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We conduct a global inverse analysis of 2010–2018 GOSAT observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes anthropogenic methane emissions and their 2010–2018 trends on a 4∘×5∘ grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane). We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. We verify our inversion results with independent methane observations from the TCCON and NOAA networks. Our inversion successfully reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high for China (coal) and Russia (oil and gas) and too low for Venezuela (oil and gas) and the US (oil and gas). We show large 2010–2018 increases in anthropogenic methane emissions over South Asia, tropical Africa, and Brazil, coincident with rapidly growing livestock populations in these regions. We do not find a significant trend in anthropogenic emissions over regions with high rates of production or use of fossil methane, including the US, Russia, and Europe. Our results indicate that the peak methane growth rates in 2014–2015 are driven by low OH concentrations (2014) and high fire emissions (2015), while strong emissions from tropical (Amazon and tropical Africa) and boreal (Eurasia) wetlands combined with increasing anthropogenic emissions drive high growth rates in 2016–2018. Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.

Emissions of methane (CH4) from tropical ecosystems, and how they respond to changes in climate, represent one of the biggest uncertainties associated with the global CH4 budget. Historically, this has been due to the dearth of pan-tropical in situ measurements, which is particularly acute in Africa. By virtue of their superior spatial coverage, satellite observations of atmospheric CH4 columns can help to narrow down some of the uncertainties in the tropical CH4 emission budget. We use proxy column retrievals of atmospheric CH4 (XCH4) from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the nested version of the GEOS-Chem atmospheric chemistry and transport model (0.5∘×0.625∘) to infer emissions from tropical Africa between 2010 and 2016. Proxy retrievals of XCH4 are less sensitive to scattering due to clouds and aerosol than full physics retrievals, but the method assumes that the global distribution of carbon dioxide (CO2) is known. We explore the sensitivity of inferred a posteriori emissions to this source of systematic error by using two different XCH4 data products that are determined using different model CO2 fields. We infer monthly emissions from GOSAT XCH4 data using a hierarchical Bayesian framework, allowing us to report seasonal cycles and trends in annual mean values. We find mean tropical African emissions between 2010 and 2016 range from 76 (74–78) to 80 (78–82) Tg yr−1, depending on the proxy XCH4 data used, with larger differences in Northern Hemisphere Africa than Southern Hemisphere Africa. We find a robust positive linear trend in tropical African CH4 emissions for our 7-year study period, with values of 1.5 (1.1–1.9) Tg yr−1 or 2.1 (1.7–2.5) Tg yr−1, depending on the CO2 data product used in the proxy retrieval. This linear emissions trend accounts for around a third of the global emissions growth rate during this period. A substantial portion of this increase is due to a short-term increase in emissions of 3 Tg yr−1 between 2011 and 2015 from the Sudd in South Sudan. Using satellite land surface temperature anomalies and altimetry data, we find this increase in CH4 emissions is consistent with an increase in wetland extent due to increased inflow from the White Nile, although the data indicate that the Sudd was anomalously dry at the start of our inversion period. We find a strong seasonality in emissions across Northern Hemisphere Africa, with the timing of the seasonal emissions peak coincident with the seasonal peak in ground water storage. In contrast, we find that a posteriori CH4 emissions from the wetland area of the Congo Basin are approximately constant throughout the year, consistent with less temporal variability in wetland extent, and significantly smaller than a priori estimates.

We use the GEOS-Chem global 3-D model and two inverse methods (the maximum a posteriori and ensemble Kalman filter) to infer regional methane (CH4) emissions and the corresponding stable-carbon-isotope source signatures from 2004–2020 across the globe using in situ and satellite remote sensing data. We use the Siegel estimator to determine linear trends from the in situ data. Over our 17-year study period, we estimate a linear increase of 3.6 Tg yr−1 yr−1 in CH4 emissions from tropical continental regions, including North Africa, southern Africa, tropical South America, and tropical Asia. The second-largest increase in CH4 emissions over this period (1.6 Tg yr−1 yr−1) is from China. For boreal regions we estimate a negative emissions trend of −0.2 Tg yr−1 yr−1, and for northern and southern temperate regions we estimate trends of 0.03 Tg yr−1 yr−1 and 0.2 Tg yr−1 yr−1, respectively. These increases in CH4 emissions are accompanied by a progressively isotopically lighter atmospheric δ13C signature over the tropics, particularly since 2012, which is consistent with an increased biogenic emissions source and/or a decrease in a thermogenic/pyrogenic emissions source with a heavier isotopic signature. Previous studies have linked increased tropical biogenic emissions to increased rainfall. Over China, we find a weaker trend towards isotopically lighter δ13C sources, suggesting that heavier isotopic source signatures make a larger contribution to this region. Satellite remote sensing data provide additional evidence of emissions hotspots of CH4 that are consistent with the location and seasonal timing of wetland emissions. The collective evidence suggests that increases in tropical CH4 emissions are from biogenic sources, with a significant fraction from wetlands. To understand the influence of our results on changes in the hydroxyl radical (OH), we also report regional CH4 emissions estimates using an alternative scenario of a 0.5 % yr−1 decrease in OH since 2004, followed by a larger 1.5 % drop in 2020 during the first COVID-19 lockdown. We find that our main findings are broadly insensitive to those idealised year-to-year changes in OH, although the corresponding change in atmospheric CH4 in 2020 is inconsistent with independent global-scale constraints for the estimated annual-mean atmospheric growth rate.

The global characteristics of retrievals of the column-averaged CO2 dry air mole fraction, XCO2, from shortwave infrared observations has been studied using the expected measurement performance of the NASA Orbiting Carbon Observatory-2 (OCO-2) mission. This study focuses on XCO2 retrieval precision and averaging kernels and their sensitivity to key parameters such as solar zenith angle (SZA), surface pressure, surface type and aerosol optical depth (AOD), for both nadir and sunglint observing modes. Realistic simulations have been carried out and the single sounding retrieval errors for XCO2 have been derived from the formal retrieval error covariance matrix under the assumption that the retrieval has converged to the correct answer and that the forward model can adequately describe the measurement. Thus, the retrieval errors presented in this study represent an estimate of the retrieval precision. For nadir observations, we find single-sounding retrieval errors with values typically less than 1 part per million (ppm) over most land surfaces for SZAs less than 70° and up to 2.5 ppm for larger SZAs. Larger errors are found over snow/ice and ocean surfaces due to their low albedo in the spectral regions of the CO2 absorption bands and, for ocean, also in the O2 A band. For sunglint observations, errors over the ocean are significantly smaller than in nadir mode with values in the range of 0.3 to 0.6 ppm for small SZAs which can decrease to values as small as 0.15 for the largest SZAs. The vertical sensitivity of the retrieval that is represented by the column averaging kernel peaks near the surface and exhibits values near unity throughout most of the troposphere for most anticipated scenes. Nadir observations over dark ocean or snow/ice surfaces and observations with large AOD and large SZA show a decreased sensitivity to near-surface CO2. All simulations are carried out for a mid-latitude summer atmospheric profile, a given aerosol type and vertical distribution, a constant windspeed for ocean sunglint and by excluding the presence of thin cirrus clouds. The impact of these parameters on averaging kernels and XCO2 retrieval errors are studied with sensitivity studies. Systematic biases in retrieved XCO2, as can be introduced by uncertainties in the spectroscopic parameters, instrument calibration or deficiencies in the retrieval algorithm itself, are not included in this study. The presented error estimates will therefore only describe the true retrieval errors once systematic biases are eliminated. It is expected that it will be possible to retrieve XCO2 for cloud free observations and for low AOD (here less than 0.3 for the wavelength region of the O2 A band) with sufficient accuracy for improving CO2 surface flux estimates and we find that on average 18% to 21% of all observations are sufficiently cloud-free with only few areas suffering from the presence of persistent clouds or high AOD. This results typically in tens of useful observations per 16 day ground track repeat cycle at a 1° × 1° resolution. Averaging observations acquired along ~1° intervals for individual ground tracks will significantly reduce the random component of the errors of the XCO2 average product for ingestion into data assimilation/inverse models. If biases in the XCO2 retrieval of the order of a few tenth ppm can be successfully removed by validation or by bias-correction in the flux inversion, then it can be expected that OCO-2 XCO2 data can lead to tremendous improvements in estimates of CO2 surface-atmosphere fluxes.

East Africa is a key location for wetland emissions of methane (CH4), driven by variations in rainfall that are in turn influenced by sea-surface temperature gradients over the Indian Ocean. Using satellite observations of CH4 and an atmospheric chemistry-transport model, we quantified East African CH4 emissions during 2018 and 2019 when there was 3-σ anomalous rainfall during the long rains (March-May) in 2018 and the short rains (October-December) in 2019. These rainfall anomalies resulted in CH4 emissions of 6.2 ± 0.3 Tg CH4 and 8.6 ± 0.3 Tg CH4, in each three month period, respectively, and represent a 10% and 37% increase compared to the equivalent season in the opposite year, when rainfall was close to the long-term seasonal mean. We find the additional short rains emissions were equivalent to over a quarter of the growth in global emissions in 2019, highlighting the disproportionate role of East Africa in the global CH4 budget.

Changes in tropical wetland, ruminant or rice emissions are thought to have played a role in recent variations in atmospheric methane (CH 4 ) concentrations. India has the world’s largest ruminant population and produces ~ 20% of the world’s rice. Therefore, changes in these sources could have significant implications for global warming. Here, we infer India’s CH 4 emissions for the period 2010–2015 using a combination of satellite, surface and aircraft data. We apply a high-resolution atmospheric transport model to simulate data from these platforms to infer fluxes at sub-national scales and to quantify changes in rice emissions. We find that average emissions over this period are 22.0 (19.6–24.3) Tg yr −1 , which is consistent with the emissions reported by India to the United Framework Convention on Climate Change. Annual emissions have not changed significantly (0.2 ± 0.7 Tg yr −1 ) between 2010 and 2015, suggesting that major CH 4 sources did not change appreciably. These findings are in contrast to another major economy, China, which has shown significant growth in recent years due to increasing fossil fuel emissions. However, the trend in a global emission inventory has been overestimated for China due to incorrect rate of fossil fuel growth. Here, we find growth has been overestimated in India but likely due to ruminant and waste sectors. India’s methane emissions have been quantified using atmospheric measurements to provide an independent comparison with reported emissions. Here Ganesan et al. find that derived methane emissions are consistent with India’s reports and no significant trend has been observed between 2010–2015.

We use a global inverse model, satellite data and flask measurements to estimate methane (CH4) emissions from South America, Brazil and the basin of the Amazon River for the period 2010–2018. We find that emissions from Brazil have risen during this period, most quickly in the eastern Amazon basin, and that this is concurrent with increasing surface temperatures in this region. Brazilian CH4 emissions rose from 49.8 ± 5.4 Tg yr−1 in 2010–2013 to 55.6 ± 5.2 Tg yr−1 in 2014–2017, with the wet season of December–March having the largest positive trend in emissions. Amazon basin emissions grew from 41.7 ± 5.3 to 49.3 ± 5.1 Tg yr−1 during the same period. We derive no significant trend in regional emissions from fossil fuels during this period. We find that our posterior distribution of emissions within South America is significantly and consistently changed from our prior estimates, with the strongest emission sources being in the far north of the continent and to the south and south-east of the Amazon basin, at the mouth of the Amazon River and nearby marsh, swamp and mangrove regions. We derive particularly large emissions during the wet season of 2013/14, when flooding was prevalent over larger regions than normal within the Amazon basin. We compare our posterior CH4 mole fractions, derived from posterior fluxes, to independent observations of CH4 mole fraction taken at five lower- to mid-tropospheric vertical profiling sites over the Amazon and find that our posterior fluxes outperform prior fluxes at all locations. In particular the large emissions from the eastern Amazon basin are shown to be in good agreement with independent observations made at Santarém, a location which has long displayed higher mole fractions of atmospheric CH4 in contrast with other basin locations. We show that a bottom-up wetland flux model can match neither the variation in annual fluxes nor the positive trend in emissions produced by the inversion. Our results show that the Amazon alone was responsible for 24 ± 18 % of the total global increase in CH4 flux during the study period, and it may contribute further in future due to its sensitivity to temperature changes.

The dramatic and sudden reduction in anthropogenic activity due to lockdown measures in the UK in response to the COVID-19 outbreak has resulted in a concerted effort to estimate local and regional changes in air quality, though changes in underlying emissions remain uncertain. Here we combine satellite observations of tropospheric NO 2 from TROPOspheric Monitoring Instrument and the Goddard Earth Observing System (GEOS)-Chem 3D chemical transport model to estimate that NO x emissions declined nationwide by ∼20% during the lockdown (23 March to 31 May 2020). Regionally, these range from 22% to 23% in the western portion of the country to 29% in the southeast and Manchester, and >40% in London. We apply a uniform 20% lockdown period emission reduction to GEOS-Chem anthropogenic emissions over the UK to determine that decline in lockdown emissions led to a national decline in PM 2.5 of 1.1 μ g m −3 , ranging from 0.6 μ g m −3 in Scotland to 2 μ g m −3 in the southwest. The decline in emissions in cities (>40%) is greater than the national average and causes an increase in ozone of ∼2 ppbv in London and Manchester. The change in ozone and PM 2.5 concentrations due to emission reductions alone is about half the total change from 2019 to 2020. This emphasizes the need to account for emissions and other factors, in particular meteorology, in future air pollution abatement strategies and regulatory action.

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 CH4 (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 CH4 and CO2 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. CH4 is particularly enhanced, due to the dominance of smouldering combustion in peatland fires, with CH4 total column values typically exceeding 35 ppb above those of background \"clean air\" soundings. By examining the CH4 and CO2 excess concentrations in the fire-affected GOSAT observations, we determine the CH4 to CO2 (CH4 CO2) fire emission ratio for the entire 2-month period of the most extreme burning (September-October 2015), and also for individual shorter periods where the fire activity temporarily peaks. We demonstrate that the overall CH4 to CO2 emission ratio (ER) for fires occurring in Indonesia over this time is 6.2 ppb ppm1. This is higher than that found over both the Amazon (5.1 ppb ppm1) and southern Africa (4.4 ppb ppm1), 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 satellite-derived Indonesian ERs (6.18-13.6 ppb ppm1) to be relatively closely matched to that of a series of close-to-source, ground-based sampling measurements made on Kalimantan at the height of the fire event (7.53-19.67 ppb ppm1), although typically the satellite-derived quantities are slightly lower on average. This seems likely because our field sampling mostly intersected smaller-scale peat-burning plumes, whereas the large-scale plumes intersected by the GOSAT Thermal And Near infrared Sensor for carbon Observation - Fourier Transform Spectrometer (TANSO-FTS) footprints would very likely come from burning that was occurring in a mixture of fuels that included peat, tropical forest and already-cleared areas of forest characterised by more fire-prone vegetation types than the natural rainforest biome (e.g. post-fire areas of ferns and scrubland, along with agricultural vegetation).The ability to determine large-scale ERs from satellite data allows the combustion behaviour of very large regions of burning to be characterised and understood in a way not possible with ground-based studies, and which can be logistically difficult and very costly to consider using aircraft observations. We therefore believe the method demonstrated here provides a further important tool for characterising biomass burning emissions, and that the GHG ERs derived for the first time for these large-scale Indonesian fire plumes during an El Niño event point to more routinely assessing spatiotemporal variations in biomass burning ERs using future satellite missions. These will have more complete spatial sampling than GOSAT and will enable the contributions of these fires to the regional atmospheric chemistry and climate to be better understood.

China’s anthropogenic methane emissions are the largest of any country in the world. A recent study using atmospheric observations suggested that recent policies aimed at reducing emissions of methane due to coal production in China after 2010 had been largely ineffective. Here, based on a longer observational record and an updated modelling approach, we find a statistically significant positive linear trend (0.36 ± 0.04 ( ± 1 σ ) Tg CH 4 yr −2 ) in China’s methane emissions for 2010–2017. This trend was slowing down at a statistically significant rate of -0.1 ± 0.04 Tg CH 4 yr −3 . We find that this decrease in growth rate can in part be attributed to a decline in China’s coal production. However, coal mine methane emissions have not declined as rapidly as production, implying that there may be substantial fugitive emissions from abandoned coal mines that have previously been overlooked. We also find that emissions over rice-growing and aquaculture-farming regions show a positive trend (0.13 ± 0.05 Tg CH 4 yr −2 for 2010–2017) despite reports of shrinking rice paddy areas, implying potentially significant emissions from new aquaculture activities, which are thought to be primarily located on converted rice paddies.