AS3.22 | Science-based, measurement-based greenhouse gas monitoring and emission estimates in support of national, sub-national and industrial climate change mitigation
EDI
Science-based, measurement-based greenhouse gas monitoring and emission estimates in support of national, sub-national and industrial climate change mitigation
Convener: Phil DeCola | Co-conveners: Tomohiro Oda, Beata BukosaECSECS, Werner Leo Kutsch, Oksana Tarasova
Orals
| Wed, 26 Apr, 08:30–10:10 (CEST), 10:45–12:25 (CEST), 14:00–15:40 (CEST)
 
Room 0.11/12
Posters on site
| Attendance Tue, 25 Apr, 16:15–18:00 (CEST)
 
Hall X5
Posters virtual
| Attendance Tue, 25 Apr, 16:15–18:00 (CEST)
 
vHall AS
Orals |
Wed, 08:30
Tue, 16:15
Tue, 16:15
Accurate and precise, long-term atmospheric measurements of greenhouse gas (GHG) concentrations reveal the rapid and unceasing rise of global GHG concentrations due to human activity. The resulting increases in global temperatures, sea-level, glacial retreat, and other negative impacts are clear. In response to this evidence, nations, states, and cities, industries and individuals have been accelerating GHG emission reduction and other mitigation efforts while working towards equitable development and environmental justice. The urgency, complexity, and economic implications of the needed GHG emission reductions and other climate action demand strategic investment in science-based information for planning and tracking emission reduction policies and actions. An example of an intergovernmental response can be seen in the World Meteorological Organization’s (WMO) Integrated Global Greenhouse Gas Information System (IG3IS). The IG3IS initiative seeks to enhance the capacity of nations, states, cities, and industries to target emissions reduction opportunities and track progress towards their goals. Success depends on infrastructure investments and the availability of measurements of atmospheric composition and emission activity data in key GHG emission source regions and relies on a multi-tiered observing strategy involving satellite, aircraft, and surface-based measurements, as well as innovative data mining and analysis methods. Such science-based services also depend on successful analysis methods and mature use-cases for which the scientific and technical skill is proven or emerging.

This session intends to gather presentations from researchers, inventory compilers, information service providers, as well as decision-maker and policy user-community. The session seeks presentations of work focused on the development, implementation, use and impact of measurement-based “top-down,” activity data-driven “bottom-up,” and the hybrid combinations of both approaches for GHG monitoring and improved emission inventory estimates that deliver actionable GHG information from granular space and time scales of explicit emission activity where climate action is achievable up to the global scale in support of Paris agreement stocktake.

Orals: Wed, 26 Apr | Room 0.11/12

Chairpersons: Beata Bukosa, Phil DeCola, Werner Leo Kutsch
08:30–08:40
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EGU23-3626
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AS3.22
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Highlight
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On-site presentation
Ken Jucks

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.

08:40–08:50
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EGU23-10714
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AS3.22
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Highlight
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Virtual presentation
Stephen Montzka, Lei Hu, Phil DeCola, Dave Godwin, Isaac Vimont, Bart Croes, Toshi Kuwayama, Geoff Dutton, David Nance, Brad Hall, Colm Sweeney, and Arlyn Andrews

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.

08:50–09:00
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EGU23-16832
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AS3.22
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ECS
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Virtual presentation
Brendan Byrne and Kevin Bowman and the other authors

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 (XCO2) 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.

09:00–09:20
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EGU23-17172
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AS3.22
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solicited
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On-site presentation
John Worden, Sudhanshu Pandey, Yuzhong Zhang, Daniel Cusworth, Qu Zhen, Anthony Bloom, Shuang Ma, Bram Maasakkers, Brendan Byrne, Riley Duren, David Crisp, Debbie Gordon, and Daniel Jacob

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.

09:20–09:30
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EGU23-1647
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AS3.22
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ECS
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On-site presentation
Ruochong Xu, Qiang Zhang, Dan Tong, Qingyang Xiao, Xinying Qin, Cuihong Chen, Liu Yan, Jing Cheng, Can Cui, Hanwen Hu, Wenyu Liu, Xizhe Yan, Huaxuan Wang, Xiaodong Liu, Guannan Geng, Dabo Guan, and Kebin He

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.

09:30–09:40
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EGU23-4558
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AS3.22
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ECS
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Highlight
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On-site presentation
Piyu Ke, Zhu Deng, Biqing Zhu, Bo Zheng, Yilong Wang, Olivier Boucher, Simon Ben Arous, Chuanlong Zhou, Xinyu Dou, Taochun Sun, Zhao Li, Feifan Yan, Duo Cui, Yifan Hu, Da Huo, Jean Pierre, Richard Engelen, Steven J. Davis, Philippe Ciais, and Zhu Liu

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.

09:40–09:50
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EGU23-12172
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AS3.22
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Virtual presentation
Jgor Arduini, Saurabh Annadate, Rita Cesari, Paolo Cristofanelli, Serena Falasca, Umberto Giostra, and Michela Maione

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.

09:50–10:00
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EGU23-7010
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AS3.22
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ECS
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Highlight
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On-site presentation
Minde An, Ronald Prinn, Luke Western, Bo Yao, Jianxin Hu, and Matthew Rigby

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.

10:00–10:10
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EGU23-10805
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AS3.22
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Highlight
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On-site presentation
Lars-Peter Riishojgaard

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.

Coffee break
Chairpersons: Phil DeCola, Werner Leo Kutsch, Tomohiro Oda
10:45–11:05
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EGU23-13336
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AS3.22
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solicited
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Virtual presentation
Ana Maria Roxana Petrescu, Glen P. Peters, Robbie M. Andrew, Matthew J. McGrath, Philippe Peylin, Frederic Chevallier, and Richard Engelen and the VERIFY and CoCO2 project participants

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.

11:05–11:15
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EGU23-301
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AS3.22
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ECS
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On-site presentation
Ivonne Albarus, Giorgia Fleischmann, Patrick Aigner, Philippe Ciais, Hugo Denier van der Gon, Rianne Droge, Jinghui Lian, Miguel Andrey Narvaez Rincon, Hervé Utard, and Thomas Lauvaux

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.

11:15–11:25
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EGU23-9456
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AS3.22
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ECS
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Virtual presentation
Taochun Sun, Yuanhao Geng, Rohith Teja Mittakola, Jinpyo Hong, Zhongyan Li, Xuanren Song, Da Huo, Zhu Deng, Lixing Wang, Chenxi Lu, and Zhu Liu

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.

11:25–11:35
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EGU23-770
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AS3.22
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ECS
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On-site presentation
Rafaela Cruz Alves Alberti, Ricard Segura Barrero, Gara Villalba Mendez, Maria Fátima Andrade, Thomas Lauvaux, Humberto Ribeiro da Rocha, Osvaldo Machado Rodrigues Cabral, and Rita Ynoue

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.

11:35–11:45
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EGU23-4138
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AS3.22
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ECS
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On-site presentation
Chuanlong Zhou, Mathieu de Castelbajac, Biqing Zhu, Da Huo, Zhu Liu, Antoine Benoit, Chandan Deuskar, Craig Mesner, Julian Akani-Guéry, Margaux Boucher, and Philippe Ciais

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.

11:45–11:55
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EGU23-7658
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AS3.22
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ECS
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On-site presentation
Enrichetta Fasano, Bradley Matthews, Francesco Vuolo, and Helmut Schume

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.

11:55–12:05
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EGU23-13451
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AS3.22
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ECS
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On-site presentation
Patrick Aigner, Michael Suhendra, Beyza Yirtar, Daniel Kühbacher, Ingrid Super, Arjan Droste, Hugo Denier van der Gon, Dominik Brunner, Helmut Kohlmeier, Thomas Althammer, and Jia Chen

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.

12:05–12:15
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EGU23-16636
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AS3.22
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On-site presentation
Ke Che, thomas Lauvaux, noemie Taquet, Yang Xu, Morgan Lopez, Wolfgang Stremme, Agustín García-Reynoso, Phillipe Ciais, Yi Liu, Michel Ramonet, and Michel Grutter

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.

12:15–12:25
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EGU23-11169
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AS3.22
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On-site presentation
Kevin Gurney, Pawlok Dass, Anna Kato, Bhaskar Mitra, and Geoffrey Roest

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.

Lunch break
Chairpersons: Phil DeCola, Werner Leo Kutsch, Oksana Tarasova
14:00–14:10
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EGU23-12722
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AS3.22
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Highlight
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On-site presentation
Daniel Zavala-Araiza, Stefan Schwietzke, Andreea Calcan, Itziar Irakulis Loitxate, Cynthia Randles, Meghan Demeter, Luis Guanter, Manfredi Caltagirone, and Steven P. Hamburg

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.

14:10–14:30
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EGU23-10863
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AS3.22
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solicited
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Highlight
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On-site presentation
Riley Duren, Daniel Cusworth, Frances Reuland, Andrew Thorpe, Alana Ayasse, and Deborah Gordon

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, 3rd 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.

14:30–14:40
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EGU23-10367
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AS3.22
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Highlight
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On-site presentation
Lei Hu, Arlyn Andrews, Stephen Montzka, Ed Dlugokencky, Scot Miller, Sergio Ibarra-Espinosa, Colm Sweeney, Lori Bruwhiler, Natasha Miles, and Kenneth Davis

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.

14:40–14:50
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EGU23-10659
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AS3.22
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Highlight
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On-site presentation
Yuzhong Zhang, Shuangxi Fang, Jianmeng Chen, Yi Lin, Yuanyuan Chen, Ruosi Liang, Ke Jiang, Robert J. Parker, Hartmut Boesch, Martin Steinbacher, Jian-Xiong Sheng, Xiao Lu, Shaojie Song, and Shushi Peng

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.

14:50–15:00
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EGU23-17282
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AS3.22
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ECS
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On-site presentation
Nathan Li, Daniel Moore, Hongming Yi, Lei Tao, James McSpiritt, Lars Wendt, Vladislav Sevostianov, Nidia Rojas Robles, Francesca Hopkins, and Mark Zondlo

We report facility-scale emissions measurements of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ammonia (NH3) from over 200 farms in California, representing 2.4% of the total dairy cow population of the United States. The dairy industry—and its 270 million cattle—are responsible for a significant proportion of global greenhouse gas and reactive nitrogen emissions. Open-path laser spectrometers mounted on top of an electric vehicle were used to conduct measurements downwind of facilities. Emission rates and associated uncertainties for each facility were quantified using in-plume observations and an inverse Gaussian plume atmospheric dispersion model with Bayesian inference. A subset of 53 farms were revisited quarterly to investigate seasonal variability and changes in management practices. 24 farms were sampled multiple times a week on different days for select seasons in order to capture inter-day variability. We partnered with 6 farms for intensive 24-hour measurements to study diurnal variability in emissions. 

Measurements of emissions were compared with national and state emissions inventories, and they were also evaluated against bottom-up estimates using activity data from the Vista-CA database. The median of the top-down CH4 emissions agreed with the median of facility-level bottom-up emissions to within 8%. Measured N2O emissions were 2.5 times higher than current inventories. N2O was responsible for about 15% of total greenhouse gas emissions in terms of CO2-equivalents (CO2e). NH3 was 40% lower than the values indicated by the 2017 US EPA national emissions inventory (NEI). 

For N2O, we found that emissions were dominated by large emission events (≈30 kg N2O hr-1) with high spatial and temporal variability. These events are potentially not captured by shorter measurement campaigns that focus only on a few farms. The top 10% largest N2O emission events were responsible for 58% of total N2O emissions. Focused studies on such events may elucidate potential opportunities for reduction of N2O emissions. 

For CH4, 14 out of the 207 dairies that we sampled had verified anaerobic digestion systems installed for manure management. The effectiveness of these systems for reducing methane emissions will be evaluated by comparing emissions from farms with digesters to emissions from farms without digesters.

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.

15:00–15:10
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EGU23-16064
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AS3.22
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On-site presentation
Dietrich G. Feist, Anke Roiger, Julia Marshall, Klaus-Dirk Gottschaldt, Friedemann Reum, Günter Lichtenberg, Andreas Baumgartner, Philipp Hochstaffl, Claas Köhler, Franz Schreier, David Krutz, Carsten Paproth, Andreas Pohl, Ilse Sebastian, Ingo Walter, and André Butz

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.

15:10–15:20
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EGU23-7757
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AS3.22
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On-site presentation
Christoph Gerbig, Frank-Thomas Koch, Saqr Munassar, Danilo Custodio, Michal Galkowski, and Christian Rödenbeck

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.

15:20–15:30
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EGU23-10975
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AS3.22
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On-site presentation
Vishnu Thilakan, Dhanyalekshmi Pillai, and Jithin S Kumar

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.

15:30–15:40
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EGU23-6154
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AS3.22
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ECS
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Highlight
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On-site presentation
Dylan Geissbühler, Thomas Laemmel, Philip Gautschi, Lukas Wacker, and Sönke Szidat

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.

Posters on site: Tue, 25 Apr, 16:15–18:00 | Hall X5

Chairpersons: Phil DeCola, Werner Leo Kutsch, Oksana Tarasova
X5.110
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EGU23-3759
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AS3.22
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Highlight
Sylvia Walter, Anita Ganesan, Thomas Röckmann, and Aoife Grant and the PARIS team

PARIS is a 4-year Horizon Europe research project that aims to significantly increase our knowledge about the emissions of climate forcers from 8 European countries. PARIS focuses on the interface between bottom-up and top-down approaches and aims to strengthen the collaboration between scientists and national inventory teams for the evaluation and development of national inventories.

Seventeen European partners will work together to quantify emissions of carbon dioxide, methane, nitrous oxide, fluorinated gases (F-gases), and black carbon.  To engage inventory teams early in the project, we focus early on emission estimates for fluorinated gases (F-gases), which have relatively simple source distributions, but poorly understood magnitudes. For greenhouse gases with a more complex mixture of sources, methane and carbon dioxide, research in PARIS focuses on the attribution of fluxes to particular sources and sinks. We will advance isotopologue measurements and multi-tracer analysis methods for source characterization, providing inventory teams with new information to target areas of uncertainty. For nitrous oxide, a greenhouse gas for which most European inventories rely on highly simplified and uncertain bottom-up methods, two process-level models will be advanced to produce time- and space-resolved estimates that will be evaluated against isotopic data. For the important, but complex, climate forcers, organic matter aerosol and black carbon, we will take the next steps required towards robust top-down emissions inference by developing source apportionment methods. To generate maximum impact, we will synthesise our efforts in the form of draft annual Annexes to National Inventory Reports (NIRs) for eight European PARIS focus countries.

The overall objectives of PARIS are:

1) Quantify top-down emissions from a selection of European countries of all the major GHGs reported under the UNFCCC (CO2, CH4, N2O and F-gases), and black carbon aerosol (BC) reported under CLRTAP.

2) Quantify the contribution of major source sectors of GHG and BC emissions and organic matter aerosol (OM) abundance through the implementation of innovative measurement and analysis technologies.

3) Derive time- and space-resolved flux estimates for GHGs with complex or uncertain source distributions (N2O, F-gases)

4) Produce draft Annexes to the annual National Inventory Reports (NIRs) for a selection of ‘focus’ countries.

This presentation will give a general overview of the PARIS project, its objectives and implementation. It aims to introduce the project to the scientific community, and to set up a network for future collaborations with related projects, e.g.EYE-CLIMA (https://cordis.europa.eu/project/id/101081395) or AVENGERS (https://cordis.europa.eu/project/id/101081322).

How to cite: Walter, S., Ganesan, A., Röckmann, T., and Grant, A. and the PARIS team: PARIS - Process Attribution of Regional emISsions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3759, https://doi.org/10.5194/egusphere-egu23-3759, 2023.

X5.111
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EGU23-6344
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AS3.22
Mattia Righi, Angelika Schulz, Johannes Hendricks, Simone Ehrenberger, Rainer Schmid, Daniel Krajzewicz, André Twele, Volker Matthias, and Markus Quante and the ELK Consortium

Transport and the related economic sectors contribute significantly to climate change. Emissions from these sectors are continuously growing, challenging the achievement of the Paris agreement. These sectors also have detrimental impacts on air quality and noise pollution. None of the currently available emission inventories can consistently account for the emissions (gas, particles and noise) of all transport modes (land transport, aviation, and shipping), while also considering the emissions from transport-related energy production, industrial processes and infrastructures. Moreover, most of the available inventories do not include information at subsector level, for example, they neither distinguish among different vehicle types nor consider key transport-related quantities other than emissions. However, emission inventories at such a level of detail are essential for a reliable quantification of the impact of transport at global, regional and national scales. In addition, consistent emission inventories are required for the development of future emission scenarios to assess the effectiveness of climate policies and other measures to improve air quality and to reduce noise pollution. To master this challenging task, the new strategic impulse project ELK – EmissionsLandKarte (en.: emission map) combines the interdisciplinary expertise of the German Aerospace Center (DLR) through a collaboration of 24 institutes and the Helmholtz-Zentrum Hereon. ELK aims to establish a primary source of information for different stakeholders and working groups, such as national policy makers, the scientific community dealing with climate, air quality and noise modelling, and the Intergovernmental Panel for Climate Change (IPCC). A collaborative and interdisciplinary approach is necessary in order to cover the wide spectrum of topics inherent to the compilation of emission inventories. This includes, for instance, transport demand, emission indices, and the development of models, together with dedicated methods for data visualization and management. The diversity of data sources and the heterogeneity of data formats from different sectors and data providers require the combination of different modelling tools and approaches, along with the use of measurements and satellite data. Available DLR data and data from external providers will be processed and used as input to quantify spatially and temporally resolved transport volumes and emission distributions. Based on already available inventories, precise criteria for the design of the ELK emission inventories will be defined and their quality and usability in real-world applications will be assessed. Finally, a database structure and user interface will be established to guarantee an easy and reliable access to the final products for both internal and external users. The ELK project links the aeronautics, space, transport and energy research programs of the DLR.

How to cite: Righi, M., Schulz, A., Hendricks, J., Ehrenberger, S., Schmid, R., Krajzewicz, D., Twele, A., Matthias, V., and Quante, M. and the ELK Consortium: The DLR project ELK: Mapping the global, regional and national emissions of transport, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6344, https://doi.org/10.5194/egusphere-egu23-6344, 2023.

X5.112
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EGU23-11521
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AS3.22
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Highlight
Wilfried Winiwarter, Glen Peters, and Rona Thompson and the EYE-CLIMA consortium

Emissions of greenhouse gases (GHG) reported by countries in their GHG inventories represent the central information used in international policies based on the Paris agreement and in the Global Stocktake process to help curb global GHG emissions. To maximize trust in these national emission inventories reported to the UNFCCC, procedures for quality control, quality assurance, and verification have been described in the IPCC 2006 national GHG inventory guidelines and extended further in its 2019 refinement. Quantifying emission fluxes via atmospheric measurements and inverse modelling provides an independent assessment of the inventories and can help determine the quality of national inventories and make improvements. While tested in scientific studies, routine applications of inverse modelling in national inventory reports are rare. As a new Horizon Europe project, EYE-CLIMA will perform inverse modelling of a range of important radiative forcers (methane, nitrous oxide, selected fluorinated gases, black carbon) as a monitoring tool on a national scale for selected European countries, together with national inventory agencies, to help develop complementary methods to ensure the robustness and lead to improvements in inventories. The presentation will lay out the overall project concept, including stakeholder involvement, and provide an overview on past experiences with inverse modelling approaches and strategies to implement them in a way useful for national inventory agencies.

How to cite: Winiwarter, W., Peters, G., and Thompson, R. and the EYE-CLIMA consortium: EYE-CLIMA: developing inverse modelling approaches for monitoring national GHG inventories, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11521, https://doi.org/10.5194/egusphere-egu23-11521, 2023.

X5.113
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EGU23-10862
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AS3.22
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Highlight
kevin gurney, Don Wuebbles, Kamal Bawa, gabrielle Dreyfus, Annmarie Eldering, fiji george, heather graven, angel Hsu, Tomohiro Oda, Irene Xueref-Remy, Rachel Silvern, Amanda Stoudt, Bridget McGovern, and Patricia Razafindrambinina

The National Academy of Sciences convened a committee to generate a report titled “Development of a Framework for Evaluating Global Greenhouse Gas Emissions Information for Decision Making”. The report:

 

  • Described approaches used to develop global anthropogenic greenhouse gas emissions inventories, including the use of surveys
  • Discussed the potential uses and limitations of these approaches
  • Provided a framework to evaluate emissions information and inventories
  • Presented several case studies to demonstrate how the framework could be applied to evaluate emissions information and inventory approaches and identify strengths and opportunities for improvement for each case study.
  • Identified ways to improve methodological transparency, sustainability and continuity of relevant observations, and product confidence in global anthropogenic greenhouse gas emissions inventories

 

To accomplish this the committee identified a series of “pillars” that were deemed ideal characteristics of GHG inventories for decisionmaking. Three dominant GHG inventory development approaches were identified from the existing work and ranked within the series of pillars. The committee made a series of recommendations for continued development of inventories to enhance their quality and utility to decisionmakers.

In this presentation I will describe the major content of the report, describing the evaluation matrix and a summary of the committee’s recommendations.

How to cite: gurney, K., Wuebbles, D., Bawa, K., Dreyfus, G., Eldering, A., george, F., graven, H., Hsu, A., Oda, T., Xueref-Remy, I., Silvern, R., Stoudt, A., McGovern, B., and Razafindrambinina, P.: US National Academy report on Greenhouse Gas Information for Decision Making, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10862, https://doi.org/10.5194/egusphere-egu23-10862, 2023.

X5.114
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EGU23-4763
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AS3.22
Yeri Kang and Chang-Keun Song

Atmospheric carbon dioxide (CO2) in the atmosphere has increased mainly due to anthropogenic fossil fuel emissions, resulting in accelerating global warming and increasing climate variability. Atmospheric chemical transport models are powerful tools for understanding mechanisms between emission/sink and the spatiotemporal distribution of atmospheric CO2.

We aim to provide information on GEOS-Chem CO2 simulation to assess the mitigation strategies over East Asia under future emission scenarios. To achieve this, we first need to evaluate the model performance of the global simulation. We investigated trends and characteristics of atmospheric CO2 from the model with ground-based in-situ observations from the World Data Centre for Greenhouse Gases; (WDCGG), Total Carbon Column Observing Network (TCCON), and satellite observations (e.g., the Greenhouse Gases Observing Satellite; GOSAT, the Orbiting Carbon Observatory 2; OCO-2).

Overall, modeled CO2 concentrations showed reasonable seasonal, annual amplitudes, and spatiotemporal distributions. They also agreed well with ground-based observations and satellite observations. Our global simulation was highly correlated with in-situ observations (Index of agreement (IOA) ≈ 0.9), and also showed excellent performance (Correlation coefficient (R) > 0.9) compared with satellite observations. Our study provides broad information on global simulation to identify features of monitoring measurements and modeling. We anticipate that our model configuration is capable of studying future emission scenarios in East Asia.

How to cite: Kang, Y. and Song, C.-K.: An evaluation of global atmospheric CO2 simulation by the GEOS-Chem using multiple observations in the period 2010-2020, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4763, https://doi.org/10.5194/egusphere-egu23-4763, 2023.

X5.115
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EGU23-16996
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AS3.22
changsub shim, jungi moon, and Jihyun han
Methane is the second largest greenhouse gas after carbon dioxide in its impact on climate change. Atmospheric methane has stagnated from 2000 to 2006, and then began to increase again in 2007, showing the largest increase since observation in 2021(19.94 ppb/yr).
As part of UNFCCC’s goals for carbon neutrality, it is necessary to verify each country's GHG’s emissions sources and the verifications using satellite observations and atmospheric models are one of the important approaches.
Currently, satellite data have been useful for methane monitoring, particularly the retrievals measured by TROPOMI with a high resolution(~7km) and good spatial coverage.
Here we investigated the spatio-temporal characteristics of national methane distribution and the spatial correlation between satellite concentrations and the national emission sources over South Korea to identify the characteristics of high-level methane distributions from August 2018 to July 2019 .
During the period, the average concentration of XCH4 in Korea was ~1858 ppb and the monthly mean concentrations of methane in Korea were higher from June to October, which in fact reflected the characteristics of rice paddy and wetlands in monsoon season.
The spatial correlation analysis (SDM) found that there are some areas showing specific contributing emissions sources with higher methane levels. There are areas with high correlations with livestock production, fossil fuel uses(gas & oils), wastes(& landfill), and rice paddies, while there are areas with high correlations with complex effects of the four fields or with no clear correlations.
Based on our analysis, the spatial correlation analysis with various emission sources and satellite data can provide the information to evaluate the CH4 emissions inventory and give some ideas to manage regional greenhouse gases reduction policies

How to cite: shim, C., moon, J., and han, J.: Investigating national methane sources with satellite retrievals: a case of South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16996, https://doi.org/10.5194/egusphere-egu23-16996, 2023.

X5.116
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EGU23-8067
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AS3.22
Jia Chen and Vigneshkumar Balamurugan

Recent developments in space-based measurements provide new possibilities for monitoring greenhouse gas (GHG) emissions at all scales, from global to local. We traced CO2 emission sources such as power plants over India using OCO-2 satellite measurements from 2014 to 2021. India is the third-largest CO2 emitter in the world, with coal accounting for nearly 60% of total fossil fuel combustion.

The Gaussian plume model was used to assess the power plant emissions. Cross-sectional (c/s) CO2 emission flux is estimated to validate the results. In 13 out of 26 cases, the estimated power plant CO2 emissions agreed within ± 25% of the emissions reported in the Carbon Brief (CB) database, and in 21 cases, the estimated emissions are within ± 50%. There is one case where the CB database significantly overestimated the CO2 emission for power plants. Further, in two cases, in which the Gaussian plume model gives much higher estimated CO2 emissions, there are emission sources other than the power plants in the vicinity. The c/s emission flux and emission inventories can be used to confirm such cases. 

In addition, the c/s emission flux method was employed to assess the emissions reported in the EDGAR and ODIAC CO2 emission inventories. Our study demonstrated the capability of OCO-2 to uncover missing or underestimated CO2 emission sources in emission inventories. Our approach could be a vital tool to conduct an initial estimate of missing or underestimated sources in the databases and emission inventories at various scales, as c/s emission flux estimation and Gaussian plume model require less computation than other modeling approaches. More sophisticated methods, such as Bayesian inversion combined with extensive transport modeling, can then be used to derive emissions with less uncertainty. Future satellites with high spatio-temporal resolution and coverage (e.g., Microcarb, Tansat2, CO2M) will enhance the possibilities of continuous monitoring of point sources as well as detecting missing or underestimated emission sources. 

How to cite: Chen, J. and Balamurugan, V.: Fossil fuel CO2 emission signatures over India captured by OCO-2 satellite measurements, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8067, https://doi.org/10.5194/egusphere-egu23-8067, 2023.

X5.117
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EGU23-10282
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AS3.22
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ECS
Sandra Porras, Eugenia González del Castillo, Omar López, Thania Arredondo, José Agustín García-Reynoso, Olivier Laurent, Michel Ramonet, Marc Delmotte, and Michel Gutter

Emission inventories are the primary source of information regarding the spatial and temporal distribution of CO2 sources and sinks in urban environments. In most cities around the world, the impact of mitigation strategies is evaluated conventionally through an inventory methodology which -by nature- is subject to high uncertainties.  In this work, we report on the utility of deploying a small network of medium-cost, non-dispersive infrared (NDIR) CO2 microsensors in Mexico City, to directly quantify the gradients and variability of the molar fraction of atmospheric CO2. The measurements obtained were compared with simulations of a high-resolution 3D transport model (WRF-Chem), prescribed with city-scale, inventory-based CO2 emission maps. The focus of this presentation is on the description of the sensors construction, the characterization of each individual sensor’s performance evaluated against a reference instrument, and the ability of the network to represent the spatial and temporal variability of CO2 in a complex urban environment. Preliminary results of a multivariate calibration of the medium-cost microsensors using a Picarro G2401 as reference instrument, along with air temperature, relative humidity and pressure, results in RMSE ranging from 1 to 5 ppm of CO2. We discuss the potential that this network of sensors offer to evaluate whether the distribution of sources and sinks declared in an inventory can result in the variability of concentrations of CO2 measured in the atmosphere, and the possibility of being used as a tool to incorporate the contribution of out-of-city emissions or mobile sources emissions, currently not accounted for in the inventory.

How to cite: Porras, S., González del Castillo, E., López, O., Arredondo, T., García-Reynoso, J. A., Laurent, O., Ramonet, M., Delmotte, M., and Gutter, M.: CO2 gradients and variability in Mexico City from in situ measurements and simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10282, https://doi.org/10.5194/egusphere-egu23-10282, 2023.

X5.118
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EGU23-14275
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AS3.22
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ECS
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Hayoung Park and Sujong Jeong

Anthropogenic emissions of greenhouse gases from fossil fuel combustion have a significant impact on the global climate. Cities, which are home to more than half of the global population, account for over 70% of anthropogenic greenhouse gas emissions and are the major sources of air pollution that we face today. However, as much as they are big emitters, cities also have great potential to be drivers of global greenhouse gas reductions. To better manage atmospheric greenhouse gases, it is necessary to accurately monitor and quantify emissions at all spatial scales from national to urban levels. In addition to using satellites which provide global coverage with high space and time resolutions for greenhouse gas monitoring, several studies have used portable ground-based remote sensing Fourier transform infrared (FTIR) spectrometers, EM27/SUNs, to measure the column-averaged concentrations of greenhouse gases. This study analyzes the column-averaged dry air mole fractions of CO2, CH4, and CO (XCO2, XCH4, XCO) in the atmosphere over Seoul using two EM27/SUNs which is the first to be done in South Korea. Moreover, we compare our measurements with satellite measurements of the Orbiting Carbon Observatory-2 (OCO-2), Orbiting Carbon Observatory-3 (OCO-3), and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). Together, this study aims to analyze the three-dimensional structure of greenhouse gases in Seoul which has been difficult to do due to the absence of domestic greenhouse gas observation satellites. Furthermore, this study identifies the need for more ground-based column measurements of greenhouse gases in cities and plume areas, as well as locally adaptable methods of greenhouse gas emissions monitoring in urban areas.

How to cite: Park, H. and Jeong, S.: Identifying needs for urban greenhouse gas monitoring in Seoul using ground-based EM27/SUN measurements, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14275, https://doi.org/10.5194/egusphere-egu23-14275, 2023.

X5.119
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EGU23-12997
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AS3.22
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ECS
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Daniel Kühbacher, Patrick Aigner, Ingrid Super, Arjan Droste, Hugo Denier van der Gon, Mario Ilic, and Jia Chen

Cities are home to more than half of the world’s population, a share that will continue to grow in the future and account for more than 70% of the global fossil fuel CO2 emissions. To avoid dangerous climate change, cities will be required to reduce their energy consumption and cut carbon emissions significantly. Emission inventories are the basis for any carbon mitigation efforts. They determine the current status, allocate emissions to various sectors and indicate their reduction potential. The ICOS-Cities project fosters this development and aims to set up integrated city observatories in three pilot cities (Paris, Zurich and Munich). Reliable prior data is essential for modeling efforts in this project and road transport is a key emission sector in urban areas.

We present a newly developed, highly spatially and temporally resolved bottom-up traffic emission inventory for the area of Munich (311 km2), covering outer circle motorways as well as inner city roads. The inventory accounts for greenhouse gases (CO2, CH4) and co-emitted species/ air pollutants (CO, NO2, O3 and PM). It has a temporal resolution of one hour and is compiled for the years 2019 to 2022. The emissions are represented as line sources along the road network, which allows for emission sampling ranging from several tens of meters in densely interconnected inner-city environments to a kilometer-scale on highways.

The inventory is based on the city’s official macroscopic traffic model (VISUM), which we validate using traffic counts from more than hundred permanent traffic monitoring stations in Munich since this data is not implemented in the traffic model. Additionally, we extrapolate the traffic model to unobserved days (e.g., weekends, holidays) by means of traffic counts, and distinguish between vehicle classes (private car, heavy duty vehicle, light duty vehicle, coach and motorbike) based on categorized traffic counts. HBEFA emission factors (Handbook for Road Transport Emission Factors) are applied to estimate the emissions.

A comparison with the official emission numbers of the City of Munich and other spatially explicit inventories available in the same region, such as TNO GHGco database, is conducted. We will present the main discrepancies and provide insights for other cities aiming to develop similar inventories.

How to cite: Kühbacher, D., Aigner, P., Super, I., Droste, A., Denier van der Gon, H., Ilic, M., and Chen, J.: Bottom-up estimation of traffic emissions in Munich based on macroscopic traffic simulation and counting data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12997, https://doi.org/10.5194/egusphere-egu23-12997, 2023.

X5.120
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EGU23-15380
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AS3.22
Tomohiro Oda, Liang Feng, Paul Palmer, and Lesley Ott

Atmospheric-based approaches have been recognized as promising tools for QA/QC and verification of greenhouse gas (GHG) emission inventories reported by countries. Atmospheric-based approaches also help provide GHG estimates for countries and regions with less robust inventory building capacities, and direct estimates for key subnational levels, which are not often covered by national inventories. Conventional CO2 flux inversion approaches, unlike urban inversion applications, often prescribe fossil fuel CO2 emissions (FFCO2) and mostly optimized natural fluxes. Thus, errors in prescribed FFCO2 impact the final flux estimates.

We implemented two sets of inversions with two different emission inventories in order to examine the impact of the prescribed FFCO2 on the inverse flux estimates. The emission inventory difference was used to approximate potential errors in the prescribed FFCO2. Our inversion result demonstrated how the FFCO2 errors, particularly due to sub-annual seasonal emission pattern differences, can have an impact on posterior emission estimates via flux optimization. Our result also demonstrated that FFCO2 errors from large emitting countries could significantly bias sub-national flux estimates by mis-attributing their flux corrections to natural fluxes to compensate for the FFCO2 errors. The magnitude of the potential errors might be small compared to that of large-scale fluxes. However, the errors could be significant in relation to small sub-national scale fluxes or emissions from lesser emitting countries. We also examined the impact of two observation systems, such as global in-situ network and a satellite, to FFCO2 errors.

We discuss the existing challenges that need to be addressed to further enhance the use of atmospheric inversions with country level inventories. In addition to improvements in inversions, improving inventories (prescribed FFCO2) should have a direct benefit to improved accuracy of inverse flux estimates. For example, extended data collection at sub-national scales should greatly mitigate potential errors in the prescribed FFCO2. This study highlights the importance of developing GHG emission information in a hybrid fashion to support science and the emission reporting and monitoring.

How to cite: Oda, T., Feng, L., Palmer, P., and Ott, L.: Impacts of prescribed fossil fuel CO2 emissions on subnational level inverse flux estimates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15380, https://doi.org/10.5194/egusphere-egu23-15380, 2023.

X5.121
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EGU23-11603
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AS3.22
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ECS
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Highlight
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Sojung Sim and Sujong Jeong

The bottom-up method of estimating carbon emissions from fossil fuel use based on socio-economic databases has significant uncertainty. In particular, this uncertainty increases when the spatial and temporal scales are fine, such as in cities. Therefore, an independent and complementary top-down method has been used to verify carbon emissions estimated by the bottom-up method. This method uses the atmospheric CO2 measurement, transport model, and the inverse model. In this study, carbon emissions provided by ODIAC were improved using ground and satellite CO2 observation data measured in 2019 for Seoul. We used atmospheric CO2 concentration from four observation sites located in Seoul and the column-averaged dry air mole fractions of CO2 from OCO-2. In order to quantify the footprint, which is the flux sensitivities on observation sites, STILT and X-STILT model was used for the ground observation and satellite observation, respectively. The result showed that prior carbon emissions in specific areas including power plants and airport were underestimated. The carbon emission uncertainty decreased through Bayesian inverse model, and it was found that the calculated observation and emission error covariance were appropriate through the reduced chi-square calculation. We assessed Bayesian inverse modelling of Seoul carbon emission from fossil fuel use using measurement from ground and space, which will enable effective carbon neutrality policy decisions for Seoul.

This work was supported by Korea Environment Industry &Technology Institute (KEITI) through "Climate Change R&D Project for New Climate Regime", funded by Korea Ministry of Environment (MOE) (2022003560006)

How to cite: Sim, S. and Jeong, S.: Seoul carbon emissions estimated with Bayesian inverse modeling of measurements from ground and space, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11603, https://doi.org/10.5194/egusphere-egu23-11603, 2023.

X5.122
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EGU23-4955
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AS3.22
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ECS
Liu Yan, Qiang Zhang, and Kebin He

Vehicles are one of the most important contributors to global anthropogenic CO2 emissions. However, lack of fuel- and vehicle- type specific information about global on-road CO2 emissions from existing datasets, which are only available at sector level, makes it insufficient to support establishment of emission mitigation strategies. Thus, a fleet model is developed in this study and CO2 emissions from global on-road vehicles during 1970-2020 are estimated at vehicle level. Here we access the fuel- and vehicle- type specific characteristics of both vehicular CO2 emissions and vehicle ownership, and highlight the trend in the intensity of CO2 emissions and ownership of on-road vehicles in hotspot regions. We find that, heavy-duty trucks and buses which account for less than 10% of global vehicle ownership but represent over 30% of on-road CO2 emissions. Contribution of diesel vehicles to global on-road CO2 emissions has doubled during 1970-2020, driven by the shift in fuel-type distribution of vehicle ownership. As the top four vehicle markets, vehicles per thousand people in the United States, European Union, China and India all increased significantly from 1970 to 2020 while vehicle intensity China and India was still lower than global average level, which indicates that developing countries have to face great challenges in vehicular decarbonization in the future. CO2 emissions per vehicle in these regions generally decreased for the last 50 years, but vehicular CO2 emission intensity in the United States and European Union were relatively higher, meaning that there're still large potentials for developed countries in vehicular CO2 emission mitigation. These findings provide better understanding of trends of historical CO2 emissions from on-road vehicles, as well as insights into the effective governance of CO2 emissions in the future.

How to cite: Yan, L., Zhang, Q., and He, K.: Modeling fuel- and vehicle- type specific CO2 emissions from global on-road vehicles during 1970-2020, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4955, https://doi.org/10.5194/egusphere-egu23-4955, 2023.

X5.123
|
EGU23-9934
|
AS3.22
Yeji Choi and Eunbin Kim

Greenhouse (GHG) gases are the primary driver of climate change. There are two approaches to measuring GHG emissions: bottom-up and top-down. Bottom-up measurement involves collecting data based on local emissions and modeling individual sources and sinks of carbon. This is useful for understanding the specific drivers of GHG emissions; however, there is a time lag for collecting data from each source, and good national statistics are required. Meanwhile, top-down measurement involves estimating GHG emissions based on atmospheric measurements and modeling. In general, the bottom-up approach is considered to be more accurate than the top-down approach, but the top-down approach is helpful for providing broad-scale estimates of GHG emissions, and it allows for spatial mapping on a global scale.

In this study, we use OCO-2 satellite products to generate a XCO2 global map using a deep-learning-based technique. Although OCO-2 measurement provides the CO2 concentrations with the highest spatial resolution on a global scale, there are limitations to the FOV coverage and the low temporal resolution of the low-earth orbit satellite. To solve this problem, we use additional satellite products, which can be a precursor to CO2, and we applied TabNet which is firstly introduced at the International Conference on Machine Learning (ICML) 2020. TabNet is an attention-based network that uses a self-attention mechanism. The preliminary results showed that the XCO2 global map for every half of the month could be provided using integrated satellite observations. The validation results will be discussed in the presentation.

How to cite: Choi, Y. and Kim, E.: Deep learning based XCO2 global map generation using satellite observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9934, https://doi.org/10.5194/egusphere-egu23-9934, 2023.

X5.124
|
EGU23-2527
|
AS3.22
Buhalqem Mamtimin, Thomas Rösch, Franziska Roth, Anusha Sunkisala, and Andrea Kaiser-Weiss

An experimental setup within ICON (ICOsahedral Non-hydrostatic)-ART (Aerosols and Reactive Trace gases) has been  carried out to simulate the atmospheric CH4 concentration over Europe during the investigation period from 01 May 2018 to 30 June 2018.

Modelling CH4 in the Limited Area Mode (Europe, 6.5 x 6.5 km), the model requires as accurate as possible initial and boundary atmospheric conditions as well as spatially highly resolved emissions. Temporal resolved emissions are to be included in the next step. While the intial data denote here the state of the atmosphere (meteorological and CH4 concentration fields) at the start of the model run, the boundary conditions shall denote the data in the lateral boundary zone where the model is forced by the meteorological and CH4 concentration data outside the domain. We have used DWD's operational numerical weather prediction output as meteorological boundary conditions. The Copernicus Atmosphere Monitoring Service (CAMS) provides the necessary initial and boundary CH4 data, which are made applicable  for the ICON-ART before the model run in Limited Area Mode. The regional CH4 emissions for Europe have been  provided by TNO and are processed with the ART module.  

Since CAMS uses a vertical coordinate of a hybrid sigma-pressure system, the data had been horizontally and vertically interpolated to the height based SLEVE coordinate system of ICON. The sectorial CH4 emissions for Europe and for Germany were mapped separately to the target ICON grid by preprocessing the corresponding reported methane emissions of various sectors (resulting in 36 distinct methane variables in the model). The 50 largest point emissions from each sector are treated separately, smaller point emissions are treated together with the area emissions.

To run a hourly experimental setup for two month the Basic Cycling environment (BACY) tool was used. The fields for meteorological parameters were initialized daily by using the DWD's operational data, while the atmospheric CH4 concentrations are taken from the previous ICON-ART CH4 simulation results (e.g., the 24 h CH4 forecast from the previous day). Then, the merged concentration fields for meterological conditions and atmospheric methane are used as “DWD first guess”, which served for a daily start of the simulation process in the ICON-ART Limited Area Mode. In order to compare the model results and measurements from the Integrated Carbon Observation System (ICOS) stations, the model equivalents have been extracted at the locations of the  ICOS montoring stations  using the “Model Equivalent Calculator”.

In this work, the ICON-ART CH4 simulation setup for Limited Area Mode (Europe) was forced by ICON meteorology and CAMS CH4 boundary data and had been started daily by the merged “DWD first guess”. These are shown to be a useful method to simulate the CH4 atmospheric concentrations at the regional scale.

How to cite: Mamtimin, B., Rösch, T., Roth, F., Sunkisala, A., and Kaiser-Weiss, A.: Simulation setup for atmospheric CH4 concentrations in the ICON-ART Limited Area Mode, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2527, https://doi.org/10.5194/egusphere-egu23-2527, 2023.

X5.125
|
EGU23-9953
|
AS3.22
Jasper Griffioen, Kees Geel, and Maartje Koning

Seabeds are a geological source for methane via natural release at seeps, dispersive methanogenic degradation of sedimentary organic matter as well as anthropogenic well leakage. Global estimates of methane seepage fluxes vary around severals tens of Tg/y. The associated release to the atmosphere is lowered by attenuation in the water column. Large uncertainties are associated with these figures as emphasized by the researchers that performed these investigations. 
Our goal is to investigate which quantitative data is available on both natural seepage and anthropogenic well leakage and which unknowns emerge from this for upscaling to regional basin scale and beyond. We focused on the Gulf of Mexico (GoM) and the North Sea basin (NSB) as these are relatively well studied marine areas.
For the two areas together, the number of quantitative studies on natural seepage is about 30 for seabed fluxes and 6 for sea/air emissions. One unknown that emerges is what flux data should be used for further upscaling: that from the studied region or from a global data-set as done before? The population statistics are different for the two. Another unknown for GoM is the fate of methane released at deep seeps (> 500 m below sea surface) that dissolves in seawater and does not reach the sea surface (as bubbles): does it get oxidised, does it interfer with biological methane, does it stay dissolved at large depth? Several quantitative studies focus on the bubble fluxes and neglect mass transport of dissolved methane. How justified is this when we recall that methane from rising bubbles dissolves into surrounding seawater and incidental storms mix seawater at shallow depth enabling transfer to air.
Globally, most (quantitative) studies on natural seepage happened at the Northern hemisphere. Natural seeps are commonly found in oil and gas producing basins and oil and gas exploration in several basins at the Southern hemisphere has been performed only recently. This means that natural seepage at such basins is probable as well but has been neglected so far. This puts another unknown forward.
The emission related to exploitation and transport of fossil fuels is a major, global anthropogenic source. For onshore wells, the contribution of subsurface leakage has obtained large attention in the past years with considerable numbers of local to national investigations. However, the contribution is far from clear for offshore wells whereas offshore wells may show more frequent well integrity issues as well as barrier integrity issues. Two blowouts have been studied in detail: the Deep Water Horizon blowout in GoM that was capped after 84 days and the UK22/4b blowout in NSB that is continuously leaking since 1990. Additionally, methane fluxes at three leaking, abandoned Norwegian wells were quantified. The data gap for GoM and NSB as well as globally will be illustrated taking into account data on well integrity issues and blowouts.
In conclusion, several major unknowns exist on methane fluxes associated with natural seeps and anthopogenic well leakage. These should be addressed to further constrain their contribution to the global methane budget.

How to cite: Griffioen, J., Geel, K., and Koning, M.: Identifying known unknowns in estimating regional methane fluxes from natural seeps and anthropogenic well leakage in the marine environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9953, https://doi.org/10.5194/egusphere-egu23-9953, 2023.

X5.126
|
EGU23-12043
|
AS3.22
|
ECS
Cameron Yeo, Chris Rennick, Emmal Safi, and Tim Arnold

Methane (CH4) is the second most important anthropogenic greenhouse gas in terms of its impact on climate to date and its current rates of emissions. It has a wide variety of emission sources including agriculture, landfills and fossil fuel combustion alongside a short atmospheric lifetime, making it a justifiable short-term target of emissions mitigation strategies. To aid the global drive on reducing emissions, it is integral to quantify emissions estimates for the validation of CH4 inventories. Each source of CH4 is identified by distinct isotopic ratios which proves a useful appliance in the sectoral division of amount fraction measurements.

We have developed Boreas, an automated preconcentrator sample preparation system coupled to an infrared laser spectrometer, capable of making hourly measurements of δ13C(CH4) and δ2H(CH4) of ambient air samples. Deployed at National Physical Laboratory’s atmospheric monitoring site in May 2021, Boreas makes continuous, high-frequency hourly measurements capable of capturing pollution events.

As concentration calibration and linearity has been verified using synthetic mixtures, we present a method of validation of Boreas measurements, comparing them to simultaneous one-minute-average measurements made by cavity-ring down spectroscopy. We use Keeling and Miller-Tans analysis to examine the source signature δ13C(CH4) and δ2H(CH4) of local pollution events, distinguishing agricultural sources from northern hemisphere background. We will show that the calibrated concentration reported by Boreas agrees with that of a Picarro G2401 making simultaneous measurements over a wide range of ambient background and polluted air. Moreover, we will show that the preconcentration step has no concentration dependence e.g., breakthrough or carry over.

How to cite: Yeo, C., Rennick, C., Safi, E., and Arnold, T.: Validation of Boreas: an instrument for simultaneous measurement of amount fraction and stable isotope ratios in methane, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12043, https://doi.org/10.5194/egusphere-egu23-12043, 2023.

X5.127
|
EGU23-13186
|
AS3.22
|
ECS
|
Ida Jandl, Leon Scheidweiler, Jochen Landgraf, Joannes D. Maasakkers, and André Butz

Satellite remote sensing techniques offer the possibility of an independent global monitoring of carbon dioxide (CO2) and methane (CH4) emissions. Recently, point sources such as oil and gas facilities and power plants, which emit a high concentration of greenhouse gases (GHG) locally, have received particular attention. Therefore, present and upcoming satellite missions focus on collecting GHG concentration images with a high spatial resolution. The PRISMA (PRecursore IperSpettrale della Missione Applicativais) spaceborne imaging spectrometer is an Italian satellite which has been launched on March 22, 2019. It is the first satellite which provides open access hyperspectral images of backscattered sunlight with a spatial resolution of 30x30 meter and a spectral resolution of around 11nm. The measured absorption spectra in the shortwave infrared range cover strong CO2 and CH4absorption bands. Various methods can be used to retrieve 2-dimensional CO2 and CH4fields above localized GHG sources.

Here, we compare data-driven and physics-based retrieval methods in application to PRISMA measurements above localized GHG sources such as oil and gas production facilities in Turkmenistan for CH4 and coal-fired power plants for CO2. The data-driven methods are variants of the matched filter technique while the physics-based methods built on spectroscopic radiative transfer modeling. While matched filter techniques use the spatial covariance of the observed scene, traditional physics-based retrievals operate on individual spectra without considering the two-dimensional scene. For a few cases, we examine the differences between both methods and conclude on strengths and weaknesses.

How to cite: Jandl, I., Scheidweiler, L., Landgraf, J., Maasakkers, J. D., and Butz, A.: Comparing imaging processing techniques with physical based inverse radiative transfer models for methane and carbon dioxide point emissions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13186, https://doi.org/10.5194/egusphere-egu23-13186, 2023.

X5.128
|
EGU23-15895
|
AS3.22
Janne Hakkarainen, Iolanda Ialongo, Tomohiro Oda, Monika Szeląg, Christopher W. O'Dell, Annmarie Eldering, and David Crisp

We characterize major anthropogenic point sources in the South African Highveld region using Orbiting Carbon Observatory-3 (OCO-3) Snapshot Area Map (SAM) carbon dioxide (CO2) and Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO2) observations. Altogether we analyze six OCO-3 SAMs. We estimate the emissions of six power stations (Kendal, Kriel, Matla, Majuba, Tutuka and Grootvlei) and the largest single emitter of greenhouse gas in the world, Secunda CTL synthetic fuel plant. We apply the cross-sectional flux method for the emission estimation, and we extend the method to fit several plumes at the same time. Overall, the satellite-based emission estimates are in good agreement (within the uncertainties) as compared to emission inventories, even for the cases where several plumes are mixed. We also discuss the advantages and challenges of the current measurement systems for greenhouse gas emission monitoring and reporting, and the applicability of different emission estimation approaches to future satellite missions such as the Copernicus CO2 Monitoring Mission (CO2M) and the Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW), including the joint analysis of CO2 and NOobservations.

How to cite: Hakkarainen, J., Ialongo, I., Oda, T., Szeląg, M., O'Dell, C. W., Eldering, A., and Crisp, D.: Characterizing major anthropogenic point sources in the South African Highveld region using OCO-3 carbon dioxide Snapshot Area Maps and Sentinel-5P/TROPOMI nitrogen dioxide columns, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15895, https://doi.org/10.5194/egusphere-egu23-15895, 2023.

X5.129
|
EGU23-11205
|
AS3.22
|
Highlight
Declining, seasonal-varying emissions of sulfur hexafluoride from the United States
(withdrawn)
Phil DeCola, Lei Hu, Deborah Ottinger, Stephanie Bogle, Stephen Montzka, Ed Dlugokencky, Arlyn Andrews, Kirk Thoning, Colm Sweeney, Geoff Geoff, Lauren Aepli, and Andrew Crotwell
X5.130
|
EGU23-13875
|
AS3.22
|
Highlight
Luke Western, Martin Vollmer, Paul Krummel, Karina Adcock, Paul Fraser, Christina Harth, Ray Langenfelds, Stephen Montzka, Jens Mühle, Simon O'Doherty, David Oram, Stefan Reimann, Matt Rigby, Isaac Vimont, Ray Weiss, Dickon Young, and Johannes Laube

Production and consumption of ozone-depleting chlorofluorocarbons (CFCs) are controlled under the Montreal Protocol. CFC production for most applications was banned globally in 2010, albeit with exemptions for uses assumed to cause negligible emissions to the atmosphere, such as in the production of other chemicals. A few years ago, emissions of CFC-11 were reported to be increasing after their phase-out, most likely due to unreported production and non-compliance, which sparked widespread, renewed attention to this topic. Here we show that emissions of five other CFCs have increased since 2010, namely CFC-13, CFC-112a, CFC-113a, CFC-114a and CFC-115. Three of these CFCs are likely increasing due to their involvement in the production of non-ozone-depleting HFCs, which have largely replaced CFCs and HCFCs in many applications. The drivers behind the increase in the other two CFCs is unclear. While the impact of these CFCs on ozone layer recovery will likely be small, these long-lived CFCs are potent greenhouse gases and their CO2-equivalent emissions in 2020 were comparable to those of a mid-sized European country.

How to cite: Western, L., Vollmer, M., Krummel, P., Adcock, K., Fraser, P., Harth, C., Langenfelds, R., Montzka, S., Mühle, J., O'Doherty, S., Oram, D., Reimann, S., Rigby, M., Vimont, I., Weiss, R., Young, D., and Laube, J.: Global emissions of five controlled CFCs have increased since 2010, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13875, https://doi.org/10.5194/egusphere-egu23-13875, 2023.

X5.131
|
EGU23-15854
|
AS3.22
|
ECS
Rakesh Subramanian, Rona Thompson, Martin Vojta, and Andreas Stohl

The source-sink estimation of greenhouse gases and the accurate quantification of their flux distributions are a major scientific challenge of our times. Although statistically developed inventory-based carbon emissions improve understanding of emissions, they are uncertain and occasionally, they do not reflect actual emissions, especially at finer scales. By using GHG observations in conjunction with numerical atmospheric models, GHG budgets can be constrained. However, the sparseness in the in-situ observation networks of GHGs, makes this a challenging problem, especially in certain regions like India. However, satellite remote sensing can fill this data gap. This study applies the Bayesian inversion system, FLEXINVERT (Thompson and Stohl 2014), coupled with a Lagrangian Particle Dispersion Model, FLEXPART (Stohl et al 1998), to assimilate TROPOMI satellite observations of CH4 and constrains the CH4 fluxes over India domain. This inverse modeling system uses the Source-Receptor Relationship derived from Flexpart as the Transport operator while minimizing the cost function for optimization.  The system will use various prior fluxes and initial concentration fields to test the set-up over Indian domain, which have strong fluxes of CH4 and are not well constrained by the existing ground-based measurement networks. The results will be compared with the limited in-situ observations available for the region.

How to cite: Subramanian, R., Thompson, R., Vojta, M., and Stohl, A.: Using Satellite Column Observations with a Bayesian Inversion System for constraining the GHG budget over India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15854, https://doi.org/10.5194/egusphere-egu23-15854, 2023.

X5.132
|
EGU23-4302
|
AS3.22
|
ECS
|
Highlight
Qinren Shi, Bo Zheng, Qiang Zhang, and Kebin He

China has pledged to peak CO2 emissions before 2030 and achieve carbon neutrality before 2060, and climate actions are urgently needed. Cites are basic administrative units and leaders in implementing emission reduction policies in China, and a comprehensive analysis of characteristics and historical emission trends in Chinese cities is of great importance. This study developed a city-level CO2 emission inventory in China during 2012-2018 by merging multiple databases. The results reveal spatial heterogeneity and inequality in Chinese cities’ CO2 emissions. In general, eastern cities emit more CO2 than western cities. According to the emission-Lorenz curve, the top 10% of 336 cities contribute over 30% of total CO2 emissions in 2018. From the perspective of emission trends, more than 60% of cities in China failed to achieve the decoupling of GDP growth and carbon emissions. The overall positive correlation between per capita GDP and per capita carbon emission in Chinese cities indicates that even cities with a relatively high level of development still need to further promote carbon emission reduction. As for sectoral drivers, industrial boilers and cement sectors were the major drivers for CO2 emission reduction in most cities, while the increase in carbon emissions from thermal power and industrial boiler sectors led to the rebound of carbon emissions in most cities.

How to cite: Shi, Q., Zheng, B., Zhang, Q., and He, K.: Emission trend and drivers of CO2 emission in Chinese cities from 2012 to 2018, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4302, https://doi.org/10.5194/egusphere-egu23-4302, 2023.

X5.133
|
EGU23-6978
|
AS3.22
|
ECS
Danilo Custódio, Saqr Munassar, Frank-Thomas Koch, Christian Rödenbeck, and Christoph Gerbig

This work was developed on the scope of the national initiative of the Integrated Greenhouse Gas Monitoring System for Germany – ITMS, which focus on reducing and characterizing uncertainties/errors introduced in the full retrieval chain of models, making use of observational data. 

Determining the magnitude, cause, and agents of carbon fluxes is important to advance our current understanding of the carbon budget and cycle, permitting more accurate predictions of its future behaviour. Sources and sinks of CO2at the Earth's surface can, in principle, be estimated from atmospheric concentrations by inverting atmospheric transport in the atmospheric tracer inversions.

Solving for CO2 fluxes in the inversions is highly desirable to better understand the carbon cycle but also to support policies aimed at reducing CO2 emissions. The Jena CarboScope inversion developed based on Bayesian inverse methods is used to obtain data-driven estimates of trace gas exchange, quantifying the large-scale sources and sinks of CO2.

To make Jena CarboScope estimates more reliable in understanding sources, sinks and transport of atmospheric CO2from the surface into the troposphere, the reliability of this data product should be evaluated based on independent observations. Quantifying the quality of the inversion estimation by decomposing the inherent uncertainty components is a challenging and key component in product reliability and its use. The overall objective in validating and evaluating uncertainties of the Bayesian data product provided by Jena CarboScope, is to explicitly answer the question: How good is the inversion estimation?

The assessment of these products is of special importance for further development and possibly allows for judging the trustworthiness of the inversion outcome.

This study explores the potential of CarboScope to reproduce CO2 concentrations recorded during regular flights and aircraft campaigns during the past two decades. The inversion estimations are accomplished by forward runs performed with the inversion having the measurement locations as receptors.

This study examines biases and uncertainties in the CarboScope estimations evaluated against flights’ data. It has been found that the CO2 simulated by CarboScope in the forward run (using the transport model TM3 for background concentration and STILT for regional signal) agree reasonably well, into the 10/90th percentile for 3-sigma of the distribution. On the other hand, the inversion exhibit some systematic biases at the edges of the distribution under and overestimating at high and lower mixing ratios, respectively. 

The CarboScope strength and concerns were enhanced by understanding the differences among observations and the inversion estimation. In comprehensive statistics comparing measurement data from hundreds of flights, we assess the compliance CarboScope`s CO2 estimation. Furthermore, we discuss the estimation and observation mismatches, exploiting the model constraints to reproduce atmospheric transport from the boundary layer to the upper troposphere. Understanding such constraints has the potential to reduce uncertainties of the atmospheric inversion estimates.

 

How to cite: Custódio, D., Munassar, S., Koch, F.-T., Rödenbeck, C., and Gerbig, C.: Using aircraft observations of atmospheric CO2 to evaluate vertical transport within the CarboScope Regional inverse model., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6978, https://doi.org/10.5194/egusphere-egu23-6978, 2023.

X5.134
|
EGU23-12535
|
AS3.22
Eight years of continuous measurements of atmospheric methane at a high-altitude South American GAW station
(withdrawn)
Marcos Andrade, Michel Ramonet, Laura Ticona, Olivier Lauremt, Paolo Laj, Fernando Velarde, Isabel Moreno, Rene Gutierrez, Ricardo Forno, and Luis Blacutt
X5.135
|
EGU23-7711
|
AS3.22
|
ECS
Jens Hellekes, Simone Ehrenberger, Nina Thomsen, Sabine Brinkop, Johannes Hendricks, Christian Weder, Paweł Banyś, Isheeka Dasgupta, Mario Feinauer, Manuel Löber, Tobias Schripp, Mattia Righi, and Angelika Schulz

The transport sector accounts for about one quarter of worldwide anthropogenic carbon dioxide emissions. Since a robust growth in transport activity is expected over the coming decades, reducing associated emissions to mitigate human-caused climate change is a particular challenge. In order to achieve high-quality comparative monitoring, to develop scenarios for future emissions, and to enable a robust assessment of climate protection measures, the allocation of emissions to the subsector level is a necessary prerequisite. The DLR project ELK – EmissionsLandKarte (en.: emission map) contributes here in several respects: (1) requirements are specified in an application-based manner, i.e. compatibility with existing inventories, such as the ones generated for IPCC, is ensured and insufficiencies in spatial resolution and methodological detail are addressed, (2) an input database congruent with both statistical data and SSP scenarios is provided, and (3) bottom-up calculations are performed that allow attribution of climate impacts to specific transport services, as well as prospective analyses where, for example, activity levels change or alternative fuels affect regional emission factors. The resulting prototype global gas and particle emission inventories for land transport, aviation and shipping reflect the status quo as of 2019.

For land transport, fine-grained activity and vehicle fleet data as well as technology-specific emission factors are applied. This allows emissions from passenger and freight transport to be disaggregated by mode and vehicle type. New approaches for spatial disaggregation of emissions will increase transparency of the methodology. For aviation, calculations are based on fleet composition and transport performance for both passenger and cargo traffic at the airport pair level, while real flight tracks serve as the foundation for spatial allocation. For both transport sectors, complementary analyses are performed to characterize particulate emissions in order to fill gaps in data availability. For shipping, transport performance on inland waterways and maritime routes are considered, including technical data describing propulsion and bunkering. Finally, all mode-specific results are subjected to an innovative uncertainty assessment aligned with the needs of other emission inventory creators through a detailed evaluation per uncertainty factor, as well as aggregated values for climate modelers and practitioners. The consistent assessment of uncertainty factors along the entire calculation chain, such as activity levels, emission factors, and proxy data used for spatial or temporal disaggregation, promotes comparability across all transport sectors. In this paper, we outline the new methodological approaches for mapping transport emissions and present first results.

How to cite: Hellekes, J., Ehrenberger, S., Thomsen, N., Brinkop, S., Hendricks, J., Weder, C., Banyś, P., Dasgupta, I., Feinauer, M., Löber, M., Schripp, T., Righi, M., and Schulz, A.: User-oriented development of global emission inventories: Bottom-up modeling of emissions from land transport, aviation and shipping in the DLR project ELK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7711, https://doi.org/10.5194/egusphere-egu23-7711, 2023.

X5.136
|
EGU23-11511
|
AS3.22
|
ECS
Blanca Fuentes Andrade, Maximilian Reuter, Michael Buchwitz, Heinrich Bovensmann, and John P. Burrows

CO2 emissions need to be rapidly reduced in order to peak greenhouse gas emissions and limit global warming to well below 2ºC. Most of these anthropogenic CO2 emissions result from the combustion of fossil fuels from localized sources. Therefore, it is essential to monitor these emissions to corroborate the compliance with the objectives of the Paris Agreement. Under this agreement, the parties report their national greenhouse gas inventories, usually computed using bottom-up methods based on statistical activity data and emission factors. Top-down approaches, based on atmospheric observations, can complement and verify these inventories. Satellite-based observations have the advantage of potential global coverage and the feasibility of providing independent emission estimates from localized sources, like cities and power plants.

In this study we present a top-down method to quantify the CO2 emissions from localized sources by using XCO2 (the column-averaged dry-air mole fraction of CO2) retrievals from the Orbiting Carbon Observatory 3 (OCO-3) in its snapshot area mode. It is a cross-sectional flux method, so that our estimate of the source rate is obtained from the flux through a number of cross-sections downwind of the source.

The detection of CO2 emission plumes is challenging due to the small enhancements resulting from anthropogenic emissions from individual source points compared to the background concentrations and the satellite’s instrument noise. NO2 is co-emitted with CO2 in the combustion of fossil fuels and its vertical column densities can exceed background values and sensor noise by orders of magnitude in emission plumes, what makes it a suitable tracer for recently emitted CO2. Therefore, our plume detection method uses TROPOMI NO2 data which is co-located with the XCO2 snapshot.

We expose the CO2 emissions estimates for 7 overpasses over the Bełchatów power plant, in Poland, together with bottom-up emission estimates for comparison, showing that we can repeatedly monitor power plant CO2 emissions.

How to cite: Fuentes Andrade, B., Reuter, M., Buchwitz, M., Bovensmann, H., and Burrows, J. P.: A method for estimating localized CO2 emissions from co-located satellite XCO2 and NO2 images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11511, https://doi.org/10.5194/egusphere-egu23-11511, 2023.

X5.137
|
EGU23-10615
|
AS3.22
Matthäus Kiel, Saswati Das, Gregory Osterman, Joshua Laughner, Vivienne Payne, and Abhishek Chatterjee

The Orbiting Carbon Observatory-2 (OCO-2), launched in 2014, is NASA’s first satellite dedicated to measure sources and sinks of carbon dioxide (CO2) in Earth’s atmosphere on regional scales. Since 2019, measurements from the Orbiting Carbon Observatory-3 (OCO-3) have complemented OCO-2’s data record. In addition, OCO-3’s Snapshot Area Mapping (SAM) mode observations over emission hotspots like cities, power plants, and volcanoes provide a novel data set for carbon cycle studies on local scales. Data from both instruments are analyzed with the Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm to estimate column-average dry-air mole fractions of carbon dioxide (XCO2) in Earth’s atmosphere. Evaluating these space-based estimates of XCO2 against independent validation data sets provides information about the quality, potential biases, and errors in the OCO-2/3 data record. Here, we present comparisons of the ACOS V10 XCO2 from OCO-3 and the new and improved ACOS V11 XCO2 from OCO-2 against ground-based measurements from the Total Carbon Column Observing Network (TCCON).

For both instruments, the root-mean-square error (RMSE) is below 1 ppm for all observational modes when compared to collocated TCCON observations. The OCO-3 V10.4 data version, an improvement over the initial vEarly data version, reduces an XCO2 time-dependent bias that was present in the previous OCO-3 data record. Consequently, data from both instruments does not indicate any significant time-dependent bias over the span of several years. Further, we evaluate differences between OCO-3 and TCCON related to different local overpass times. On average, OCO-3’s equator crossing time occurs about 20 minutes earlier every day. Comparisons against TCCON indicate no significant local time of day bias in the OCO-3 XCO2 data, however, comparisons over individual TCCON sites indicate a dependence which provides insight into potential airmass and viewing geometry related biases. The improved OCO-2 V11 data version reduces the mean bias against TCCON to ~0.15ppm from previously ~0.4ppm in V10. We find the largest reduction in RMSE over ocean due to an improved ocean glint surface treatment in V11. Both OCO data products are of comparable quality and are an improvement over earlier OCO data versions. Finally, we analyze how well OCO-2 captures the mean seasonal cycle amplitudes and growth rates over selected TCCON sites.

How to cite: Kiel, M., Das, S., Osterman, G., Laughner, J., Payne, V., and Chatterjee, A.: Evaluation of the OCO-2 and OCO-3 ACOS data products against TCCON, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10615, https://doi.org/10.5194/egusphere-egu23-10615, 2023.

Posters virtual: Tue, 25 Apr, 16:15–18:00 | vHall AS

Chairpersons: Tomohiro Oda, Beata Bukosa
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Saswati Das, Matthäus Kiel, Joshua Laughner, Vivienne Payne, and Gregory Osterman

Carbon dioxide (CO2) is the primary greenhouse gas emitted from anthropogenic activities. Although it is naturally present as a part of Earth’s carbon-cycle, human activities influence the ability of natural sinks to reduce CO2 from the atmosphere, thus altering the carbon-cycle and necessitating the long-term monitoring of atmospheric CO2. Precise, accurate and continuous measurements of CO2 are important to this end.

The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014. It is NASA’s first Earth-orbiting satellite dedicated to making observations of CO2 in the atmosphere and measuring its column-averaged dry-air mole fraction (XCO2). The primary goal of the OCO-2 mission is to provide XCO2 measurements with sufficient precision and accuracy alongside quantifying its seasonal and interannual variability. In this study, we use the new and improved OCO-2 B11 data set.

In the past, the space-based XCO2 measurements from OCO-2 data have been validated against independent data sets such as the Total Carbon Column Observing Network (TCCON). In this study, we use independent measurements from the COllaborative Carbon Column Observing Network (COCCON) to identify potential biases and errors in the B11 data version and establish its robustness for use by the science community. COCCON uses portable Fourier-Transform InfraRed (FTIR) spectrometers (EM27/SUN) to measure greenhouse gases at several global sites.  

Comparison of OCO-2 measurements against COCCON sites indicate similar temporal trends in XCO2 variability, with OCO-2 typically reporting higher values. Further, we evaluate the differences between the B11 OCO-2 and COCCON data sets. Finally, we analyze how OCO-2’s B11 version compares to selected COCCON and TCCON sites’ measurements in terms of capturing the seasonal cycle and growth rate of XCO2.

How to cite: Das, S., Kiel, M., Laughner, J., Payne, V., and Osterman, G.: Quantification and Evaluation of OCO-2 measured XCO2 against COCCON, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10545, https://doi.org/10.5194/egusphere-egu23-10545, 2023.