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Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2 , i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2 , MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets. [ABSTRACT FROM AUTHOR]

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

Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas and its concentration is continuously increasing mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and designing mitigation strategies aimed at stabilizing the CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of column-averaged dry-air mole fraction of CO2 i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory 2 (OCO-2) to estimate the anthropogenic CO2 emissions using Generalized Regression Neural Network over East and West Asia. OCO-2 XCO2 and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015-2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variabilities. Then the XCO2 anomaly and ODIAC emission datasets from 2015 to 2018 were used to train the GRNN model, and finally, the anthropogenic CO2 emissions were estimated for 2019 based on the XCO2 anomalies derived for the same year. The XCO2-based estimated and the ODIAC actual CO2 emissions were compared and the results showed a good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional scale anthropogenic CO2 emissions and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets. [ABSTRACT FROM AUTHOR]

Through 1959-2012, an airborne fraction (AF) of 44% of total anthropogenic CO2 emissions remained in the atmosphere, with the rest being taken up by land and ocean CO2 sinks. Understanding of this uptake is critical because it greatly alleviates the emissions reductions required for climate mitigation. An observable quantity that reflects sink properties more directly than the AF is the CO2 sink rate (kS), the combined land-ocean CO2 sink flux per unit excess atmospheric CO2 above preindustrial levels. Here we show from observations that kS declined over 1959-2012 by a factor of about 1/3, implying that CO2 sinks increased more slowly than excess CO2. We attribute the decline in kS to four mechanisms: slower-than-exponential CO2 emissions growth (~ 35% of the trend), volcanic eruptions (~ 25 %), sink responses to climate change (~ 20 %), and nonlinear responses to increasing CO2, mainly oceanic (~ 20 %). The first of these mechanisms is associated purely with extrinsic forcings, and the last two with intrinsic, nonlinear responses of sink processes to changes in climate and atmospheric CO2. Our results indicate that the effects of these intrinsic, nonlinear responses are already detectable in the global carbon cycle. Although continuing future decreases in kS will occur under all plausible CO2 emission scenarios, the rate of decline varies between scenarios in non-intuitive ways because extrinsic and intrinsic mechanisms respond in opposite ways to changes in emissions: extrinsic mechanisms cause kS to decline more strongly with increasing mitigation, while intrinsic mechanisms cause kS to decline more strongly under high-emission, low-mitigation scenarios as the carbon- climate system is perturbed further from a near-linear regime. [ABSTRACT FROM AUTHOR]

Vast areas of peatlands have been drained globally for agriculture and forestry causing loss of peat and consequently carbon dioxide and nitrous oxide emissions. Rewetting of these peatlands has been proposed to reduce greenhouse gas emissions for climate change mitigation.During recent years, evidence has accumulated showing that the carbon dioxide and nitrous oxide emissions from these drained soils vary substantially depending on land-use, soil nutrient status and climate. Rewetting effectively reduces or even completely stops these emissions. On the other hand, rewetting induces methane emissions from soil, their magnitude depending largely on soil nutrient status. The effect of rewetting on climate is further complicated because of the largely different radiative efficacies and atmospheric lifetimes of carbon dioxide, nitrous oxide and methane. Thus, the effect of peatland rewetting on climate also depends strongly on the studied time interval. On peatlands drained for forestry, also carbon sequestration by trees can have a considerable effect on radiative forcing. We combined area estimates of drained peatlands for different land-uses and climate zones with estimates on greenhouse gas emissions and removals from drained and rewetted peat soils. We then calculated the effect of global peatland rewetting on the emission/removal time series of different gases. Further, radiative forcing scenarios were constructed for each land-use and climate zone. The effect of carbon sequestration by trees was assessed in a sub study on Finnish forestry-drained peatlands. We answer the following questions: How big effect on climate a global rewetting of drained peat soils would have? How much the increased methane emissions offset the benefit of decreased carbon dioxide and nitrous oxide emissions? For which time intervals, land-uses and climate zones rewetting is an effective tool for climate change mitigation? [ABSTRACT FROM AUTHOR]