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Improved urban greenhouse gas (GHG) flux estimates are crucial for informing policy and mitigation efforts. Atmospheric inversion modeling (AIM) is a widely used technique combining atmospheric measurements of trace gas, meteorological modeling, and a prior emission map to infer fluxes. Traditionally, AIM relies on mid‐afternoon observations due to the well‐represented atmospheric boundary layer in meteorological models. However, confining flux assessment to daytime observations is problematic for the urban scale, where air masses typically move over a city in a few hours and AIM therefore cannot provide improved constraints on emissions over the full diurnal cycle. We hypothesized that there are atmospheric conditions beyond the mid‐afternoon under which meteorological models also perform well. We tested this hypothesis using tower‐based measurements of CO2 and CH4, wind speed observations, weather model outputs from INFLUX (Indianapolis Flux Experiment), and a prior emissions map. By categorizing trace gas vertical gradients according to wind speed classes and identifying when the meteorological model satisfactorily simulates boundary layer depth (BLD), we found that non‐afternoon observations can be assimilated when wind speed is >5 m/s. This condition resulted in small modeled BLD biases (<40%) when compared to calmer conditions (>100%). For Indianapolis, 37% of the GHG measurements meet this wind speed criterion, almost tripling the observations retained for AIM. Similar results are expected for windy cities like Auckland, Melbourne, and Boston, potentially allowing AIM to assimilate up to 60% of the total (24‐hr) observations. Incorporating these observations in AIMs should yield a more diurnally comprehensive evaluation of urban GHG emissions. Plain Language Summary: It is crucial to improve greenhouse gas (GHG) emission estimates to inform policy and mitigation strategies. However, the current model techniques used to estimate such emissions rely on incorporating only mid‐afternoon observations of atmospheric concentrations of GHGs. For cities, this limits a detailed understanding of emissions during hours of the day when emissions are the highest, such as the morning rush hours. This constraint is due to the limitations on how well meteorological models can describe the atmosphere during stable conditions, such as when calm winds prevail. To understand if there are any atmospheric conditions when meteorological models have good performance, for non‐afternoon hours, we used atmospheric measurements of carbon dioxide and methane, alongside meteorological model outputs. We found that observations during non‐afternoon hours are suitable for use in models when wind speed is greater than 5 m/s. This means that it is possible to double the amount of data that goes into the modeled GHG emission estimates. With this finding, emission estimates will potentially be improved, leading to a better evaluation of the diurnal cycle of GHG emissions. Key Points: Assimilating non‐afternoon greenhouse gas (GHG) observations in atmospheric inversions is reliable when wind speeds are greater than 5 m/sInclusion of non‐afternoon atmospheric observations during windy conditions doubles the current data assimilation in atmospheric inversionsAdditional observations in atmospheric inversions have the potential to improve GHG emissions estimates [ABSTRACT FROM AUTHOR]

Monitoring methane emissions is crucial in mitigating climate change as it has a relatively short atmospheric lifetime of about 12 years and a significant radiative forcing impact. To measure the impact of methane-controlling policies and techniques, a deep understanding of methane emissions is of great importance. Remote sensing offers scalable approaches for monitoring methane emissions at various scales, from point-source high-resolution monitoring to regional and global estimates. The TROPOMI satellite instrument provides daily XCH4 data globally, offering the opportunity to monitor methane at a moderate spatial resolution with an acceptable level of sensitivity. To infer emissions from TROPOMI data, we used the prior emission estimates from global and national inventories and the GEOS-Chem chemical transport model to simulate atmospheric methane along with actual observations of TROPOMI. In this study, methane emissions from Toronto, Montreal, New York, Los Angeles, Houston, and Mexico City have been estimated using the analytical solution of Bayesian inversion using the cloud-based Integrated Methane Inversion (IMI) framework. Using the result from ensemble inversions, and city boundaries, the average total emissions were as follows: Toronto 230.52 Gg a−1, Montreal 111.54 Gg a−1, New York 144.38 Gg a−1, Los Angeles 207.03 Gg a−1, Houston 650.16 Gg a−1, and Mexico City 280.81 Gg a−1. The resulting gridded scale factors ranged from 0.22 to 6.2, implying methane prior emission underestimations in most of these cities. As such, this study underscores the key role of remote sensing in accurately assessing urban methane emissions, informing essential climate mitigation efforts. [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NOx), significant contributors to air pollution and climate change, form aerosols and ozone in the atmosphere. Accurate, timely, and transparent information on NOx emissions is essential for decision-making to mitigate both haze and ozone pollution. However, a comprehensive understanding of the trends and drivers behind anthropogenic NOx emissions from China—the world's largest emitter—has been lacking since 2020 due to delays in emissions reporting. Here we show a consistent decline in China's NOx emissions from 2020 to 2022, despite increased fossil fuel consumption, utilizing satellite observations as constraints for NOx emission estimates through atmospheric inversion. This reduction is corroborated by data from two independent spaceborne instruments: the TROPOspheric Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI). Notably, a reduction in transport emissions, largely due to the COVID-19 lockdowns, slightly decreased China's NOx emissions in 2020. In subsequent years, 2021 and 2022, reductions in NOx emissions were driven by the industry and transport sectors, influenced by stringent air pollution controls. The satellite-based inversion system developed in this study represents a significant advancement in the real-time monitoring of regional air pollution emissions from space.

Drivers of natural high-latitude biogenic methane fluxes were studied by combining atmospheric inversion modelling results of methane fluxes (CTE-CH4 model) with datasets on permafrost (ESA Permafrost CCI), climate (Köppen–Geiger classes) and wetland classes (BAWLD) and seasonality of soil freezing (ESA SMOS F/T) for the years 2011–2019. The highest emissions were found in the southern parts of the study region, while areas with continuous permafrost, tundra climate, and tundra wetlands had the lowest emissions. The magnitude of the methane flux per wetland area followed the order of permafrost zones excluding non-permafrost, continuous permafrost having the smallest flux and sporadic the largest. Fens had higher fluxes than bogs in the thaw period, but bogs had higher fluxes in the colder seasons. The freezing period when the soil status is between complete thaw and frozen contributed to annual emissions more in the warmest regions studied than in other regions. In the coldest areas, freezing period fluxes were lower and closer to wintertime values than elsewhere. Emissions during freezing periods were smaller than those during winter periods, but were of comparable magnitude in warm regions. The contribution of the thaw period to the total annual emission varied from 86% in warmest areas to 97% in the coldest areas, suggesting that the longest winter periods did not contribute significantly to the annual budget. [ABSTRACT FROM AUTHOR]

Accurate assessment of the size and distribution of carbon dioxide (CO2) sources and sinks is important for efforts to understand the carbon cycle and support policy decisions regarding climate mitigation actions. Satellite retrievals of the column-averaged dry-air mole fractions of CO2 (XCO2) have been widely used to infer spatial and temporal variations of carbon fluxes through atmospheric inversion techniques. In this study, we present a global spatially resolved terrestrial and ocean carbon flux dataset for 2015–2022. The dataset was generated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through the assimilation of Orbiting Carbon Observatory 2 (OCO-2) XCO2 retrievals. We describe the carbon budget, interannual variability, and seasonal cycle for the global scale and a set of TransCom regions. The 8-year mean net biosphere exchange and ocean carbon fluxes were −2.22 ± 0.75 PgC yr−1 and –2.32 ± 0.18 PgC yr−1, absorbing approximately 23 % and 24 % of contemporary fossil fuel CO2 emissions, respectively. The annual mean global atmospheric CO2 growth rate was 5.17 ± 0.68 PgC yr−1, which is consistent with the National Oceanic and Atmospheric Administration (NOAA) measurement (5.24 ± 0.59 PgC yr−1). Europe has the largest terrestrial sink among the 11 TransCom land regions, followed by Boreal Asia and Temperate Asia. The dataset was evaluated by comparing posterior CO2 simulations with the observations from Total Carbon Column Observing Network (TCCON) and Observation Package (ObsPack). Compared with CO2 simulations using the unoptimized fluxes, the bias and root mean square error of posterior CO2 simulations were largely reduced across the full range of locations, confirming that the GONGGA system improves the estimates of spatial and temporal variations in carbon fluxes by assimilating OCO-2 XCO2 data. This dataset will improve the broader understanding of global carbon cycle dynamics and their response to climate change. [ABSTRACT FROM AUTHOR]

Accurate estimate of the size of land carbon sink is essential for guiding climate mitigation actions to fulfill China's net-zero ambitions before 2060. The atmospheric inversion is an effective approach to provide spatially explicit estimate of surface CO 2 fluxes that are optimally consistent with atmospheric CO 2 measurements. But atmospheric inversion of China's land carbon sink has enormous uncertainties, with one major source arising from the poor coverage of CO 2 observation stations. Here we use a regional atmospheric inversion framework to design an observation network that could minimize uncertainties in inverted estimate of China's land carbon sink. Compared with the large spread of inverted sink (∼1 Pg C a−1) from state-of-the-art inversions using existing CO 2 observations, the uncertainty is constrained within 0.3 Pg C a−1 when a total of 30 stations were deployed, and is further reduced to approximately 0.2 Pg C a−1 when 60 stations were deployed. The proposed stations are mostly distributed over areas with high biosphere productivity during the growing season, such as Southeast China, Northeast China, North China, and the Tibetan Plateau. Moreover, the proposed stations can cover areas where existing satellites have limited coverage due to cloud shadowing in the monsoon season or over complex topography. Such ground-based observation network will be a critical component in the future integrated observing system for monitoring China's land carbon fluxes. [ABSTRACT FROM AUTHOR]

The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient observational constraints; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals may fill some of this gap. Here, we estimate China's carbon sink using atmospheric inversions of the Orbiting Carbon Observatory 2 (OCO‐2) XCO2 retrievals within different platforms, including the Global Carbon Assimilation System (GCAS) v2, the Copernicus Atmosphere Monitoring Service, and the OCO‐2 Model Inter‐comparison Project (MIP). We find that they consistently place the largest net biome production (NBP) in the south on an annual basis compared to the northeast and other main agricultural areas during peak growing season, coinciding well with the distribution of forests and crops, respectively. Moreover, the mean seasonal cycle amplitude of NBP in OCO‐2 inversions is obviously larger than that of biosphere model simulations and slightly greater than surface CO2 inversions. More importantly, the mean seasonal cycle of the OCO‐2 inversions is well constrained in the temperate, tropical, and subtropical monsoon climate zones, with better inter‐model consistency at a sub‐regional scale compared to in situ inversions and biosphere model simulations. In addition, the OCO‐2 inversions estimate the mean annual NBP in China for 2015–2019 to be between 0.34 (GCASv2) and 0.47 ± 0.16 PgC/yr (median ± std; OCO‐2 v10 MIP), and indicate the impacts of climate extremes (e.g., the 2019 drought) on the interannual variations of NBP. Our results suggest that assimilating OCO‐2 XCO2 retrievals is crucial for improving our understanding of China's terrestrial carbon sink regime. Plain Language Summary: The magnitude and distribution of China's terrestrial carbon sink remain underconstrained; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals from NASA's Carbon Observatory 2 (OCO‐2) could help reduce this uncertainty. This study revisited China's terrestrial carbon sink estimates based on state‐of‐the‐art OCO‐2 XCO2 inversions, including the Global Carbon Assimilation System OCO‐2 inversion, the Copernicus Atmosphere Monitoring Service OCO‐2 inversion, and those in the OCO‐2 Model Inter‐comparison Project. We found that the assimilation of OCO‐2 XCO2 retrievals offers effective constraints on the spatiotemporal patterns of the terrestrial carbon sink of China. This result suggests that the OCO‐2 XCO2 inversions allow an improved understanding of China's land carbon sink over in situ CO2 inversions and bottom‐up biosphere model simulations, including better representations in spatial distributions and seasonal cycles and more plausible interannual variations. These improvements suggest that the assimilation of OCO‐2 XCO2 retrievals offers effective constraints on the spatiotemporal patterns of the terrestrial carbon sink of China. Key Points: Orbiting Carbon Observatory 2 (OCO‐2) inversions reveal the largest carbon sink in China is in the south on an annual basis, while in the northeast during peak growing seasonThe seasonal cycle appears to be well constrained in the monsoon climate zonesOCO‐2 inversions are able to capture the impacts of climate extremes on China's carbon sink interannual variability [ABSTRACT FROM AUTHOR]

North American nitrous oxide (N2O) emissions over 2011–2018 are estimated using the CarbonTracker‐Lagrange regional inversion framework. Emissions are strongest in the Midwestern corn/soybean belt and display a distinct dual maxima seasonal pattern. The first maximum occurs in late winter/early spring, suggestive of freeze‐thaw (FT) effects on denitrification rates and associated N2O emissions. The second maximum occurs in late spring/early summer, consistent with a growing season nitrogen fertilizer‐driven source, although fertilizer applied in late fall may contribute to the FT pulse as well. Interannual variability in the first maximum correlates significantly to soil freeze thaw status derived from remote sensing data. A requisite frozen period in the preceding early winter appears necessary to create conditions for the N2O pulse after thawing. The FT pulse is a prominent feature of the annual cycle in Canadian cropland, where it may be of comparable magnitude to growing season emissions. In contrast, the growing season peak in N2O dominates the FT peak in the Midwestern Corn‐Soybean region of the United States. Key Points: Early spring peaks in N2O flux from North American agriculture correlate to satellite soil freeze/thaw status from the previous winterA requisite frozen period in early winter appears necessary to prepare the soil for the freeze‐thaw (FT) N2O pulseGrowing season N2O emissions dominate FT emissions in U.S. agriculture but the two have comparable magnitude in Canada [ABSTRACT FROM AUTHOR]

Coal mining ranks as the largest anthropogenic CH4 source in China with emission factors (EFs) varying up to 30‐fold among inventories when applied to different provinces. The lack of independent evaluation of coal mining CH4 EFs in China is one of the main uncertainties in estimating national total CH4 emissions. Shanxi province, which supplies 25% of the national coal production, is the largest coal mining CH4 emission region in China and even among the world's largest coal production regions. This area is also a significant anthropogenic CO2 source because of high‐density power and industrial activities. Given the large uncertainties in CH4 and CO2 inventories from provincial to city scales, questions remain whether state‐of‐the‐art inventories have accurately estimated these emission hotspots. Here, we evaluate CH4 and CO2 emissions from one of the world's largest coal production regions near Taiyuan City, the capital of Shanxi province, China. CH4 and CO2 concentrations were measured from March 2018 to February 2019 from a 30‐m tower. These data were used within an inverse modeling framework to simulate both CH4 and CO2 concentrations and to evaluate EFs for this region. Results show generally good agreement between observed and simulated CH4 concentrations. However, the CO2 simulations were much lower compared to the observations. Given the minor role of NEE‐induced CO2 enhancements, we believe that the large difference is attributed to the underestimation of anthropogenic CO2 emissions. In general, the derived posteriori anthropogenic CH4 emissions were 85.2(±18.1)% of a priori emissions, where fugitive CH4 from coal mining accounted for ∼92.7% of total anthropogenic emissions. The derived coal mining EF was 23.2(±4.9) m3 CH4/ton coal, close to the default value of high CH4‐content coal, but twofold the province average that were reported by previous observation‐based studies in Shanxi province, indicating large spatial inhomogeneity in the coal mining CH4 EF. The posteriori CO2 emissions were 1.6‐fold of the a priori emissions, highlighting underestimation of CO2 emissions in industrial cities and some potential large emission sources that are missing from state‐of‐the‐art inventories. Finally, we also emphasize the use of satellite observations and denser tower‐based networks are essential in resolving the spatial inhomogeneity of greenhouse gas emissions. Plain Language Summary: The understanding of anthropogenic CH4 and CO2 emissions is basis for climate mitigation especially for global top emitting countries, but the largest issue before addressing above question is that many previous studies have found considerable bias of greenhouse gas emission for almost all inventories from city to regional scales. These facts hindered the government to make and evaluate corresponding mitigation policies. Here, to quantify CH4 and CO2 emissions at one of global largest CH4 and CO2 hotspot in China, we conducted 1 year tower‐based atmospheric CH4 and CO2 concentration measurements and used atmospheric inversion method to constrain and evaluate their emissions, we found coal mining CH4 emission factor has less bias but CO2 emissions were underestimated by 1.6‐fold, highlighting underestimation of CO2 emissions in industrial cities and some potential large emission sources that are missing from state‐of‐the‐art inventories. Our findings indicate more work is needed for urban government to fully understand their greenhouse gases emissions. Key Points: CH4 emission factor for coal mining was 23.2(±4.9) m3 CH4/ton coal, close to the default value of high CH4‐content coal in ChinaThe posteriori CO2 emissions were 1.6‐fold of the a priori emissions, indicating CO2 emissions in industrial cities were largely underestimatedSome large CO2 emissions are missing and more work is needed for urban government to fully understand their greenhouse gases emissions [ABSTRACT FROM AUTHOR]

We previously introduced the parametric variance Kalman filter (PvKF) assimilation as a cost-efficient system to estimate the dynamics of methane analysis concentrations. As an extension of our development, this study demonstrates the linking of PvKF to a 4D-Var inversion aiming to improve on methane emissions estimation in comparison with the traditional 4D-Var. Using the proposed assimilation–inversion framework, we revisit fundamental assumptions of the perfect and already optimal model state that is typically made in the 4D-Var inversion algorithm. In addition, the new system objectively accounts for error correlations and the evolution of analysis error variances, which are non-trivial or computationally prohibitive to maintain otherwise. We perform observing system simulation experiments (OSSEs) aiming to isolate and explore various effects of the assimilation analysis on the source inversion. The effect of the initial field of analysis, forecast of analysis error covariance, and model error is examined through modified 4D-Var cost functions, while different types of perturbations of the prior emissions are considered. Our results show that using PvKF optimal analysis instead of the model forecast to initialize the inversion improves posterior emissions estimate (~35% reduction in the normalized mean bias, NMB) across the domain. The propagation of analysis error variance using the PvKF formulation also tends to retain the effect of background correlation structures within the observation space and, thus, results in a more reliable estimate of the posterior emissions in most cases (~50% reduction in the normalized mean error, NME). Our sectoral analysis of four main emission categories indicates how the additional information of assimilation analysis enhances the constraints of each emissions sector. Lastly, we found that adding the PvKF optimal analysis field to the cost function benefits the 4D-Var inversion by reducing its computational time (~65%), while including only the error covariance in the cost function has a negligible impact on the inversion time (10–20% reduction). [ABSTRACT FROM AUTHOR]

Satellite remote sensing is currently the best monitoring means to obtain global carbon source and sink data. The United States, Japan, China and other countries are vigorously developing spaceborne detection technology. However, the important factors that restrict the application of greenhouse gas satellite remote sensing technology include the limited accuracy of data products. How to improve the retrieval level of greenhouse gas payloads is a problem that needs to be solved urgently. One effective way to improve data quality is to carry out satellite ground synchronous authenticity verification and system error correction. This paper mainly aims at the shortcomings of the existing TCCON and the portable verification equipment EM27/SUN, and develops a High-Resolution Fourier Transform Spectrometer (HRFTS) based on dynamic collimation technology. Through the gas absorption method and the band scanning method of the hyperspectral monochromatic light source, the instrument's absorption spectrum measurement capability and the Instrument Line Shape (ILS) are demonstrated. The instrument's spectral resolution is consistent with the on-orbit greenhouse gas satellite load, reaching 0.26 cm−1. For the interference data obtained by the spectrometer, spectral restoration processing, data quality control and inversion algorithm optimization were carried out to solve the problems of baseline correction, spectral fine registration, and environmental parameter profile reconstruction, and cross comparison experiments with EM27/SUN were carried out simultaneously. Finally, for the gases monitoring instrument (GMI) of the GF5-02 satellite launched on 7 September 2021, the first satellite ground synchronization verification experiment with high space-time matching was carried out. The results showed that the CO2 column concentration deviation of the satellite ground synchronization inversion was about 1.5 ppm, and the CH4 column concentration deviation was about 11.3 ppb, which verified the on-orbit detection accuracy of the GMI, and laid a foundation for the subsequent satellite inversion algorithm optimization and systematic error correction. [ABSTRACT FROM AUTHOR]

Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane ( XCH 4 ) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH 4 fluxes using the CarbonTracker Europe- CH 4 (CTE- CH 4 ) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research's (SRON) operational and University of Bremen's Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where CH 4 emission maximum is later, when the SRON's operational data are assimilated. The TROPOMI inversions show higher CH 4 emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates. [ABSTRACT FROM AUTHOR]

Satellite carbon dioxide (CO2) retrievals provide important constraints on surface carbon fluxes in regions that are undersampled by global in situ networks. In this study, we developed an atmospheric inversion system to infer CO2 sources and sinks from Orbiting Carbon Observatory-2 (OCO-2) column CO2 retrievals during 2015–2019, and compared our estimates to five other state-of-the-art inversions. By assimilating satellite CO2 retrievals in the inversion, the global net terrestrial carbon sink (net biome productivity, NBP) was found to be 1.03±0.39 petagrams of carbon per year (PgC yr−1); this estimate is lower than the sink estimate of 1.46–2.52 PgC yr−1, obtained using surface-based inversions. We estimated a weak northern uptake of 1.30 PgC yr−1 and weak tropical release of −0.26 PgC yr−1, consistent with previous reports. By contrast, the other inversions showed a strong northern uptake (1.44–2.78 PgC yr−1), but diverging tropical carbon fluxes, from a sink of 0.77 PgC yr−1 to a source of −1.26 PgC yr−1. During the 2015–2016 El Niño event, the tropical land biosphere was mainly responsible for a higher global CO2 growth rate. Anomalously high carbon uptake in the northern extratropics, consistent with concurrent extreme Northern Hemisphere greening, partially offset the tropical carbon losses. This anomalously high carbon uptake was not always found in surface-based inversions, resulting in a larger global carbon release in the other inversions. Thus, our satellite constraint refines the current understanding of flux partitioning between northern and tropical terrestrial regions, and suggests that the northern extratropics acted as anomalous high CO2 sinks in response to the 2015–2016 El Niño event. [ABSTRACT FROM AUTHOR]

China, the Unite States (US), the European Union (EU), India, and Russia are the world's top 5 fossil fuel and cement CO2 (FFC) emitting countries or regions (CRs). It is very important to understand their status of carbon neutrality, and to monitor their future changes of net carbon fluxes (NCFs). In this study, we implemented a well-established global carbon assimilation system (GCAS, Version 2) to infer global surface carbon fluxes from May 2009 to December 2019 using both GOSAT and OCO-2 XCO2 retrievals. The reductions of flux uncertainty and XCO2 bias, and the evaluation of posterior flux show that GCAS has comparable and good performance in the 5 CRs. The results suggest that Russia has achieved carbon neutrality, but the other 4 are still far from being carbon neutral, especially China. The mean annual NCFs in China, the US, the EU, India, and Russia are 2.33 ± 0.29, 0.82 ± 0.20, 0.42 ± 0.16, 0.50 ± 0.12, and -0.33 ± 0.23 PgC yr−1, respectively. From 2010 to 2019, the NCFs showed an increasing trend in the US and India, a slight downward trend after 2013 in China, and were stable in the EU. The changes of land sinks in China and the US might be the main reason for their trends. India's trend was mainly due to the increase of FFC emission. The relative contributions of NCFs to the global land net carbon emission of China and the EU have decreased, while those of the US and India have increased, implying the US and India must take more active measures to control carbon emissions or increase their sinks. This study indicates that satellite XCO2 could be successfully used to monitor the changes of regional NCFs, which is of great significance for major countries to achieve greenhouse gas control goals.

Southwestern North America (SWNA) continuously experienced megadroughts and large wildfires in 2020 and 2021. Here, we quantified their impact on the terrestrial carbon budget using net biome production (NBP) estimates from an ensemble of atmospheric inversions assimilating in-situ CO _2 and Carbon Observatory – 2 (OCO-2) satellite XCO _2 retrievals (OCO-2 v10 MIP Extension), two satellite-based gross primary production (GPP) datasets, and two fire CO _2 emission datasets. We found that the 2020 – 2021 drought and associated wildfires in SWNA led to a large CO _2 loss, an ensemble mean of 95.07 TgC estimated by the satellite inversions using both nadir and glint XCO _2 retrievals (LNLG) within the OCO-2 v10 MIP, greater than 80% of SWNA’s annual total carbon sink. Moreover, the carbon loss in 2020 was mainly contributed by fire emissions while in 2021 mainly contributed by drought impacts on terrestrial carbon uptake. In addition, the satellite inversions indicated the huge carbon loss was mainly contributed by fire emissions from forests and grasslands along with carbon uptake reductions due to drought impacts on grasslands and shrublands. This study provides a process understanding of how some droughts and following wildfires affect the terrestrial carbon budget on a regional scale.

Analysis systems incorporating atmospheric observations could provide a powerful tool for validating fossil fuel CO2 (ffCO2) emissions reported for individual regions, provided that fossil fuel sources can be separated from other CO2 sources or sinks and atmospheric transport can be accurately accounted for. We quantified ffCO2 by measuring radiocarbon (14C) in CO2, an accurate fossil-carbon tracer, at nine observation sites in California for three months in 2014-15. There is strong agreement between the measurements and ffCO2 simulated using a high-resolution atmospheric model and a spatiotemporally-resolved fossil fuel flux estimate. Inverse estimates of total in-state ffCO2 emissions are consistent with the California Air Resources Board's reported ffCO2 emissions, providing tentative validation of California's reported ffCO2 emissions in 2014-15. Continuing this prototype analysis system could provide critical independent evaluation of reported ffCO2 emissions and emissions reductions in California, and the system could be expanded to other, more data-poor regions.

Land carbon sink is a vital component for the achievement of China's ambitious carbon neutrality goal, but its magnitude is poorly known. Atmospheric observations and inverse models are valuable tools to constrain the China's land carbon sink. Space‐based CO2 measurements from satellites form an emerging data stream for application of such atmospheric inversions. Here, we reviewed the satellite missions that is dedicated to the monitoring of CO2, and the recent progresses on the inversion of China's land carbon sink using satellite CO2 measurements. We summarized the limitations and challenges in current space platforms, retrieval algorithms, and the inverse modeling. It is shown that there are large uncertainties of contemporary satellite‐based estimates of China's land carbon sink. We discussed future opportunities of continuous improvements in three aspects to better constrain China's land carbon sink with space‐based CO2 measurements. [ABSTRACT FROM AUTHOR]

This study aims at estimating errors to be accounted for in atmospheric inversions of methane (CH4) emissions at the European scale. Four types of errors are estimated in the concentration space over the model domain and at selected measurement sites. Furthermore, errors in emission inventories are estimated at country and source sector scales. A technically ready method is used, which is implemented by running a set of simulations of hourly CH4 mixing ratios for 2015 using two area-limited transport models at three horizontal resolutions with multiple data sets of emissions and boundary and initial conditions as inputs. The obtained error estimates provide insights into how these errors could be treated in an inverse modelling system for inverting CH4 emissions over Europe. The main results show that sources of transport errors may better be controlled alongside the emissions, which differs from usual inversion practices. The average total concentration error is estimated at 29 ppb. The assessed error of total CH4 emissions is 22% and emission errors are heterogeneous at sector (23–49%) and country scales (16–124%), with largest errors occurring in the waste sector due to uncertainties in activity data and emission factors and in Finland due to uncertainties in natural wetland emissions.

This study evaluates two local‐scale atmospheric inversion approaches for the monitoring of methane (CH4) emissions from industrial sites based on in situ atmospheric CH4 mole fraction measurements from stationary or mobile sensors. We participated in a two‐week campaign of CH4 controlled‐release experiments at TotalEnergies Anomaly Detection Initiatives (TADI) in Lacq, France in October 2019. We analyzed releases from various points within a 40 m × 50 m area with constant rates of 0.16 to 30 g CH4 s−1 over 25 to 75 mins, using fixed‐point and mobile measurements, and testing different inversion configurations with a Gaussian dispersion model. An inlet switching system, combining a limited number (6–7) of high‐precision gas analyzers with a higher number (16) of sampling lines, ensured that a sufficient number of fixed measurement points sampled the plume downwind of the sources and the background mole fractions for any wind direction. The inversions using these fixed‐point measurements provide release rate estimates with approximately 23%–30% average errors and estimates of the location of the releases with approximately 8–10 m average errors. The inversions using the mobile measurements provide estimates with approximately 20%–30% average errors for the release rates and approximately 30 m average errors for the release locations. The precision of the release rate estimates from both inversion frameworks corresponds to the best estimation precision documented on site‐scale CH4 inversions. However, the use of continuous measurements from fixed stations provides much more robust estimates of the source locations than that of the mobile measurements. [ABSTRACT FROM AUTHOR]

The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient constraints at large scales, whereby satellite data offer great potential for reducing the uncertainty. Here, we present two carbon sink estimates for China constrained either by satellite CO2 column concentrations (XCO2) within the Global Carbon Assimilation System or by remotely sensed soil moisture and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in addition to in situ CO2 observations within the Carbon Cycle Data Assimilation System. They point to a moderate size of carbon sinks of 0.34 ± 0.14 (mean ± unc.) and 0.43 ± 0.09 PgC/yr during 2010–2015, which are supported by an inventory‐based estimate for forest and soil carbon sink (0.26 PgC/yr) and fall in the range of contemporary ensemble atmospheric inversions (0.25–0.48 PgC/yr). They also agree reasonably well on interannual variations, which reflect the carbon sink anomalies induced by regional droughts in southwest China. Furthermore, their spatial distributions are broadly consistent that of the forest inventory‐based estimate, indicating that the largest carbon sinks locate in central and eastern China. Their estimates for forest carbon sink coincide fairly well with the inventory‐based estimate across different regions, especially when aggregated to the north and south of China. Although enhanced recently by afforestation, China's carbon sink was also significantly weakened by regional droughts, which were often not fully represented in previous in situ CO2‐based inversions due to insufficient observations. Our results suggest that satellite‐based atmospheric CO2 and land surface observations are vital in characterizing terrestrial net carbon fluxes at regional scales. Plain Language Summary: Limited by available in situ CO2 observations, the size and geographical distribution of China's terrestrial carbon sink remain not well known, whereby satellite observations offer great potential for improving the situation. In this study, we estimate the carbon sink of China from either satellite CO2 column concentrations (XCO2) or remotely sensed land surface variables in addition to in situ CO2 observations. They point to a moderate size of carbon sinks of 0.34 ± 0.14 (mean ± unc.) and 0.43 ± 0.09 PgC/yr during 2010–2015. The size of China's carbon sink estimate is supported by an inventory‐based estimate for forest and soil carbon sink (0.26 PgC/yr) and in line with contemporary atmospheric inversions (0.25–0.48 PgC/yr). They can detect the carbon sink anomalies induced by regional droughts in southwest China. Their estimates for forest carbon sink coincide fairly well with the inventory‐based estimate across different regions, especially when aggregated to the north and south of China. China's carbon sink was significantly weakened by regional droughts, which were often not fully represented in previous in situ CO2‐based inversions because of lacking observations. This study demonstrates that satellite‐based atmospheric CO2 and land surface observations are vital in characterizing terrestrial net carbon fluxes at regional scales. Key Points: Two estimates of China's recent carbon flux constrained by different satellite observations consistently reveal a moderate carbon sinkThe two satellite‐based data assimilation systems point to a largely reduced carbon uptake in southwest China due to droughtsTheir estimates for forest carbon sink coincide fairly well with the inventory‐based estimate across different regions [ABSTRACT FROM AUTHOR]

The effect of the 2018 extreme meteorological conditions in Europe on methane (CH4) emissions is examined using estimates from four atmospheric inversions calculated for the period 2005-2018. For most of Europe, we find no anomaly in 2018 compared to the 2005-2018 mean. However, we find a positive anomaly for the Netherlands in April, which coincided with positive temperature and soil moisture anomalies suggesting an increase in biogenic sources. We also find a negative anomaly for the Netherlands for September-October, which coincided with a negative anomaly in soil moisture, suggesting a decrease in soil sources. In addition, we find a positive anomaly for Serbia in spring, summer and autumn, which coincided with increases in temperature and soil moisture, again suggestive of changes in biogenic sources, and the annual emission for 2018 was 33±38% higher than the 2005-2017 mean. These results indicate that CH4 emissions fromareas where the natural source is thought to be relatively small can still vary due to meteorological conditions. At the European scale though, the degree of variability over 2005-2018 was small, and there was negligible impact on the annual CH4 emissions in 2018 despite the extreme meteorological conditions. [ABSTRACT FROM AUTHOR]

This study aims at estimating errors to be accounted for in atmospheric inversions of methane (CH4) emissions at the European scale. Four types of errors are estimated in the concentration space over the model domain and at selected measurement sites. Furthermore, errors in emission inventories are estimated at country and source sector scales. A technically ready method is used, which is implemented by running a set of simulations of hourly CH4 mixing ratios for 2015 using two area-limited transport models at three horizontal resolutions with multiple data sets of emissions and boundary and initial conditions as inputs. The obtained error estimates provide insights into how these errors could be treated in an inverse modelling system for inverting CH4 emissions over Europe. The main results show that sources of transport errors may better be controlled alongside the emissions, which differs from usual inversion practices. The average total concentration error is estimated at 29 ppb. The assessed error of total CH4 emissions is 22% and emission errors are heterogeneous at sector (23-49%) and country scales (16-124%), with largest errors occurring in the waste sector due to uncertainties in activity data and emission factors and in Finland due to uncertainties in natural wetland emissions. [ABSTRACT FROM AUTHOR]

The 2018 drought was one of the worst European droughts of the twenty-first century in terms of its severity, extent and duration. The effects of the drought could be seen in a reduction in harvest yields in parts of Europe, as well as an unprecedented browning of vegetation in summer. Here, we quantify the effect of the drought on net ecosystem exchange (NEE) using five independent regional atmospheric inversion frameworks. Using a network of atmospheric CO2 mole fraction observations, we estimate NEE with at least monthly and 0.5° × 0.5° resolution for 2009–2018. We find that the annual NEE in 2018 was likely more positive (less CO2 uptake) in the temperate region of Europe by 0.09 ± 0.06 Pg C yr−1 (mean ± s.d.) compared to the mean of the last 10 years of −0.08 ± 0.17 Pg C yr−1, making the region close to carbon neutral in 2018. Similarly, we find a positive annual NEE anomaly for the northern region of Europe of 0.02 ± 0.02 Pg C yr−1 compared the 10-year mean of −0.04 ± 0.05 Pg C yr−1. In both regions, this was largely owing to a reduction in the summer CO2 uptake. The positive NEE anomalies coincided spatially and temporally with negative anomalies in soil water. These anomalies were exceptional for the 10-year period of our study. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'. [ABSTRACT FROM AUTHOR]

In 2018, central and northern parts of Europe experienced heat and drought conditions over many months from spring to autumn, strongly affecting both natural ecosystems and crops. Besides their impact on nature and society, events like this can be used to study the impact of climate variations on the terrestrial carbon cycle, which is an important determinant of the future climate trajectory. Here, variations in the regional net ecosystem exchange (NEE) of CO2 between terrestrial ecosystems and the atmosphere were quantified from measurements of atmospheric CO2 mole fractions. Over Europe, several observational records have been maintained since at least 1999, giving us the opportunity to assess the 2018 anomaly in the context of at least two decades of variations, including the strong climate anomaly in 2003. In addition to an atmospheric inversion with temporally explicitly estimated anomalies, we use an inversion based on empirical statistical relations between anomalies in the local NEE and anomalies in local climate conditions. For our analysis period 1999–2018, we find that higher-than-usual NEE in hot and dry summers may tend to arise in Central Europe from enhanced ecosystem respiration due to the elevated temperatures, and in Southern Europe from reduced photosynthesis due to the reduced water availability. Despite concerns in the literature, the level of agreement between regression-based NEE anomalies and temporally explicitly estimated anomalies indicates that the atmospheric CO2 measurements from the relatively dense European station network do provide information about the year-to-year variations of Europe's carbon sources and sinks, at least in summer. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'. [ABSTRACT FROM AUTHOR]

Robust estimates of CO2 budget, CO2 exchanged between the atmosphere and terrestrial biosphere, are necessary to better understand the role of the terrestrial biosphere in mitigating anthropogenic CO2 emissions. Over the past decade, this field of research has advanced through understanding of the differences and similarities of two fundamentally different approaches: "top‐down" atmospheric inversions and "bottom‐up" biosphere models. Since the first studies were undertaken, these approaches have shown an increasing level of agreement, but disagreements in some regions still persist, in part because they do not estimate the same quantity of atmosphere–biosphere CO2 exchange. Here, we conducted a thorough comparison of CO2 budgets at multiple scales and from multiple methods to assess the current state of the science in estimating CO2 budgets. Our set of atmospheric inversions and biosphere models, which were adjusted for a consistent flux definition, showed a high level of agreement for global and hemispheric CO2 budgets in the 2000s. Regionally, improved agreement in CO2 budgets was notable for North America and Southeast Asia. However, large gaps between the two methods remained in East Asia and South America. In other regions, Europe, boreal Asia, Africa, South Asia, and Oceania, it was difficult to determine whether those regions act as a net sink or source because of the large spread in estimates from atmospheric inversions. These results highlight two research directions to improve the robustness of CO2 budgets: (a) to increase representation of processes in biosphere models that could contribute to fill the budget gaps, such as forest regrowth and forest degradation; and (b) to reduce sink–source compensation between regions (dipoles) in atmospheric inversion so that their estimates become more comparable. Advancements on both research areas will increase the level of agreement between the top‐down and bottom‐up approaches and yield more robust knowledge of regional CO2 budgets. [ABSTRACT FROM AUTHOR]

The northern wetland methane emission estimates have large uncertainties. Inversion models are a qualified method to estimate the methane fluxes and emissions in northern latitudes but when atmospheric observations are sparse, the models are only as good as their a priori estimates. Thus, improving a priori estimates is a competent way to reduce uncertainties and enhance emission estimates in the sparsely sampled regions. Here, we use a novel way to integrate remote sensing soil freeze/thaw (F/T) status from SMOS satellite to better capture the seasonality of methane emissions in the northern high latitude. The SMOS F/T data provide daily information of soil freezing state in the northern latitudes, and in this study, the data is used to define the cold season in the high latitudes and, thus, improve our knowledge of the seasonal cycle of biospheric methane fluxes. The SMOS F/T data is implemented to LPX-Bern DYPTOP model estimates and the modified fluxes are used as a biospheric a priori in the inversion model CarbonTracker Europe-CH4. The implementation of the SMOS F/T soil state is shown to be beneficial in improving the inversion model’s cold season biospheric flux estimates. Our results show that cold season biospheric CH4 emissions in northern high latitudes are approximately 0.60 Tg lower than previously estimated, which corresponds to 17% reduction in the cold season biospheric emissions. This reduction is partly compensated by increased anthropogenic emissions in the same area (0.23 Tg), and the results also indicates that the anthropogenic emissions could have even larger contribution in cold season than estimated here.

Canadian nitrous oxide (N 2 O) emissions over 2011–2015 are estimated using the CarbonTracker-Lagrange (CT-L) regional inversion. The uncertainty in the whole-country total is high, on the order of 100% or more, with a net flux not significantly different from zero. Emissions are better resolved in Canadian cropland, primarily in Alberta, Saskatchewan, and Manitoba, where the total flux is estimated at 0.08 ± 0.08 Tg N/yr. The uncertainty is improved by the addition of 4 new Canadian sites to the inversion, but remains large, mainly due to the low signal to background ratio at all Canadian N 2 O measurement sites. The seasonal patterns in Canadian cropland emissions suggest a dual maximum, with a late winter freeze-thaw pulse and a growing season flux of similar magnitude. Overall, Canadian cropland accounts for ∼1% of the global anthropogenic N 2 O source according to the inversion, although some process-based models suggest a source more on the order of 2%. • Canadian cropland emits 0.08 ± 0.08 Tg N 2 O–N/yr according to a regional inversion. • This represents ∼1% of the global anthropogenic N 2 O source. • Including 4 new Canadian monitoring sites modestly improves inversion uncertainty. • Canadian cropland N 2 O fluxes have comparable freeze-thaw and growing season peaks. [ABSTRACT FROM AUTHOR]

The Indianapolis Flux Experiment aims to utilize a variety of atmospheric measurements and a high-resolution inversion system to estimate the temporal and spatial variation of anthropogenic greenhouse gas emissions from an urban environment. We present a Bayesian inversion system solving for fossil fuel and biogenic CO2 fluxes over the city of Indianapolis, IN. Both components were described at 1 km resolution to represent point sources and fine-scale structures such as highways in the a priori fluxes. With a series of Observing System Simulation Experiments, we evaluate the sensitivity of inverse flux estimates to various measurement deployment strategies and errors. We also test the impacts of flux error structures, biogenic CO2 fluxes and atmospheric transport errors on estimating fossil fuel CO2 emissions and their uncertainties. The results indicate that high-accuracy and high-precision measurements produce significant improvement in fossil fuel CO2 flux estimates. Systematic measurement errors of 1 ppm produce significantly biased inverse solutions, degrading the accuracy of retrieved emissions by about 1 'μ'mol m–2 s–1 compared to the spatially averaged anthropogenic CO2 emissions of 5 'μ'mol m–2 s–1. The presence of biogenic CO2 fluxes (similar magnitude to the anthropogenic fluxes) limits our ability to correct for random and systematic emission errors. However, assimilating continuous fossil fuel CO2 measurements with 1 ppm random error in addition to total CO2 measurements can partially compensate for the interference from biogenic CO2 fluxes. Moreover, systematic and random flux errors can be further reduced by reducing model-data mismatch errors caused by atmospheric transport uncertainty. Finally, the precision of the inverse flux estimate is highly sensitive to the correlation length scale in the prior emission errors. This work suggests that improved fossil fuel CO2 measurement technology, and better understanding of both prior flux and atmospheric transport errors are essential to improve the accuracy and precision of high-resolution urban CO2 flux estimates.

Abstract: North American nitrous oxide (N2O) emissions of 1.6 ± 0.3 Tg N/yr over 2008–2014 are estimated using the CarbonTracker‐Lagrange regional inversion framework. The estimated N2O emissions are largely consistent with the EDGAR (Emission Database for Global Atmospheric Research) global inventory and with the results of global atmospheric inversions but offer more spatial and temporal detail over North America. Emissions are strongest from the Midwestern Corn/Soybean Belt, which accounts for nearly one third of the total North American N2O source. The emissions are maximum in spring/early summer, consistent with a nitrogen fertilizer‐driven source, and also show a late winter spike suggestive of freeze‐thaw effects. Interannual variability in emissions across the primary months of fertilizer application is positively correlated to mean precipitation. The estimated N2O flux from the Midwestern Corn/Soybean Belt and the more northerly United States/Canadian wheat belt corresponds to 4.2–4.6% and 2.2–3.0%, respectively, of total synthetic + organic N fertilizer applied to those regions. Consideration of nonagricultural sources and additional N inputs from soybean N2 fixation could reduce the N2O yield from the Midwestern Corn/Soybean Belt to ~2.2–2.4% of total N inputs. [ABSTRACT FROM AUTHOR]

Ny-Ålesund is an international research settlement where the thermodynamics and chemical composition of the air are monitored. The present work investigates the effects of micrometeorological conditions, mesoscale dynamics and local air pollution on the data collected at two different locations around the village. Daily filter measurements of sulphur dioxide and non-sea salt sulphate from the temporary Ny-Ålesund station and permanent Zeppelin mountain station have been analysed along with meteorological data. The influence of different factors representing micrometeorological phenomena and local pollution from ships has been statistically investigated. Seasonal variation of the correlation between the data from Ny-Ålesund and Zeppelin stations is revealed, and the seasonal dependence of the relative contribution of different factors has been analysed. The median concentrations of SO42- measured in Ny-Ålesund increased significantly on days with temperature inversions in winter. In spring, concentrations of SO2 and SO42- were higher than normal at both stations on days with temperature inversions, but lower on days with strong humidity inversions. In summer, local ship traffic affects the SO2 data set from Ny-Ålesund, while no statistically significant influence on the Zeppelin data set has been observed. The pollution from ships has an effect on SO42- values at both stations; however, the concentrations in Ny-Ålesund were higher when local pollution accumulated close to the ground in days with strong humidity inversions.

Quantifying greenhouse gas (GHG) emissions from cities is a key challenge towards effective emissions management. An inversion analysis from the INdianapolis FLUX experiment (INFLUX) project, as the first of its kind, has achieved a top-down emission estimate for a single city using CO2 data collected by the dense tower network deployed across the city. However, city-level emission data, used as 'a priori' emissions, are also a key component in the atmospheric inversion framework. Currently, fine-grained emission inventories (EIs) able to resolve GHG city emissions at high spatial resolution, are only available for few major cities across the globe. Following the INFLUX inversion case with a global 1 . 1 km ODIAC fossil fuel CO2 emission dataset, we further improved the ODIAC emission field and examined its utility as a prior for the city scale inversion. We disaggregated the 1 . 1 km ODIAC non-point source emissions using geospatial datasets such as the global road network data and satellite-data driven surface imperviousness data to a 30 . 30 m resolution. We assessed the impact of the improved emission field on the inversion result, relative to priors in previous studies (Hestia and ODIAC). The posterior total emission estimate (5.1 MtC/yr) remains statistically similar to the previous estimate with ODIAC (5.3 MtC/yr). However, the distribution of the flux corrections was very close to those of Hestia inversion and the model-observation mismatches were significantly reduced both in forward and inverse runs, even without hourly temporal changes in emissions. EIs reported by cities often do not have estimates of spatial extents. Thus, emission disaggregation is a required step when verifying those reported emissions using atmospheric models. Our approach offers gridded emission estimates for global cities that could serves as a prior for inversion, even without locally reported EIs in a systematic way to support city-level Measuring, Reporting and Verification (MRV) practice implementation.

Nitrous oxide (N2O) has a global warming potential that is 300 times that of carbon dioxide on a 100-y timescale, and is of major importance for stratospheric ozone depletion. The climate sensitivity of N2O emissions is poorly known, which makes it difficult to project how changing fertilizer use and climate will impact radiative forcing and the ozone layer. Analysis of 6 y of hourly N2O mixing ratios from a very tall tower within the US Corn Belt--one of the most intensive agricultural regions of the world--combined with inverse modeling, shows large interannual variability in N2O emissions (316 Gg N2O-N⋅y-1 to 585 Gg N2O-N⋅y-1). This implies that the regional emission factor is highly sensitive to climate. In the warmest year and spring (2012) of the observational period, the emission factor was 7.5%, nearly double that of previous reports. Indirect emissions associated with runoff and leaching dominated the interannual variability of total emissions. Under current trends in climate and anthropogenic N use, we project a strong positive feedback to warmer and wetter conditions and unabated growth of regional N2O emissions that will exceed 600 Gg N2O-N⋅y-1, on average, by 2050. This increasing emission trend in the US Corn Belt may represent a harbinger of intensifying N2O emissions from other agricultural regions. Such feedbacks will pose a major challenge to the Paris Agreement, which requires large N2O emission mitigation efforts to achieve its goals. [ABSTRACT FROM AUTHOR]

Recent instrumental deployments of regional observation networks of atmospheric CO2 mixing ratios have been used to constrain carbon sources and sinks using inversion methodologies. In this study, we performed sensitivity experiments using observation sites from the Mid Continent Intensive experiment to evaluate the required spatial density and locations of CO2 concentration towers based on flux corrections and error reduction analysis. In addition, we investigated the impact of prior flux error structures with different correlation lengths and biome information. We show here that, while the regional carbon balance converged to similar annual estimates using only two concentration towers over the region, additional sites were necessary to retrieve the spatial flux distribution of our reference case (using the entire network of eight towers). Local flux corrections required the presence of observation sites in their vicinity, suggesting that each tower was only able to retrieve major corrections within a hundred of kilometres around, despite the introduction of spatial correlation lengths (~100 to 300 km) in the prior flux errors. We then quantified and evaluated the impact of the spatial correlations in the prior flux errors by estimating the improvement in the CO2 model-data mismatch of the towers not included in the inversion. The overall gain across the domain increased with the correlation length, up to 300 km, including both biome-related and non-biome-related structures. However, the spatial variability at smaller scales was not improved. We conclude that the placement of observation towers around major sources and sinks is critical for regional-scale inversions in order to obtain reliable flux distributions in space. Sparser networks seem sufficient to assess the overall regional carbon budget with the support of flux error correlations, indicating that regional signals can be recovered using hourly mixing ratios. However, the smaller spatial structures in the posterior fluxes are highly constrained by assumed prior flux error correlation lengths, with no significant improvement at only a few hundreds of kilometres away from the observation sites.

Based on the measurements of the OCO-2 satellite, Noel Cressie addresses a particularly hard challenge for Earth observation, arguably an extreme case in remote sensing. He is one of the very few who has expertise in most of the processing chain and his article brilliantly discusses the diverse underlying statistical challenges. In this comment, we provide a complementary view of the topic to qualify its prospects as drawn by N. Cressie at the end of his article. We first summarize the motivation of OCO-2-type programs; we then expose the corresponding challenges before discussing the prospects. [ABSTRACT FROM AUTHOR]

Analysis systems incorporating atmospheric observations could provide a powerful tool for validating fossil fuel CO _2 (ffCO _2 ) emissions reported for individual regions, provided that fossil fuel sources can be separated from other CO _2 sources or sinks and atmospheric transport can be accurately accounted for. We quantified ffCO _2 by measuring radiocarbon ( ^14 C) in CO _2 , an accurate fossil-carbon tracer, at nine observation sites in California for three months in 2014–15. There is strong agreement between the measurements and ffCO _2 simulated using a high-resolution atmospheric model and a spatiotemporally-resolved fossil fuel flux estimate. Inverse estimates of total in-state ffCO _2 emissions are consistent with the California Air Resources Board’s reported ffCO _2 emissions, providing tentative validation of California’s reported ffCO _2 emissions in 2014–15. Continuing this prototype analysis system could provide critical independent evaluation of reported ffCO _2 emissions and emissions reductions in California, and the system could be expanded to other, more data-poor regions.

Background: More than a quarter century has passed since Methyl Isocyanate disaster took place at Union Carbide’s Sevin manufacturing plant at Bhopal on the night of 2nd /3rd December 1984. Mixture of toxic gases settled down on densely populated old city of Bhopal affecting human beings, animals, plants and even microflora. The stored stockpiles and the products of reactions contaminated air, water, soil and human as well as animal bodies. Methods and Procedures: field visit; monthly /six monthly and annually were made to have firsthand information on the affected area and the localities surrounding it. Extensive secondary data review was done to understand the genesis, quantum and the potential of the contaminants to harm the environment and the human health. Results: It was found that the air was clean by 6th December 1984 and threat to environment by stored MIC was removed by 22nd December 1984. Soil was evaluated and observed that the potential to cause acute toxicity was at minimal by 2002. In 2013 water from local tube wells was found contaminated with inorganic Lead and HCH (>permissible limit) and HCH γ under permissible limit and it is recommended to be used for only non-internal consumption purposes. Soil which at present is the main source of future contamination needs immediate remediation. Conclusion: Fear of contamination and resultant ill health is so far unsubstantiated. However, further study should be conducted to link toxicant present in water/soil, pathways to reach food chain and ultimately human body and with their present health status. Soil remediation should be under taken on war footing.

From 1980 until 2010, the combined CO2 sink strengths of ocean and land increased by nearly 50% (−0.55 Pg C yr−1 decade−1), but the spatial distribution of this trend is not well known. We address this by performing a joint cyclostationary ocean-atmosphere inversion for the three decades 1980-1989, 1990-1999, and 2000-2008, using only carbon data from the ocean and atmosphere as constraints, i.e., without applying any prior information about the land fluxes. We find that in the inversion, most of the 30 year sink trend stems from the ocean (−0.44 Pg C yr−1 decade−1). The contribution of the terrestrial biosphere is commensurably smaller but has more decadal variability. First, the land sink strength intensified in the 1990s by 0.4 (±0.3) Pg C yr−1 compared to the 1980s but then weakened slightly by 0.2 (±0.4) Pg C yr−1 in the 2000s. The different land regions contributed very variedly to these global trends. While the northern extratropical land acted as an increasing carbon sink throughout the examined period primarily driven by boreal regions, the tropical land is estimated to have acted as an increasing source of CO2, with source magnitude and trend dominated by enhanced release in tropical America during the Amazon mean wet season. This pattern is largely unchanged if the oceanic inversion constraint, which is based on a stationary ocean circulation, is replaced by an estimate based on simulation results from an ocean biogeochemical general circulation model that includes year-to-year variability in the air-sea CO2 fluxes and also has a trend (−0.07 Pg C yr−1 decade−1) that is at the very low end of current estimates. However, the land/ocean partitioning of the trend contribution is adjusted accordingly. Oceanic carbon data has a major impact on carbon exchange for all tropical regions and southern Africa but also for observationally better constrained regions in North America and temperate Asia. The European trend exhibits a strong sensitivity to the choice of the atmospheric CO2 network. [ABSTRACT FROM AUTHOR]