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.

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.

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.

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.

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.

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. >4 ppm). 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 (>2 ppm) over India and the African regions typically associated with biomass burning. There is also a moderate level of uncertainty (>1 ppm) 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.

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. .

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 .

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.

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 (>4 ppm) 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 .