Suchergebnisse

The intensive measurement campaign CoMet 1.0 (Carbon Dioxide and Methane Mission) took place during May and June 2018, with a focus on greenhouse gases over Europe. CoMet 1.0 aimed at characterising the distribution of CH 4 and CO 2 over significant regional sources with the use of a fleet of research aircraft as well as validating remote sensing measurements from state-of-the-art instrumentation installed on board against a set of independent in situ observations. Here we present the results of over 55 h of accurate and precise in situ measurements of CO 2 , CH 4 and CO mole fractions made during CoMet 1.0 flights with a cavity ring-down spectrometer aboard the German research aircraft HALO (High Altitude and LOng Range Research Aircraft), together with results from analyses of 96 discrete air samples collected aboard the same platform. A careful in-flight calibration strategy together with post-flight quality assessment made it possible to determine both the single-measurement precision as well as biases against respective World Meteorological Organization (WMO) scales. We compare the result of greenhouse gas observations against two of the available global modelling systems, namely Jena CarboScope and CAMS (Copernicus Atmosphere Monitoring Service). We find overall good agreement between the global models and the observed mole fractions in the free tropospheric range, characterised by very low bias values for the CAMS CH 4 and the CarboScope CO 2 products, with a mean free tropospheric offset of 0 (14) nmol mol −1 and 0.8 (1.3) µmol mol −1 respectively, with the numbers in parentheses giving the standard uncertainty in the final digits for the numerical value. Higher bias is observed for CAMS CO 2 (equal to 3.7 (1.5) µmol mol −1 ), and for CO the model–observation mismatch is variable with height (with offset equal to −1.0 (8.8) nmol mol −1 ). We also present laboratory analyses of air samples collected throughout the flights, which include information on the isotopic composition of CH 4 , and we demonstrate the potential of simultaneously measuring δ 13 C−CH 4 and δ 2 H−CH 4 from air to determine the sources of enhanced methane signals using even a limited number of discrete samples. Using flasks collected during two flights over the Upper Silesian Coal Basin (USCB, southern Poland), one of the strongest methane-emitting regions in the European Union, we were able to use the Miller–Tans approach to derive the isotopic signature of the measured source, with values of δ 2 H equal to −224.7 (6.6) ‰ and δ 13 C to −50.9 (1.1) ‰, giving significantly lower δ 2 H values compared to previous studies in the area.

The intensive measurement campaign CoMet 1.0 (Carbon Dioxide and Methane Mission) took place during May and June 2018, with a focus on greenhouse gases over Europe. CoMet 1.0 aimed at characterising the distribution of CH4 and CO2 over significant regional sources with the use of a fleet of research aircraft as well as validating remote sensing measurements from state-of-the-art instrumentation installed on board against a set of independent in situ observations. Here we present the results of over 55 h of accurate and precise in situ measurements of CO2, CH4 and CO mole fractions made during CoMet 1.0 flights with a cavity ring-down spectrometer aboard the German research aircraft HALO (High Altitude and LOng Range Research Aircraft), together with results from analyses of 96 discrete air samples collected aboard the same platform. A careful in-flight calibration strategy together with post-flight quality assessment made it possible to determine both the single-measurement precision as well as biases against respective World Meteorological Organization (WMO) scales. We compare the result of greenhouse gas observations against two of the available global modelling systems, namely Jena CarboScope and CAMS (Copernicus Atmosphere Monitoring Service). We find overall good agreement between the global models and the observed mole fractions in the free tropospheric range, characterised by very low bias values for the CAMS CH4 and the CarboScope CO2 products, with a mean free tropospheric offset of 0 (14) nmol mol−1 and 0.8 (1.3) µmol mol−1 respectively, with the numbers in parentheses giving the standard uncertainty in the final digits for the numerical value. Higher bias is observed for CAMS CO2 (equal to 3.7 (1.5) µmol mol−1), and for CO the model–observation mismatch is variable with height (with offset equal to −1.0 (8.8) nmol mol−1). We also present laboratory analyses of air samples collected throughout the flights, which include information on the isotopic composition of CH4, and we demonstrate the potential of simultaneously measuring δ13C−CH4 and δ2H−CH4 from air to determine the sources of enhanced methane signals using even a limited number of discrete samples. Using flasks collected during two flights over the Upper Silesian Coal Basin (USCB, southern Poland), one of the strongest methane-emitting regions in the European Union, we were able to use the Miller–Tans approach to derive the isotopic signature of the measured source, with values of δ2H equal to −224.7 (6.6) ‰ and δ13C to −50.9 (1.1) ‰, giving significantly lower δ2H values compared to previous studies in the area.

The intensive measurement campaign CoMet 1.0 (Carbon dioxide and Methane mission) took place during May and June 2018, with a focus on greenhouse gases over Europe. CoMet 1.0 aimed at characterising the distribution of CH 4 and CO 2 over significant regional sources with the use of a fleet of research aircraft, as well as validating remote sensing measurements from state-of-the-art instrumentation installed on-board against a set of independent in-situ observations. Here we present the results of over 55 hours of accurate and precise in situ measurements of CO 2 , CH 4 and CO mixing ratios made during CoMet 1.0 flights with a cavity ring-down spectrometer aboard the German research aircraft HALO, together with results from analyses of 96 discrete air samples collected aboard the same platform. A careful in-flight calibration strategy together with post-flight quality assessment made it possible to determine both the single measurement precision as well as biases against respective WMO scales. We compare the result of greenhouse gas observations against two of the available global modelling systems, namely Jena CarboScope and CAMS (Copernicus Atmosphere Monitoring Service). We find overall good agreement between the global models and the observed mixing ratios in the free-tropospheric range, characterised by very low bias values for the CAMS CH 4 and the CarboScope CO 2 products, with a mean free tropospheric offset of 0 (14) ppb and 0.8 (1.3) ppm respectively, with the quoted number giving the standard uncertainty in the final digits for the numerical value. Higher bias is observed for CAMS CO 2 (equal to 3.7 (1.5) ppm), and for CO the model-observation mismatch is variable with height (with offset equal to −1.0 (8.8)). We also present laboratory analyses of air samples collected throughout the flights, which include information on the isotopic composition of CH_4, and we demonstrate the potential of simultaneously measuring δ 13 C-CH 4 and δ 2 H-CH 4 from air to determine the sources of enhanced methane signals using even a limited amount of discrete samples. Using flasks collected during two flights over the Upper Silesian Coal Basin (USCB, southern Poland), one of the strongest methane-emitting regions in the European Union, we were able to use the Miller-Tans approach to derive the isotopic signature of the measured source, with values of δ 2 H equal to −224.7 (6.6) permil and δ 13 C to −50.9 (1.1) permil, giving significantly lower d2H values compared to previous studies in the area.

The growth in anthropogenic carbon dioxide ( CO 2 ) emissions acts as a major climate change driver, which has widespread implications across society, influencing the scientific, political, and public sectors. For an increased understanding of the CO 2 emission sources, patterns, and trends, a link between the emission inventories and observed CO 2 concentrations is best established via Earth system modelling and data assimilation. Bringing together the different pieces of the puzzle of a very different nature (measurements, reported statistics, and models), it is of utmost importance to know their level of confidence and boundaries well. Inversions disaggregate the variation in observed atmospheric CO 2 concentration to variability in CO 2 emissions by constraining the regional distribution of CO 2 fluxes, derived either bottom-up from statistics or top-down from observations. The level of confidence and boundaries for each of these CO 2 fluxes is as important as their intensity, though often not available for bottom-up anthropogenic CO 2 emissions. This study provides a postprocessing tool CHE_UNC_APP for anthropogenic CO 2 emissions to help assess and manage the uncertainty in the different emitting sectors. The postprocessor is available under https://doi.org/10.5281/zenodo.5196190 (Choulga et al., 2021). Recommendations are given for regrouping the sectoral emissions, taking into account their uncertainty instead of their statistical origin; for addressing local hot spots; for the treatment of sectors with small budget but uncertainties larger than 100 %; and for the assumptions around the classification of countries based on the quality of their statistical infrastructure. This tool has been applied to the EDGARv4.3.2_FT2015 dataset, resulting in seven input grid maps with upper- and lower-half ranges of uncertainty for the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System. The dataset is documented and available under https://doi.org/10.5281/zenodo.3967439 (Choulga et al., 2020). While the uncertainty in most emission groups remains relatively small (5 %–20 %), the largest contribution (usually over 40 %) to the total uncertainty is determined by the OTHER group (of fuel exploitation and transformation but also agricultural soils and solvents) at the global scale. The uncertainties have been compared for selected countries to those reported in the inventories submitted to the United Nations Framework Convention on Climate Change and to those assessed for the European emission grid maps of the Netherlands Organisation for Applied Scientific Research. Several sensitivity experiments are performed to check (1) the country dependence (by analysing the impact of assuming either a well- or less well-developed statistical infrastructure), (2) the fuel type dependence (by adding explicit information for each fuel type used per activity from the Intergovernmental Panel on Climate Change), and (3) the spatial source distribution dependence (by aggregating all emission sources and comparing the effect against an even redistribution over the country). The first experiment shows that the SETTLEMENTS group (of energy for buildings) uncertainty changes the most when development level is changed. The second experiment shows that fuel-specific information reduces uncertainty in emissions only when a country uses several different fuels in the same amount; when a country mainly uses the most globally typical fuel for an activity, uncertainty values computed with and without detailed fuel information are the same. The third experiment highlights the importance of spatial mapping.

The growth in anthropogenic carbon dioxide (CO2) emissions acts as a major climate change driver, which has widespread implications across society, influencing the scientific, political, and public sectors. For an increased understanding of the CO2 emission sources, patterns, and trends, a link between the emission inventories and observed CO2 concentrations is best established via Earth system modelling and data assimilation. Bringing together the different pieces of the puzzle of a very different nature (measurements, reported statistics, and models), it is of utmost importance to know their level of confidence and boundaries well. Inversions disaggregate the variation in observed atmospheric CO2 concentration to variability in CO2 emissions by constraining the regional distribution of CO2 fluxes, derived either bottom-up from statistics or top-down from observations. The level of confidence and boundaries for each of these CO2 fluxes is as important as their intensity, though often not available for bottom-up anthropogenic CO2 emissions. This study provides a postprocessing tool CHE_UNC_APP for anthropogenic CO2 emissions to help assess and manage the uncertainty in the different emitting sectors. The postprocessor is available under https://doi.org/10.5281/zenodo.5196190 (Choulga et al., 2021). Recommendations are given for regrouping the sectoral emissions, taking into account their uncertainty instead of their statistical origin; for addressing local hot spots; for the treatment of sectors with small budget but uncertainties larger than 100 %; and for the assumptions around the classification of countries based on the quality of their statistical infrastructure. This tool has been applied to the EDGARv4.3.2_FT2015 dataset, resulting in seven input grid maps with upper- and lower-half ranges of uncertainty for the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System. The dataset is documented and available under https://doi.org/10.5281/zenodo.3967439 (Choulga et al., 2020). While the uncertainty in most emission groups remains relatively small (5 %–20 %), the largest contribution (usually over 40 %) to the total uncertainty is determined by the OTHER group (of fuel exploitation and transformation but also agricultural soils and solvents) at the global scale. The uncertainties have been compared for selected countries to those reported in the inventories submitted to the United Nations Framework Convention on Climate Change and to those assessed for the European emission grid maps of the Netherlands Organisation for Applied Scientific Research. Several sensitivity experiments are performed to check (1) the country dependence (by analysing the impact of assuming either a well- or less well-developed statistical infrastructure), (2) the fuel type dependence (by adding explicit information for each fuel type used per activity from the Intergovernmental Panel on Climate Change), and (3) the spatial source distribution dependence (by aggregating all emission sources and comparing the effect against an even redistribution over the country). The first experiment shows that the SETTLEMENTS group (of energy for buildings) uncertainty changes the most when development level is changed. The second experiment shows that fuel-specific information reduces uncertainty in emissions only when a country uses several different fuels in the same amount; when a country mainly uses the most globally typical fuel for an activity, uncertainty values computed with and without detailed fuel information are the same. The third experiment highlights the importance of spatial mapping.

Anthropogenic carbon dioxide (CO 2 ) emissions and their observed growing trends raise awareness in scientific, political and public sectors of the society as the major driver of climate-change. For an increased understanding of the CO 2 emission sources, patterns and trends, a link between the emission inventories and observed CO 2 concentrations is best established via Earth system modelling and data assimilation. In this study anthropogenic CO 2 emission inventories are processed into gridded maps to provide an estimate of prior CO 2 emissions for 7 main emissions groups: 1) power generation super-emitters and 2) energy production average-emitters, 3) manufacturing, 4) settlements, 5) aviation, 6) transport and 7) others, with estimation of their uncertainty and covariance to be included in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). The emission inventories are sourced from the Intergovernmental Panel on Climate Change (IPCC) 2006 Guidelines for National Greenhouse Gas Inventories and revised information from its 2019 Refinements, and the global grid-maps of Emissions Database for Global Atmospheric Research (EDGAR) inventory. The anthropogenic CO 2 emissions for 2012 and 2015, (EDGAR versions 4.3.2 and 4.3.2_FT2015 respectively) are considered, updated with improved apportionment of the energy sector, energy usage for manufacturing and diffusive CO 2 emissions from coal mines. These emissions aggregated into 7 ECMWF groups with their emission uncertainties are calculated per country considering its statistical infrastructure development level and sector considering the most typical fuel type and use the IPCC recommended error propagation method assuming fully uncorrelated emissions to generate covariance matrices of parsimonious dimension (7×7). While the uncertainty of most groups remains relatively small, the largest contribution to the total uncertainty is determined by the group with usually the smallest budget, consisting of oil refineries and transformation industry, fuel exploitation, coal production, agricultural soils and solvents and products use emissions. Several sensitivity studies are performed: for country type (with well-/less well-developed statistical infrastructure), for fuel type specification, and for national emission source distribution (highlights the importance of 30 accurate point source mapping). Uncertainties are compared with United Nations Framework Convention on Climate Change (UNFCCC) and the Netherlands Organisation for Applied Scientific Research (TNO) data. Upgraded anthropogenic CO 2 emission maps with their yearly and monthly uncertainties are combined into the CHE_EDGAR-ECMWF_2015 dataset (Choulga et al., 2020) available from https://doi.org/10.5281/zenodo.3712339 .