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We use a global transport model and satellite retrievals of the carbon dioxide (CO2) column average to explore the impact of CO2 emissions reductions that occurred during the economic downturn at the start of the Covid-19 pandemic. The changes in the column averages are substantial in a few places of the model global grid, but the induced gradients are most often less than the random errors of the retrievals. The current necessity to restrict the quality-assured column retrievals to almost cloud-free areas appears to be a major obstacle in identifying changes in CO2 emissions. Indeed, large changes have occurred in the presence of clouds, and in places that were cloud free in 2020, the comparison with previous years is hampered by different cloud conditions during these years. We therefore recommend to favor all-weather CO2 monitoring systems, at least in situ, to support international efforts to reduce emissions.

In the framework of the RECCAP2 initiative, we present the greenhouse gas (GHG) and carbon (C) budget of Europe. For the decade of the 2010s, we present a bottom‐up (BU) estimate of GHG net‐emissions of 3.9 Pg CO2‐eq. yr−1 (using a global warming potential on a 100 years horizon), which are largely dominated by fossil fuel emissions. In this decade, terrestrial ecosystems acted as a net GHG sink of 0.9 Pg CO2‐eq. yr−1, dominated by a CO2 sink that was partially counterbalanced by net emissions of CH4 and N2O. For CH4 and N2O, we find good agreement between BU and top‐down (TD) estimates from atmospheric inversions. However, our BU land CO2 sink is significantly higher than the TD estimates. We further show that decadal averages of GHG net‐emissions have declined by 1.2 Pg CO2‐eq. yr−1 since the 1990s, mainly due to a reduction in fossil fuel emissions. In addition, based on both data driven BU and TD estimates, we also find that the land CO2 sink has weakened over the past two decades. A large part of the European CO2 and C sinks is located in Northern Europe. At the same time, we find a decreasing trend in sink strength in Scandinavia, which can be attributed to an increase in forest management intensity. These are partly offset by increasing CO2 sinks in parts of Eastern Europe and Northern Spain, attributed in part to land use change. Extensive regions of high CH4 and N2O emissions are mainly attributed to agricultural activities and are found in Belgium, the Netherlands and the southern UK. We further analyzed interannual variability in the GHG budgets. The drought year of 2003 shows the highest net‐emissions of CO2 and of all GHGs combined. Plain Language Summary: We have synthesized the European budgets of carbon and the greenhouse gases (GHG) carbon dioxide, methane and nitrous oxide. This synthesis includes estimates of direct emissions from fossil fuel burning, industrial production, waste management and agriculture, as well as of sources and sinks in the terrestrial biosphere. Summing up the sources and sinks of the three GHGs, we estimate for the decade of the 2010s an average annual net‐emission of 3.9 billion tons of carbon dioxide equivalents. These net‐emissions are dominated by carbon dioxide from fossil fuel emissions (4.1 billion tons of carbon dioxide). In contrast, the terrestrial biosphere acts as a net sink of carbon dioxide, the effect of which is only partly counterbalanced by net emissions of methane and nitrous oxide. The net‐effect of the terrestrial biosphere's GHG budget is a sink of 0.9 billion tons of carbon dioxide equivalents per year. Over the last three decades, European GHG emissions have declined by 1.2 billion tons carbon dioxide equivalents per year, mainly due to reductions in fossil fuel emissions. However, the sink capacity of the terrestrial biosphere has diminished since the 2000s. Key Points: We provide a bottom‐up estimate of CO2, CH4, N2O emissions of 3.9 Pg CO2‐eq. yr−1 over Europe, 2010–2019Terrestrial ecosystems acted as a greenhouse gas net sink of 0.9 Pg CO2‐eq. yr−1, dominated by CO2 sinkNet‐greenhouse gas emissions decreased by ∼1/4 since the 1990s, but land carbon sink is weakening since the 2000s [ABSTRACT FROM AUTHOR]

Atmospheric emissions from anthropogenic hotspots, i.e., cities, power plants and industrial facilities, can be determined from remote sensing images obtained from airborne and space-based imaging spectrometers. In this paper, we present a Python library for data-driven emission quantification (ddeq) that implements various computationally light methods such as the Gaussian plume inversion, cross-sectional flux method, integrated mass enhancement method and divergence method. The library provides a shared interface for data input and output and tools for pre- and post-processing of data. The shared interface makes it possible to easily compare and benchmark the different methods. The paper describes the theoretical basis of the different emission quantification methods and their implementation in the ddeq library. The application of the methods is demonstrated using Jupyter notebooks included in the library, for example, for NO 2 images from the Sentinel-5P/TROPOMI satellite and for synthetic CO 2 and NO 2 images from the Copernicus CO 2 Monitoring (CO2M) satellite constellation. The library can be easily extended for new datasets and methods, providing a powerful community tool for users and developers interested in emission monitoring using remote sensing images. [ABSTRACT FROM AUTHOR]

The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M (Copernicus Anthropogenic Carbon Dioxide Monitoring) satellite constellation in 2026 is expected to provide high-resolution images of CO2 (carbon dioxide) column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the low CO2 plume signal-to-noise ratio due to the variability in the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we develop a strategy employing convolutional neural networks (CNNs) to estimate the emission fluxes from a plume in a pseudo- XCO2 image. Our dataset used to train and test such methods includes pseudo-images based on simulations of hourly XCO2 , NO2 (nitrogen dioxide), and wind fields near various power plants in eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state-of-the-art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method and an absolute relative error of ∼ 20 % when only the XCO2 and wind fields are used as inputs. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images. [ABSTRACT FROM AUTHOR]

Various satellite imagers of the vertically integrated column of carbon dioxide (XCO2) are under development to enhance the capabilities for the monitoring of fossil fuel (FF) CO2 emissions. XCO2 images can be used to detect plumes from cities and large industrial plants and to quantify the corresponding emission using atmospheric inversions techniques. However, this potential and the ability to catch the signal from more diffuse FF CO2 sources can be hampered by the mix between these FF signals and a background signal from other types of CO2 surface fluxes, and in particular of biogenic CO2 fluxes. The deployment of dense ground-based air-sampling networks for CO2 and radiocarbon (14CO2) could complement the spaceborne imagery by supporting the separation between the fossil fuel and biogenic or biofuel (BF) CO2 signals. We evaluate this potential complementarity with a high-resolution analytical inversion system focused on northern France, western Germany, Belgium, Luxembourg, and a part of the Netherlands and with pseudo-data experiments. The inversion system controls the FF and BF emissions from the large urban areas and plants, in addition to regional budgets of more diffuse emissions or of biogenic fluxes (NEE, net ecosystem exchange), at an hourly scale over a whole day. The system provides results corresponding to the assimilation of pseudo-data from a single track of a 300 km swath XCO2 imager at 2 km resolution and from surface ground-based CO2 and/or 14CO2 networks. It represents the diversity of 14CO2 sources and sinks and not just the dilution of radiocarbon-free FF CO2 emissions. The uncertainty in the resulting FF CO2 emissions at local (urban area/plant) to regional scales is directly derived and used to assess the potential of the different combinations of observation systems. The assimilation of satellite observations yields estimates of the morning regional emissions with an uncertainty down to 10 % (1 σ) in the satellite field of view, from an assumed uncertainty of 15 % in the prior estimates. However, it does not provide direct information about emissions outside the satellite field of view or about afternoon or nighttime emissions. The co-assimilation of 14CO2 and CO2 surface observations leads to a further reduction of the uncertainty in the estimates of FF emissions. However, this further reduction is significant only in administrative regions with three or more 14CO2 and CO2 sampling sites. The uncertainty in the estimates of 1 d emission in North Rhine-Westphalia, a region with three sampling sites, decreases from 8 % to 6.6 % when assimilating the in situ 14CO2 and CO2 data in addition to the satellite data. Furthermore, this additional decrease appears to be larger when the ground stations are close to large FF emission areas, providing an additional direct constraint for the estimate of these sources rather than supporting the characterization of the background signal from the NEE and its separation from that of the FF emissions. More generally, the results indicate no amplification of the potential of each observation subsystem when they are combined into a large observation system with satellite and surface data. [ABSTRACT FROM AUTHOR]

Using the multiyear archive of the two Orbiting Carbon Observatories (OCO) of NASA, we have retrieved large fossil fuel CO2 emissions (larger than 1.0 ktCO2 h−1 per 10−2 square degree grid cell) over the globe with a simple plume cross‐sectional inversion approach. We have compared our results with a global gridded and hourly inventory. The corresponding OCO emission retrievals explain more than one third of the inventory variance at the corresponding cells and hours. We have binned the data at diverse time scales from the year (with OCO‐2) to the average morning and afternoon (with OCO‐3). We see consistent variations of the median emissions, indicating that the retrieval‐inventory differences (with standard deviations of a few tens of percent) are mostly random and that trends can be calculated robustly in areas of favorable observing conditions, when the future satellite CO2 imagers provide an order of magnitude more data. Plain Language Summary: In the wake of the Paris Climate Agreement, there is an increasing need to monitor emissions from fossil fuel combustion around the world. For CO2 in particular, satellite imagers are being designed to observe the emission plumes from large point sources and intense urban area sources. In order to assess their potential, we have tested a simple emission retrieval scheme on the multi‐year archive of the two NASA Orbiting Carbon Observatories which provide dense observations along the orbit line, but with a narrow swath. We have compared our results with a global gridded and hourly inventory. The corresponding emission retrievals explain a large part of the inventory variability, despite uncertainty in both datasets. We also see consistent variations in the middle emission values at different time scales. These results suggest that the differences between retrievals and inventory are mostly random and that the trends can be calculated robustly in areas of favorable observation conditions, when future satellite CO2 imagers provide an order of magnitude more data. Key Points: We have retrieved some instantaneous CO2 emissions for one third of the large emission cells of a global high‐resolution hourly inventoryThe emission retrievals explain more than one third of the inventory variance at the corresponding cells and hoursConsistent temporal variations of median emissions suggest that trends can be robustly calculated when more data become available [ABSTRACT FROM AUTHOR]

Various satellite imagers of the vertically integrated column of carbon dioxide (XCO2) are under development to enhance the capabilities for the monitoring of the fossil fuel (FF) CO2 emissions. XCO2 images can be used to detect plumes from cities and large industrial plants, and to quantify the corresponding emissions using atmospheric inversions techniques. However, this potential and the ability to catch the signal from more diffuse FF CO2 sources can be hampered by the mix between these FF signals and a background signal from other types of CO2 surface fluxes, and in particular of biogenic CO2 fluxes. The deployment of dense ground-based air-sampling networks for CO2 and radiocarbon (14CO2) could complement the spaceborne imagery by supporting the separation between the fossil fuel and biogenic or biofuel (BF) CO2 signals. We evaluate this potential complementarity with a high resolution analytical inversion system focused on Northern France, Western Germany, Belgium, Luxembourg and a part of the Netherlands, and with pseudo-data experiments. The inversion system controls the FF and BF emissions from the large urban areas and plants, in addition to regional budgets of more diffuse emissions or of biogenic fluxes (NEE, Net Ecosystem Exchange), at an hourly scale over a whole day. The system assimilates pseudo data from a single track of a 300-km swath XCO2 imager at 2 km resolution and from surface ground-based CO2 and/or 14CO2 networks. It represents the diversity of 14CO2 sources and sinks and not just the dilution of radiocarbon-free FF CO2 emissions. The uncertainty in the resulting FF CO2 emissions at local (urban area/ plant) to regional scales is directly derived and used to assess the potential of the different combinations of observation systems. The assimilation of satellite observations yield estimates of the morning regional emissions with an uncertainty down to 10 % (1 sigma) in the satellite field of view, from an assumed uncertainty of 15 % in the prior estimates. However, it does not provide direct information about emissions outside the satellite field of view and neither about afternoon or nighttime emissions. The co-assimilation of 14CO2 and CO2 data lead to a further reduction of the uncertainty in the estimates of FF emissions. However, this further reduction is significant only in administrative regions with three or more 14CO2 and CO2 sampling sites. The uncertainty in the estimates of 1-day emission in North Rhine-Westphalia, a region with three sampling sites, decreases from 8 to 6.6 % when assimilating the in situ 14CO2 and CO2 data in addition to the satellite data. Furthermore, this new decrease appears to be larger when the ground stations are close to large FF emission areas, providing an additional direct constraint for the estimate of these sources rather than supporting the characterization of the background signal from the NEE and its separation from that of the FF emissions. [ABSTRACT FROM AUTHOR]

We present a local-scale atmospheric inversion framework to estimate the location and rate of methane (CH 4) and carbon dioxide (CO 2) releases from point sources. It relies on mobile near-ground atmospheric CH 4 and CO 2 mole fraction measurements across the corresponding atmospheric plumes downwind of these sources, on high-frequency meteorological measurements, and on a Gaussian plume dispersion model. The framework exploits the scatter of the positions of the individual plume cross sections, the integrals of the gas mole fractions above the background within these plume cross sections, and the variations of these integrals from one cross section to the other to infer the position and rate of the releases. It has been developed and applied to provide estimates of brief controlled CH 4 and CO 2 point source releases during a 1-week campaign in October 2018 at the TOTAL experimental platform TADI in Lacq, France. These releases typically lasted 4 to 8 min and covered a wide range of rates (0.3 to 200 g CH 4 /s and 0.2 to 150 g CO 2 /s) to test the capability of atmospheric monitoring systems to react fast to emergency situations in industrial facilities. It also allowed testing of their capability to provide precise emission estimates for the application of climate change mitigation strategies. However, the low and highly varying wind conditions during the releases added difficulties to the challenge of characterizing the atmospheric transport over the very short duration of the releases. We present our series of CH 4 and CO 2 mole fraction measurements using instruments on board a car that drove along roads ∼50 to 150 m downwind of the 40 m × 60 m area for controlled releases along with the estimates of the release locations and rates. The comparisons of these results to the actual position and rate of the controlled releases indicate ∼10 %–40 % average errors (depending on the inversion configuration or on the series of tests) in the estimates of the release rates and ∼30 –40 m errors in the estimates of the release locations. These results are shown to be promising, especially since better results could be expected for longer releases and under meteorological conditions more favorable to local-scale dispersion modeling. However, the analysis also highlights the need for methodological improvements to increase the skill for estimating the source locations. [ABSTRACT FROM AUTHOR]

The top-down atmospheric inversion method that couples atmospheric CO 2 observations with an atmospheric transport model has been used extensively to quantify CO 2 emissions from cities. However, the potential of the method is limited by several sources of misfits between the measured and modeled CO 2 that are of different origins than the targeted CO 2 emissions. This study investigates the critical sources of errors that can compromise the estimates of the city-scale emissions and identifies the signal of emissions that has to be filtered when doing inversions. A set of 1-year forward simulations is carried out using the WRF-Chem model at a horizontal resolution of 1 km focusing on the Paris area with different anthropogenic emission inventories, physical parameterizations, and CO 2 boundary conditions. The simulated CO 2 concentrations are compared with in situ observations from six continuous monitoring stations located within Paris and its vicinity. Results highlight large nighttime model–data misfits, especially in winter within the city, which are attributed to large uncertainties in the diurnal profile of anthropogenic emissions as well as to errors in the vertical mixing near the surface in the WRF-Chem model. The nighttime biogenic respiration to the CO 2 concentration is a significant source of modeling errors during the growing season outside the city. When winds are from continental Europe and the CO 2 concentration of incoming air masses is influenced by remote emissions and large-scale biogenic fluxes, differences in the simulated CO 2 induced by the two different boundary conditions (CAMS and CarbonTracker) can be of up to 5 ppm. Nevertheless, our results demonstrate the potential of our optimal CO 2 atmospheric modeling system to be utilized in atmospheric inversions of CO 2 emissions over the Paris metropolitan area. We evaluated the model performances in terms of wind, vertical mixing, and CO 2 model–data mismatches, and we developed a filtering algorithm for outliers due to local contamination and unfavorable meteorological conditions. Analysis of model–data misfit indicates that future inversions at the mesoscale should only use afternoon urban CO 2 measurements in winter and suburban measurements in summer. Finally, we determined that errors related to CO 2 boundary conditions can be overcome by including distant background observations to constrain the boundary inflow or by assimilating CO 2 gradients of upwind–downwind stations rather than by assimilating absolute CO 2 concentrations. [ABSTRACT FROM AUTHOR]

This work presents a flux inversion system which assesses the potential of new satellite imagery measurements of atmospheric CO 2 for monitoring anthropogenic emissions at scales ranging from local intense point sources to regional and national scales. Such imagery measurements will be provided by the future Copernicus Anthropogenic Carbon Dioxide Monitoring Mission (CO2M). While the modeling framework retains the complexity of previous studies focused on individual and large cities, this system encompasses a wide range of sources to extend the scope of the analysis. This atmospheric inversion system uses a zoomed configuration of the CHIMERE regional transport model which covers most of western Europe with a 2 km resolution grid over northern France, western Germany and Benelux. For each day of March and May 2016, over the 6 h before a given satellite overpass, the inversion separately controls the hourly budgets of anthropogenic emissions in this area from ∼ 300 cities, power plants and regions. The inversion also controls hourly regional budgets of the natural fluxes. This enables the analysis of results at the local to regional scales for a wide range of sources in terms of emission budget and spatial extent while accounting for the uncertainties associated with natural fluxes and the overlapping of plumes from different sources. The potential of satellite data for monitoring CO 2 fluxes is quantified with posterior uncertainties or uncertainty reductions (URs) from prior inventory-based statistical knowledge. A first analysis focuses on the hourly to 6 h budgets of the emissions of the Paris urban area and on the sensitivity of the results to different characteristics of the images of vertically integrated CO 2 (XCO 2) corresponding to the spaceborne instrument: the pixel spatial resolution, the precision of the XCO 2 retrievals per pixel and the swath width. This sensitivity analysis provides a correspondence between these parameters and thresholds on the targeted precisions of emission estimates. However, the results indicate a large sensitivity to the wind speed and to the prior flux uncertainties. The analysis is then extended to the large ensemble of point sources, cities and regions in the study domain, with a focus on the inversion system's ability to separately monitor neighboring sources whose atmospheric signatures overlap and are also mixed with those produced by natural fluxes. Results highlight the strong dependence of uncertainty reductions on the emission budgets, on the wind speed and on whether the focus is on point or area sources. With the system hypothesis that the atmospheric transport is perfectly known, the results indicate that the atmospheric signal overlap is not a critical issue. All of the tests are conducted considering clear-sky conditions, and the limitations from cloud cover are ignored. Furthermore, in these tests, the inversion system is perfectly informed about the statistical properties of the various sources of errors that are accounted for, and systematic errors in the XCO 2 retrievals are ignored; thus, the scores of URs are assumed to be optimistic. For the emissions within the 6 h before a satellite overpass, URs of more than 50 % can only be achieved for power plants and cities whose annual emissions are more than ∼ 2 MtC yr -1. For regional budgets encompassing more diffuse emissions, this threshold increases up to ∼ 10 MtC yr -1. The results therefore suggest an imbalance in the monitoring capabilities of the satellite XCO 2 spectro-imagery towards high and dense sources. [ABSTRACT FROM AUTHOR]

We investigate the potential of the Copernicus Anthropogenic Carbon Dioxide (CO2) Monitoring (CO2M) mission, a proposed constellation of CO2 imaging satellites, to estimate the CO2 emissions of a city on the example of Berlin, the capital of Germany. On average, Berlin emits about 20 MtCO2yr-1 during satellite overpass (11:30 LT). The study uses synthetic satellite observations of a constellation of up to six satellites generated from 1 year of high-resolution atmospheric transport simulations. The emissions were estimated by (1) an analytical atmospheric inversion applied to the plume of Berlin simulated by the same model that was used to generate the synthetic observations and (2) a mass-balance approach that estimates the CO2 flux through multiple cross sections of the city plume detected by a plume detection algorithm. The plume was either detected from CO2 observations alone or from additional nitrogen dioxide (NO2) observations on the same platform. The two approaches were set up to span the range between (i) the optimistic assumption of a perfect transport model that provides an accurate prediction of plume location and CO2 background and (ii) the pessimistic assumption that plume location and background can only be determined reliably from the satellite observations. Often unfavorable meteorological conditions allowed us to successfully apply the analytical inversion to only 11 out of 61 overpasses per satellite per year on average. From a single overpass, the instantaneous emissions of Berlin could be estimated with an average precision of 3.0 to 4.2 Mtyr-1 (15 %–21 % of emissions during overpass) depending on the assumed instrument noise ranging from 0.5 to 1.0 ppm. Applying the mass-balance approach required the detection of a sufficiently large plume, which on average was only possible on three overpasses per satellite per year when using CO2 observations for plume detection. This number doubled to six estimates when the plumes were detected from NO2 observations due to the better signal-to-noise ratio and lower sensitivity to clouds of the measurements. Compared to the analytical inversion, the mass-balance approach had a lower precision ranging from 8.1 to 10.7 Mtyr-1 (40 % to 53 %), because it is affected by additional uncertainties introduced by the estimation of the location of the plume, the CO2 background field, and the wind speed within the plume. These uncertainties also resulted in systematic biases, especially without the NO2 observations. An additional source of bias was non-separable fluxes from outside of Berlin. Annual emissions were estimated by fitting a low-order periodic spline to the individual estimates to account for the seasonal variability of the emissions, but we did not account for the diurnal cycle of emissions, which is an additional source of uncertainty that is difficult to characterize. The analytical inversion was able to estimate annual emissions with an accuracy of < 1.1 Mtyr-1 (< 6 %) even with only one satellite, but this assumes perfect knowledge of plume location and CO2 background. The accuracy was much smaller when applying the mass-balance approach, which determines plume location and background directly from the satellite observations. At least two satellites were necessary for the mass-balance approach to have a sufficiently large number of estimates distributed over the year to robustly fit a spline, but even then the accuracy was low (> 8 Mtyr-1 (>40 %)) when using the CO2 observations alone. When using the NO2 observations to detect the plume, the accuracy could be greatly improved to 22 % and 13 % with two and three satellites, respectively. Using the complementary information provided by the CO2 and NO2 observations on the CO2M mission, it should be possible to quantify annual emissions of a city like Berlin with an accuracy of about 10 % to 20 %, even in the pessimistic case that plume location and CO2 background have to be determined from the observations alone. This requires, however, that the temporal coverage of the constellation is sufficiently high to resolve the temporal variability of emissions. [ABSTRACT FROM AUTHOR]

This study assesses the potential of satellite imagery of vertically integrated columns of dry-air mole fractions of CO2 (XCO2) to constrain the emissions from cities and power plants (called emission clumps) over the whole globe during 1 year. The imagery is simulated for one imager of the Copernicus mission on Anthropogenic Carbon Dioxide Monitoring (CO2M) planned by the European Space Agency and the European Commission. The width of the swath of the CO2M instruments is about 300 km and the ground horizontal resolution is about 2 km resolution. A Plume Monitoring Inversion Framework (PMIF) is developed, relying on a Gaussian plume model to simulate the XCO2 plumes of each emission clump and on a combination of overlapping assimilation windows to solve for the inversion problem. The inversion solves for the 3 h mean emissions (during 08:30–11:30 local time) before satellite overpasses and for the mean emissions during other hours of the day (over the aggregation between 00:00–08:30 and 11:30–00:00) for each clump and for the 366 d of the year. Our analysis focuses on the derivation of the uncertainty in the inversion estimates (the "posterior uncertainty") of the clump emissions. A comparison of the results obtained with PMIF and those from a previous study using a complex 3-D Eulerian transport model for a single city (Paris) shows that the PMIF system provides the correct order of magnitude for the uncertainty reduction of emission estimates (i.e., the relative difference between the prior and posterior uncertainties). Beyond the one city or few large cities studied by previous studies, our results provide, for the first time, the global statistics of the uncertainty reduction of emissions for the full range of global clumps (differing in emission rate and spread, and distance from other major clumps) and meteorological conditions. We show that only the clumps with an annual emission budget higher than 2 MtC yr -1 can potentially have their emissions between 08:30 and 11:30 constrained with a posterior uncertainty smaller than 20 % for more than 10 times within 1 year (ignoring the potential to cross or extrapolate information between 08:30–11:30 time windows on different days). The PMIF inversion results are also aggregated in time to investigate the potential of CO2M observations to constrain daily and annual emissions, relying on the extrapolation of information obtained for 08:30–11:30 time windows during days when clouds and aerosols do not mask the plumes, based on various assumptions regarding the temporal auto-correlations of the uncertainties in the emission estimates that are used as a prior knowledge in the Bayesian framework of PMIF. We show that the posterior uncertainties of daily and annual emissions are highly dependent on these temporal auto-correlations, stressing the need for systematic assessment of the sources of uncertainty in the spatiotemporally resolved emission inventories used as prior estimates in the inversions. We highlight the difficulty in constraining the total budget of CO2 emissions from all the cities and power plants within a country or over the globe with satellite XCO2 measurements only, and calls for integrated inversion systems that exploit multiple types of measurements. [ABSTRACT FROM AUTHOR]

Atmospheric inversions have been used for the past two decades to derive large-scale constraints on the sources and sinks of CO2 into the atmosphere. The development of dense in situ surface observation networks, such as ICOS in Europe, enables in theory inversions at a resolution close to the country scale in Europe. This has led to the development of many regional inversion systems capable of assimilating these high-resolution data, in Europe and elsewhere. The EUROCOM (European atmospheric transport inversion comparison) project is a collaboration between seven European research institutes, which aims at producing a collective assessment of the net carbon flux between the terrestrial ecosystems and the atmosphere in Europe for the period 2006–2015. It aims in particular at investigating the capacity of the inversions to deliver consistent flux estimates from the country scale up to the continental scale. The project participants were provided with a common database of in situ-observed CO2 concentrations (including the observation sites that are now part of the ICOS network) and were tasked with providing their best estimate of the net terrestrial carbon flux for that period, and for a large domain covering the entire European Union. The inversion systems differ by the transport model, the inversion approach, and the choice of observation and prior constraints, enabling us to widely explore the space of uncertainties. This paper describes the intercomparison protocol and the participating systems, and it presents the first results from a reference set of inversions, at the continental scale and in four large regions. At the continental scale, the regional inversions support the assumption that European ecosystems are a relatively small sink (-0.21±0.2 Pg C yr -1). We find that the convergence of the regional inversions at this scale is not better than that obtained in state-of-the-art global inversions. However, more robust results are obtained for sub-regions within Europe, and in these areas with dense observational coverage, the objective of delivering robust country-scale flux estimates appears achievable in the near future. [ABSTRACT FROM AUTHOR]

In order to track progress towards the global climate targets, the parties that signed the Paris Climate Agreement will regularly report their anthropogenic carbon dioxide (CO2) emissions based on energy statistics and CO2 emission factors. Independent evaluation of this self-reporting system is a fast-growing research topic. Here, we study the value of satellite observations of the column CO2 concentrations to estimate CO2 anthropogenic emissions with 5 years of the Orbiting Carbon Observatory-2 (OCO-2) retrievals over and around China. With the detailed information of emission source locations and the local wind, we successfully observe CO2 plumes from 46 cities and industrial regions over China and quantify their CO2 emissions from the OCO-2 observations, which add up to a total of 1.3 Gt CO2 yr -1 that accounts for approximately 13 % of mainland China's annual emissions. The number of cities whose emissions are constrained by OCO-2 here is 3 to 10 times larger than in previous studies that only focused on large cities and power plants in different locations around the world. Our satellite-based emission estimates are broadly consistent with the independent values from China's detailed emission inventory MEIC but are more different from those of two widely used global gridded emission datasets (i.e., EDGAR and ODIAC), especially for the emission estimates for the individual cities. These results demonstrate some skill in the satellite-based emission quantification for isolated source clusters with the OCO-2, despite the sparse sampling of this instrument not designed for this purpose. This skill can be improved by future satellite missions that will have a denser spatial sampling of surface emitting areas, which will come soon in the early 2020s. [ABSTRACT FROM AUTHOR]

We present a local-scale atmospheric inversion framework to estimate the location and rate of methane (CH4) and carbon dioxide (CO2) releases from point sources. It relies on mobile near-ground atmospheric CH4 and CO2 mole fraction measurements across the corresponding atmospheric plumes downwind the sources, on high-frequency meteorological measurements, and a Gaussian plume dispersion model. It exploits the spread of the positions of individual plume cross-sections and the integrals of the gas mole fractions above the background within these plume cross-sections to infer the position and rate of the releases. It has been developed and applied to provide estimates of brief controlled CH4 and CO2 point source releases during a one-week campaign in October 2018 at the TOTAL's experimental platform TADI in Lacq, France. These releases lasted typically 4 to 8 minutes and covered a wide range of rates (0.3 to 200 gCH4/s and 0.2 to 150 gCO2/s) to test the capability of atmospheric monitoring systems to react fast to emergency situations in industrial facilities. It also allowed testing their capability to provide precise emission estimates for the application of climate change mitigation strategies. However, the low and highly varying wind conditions during the releases added difficulties to the challenge of characterizing the atmospheric transport over the very short duration of the releases. We present our series of measurements of CH4 and CO2 mole fractions using instruments onboard a car that drives along the roads ~50 to 150 m downwind the 40 m x 60 m area of controlled releases for each of the releases and the results from the inversions of the release locations and rates. The comparisons of these results to the actual position and rate of the controlled release indicate a 20 %-30 % average error on the release rates and a ~30-40 m errors in the estimates of the release locations. These results are shown to be promising especially since better results could be expected for longer releases and under meteorological conditions more favorable to local scale dispersion modeling. [ABSTRACT FROM AUTHOR]

High-resolution atmospheric transport simulations were used to investigate the potential for detecting carbon dioxide (CO2) plumes of the city of Berlin and neighboring power stations with the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission, which is a proposed constellation of CO2 satellites with imaging capabilities. The potential for detecting plumes was studied for satellite images of CO2 alone or in combination with images of nitrogen dioxide (NO2) and carbon monoxide (CO) to investigate the added value of measurements of other gases coemitted with CO2 that have better signal-to-noise ratios. The additional NO2 and CO images were either generated for instruments on the same CO2M satellites (2 km × 2 km resolution) or for the Sentinel-5 instrument (7.5 km × 7.5 km) assumed to fly 2 h earlier than CO2M. Realistic CO2 , CO and NOX(=NO+NO2) fields were simulated at 1 km × 1 km horizontal resolution with the Consortium for Small-scale Modeling model extended with a module for the simulation of greenhouse gases (COSMO-GHG) for the year 2015, and they were used as input for an orbit simulator to generate synthetic observations of columns of CO2 , CO and NO2 for constellations of up to six satellites. A simple plume detection algorithm was applied to detect coherent structures in the images of CO2 , NO2 or CO against instrument noise and variability in background levels. Although six satellites with an assumed swath of 250 km were sufficient to overpass Berlin on a daily basis, only about 50 out of 365 plumes per year could be observed in conditions suitable for emission estimation due to frequent cloud cover. With the CO2 instrument only 6 and 16 of these 50 plumes could be detected assuming a high-noise (σVEG50=1.0 ppm) and low-noise (σVEG50=0.5 ppm) scenario, respectively, because the CO2 signals were often too weak. A CO instrument with specifications similar to the Sentinel-5 mission performed worse than the CO2 instrument, while the number of detectable plumes could be significantly increased to about 35 plumes with an NO2 instrument. CO2 and NO2 plumes were found to overlap to a large extent, although NOX had a limited lifetime (assumed to be 4 h) and although CO2 and NOX were emitted with different NOX:CO2 emission ratios by different source types with different temporal and vertical emission profiles. Using NO2 observations from the Sentinel-5 platform instead resulted in a significant spatial mismatch between NO2 and CO2 plumes due to the 2 h time difference between Sentinel-5 and CO2M. The plumes of the coal-fired power plant Jänschwalde were easier to detect with the CO2 instrument (about 40–45 plumes per year), but, again, an NO2 instrument could detect significantly more plumes (about 70). Auxiliary measurements of NO2 were thus found to greatly enhance the capability of detecting the location of CO2 plumes, which will be invaluable for the quantification of CO2 emissions from large point sources. [ABSTRACT FROM AUTHOR]

Global terrestrial nitrogen (N) and phosphorus (P) cycles are coupled to the global carbon (C) cycle for net primary production (NPP), plant C allocation, and decomposition of soil organic matter, but N and P have distinct pathways of inputs and losses. Current C-nutrient models exhibit large uncertainties in their estimates of pool sizes, fluxes, and turnover rates of nutrients, due to a lack of consistent global data for evaluating the models. In this study, we present a new model-data fusion framework called the Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines the CARbon DAta MOdel fraMework (CARDAMOM) data-constrained C-cycle analysis with spatially explicit data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. We calculated the steady-state N- and P-pool sizes and fluxes globally for large biomes. Our study showed that new N inputs from biological fixation and deposition supplied > 20% of total plant uptake in most forest ecosystems but accounted for smaller fractions in boreal forests and grasslands. New P inputs from atmospheric deposition and rock weathering supplied a much smaller fraction of total plant uptake than new N inputs, indicating the importance of internal P recycling within ecosystems to support plant growth. Nutrient-use efficiency, defined as the ratio of gross primary production (GPP) to plant nutrient uptake, were diagnosed from our model results and compared between biomes. Tropical forests had the lowest N-use efficiency and the highest P-use efficiency of the forest biomes. An analysis of sensitivity and uncertainty indicated that the NPP-allocation fractions to leaves, roots, and wood contributed the most to the uncertainties in the estimates of nutrient-use efficiencies. Correcting for biases in NPP-allocation fractions produced more plausible gradients of N- and P-use efficiencies from tropical to boreal ecosystems and highlighted the critical role of accurate measurements of C allocation for understanding the N and P cycles. [ABSTRACT FROM AUTHOR]

Fossil-fuel (FF) burning releases carbon dioxide (CO2/ together with many other chemical species, some of which, such as nitrogen dioxide (NO2/ and carbon monoxide (CO), are routinely monitored from space. This study examines the feasibility of estimation of FF CO2 emissions from large industrial regions by using NO2 and CO column retrievals from satellite measurements in combination with simulations by a mesoscale chemistry transport model (CTM). To this end, an inverse modeling method is developed that allows estimating FF CO2 emissions from different sectors of the economy, as well as the total CO2 emissions, in a given region. The key steps of the method are (1) inferring "top-down" estimates of the regional budget of anthropogenic NOx and CO emissions from satellite measurements of proxy species (NO2 and CO in the case considered) without using formal a priori constraints on these budgets, (2) the application of emission factors (the NOx - to-CO2 and CO-to-CO2 emission ratios in each sector) that relate FF CO2 emissions to the proxy species emissions and are evaluated by using data of "bottom-up" emission inventories, and (3) cross-validation and optimal combination of the estimates of CO2 emission budgets derived from measurements of the different proxy species. Uncertainties in the top-down estimates of the NOx and CO emissions are evaluated and systematic differences between the measured and simulated data are taken into account by using original robust techniques validated with synthetic data. To examine the potential of the method, it was applied to the budget of emissions for a western European region including 12 countries by using NO2 and CO column amounts retrieved from, respectively, the OMI and IASI satellite measurements and simulated by the CHIMERE mesoscale CTM, along with the emission conversion factors based on the EDGAR v4.2 emission inventory. The analysis was focused on evaluation of the uncertainty levels for the top-down NOx and CO emission estimates and "hybrid" estimates (that is, those based on both atmospheric measurements of a given proxy species and respective bottom-up emission inventory data) of FF CO2 emissions, as well as on examining consistency between the FF NO2 emission estimates derived from measurements of the different proxy species. It is found that NO2 measurements can provide much stronger constraints to the total annual FF CO2 emissions in the study region than CO measurements, the accuracy of the NO2-measurement-based CO2 emission estimate being mostly limited by the uncertainty in the top-down NOx emission estimate. Nonetheless, CO measurements are also found to be useful as they provide additional constraints to CO2 emissions and enable evaluation of the hybrid FF CO2 emission estimates obtained from NO2 measurements. Our most reliable estimate for the total annual FF CO2 emissions in the study region in 2008 (2.71 ± 0.30 PgCO2/ is found to be about 11 and 5% lower than the respective estimates based on the EDGAR v.4.2 (3.03 PgCO2/ and CDIAC (2.86 PgCO2/ emission inventories, with the difference between our estimate and the CDIAC inventory data not being statistically significant. In general, the results of this study indicate that the proposed method has the potential to become a useful tool for identification of possible biases and/or inconsistencies in the bottom-up emission inventory data regarding CO2, NOx, and CO emissions from fossil-fuel burning in different regions of the world. [ABSTRACT FROM AUTHOR]

Urban areas, where more than 55% of the global population gathers, contribute more than 70% of anthropogenic fossil fuel carbon dioxide (CO 2ff) emissions. Accurate quantification of CO 2ff emissions from urban areas is of great importance for formulating global warming mitigation policies to achieve carbon neutrality by 2050. Satellite-based inversion techniques are unique among "top-down" approaches, potentially allowing us to track CO 2ff emission changes over cities globally. However, their accuracy is still limited by incomplete background information, cloud blockages, aerosol contamination, and uncertainties in models and priori emission inventories. To evaluate the current potential of space-based quantification techniques, we present the first attempt to monitor long-term changes in CO 2ff emissions based on the OCO-2 satellite measurements of column-averaged dry-air mole fractions of CO 2 (X CO2) over a fast-growing Asian metropolitan area: Lahore, Pakistan. We first examined the OCO-2 data availability at global scale. About 17% of OCO-2 soundings over the global 70 most populated cities from 2014 to 2019 are marked as high-quality. Cloud blockage and aerosol contamination are the two main causes of data loss. As an attempt to recover additional soundings, we evaluated the effectiveness of OCO-2 quality flags at the city level by comparing three flux quantification methods (WRF-Chem, X-STILT, and the flux cross-sectional integration method). The satellite/bottom-up emissions (OCO-2/ODIAC) ratios of the high-quality tracks with reduced uncertainties in emissions are better agreed across the three methods compared to the all-data tracks. This demonstrates that OCO-2 quality flags are useful filters of low-quality OCO-2 retrievals at local scales. All three methods consistently suggested that the ratio medians are greater than 1, implying that the ODIAC slightly underestimated CO 2ff emissions over Lahore. Additionally, our estimation of the a posteriori CO 2ff emission trend was about 734 kt C/year (i.e., an annual 6.7% increase). 10,000 Monte Carlo simulations of the Mann-Kendall upward trend test showed that less than 10% prior uncertainty for 8 tracks (or less than 20% prior uncertainty for 25 tracks) is required to achieve a greater-than-50% trend significant possibility at a 95% confidence level. It implies that the trend is driven by the prior and not due to the assimilation of OCO-2 retrievals. The key to improving the role of satellite data in CO 2 emission trend detection lies in collecting more frequent high-quality tracks near metropolitan areas to achieve significant constraints from X CO2 retrievals. • 17% of OCO-2 soundings over the 70 most populated cities are of high-quality. • OCO-2 quality flags are useful filters of low-quality retrievals at city scale. • Posteriori fossil fuel CO 2 emissions over Lahore showed a 6.7% annual increase. • More high-quality tracks are needed to better constrain urban CO 2 emission trends. [ABSTRACT FROM AUTHOR]