NASA’s Carbon Monitoring System (CMS) has a 12-year record of production of prototype data products from across Carbon Cycle Science that have potential usefulness for stakeholders outside the standard science community.   CMS originated from US Congressional direction through the budget process back in 2010 and remains currently.  CMS activities all include significant use of remote sensing data, as that is NASA’s strong suit.  We have been emphasizing increased engagement with stakeholders as CMS has progressed.  Prototype products exist currently related to GHG fluxes, terrestrial biomass, and ocean/coastal carbon.  Satellite sensors currently employed in CMS prototype products include OCO-2, OCO-3, S5P, GOSAT, GEDI, MODIS, and LandSat.  These various product development teams are coordinated through related working groups to help learn from the other projects, exchange ideas to improve outreach to stakeholders, and set potential direction for future CMS solicitations.  Many of the CMS products, that have existed for years and further developing, especially those related to GHG fluxes, have participants from numerous US agencies and have direct relationship to the coordination activities being discussed by many nations. 

How to cite: Jucks, K.: NASA’s Carbon Monitoring System; lessons learned from 12 years of producing society-relevant, science-based, prototype Carbon-related products., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3626, https://doi.org/10.5194/egusphere-egu23-3626, 2023.

The likelihood for successful emission control and mitigation efforts of trace gases having adverse environmental effects can be enhanced by using a multi-faceted framework for quantifying and understanding emissions. While bottom-up activity-based inventories provide a quantification of various source sectors, appropriately designed atmosphere-based (top-down) approaches are able to independently evaluate the inventory and further refine temporal changes and spatial distributions.  Differences between bottom-up and top-down estimates are oftentimes observed and represent prime opportunities for increasing understanding and refining estimates of emissions.  Here we will present results derived from atmospheric observations made in the remote global atmosphere as well as from our North American measurement network.  The remote global observations enabled the identification of an apparent violation of the Montreal Protocol.  After our atmospheric measurements identified this unexpected issue, fairly quick resolution appears to have been achieved, in part due to the additional understanding of likely underlying causes provided by industry experts. Our North American measurement network also allows for trace-gas emission estimates on national and state scales.  Results from these efforts will be discussed, with an emphasis on describing how the interaction between inventory-derived and atmosphere-based information has led to an improved understanding of emission magnitudes along with identifying areas needing additional study.

How to cite: Montzka, S., Hu, L., DeCola, P., Godwin, D., Vimont, I., Croes, B., Kuwayama, T., Dutton, G., Nance, D., Hall, B., Sweeney, C., and Andrews, A.: Making best use of atmosphere- and inventory-based approaches for quantifying and understanding emissions of greenhouse gases and ozone-depleting substances on a range of spatial scales., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10714, https://doi.org/10.5194/egusphere-egu23-10714, 2023.

Accurate accounting of emissions and removals of CO₂ is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries’ carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling inter-comparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (X_CO2) retrievals (ACOS v10), in situ CO₂ measurements, or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1° × 1° gridded dataset and as a country-level dataset. Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 PgCO₂ yr⁻¹ (0.90–1.25 PgC yr⁻¹). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

How to cite: Byrne, B. and Bowman, K. and the other authors: National CO2 budgets (2015–2020) inferred from atmospheric CO2observations in support of the Global Stocktake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16832, https://doi.org/10.5194/egusphere-egu23-16832, 2023.

The 2015 Paris Climate Agreement and Global Methane Pledge formalized agreement for countries to report and reduce methane emissions to mitigate near-term climate change. Emission inventories generated through surface activity measurements are reported annually or bi-annually and evaluated periodically through a “Global Stocktake”.  Emissions inverted from atmospheric data support evaluation of reported inventories, but their systematic use is stifled by spatially variable biases from prior errors combined with limited sensitivity of observations to emissions (smoothing error), as-well-as poorly characterized information content. Here, we demonstrate a Bayesian, optimal estimation (OE) algorithm for evaluating a state-of-the-art inventory (EDGAR v6.0) using satellite-based emissions from 2009 to 2018. The OE algorithm quantifies the information content (uncertainty reduction, sectoral attribution, spatial resolution) of the satellite-based emissions and disentangles the effect of smoothing error when comparing to an inventory. We find robust differences between satellite and EDGAR for total livestock, rice, and coal emissions: 14 ± 9, 12 ± 8, -11 ± 6 Tg CH₄/yr respectively. EDGAR and satellite agree that livestock emissions are increasing (0.25 to 1.3 Tg CH₄/ yr / yr), primarily in the Indo-Pakistan region, sub-tropical Africa, and the Brazilian arc of deforestation; East Asia rice emissions are also increasing, highlighting the importance of agriculture on the atmospheric methane growth rate. In contrast, low information content for the waste and fossil emission trends confounds comparison between EDGAR and satellite;  increased sampling and spatial resolution of satellite observations are therefore needed to evaluate reported changes to emissions in these sectors.

How to cite: Worden, J., Pandey, S., Zhang, Y., Cusworth, D., Zhen, Q., Bloom, A., Ma, S., Maasakkers, B., Byrne, B., Duren, R., Crisp, D., Gordon, D., and Jacob, D.: A Bayesian Framework for Verifying Methane Inventories and Trends With Atmospheric Methane Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17172, https://doi.org/10.5194/egusphere-egu23-17172, 2023.

CO₂ emission database lays the foundation of climate research and climate governance. Most current global CO₂ emission inventories were developed with energy statistics from International Energy Agency (IEA) and were available at country level with limited source categories. Here, as the first step toward a high-resolution and dynamic updated global CO₂ emission database, we developed a data-driven approach to construct seamless and highly-resolved energy consumption data cubes for 208 countries/territories, 797 sub-country administrative divisions in 29 countries, 42 fuel types, and 52 sectors, with the fusion of energy consumption from 24 international statistics and 66 regional/local statistics. Global CO₂ emissions from fossil fuel combustion and cement production in 1970-2021 were then estimated with highly-resolved source category (1484 of total) and sub-country information (797 of total). Specifically, 73% of global CO₂ emissions in 2021 were calculated based on sub-country information, providing considerably improved spatial resolution for global CO₂ accounting. With the support of detailed information, the dynamics of global CO₂ emissions across sectors and fuels were presented, representing the evolution of global economy and progress of climate governance. Remarkable differences of sectoral contribution were found across sub-country administrative divisions within a given country, revealing the uneven distribution of energy and economic structure among different regions. Our estimates were generally consistent with existing databases at aggregated level for global total or large emitters, while large discrepancies were observed for middle and small emitters. Our database, named Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC) is publicly available through http://meicmodel.org.cn with highly-resolved information and timely update, which provides an independent carbon emission accounting data source for climate research.

How to cite: Xu, R., Zhang, Q., Tong, D., Xiao, Q., Qin, X., Chen, C., Yan, L., Cheng, J., Cui, C., Hu, H., Liu, W., Yan, X., Wang, H., Liu, X., Geng, G., Guan, D., and He, K.: Development of a new global CO2 emission database with highly-resolved source category and sub-country information: methodology and 1970-2021 emissions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1647, https://doi.org/10.5194/egusphere-egu23-1647, 2023.

With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan to reduce greenhouse gases emissions, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO₂ emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, supply chains tensions and rising prices of energy and commodities, war in Ukraine. Here we present a near-real-time dataset of daily fossil fuel and cement emissions to monitor country-level emissions from January 2019 through December 2021 for 27 European Union countries and the United Kingdom. This dataset is called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry (incl. cement production), ground transportation, domestic aviation, international aviation, residential emissions which includes the built environment. Daily CO₂ emissions are estimated from a large set of activity data compiled from different sources, including hourly to daily electrical power generation data, monthly production data and production indices of industry, daily mobility data and indices for the ground transportation. Individual flight location data and monthly data were for aviation sector estimates. Monthly fuel consumption data downscaled in time with daily air temperature are used to estimate daily emissions from commercial and residential buildings. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.

How to cite: Ke, P., Deng, Z., Zhu, B., Zheng, B., Wang, Y., Boucher, O., Ben Arous, S., Zhou, C., Dou, X., Sun, T., Li, Z., Yan, F., Cui, D., Hu, Y., Huo, D., Pierre, J., Engelen, R., J. Davis, S., Ciais, P., and Liu, Z.: Carbon Monitor Europe, a near-real-time and country-level monitoring of daily CO2 emissions for European Union and the United Kingdom, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4558, https://doi.org/10.5194/egusphere-egu23-4558, 2023.

Long term measurement activities carried out at the WMO GAW global station of Monte Cimone (CMN) provide a useful insight into the characterization of the composition of the Southern European atmosphere. Since 2003, ozone depleting substances (ODSs) and halogenated Greenhouse Gases (GHGs) have been measured at CMN in the frame of the AGAGE (Advanced Global Atmospheric Gases Experiment) programme, with the aim of tracking progress towards the implementation of the international treaties on stratospheric ozone depletion and climate, and getting a better understanding of emissions both in terms of magnitude and localisation at the regional (EU) scale. CMN is often influenced by the advection of polluted air masses from the Po basin, one of the most polluted areas in Europe, as well as by the transport from other highly anthropised regions in Central EU. However, during the cold months and at night-time in the warm season, the site is representative of the free troposphere.

By providing high quality, high frequency, continuous -almost uninterrupted- observations of ODSs (chlorofluorocarbons and hydrochlorofluorocarbons) and their radiatively active substitutes (hydrofluorocarbons) to modellers, the monitoring activities at CMN  are crucial for improving the overall sensitivity of the inverse modelling techniques used to derive emissions through the so called “top-down” approach. Such approach represents an important quasi-independent cross-check of national GHG emission inventories submitted annually by the parties to the United Nations Framework Convention on Climate Change (UNFCCC). Here we will present results of the Bayesian inverse modelling technique used to derive emissions at the EU national scale based on the observations described above.

How to cite: Arduini, J., Annadate, S., Cesari, R., Cristofanelli, P., Falasca, S., Giostra, U., and Maione, M.: Observations of radiatively active halogenated species at MonteCImone WMO-GAW station and their use in inverse modelling techniques to derive emission estimates at the regional scale., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12172, https://doi.org/10.5194/egusphere-egu23-12172, 2023.

Sulfur hexafluoride (SF₆) is the most potent non-CO₂ Greenhouse Gas currently incorporated in the Paris Agreement, with a global warming potential of around 25,000 over a 100-year time horizon and lifetime of around 1,000 years. Global mole fractions and emissions of SF₆ have increased substantially since the 2000s. The increasing SF₆ emissions worldwide are thought to originate from its growing emissions in Kyoto Protocol non-Annex-I countries, where China is a major contributor. 

Top-down emission estimates provide evaluation of national bottom-up inventories, based on information from atmospheric observations. Previous top-down emissions of SF₆ in China were determined by observations made outside of China (e.g., in Korea and Japan), which lack sensitivity to emissions in regions far from the measurement sites (like the western or southern parts of China). In this study, emissions of SF₆ in China over 2011-2020 were derived using observations of SF₆ from 9 sites within China, coupled with a Lagrangian transport model and a hierarchical Bayesian inference algorithm. Analysis of the sensitivity maps (footprints) of these measurement sites suggest broad sensitivity to the major emission areas in China. The emissions in China show a substantial increase throughout the study period and contribute substantially to the rise in global emissions. The spatial distribution of SF₆ emissions in different regions or provinces in China and their changes are further analyzed. Finally, the potential industrial drivers behind the changes in emissions in China, and the necessity of continuous atmospheric observations in some key regions like in the northwest of China are discussed.

How to cite: An, M., Prinn, R., Western, L., Yao, B., Hu, J., and Rigby, M.: Emissions of SF6 in China inferred from atmospheric observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7010, https://doi.org/10.5194/egusphere-egu23-7010, 2023.

Beyond water vapor, the three most important greenhouse gases (GHGs) in terms of their radiative forcing are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Increasing concentrations of these gases driven by human activities are the primary cause of the observed climate change according to the Intergovernmental Panel on Climate Change (ref AR6 WG1). The Paris Agreement, adopted by 196 countries at the UNFCCC Conference of the Parties in 2015, sets specific targets for maximum rise in global mean temperature and identifies reduction in net greenhouse emissions as the primary means to achieve this target.
However, even very accurate estimates of anthropogenic emissions alone will not be enough to design meaningful mitigation efforts or to monitor their effectiveness. Greenhouse gas concentrations are influenced by both natural and anthropogenic processes, and some of the natural carbon sources and sinks in particular are associated with very large uncertainties, both as they currently operate and as they may change in the future in response to climate change. 
Sustained, routine monitoring of greenhouse gas concentrations, using monitoring of weather and climate as a paradigm and role model, will provide a wealth of quantitative data to help constrain the modelling of all parts of the carbon cycle. This will be extremely valuable for the work of the World Climate Research Programme (WCRP) and IPCC, it will complement and supplement existing methodologies used to estimate anthropogenic emissions, and it will help put mitigation steps taken by Parties to the Paris Agreement on a solid scientific footing. 
This presentation introduces a WMO-coordinated effort to establish an operational Global Greenhouse Monitoring Infrastructure to directly observe and model greenhouse gas concentrations in the atmosphere, and thereby support/enable estimates of net greenhouse gas fluxes between atmosphere, land, and oceans. The atmospheric component of this infrastructure builds on the research infrastructure for greenhouse gas observations and modelling supported by WMO since 1975, and promotes its operationalization and further advancement by utilizing the existing infrastructure and methodologies employed for more than 50 years for operational weather forecasting. 

How to cite: Riishojgaard, L.-P.: A WMO-coordinated Global Greenhouse Gas Monitoring Infrastructure, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10805, https://doi.org/10.5194/egusphere-egu23-10805, 2023.

Knowledge of the spatial distribution of the fluxes of greenhouse gases and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its Global Stocktake.

This study identifies, quantifies and explains possible divergences between global inventories, atmospheric inversions, process-based models, and national inventories submitted to the UNFCCC. The focus is on the EU27, the world’s largest emitters, or other smaller regions that can be used to draw generic results or lessons. The analysis builds on the methodological approach developed previously in the EU-funded VERIFY project’s GHG syntheses (Petrescu et al., 2020, 2021a, 2021b, 2022 and McGrath et al., 2022). Most of the data products are from the VERIFY project, with a gradual inclusion of EU-funded CoCO₂ project’s specific products as the project evolves. Reported inventory-based emissions and removals are generally estimated using bottom-up statistical inventory estimates. Top-down, observation-based estimates are required by multiple stakeholders and at multiple scales to verify bottom-up emission estimates. These estimates are performed at different scales for a variety of applications: the global and continental scale for science purposes, country scale for reporting to the UNFCCC, sub-country scale for urban planning, and point sources like large power plants for verification (Pinty et al., 2019). Several examples will be provided for different sectors of CO₂/CH₄ budgets to illustrate (e.g., EU27) the aforementioned divergences as well as the current level of convergence between methods.

A key conclusion across all components analysed is the difficulty of harmonising datasets into a comparable format. The tradition of comparing datasets as published is easy, but problematic and potentially misleading. To reconcile differences between alternative datasets requires a much deeper understanding of each dataset, such as the system boundaries, methods, and input data sources. Often the necessary data is not available or time consuming to access. A systematic reconciliation and comparison often requires a close dialogue between analysts, data providers, and modelers.

Building on our experiences, we discuss a Decision Support Blueprint, outlining potential mechanisms and tools to provide diverse, but targeted, information to the relevant users wanting to reconcile these different datasets. The blueprint is informed by several user consultation meetings and workshops. The blueprint will help tailor a Decision Support System (DSS), as a component of the Copernicus CO₂MVS, to help inventory agencies, governments and their initiatives (e.g. Covenant of Mayors, C40, ICLEI), industry, NGOs, and other interested actors to utilize the expanding observation datasets to support monitoring and verification activities.

How to cite: Petrescu, A. M. R., Peters, G. P., Andrew, R. M., McGrath, M. J., Peylin, P., Chevallier, F., and Engelen, R. and the VERIFY and CoCO2 project participants: CO2 and CH4 observation-based budgets in support to future Copernicus CO2 emissions Monitoring and Verification Support (CO2MVS) capacity user communities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13336, https://doi.org/10.5194/egusphere-egu23-13336, 2023.

Urban agglomerations play a crucial role in reaching global climate objectives. Many cities have committed to reducing their greenhouse gas emissions, but current emission trends remain unverifiable. The quantification of mitigation policies is often incomplete or unavailable, raising serious concerns about how and if climate mitigation targets will be achieved. To support the effective traceability of urban emissions trajectories, atmospheric monitoring of greenhouse gases (GHG) has been deployed over several metropolitan areas. This approach offers an independent and transparent solution to measuring urban emissions. However, careful design of the monitoring network is crucial to be able to monitor the most important sectors as well as to adjust to rapidly changing urban landscapes.

We present here a joint study of Paris and Munich emissions trajectories to demonstrate how climate action plans, carbon emission inventories, and urban development plans can be combined to construct high-resolution projected emissions maps and to help design optimal atmospheric monitoring networks. We show that these two European cities will encounter widely different GHG emission trajectories in space and time, reflecting different emission reduction strategies and different constraints due to administrative boundaries. The projected CO₂ emissions for the milestone years 2030 and 2050 are based on the 2019 spatially distributed 1km x 1km TNO inventory. Future emissions scenarios are based on the analysis of their respective Climate Action Plan. Individual mitigation measures are quantified, sectorized and the resulting saving potentials are applied to the 2019 TNO inventory, used as baseline. The projected CO₂ emissions rely on future actions, hence uncertain, but we demonstrate how emission reductions vary significantly at the sub-city level. 

We show here how climate actions, population growth, and urbanization plans produce mixed spatial patterns across both cities. We conclude that quantified individual cities’ climate actions are essential for strengthening climate policies and their effectiveness at the city scale. Harmonization and compatibility of climate plans from various cities are necessary to make intercity comparisons of climate targets possible. In terms of atmospheric monitoring, our results demonstrate the need for additional measurement stations located inside the densest areas of the two cities but also in the cities’ outskirts to track local positive and negative emission trends over the coming decades. 

How to cite: Albarus, I., Fleischmann, G., Aigner, P., Ciais, P., Denier van der Gon, H., Droge, R., Lian, J., Narvaez Rincon, M. A., Utard, H., and Lauvaux, T.: From political pledges to the quantitative mapping of climate mitigation plans: comparison of two European cities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-301, https://doi.org/10.5194/egusphere-egu23-301, 2023.

Carbon monitoring is crucial for mitigating urban climate change and expediting urban responses to emission changes. This requires monitoring and reporting emissions on a timely basis. However, existing inventories mainly consider scope-1 (in boundary emissions) with a time lag of more than 2 years. The Carbon Monitor (carbonmonitor.org) and the following Carbon Monitor Cities (cities.carbonmonitor.org) project thus developed the near-real-time (NRT) inventories for the global high emitters worldwide at the country level and city level, pushing forwards the efforts of global NRT monitoring in an around time lag of 3 months to ensure the unprecedented timeliness of carbon data. However, the immediate monitoring of emissions in more rapid governmental responses and short-term future policy design is still urgently needed in the future, especially for getting real-time estimates of major emitters by developing the “nowcasting” and even forecasting framework. We choose the residential sector as the initial exploration of this kind given the sectoral characteristics of Asian big emitters, including China, Japan, and India.

By developing an automatic machine learning ensemble framework (Carbon Monitor AutoForecast-Asia), we essentially achieve the nowcasting from the perspective of city-level emission dynamics. Specifically, this framework utilizes data from multiple sources, including the Carbon Monitor datasets, Carbon Monitor Cities datasets, and the ERA5 reanalysis datasets. Time-series and tree-based models are incorporated into the entire framework with automatic finetuning pipelines, as well as designed algorithms for capturing the seasonalities of daily emissions, and the holiday impacts as demonstrated in our previous studies. We use the Carbon Monitor and the Carbon Monitor Cities datasets up to the end of 2021 to train and test the entire framework by parallelizing the framework of more than 400 cities for acceleration. After running correction models to reduce the induced uncertainties from the Carbon Monitor to the Carbon Monitor Cities, we get the R squared metrics of 0.95, 0.94, 0.97 for China, India, and Japan respectively at the country level. Note that, we use the Carbon Monitor data from 2022.1.1 to the latest for comparisons in this procedure, and the framework could nowcast the residential emissions in the time lag of 1 week.

Subsequently, deep learning-based models (i.e., LSTM) are used as the baselines with the same configurations (i.e., train and test splits) and we find that the ensemble results could outperform the baseline models in terms of common metrics, including MAE, MSE, RMSE, MAPE. This suggests that the real-time estimates of emissions may depend on more complicated ensemble methods rather than the deep learning models due to the trade-off between the small volume of near-real-time data and the complex patterns of daily emissions.

In the future, we may include more high emitters globally by extending the developed framework to at least satisfy the needs of real-time and even future estimates of the residential sector.

How to cite: Sun, T., Geng, Y., Teja Mittakola, R., Hong, J., Li, Z., Song, X., Huo, D., Deng, Z., Wang, L., Lu, C., and Liu, Z.: Carbon Monitor AutoForecast-Asia: a real-time emission estimates of the residential sector for Asian major emitters with an automatic machine learning framework , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9456, https://doi.org/10.5194/egusphere-egu23-9456, 2023.

Sao Paulo Metropolitan Area, with 39 municipalities and a population of about 22 million inhabitants, aims to reach carbon neutrality by 2050. More than half of its population resides in Sao Paulo city, which, in 2018, was responsible for emitting nearly 18 million tons of CO2 equivalent. Two high-accuracy CO2 sensors have been deployed (Picarro CRDS analyzers)  from the first conventional in situ measurement network installed in South America for GHG monitoring.  The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem V4.0) with a modified version of the greenhouse gas chemistry module (WRF-GHG) was used to simulate the transport of the mole fraction of carbon dioxide (CO2) at a horizontal resolution of 3 km in the São Paulo Metropolitan Area during August 2020. Biogenic CO2 fluxes were simulated using an improved version of the “Vegetation Photosynthesis and Respiration Model” (VPRM) (Mahadevan et al., 2008) included offline in WRF-GHG. The VPRM parameters were optimized using flux tower data (Net Ecosystem Exchange) for the three main vegetation types in the area (Atlantic Forest, Sugarcane, and Cerrado). Anthropogenic CO2 emissions were simulated with the vehicle emission model VEIN (Ibarra et al., 2018) combined with industrial emissions from EDGAR (Crippa et al., 2020) and ODIAC (Oda et al., 2018). The initial and lateral boundary conditions (IC-BCs) were imported from CAMS and from CARBON-TRACKER global reanalysis for greenhouse gases.

The simulated CO2 concentrations from the WRF-GHG model captured both day-to-day and diurnal variations compared to in situ observations in suburban and urban areas (Pico do Jaraguá and IAG). We examined the magnitude of the fossil fuel contribution compared to biogenic signals across the domain, and the anthropogenic signals are heavily influenced by local and nearby sources and sinks. Additionally, the signal includes respiration from vegetation that is carried by winds from vegetation regions.

 We conducted a long-term analysis of CO2 concentration measurements at the two stations (2019-2022) to determine the seasonality and its relationship with both flux variability and local circulation. Finally, we estimated CO2 concentration gradients from three additional measurement stations around the Sao Paulo metropolitan area to assess the potential of our future urban atmospheric inversion system. 

How to cite: Cruz Alves Alberti, R., Segura Barrero, R., Villalba Mendez, G., Fátima Andrade, M., Lauvaux, T., Ribeiro da Rocha, H., Machado Rodrigues Cabral, O., and Ynoue, R.: Atmospheric CO2 monitoring over a large tropical metropolitan area: fossil fuel and biogenic CO2 fluxes over the Sao Paulo Metropolitan Area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-770, https://doi.org/10.5194/egusphere-egu23-770, 2023.

Cities generate the majority of CO2 emissions, the largest climate change contributor. Near real-time CO2 emission monitoring and modeling at relatively high resolutions are beneficial to fill the knowledge gaps for the spatial and temporal emission patterns in different regions, and to provide the public and the policymakers with accurate and timely information on major emission sources and emission amounts for better public awareness and decision making. On the other hand, there are even larger knowledge and information shortage in Global South counties for the city-/community- level CO2 emissions due to the lack of well-monitored official data with high latency and low transparency.

Data-driven models with big data embedded can be one of the most robust and efficient approaches for addressing those challenges in regions without well-documented data. Therefore, we developed the Building Integral Gridded Carbon Emission Disaggregating Model (BIGCarbonEDM) that disaggregates Scope 1&2 CO2 emissions to community-level (with a resolution at 500 meters) using machine learning models trained with building- to regional-level features. Multiple open datasets were used as model inputs, 1) building-level datasets: Microsoft Building Footprints, OpenStreetMap, and OpenStreetMap Building, 2) regional-level datasets: Global Human Settlement, World Settlement Footprint, VIIRS Nightlights, Local Climate Zone, Copernicus Digital Elevation, and land surface temperature, 3) economical and census datasets were collected from the national statistical report and world bank surveys, and 4）city-level near real-time CO2 emission: Carbon Monitor City (https://cities.carbonmonitor.org/), one of our previous projects.

BIGCarbonEDM for the first time proposed the approach for capturing the emission patterns for the community level based on building-level and regional-level features and provides the near real-time CO2 emission for twelve major cities in Egypt, South Africa, and Turkey. The cities are Adana, Trabzon, Ordu, and Manisa for Turkey; Cairo, Alexandria, Luxor, and Sheikh Zayed for Egypt; and Johannesburg, Tshwane, Ekurhuleni, and eThekwini for South Africa. BIGCarbonEDM was designed as a modular platform including modules for data collection, data fusion, spatial and temporal emission feature learning, emission estimation model, and data visualization. BIGCarbonEDM modules can be updated and modified separately, which simplifies the improvements and extensions of the final delivered dataset, also all the individual modules can be used by the research community for other relevant research.

How to cite: Zhou, C., de Castelbajac, M., Zhu, B., Huo, D., Liu, Z., Benoit, A., Deuskar, C., Mesner, C., Akani-Guéry, J., Boucher, M., and Ciais, P.: Building Integral Gridded Carbon Emission Disaggregating Model (BIGCarbonEDM): Near real-time community-level CO2 emission evaluations for twelve cities in Egypt, South Africa, and Turkey, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4138, https://doi.org/10.5194/egusphere-egu23-4138, 2023.

Curbing carbon dioxide (CO₂) emissions from cities is critical for climate change mitigation given the substantial urban contribution to global anthropogenic greenhouse gas (GHG) emissions. Despite relatively robust inventory estimates of total urban CO₂ emissions at regional and global scales, emission inventories at the level of individual cities can be very uncertain due to unavailability of input data and/or uncertainties in downscaling aggregated statistics or emissions. A growing field of research is thus investigating the application of atmospheric measurement and modelling to support CO₂ emissions monitoring in cities. Here we present ongoing research comparing local emission inventories of the city of Vienna, Austria with tall tower eddy covariance measurements of CO₂ fluxes. In contrast to inverse modelling methods, eddy covariance allows net surface emissions to be directly inferred from the  measured vertical turbulent fluxes in the surface layer above cities. For this analysis local emission inventories were processed with external data (e.g., measurements of local traffic counts and air temperature, proxies from literature) to produce temporally-resolved emissions maps (hour-hectare resolution) from the annual, aggregate inventory estimates for the years 2018 to 2020. For the comparison with the flux measurements, these emission maps were cropped after overlapping these layers with an average flux footprint calculated from flux measurements made during northwesterly flows, when the most densely inhabited districts of the city were sampled. On an annual scale, the flux measurements and inventory estimates of total CO₂ emissions agree well with one another. Furthermore, encouraging results were obtained when comparing annual space-heating and traffic emissions from the inventories with respective estimates derived from regression analyses of the eddy fluxes against local air temperature and traffic counts. At sub-annual scales, seasonal and hourly divergences between the inventories and the eddy covariance measurements were indicative of boundary layer dynamics (decoupling between turbulent exchange and fluxes at the surface) as well as a seasonal influence of urban vegetation on net CO₂ fluxes.

How to cite: Fasano, E., Matthews, B., Vuolo, F., and Schume, H.: Cross-verification of local inventory CO2 emissions and tall tower eddy covariance fluxes measurements in Vienna, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7658, https://doi.org/10.5194/egusphere-egu23-7658, 2023.

Cities’ climate action efforts towards carbon neutrality will be challenged in the next decades by more and more people moving into cities and the correspondingly growing demand for services and infrastructure. By 2050, over 70% of the world's population is projected to be living in densely populated urban areas. This will add another level of difficulty to fulfilling the demand for clean energy and heating considering the available technology and infrastructure. It will be important for city stakeholders to understand current and future demands in detail to make informed decisions, implement effective carbon mitigation measures, and achieve a good return on investment. To kick this off, the ICOS-cities project chose three pilot cities (Paris, Munich, and Zurich) to generate high-resolution spatial and temporal bottom-up inventories for CO₂ and co-emitted species. 

Existing municipal emission inventories for Munich report annual emissions estimates without spatial information. We present a temporal (1h) and spatial (100m x 100m) explicit high-resolution bottom-up inventory for public power production consisting of electricity and district heating (GNFR A) and other stationary combustion (GNFR-C) in Munich. Both sectors are derived from power and heating plant data of the year 2019 provided by the Stadtwerke München (SWM) and the latest municipal geospatial datasets provided by the City of Munich. Furthermore, we compare state-of-the-art but more generic TNO activity and temporal profiles with temporal profiles derived from data from local CHP plants data and a heat demand function validated with Munich’s reported yearly heat demand.  Additionally, we present emission factors calculated from the fuel composition (2019) of inflowing gas and burned waste alongside available state-of-the-art emissions factors from IPCC (2019), EPA (2022), and UBA (2022). 

How to cite: Aigner, P., Suhendra, M., Yirtar, B., Kühbacher, D., Super, I., Droste, A., Denier van der Gon, H., Brunner, D., Kohlmeier, H., Althammer, T., and Chen, J.: CO2 bottom-up emission inventory based on municipal power generation and heating data in Munich, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13451, https://doi.org/10.5194/egusphere-egu23-13451, 2023.

The Mexico City Metropolitan Area (MCMA) has become the most populous urban region in North America and in the top five megacities worldwide (around 22 million inhabitants). To quantify the urban CO2 emissions of MCMA, a dense network of  Fourier Transform Infrared (FTIR) spectrometers with 6 portable EM27/SUN and 1 IFS 125HR were deployed within and around the MCMA  tracking gradients in atmospheric column CO2 concentrations (XCO2) from October 2020 to May 2021 (part of the French-Mexican MERCI-CO2 project). During these 7 months, twenty  XCO2 images (Snapshot Area Mode)  were collected by the NASA Orbiting Carbon Observatory (OCO-3) mission over the MCMA. By comparing  ground- and space-based observations, we found a positive XCO2 difference with a mean (± one standard uncertainty) of 1.16 (± 1.14) ppm between OCO-3 and FTIR column measurements, most probably caused by their respective calibration procedures. XCO2 gradients observed between the urban plume and its surroundings  show a good agreement between OCO-3 and the FTIR stations with a correlation coefficient (R) of 0.92,  decreasing significantly when comparing intra-city gradients (R=0.24). 

In a second phase, we assimilated these two types of dense column-integrated observations (FTIR network and OCO-3 SAM observations) separately to optimize the anthropogenic emissions from the MCMA and the biogenic CO2 fluxes in and around the city limits, in addition to the CO2 background concentrations. The X-Stochastic Time-Inverted Lagrangian Transport (X-STILT) model driven by the Weather Research and Forecasting (WRF) at 1-km resolution was used here to relate our atmospheric observations to surface fluxes and background conditions. An analytical Bayesian inversion technique was used here to robustly update the prior estimates at 1-km and 1-hour resolution. High-resolution prior biogenic CO2 emissions were computed with the light-use efficiency model CASA and our prior backgrounds from the global CAMS CO2 atmospheric inversion (version: v21r2). Prior anthropogenic CO2 emissions at 1 km are coming from a Mexico-specific inventory UNAM and from the global inventory ODIAC. These two inventories contain large discrepancies over MCMA (~40 %), while UNAM provides more detailed  information of point sources across our inversion domain. We performed sensitivity control experiments to quantify the effects from different prior fluxes and different covariance parameters. Furthermore, we combine OCO-3 and FTIR together in the same inversion process, a promising step toward providing a verification to  combine multiple data streams over a specific city. 

How to cite: Che, K., Lauvaux, T., Taquet, N., Xu, Y., Lopez, M., Stremme, W., García-Reynoso, A., Ciais, P., Liu, Y., Ramonet, M., and Grutter, M.: CO2 emissions estimate from Mexico City using ground- and space-based remote sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16636, https://doi.org/10.5194/egusphere-egu23-16636, 2023.

The Hestia Project estimates fossil fuel carbon dioxide (FFCO2) emissions to the scale of buildings, factories, and road segments for whole cities every hour. Recent updates to the Vulcan Project (version 4) are now realized in 3 large urban domains in the US: the LA Megacity, the NE corridor (Washington DC to Baltimore and beyond) and the city of Indianapolis. Here, we present the latest developments in the estimation from these 3 urban areas and apply a series of hypothetical mitigation efforts, demonstrating the enabling features of high space/time resolution urban FFCO2 emissions estimation. Furthermore, we analyze the spatial patterns of emissions and place them in the context of indicators of social and environmental justice.

How to cite: Gurney, K., Dass, P., Kato, A., Mitra, B., and Roest, G.: Building and road segment scale fossil fuel CO2 emissions estimation across multiple US cities spanning the 2010-2021 time period, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11169, https://doi.org/10.5194/egusphere-egu23-11169, 2023.

To fill gaps in knowledge and refine global understanding of the location and magnitude of methane emissions across sectors, integration and use of data from a range of sources is needed. As countries and industry establish ambitious mitigation targets, accurate and measurement-based emission estimates are critical to accelerate emission reductions and assess progress by tracking changes in emissions over time.

The International Methane Emissions Observatory (IMEO) was launched in 2021 at the G20 summit and is hosted by the United Nations Environment Program (UNEP). IMEO is providing reliable, public, policy-relevant data to facilitate actions to reduce methane emissions.

IMEO is collecting and integrating diverse methane emissions data streams, including satellite remote sensing data, science studies, inventories, and measurement-based industry reporting to establish a global, centralized public record of empirically verified methane emissions.

In this work, we will summarize (i) IMEO’s current efforts to integrate spatio-temporally dynamic methane emissions data (ii) details of the Methane Alert and Response System (MARS) -a recently-launched system for satellite-based detection, attribution, and monitoring of methane sources to notify emitters and assist mitigation efforts, (iii) insights from measurement campaigns across the world, and (iv) activities for the direct engagement with relevant stakeholders to accelerate mitigation of methane emissions.

How to cite: Zavala-Araiza, D., Schwietzke, S., Calcan, A., Irakulis Loitxate, I., Randles, C., Demeter, M., Guanter, L., Caltagirone, M., and Hamburg, S. P.: The International Methane Emissions Observatory (IMEO): Bringing together policy-relevant methane emissions data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12722, https://doi.org/10.5194/egusphere-egu23-12722, 2023.

Success in achieving the methane emission reduction targets of industry, national and subnational governments, the Paris agreement, and Global Methane Pledge will critically depend on access to emissions data that is actionable, complete and trustworthy. Despite recent improvements, current methane monitoring systems are still limited in their ability to offer timely delivery of precise, quantitative, and reproduceable emissions data to facility operators, regulators, front-line communities and other stakeholders. There are multiple use-cases for accelerating data-enabled methane mitigation including but not limited to leak detection and repair programs, diagnostics to guide process emission reductions and infrastructure model improvements, emissions trending, and supply-chain methane intensity certification. Each use-case translates to specific requirements on methane data products and the observational and analytic frameworks that deliver them. Meanwhile, a nascent global system of systems for operational methane emissions monitoring is emerging that offers the potential to synergistically combine observations from multiple sensor types, vantage points, and quantification methods.  Additionally, new programs offering improved data access, transparency, independent validation, 3^rd party reporting, and user capacity-building are also gaining traction.

Remote sensing offers unique contributions to the expanding methane tiered observing system. Satellites in particular can complement continuous data from surface sensors at selected facilities and periodic regional airborne surveys by providing dense and sustained sampling of diverse jurisdictions globally without being constrained by access restrictions. We discuss use-cases for regional- to facility-scale monitoring and challenges in scaling up methane remote sensing systems for sustained operational decision support. We also present pilot studies involving collaborative data sharing between researchers, regulators and facility operators that resulted in measurable and verifiable emission reductions.

How to cite: Duren, R., Cusworth, D., Reuland, F., Thorpe, A., Ayasse, A., and Gordon, D.: The role(s) of remote sensing in reducing methane emissions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10863, https://doi.org/10.5194/egusphere-egu23-10863, 2023.

Methane is a major greenhouse gas (GHG) that has contributed to one third of the warming induced by all GHGs since the preindustrial era (IPCC, 2021).  Reliable quantification of methane emissions is critical for tracking progress towards methane mitigation, especially for the countries that join the Global Methane Pledge and that have firm plans for reducing methane emissions over the next decades, such as the US.  In this study, we quantified US methane emissions using inverse modeling of ground- and airborne- methane measurements made from the NOAA Greenhouse Gas Reference Network for 2007 – 2021. We conducted 12 inversion runs using two high-resolution transport models (HYSPLIT-NAMs and WRF-STILT), three background estimates, and two prior emission fields.  Multiple estimates of wetland emissions were considered and subtracted from our inversion-derived total emission estimates.  Our derived anthropogenic emissions show a consistent increasing trend after 2015 across our inversion ensemble members and a seasonal cycle in emission magnitude that repeats every year during our study period.  Both the trend and seasonal variation seem to correlate with US natural gas consumption.  A similar seasonal variability has been reported previously, but only on an urban scale; this is the first time it has been derived on a national scale.  Furthermore, we also compared our estimates with US EPA’s national GHG inventory reporting and investigated the impact of the COVID-19 pandemic on emissions in 2020 relative to 2019 and 2021.    

How to cite: Hu, L., Andrews, A., Montzka, S., Dlugokencky, E., Miller, S., Ibarra-Espinosa, S., Sweeney, C., Bruwhiler, L., Miles, N., and Davis, K.: Trend and seasonal cycle of US methane emissions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10367, https://doi.org/10.5194/egusphere-egu23-10367, 2023.

China is set to actively reduce its methane emissions in the coming decade. A comprehensive evaluation of the current situation can provide a reference point for tracking the country’s future progress. Here, using satellite and surface observations, we quantify China’s methane emissions during 2010–2017. Including newly available data from a surface network across China greatly improves our ability to constrain emissions at subnational and sectoral levels. Our results show that recent changes in China’s methane emissions are linked to energy, agricultural, and environmental policies. We find contrasting methane emission trends in different regions attributed to coal mining, reflecting region-dependent responses to China’s energy policy of closing small coal mines (decreases in Southwest) and consolidating large coal mines (increases in North). Coordinated production of coalbed methane and coal in southern Shanxi effectively decreases methane emissions, despite increased coal production there. We also detect unexpected increases from rice cultivation over East and Central China, which is contributed by enhanced rates of crop-residue application, a factor not accounted for in current inventories. Our work identifies policy drivers of recent changes in China’s methane emissions, providing input to formulating methane policy toward its climate goal.

How to cite: Zhang, Y., Fang, S., Chen, J., Lin, Y., Chen, Y., Liang, R., Jiang, K., Parker, R. J., Boesch, H., Steinbacher, M., Sheng, J.-X., Lu, X., Song, S., and Peng, S.: Observed changes in China’s methane emissions linked to policy drivers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10659, https://doi.org/10.5194/egusphere-egu23-10659, 2023.

How to cite: Li, N., Moore, D., Yi, H., Tao, L., McSpiritt, J., Wendt, L., Sevostianov, V., Robles, N. R., Hopkins, F., and Zondlo, M.: Dairy sector greenhouse gas and ammonia emissions estimates based on seasonal measurements from 200 farms in California, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17282, https://doi.org/10.5194/egusphere-egu23-17282, 2023.

Space-based observations of carbon dioxide (CO₂) are the backbone of the global and national-scale carbon monitoring systems that are currently being developed to support and verify greenhouse gas emission reduction measures. Current and planned public satellite missions, such as GOSAT 1+2, OCO 1-3 and the European Union's Anthropogenic Carbon Dioxide Monitoring mission CO2M, aim at constraining national and regional-scale emissions down to scales of urban agglomerations and large point sources with emissions in excess of ~10 MtCO₂/year.

We report on the DLR demonstrator mission CO2Image, which is planned for launch in 2026. The mission will complement the suite of planned CO₂ sensors by zooming in on facility-scale emissions, detecting and quantifying emissions from point sources as small as 1 MtCO2/year. A fleet of CO2Image sensors would be able to monitor roughly 90% of the CO₂ emissions from coal-fired power plants worldwide. The key feature of the mission is a target region approach, measuring approximately 75 tiles of size ~50 x 50 km² per day at a resolution of 50 x 50 m². Thus, CO2Image will be able to resolve plumes from individual localized sources, essentially providing super-resolution nests for survey missions such as CO2M. In addition, the choice of the spectral window will allow the detection of point sources of methane as small as 100 kg CH₄/hr will also be possible.

We present the instrument concept, a spaceborne push-broom imaging grating spectrometer developed and built by DLR. It will measure spectra of reflected solar radiation in the short wave infrared spectral band around 2000 nm. It relies on a comparatively compact design with a single spectral window and a spectral resolution of approximately ~1 nm. This spectral resolution has been optimized for greenhouse gas retrieval and should provide improved precision and accuracy compared to hyperspectral sensors with comparable spatial resolution. We will further discuss the overall mission concept in terms of the sampling strategy, outlining how target scenes will be selected. As a publicly-funded mission, CO2Image will provide public, transparent information about anthropogenic greenhouse gas emissions from space.

How to cite: Feist, D. G., Roiger, A., Marshall, J., Gottschaldt, K.-D., Reum, F., Lichtenberg, G., Baumgartner, A., Hochstaffl, P., Köhler, C., Schreier, F., Krutz, D., Paproth, C., Pohl, A., Sebastian, I., Walter, I., and Butz, A.: Quantifying localized carbon dioxide emissions from space: the CO2Image mission, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16064, https://doi.org/10.5194/egusphere-egu23-16064, 2023.

Within the Integrated Greenhouse Gas Monitoring System for Germany (ITMS), a national initiative targeted at the provision of estimates of GHG fluxes for Germany, the CarboScope-Regional (CSR) inversion system operated at the MPI-BGC is envisioned as a back-bone and reference system for future developments using the ICON-ART modelling system at the German Weather Service (DWD). It utilizes ICOS atmospheric observations

Additional focus of the research involving CSR is the evaluation and improvement of vertical transport in atmospheric models, as well as the inclusion of additional data streams (e.g. vertical profile information) to improve both, GHG flux estimates and their uncertainty characterization. In this context, the assessment of mixing heights as represented within CSR against independent information derived from radiosondes over a timespan of more than a decade has revealed problems vertical mixing during wintertime that have the potential to cause biased flux retrievals. First results related to this and other experiments will be presented.

How to cite: Gerbig, C., Koch, F.-T., Munassar, S., Custodio, D., Galkowski, M., and Rödenbeck, C.: Inclusion of additional data streams within atmospheric inverse modelling systems: first results from ITMS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7757, https://doi.org/10.5194/egusphere-egu23-7757, 2023.

India needs a high-resolution estimation of carbon sources and sinks to implement the country’s climate change action plans and mitigation strategy effectively. Current carbon estimates over the Indian region based on the “Bottom-up” approach suffer from significant uncertainty, which calls for more process-based models and atmospheric inverse modelling to obtain a more accurate budget. Inverse models constrain the carbon fluxes based on atmospheric observation of CO₂ mole fractions. The unavailability of amble observations over the Indian domain critically impacts estimation accuracy. Fortunately, there are increasing efforts to improve the availability of CO₂ observation over the domain. Along with the observations, the availability of a suitable transport model to simulate the CO₂ distribution is essential to the accurate inverse estimation of carbon fluxes. The inability of coarse-resolution global models to simulate the fine-scale variability in CO₂ distribution warrants developing a regional high-resolution modelling system. Here we evaluate the performance of a regional high-resolution modelling system which utilises meteorological fields from the Weather Research and Forecasting (WRF) model to simulate the CO₂ transport over the Indian domain using a lagrangian particle dispersion model, Stochastic Time-Inverted Lagrangian Transport Model (STILT). Using lagrangian models enables us to study the CO₂ distribution at very high resolution (even at sub-grid scale) with reduced cost. We use the vegetation photosynthesis and Respiration Model (VPRM), coupled with the modelling system, to simulate the biospheric fluxes. The anthropogenic and biomass burning fluxes are obtained from different available inventories. We use CO₂ in-situ observations from different parts of the Indian domain, which utilises flask measurements and PICARRO CRDS instruments, to evaluate the modelling system. Our high-resolution modelling framework shows good skill in simulating the CO₂ variability over the region. The results of the evaluation will be discussed in detail during the presentation.

How to cite: Thilakan, V., Pillai, D., and S Kumar, J.: Evaluation of the performance of regional transport models in simulating CO2 variability over India employing WRF-STILT modelling framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10975, https://doi.org/10.5194/egusphere-egu23-10975, 2023.

Perturbations to the natural carbon cycle due to anthropogenic and induced natural emissions of carbon dioxide (CO₂) into the atmosphere are strongly linked with the current trend of global climate change. Efforts aiming at measuring amounts of CO₂ emitted by different ecosystems and industrial activities have been increasing in the past years, in order to gather information about necessary mitigations efforts towards reduced future emissions.

Radiocarbon (¹⁴C) measurements of atmospheric CO₂ are unique in their capabilities to provide information on carbon sources and transport. The radiocarbon method allows an apportionment between "modern" sources, with ¹⁴CO₂ signatures close to the global atmospheric average and fossil-fuel derived sources which are ¹⁴C-depleted. The capability to determine the fraction of fossil CO₂ in atmospheric samples provides insight on the contribution of different emissions to the current rise in atmospheric CO₂ concentration. When associated with meteorological data and atmospheric dispersion models, radiocarbon data can be used to identify fossil-fuel emissions patterns from a local to a regional scale, across time.

The Radiocarbon Inventories of Switzerland (RICH) project aims to build the first database and model of the distribution and cycling of ¹⁴C at a national scale and across the atmosphere, soils, rivers and lakes of the country. The project presented here (RICH-Air) will serve to construct complementary monitoring and snapshots approaches of atmospheric ¹⁴CO₂ measurement in this larger scope. For the monitoring aspect, air masses passing over Switzerland are collected and measured every two weeks at three tall tower sites situated over the populated Swiss plateau and one background site (Jungfraujoch). As for the snapshots, we focus on three industrial point sources (two cement plants, and a combined refinery-cement site) and use an Upwind-Downwind approach to have emissions and background samples at each site. As a complementary method, tree leaf samples will also be collected close to sites of interest, to have more temporally-integrated data.

First results show that the seasonality has a huge influence on the monitoring of the ¹⁴CO₂ signature, with a decrease in the winter months, due to limited atmospheric mixing, and accumulation of ground emissions. For the industrial hotspots, plume catching was shown to be challenging, even though an increased signal of a few ppm was generally visible.

How to cite: Geissbühler, D., Laemmel, T., Gautschi, P., Wacker, L., and Szidat, S.: Following the temporal and spatial variability of atmospheric 14CO2 across Switzerland to estimate source contributions to the national CO2 emissions., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6154, https://doi.org/10.5194/egusphere-egu23-6154, 2023.