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Extreme wildfires are becoming more common and increasingly affecting Earth’s climate. Wildfires in boreal forests have attracted much less attention than those in tropical forests, although boreal forests are one of the most extensive biomes on Earth and are experiencing the fastest warming. We used a satellite-based atmospheric inversion system to monitor fire emissions in boreal forests. Wildfires are rapidly expanding into boreal forests with emerging warmer and drier fire seasons. Boreal fires, typically accounting for 10% of global fire carbon dioxide emissions, contributed 23% (0.48 billion metric tons of carbon) in 2021, by far the highest fraction since 2000. 2021 was an abnormal year because North American and Eurasian boreal forests synchronously experienced their greatest water deficit. Increasing numbers of extreme boreal fires and stronger climate–fire feedbacks challenge climate mitigation efforts.

The Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel 5 Precursor (S5P) satellite, launched in October 2017, provides a wealth of atmospheric composition data, including total columns of carbon monoxide (TCCO) at high horizontal resolution (5.5 km × 7 km). Near-real-time TROPOMI TCCO data have been monitored in the global data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS) since November 2018 to assess the quality of the data. The CAMS system already routinely assimilates TCCO data from the Measurement of Pollution in the Troposphere (MOPITT) instrument and the Infrared Atmospheric Sounding Interferometer (IASI) outside the polar regions. The assimilation of TROPOMI TCCO data in the CAMS system was tested for the period 6 July to 31 December 2021, i.e. after the TROPOMI algorithm update to version 02.02.00 in July 2021. By assimilating TROPOMI TCCO observations, the CAMS CO columns increase by on average 8 %, resulting in an improved fit to independent observations (IAGOS aircraft profiles and NDACC Fourier transform infrared (FTIR) tropospheric and total-column CO data) compared to a version of the CAMS system where only TCCO from MOPITT and IASI is assimilated. The largest absolute and relative changes from the assimilation of TROPOMI CO are found in the lower and middle troposphere, i.e. that part of the atmosphere that is not already well constrained by the assimilated TIR MOPITT and IASI data. The largest impact near the surface comes from clear-sky TROPOMI data over land, and additional vertical information comes from the retrievals of measurements in cloudy conditions. July and August 2021 saw record numbers of boreal wildfires over North America and Russia, leading to large amounts of CO being released into the atmosphere. The paper assesses the impact of TROPOMI CO assimilation on selected CO plumes more closely. While the CO column can be well constrained by the assimilation of TROPOMI CO data, and the fit to individual IAGOS CO profiles in the lower and middle troposphere is considerably improved, the TROPOMI CO columns do not provide further constraints on individual plumes that are transported across continents and oceans at altitudes above 500 hPa.

We apply atmospheric methane column retrievals from two different satellite instruments (Greenhouse gases Observing SATellite – GOSAT; TROPOspheric Monitoring Instrument – TROPOMI) to a regional inversion framework to quantify East Asian methane emissions for 2019 at 0.5∘ × 0.625∘ horizontal resolution. The goal is to assess if GOSAT (relatively mature but sparse) and TROPOMI (new and dense) observations inform consistent methane emissions from East Asia with identically configured inversions. Comparison of the results from the two inversions shows similar correction patterns to the prior inventory in central northern China, central southern China, northeastern China, and Bangladesh, with less than 2.6 Tg a−1 differences in regional posterior emissions. The two inversions, however, disagree over some important regions, particularly in northern India and eastern China. The methane emissions inferred from GOSAT observations are 7.7 Tg a−1 higher than those from TROPOMI observations over northern India but 6.4 Tg a−1 lower over eastern China. The discrepancies between the two inversions are robust against varied inversion configurations (i.e., assimilation window and error specifications). We find that the lower methane emissions from eastern China inferred by the GOSAT inversion are more consistent with independent ground-based in situ and total column (TCCON) observations, indicating that the TROPOMI retrievals may have high XCH4 biases in this region. We also evaluate inversion results against tropospheric aircraft observations over India during 2012–2014 by using a consistent GOSAT inversion of earlier years as an intercomparison platform. This indirect evaluation favors lower methane emissions from northern India inferred by the TROPOMI inversion. We find that in this case the discrepancy in emission inference is contributed by differences in data coverage (almost no observations by GOSAT vs. good spatial coverage by TROPOMI) over the Indo-Gangetic Plain. The two inversions also differ substantially in their posterior estimates for northwestern China and neighboring Kazakhstan, which is mainly due to seasonally varying biases between GOSAT and TROPOMI XCH4 data that correlate with changes in surface albedo.

We quantify weekly methane emissions at 0.25∘ × 0.3125∘ (≈25 × 25 km2) resolution from the Permian Basin, the largest oil production basin in the US, by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) from May 2018 to October 2020. The mean oil and gas emission from the region (± standard deviation of weekly estimates) was 3.7 ± 0.9 Tg a−1, higher than previous TROPOMI inversion estimates that may have used biased prior emissions or background assumptions. We find strong week-to-week variability in emissions superimposed on longer-term trends, and these are consistent with independent inferences of temporal emission variability from tower, aircraft, and multispectral satellite data. New well development and natural gas spot price were significant drivers of variability in emissions over our study period but the concurrent 50 % increase in oil and gas production was not. The methane intensity (methane emitted per unit of methane gas produced) averaged 4.6 % ± 1.3 % and steadily decreased from 5 %–6 % in 2018 to 3 %–4 % in 2020. While the decreasing trend suggests improvement in operator practices during the study period, methane emissions from the Permian Basin remained high, with methane intensity an order of magnitude above the industry target of <0.2 %. Our success in using TROPOMI satellite observations for weekly estimates of emissions from a major oil production basin shows promise for application to near-real-time monitoring in support of climate change mitigation efforts.

The reduction of anthropogenic methane emissions, and especially those from the fossil fuel industry, is one of the most effective ways to slow down global warming in the next decade. Methane emissions from fossil fuel activities often happen as plumes emanating from strong point sources, such as flares, compressor stations, storage tanks, and mine vents.The Sentinel-5P TROPOMI mission can detect the largest plumes from these sources daily and at a global scale thanks to its continuous spatial coverage and daily temporal resolution. Complementary to TROPOMI, several high spatial resolution satellite missions with sensitivity to methane are now being used to attribute plumes to point sources. These missions, so-called point-source imagers in the methane mapping context, sample the methane absorption features in the shortwave infrared with a spatial resolution of 20-60 m, enabling the detection and attribution of plumes from point sources >200-1000 kg/h. Point-source imagers can be hyperspectral (also known as imaging spectrometers), including the GHGSat, PRISMA, EnMAP, and EMIT missions, and multispectral instruments, such as Sentinel-2, Landsat, and WorldView-3. The observations by these point-source imagers often follow a previous identification of large methane hotspot areas by TROPOMI.In connection with this increasing landscape of methane-observing satellites, the United Nations Environment Programme (UNEP) is implementing a Methane Alert and Response system (MARS) as part of its International Methane Emissions Observatory (IMEO). MARS was recently launched at COP27 in direct support to the Global Methane Pledge. It relies on the combined use of multiple methane-observing satellites for the systematic detection, attribution and monitoring of methane sources. In its initial phase, the focus is on point sources associated to the energy sector. In this contribution, we will describe the satellite component of MARS and will provide examples of how satellites are being used to detect and mitigate active methane point sources from the energy sector around the world.

A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called super-emitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit providing daily global coverage of methane mixing ratios at a resolution of up to 7x5.5 km2, enabling the detection of these super-emitters for the first time at global scale. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a Convolutional Neural Network to detect plume-like structures in the methane data and subsequently apply a Support Vector Classifier to distinguish emission plumes from retrieval artefacts. The models are trained on pre-2021 data, and subsequently applied to all 2021 observations. We detect 2974 plumes in 2021 with a mean estimated source rate of 44 t h-1 and 5-95th percentile range of 8-122 t h-1. These emissions originate from 94 persistent emission clusters and hundreds of transient sources. Based on bottom-up emission inventories, we find that most detected plumes are related to urban areas / landfills (35%), followed by plumes from gas infrastructure (24%), oil infrastructure (21%) and coal mines (20%). For twelve (clusters of) TROPOMI detections, we "tip-and-cue" targeted observations and analysis of high-resolution satellite instruments to identify the exact sources responsible for these plumes. Using the high-resolution observations from GHGSat, PRISMA and Sentinel-2, we detect and analyze both persistent and transient facility-level emissions underlying the TROPOMI detections. We will show observations of emissions from landfills and fossil fuel exploitation facilities, for the latter we find up to ten facilities contributing to one TROPOMI detection. In addition to our examination of 2021, we will show results from applying our automated machine learning pipeline continuously on TROPOMI data from as recently as three days ago. Our automated TROPOMI-based monitoring system in combination with high-resolution satellite data allows for the detection, precise identification and monitoring of these methane super-emitters, which is essential for mitigating their emissions and reaching the goals of the Global Methane Pledge of reducing global anthropogenic methane emissions with 30% by 2030.

Coal mines are globally an important source of methane and also one of the largest point sources of methane. We present a high-resolution 0.1∘ * 0.1∘ bottom-up gridded emission inventory for methane emissions from coal mines in India and Australia, which are among the top 5 coal producing countries in 2018. The aim is to reduce the uncertainty in local coal mine methane emissions and to improve the spatial localization to support monitoring and mitigation of these emissions. For India, we improve the spatial allocation of the emissions (CH4 emissions: 825 [min: 166 - max: 1484] Gg yr-1) by identifying the exact location of surface and underground coal mines and we use a Tier-2 Intergovernmental Panel on Climate Change (IPCC) methodology to estimate the emissions from each coal mine using country-specific emission factors. For Australia (CH4 emissions: 972 [min: 863 - max: 1081] Gg yr-1), we estimate the emission for each coal mine by distributing the state-level reported total emissions using proxies of coal production and the coal basin-specific gas content profile of underground mines. Comparison of our total coal mine methane emission from India with existing global inventories showed our estimates are about a factor 3 lower, but well within range of the national Indian estimate reported to United Nations Framework Convention on Climate Change. For both the countries, the new spatial distribution of the emissions show large difference from the current global inventories. Our improved emissions dataset will be useful for air quality or climate modeling and while assessing the satellite methane observations.

The SWIR module of the TROPOMI instrument on board ESA's Sentinel-5p mission has been very stable during its five years in orbit. Calibration was performed on-ground, complemented by measurements during inflight instrument commissioning. The radiometric response and general performance of the SWIR module are monitored by onboard calibration sources. We show that after five years in orbit, TROPOMI-SWIR has continued to show excellent performance with degradation of at most 0.1 % in transmission and having lost less than 0.3 % of the detector pixels. Independent validation of the instrument calibration, via vicarious calibration, can be done through comparisons with ground-based reflectance data. In this work, measurements at the Railroad Valley Playa are used to perform vicarious calibration of the TROPOMI-SWIR measurements, using both dedicated measurement campaigns, as well as automated reflectance measurements through RADCALNET. As such, TROPOMI-SWIR is an excellent test case to explore the methodology of vicarious calibration applied to infrared spectroscopy. Using methodology developed for the vicarious calibration of the OCO-2 and GOSAT missions, the absolute radiometry of TROPOMI-SWIR performance is independently verified to be stable down to ~ 6–10 % using the Railroad Valley, both on the absolute and, thus, relative radiometric calibration. Differences with the onboard calibration originate from the BRDF effects of the desert surface, the large variety in viewing angles, and the different sizes of footprints of the TROPOMI pixels. However, vicarious calibration is shown to be an additional valuable tool in validating radiance-level performances of infra-red instruments such as TROPOMI-SWIR in the field of atmospheric composition.

Carbon monoxide (CO) is an air pollutant that plays an important role in atmospheric chemistry and is mostly emitted by forest fires and incomplete combustion in for example road transport, residential heating, and industry. As CO is co-emitted with fossil fuel CO2 combustion emissions, it can be used as a proxy for CO2. Following the Paris agreement, there is a need for independent verification of reported activity-based bottom-up CO2 emissions through atmospheric measurements. CO can be observed daily at global scale with the TROPOMI satellite instrument with daily global coverage at a resolution down to 5.5 × 7 km2. To take advantage of this unique TROPOMI dataset, we develop a cross-sectional flux-based emission quantification method that can be applied to quantify emissions from a large number of cities, without relying on computationally expensive inversions. We focus on Africa as a region with quickly growing cities and large uncertainties in current emission estimates. We use a full year of high-resolution WRF-simulations over three cities to evaluate and optimize the performance of our cross-sectional flux emission quantification method and show its reliability down to emission rates of 0.1 Tg CO yr−1. Comparison of the TROPOMI-based emission estimates to the DACCIWA and EDGAR bottom-up inventories shows CO emission rates in northern Africa are underestimated in EDGAR, suggesting overestimated combustion efficiencies. We see the opposite when comparing TROPOMI to the DACCIWA inventory in South Africa and Côte d'Ivoire, where CO emission factors appear to be overestimated. Over Lagos and Kano (Nigeria) we find that potential errors in the spatial disaggregation of national emissions cause errors in DACCIWA and EDGAR, respectively. Finally, we show that our computationally-efficient quantification method combined with the daily TROPOMI observations can identify a weekend effect in the road transport-dominated CO emissions from Cairo and Algiers.

The Copernicus Atmosphere Monitoring Service (CAMS) has recently produced a greenhouse gas reanalysis (version egg4) that covers almost 2 decades from 2003 to 2020 and which will be extended in the future. This reanalysis dataset includes carbon dioxide (CO2) and methane (CH4). The reanalysis procedure combines model data with satellite data into a globally complete and consistent dataset using the European Centre for Medium-Range Weather Forecasts' Integrated Forecasting System (IFS). This dataset has been carefully evaluated against independent observations to ensure validity and to point out deficiencies to the user. The greenhouse gas reanalysis can be used to examine the impact of atmospheric greenhouse gas concentrations on climate change (such as global and regional climate radiative forcing), assess intercontinental transport, and serve as boundary conditions for regional simulations, among other applications and scientific uses. The caveats associated with changes in assimilated observations and fixed underlying emissions are highlighted, as is their impact on the estimation of trends and annual growth rates of these long-lived greenhouse gases.

As atmospheric methane concentrations increase at record pace, it is critical to identify individual emission sources with high potential for mitigation. Landfills are responsible for large methane emissions that can be readily abated but have been sparsely observed. Here we leverage the synergy between satellite instruments with different spatiotemporal coverage and resolution to detect and quantify emissions from individual landfill facilities. We use the global surveying Tropospheric Monitoring Instrument (TROPOMI) to identify large emission hot spots, and then zoom in with high-resolution target-mode observations from the GHGSat instrument suite to identify the responsible facilities and characterize their emissions. Using this ‘tip and cue’ approach, we detect and analyze strongly emitting landfills (3-29 t hr−1) in Buenos Aires (Argentina), Delhi (India), Lahore (Pakistan), and Mumbai (India). We find that city-level emissions are 1.6-2.8 times larger than reported in commonly used emission inventories and that the landfills contribute 5-47% of those emissions. Our work demonstrates how complementary satellites enable global detection, identification, and monitoring of methane super-emitters at the facility-level.

The Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel 5 Precursor (S5P) satellite, launched in October 2017, provides a wealth of atmospheric composition data, including total columns of carbon monoxide (TCCO) at high horizontal resolution (5.5 km x 7 km). Near-real time TROPOMI TCCO data have been monitored in the global data assimilation system of the Copernicus Atmospheric Monitoring Service (CAMS) since November 2018 to assess the quality of the data. The CAMS system already routinely assimilates TCCO data from the Measurement of Pollution in the Troposphere (MOPITT) instrument and the Atmospheric Sounding Interferometer (IASI) outside the polar regions. In the global mean, CAMS TCCO analysis values are about 10 % lower than TROPOMI TCCO when only IASI and MOPITT thermal infrared (TIR) TCCO data are assimilated (averaged over the period November 2018 to Dec 2021), with the largest relative differences (11–14 %) found in the polar latitude bands, i.e., the areas where no satellite CO retrievals are assimilated in the global CAMS system. Most of these differences are due to a low TCCO bias of the CAMS model, rather than a high bias of TROPOMI. The assimilation of NRT TROPOMI TCCO data in the CAMS system was tested for the period 2021-07-06 to 2021-12-31, i.e., after the TROPOMI algorithm update to version 02.02.00 in July 2021. It leads to a much-improved CO analysis field, with increased CO values and improved fit to independent observations, such as IAGOS aircraft profiles, NDACC FTIR tropospheric and total column CO data, as well as surface CO data from the Air Now, AirBase and Chinese air quality networks. The largest absolute and relative changes from the assimilation of TROPOMI CO, in addition to the already assimilated IASI and MOPITT TIR TCCO data, are found in the lower and mid troposphere, i.e., that part of the atmosphere that is not already well constrained by the already assimilated TIR MOPITT and IASI data. The largest impact near the surface comes from clear-sky TROPOMI data over land, and additional vertical information comes from the retrievals of measurements in cloudy conditions. July and August 2021 saw record numbers of boreal wildfires over North America and Russia leading to large amounts of CO being released into the atmosphere. The CAMS CO analysis captures the high CO columns resulting from these fires and also shows plumes of high CO from the boreal wildfires that are transported from Siberia over the North Pole and from North America over the North Atlantic reaching as far as Europe, even though some of the high CO values detected in the upper troposphere by IAGOS aircraft that intersected parts of the plumes are not quite reached in the CAMS CO analysis.

In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local CH4 concentration anomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget and by biases in the satellite retrieval data. The method uses high-resolution (7 km × 7 km) retrievals of total column CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are combined with high-resolution CH4 forecasts (∼ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) at close to the satellite's native resolution at appropriate time. Investigating these departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the synoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered departures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or overreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to local satellite retrieval biases linked to albedo and scattering issues.

This study investigates the use of co-located nitrogen dioxide (NO2) and carbon monoxide (CO) retrievals from the TROPOMI satellite to improve the quantification of burning efficiency and emission factors (EFs) over the megacities of Tehran, Mexico City, Cairo, Riyadh, Lahore, and Los Angeles. Efficient combustion is characterized by high NOx (NO+NO2) and low CO emissions, making the NO2∕CO ratio a useful proxy for combustion efficiency (CE). The local enhancement of CO and NO2 above megacities is well captured by TROPOMI at short averaging times compared with previous satellite missions. In this study, the upwind background and plume rotation methods are used to investigate the accuracy of satellite-derived ΔNO2∕ΔCO ratios. The column enhancement ratios derived using these two methods vary by 5 % to 20 % across the selected megacities. TROPOMI-derived column enhancement ratios are compared with emission ratios from the EDGAR v4.3.2 (Emission Database for Global Atmospheric Research v4.3.2) and the MACCity (Monitoring Atmospheric Chemistry and Climate and CityZen) 2018 emission inventories. TROPOMI correlates strongly (r=0.85 and 0.7) with EDGAR and MACCity, showing the highest emission ratio for Riyadh and lowest emission ratio for Lahore. However, inventory-derived emission ratios are 60 % to 85 % higher than TROPOMI column enhancement ratios across the six megacities. The short lifetime of NO2 and the different vertical sensitivity of TROPOMI NO2 and CO explain most of this difference. We present a method to translate TROPOMI-retrieved column enhancement ratios into corresponding emission ratios, thereby accounting for these influences. Except for Los Angeles and Lahore, TROPOMI-derived emission ratios are close (within 10 % to 25 %) to MACCity values. For EDGAR, however, emission ratios are ∼65 % higher for Cairo and 35 % higher for Riyadh. For Los Angeles, EDGAR and MACCity are a factor of 2 and 3 higher than TROPOMI respectively. The air quality monitoring networks in Los Angeles and Mexico City are used to validate the use of TROPOMI. For Mexico City and Los Angeles, these measurements are consistent with TROPOMI-derived emission ratios, demonstrating the potential of TROPOMI with respect to monitoring burning efficiency.

Algeria is one of the world's largest oil and gas (O&G) producers and exporters and a major emitter of methane from its O&G extraction and transportation activities. In recent years satellites have proven to be a key tool for detecting point emitters on a global scale. The TROPOMI sensor onboard Sentinel 5P provides a daily large-scale view, and recent advances allow pinpointing single emitters with high spatial resolution sensors. In this study, we aim to understand and identify methane emission sources in Algeria using satellites as a tool to obtain continuous and objective information from the whole study area. We have used the synergy between different satellites to detect and analyze Algeria's point emitters. We have combined low spatial resolution TROPOMI data with high-resolution images from PRISMA and ZY1-AHSI hyperspectral satellites and Sentinel-2, Landsat 8, and WV3 multispectral satellites. We have identified about 60 point emitters spread over different O&G fields, of which the vast majority are flares venting gas. In addition, we have analyzed the persistence in time of the emitters identified in the most recent years (2017-present) by observing them in the past years with the Landsat constellation historical image record (1984-present). The results obtained so far show a wide diversity in the persistence of emission over time, from facilities emitting for decades to active flares that occasionally deactivate and vent gas.

We quantify and characterize methane emissions from a natural gas well blowout in Louisiana, USA in 2019 using TROPOMI (TROPOspheric Monitoring Instrument) methane observations and VIIRS (Visible Infrared Imaging Radiometer Suite) gas flare detections. We use TROPOMI data in a Bayesian inversion to optimize daily methane emissions while VIIRS-based radiant heat data are used to estimate the amount of gas flared. This hybrid approach allows for the characterization of methane emissions during both the flaring and venting phases of the blowout, where more than half of the blowout duration was associated with flaring. We estimate overall emissions of this 38-day event at 49 (21–63) Gg, which is lower than reported by the gas company but exceeds all known US point sources emitting at the time in terms of emission rate. We estimate that over 80% of total emissions come from the venting phase, leaving the flaring phase responsible for only a small fraction of total emissions. We show that routine satellite operations from global-scale observing instruments such as TROPOMI and VIIRS when combined can provide a detailed assessment of quantitative greenhouse gas emissions from large point sources including stochastic events and thus these data could be used for regulatory enforcement.

AirCore in situ vertical profiles sample the atmosphere from near the surface to the lower stratosphere, making them ideal for the validation of satellite tropospheric trace gas data. Here we present intercomparison results of AirCore carbon monoxide (CO) measurements with respect to retrievals from MOPITT (Measurements of Pollution In The Troposphere; version 8) and TROPOMI (TROPOspheric Monitoring Instrument), on board the NASA Terra and ESA Sentinel 5-Precursor satellites, respectively. Mean MOPITT/AirCore total column bias values and their standard deviation (0.4 ± 5.5, 1.7 ± 5.6, and 0.7 ± 6.0 for MOPITT thermal-infrared, near-infrared, and multispectral retrievals, respectively; all in %) are similar to results obtained in MOPITT/NOAA aircraft flask data comparisons from this study and from previous validation efforts. MOPITT CO retrievals are systematically validated using in situ vertical profiles from a variety of aircraft campaigns. Because most aircraft vertical profiles do not sample the troposphere's entire vertical extent, they must be extended upwards in order to be usable in validation. Here we quantify, for the first time, the error introduced in MOPITT CO validation by the use of shorter aircraft vertical profiles extended upwards by analyzing validation results of MOPITT with respect to full and truncated AirCore CO vertical profiles. Our results indicate that the error is small, affects mostly upper tropospheric retrievals (at 300 hPa: ∼ 2.6, 0.8, and 3.2 percent points for MOPITT thermal-infrared, near-infrared, and multispectral, respectively), and may have resulted in the overestimation of MOPITT retrieval biases in that region. TROPOMI can retrieve CO under both clear and cloudy conditions. The latter is achieved by quantifying interfering trace gases and parameters describing the cloud contamination of the measurements together with the CO column; then, the reference CO profiles used in the retrieval are scaled based on estimated above-cloud CO rather than on estimated total CO. We use AirCore measurements as the reference to evaluate the error introduced by this approach in cloudy TROPOMI retrievals over land after accounting for TROPOMI's vertical sensitivity to CO (relative bias and its standard deviation = 2.02 % ± 11.13 %). We also quantify the null-space error, which accounts for differences between the shape of TROPOMI reference profiles and that of AirCore measured profiles (for TROPOMI cloudy enull=0.98 % ± 2.32 %).

Reduction of fossil fuel-related methane emissions has been identified as an essential means for climate change mitigation, but emission source identification remains elusive for most oil and gas production basins in the world. We combine three complementary satellite data sets to survey single methane emission sources on the west coast of Turkmenistan, one of the largest methane hotspots in the world. We found 29 different emitters, with emission rates1800 kg/h, active in the 2017-2020 time period, although older satellite data show that this type of emission has been occurring for decades. We find that all sources are linked to extraction fields mainly dedicated to crude oil production, where 24 of them are inactive flares venting gas. The analysis of time series suggests a causal relationship between the decrease in flaring and the increase in venting. At the regional level, 2020 shows a substantial increase in the number of methane plume detections concerning previous years. Our results suggest that these large venting point sources represent a key mitigation opportunity as they emanate from human-controlled facilities, and that new satellite methods promise a revolution in the detection and monitoring of methane point emissions worldwide.

A new method is presented for estimating urban hydroxyl radical (OH) concentrations using the downwind decay of the ratio of nitrogen dioxide over carbon monoxide column-mixing ratios (XNO2/XCO) retrieved from the Tropospheric Monitoring Instrument (TROPOMI). The method makes use of plumes simulated by the Weather Research and Forecast model (WRF-Chem) using passive-tracer transport, instead of the encoded chemistry, in combination with auxiliary input variables such as Copernicus Atmospheric Monitoring Service (CAMS) OH, Emission Database for Global Atmospheric Research v4.3.2 (EDGAR) NOx and CO emissions, and National Center for Environmental Protection (NCEP)-based meteorological data. NO2 and CO mixing ratios from the CAMS reanalysis are used as initial and lateral boundary conditions. WRF overestimates NO2 plumes close to the center of the city by 15 % to 30 % in summer and 40 % to 50 % in winter compared to TROPOMI observations over Riyadh. WRF-simulated CO plumes differ by 10 % with TROPOMI in both seasons. The differences between WRF and TROPOMI are used to optimize the OH concentration, NOx, CO emissions and their backgrounds using an iterative least-squares method. To estimate OH, WRF is optimized using (a) TROPOMI XNO2/XCO and (b) TROPOMI-derived XNO2 only. For summer, both the NO2/CO ratio optimization and the XNO2 optimization increase the prior OH from CAMS by 32 ± 5.3 % and 28.3 ± 3.9 %, respectively. EDGAR NOx and CO emissions over Riyadh are increased by 42.1 ± 8.4 % and 101 ± 21 %, respectively, in summer. In winter, the optimization method doubles the CO emissions while increasing OH by ∼ 52 ± 14 % and reducing NOx emissions by 15.5 ± 4.1 %. TROPOMI-derived OH concentrations and the pre-existing exponentially modified Gaussian function fit (EMG) method differ by 10 % in summer and winter, confirming that urban OH concentrations can be reliably estimated using the TROPOMI-observed NO2/CO ratio. Additionally, our method can be applied to a single TROPOMI overpass, allowing one to analyze day-to-day variability in OH, NOx and CO emission.

The aim of this paper is two-fold: to provide guidance on how to best interpret TROPOMI trace gas retrievals and to highlight how TROPOMI trace gas data can be used to understand event-based impacts on air quality from regional to city-scales around the globe. For this study, we present the observed changes in the atmospheric column amounts of five trace gases (NO2, SO2, CO, HCHO and CHOCHO) detected by the Sentinel-5P TROPOMI instrument, driven by reductions of anthropogenic emissions due to COVID-19 lockdown measures in 2020. We report clear COVID-19-related decreases in NO2 concentrations on all continents. For megacities, reductions in column amounts of tropospheric NO2 range between 14 % and 63 %. For China and India supported by NO2 observations, where the primary source of anthropogenic SO2 is coal-fired power generation, we were able to detect sector-specific emission changes using the SO2 data. For HCHO and CHOCHO, we consistently observe anthropogenic changes in two-week averaged column amounts over China and India during the early phases of the lockdown periods. That these variations over such a short time scale are detectable from space, is due to the high resolution and improved sensitivity of the TROPOMI instrument. For CO, we observe a small reduction over China which is in concert with the other trace gas reductions observed during lockdown, however large, interannual differences prevent firm conclusions from being drawn. The joint analysis of COVID-19 lockdown-driven reductions in satellite observed trace gas column amounts, using the latest operational and scientific retrieval techniques for five species concomitantly is unprecedented. However, the meteorologically and seasonally driven variability of the five trace gases does not allow for drawing fully quantitative conclusions on the reduction of anthropogenic emissions based on TROPOMI observations alone. We anticipate that in future, the combined use of inverse modelling techniques with the high spatial resolution data from S5P/TROPOMI for all observed trace gases presented here, will yield a significantly improved sector-specific, space-based analysis of the impact of COVID-19 lockdown measures as compared to other existing satellite observations. Such analyses will further enhance the scientific impact and societal relevance of the TROPOMI mission.

[EN] The first satellite-based global retrievals of terrestrial sun-induced chlorophyll fluorescence (SIF) were achieved in 2011. Since then, a number of global SIF datasets with different spectral, spatial, and temporal sampling characteristics have become available to the scientific community. These datasets have been useful to monitor the dynamics and productivity of a range of vegetated areas worldwide, but the coarse spatiotemporal sampling and low signal-to-noise ratio of the data hamper their application over small or fragmented ecosystems. The recent advent of the Copernicus Sentinel-5P TROPOMI mission and the high quality of its data products promise to alleviate this situation, as TROPOMI provides daily global measurements at a much denser spatial and temporal sampling than earlier satellite instruments. In this work, we present a global SIF dataset produced from TROPOMI measurements within the TROPOSIF project funded by the European Space Agency. The current version of the TROPOSIF dataset covers the time period between May 2018 and April 2021. Baseline SIF retrievals are derived from the 743¿758¿nm window. A secondary SIF dataset derived from an extended fitting window (735¿758¿nm window) is included. This provides an enhanced signal-to-noise ratio at the expense of a higher sensitivity to atmospheric effects. Spectral reflectance spectra at seven 3¿nm windows devoid of atmospheric absorption within the 665¿785¿nm range are also included in the TROPOSIF dataset as an important ancillary variable to be used in combination with SIF. The methodology to derive SIF and ancillary data as well as results from an initial data quality assessment are presented in this work. The TROPOSIF dataset is available through the following digital object identifier, This research has been supported by the European Space Agency (grant no. 4000127461/19/I-NS) and the National Aeronautics and Space Administration (grant no. NNX15AH95G)

In this study, the GOSAT Proxy Retrieval (v9.0) data product of column-averaged dry-air mole fractions of atmospheric methane (XCH₄) for the period 2009–2019 was analyzed to detect methane (CH₄) trends in the three western Canadian provinces where oil and gas development activities have changed significantly over the last decade. Although we found statistically significant increasing XCH₄ trends in all subdomains (northeast British Columbia-NE, Alberta-AB, southern Saskatchewan-SK), XCH₄ trends are not higher than the background trend (7.25 ± 0.30 ppb/yr) and enhancement trends (ΔXCH₄, after removing the background quantity) are not detectable at any subdomain during 2009–2019. For further insight into trends in all subdomains, we divided the whole period (2009–2019) into two shorter periods (2009–2013 and 2014–2019) and estimated trends. We found XCH₄ trends are higher than background trends particularly in the AB and SK subdomains during 2009–2013, and their ΔXCH₄ trends are positive and also marginally statistically significant. However, we do not find any detectable ΔXCH₄ trend if we consider either long-term (2009–2019) or the second shorter period (2014–2019), suggesting local emission sources are dominating year to year fluctuation. From the source attribution analysis, we found both wetland and oil and gas sectors are controlling the CH₄ growth rate in western Canada, but the oil and gas sector is the dominant driver in NE and SK subdomains. We also found the satellite-based average ΔXCH₄ trend (15.43 ± 8.19%/yr) between 2009 and 2013 likely reflects a trend in oil and gas CH₄ emissions in AB and SK for the same period.

Southeast Australia experienced intensive and geographically extensive wildfires during the 2019–2020 summer season1,2. The fires released substantial amounts of carbon dioxide into the atmosphere3. However, existing emission estimates based on fire inventories are uncertain4, and vary by up to a factor of four for this event. Here we constrain emission estimates with the help of satellite observations of carbon monoxide5, an analytical Bayesian inversion6 and observed ratios between emitted carbon dioxide and carbon monoxide7. We estimate emissions of carbon dioxide to be 715 teragrams (range 517–867) from November 2019 to January 2020. This is more than twice the estimate derived by five different fire inventories8–12, and broadly consistent with estimates based on a bottom-up bootstrap analysis of this fire episode13. Although fires occur regularly in the savannas in northern Australia, the recent episodes were extremely large in scale and intensity, burning unusually large areas of eucalyptus forest in the southeast13. The fires were driven partly by climate change14,15, making better-constrained emission estimates particularly important. This is because the build-up of atmospheric carbon dioxide may become increasingly dependent on fire-driven climate–carbon feedbacks, as highlighted by this event16. The amount of carbon dioxide released by the Australian wildfires of 2019–2020 is uncertain, but is estimated here using satellite observations of carbon monoxide to be more than twice the amount suggested by fire inventories.

During its first year in operation the short-wave infrared (SWIR) Tropospheric Monitoring Instrument (TROPOMI) was calibrated in-flight and its performance was monitored. In this paper we present the results of the in-flight calibration and the ongoing instrument monitoring. This includes the determination of the background signals, noise performance, instrument spectral response function (ISRF) stability, and stray-light stability. From these results, the number of incurred dead and bad pixels due to cosmic-ray impacts is determined. The light-path transmission is checked by monitoring internal lamp and diffuser stabilities. Due to its high sensitivity to Earth radiation on the eclipse side, the calibration strategy for the background (i.e. dark current and offset) monitoring was adjusted. Trends over the first full year of nominal operations reveal a very stable SWIR module. The number of newly incurred dead and bad pixels is less than 0.1 % over nearly a full year since the start of operations. Assuming linear degradation of various components, the SWIR module is expected to keep performing within expected parameters for the full operational lifetime.

Rapid identification of anomalous methane sources in oil/gas fields could enable corrective action to fight climate change. The GHGSat-D satellite instrument measuring atmospheric methane with 50-meter spatial resolution was launched in 2016 to demonstrate space-based monitoring of methane point sources. Here we report the GHGSat-D discovery of an anomalously large, persistent methane source (10–43 metric tons per hour, detected in over 50% of observations) at a gas compressor station in Central Asia, together with additional sources (4–32 metric tons per hour) nearby. The TROPOMI satellite instrument confirms the magnitude of these large emissions going back to at least November 2017. We estimate that these sources released 142 ± 34 metric kilotons of methane to the atmosphere from February 2018 through January 2019, comparable to the 4-month total emission from the well-documented Aliso Canyon blowout.

Passive remote sensing of atmospheric carbon dioxide uses spectroscopic measurements of sunlight backscattered by the Earth's surface and atmosphere. The current state-of-the-art retrieval methods use three different spectral bands, the oxygen A band at 0.76 µm and the weak and strong CO2 absorption bands at 1.61 and 2.06 µm, respectively, to infer information on light scattering and the carbon dioxide column-averaged dry-air mole fraction XCO2. In this study, we propose a one-band XCO2 retrieval technique which uses only the 2.06 µm band measurements from the Orbiting Carbon Observatory-2 (OCO-2) satellite. We examine the data quality by comparing the OCO-2 XCO2 with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON). Over land and ocean the OCO-2 one-band retrieval shows differences from TCCON observations with a standard deviation of ∼1.30 ppm and a station-to-station variability of ∼0.50 ppm. Moreover, we compare one-band and three-band retrievals over Europe, the Middle East, and Africa and see high correlation between the two retrievals with a SD of 0.93 ppm. Compared to the three-band retrievals, XCO2 retrievals using only the 2.06 µm band have similar retrieval accuracy, precision, and data yield.

The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite provides measurements of carbon monoxide (CO) total column concentrations based on earthshine radiance measurements in the 2.3 µm spectral range with a spatial resolution of 7 km×7 km and daily global coverage. Due to the high accuracy of the observations, CO pollution can be detected over cities and industrial areas using single orbit overpasses. In this study, we analyzed local CO enhancements in an area around Iran from 1 November to 20 December 2017. We employed the Weather Research and Forecasting (WRF) model v3.8.1 using the EDGAR v4.2 emission inventory and evaluated CO emissions from the cities of Tehran, Yerevan, Urmia, and Tabriz on a spatial resolution comparable to that of TROPOMI. For background conditions, the WRF simulation agrees well with TROPOMI CO, with a mean difference of 5.7 %. However, the emissions for the city area had to be significantly increased in order to match the observations. Moreover, significant differences at the sub-city scale remain. To match the TROPOMI CO observations around the Armenian city of Yerevan, it is necessary to introduce CO emissions along a southeast arterial road of Yerevan. Overall, this hints at deficits in the EDGAR inventory in the region around Iran and indicates TROPOMI's capability to identify localized CO pollution on sub-city scales, which at the same time challenges current atmospheric modeling at high spatial and temporal resolution.

We quantify the impact of atmospheric transport and limited marine boundary layer sampling on changes in global and regional methane burdens estimate using tracer transport model simulations with annually repeating methane emissions and sinks but varying atmospheric transport patterns. We find the 1σ error due to this transport and sampling effect on annual global methane increases to be 1.11A ppb/year and on zonal growth rates to be 3.8A ppb/year, indicating that it becomes more critical at smaller spatiotemporal scales. We also find that the trends in inter-hemispheric and inter-polar difference of methane are significantly influenced by the effect. Contrary to a negligible trend in the inter-hemispheric difference of measurements, we find, after adjusting for the transport and sampling, a trend of 0.37A A±A 0.06A ppb/year. This is consistent with the emission trend from a 3-D inversion of the measurements, suggesting a faster increase in emissions in the Northern Hemisphere than in the Southern Hemisphere. © 2019. The Authors.

The TROPOspheric Monitoring Instrument (TROPOMI), launched 13 October 2017, has been measuring carbon monoxide (CO) concentrations in the Earth's atmosphere since early November 2017. In the first measurements, TROPOMI was able to measure CO concentrations of the high-pollution event in India of November 2017. In this paper, we studied the extent of the pollution in India, comparing the TROPOMI CO with modeled data from the Weather Research and Forecasting model (WRF) to identify the most important sources contributing to the high pollution, both at ground level and in the total column. We investigated the period 11–19 November 2017. We found that residential and commercial combustion was a much more important source of CO pollution than the post-monsoon crop burning during this period, which is in contrast to what media suggested and some studies on aerosol emissions found. Also, the high pollution was not limited to Delhi and its direct neighborhood but the accumulation of pollution extended over the whole Indo-Gangetic Plain (IGP) due to the unfavorable weather conditions in combination with extensive emissions. From the TROPOMI data and WRF simulations, we observed a buildup of CO during 11–14 November and a decline in CO after 15 November. The meteorological conditions, characterized by low wind speeds and shallow atmospheric boundary layers, were most likely the primary explanation for the temporal accumulation and subsequent dispersion of regionally emitted CO in the atmosphere. This emphasizes the important role of atmospheric dynamics in determining the air quality conditions at ground level and in the total column. Due to its rapidly growing population and economy, India is expected to encounter similar pollution events more often in future post-monsoon and winter seasons unless significant policy measures are taken to reduce residential and commercial emissions.

Coal is a principal source of energy and the combustion of coal supplies around one-third of the global electricity generation. Coal mines are also an important source of CH4 emissions, the second most important greenhouse gas. Monitoring CH4 emissions caused by coal mining using earth observation will require the exact location of coal mines. This paper aims to determine surface coal mines from satellite images through deep learning techniques by treating them as a land use/land cover classification task. This is achieved using Convolutional Neural Networks (CNN) that has proven to be capable of complex land use/land cover classification tasks. With a list of known coal mine locations from various countries, a training dataset of “Coal Mine” and “No Coal Mine” image patches is prepared using Sentinel-2 satellite images with 13 spectral bands. Various pre-trained CNN network architectures (VGG, ResNet, DenseNet) are trained and validated with our prepared coal mine dataset of 3500 “Coal Mine” and 3000 “No Coal Mine” image patches. After several experiments with the VGG network combined with transfer learning is found to be an optimal model for this task. Classification accuracy of 98% has been achieved for the validation dataset of the pre-trained VGG architecture. The model produces more than 95% overall accuracy when tested on unseen satellite images from different countries outside the training dataset and evaluated against visual classification.

The total amount of CO2 absorbed and turned into organic matter through photosynthesis can be quantified as Gross Primary Productivity (GPP). Terrestrial vegetation GPP is responsible for offsetting about 30% of carbon dioxide emissions released by anthropogenic activities into the atmosphere. This corresponds to approximately 120 to 170 PgC/year, making terrestrial vegetation both the largest and most uncertain component of the global carbon cycle. Over the last few years, solar- induced chlorophyll fluorescence (SIF) observations from space have emerged as a promising resource for evaluating the spatio-temporal distribution of GPP by terrestrial ecosystems. SIF is an electromagnetic signal emitted by the chlorophyll a of green plants: part of the absorbed energy not used for photosynthesis is emitted at longer wavelengths as a two-peak spectrum roughly covering the 650–850 nm spectral range. The SIF signal responds instantaneously to perturbations in environmental conditions such as light and water stress, which makes it a direct proxy for photosynthetic activity.SIF has been estimated at the global scale from several space-borne spectrometers originally intended for atmospheric research because they provide the necessary spectral resolution and radiometric sensitivity. However, the exploitation of SIF estimates has remained limited by their coarse spatial resolution (GOSAT, GOME-2) or low number of observations (OCO-2). This has been strongly alleviated by the advent of the TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5 Precursor which combines a global continuous spatial sampling with a 5.5 km x 3.5 km pixel size at nadir, a daily revisit time, a wide spectral coverage and an enormous increase in the number of clear-sky measurements per day in comparison to previous missions, as demonstrated by the first SIF retrievals from TROPOMI derived by Caltech (Köhler et al. 2018).In this study, we present a new independent SIF product derived from TROPOMI observations using two fitting windows at 743-758 nm (all sky SIF) and 735-758 nm (clear sky SIF). The first one is very robust against atmospheric effects (especially cloud contamination) whereas the second one is less sensitive to estimation errors (due to the greater number of spectral points in the fitting window). The retrieval scheme is not much different from the one from Caltech (in 743-758 nm) but relies on different cloud fraction inputs. We will present the principles of the retrieval scheme, the format of the output TROPOSIF product, and the results of the comparison of our SIF estimates against the Caltech ones as well as SIF data derived from OCO-2 which show a very good consistency between all SIF products.

In the context of the Paris Agreement goal of limiting global warming to below 2 degrees Celsius, the Representative Concentration Pathways (RCP) 2.6 of the Intergovernmental Panel on Climate Change (IPCC) have framed greenhouse gas emission scenarios emphasizing a sharp reduction in methane (CH4) emissions with the current increasing trend. Recent studies have shown that satellite observations of atmospheric methane can be used to detect and quantify localized methane sources on a facility-level for the oil and gas industry. We use satellite observations from TROPOMI to understand the high and persistent methane signals from ventilation shafts in the coal mining industry. Even the bottom-up and top-down global estimates infer coal mine methane responsible for ~12% of the anthropogenic methane emissions. TROPOMI onboard Sentinel-5P has a ground pixel resolution of 5 × 7 km2 at nadir, which allows detection of large local to point sources. With its daily global coverage, we identify high methane emission sources over coal mine regions in Australia during 2018 and 2019 and quantify methane emissions using the fast data-driven cross-sectional flux method. Our initial results show that TROPOMI estimates are higher than bottom-up global emission inventories. We will present emission estimates using satellite-based quantification for super-emitter coal mines and evaluate its implication on national greenhouse gas reporting.

Imaging spectroscopy, also known as hyperspectral imaging, is a remote sensing technique in which images of the solar radiation reflected by the Earth are produced in hundreds of spectral channels between the visible and the shortwave infrared part of the electromagnetic spectrum (roughly 400–2500 nm). The 2100-2450 nm spectral window can be used for methane retrievals, as it has been demonstrated over the last years with airborne imaging spectrometers, and very recently also with space-based instruments. Satellite-based hyperspectral images are acquired with a typical spatial sampling for satellite data of 30 m, a spatial coverage between 30x30 and 60x60 km per scene, and a spectral sampling of 10 nm. In this work, we will present an overview of the state-of-the-art of methane mapping with imaging spectroscopy missions. We will review the characteristics of the available missions, the main retrieval approaches, and will present examples of methane emission detection from a number of missions and locations around the Earth.

The lockdown measures taken to combat the COVID-19 virus implemented in a majority of countries worldwide have had a dramatic impact on the anthropogenic pollutant emissions, related to a drastic reduction of road and air traffic, as well as part of the industrial activities. In our contribution we investigate the presence of COVID-19-related imprints in air quality as observed from space, focussing on worldwide industrial/highly populated regions where strong lockdown measures have been taken (e.g., China, Europe, US). This is done by exploiting the observations of the TROPOMI instrument onboard the Copernicus Sentinel-5P platform, for a number of trace gases which are indicators of anthropogenic activity. We make use of the TROPOMI operational product portfolio, which includes tropospheric NO2, CO, SO2, and HCHO. These operational data products are complemented by other scientific products such as the BIRA-IASB glyoxal (CHOCHO) retrievals and a new SO2 retrieval algorithm called COBRA. The reductions in NO2 observed by TROPOMI have been documented already in the recent literature for several regions and countries worldwide. In our contribution we focus on the combined observations of multiple trace gases, which provides not only information about how much primary (NOx) emissions decreased, but also gives region-to-region insights and constraints on the overall changes in atmospheric composition as a result of these lockdowns.

The Permian Basin is known for its extensive oil and gas production, which has increased rapidly in recent years becoming the largest producing basin in the United States. It is also responsible for almost half of the methane emissions from all oil and gas producing regions in the country. Given the urgent need to reduce greenhouse gas emissions, it is crucial to identify and characterize the point sources of emissions. To this end, we have combined three new high-resolution hyperspectral sensors data onboard the GF-5, ZY1 and PRIMA satellites to create the first regional study to identify methane sources and measure the emitted quantities from each source. With data collected over several days in 2019 and 2020, we have identified a total of 37 point source emissions with flux rates >500kg/h, that is, a high concentration of extreme emission point sources that account for nearly 40% of the Permian annual emissions. Also, we have found that new infrastructure (post-2018) is responsible for almost 60% of the detected emissions, in many cases (21% of the cases) due to inefficient use of flaring of the gas that they cannot store. With this study, we demonstrate that hyperspectral satellite data are a powerful tool for the detection and quantification of strong methane point emissions.

The TROPOspheric Monitoring Instrument (TROPOMI) provides observations of atmospheric methane (CH4) at an unprecedented combination of high spatial resolution and daily global coverage. Hu et al. (2018) reported unexpectedly large methane enhancements over South Sudan in these observations. Here we assess methane emissions from the wetlands of South Sudan using 2 years (December 2017–November 2019) of TROPOMI total column methane observations. We estimate annual wetland emissions of 7.4 ± 3.2 Tg yr−1, which agrees with the multiyear GOSAT inversions of Lunt et al. (2019) but is an order of magnitude larger than estimates from wetland process models. This disagreement may be explained by the underestimation (by up to 4 times) of inundation extent by the hydrological schemes used in those models. We investigate the seasonal cycle of the emissions and find the lowest emissions during the June–August season when the process models show the largest emissions. Using satellite-altimetry-based river water height measurements, we infer that this seasonal mismatch is likely due to a seasonal mismatch in inundation extent. In models, inundation extent is controlled by regional precipitation scaled to static wetland extent maps, whereas the actual inundation extent is driven by water inflow from rivers like the White Nile and the Sobat. We find the lowest emissions in the highest precipitation and lowest temperature season (June–August, JJA) when models estimate large emissions. In general, our emission estimates show better agreement in terms of both seasonal cycle and annual mean with model estimates that use a stronger temperature dependence. This suggests that temperature might be a stronger control for the South Sudan wetlands emissions than currently assumed by models. Our findings demonstrate the use of satellite instruments for quantifying emissions from inaccessible and uncertain tropical wetlands, providing clues for the improvement of process models and thereby improving our understanding of the currently uncertain contribution of wetlands to the global methane budget.

Southeast Australia experienced intensive and geographically extensive wildfires during the 2019-2020 summer season

[EN] Industrial emissions play a major role in the global methane budget. The Permian basin is thought to be responsible for almost half of the methane emissions from all U.S. oil- and gas-producing regions, but little is known about individual contributors, a prerequisite for mitigation. We use a new class of satellite measurements acquired during several days in 2019 and 2020 to perform the first regional-scale and high-resolution survey of methane sources in the Permian. We find an unexpectedly large number of extreme point sources (37 plumes with emission rates >500 kg hour(-1)), which account for a range between 31 and 53% of the estimated emissions in the sampled area. Our analysis reveals that new facilities are major emitters in the area, often due to inefficient flaring operations (20% of detections). These results put current practices into question and are relevant to guide emission reduction efforts., I.I.-L., L.G., J.G., and E.S.-G. are partly funded by an UPV-GFZ Potsdam EnMAP collaboration contract. Yuzhong Zhang is funded by the National Natural Science Foundation of China (project 42007198) and foundation of Westlake University.

The COVID-19 pandemic resulted in reduced anthropogenic carbon dioxide (CO2) emissions during 2020 in large parts of the world. We report results from a first attempt to determine whether a regional-scale reduction of anthropogenic CO2 emissions during the COVID-19 pandemic can be detected using space-based observations of atmospheric CO2. For this purpose, we have analysed a small ensemble of satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2. We focus on East China because COVID-19 related CO2 emission reductions are expected to be largest there early in the pandemic. We analysed four XCO2 data products from the satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse gases Observing SATellite (GOSAT). We use a data-driven approach that does not rely on a priori information about CO2 sources and sinks and ignores atmospheric transport. Our approach utilises the computation of XCO2 anomalies, ΔXCO2, from the satellite Level 2 data products using a method called DAM (Daily Anomalies via (latitude band) Medians). DAM removes large-scale, daily XCO2 background variations, yielding XCO2 anomalies that correlate with the location of major CO2 source regions such as East China. We analysed satellite data between January 2015 and May 2020 and compared monthly XCO2 anomalies in 2020 with corresponding monthly XCO2 anomalies of previous years. In order to link the XCO2 anomalies to East China fossil fuel (FF) emissions, we used XCO2 and corresponding FF emissions from NOAA’s (National Oceanic and Atmospheric Administration) CarbonTracker version CT2019 from 2015 to 2018. Using this CT2019 data set, we found that the relationship between target region ΔXCO2 and the FF emissions of the target region is approximately linear and we quantified slope and offset via a linear fit. We use the empirically obtained linear equation to compute ΔXCO2FF, an estimate of the target region FF emissions, from the satellite-derived XCO2 anomalies, ΔXCO2. We focus on October to May periods to minimize contributions from biospheric carbon fluxes and quantified the error of our FF estimation method for this period by applying it to CT2019. We found that the difference of the retrieved FF emissions and the CT2019 FF emissions in terms of the root-mean-square-error (RMSE) is 0.39 GtCO2/year (4 %). We applied our method to NASA’s (National Aeronautics and Space Administration) OCO-2 XCO2 data product (version 10r) and to three GOSAT products. We focus on estimates of the relative change of East China monthly emissions in 2020 relative to previous months. Our results show considerable month-to-month variability (especially for the GOSAT products) and significant differences across the ensemble of satellite data products analysed. The ensemble mean indicates emission reductions by approximately 8 % ± 10 % in March 2020 and 10 % ± 10 % in April 2020 (uncertainties are 1-sigma) and somewhat lower reductions for the other months in 2020. Using only the OCO-2 data product, we obtain smaller reductions of 1–2 % (depending on month) with an uncertainty of ± 2 %. The large uncertainty and the differences of the results obtained for the individual ensemble members indicates that it is challenging to reliably detect and to accurately quantify the emission reduction. There are several reasons for this including the weak signal (the expected regional XCO2 reduction is only on the order of 0.1–0.2 ppm), the sparseness of the satellite data, remaining biases and limitations of our relatively simple data-driven analysis approach. Inferring COVID-19 related information on regional-scale CO2 emissions using current satellite XCO2 retrievals likely requires, if at all possible, a more sophisticated analysis method including detailed transport modelling and considering a priori information on anthropogenic and natural CO2 surface fluxes.