 ppb) and
σΔXCH4 (5.7–10.7 ppb) values, again
indicating that considerable value is present in the satellite data.
The Eurasian Boreal region behaves similarly to the North American
Boreal region. The μΔXCH4 and
σΔXCH4 is of similar phase and magnitude to
the retrieval a posteriori error, suggesting little information will
be added to any inversion over this region beyond what is available
from the in situ measurements. In contrast, the
μΔXCH4 values over the Eurasian Temperate
region show a large variability with the differences in winter months
much larger than the total uncertainty (9.9 ppb
vs. 5.8 ppb), while in summer months the magnitudes become
much more similar (5.5 ppb
vs. 7.3 ppb). Interestingly, the
σΔXCH4 values appear to be of similar
magnitude (5–20 ppb) but directly out of phase with the
μΔXCH4 values. Even during summer months when
the a posteriori (4.3 ppb) and model XCO2
(5.8 ppb) uncertainties are comparable to the
μΔXCH4 (5.5 ppb), the high variability
in the ΔXCH4 values, as indicated by
σΔXCH4 values of up to 20 ppb (and
a summertime mean value of 15.0 ppb), suggests that the
observations are capable of providing useful information to the
inversion.
The Tropical Asian region, which encompasses parts of India, China, and
Indonesia, typically has low values for both the a posteriori
(5.6–6.9 ppb) and XCO2 model (4.4–6.0 ppb)
uncertainties, with neither exhibiting much seasonal
variability. The μΔXCH4 and
σΔXCH4 values however are much more variable
(8.9–11.7 and 9.0–16.8 ppb) and generally much
higher than the uncertainties, suggesting that useful information from
the satellite data is present.
The European Transcom region has uncertainties in the satellite data
(seasonal averages of 7.6–10.0 ppb) that are of comparable
magnitude to the μΔXCH4 values
(8.1–10.7 ppb), especially when considering the combination
of the a posteriori and model XCO2 uncertainties. However, the
standard deviation of the μΔXCH4 values is
highly variable (8.7–13.2 ppb) which suggests that there is
scope for the observational data to aid in constraining the European
XCH4 fluxes.
Finally, the Australian Transcom region shows very small uncertainties
in the satellite data. The uncertainty associated with the model
XCO2 is comparable to the a posteriori error during the
Australian spring months but even in those circumstances, the
μΔXCH4 values are far larger (11.4 ppb
vs. 4.7 ppb), demonstrating that the satellite data are
capable of providing some information to the inversion over Australia,
although this may be limited in its ability to provide specific
information on Australian sources as the
σΔXCH4 values over this region are similar to
the estimated uncertainties with seasonal averages of
4.5–5.2 ppb compared to the total uncertainty values of
4.1–5.0 ppb.
Summary and conclusions
We present details of the update to the University of Leicester GOSAT
Proxy XCH4 v5.0 data set with 5 years of GOSAT data now
processed. The data are validated against the latest ground-based
TCCON data and found to agree well with on average a small bias of
4.8 ppb (∼0.27 %), a single-sounding precision of
13.4 ppb (∼0.74 %), and a relative accuracy of
4.2 ppb. For the first time the XCH4/XCO2
ratio component of the proxy retrieval is validated and also found to
agree well with TCCON with a bias of 0.014 ppbppm-1
(∼0.3 %) and a single-sounding precision of
0.033 ppbppm-1 (∼0.72 %).
A major unknown uncertainty in previous Proxy XCH4 products
was the uncertainty associated with the model XCO2. In this
work we validate three separate state-of-the-art chemistry transport
models against the TCCON data and find that although the models can
differ greatly (>4ppm) away from the TCCON stations, at
the validation locations it is difficult to distinguish which model
performs better from our comparisons. We therefore decide to use the
median of the three models to act as the model XCO2 in the
calculation of the Proxy XCH4 and use the maximum difference
to the median as a measure of the uncertainty in the model
XCO2. This model uncertainty is found to vary greatly in time
and space but is typically largest over regions associated with
biomass burning such as central Africa and in particular over the
Eurasian regions during summer months where large uptake in CO2 leads to large differences between the models.
In order to assess the relative importance of these uncertainties, we
compare this model XCO2 uncertainty to the a posteriori
retrieval error over the different Transcom regions and find typically
that where there is seasonality in the uncertainties, it is typically
directly out of phase between the two, resulting in the model
XCO2 uncertainty becoming significant during summer months
where the a posteriori error is at its lowest. This relates to the
fact that more sunlight leads to a reduction in the a posteriori
uncertainty (by virtue of providing a greater signal in the SWIR) and
at the same time is associated with an increase in photosynthesis and,
hence, more potential for differences in the model XCO2.
We assess the significance of these uncertainties on any flux
inversion by comparing the mean and standard deviation of the
GOSAT-MACC differences (μΔXCH4 and
σΔXCH4) to the estimated uncertainties. We
find that for the majority of regions the mean and standard deviation
of the ΔXCH4 values are much larger than the estimated
uncertainties, even taking into account the uncertainty related to the
model XCO2. Our findings show that useful information will be
provided to the inversions for the majority of regions, with the
exceptions being the boreal regions (North American Boreal and
Eurasian Boreal) where the uncertainty is of a similar magnitude to
the μΔXCH4 and σΔXCH4
values. It is important to note that the MACC data are already
constrained by NOAA background sites.
One final consideration for users of the Proxy XCH4 data who
are performing atmospheric inversions is that, should they have their
own XCO2 model which they believe is consistent with their
XCH4 model, it may be beneficial to only take the GOSAT
XCH4/XCO2 ratio and apply their own model
XCO2 (with appropriate averaging kernels) in order to minimise
transport model errors between the different models. Alternatively the
XCH4/XCO2 ratio can also be inverted directly as
shown in and .
Data sets
The GOSAT Proxy XCH4 data used in this publication are freely
available from
http://www.leos.le.ac.uk/GHG/ghg_cci/CRDP/data_2/ESACCI/GHG/GOSAT/CH4_GOS_OCPR/5.1/
upon request of a password. An updated version of this data set is now
available (version 6.0) covering 2009–2014. Additionally, these data now
contain both the raw XCH4 and XCO2 values as well as the
uncertainty associated with the model XCO2.
Table showing the comparison statistics between each
XCO2 model (sampled as per the GOSAT measurements) within
± 2∘ of each TCCON site against the TCCON validation
data. The difference (model-TCCON), the standard deviation of the
difference, and the correlation coefficient are all provided as is
the total number of measurements for each site, N, and the percentage
“share”
of the median for each model, %. For each of
the three models, GEOS-Chem, MACC-II, and CarbonTracker, the best
(bold) and worst (italic) value for each metric is
highlighted. For the ensemble median data, all values which are
better than the best individual model value are highlighted in
bold-italic. The lower panel provides overall statistics across all sites.
These include the relative accuracy (the standard deviation of the individual site biases),
the overall precision (the standard deviation of the GOSAT-TCCON
differences),
and the overall share that each model contributes to the median ensemble.
2∘×2∘Coincident criteriaGEOS-Chem MACC-II CarbonTracker Ensemble median TCCON SiteNDiff (ppm)SD (ppm)r%Diff (ppm)SD (ppm)r%Diff (ppm)SD (ppm)r%Diff (ppm)SD (ppm)rSodankylä5841.11.10.97200.90.90.98371.20.90.99421.10.80.99Bialystok14290.61.50.95250.41.10.97330.61.00.98440.61.00.97Karlsruhe1569-0.21.40.9222-0.61.10.9533-0.41.10.9545-0.41.10.95Orleans16500.31.20.95220.30.90.98330.40.90.97460.30.80.98Garmisch15270.81.30.93220.61.30.94340.81.20.95430.71.10.95Park Falls24340.41.10.97230.11.00.98380.51.00.98400.30.90.98Lamont7464-0.21.60.9220-0.10.90.98390.00.90.9841-0.10.90.98Saga379-0.61.10.9327-1.00.90.9533-0.30.90.9540-0.60.90.96Darwin24910.00.80.97120.50.70.97270.40.60.98600.30.60.98Wollongong2601-0.10.80.9636-0.10.80.96440.20.90.95210.00.80.96Lauder_120124-0.10.90.8227-0.30.70.8644-0.20.80.8429-0.20.80.84Lauder_1253680.30.40.99300.20.30.99400.40.40.99300.30.30.99Statistics for all sitesRelative accuracy (ppm)0.480.530.470.48Overall precision (ppm)1.30.970.960.92Total median share (%)223641
Table showing the seasonal averages of the data plotted in Fig. for each of the Transcom regions. The retrieved a posteriori error, the uncertainty related to the model XCO2, their combined total, and the mean and standard deviation of the GOSAT-MACC difference are all provided for each season and for each Transcom region.
The TCCON XCH4 and XCO2 data used in this publication
are publicly available from http://tccon.ornl.gov. The following
data have been used: Sodankylä , Bialystok
, Karlsruhe , Orleans
, Garmisch , Park Falls
, Lamont , Saga
, Darwin , Wollongong
, Lauder120 , and Lauder125
.
Acknowledgements
We thank Japanese Aerospace Exploration Agency, National Institute for Environmental Studies, and the Ministry
of Environment for the GOSAT data and their continuous support as
part of the Joint Research Agreement. R. J. Parker is funded through an ESA Living Planet Fellowship. The
work at the University of Leicester and University of Edinburgh was
supported by funding through the UK National Centre for Earth
Observation (NCEO), the Natural Environment Research Council (NERC),
and the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI). Part
of this work was funded by the NERC Amazonian Carbon Observatory
project (NE/J016284/1) and the NERC GAUGE project (NE/K002465/1).
This research used the ALICE High Performance Computing Facility at the University of Leicester.
The authors would like to thank Paul Wennberg as the TCCON PI for
provision of TCCON data. The European TCCON groups acknowledge
financial support by the EU project InGOS. The RAMCES team at LSCE
(Gif-sur-Yvette, France) is acknowledged for maintenance and
logistical work for the Orléans TCCON site. The University of Bremen
acknowledges support from the Senate of Bremen, the EU-project
ICOS-INWIRE, and operational funding from the National Institute for
Environmental Studies (NIES, Japan). A part of the work at JAXA was
supported by the Environment Research and Technology Development
Fund (A-1102) of the Ministry of the Environment, Japan. A part of
this work has been supported by the European Commission Seventh
Framework Programme (FP7/2007–2013) projects MACC under grant
agreement 218793 and MACC-II under grant agreement
283576.Edited by: I. Aben
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