Abstract. We present 5 years of GOSAT XCH 4 retrieved using the "proxy" approach. The Proxy XCH 4 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 XCH 4 / XCO 2 ratio component of the Proxy retrieval is validated (bias of 0.014 ppb ppm −1 (∼ 0.30 %), single-sounding precision of 0.033 ppb ppm −1 (∼ 0.72 %)).The uncertainty relating to the model XCO 2 component of the Proxy XCH 4 is assessed through the use of an ensemble of XCO 2 models. While each individual XCO 2 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 XCO 2 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 XCO 2 is used to calculate the Proxy XCH 4 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 XCO 2 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 XCH 4 against modelled XCH 4 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.

Abstract. This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4∕XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).

Abstract. The 2015-2016 strong El Niño event has had a dramatic impact on the amount of Indonesian biomass burning, with the El Niño-driven drought further desiccating the already-drier-than-normal landscapes that are the result of decades of peatland draining, widespread deforestation, anthropogenically driven forest degradation and previous large fire events. It is expected that the 2015-2016 Indonesian fires will have emitted globally significant quantities of greenhouse gases (GHGs) to the atmosphere, as did previous El Niño-driven fires in the region. The form which the carbon released from the combustion of the vegetation and peat soils takes has a strong bearing on its atmospheric chemistry and climatological impacts. Typically, burning in tropical forests and especially in peatlands is expected to involve a much higher proportion of smouldering combustion than the more flaming-characterised fires that occur in fine-fuel-dominated environments such as grasslands, consequently producing significantly more CH 4 (and CO) per unit of fuel burned. However, currently there have been no aircraft campaigns sampling Indonesian fire plumes, and very few ground-based field campaigns (none during El Niño), so our understanding of the large-scale chemical composition of these extremely significant fire plumes is surprisingly poor compared to, for example, those of southern Africa or the Amazon.Here, for the first time, we use satellite observations of CH 4 and CO 2 from the Greenhouse gases Observing SATellite (GOSAT) made in large-scale plumes from the 2015 El Niño-driven Indonesian fires to probe aspects of their chemical composition. We demonstrate significant modifications in the concentration of these species in the regional atmosphere around Indonesia, due to the fire emissions.Using CO and fire radiative power (FRP) data from the Copernicus Atmosphere Service, we identify fire-affected GOSAT soundings and show that peaks in fire activity are followed by subsequent large increases in regional greenhouse gas concentrations. CH 4 is particularly enhanced, due to the dominance of smouldering combustion in peatland fires, with CH 4 total column values typically exceeding 35 ppb above those of background "clean air" soundings. By examining the CH 4 and CO 2 excess concentrations in the fire-affected GOSAT observations, we determine the CH 4 to CO 2 (CH 4 / CO 2 ) fire emission ratio for the entire 2-month period of the most extreme burning (SeptemberOctober 2015), and also for individual shorter periods where the fire activity temporarily peaks. We demonstrate that the overall CH 4 to CO 2 emission ratio (ER) for fires occurring in Indonesia over this time is 6.2 ppb ppm −1 . This is higher than that found over both the Amazon (5.1 ppb ppm −1 ) and southern Africa (4.4 ppb ppm −1 ), consistent with the Indonesian fires being characterised by an increased amount of smouldering combustion due to the large amount of organic soil (peat) burning involved. We find the range of our satellitePublished by Copernicus Publicati...

Abstract. We use the GEOS-Chem global 3-D model of atmospheric chemistry and transport and an ensemble Kalman filter to simultaneously infer regional fluxes of methane (CH 4 ) and carbon dioxide (CO 2 ) directly from GOSAT retrievals of XCH 4 : XCO 2 , using sparse ground-based CH 4 and CO 2 mole fraction data to anchor the ratio. This work builds on the previously reported theory that takes into account that (1) these ratios are less prone to systematic error than either the full-physics data products or the proxy CH 4 data products; and (2) the resulting CH 4 and CO 2 fluxes are self-consistent. We show that a posteriori fluxes inferred from the GOSAT data generally outperform the fluxes inferred only from in situ data, as expected. GOSAT CH 4 and CO 2 fluxes are consistent with global growth rates for CO 2 and CH 4 reported by NOAA and have a range of independent data including new profile measurements (0-7 km) over the Amazon Basin that were collected specifically to help validate GOSAT over this geographical region. We find that large-scale multi-year annual a posteriori CO 2 fluxes inferred from GOSAT data are similar to those inferred from the in situ surface data but with smaller uncertainties, particularly over the tropics. GOSAT data are consistent withPublished by Copernicus Publications on behalf of the European Geosciences Union. smaller peak-to-peak seasonal amplitudes of CO 2 than either the a priori or in situ inversion, particularly over the tropics and the southern extratropics. Over the northern extratropics, GOSAT data show larger uptake than the a priori but less than the in situ inversion, resulting in small net emissions over the year. We also find evidence that the carbon balance of tropical South America was perturbed following the droughts of 2010 and 2012 with net annual fluxes not returning to an approximate annual balance until 2013. In contrast, GOSAT data significantly changed the a priori spatial distribution of CH 4 emission with a 40 % increase over tropical South America and tropical Asia and a smaller decrease over Eurasia and temperate South America. We find no evidence from GOSAT that tropical South American CH 4 fluxes were dramatically affected by the two large-scale Amazon droughts. However, we find that GOSAT data are consistent with double seasonal peaks in Amazonian fluxes that are reproduced over the 5 years we studied: a small peak from January to April and a larger peak from June to October, which are likely due to superimposed emissions from different geographical regions.