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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 high density 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 project (EUROpean atmospheric transport inversion COMparison) 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 PgC/year). 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 2015, the Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE™) measurement system was deployed for a long-duration experiment in the center of Paris, France. The system measures near-surface atmospheric CO2 concentrations integrated along 30 horizontal chords ranging in length from 2.3 to 5.2 km and covering an area of 25 km 2 over the complex urban environment. In this study, we use this observing system together with six conventional in situ point measurements and the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and two urban canopy schemes (Urban Canopy Model – UCM; Building Effect Parameterization – BEP) at a horizontal resolution of 1 km to analyze the temporal and spatial variations in CO2 concentrations within the city of Paris and its vicinity for the 1-year period spanning December 2015 to November 2016. Such an analysis aims at supporting the development of CO2 atmospheric inversion systems at the city scale. Results show that both urban canopy schemes in the WRF-Chem model are capable of reproducing the seasonal cycle and most of the synoptic variations in the atmospheric CO2 point measurements over the suburban areas as well as the general corresponding spatial differences in CO2 concentration that span the urban area. However, within the city, there are larger discrepancies between the observations and the model results with very distinct features during winter and summer. During winter, the GreenLITE™ measurements clearly demonstrate that one urban canopy scheme (BEP) provides a much better description of temporal variations and horizontal differences in CO2 concentrations than the other (UCM) does. During summer, much larger CO2 horizontal differences are indicated by the GreenLITE™ system than both the in situ measurements and the model results, with systematic east–west variations. [ABSTRACT FROM AUTHOR]

Up-to-date and accurate emission inventories for air pollutants are essential for understanding their role in the formation of tropospheric ozone and particulate matter at various temporal scales, for anticipating pollution peaks and for identifying the key drivers that could help mitigate their emissions. This paper describes the Bayesian variational inverse system PYVAR-CHIMERE, which is adapted to the inversion of reactive species. Complementarily with bottom-up inventories, this system aims at updating and improving the knowledge on the high spatio-temporal variability of emissions of air pollutants and their precursors. The system is designed to use any type of observations, such as satellite observations or surface stations. The potential of PYVAR-CHIMERE is illustrated with one-day inversions of CO and NO2 emissions in Europe, using the MOPITT and OMI satellite observations (for CO and for NO2, respectively). [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 co-emitted 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 (2×2 km2 resolution) or for the Sentinel-5 instrument (7×7 km2) assumed to fly two hours earlier than CO2M. Realistic CO2, CO and NO2 fields were simulated at 1×1 km2 horizontal resolution with COSMO-GHG model for the year 2015, and 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 new 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 (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. Using NO2 observations from the Sentinel-5 platform instead resulted in a significant spatial mismatch between NO2 and CO2 plumes due to the two hour 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 performed significantly better (about 70 plumes). 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]

Inverse modeling of anthropogenic and biospheric CO2 fluxes from ground-based and satellite observations critically depends on the accuracy of atmospheric transport simulations. Previous studies emphasized the impact of errors in simulated winds and vertical mixing in the planetary boundary layer, whereas the potential importance of releasing emissions not only at the surface but distributing them in the vertical was largely neglected. Accounting for elevated emissions may be critical, since more than 50% of CO2 in Europe is emitted by large point sources such as power plants and industrial facilities. In this study we conduct high-resolution atmospheric simulations of CO2 with the mesoscale model COSMO-GHG over a domain covering the city of Berlin and several coal-fired power plants in eastern Germany, Poland and the Czech Republic. By including separate tracers for anthropogenic CO2 emitted only at the surface or according to realistic, source-dependent profiles, we find that releasing CO2 only at the surface overestimates near-surface CO2 concentrations in the afternoon on average by 14% in summer and 43% in winter over the selected model domain. Differences in column mean dry air mole fractions XCO2 are smaller, between 5% in winter and 8% in summer, suggesting smaller yet non-negligible sensitivities for inversion modeling studies assimilating satellite rather than surface observations. The results suggests that the traditional approach of emitting CO2 only at the surface is problematic and that a proper allocation of emissions in the vertical deserves as much attention as an accurate simulation of atmospheric transport. [ABSTRACT FROM AUTHOR]

A large fraction of fossil fuel CO2 emissions occur within "hotspots", such as cities and power plants, which cover a very small fraction of the land surface. Although some of these emission hotspots are monitored closely, there is no detailed emission inventory for most of them. Space-borne imagery of atmospheric CO2 has the potential to provide independent estimates of CO2 emissions from hotspots. But first, what is a hotspot needs to be defined for the purpose of satellite observations. The proposed space-borne imagers with global coverage planned for the coming decade have a pixel size on the order of a few square kilometers, and a XCO2 accuracy and precision of < 1 ppm for individual pixels. This resolution and precision is insufficient to provide a cartography of emissions for each individual pixel. Rather, the integrated emission of the diffuse emitting area and the intense point sources are sought. In this study, we address the question of the global characterization of area and point fossil fuel CO2 emitting sources (those hotspots are called emission clumps hereafter) that may cause coherent XCO2 plumes in space-borne CO2 images. An algorithm is proposed to identify emission clumps worldwide, based on the ODIAC global high resolution 1 km fossil fuel emission data product. The clump algorithm selects the major urban areas from a GIS (geographic information system) file and two emission thresholds. The selected urban areas and a high emission threshold are used to identify clump cores such as inner city areas or large power plants. A low threshold and a random walker (RW) scheme are then used to aggregate all grid cells contiguous to cores in order to define a single clump. With our definition of the thresholds, which are appropriate for a space imagery with 0.5 ppm precision for a single XCO2 measurement, a total of 11,314 individual clumps, with 5,088 area clumps and 6,226 point-source clumps (power plants), are identified. These clumps contribute 72 % of the global fossil fuel CO2 emissions according to the ODIAS inventory. The emission clumps is a new tool for comparing fossil fuel CO2 emissions from different inventories, and objectively identifying emitting areas that have a potential to be detected by future global satellite imagery of XCO2. The emission clump data product is distributed from https://doi.org/10.6084/m9.figshare.7217726.v1. [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 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 that the terrestrial C sink may ultimately be constrained by low P. Nutrient-use efficiency, defined as the ratio of gross primary production (GPP) to plant nutrient uptake, can be 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]

Combining measurements of atmospheric CO2 and its radiocarbon (14CO2) fraction and transport modeling in atmospheric inversions offers a way to derive improved estimates of CO2 emitted from fossil fuel (FFCO2). In this study, we solve for the monthly FFCO2 emission budgets at regional scale (i.e., the size of a medium-sized country in Europe) and investigate the performance of different observation networks and sampling strategies across Europe. The inversion system is built on the LMDZv4 global transport model at 3.75° ×2.5° resolution. We conduct Observing System Simulation Experiments (OSSEs) and use two types of diagnostics to assess the potential of the observation and inverse modeling frameworks. The first one relies on the theoretical computation of the uncertainty in the estimate of emissions from the inversion, known as "posterior uncertainty", and on the uncertainty reduction compared to the uncertainty in the inventories of these emissions, which are used as a prior knowledge by the inversion (called "prior uncertainty"). The second one is based on comparisons of prior and posterior estimates of the emission to synthetic "true" emissions when these true emissions are used beforehand to generate the synthetic fossil fuel CO2 mixing ratio measurements that are assimilated in the inversion. With 17 stations currently measuring 14CO2 across Europe using 2-week integrated sampling, the uncertainty reduction for monthly FFCO2 emissions in a country where the network is rather dense like Germany, is larger than 30%. With the 43 14CO2 measurement stations planned in Europe, the uncertainty reduction for monthly FFCO2 emissions is increased for the UK, France, Italy, eastern Europe and the Balkans, depending on the configuration of prior uncertainty. Further increasing the number of stations or the sampling frequency improves the uncertainty reduction (up to 40 to 70%) in high emitting regions, but the performance of the inversion remains limited over low-emitting regions, even assuming a dense observation network covering the whole of Europe. This study also shows that both the theoretical uncertainty reduction (and resulting posterior uncertainty) from the inversion and the posterior estimate of emissions itself, for a given prior and "true" estimate of the emissions, are highly sensitive to the choice between two configurations of the prior uncertainty derived from the general estimate by inventory compilers or computations on existing inventories. In particular, when the configuration of the prior uncertainty statistics in the inversion system does not match the difference between these prior and true estimates, the posterior estimate of emissions deviates significantly from the truth. This highlights the difficulty of filtering the targeted signal in the model-data misfit for this specific inversion framework, the need to strongly rely on the prior uncertainty characterization for this and, consequently, the need for improved estimates of the uncertainties in current emission inventories for real applications with actual data. We apply the posterior uncertainty in annual emissions to the problem of detecting a trend of FFCO2, showing that increasing the monitoring period (e.g., more than 20 years) is more efficient than reducing uncertainty in annual emissions by adding stations. The coarse spatial resolution of the atmospheric transport model used in this OSSE (typical of models used for global inversions of natural CO2 fluxes) leads to large representation errors (related to the inability of the transport model to capture the spatial variability of the actual fluxes and mixing ratios at subgrid scales), which is a key limitation of our OSSE setup to improve the accuracy of the monitoring of FFCO2 emissions in European regions. Using a high-resolution transport model should improve the potential to retrieve FFCO2 emissions, and this needs to be investigated. [ABSTRACT FROM AUTHOR]

Most of the global fossil fuel CO2 emissions arise from urbanized and industrialized areas. Bottom-up inventories quantify them but with large uncertainties. In 2010-2011, the first atmospheric in situ CO2 measurement network for Paris, the capital of France, began operating with the aim of monitoring the regional atmospheric impact of the emissions coming from this megacity. Five stations sampled air along a northeast--southwest axis that corresponds to the direction of the dominant winds. Two stations are classified as rural (Traînou -- TRN; Montgé-en-Goële -- MON), two are peri-urban (Gonesse -- GON; Gif-sur-Yvette -- GIF) and one is urban (EIF, located on top of the Eiffel Tower). In this study, we analyze the diurnal, synoptic and seasonal variability of the in situ CO2 measurements over nearly 1 year (8 August 2010-13 July 2011).We compare these datasets with remote CO2 measurements made at Mace Head (MHD) on the Atlantic coast of Ireland and support our analysis with atmospheric boundary layer height (ABLH) observations made in the center of Paris and with both modeled and observed meteorological fields. The average hourly CO2 diurnal cycles observed at the regional stations are mostly driven by the CO2 biospheric cycle, the ABLH cycle and the proximity to urban CO2 emissions. Differences of several µmol mol-1(ppm) can be observed from one regional site to the other. The more the site is surrounded by urban sources (mostly residential and commercial heating, and traffic), the more the CO2 concentration is elevated, as is the associated variability which reflects the variability of the urban sources. Furthermore, two sites with inlets high above ground level (EIF and TRN) show a phase shift of the CO2 diurnal cycle of a few hours compared to lower sites due to a strong coupling with the boundary layer diurnal cycle. As a consequence, the existence of a CO2 vertical gradient above Paris can be inferred, whose amplitude depends on the time of the day and on the season, ranging from a few tenths of ppm during daytime to several ppm during nighttime. The CO2 seasonal cycle inferred from monthly means at our regional sites is driven by the biospheric and anthropogenic CO2 flux seasonal cycles, the ABLH seasonal cycle and also synoptic variations. Enhancements of several ppm are observed at peri-urban stations compared to rural ones, mostly from the influence of urban emissions that are in the footprint of the peri-urban station. The seasonal cycle observed at the urban station (EIF) is specific and very sensitive to the ABLH cycle. At both the diurnal and the seasonal scales, noticeable differences of several ppm are observed between the measurements made at regional rural stations and the remote measurements made at MHD, that are shown not to define background concentrations appropriately for quantifying the regional (~100 km) atmospheric impact of urban CO2 emissions. For wind speeds less than 3 m s-1, the accumulation of local CO2 emissions in the urban atmosphere forms a dome of several tens of ppm at the peri-urban stations, mostly under the influence of relatively local emissions including those from the Charles de Gaulle (CDG) Airport facility and from aircraft in flight. When wind speed increases, ventilation transforms the CO2 dome into a plume. Higher CO2 background concentrations of several ppm are advected from the remote Benelux--Ruhr and London regions, impacting concentrations at the five stations of the network even at wind speeds higher than 9 m s-1. For wind speeds ranging between 3 and 8 m s-1, the impact of Paris emissions can be detected in the peri-urban stations when they are downwind of the city, while the rural stations often seem disconnected from the city emission plume. As a conclusion, our study highlights a high sensitivity of the stations to wind speed and direction, to their distance from the city, but also to the ABLH cycle depending on their elevation. We learn some lessons regarding the design of an urban CO2 network: (1) careful attention should be paid to properly setting regional (~100 km) background sites that will be representative of the different wind sectors; (2) the downwind stations should be positioned as symmetrically as possible in relation to the city center, at the peri-urban/rural border; (3) the stations should be installed at ventilated sites (away from strong local sources) and the air inlet set up above the building or biospheric canopy layer, whichever is the highest; and (4) high-resolution wind information should be available with the CO2 measurements. [ABSTRACT FROM AUTHOR]

This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the shortwave infrared measurements of a space-borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on observing system simulation experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 h before a given satellite overpass. These 6 h correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge with 50% uncertainty on the hourly or sectorial emissions, and with ~25% uncertainty on the 6 h mean emissions, from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbiting missions, corresponding to the specifications of CarbonSat and Sentinel- 5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the statistics of the limited number of error sources. These OSSEs indicate that the image resolution has to be finer than 4 km to decrease the uncertainty in the 6 h mean emissions by more than 50 %. More complex experiments assess the impact of more realistic error estimates that current inversion methods do not properly account for, in particular, the systematic measurement errors with spatially correlated patterns. These experiments highlight the difficulty to improve current knowledge on CO2 emissions for urban areas like Paris with CO2 observations from satellites, and call for more technological innovations in the remote sensing of vertically integrated columns of CO2 and in the inversion systems that exploit it. [ABSTRACT FROM AUTHOR]

Methane emissions at the national scale in France in 2012 are inferred by assimilating continuous atmospheric mixing ratio measurements from nine stations of the European network ICOS located in France and surrounding countries. To assess the robustness of the fluxes deduced by our inversion system based on an objectified quantification of uncertainties, two complementary inversion set-ups are computed and analysed: i) a regional run correcting for the spatial distribution of fluxes in France, and ii) a sectorial run correcting fluxes for activity sectors at the national scale. In addition, our results for the two set-ups are compared with fluxes produced in the framework of the inversion inter-comparison exercise of the InGOS project. The seasonal variability of fluxes is consistent between different set-ups, with maximum emissions in summer, likely due to agricultural activity. However, very high monthly posterior uncertainties (up to ≈ 65% to 74% in the sectorial run in May and June) makes it difficult to attribute maximum emissions to a specific sector. At the yearly national scale, the two inversions range to 3835-4050 Gg CH4 and 3570-4190 Gg CH4 for the regional and sectorial run, respectively, consistently with the InGOS products. These estimates are 25 to 55% higher than the total national emissions from bottom-up approaches (biogeochemical models from natural emissions, plus inventories for anthropogenic ones), consistently pointing at missing or under-estimated sources in the inventories and/or in natural sources. More specifically, in the sectorial set-up, agricultural emissions are inferred as 66% larger than estimates reported to UNFCCC. Uncertainties on the total annual national budget are 108 Gg CH4 and 312 Gg CH4, i.e, 3 to 8%, for the regional and sectorial run respectively, smaller than uncertainties in available bottom-up products, proving the added value of top-down atmospheric inversions. Therefore, even though the surface network used in 2012 does not allow to fully constrain all regions in France accurately, a regional inversion set-ups makes it possible to provide estimates of French methane fluxes with an uncertainty on the total budget less than 10% at the yearly scale. Additional sites deployed since 2012 would help to constrain French emissions at finer spatial and temporal scales and attributing missing emissions to specific sectors. [ABSTRACT FROM AUTHOR]

This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the Short Wave Infra Red measurements of a space borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on Observing System Simulation Experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 hours before a given satellite overpass. These 6 hours correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge about the hourly emissions from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically-integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbing missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the statistics of the limited number of error sources. These OSSEs indicate that the image resolution has to be finer than 4 km to decrease the uncertainty in the 6-hour mean emissions by more than 50 %. More complex experiments assess the impact of more realistic error estimates that current inversion methods do not properly account for, in particular the systematic measurement errors with spatially correlated patterns. These experiments highlight the difficulty to improve current knowledge on CO2 emissions for urban areas like Paris with CO2 observations from satellites, and call for more technological innovations in the remote sensing of vertically integrated columns of CO2 and in the inversion systems that exploit it. [ABSTRACT FROM AUTHOR]

Combining measurements of atmospheric CO2 and its radiocarbon (14CO2) fraction and transport modeling in atmospheric inversions offers a way to derive improved estimates of CO2 emitted from fossil fuel (FFCO2). In this study, we solve for the monthly FFCO2 emission budgets at regional scale (i.e. the size of a medium-sized country in Europe) and investigate the performance of different observation networks and sampling strategies across Europe. The inversion system is built on the LMDZv4 global transport model at 3.75° × 2.5° resolution. We conduct Observing System Simulation Experiments (OSSE) and use two types of diagnostics to assess the potential of the observation and inverse modeling frameworks. The first one relies on the theoretical computation of the uncertainty in the estimate of emissions from the inversion, known as "posterior uncertainty", and on the uncertainty reduction compared to the uncertainty in the inventories of these emissions which are used as a prior knowledge by the inversion (called "prior uncertainty"). The second one is based on comparisons of prior and posterior estimates of the emission to synthetic "true" emissions when these true emissions are used beforehand to generate the synthetic fossil fuel CO2 mixing ratio measurements that are assimilated in the inversion. With 17 stations currently measuring 14CO2 across Europe using 2-week integrated sampling, the uncertainty reduction for monthly FFCO2 emissions in a country where the network is rather dense like Germany, is larger than 30 %. With the 43 14CO2 measurement stations planned in Europe, the uncertainty reduction for monthly FFCO2 emissions is increased for UK, France, Italy, Eastern Europe and the Balkans, depending on the configuration of prior uncertainty. Further increasing the number of stations or the sampling frequency improve the uncertainty reduction (up to 40 % to 70 %) in high emitting regions, but the performance of the inversion remains limited over low-emitting regions, even assuming a dense observation network covering the whole of Europe. This study also shows that both the theoretical uncertainty reduction (and resulting posterior uncertainty) from the inversion and the posterior estimate of emissions itself, for a given prior and "true" estimate of the emissions, are highly sensitive to the choice between two configurations of the prior uncertainty derived from the general estimate by inventory compilers or computations on existing inventories. In particular, when the configuration of the prior uncertainty statistics in the inversion system does not match the difference between these prior and true estimates, the posterior estimate of emissions deviate significantly from the truth. This highlights the difficulty to filter the targeted signal in the model-data misfit for this specific inversion framework, the need to strongly rely on the prior uncertainty characterization for this, and, consequently the need for improved estimates of the uncertainties in current emission inventories for real applications with actual data. We apply the posterior uncertainty in annual emissions to the problem of detecting a trend of FFCO2, showing that increasing the monitoring period (e.g. more than 20 years) is more efficient than reducing uncertainty in annual emissions by adding stations. The coarse spatial resolution of the atmospheric transport model used in this OSSE (typical of models used for global inversions of natural CO2 fluxes) leads to large representation errors (related to the inability of the transport model to capture the spatial variability of the actual fluxes and mixing ratios at sub-grid scales), which is a key limitation of our OSSE setup to improve the accuracy of the monitoring of FFCO2 emissions in European regions. Using a high-resolution transport model should improve the potential to retrieve FFCO2 emissions, and this needs to be investigated. [ABSTRACT FROM AUTHOR]

This study presents a new method for estimating the pollutant emission rates of a site and its main facilities using a series of atmospheric measurements across the pollutant plumes. This approach is based on a combination of the tracer release method, a Gaussian atmospheric transport model and a statistical atmospheric inversion approach. The conversion between the tracer controlled emission and the measured atmospheric concentrations across the plume provides knowledge on the atmospheric transport. The concept of the method consists of using this knowledge to optimize the configuration of the Gaussian model parameters and the model uncertainty statistics in the inversion system. The pollutant rates of each source are inverted to optimize the match between the concentrations simulated with the Gaussian model and the pollutants' measured atmospheric concentrations, accounting for the Gaussian model uncertainty. This new approach is evaluated with a series of inversions of controlled methane point sources using acetylene as a tracer gas. In these experiments, different configurations of methane and acetylene point source locations are tested to assess the efficiency of this method in comparison with the classic tracer release technique to cope with the distances between the different methane and acetylene sources. The results from these controlled experiments demonstrate that when the targeted and tracer gases are not well collocated, this new approach provides a better estimate of the emission rates than the tracer release technique. As an example, the relative error between the estimated and actual emission rates is reduced from 29% with the tracer release technique to 8% with the combined approach in the case of a tracer located 60 metres upwind of a methane source. This method also enables an estimate of different sources within the same site to be provided. [ABSTRACT FROM AUTHOR]

Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty. Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41% and in O by 46%. The L uncertainty decreases by 47%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29± 8 and 37± 17 Tg C·y-2 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes. [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]

Cities currently covering only a very small portion (<3%) of the world's land surface directly release to the atmosphere about 44% of global energy-related CO2, but they are associated with 71-76% of CO2 emissions from global final energy use. Although many cities have set voluntary climate plans, their CO2 emissions are not evaluated by the monitoring, reporting, and verification (MRV) procedures that play a key role for market- or policy-based mitigation actions. Here we analyze the potential of a monitoring tool that could support the development of such procedures at the city scale. It is based on an atmospheric inversion method that exploits inventory data and continuous atmospheric CO2 concentration measurements from a network of stations within and around cities to estimate city CO2 emissions. This monitoring tool is configured for the quantification of the total and sectoral CO2 emissions in the Paris metropolitan area (~12 million inhabitants and 11.4 TgC emitted in 2010) during the month of January 2011. Its performances are evaluated in terms of uncertainty reduction based on observing system simulation experiments (OSSEs). They are analyzed as a function of the number of sampling sites (measuring at 25ma.g.l.) and as a function of the network design. The instruments presently used to measure CO2 concentrations at research stations are expensive (typically ~EUR 50 k per sensor), which has limited the few current pilot city networks to around 10 sites. Larger theoretical networks are studied here to assess the potential benefit of hypothetical operational lower-cost sensors. The setup of our inversion system is based on a number of diagnostics and assumptions from previous city-scale inversion experiences with real data. We find that, given our assumptions underlying the configuration of the OSSEs, with 10 stations only the uncertainty for the total city CO2 emission during 1 month is significantly reduced by the inversion by ~42%. It can be further reduced by extending the network, e.g., from 10 to 70 stations, which is promising for MRV applications in the Paris metropolitan area. With 70 stations, the uncertainties in the inverted emissions are reduced significantly over those obtained using 10 stations: by 32% for commercial and residential buildings, by 33% for road transport, by 18% for the production of energy by power plants, and by 31% for total emissions. These results indicate that such a high number of stations would be likely required for the monitoring of sectoral emissions in Paris using this observation-model framework. They demonstrate some high potential that atmospheric inversions can contribute to the monitoring and/or the verification of city CO2 emissions (baseline) and CO2 emission reductions (commitments) and the advantage that could be brought by the current developments of lower-cost medium precision (LCMP) sensors. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2/ and methane (CH4/ mole fractions were measured at four near-ground sites located in and around London during the summer of 2012 with a view to investigating the potential of assimilating such measurements in an atmospheric inversion system for the monitoring of the CO2 and CH4 emissions in the London area. These data were analysed and compared with simulations using a modelling framework suited to building an inversion system: a 2 km horizontal resolution south of England configuration of the transport model CHIMERE driven by European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological forcing, coupled to a 1 km horizontal resolution emission inventory (the UK National Atmospheric Emission Inventory). First comparisons reveal that local sources, which cannot be represented in the model at a 2 km resolution, have a large impact on measurements. We evaluate methods to filter out the impact of some of the other critical sources of discrepancies between the measurements and the model simulation except that of the errors in the emission inventory, which we attempt to isolate. Such a separation of the impact of errors in the emission inventory should make it easier to identify the corrections that should be applied to the inventory. Analysis is supported by observations from meteorological sites around the city and a 3-week period of atmospheric mixing layer height estimations from lidar measurements. The difficulties of modelling the mixing layer depth and thus CO2 and CH4 concentrations during the night, morning and late afternoon lead to focusing on the afternoon period for all further analyses. The discrepancies between observations and model simulations are high for both CO2 and CH4 (i.e. their root mean square (RMS) is between 8 and 12 parts per million (ppm) for CO2 and between 30 and 55 parts per billion (ppb) for CH4 at a given site). By analysing the gradients between the urban sites and a suburban or rural reference site, we are able to decrease the impact of uncertainties in the fluxes and transport outside the London area and in the model domain boundary conditions. We are thus able to better focus attention on the signature of London urban CO2 and CH4 emissions in the atmospheric CO2 and CH4 concentrations. This considerably improves the statistical agreement between the model and observations for CO2 (with model- data RMS discrepancies that are between 3 and 7 ppm) and to a lesser degree for CH4 (with model-data RMS discrepancies that are between 29 and 38 ppb). Between one of the urban sites and either the rural or suburban reference site, selecting the gradients during periods wherein the reference site is upwind of the urban site further decreases the statistics of the discrepancies in general, though not systematically. In a further attempt to focus on the signature of the city anthropogenic emission in the mole fraction measurements, we use a theoretical ratio of gradients of carbon monoxide (CO) to gradients of CO2 from fossil fuel emissions in the London area to diagnose observation-based fossil fuel CO2 gradients, and compare them with the fossil fuel CO2 gradients simulated with CHIMERE. This estimate increases the consistency between the model and the measurements when considering only one of the two urban sites, even though the two sites are relatively close to each other within the city. While this study evaluates and highlights the merit of different approaches for increasing the consistency between the mesoscale model and the near-ground data, and while it manages to decrease the random component of the analysed model-data discrepancies to an extent that should not be prohibitive to extracting the signal from the London urban emissions, large biases, the sign of which depends on the measurement sites, remain in the final model-data discrepancies. Such biases are likely related to local emissions to which the urban near-ground sites are highly sensitive. This questions our current ability to exploit urban near-ground data for the atmospheric inversion of city emissions based on models at spatial resolution coarser than 2 km. Several measurement and modelling concepts are discussed to overcome this challenge. [ABSTRACT FROM AUTHOR]

Abstract. The ability of a Bayesian atmospheric inversion to quantify the Paris region’s fossil fuel CO2 emissions on a monthly basis, based on a network of three surface stations operated during one year as part of the CO2-MEGAPARIS experiment (August 2010–July 2011), is analysed. Differences in hourly CO2 atmospheric mole fraction between the near-ground monitoring sites (CO2 gradients), located at the north-eastern and south-western edges of the urban area, are used to estimate the 6-h mean fossil fuel CO2 emission. The inversion relies on the CHIMERE transport model run at 2 km × 2 km horizontal resolution, on the spatial distribution of fossil fuel CO2 emissions in 2008 from a local inventory established at 1 km × 1 km horizontal resolution by the AIRPARIF air quality agency, and on the spatial distribution of the biogenic CO2 fluxes from the C-TESSEL land surface model. It corrects a prior estimate of the 6-h mean budgets of the fossil fuel CO2 emissions given by the AIRPARIF 2008 inventory. We found that a stringent selection of CO2 gradients is necessary for reliable inversion results, due to large modelling uncertainties. In particular, the most robust data selection analysed in this study uses only mid-afternoon gradients if wind speeds are larger than 3 m s−1 and if the modelled wind at the upwind site is within ±15° of the transect between downwind and upwind site. This stringent data selection removes 92 % of the hourly observations. Even though this leaves few remaining data to constrain the emissions, the inversion system diagnoses that their assimilation significantly reduces the uncertainty in monthly emissions, by 9 % in November 2010 to 50 % in October 2010. The inverted monthly mean emissions correlate well with independent monthly mean air temperature. Furthermore, the inverted annual mean emission is consistent with the independent revision of the AIRPARIF inventory for the year 2010, which better corresponds to the measurement period than the 2008 inventory. Several tests of the inversion's sensitivity to prior emission estimates, to the assumed spatial distribution of the emissions, and to the atmospheric transport modelling demonstrate the robustness of the measurement constraint on inverted fossil fuel CO2 emissions. The results, however, show significant sensitivity to the description of the emissions' spatial distribution in the inversion system, demonstrating the need to rely on high-resolution local inventories such as that from AIRPARIF. Although the inversion constrains emissions through the assimilation of CO2 gradients, the results are hampered by the improperly-modelled influence of remote CO2 fluxes when air masses originate from urbanised and industrialised areas north-east of Paris. The drastic data selection used in this study limits the ability to continuously monitor Paris fossil fuel CO2 emissions: the inversion results for specific months like September 2010 or November 2010 are poorly constrained by too few CO2 measurements. The high sensitivity of the inverted emissions to the prior emissions' day-to-day variations highlights the limitations induced by assimilating data during a limited number of suitable days. Therefore, even though the inversion improves the seasonal variation and the annual budget of the city's emissions, it does not necessarily yield robust estimates for individual months. These limitations, could be overcome through a refinement of the data processing for a wider data selection, and through the expansion of the observation network. [ABSTRACT FROM AUTHOR]

Most of the global fossil fuel CO2 emissions arise out of urbanized and industrialized areas. Bottom-up inventories quantify them but with large uncertainties. In 2010-2011, the first atmospheric in-situ CO2 measurement network for Paris, the capital of France, has been operated with the aim of monitoring the regional atmospheric impact of the emissions out coming from this megacity. Five stations sampled air along a northeast-southwest axis that corresponds to the direction of the dominant winds. Two stations are classified as rural (TRN and MON), two are peri-urban (GON and GIF) and one is urban (EIF, located on top of the Eiffel tower). In this study, we analyze the diurnal, synoptic and seasonal variability of the in-situ CO2 measurements over nearly one year (8 August 2010-13 July 2011). We compare these datasets with remote CO2 measurements made at Mace Head (MHD) on the Atlantic coast of Ireland, and support our analysis with atmospheric boundary layer height (ABLH) observations made in the centre of Paris and with both modeled and observed meteorological fields. The average hourly CO2 diurnal cycles observed at the regional stations are mostly driven by the CO2 biospheric cycle, the ABLH cycle, and the proximity to urban CO2 emissions. Differences of several μmol mol-1 (ppm) can be observed from one regional site to the other. The more the site is surrounded by urban sources (mostly traffic, residential and commercial heating), the more the CO2 concentration is elevated, as is the associated variability which reflects the variability of the urban sources. Furthermore, two elevated sites (EIF and TRN) show a phase shift of the CO2 diurnal cycle of a few hours compared to lower sites due to a strong coupling with the boundary layer diurnal cycle. As a consequence, the existence of a CO2 vertical gradient above Paris can be inferred, whose amplitude depends on the time of the day and on the season, ranging from a few tenths of ppm during daytime to several ppm during nighttime. The CO2 seasonal cycle inferred from monthly means at our regional sites are driven by the biospheric and anthropogenic CO2 flux seasonal cycles, by the ABLH seasonal cycle and also by synoptic variations. Gradients of several ppm are observed between the rural and peri-urban stations, mostly from the influence of urban emissions that are in the footprint of the peri-urban station. The seasonal cycle observed at the urban station (EIF) is specific and very sensitive to the ABLH cycle. At both the diurnal and the seasonal scales, noticeable differences of several ppm can be observed between the measurements made at regional rural stations and the remote measurements made at MHD, that are shown not to define background concentrations appropriately for quantifying the regional atmospheric impact of urban CO2 emissions. For wind speeds less than 3 m s-1, the accumulation of the local CO2 emissions in the urban atmosphere forms a dome of several tens of ppm at the peri-urban stations, mostly under the influence of relatively local emissions including those from the Charles-De-Gaulle (CDG) airport facility and from aircrafts in flight. When wind speed increases, ventilation transforms the CO2 dome into a plume. Higher CO2 background concentrations of several ppm are advected from the remote Benelux-Ruhr and London regions, impacting concentrations at the five stations of the network even at wind speeds higher than 9 m s-1. For wind speeds ranging between 3 and 8 m s-1, the impact of Paris emissions can be detected in the peri-urban stations when they are downwind of the city, while the rural stations often seem disconnected from the city emission plume. As a conclusion, our study highlights a high sensitivity of the stations to wind speed and direction, to their distance from the city, but also to the ABLH cycle depending on their elevation. We learn some lessons regarding the design of an urban CO2 network: 1/ careful attention should be paid to properly setting background sites that will be representative of the different wind sectors; 2/ the downwind stations should as much as possible be positioned symmetrically in relation to the city centre, at the peri-urban/rural border; 3/ the stations should be installed at ventilated sites (away from strong local sources) and the air inlet set-up above the building or biospheric canopy layer, whichever is the greatest; and 4/ high resolution wind information should be available with the CO2 measurements. [ABSTRACT FROM AUTHOR]

Landfills are a significant source of fugitive methane (CH4) emissions which should be precisely and regularly monitored to reduce and mitigate net greenhouse gas emissions. In this study, we present long-term in-situ near-surface mobile atmospheric CH4 mole fraction measurements (complemented by meteorological measurements from a fixed station) from 21 campaigns that cover approximately four-years from September 2016 to December 2020. These campaigns were utilized to regularly quantify the total CH4 emissions from an active landfill in France. We use a simple atmospheric inversion approach based on a Gaussian plume dispersion model to derive CH4 emissions. Together with the measurements near the soil surface mainly dedicated to the identification of sources within the landfill, measurements of CH4 made on the landfill perimeter (near-field) helped us to provide some qualitative insights about the respective weight of the main areas of emissions. However, we hardly managed to extract a signal representative of the overall landfill emissions from these measurements, which limited our ability to derive robust estimates of the emissions when assimilating them in the atmospheric inversions. The analysis shows that the inversions based on the measurements from a remote road further away from the landfill (far-field) yielded more reliable estimates. According to these estimates, the total CH4 emissions have a large temporal variability and range from ~0.4 t CH4/d to ~7 t CH4/d, with an average value of ~2.1 t CH4/d. We find a weak negative correlation between these estimates of the CH4 emissions and atmospheric pressure for the active landfill. However, this weak emission-pressure relationship is based on a relatively small sample of reliable emission estimates with large sampling gaps. More frequent robust estimations are required to better understand this relationship for an active landfill. [ABSTRACT FROM AUTHOR]

This paper presents the development and application of a deep learning-based method for inverting CO2 atmospheric plumes from power plants using satellite imagery of the CO2 total column mixing ratios (XCO2). We present an end-to-end CNN approach, processing the satellite XCO2 images to derive estimates of the power plant emissions, that is resilient to missing data in the images due to clouds or to the partial view of the plume due to the limited extent of the satellite swath. The CNN is trained and validated exclusively on CO2 simulations from 8 power plants in Germany in 2015. The evaluation on this synthetic dataset shows an excellent CNN performance with relative errors close to 20 %, which is only significantly affected by substantial cloud cover. The method is then applied to 39 images of the XCO2 plumes from 9 power plants, acquired by the Orbiting Carbon Observatory-3 Snapshot Area Maps (OCO3-SAMs), and the predictions are compared to average annual reported emissions. The results are very promising, showing a relative difference of the predictions to reported emissions only slightly higher than the relative error diagnosed from the experiments with synthetic images. Furthermore, the analysis of the area of the images in which the CNN-based inversion extract the information for the quantification of the emissions, based on integrated gradient techniques, demonstrates that the CNN effectively identifies the location of the plumes in the OCO-3 SAM images. This study demonstrates the feasibility of applying neural networks that have been trained on synthetic datasets for the inversion of atmospheric plumes in real satellite imagery of XCO2, and provides the tools for future applications. [ABSTRACT FROM AUTHOR]

To effectively monitor highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. These dense measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or to separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm for hourly mean measurements. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision cavity ring-down spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12:00–17:00 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects for integrating mid-cost measurements along with high-precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area. [ABSTRACT FROM AUTHOR]