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Top-down (atmospheric measurement-based) methane fluxes from individual emitting facilities are needed to reduce uncertainties in the global methane budget. This typically requires in situmethane mole fraction ([CH4]), traditionally measured using high-precision optical sensors. We show that the semiconductor-based Figaro Taguchi Gas Sensor (TGS) is a cheaper alternative. Two TGS loggers were deployed near a landfill site. Logger-1 uses a pumped cell, containing one TGS 2602, two TGS 2611-C00 and one TGS 2611-E00; laboratory testing showed methane, ethane, carbon monoxide and hydrogen sulphide sensitivity for each TGS. Logger-2 uses an external fan, containing one TGS 2611-C00. The tested TGS 2611-C00 and TGS 2611-E00 units could yield [CH4] during landfill deployment, by first modelling a reference baseline resistance in field conditions, representative of background (reference) [CH4] sampling. Background sampling was identified using wind direction from a designated background segment, which yielded a baseline resistance model as a function of time (incorporating long-term background effects), water mole fraction and temperature. The ratio between measured TGS resistance and modelled baseline resistance was converted into [CH4], using a two-term modified power fit. Logger-1 methane fitting coefficients were derived during laboratory testing, while Logger-2 coefficients used a 1.49% field sampling subset, alongside a high-precision reference (HPR) instrument. Reconstructed minute-averaged Logger-2 [CH4] for TGS 2611-C00 was compared to the HPR up to 31.5 ppm [CH4] (excluding [CH4] fitting data), resulting in a ±0.55 ppm [CH4] root-mean squared error (RMSE), for 295.2 overall sampling days (excluding data gaps). Reconstructed Logger-1 [CH4] RMSE compared to the HPR was ±0.67 ppm and ±0.77 ppm for the two TGS 2611-C00 and ±1.17 ppm for the TGS 2611-E00, up to 29.3 ppm [CH4], for 147.9 overall sampling days. Field TGS 2611-C00 superiority above other Logger-1 sensors is supported by laboratory tests, which showed TGS 2611-C00 to be most methane-sensitive. In summary, we show that the TGS 2611-C00 is an ideal low-cost sensor to measure [CH4] from facility-scale sources, with a field RMSE below ±1 ppm. This work represents the first application of TGS resistance ratios to yield parts-per-million level [CH4] field measurements, using a dynamic baseline resistance model.

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 CO2sink that was partially counterbalanced by net emissions of CH4and N2O. For CH4and N2O, we find good agreement between BU and top‐down (TD) estimates from atmospheric inversions. However, our BU land CO2sink 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−1since 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 CO2sink has weakened over the past two decades. A large part of the European CO2and 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 CO2sinks in parts of Eastern Europe and Northern Spain, attributed in part to land use change. Extensive regions of high CH4and 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 CO2and of all GHGs combined. 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. We provide a bottom‐up estimate of CO2, CH4, N2O emissions of 3.9 Pg CO2‐eq. yr−1over Europe, 2010–2019Terrestrial ecosystems acted as a greenhouse gas net sink of 0.9 Pg CO2‐eq. yr−1, dominated by CO2sinkNet‐greenhouse gas emissions decreased by ∼1/4 since the 1990s, but land carbon sink is weakening since the 2000s We provide a bottom‐up estimate of CO2, CH4, N2O emissions of 3.9 Pg CO2‐eq. yr−1over Europe, 2010–2019 Terrestrial ecosystems acted as a greenhouse gas net sink of 0.9 Pg CO2‐eq. yr−1, dominated by CO2sink Net‐greenhouse gas emissions decreased by ∼1/4 since the 1990s, but land carbon sink is weakening since the 2000s

To assess global carbon cycle variability, we decompose the net land carbon sink into the sum of gross primary productivity (GPP), terrestrial ecosystem respiration (TER), and fire emissions and apply a Bayesian framework to constrain these fluxes between 1980 and 2014. The constrained GPP and TER fluxes show an increasing trend of only half of the prior trend simulated by models. From the optimization, we infer that TER increased in parallel with GPP from 1980 to 1990, but then stalled during the cooler periods, in 1990–1994 coincident with the Pinatubo eruption, and during the recent warming hiatus period. After each of these TER stalling periods, TER is found to increase faster than GPP, explaining a relative reduction of the net land sink. These results shed light on decadal variations of GPP and TER and suggest that they exhibit different responses to temperature anomalies over the last 35 years. A novel Bayesian fusion is applied to infer GPP and TER from the global carbon budget equationGrowth trends of GPP and TER are reduced after optimizationTER growth stalled after Pinatubo eruption and during the hiatus period

The column‐average dry air‐mole fraction of carbon dioxide in the atmosphere (XCO2) is measured by scattered satellite measurements like those from the Orbiting Carbon Observatory (OCO‐2). We show that global continuous maps of XCO2(corresponding to level 3of the satellite data) at daily or coarser temporal resolution can be inferred from these data with a Kalman filter built on a model of persistence. Our application of this approach on 2 years of OCO‐2 retrievals indicates that the filter provides better information than a climatology of XCO2at both daily and monthly scales. Provided that the assigned observation uncertainty statistics are tuned in each grid cell of the XCO2maps from an objective method (based on consistency diagnostics), the errors predicted by the filter at daily and monthly scales represent the true error statistics reasonably well, except for a bias in the high latitudes of the winter hemisphere and a lack of resolution (i.e., a too small discrimination skill) of the predicted error standard deviations. Due to the sparse satellite sampling, the broad‐scale patterns of XCO2described by the filter seem to lag behind the real signals by a few weeks. Finally, the filter offers interesting insights into the quality of the retrievals, both in terms of random and systematic errors. A satellite level 3 XCO2product at daily and 2° × 2° resolution has been generated with a Kalman filter for OCO‐2The filter provides some preliminary information about the quality of the XCO2retrievals both in terms of random and systematic errorsThe error statistics of the filter are finer than the statistics of a climatological distribution of XCO2and are fairly predicted by the filter

Using the multiyear archive of the two Orbiting Carbon Observatories (OCO) of NASA, we have retrieved large fossil fuel CO2emissions (larger than 1.0 ktCO2h−1per 10−2square 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 CO2imagers provide an order of magnitude more data. In the wake of the Paris Climate Agreement, there is an increasing need to monitor emissions from fossil fuel combustion around the world. For CO2in 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 CO2imagers provide an order of magnitude more data. We have retrieved some instantaneous CO2emissions 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 We have retrieved some instantaneous CO2emissions for one third of the large emission cells of a global high‐resolution hourly inventory The emission retrievals explain more than one third of the inventory variance at the corresponding cells and hours Consistent temporal variations of median emissions suggest that trends can be robustly calculated when more data become available

The Paris metropolitan area, the largest urban region in the European Union, has experienced two national COVID-19 confinements in 2020 with different levels of restrictions on mobility and economic activity, which caused reductions in CO2emissions. To quantify the timing and magnitude of daily emission reductions during the two lockdowns, we used continuous atmospheric CO2monitoring, a new high-resolution near-real-time emission inventory, and an atmospheric Bayesian inverse model. The atmospheric inversion estimated the changes in fossil fuel CO2emissions over the Greater Paris region during the two lockdowns, in comparison with the same periods in 2018 and 2019. It shows decreases by 42–53% during the first lockdown with stringent measures and by only 20% during the second lockdown when traffic reduction was weaker. Both lockdown emission reductions are mainly due to decreases in traffic. These results are consistent with independent estimates based on activity data made by the city environmental agency. We also show that unusual persistent anticyclonic weather patterns with north-easterly winds that prevailed at the start of the first lockdown period contributed a substantial drop in measured CO2concentration enhancements over Paris, superimposed on the reduction of urban CO2emissions. We conclude that atmospheric CO2monitoring makes it possible to identify significant emission changes (>20%) at subannual time scales over an urban region.

Resolving regional carbon budgets is critical for informing land-based mitigation policy. For nine regions covering nearly the whole globe, we collected inventory estimates of carbon-stock changes complemented by satellite estimates of biomass changes where inventory data are missing. The net land–atmospheric carbon exchange (NEE) was calculated by taking the sum of the carbon-stock change and lateral carbon fluxes from crop and wood trade, and riverine-carbon export to the ocean. Summing up NEE from all regions, we obtained a global ‘bottom-up’ NEE for net land anthropogenic CO2uptake of –2.2 ± 0.6 PgC yr−1consistent with the independent top-down NEE from the global atmospheric carbon budget during 2000–2009. This estimate is so far the most comprehensive global bottom-up carbon budget accounting, which set up an important milestone for global carbon-cycle studies. By decomposing NEE into component fluxes, we found that global soil heterotrophic respiration amounts to a source of CO2of 39 PgC yr−1with an interquartile of 33–46 PgC yr−1—a much smaller portion of net primary productivity than previously reported.

We use a global transport model and satellite retrievals of the carbon dioxide (CO2) column average to explore the impact of CO2emissions 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 CO2emissions. 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 CO2monitoring systems, at least in situ, to support international efforts to reduce emissions. The first half of 2020 represents an extreme period for the global carbon cycle with both exceptionally low carbon dioxide emissions from the use of fossil fuels and exceptionally high temperatures around the world. Here, we study the changes in human emissions witnessed from space by NASA's second Orbiting Carbon Observatory (OCO‐2) from February to May 2020. We then reassess user needs for future carbon‐dioxide observing systems that will monitor human emissions. Covid‐19 impacted the CO2column mostly in the vicinity of a few emission locations that changed over timeThese places have not been well observed by the OCO‐2 satellite because of frequent or persistent cloud conditionsTo support the Paris Agreement on climate, priority should be given on the development of all‐weather carbon‐monitoring systems