AS3.38 | Science-based, measurement-based greenhouse gas monitoring and emission estimates in support of national, sub-national, city and industrial climate change mitigation
EDI
Science-based, measurement-based greenhouse gas monitoring and emission estimates in support of national, sub-national, city and industrial climate change mitigation
Co-organized by BG8
Convener: Phil DeCola | Co-conveners: Beata BukosaECSECS, Oksana Tarasova, Werner Leo Kutsch, Tomohiro Oda
Orals
| Thu, 18 Apr, 14:00–17:55 (CEST)
 
Room 1.85/86
Posters on site
| Attendance Fri, 19 Apr, 16:15–18:00 (CEST) | Display Fri, 19 Apr, 14:00–18:00
 
Hall X5
Posters virtual
| Attendance Fri, 19 Apr, 14:00–15:45 (CEST) | Display Fri, 19 Apr, 08:30–18:00
 
vHall X5
Orals |
Thu, 14:00
Fri, 16:15
Fri, 14:00
Accurate and precise, long-term measurements of greenhouse gas (GHG) concentrations continue to show 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, implementing, 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) and WMO’s newly established Global Greenhouse Gas Watch (GGGW) initiative. These initiatives, together with other national and international, efforts seek 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, GHG fluxes, 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, modeling and analysis methods.

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, together with statistical and novel means of tracking emissions activity data-driven, as well as hybrid combinations of both approaches for GHG monitoring and improved emission inventory estimates that deliver actionable GHG information. Actionable information must have the needed temporal and spatial details to target and track explicit emission activity where climate action is achievable from facilities to cities to nations and ultimately our ability to determine the integrated efficacy of our emission reduction efforts at the global scale in support of Paris agreement stocktake.

Orals: Thu, 18 Apr | Room 1.85/86

Chairperson: Phil DeCola
14:00–14:05
14:05–14:15
|
EGU24-19389
|
On-site presentation
Gianpaolo Balsamo and Lars Peter Riishojgaard

Greenhouse gas emissions, greenhouse gas concentrations and global mean temperature all continue to rise, and in order to stay within the temperature limits stipulated in the text of the Paris Agreement, mitigation action is becoming increasingly urgent However, the fact that we cannot quantitatively and reliably predict future GHG concentrations – and therefore climate scenarios – from assumed future emission pathways is a complicating factor when designing mitigation action. Even more problematic is the assessment the impact or effectiveness of many current or proposed mitigation activities, since it often has to be based on indirect measures such as avoided emissions with respect to a hypothetical baseline, or carbon stored, e.g. in the land or ocean biosphere, neither of which can be directly linked to atmospheric concentrations.

In order to provide robust, actionable data that will help Parties to the UNFCCC and other stakeholder design and develop mitigation action and monitor its effectiveness, the World Meteorological Congress in May 2023 endorsed the Global Greenhouse Gas Watch (G3W) as an internationally coordinated framework to provide near-real time GHG (CO2, CH4 and N2O) flux estimates based on atmospheric modelling and atmospheric observations.  At COP28 in Dubai, the G3W was formally recognized by the Subsidiary Body for Scientific and Technological Advice (SBSTA-59) to the UNFCCC.

Currently a G3W implementation plan is in development, with the aim of submitting it for approval by the WMO Executive Council by mid-2024. Some of the key elements of the plan are a significant strengthening of the global GHG observing capabilities, improved near-real time exchange of both observational data and flux estimates, and routine intercomparision of model output among all participating flux estimation centers.

The presentation will introduce the overall G3W development timeline which aims for a full operational capability to be ready for the Second Global Stocktake in 2027-28, with the main focus on the near-time activities planned for 2024-25.

How to cite: Balsamo, G. and Riishojgaard, L. P.: The Global Greenhouse Gas Watch, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19389, https://doi.org/10.5194/egusphere-egu24-19389, 2024.

14:15–14:25
|
EGU24-22506
|
Virtual presentation
Red Willow Coleman, Philip Brodrick, K. Dana Chadwick, Adam Chlus, Michael Eastwood, Clayton Elder, Jay Fahlen, Sergio Gomezbeltran, Francesca Hopkins, David Thompson, Andrew Thorpe, and Robert Green

Spaceborne and airborne imaging spectrometers can identify methane (CH4) plumes and enable emission quantification and direct sectoral attribution necessary to better constrain methane emissions and inform mitigation strategies. We will show CH4 emission quantification results and accompanying uncertainty products for CH4 plume observations from NASA’s Earth surface Mineral dust source InvesTigation (EMIT) imaging spectrometer onboard the International Space Station, as well as the recently developed Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3). The differing spatial resolution and instrument sensitivity of the EMIT and AVIRIS-3 sensors are highly complementary for tiered CH4 plume detection and quantification. The large spatial coverage from EMIT allows us to identify and quantify previously unknown emissions from CH4 point sources across large regions of the Earth’s surface, while AVIRIS-3 has higher sensitivity and increased spatial resolution for characterizing CH4 emissions below EMIT’s detection limit.

Building on a legacy of greenhouse gas retrievals first developed for airborne imaging spectrometers (e.g., AVIRIS, AVIRIS-NG), we use a matched filter approach to retrieve CH4 enhancements and the per-plume integrated mass enhancement (IME) method with windspeed data to estimate hourly CH4 emission rates. We take a two-pronged approach to validating our CH4 emission detection and quantification method: (1) an AVIRIS-3 CH4 controlled release experiment with multiple flow rates, and (2) evaluation of a simultaneous collection of AVIRIS-3 and EMIT in West Texas’ Permian Basin oil-and-gas producing region. This validation work will help provide confidence in EMIT’s plume quantification approach, which is important as imaging spectrometers are necessary for more comprehensive understanding of global CH4 point source emissions and greenhouse gas budgets, particularly in areas with limited reporting requirements. Lastly, the EMIT greenhouse gas portal (https://earth.jpl.nasa.gov/emit/data/data-portal/Greenhouse-Gases/) is actively distributing methane data products in support of NASA’s Open Source Science Initiative and AVIRIS-3 data will soon be publicly available for interested decision-makers and users (e.g., U.S. Greenhouse Gas Center).

How to cite: Coleman, R. W., Brodrick, P., Chadwick, K. D., Chlus, A., Eastwood, M., Elder, C., Fahlen, J., Gomezbeltran, S., Hopkins, F., Thompson, D., Thorpe, A., and Green, R.: Quantification of CH4 Emissions from the EMIT and AVIRIS-3 Imaging Spectrometers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22506, https://doi.org/10.5194/egusphere-egu24-22506, 2024.

14:25–14:35
|
EGU24-6185
|
ECS
|
On-site presentation
Mengyao Liu, Ronald van der A, Ruoqi Liu, Michiel van Weele, Geli Zhang, Jos de Laat, and Pepijn Veefkind

Wetland methane emissions are an important source of uncertainty in the methane budget due to their significant spatial and temporal variabilities. The Lake Chad Basin is located in central Africa and comprises a number of transboundary waters, which exhibit dramatic expansion and contraction. However, methane emissions from Lake Chad seem not to be properly captured in bottom-up emission inventories. An improved divergence method has been developed to estimate gridded methane (CH4) emissions from satellite observations of the TROPOspheric Monitoring Instrument (TROPOMI). Significant annual methane emissions over the Lake Chad Basin are identified by both the official reprocessed (S5P_RPRO_L2__CH4) and WFM-DOAS (TROPOMI/WFMD v1.8) XCH4 products. The maximum methane emissions appear from December to February while the minimum emissions are found during June to August. We further extract the monthly surface water areas using Landsat satellite imagery and wetland areas based on the MODIS vegetation index. The monthly variations of methane emissions are consistent with monthly surface water areas and wetlands areas but in contrast to the monthly rainfall. The seasonal emissions during the period of 2018 to 2022 over the Lake Chad Basin have been studied to better understand the role of driving factors such as rainfall, temperature, and waterlogged soils.

How to cite: Liu, M., van der A, R., Liu, R., van Weele, M., Zhang, G., de Laat, J., and Veefkind, P.: Using TROPOMI observations to derive methane emissions and its driving factors over Lake Chad, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6185, https://doi.org/10.5194/egusphere-egu24-6185, 2024.

14:35–14:45
|
EGU24-16000
|
On-site presentation
Marc Guevara, Carles Tena, Oriol Jorba, Stijn Dellaert, Hugo Denier van der Gon, and Carlos Pérez García-Pando

A correct representation of the spatial and temporal distribution of anthropogenic emissions is important for verification of global CO2 emissions through current and future satellite emission monitoring. This work presents the results derived from the CoCO2 and CORSO Horizon Europe projects on improving the spatiotemporal representation of CO2 and co-emitted anthropogenic bottom-up emissions (i.e., CO, NOx) as part of the CO2 Monitoring and Verification Support capacity (CO2MVS) developments. The global CO2MVS system is envisioned to become a part of the European Union’s Copernicus Atmosphere Monitoring Service (CAMS). To improve the emission timing, we built a new set of activity and meteorology based global temporal profiles for the road transport, residential combustion, aviation, shipping and energy industry sectors. Their associated uncertainty is quantified by creating an ensemble of profiles from different years / countries / oceans and seas, so that the full range of possibilities is included. Regarding the improvement of the spatial representation, we constructed a global point source emission catalogue that contains emission information for individual facilities at their exact geographical location. The two developed datasets were compared against state-of-the-art bottom-up emission inventories that are widely used in modelling efforts, including the Emissions Database for Global Atmospheric Research (EDGAR), as well as independent TROPOMI satellite-based estimates for the co-emitted species. Main discrepancies between datasets were found in developing regions where information to derive bottom-up emissions such as energy use or pollution control strategies is still poorly characterized, indicating the need to complement the information with top-down estimates.

How to cite: Guevara, M., Tena, C., Jorba, O., Dellaert, S., Denier van der Gon, H., and Pérez García-Pando, C.: Improving the spatiotemporal representation of anthropogenic CO2 and co-emitted species to support verification using earth observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16000, https://doi.org/10.5194/egusphere-egu24-16000, 2024.

14:45–14:55
|
EGU24-22504
|
On-site presentation
Colm Sweeney, Vanda Grubisic, Alryn Andrews, John Miller, Lesley Ott, Rik Wanninkhof, Anna Karion, and Sourish Basu

Direct and remote observations of ocean and atmospheric greenhouse gases (GHGs) provide a critical constraint on global atmospheric burden of GHGs as well as the key natural and anthropogenic processes that transfer GHGs between atmosphere and land and ocean reservoirs. The United States (US) has played a large role in providing observations that span global to local scale in both the ocean and the atmosphere relying on both direct measurements of the atmosphere and ocean from ground, tower, ship, balloon and aircraft-based platforms and remote measurements from satellite, upward looking spectrometers, floats and ocean profilers. While these networks have been instrumental in providing a basic understanding of the carbon cycle there are many gaps that need to be filled over the next decade to assess interannual variability in both natural and anthropogenic sources and sinks of GHGs. With no planned US satellite missions for carbon dioxide in this time period there is an urgent need to take advantage of other gap filling opportunities. For methane, the focus on large point sources for satellites also represents a gap that many assumed would be filled in the next decade. These gaps in planned remote sensing satellite missions reinforce the need to focus development of new planforms, networks and tracers for observing atmospheric and ocean GHGs gradients and processes driving these gradients. These processes include climate/carbon feedbacks as well as changes in anthropogenic emissions across multiple scales that allow stakeholders in pursuit of GHG mitigation and carbon capture efforts to be informed and act with the most up-to-date understanding of critical processes in the global and local carbon budgets. We provide an overview of the observing, analysis and information systems that build on "bottom up" systems that currently inform the Global Stocktake. We also will report on efforts to make this information more actionable for emissions mitigation.

How to cite: Sweeney, C., Grubisic, V., Andrews, A., Miller, J., Ott, L., Wanninkhof, R., Karion, A., and Basu, S.: The US global and regional observing and analysis systems strategies for monitoring and delivering GHG natural and anthropogenic emissions estimates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22504, https://doi.org/10.5194/egusphere-egu24-22504, 2024.

14:55–15:05
|
EGU24-4894
|
ECS
|
On-site presentation
Alberth Nahas, Muhammad Rezza Ferdiansyah, and Ardhasena Sopaheluwakan

In Indonesia, the monitoring of Greenhouse Gases (GHGs) is a vital part of the nation's planning strategy, primarily spearheaded by the Meteorological, Climatological, and Geophysical Agency (BMKG) in response to the World Meteorological Organization's (WMO) mandate through the Global Atmosphere Watch (GAW) program. This initiative is of paramount importance as it aims to provide comprehensive and robust GHG monitoring to support global and national efforts in understanding and combating climate change. Despite existing efforts, there remains a pressing need to expand these services to ensure more accurate and extensive data collection, which is crucial for informing government policies and international climate negotiations. Indonesia's approach to GHG monitoring is multifaceted, encompassing global, national, and sub-national strategies to provide a comprehensive understanding of GHG dynamics and contribute effectively to global efforts. At a global and regional level, Indonesia boasts the longest GHG dataset in Southeast Asia, as well as in the equatorial region, from Bukit Kototabang. This data is invaluable, feeding into the WMO GAW international network and providing insights that aid in refining GHG inventories worldwide. It represents a significant contribution to the global understanding of GHG trends and helps position Indonesia as a critical player in international climate dialogues, especially concerning carbon budgeting and emission reduction strategies. Additionally, the implementation plan for the Global Greenhouse Gas Watch (G3W) program, a WMO initiative for a GHG monitoring effort worldwide,  is incorporated in the nation’s GHG monitoring plan, aiming for a more inclusive and extensive GHG monitoring network. Nationally, Indonesia's strategy leverages the potential of satellite-driven information.  This approach can be considered as complementary as it offers an advantage in providing better spatial resolution, and fully representing the differences in land-cover types. At a sub-national level, the focus is on atmospheric-based monitoring to provide localized GHG estimates through a roadmap for the adoption of the Integrated Global Greenhouse Gas Information System (IG3IS). This ambitious program aims to monitor atmospheric GHGs in an integrated manner, combining this with atmospheric modeling to yield a range of benefits. It enables the estimation of carbon emissions across various sectors and complement in calculating carbon sequestration, particularly in forestry initiatives. Together, these strategies illustrate Indonesia's nuanced and robust approach to GHG monitoring. By continuously enhancing its GHG monitoring plans, adopting advanced satellite technology, and focusing on localized atmospheric monitoring, Indonesia not only contributes valuable data to the global scientific community but also strengthens its own capacity to address climate change. This integrated approach is crucial for developing a comprehensive understanding of GHG dynamics, informing policy and international negotiations, and ultimately guiding the nation towards a sustainable and resilient future in the face of global environmental challenges. 

How to cite: Nahas, A., Ferdiansyah, M. R., and Sopaheluwakan, A.: Indonesia's Multifaceted Approach to Navigating the Challenges of Greenhouse Gas Observations , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4894, https://doi.org/10.5194/egusphere-egu24-4894, 2024.

15:05–15:15
|
EGU24-7594
|
On-site presentation
Christoph Gerbig, Andrea Kaiser-Weiss, Heinrich Bovensmann, Ralf Kiese, Clemens Scheer, Rachael Akinyede, Beatrice Ellerhoff, Maximilian Reuter, Hannes Imhof, Christian Plaß-Dülmer, and Andreas Fix

The Integrated Greenhouse Gas Monitoring System for Germany (ITMS) is a national initiative to establish an operational service for the provision of independent estimates of GHG fluxes for Germany. The main aim is to enhance transparency in reporting of emissions and natural fluxes on the path to net zero emissions. ITMS is a highly interdisciplinary project, bringing together diverse scientific communities involved in atmospheric observations, satellite observations, biosphere and agriculture research, inventory experts, and atmospheric transport and inverse modelling. ITMS utilizes observational datastreams from research infrastructures such as ICOS and IAGOS, and tailored remote sensing products, to constrain Germany’s GHG fluxes into the atmosphere using inverse atmospheric transport modelling. Detailed a priori emissions are generated consistent with UNFCCC reported emissions, while priors for natural fluxes are based on various process based as well as diagnostic models. Inverse modelling is deployed at mesoscale resolution, using the CarboScope-Regional (CSR) inversion system operated at the MPI-BGC as a back-bone and reference system, while developing ICON-ART based data assimilation for future operational services. The presentation will give an overview of recent progress and show some research highlights achieved so far.

How to cite: Gerbig, C., Kaiser-Weiss, A., Bovensmann, H., Kiese, R., Scheer, C., Akinyede, R., Ellerhoff, B., Reuter, M., Imhof, H., Plaß-Dülmer, C., and Fix, A.: The Integrated Greenhouse gas Monitoring System (ITMS) for Germany: Update on recent progress, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7594, https://doi.org/10.5194/egusphere-egu24-7594, 2024.

15:15–15:25
|
EGU24-13756
|
Highlight
|
On-site presentation
US fossil-CO2 emissions from 2010 - 2019 based on measurements of atmospheric radiocarbon
(withdrawn)
John Miller, Nazrul Islam, Scott Lehman, Sourish Basu, Arlyn Andrews, Colm Sweeney, Pieter Tans, Xiaomei Xu, and Kevin Gurney
15:25–15:35
|
EGU24-19246
|
ECS
|
On-site presentation
Eric Saboya, Alistair J. Manning, Peter Levy, Stephan Henne, Kieran M. Stanley, Joseph Pitt, Dickon Young, Daniel Say, Aoife Grant, Tim Arnold, Chris Rennick, Sam J. Tomlinson, Edward J. Carnell, Yuri Artoli, Ann Stavart, T. Gerard Spain, Simon O'Doherty, Matthew Rigby, and Anita Ganesan

Atmospheric trace gas measurements can be used to independently assess national greenhouse gas inventories through inverse modelling. Here, atmospheric nitrous oxide (N2O) measurements are used to derive monthly U.K. N2O emissions for 2013-2022 – using the InTEM and RHIME inverse methods – and Swiss N2O emissions for 2017-2022 – using the ELRIS inverse method. We find mean U.K. emissions of 90.5±23.0 and 111.7±32.1 Gg N2O yr-1 for 2013-2022 and corresponding trends of -0.68±0.48 and -2.10±0.72 Gg N2O yr-2, respectively, derived using InTEM and RHIME. The 2013-2022 mean U.K. N2O emissions as reported by the U.K. National Atmospheric Emissions Inventory were relatively constant at 74 Gg N2O yr-1 across this period, which is 14-33% smaller than the U.K. emissions derived from atmospheric data. Top-down Swiss emissions of 10.8±3.8 Gg N2O yr-1 derived using atmospheric measurements were very comparable to those reported in the Swiss National Inventory: 11.5 (8.3 to 14.9) Gg N2O yr-1 over 2017-2021. Pronounced seasonal N2O emissions cycles are inferred in the U.K. and Swiss data with similar seasonal magnitudes observed in both countries. In the U.K., the primary seasonal peak occurs in the spring with a second smaller peak occurring in the late summer for certain years. The springtime peak has a long seasonal decline that contrasts with the sharp rise and fall of N2O emissions estimated from the bottom-up U.K. Emissions Model (UKEM). Similarly, Swiss seasonal N2O emissions peak during the summer with a second smaller peak also occurring in the late summer/early autumn for certain years. Bayesian inference is used to minimize the U.K. seasonal cycle mismatch between the average top-down (atmospheric data-based) and UKEM bottom-up (process model and inventory-based) seasonal emissions at a sub-sector level. Increasing agricultural manure management and decreasing synthetic fertiliser N2O emissions reduces some of the discrepancy between the average U.K. top-down and bottom-up seasonal cycles. Other possibilities could also explain these discrepancies, such as missing emissions from NH3 deposition, but these require further investigation.

How to cite: Saboya, E., Manning, A. J., Levy, P., Henne, S., Stanley, K. M., Pitt, J., Young, D., Say, D., Grant, A., Arnold, T., Rennick, C., Tomlinson, S. J., Carnell, E. J., Artoli, Y., Stavart, A., Spain, T. G., O'Doherty, S., Rigby, M., and Ganesan, A.: Using atmospheric measurements to evaluate recent bottom-up trends and seasonal patterns in U.K. and Swiss N2O emissions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19246, https://doi.org/10.5194/egusphere-egu24-19246, 2024.

15:35–15:45
|
EGU24-11352
|
ECS
|
On-site presentation
Katharina Meixner, Andreas Engel, Tanja J. Schuck, Thomas Wagenhäuser, Cedric Couret, Frank Meinhardt, Kieran M. Stanley, Alistair J. Manning, Armin Jordan, Xochilt Gutièrrez, Tobias Kneuer, Dagmar Kubistin, Matthias Lindauer, and Jennifer Mueller-Williams

Sulfur hexafluoride (SF6) is a greenhouse gas with an estimated atmospheric lifetime of about 850-1280 years and a global warming potential of 24,700 over 100 years. As this strong greenhouse gas continues to be used in switchgear, circuit breakers, transformers and in other applications; monitoring emissions worldwide is essential. Some global and regional measurement networks, including the AGAGE, NOAA and ICOS programmes, have been measuring surface-based SF6 for several years. Through these measurements and inverse modelling, it has been shown that there are still significant SF6 emissions in western Europe, the largest source estimated to be in southern Germany.

Here we present the first time series of all available SF6 observations in Germany to localise the most important source regions of SF6. Data from the following stations were used: Taunus Observatory (AGAGE), Zugspitze / Schneefernerhaus (UBA Germany, GAW, ICOS), Karlsruhe (DWD, ICOS), Hohenpeissenberg (DWD, GAW, ICOS), Lindenberg (DWD, ICOS), Ochsenkopf (MPI-BGC, ICOS), Steinkimmen (ICOS), Gartow (ICOS) and Schauinsland (UBA Germany, GAW, ICOS). This distribution of observation sites provides good resolution of SF6 emissions in Germany. Despite the annual National Inventory Reports to the UNFCCC suggesting a decline in SF6 emissions in Germany, observations show continued episodes of elevated mixing ratios. This is indicative of continuing local emissions in Germany. Depending on wind direction, the highest levels of SF6 were measured at Zugspitze, Schauinsland, Karlsruhe and the Taunus Observatory, consistent with a source in southern to south-western Germany.  The Karlsruhe station stands out in particular, with maximum mixing ratios of more than 70 ppt. In addition to an analysis of such pollution events, the observations are also used in the top-down inverse model InTEM (Inversion Technique for Emission Modelling) coupled to the atmospheric transport model NAME (Numerical Atmospheric Dispersion Modelling Environment).

How to cite: Meixner, K., Engel, A., Schuck, T. J., Wagenhäuser, T., Couret, C., Meinhardt, F., Stanley, K. M., Manning, A. J., Jordan, A., Gutièrrez, X., Kneuer, T., Kubistin, D., Lindauer, M., and Mueller-Williams, J.: Evidence of ongoing SF6 emissions in Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11352, https://doi.org/10.5194/egusphere-egu24-11352, 2024.

Coffee break
Chairperson: Oksana Tarasova
16:15–16:25
|
EGU24-19900
|
ECS
|
On-site presentation
Lilja Dahl, Rona L. Thompson, and Alessandro Bigi

The Po valley, situated in Northern Italy, is a flat region with mountains on the northern and southern ends, characterized by intensive animal farming and agriculture practices (including rice cultivation), all of which are significant sources of methane (CH4) emissions. Although both sources and sinks of strong greenhouse gases are well identified, large uncertainties still remain in estimating the CH4  budget. However, inverse modeling is an observation-based approach that can be used independently to verify existing emission inventories.
The objective is to estimate CH4  surface-atmosphere fluxes in Northern Italy for the year 2019 over a nested grid covering the Po valley at 0.1°x0.1° horiz. grid res. using the atmospheric inversion framework FLexInvert+. The framework integrates atmospheric CH4  mixing ratios from 17 ICOS sites, background mixing ratios using the CAMS inversion product, prior information (with total emissions of 594 Tg y-1), and an atmospheric transport model to optimize surface fluxes to best match the observation. The FlexPart model is used to simulate the source-receptor relationship using ECMWF ERA5 windfields at 0.5°x0.5° horiz. res., and relates the surface fluxes to the changes in CH4  mixing ratios. Modeling the dispersion of particles over complex terrain, i.e. the Alps, is challenging due to processes interacting with the orography, and a coarse model resolution smoothens the slope and elevation of the mountain. Hence, a sensitivity analysis was carried out for the six mountain sites >1000 m a.s.l. located within the nested domain to assess the optimal particle release height using 7-days backtrajectories. The inversion results from three different release heights were examined using 1) the original sampling height of CH4 a.s.l., 2) the pressure-based height, determined by identifying the model-level height that minimized the difference between the modeled pressure and observed pressure at the receptor site, and 3) the potential temperature-based height, determined by matching the modeled potential temperature with observations to identify the model-level height. The hypsometric equation was applied to obtain the release height, and an averaged particle release height was selected for the entire year based on nighttime observations. Initial findings highlight significant differences in posterior estimates among the three release heights and hold promising prospects for achieving improved inversion results.

How to cite: Dahl, L., L. Thompson, R., and Bigi, A.: Estimating methane fluxes in Northern Italy by inverse modeling: Evaluating optimal particle transport release heights in mountainous regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19900, https://doi.org/10.5194/egusphere-egu24-19900, 2024.

16:25–16:35
|
EGU24-7180
|
ECS
|
Virtual presentation
Sougol Aghdasi, Peter Rayner, Nicholas Deutscher, and Jeremy Silver

Quantifying methane emissions in Gippsland, Victoria, Australia is challenging due to the presence of multiple emission sources, resulting in overlapping emissions and considerable uncertainty in estimation. To address this challenge, our study investigates the potential to reduce uncertainties in methane emissions in Gippsland through the combination of in-situ data, models, and prior information using a Bayesian inverse modeling and variational approach. We employ a four-dimensional variational in-situ data assimilation technique built around the Community Multiscale Air Quality (CMAQ) model at 2 km resolution for four months in 2019.

Initially, we used the Emission Database for Global Atmospheric Research (EDGAR) as a baseline but identified a number of shortcomings in capturing local emissions. To address this issue, we introduced prior estimates from the "openmethane" prior at https://openmethane.org/. We evaluated the underlying Weather Research and Forecasting Model (WRF) meteorological predictions against nearby weather station data, revealing good performance at most times. We validated the performance of our concentration model by comparing it with observational data at the three sites used in the study.

We will discuss the results and present the reductions in emission uncertainties. Next steps in the study will integrate these findings to further rectify biases and improve the accuracy of methane emission estimates in the Gippsland region, especially during the intense fire period of 2019-2020.

How to cite: Aghdasi, S., Rayner, P., Deutscher, N., and Silver, J.: Investigating high-resolution methane emission uncertainty reduction in Gippsland using in-situ data: A Bayesian inverse modeling and variational assimilation approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7180, https://doi.org/10.5194/egusphere-egu24-7180, 2024.

16:35–16:45
|
EGU24-19215
|
ECS
|
On-site presentation
Saurabh Annadate, Michela Maione, Rita Cesari, Serena Falasca, Umberto Giostra, Barbara Gonella, Federica Moricci, and Jgor Arduini

Hydrofluorocarbons (HFCs) are a class of greenhouse gases (GHGs) primarily used as substitutes for ozone-depleting substances like chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs), phased out under the Montreal Protocol. However, HFCs significantly impact global warming due to their high global warming potential. In light of the pressing need to tackle climate change and mitigate the effects of GHG emissions, the United Nations Framework Convention on Climate Change (UNFCCC) has established rigorous commitments on emission reduction. As a commitment to the UNFCCC, Annex-I countries need to report their national emission estimates for regulated GHGs, including HFCs, based on the methodologies reported in the IPCC guidelines (Intergovernmental Panel on Climate Change). According to the guidelines, the comparison of estimates with top-down (models based on atmospheric measurements) is indicated as an effective tool for verifying the accuracy of inventories and there is a growing need for independent verification of these estimates. This study reports the most recent update on emissions of 1,1,1,2-Tetrafluoroethane (CH2FCF3) from 2008 to 2023, employing inverse modelling within the European domain, with a specific focus on Italy. CH2FCF3, commercially known as HFC-134a, stands as the most prevalent HFC on a global scale. Its thermodynamic properties, akin to those of dichlorodifluoromethane (CFC-12), render it an effective refrigerant for the RAC (refrigeration and air conditioning) sector. This study reveals a notable decline in HFC-134a emissions over the past decade, followed by a recent resurgence. Specifically, Italian emissions in 2020 show a 48% reduction compared to the levels of 2011 and a subsequent increase, with emissions rebounding by 25% in 2022. The availability of near real-time validated observations combined with the most recent inversion frameworks -such as Flexpart/flexinvert+ used here, could be a valuable tool to support the Inventories used to track progress and the effectiveness of the mitigation policies adopted by each country for this class of compounds (that could be extended to others major GHGs) to maximise the effectiveness of their investments.

How to cite: Annadate, S., Maione, M., Cesari, R., Falasca, S., Giostra, U., Gonella, B., Moricci, F., and Arduini, J.: Estimates of HFC-134a Emissions over Europe informed by observations show a recent increase, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19215, https://doi.org/10.5194/egusphere-egu24-19215, 2024.

16:45–16:55
|
EGU24-5321
|
ECS
|
On-site presentation
Anna Sommani, Maximilian May, Alexander J. Turner, Ronald C. Cohen, and Sanam N. Vardag

Urban areas are responsible for about 70% of anthropogenic CO2 emissions and are therefore an important system in which to develop mitigation strategies to reduce emissions. To assess these strategies and monitor mitigation efforts, independent knowledge of urban CO2 sources is required. A measurement-based estimation of emissions can be obtained using CO2 measurements, along with prior information on emissions from inventories and a high-resolution transport model.

Here we use the forward model system GRAMM/GRAL. This consists of two nested models, a prognostic mesoscale model (GRAMM), and a microscale computational fluid dynamics and Lagrangian dispersion model (GRAL). We run GRAL on a 15 km x 15 km grid over the city of Oakland, California, at a horizontal resolution of 10 m x 10 m. This resolution of 10 meters is sufficient to resolve street canyon effects. We utilize the Berkeley Atmospheric CO2 Observation Network (BEACO2N), a unique high-density network of CO2 monitoring stations consisting of mid-cost sensors. To optimize computational time, GRAMM/GRAL is run in a steady-state mode where we compute hourly steady-state wind and concentration fields, corresponding to different synoptic meteorological situations. To infer the temporal evolution of the simulated CO2 concentration over a whole year we then use a match-to-observation algorithm that for each hour chooses the hourly steady-state wind field which minimizes the difference between the simulated wind and the observed wind time series from an urban network of wind measuring stations (May et al. 2024).

In our study, we assess the performance of the GRAMM/GRAL model in Oakland and compare the modelled and measured wind and concentration fields over a year. In general, we find a good agreement between modelled and observed wind fields. Comparing the time series of simulated CO2 concentration to the observed CO2 concentration from the BEACO2N network, we analyze the agreement and difference between the modelled and simulated CO2 concentration and propose possible improvements in the modelling framework. Finally, we propose an inversion set-up to infer emission estimates at high resolution given the observations and discuss remaining challenges and limitations.

How to cite: Sommani, A., May, M., Turner, A. J., Cohen, R. C., and Vardag, S. N.: Towards CO2 emission estimation in urban areas using a dense sensor network and the high-resolution GRAMM/GRAL model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5321, https://doi.org/10.5194/egusphere-egu24-5321, 2024.

16:55–17:05
|
EGU24-6957
|
On-site presentation
Russell Dickerson, Xinrong Ren, Anna Karion, Paul Shepson, Phil Stratton, Jiayang Sun, Sahu Sayatan, Hao He, and Hannah Daley

The cities of Baltimore, MD and Washington, DC generate substantial amounts of air pollutants with adverse effects on health and climate, but the magnitude and origins of these contaminants remain uncertain.  The State of Maryland has committed to reducing statewide greenhouse gas emissions by 60% (relative to 2006 levels) by the year 2031. A team of scientists from the Maryland Department of the Environment (MDE), the National Institute of Standards and Technology (NIST), the National Oceanic and Atmospheric Administration (NOAA), the University of Maryland, and Stony Brook University have established a coordinated program of measurements and models to quantify and allocate emissions.  These include observations from aircraft, a mobile laboratory, a tower array, and surface monitors as well as Lagrangian and Eulerian models.  Results thus far indicate that methane emissions substantially exceed initial, traditional, bottom-up, inventory data and that leakage from the natural gas delivery system and landfills are major sources.  Urban methane emissions show a strong seasonality, consistent with natural gas usage – the flux in winter was 44% greater than in summer.  Model inversions suggest urban methane emissions in Washington and Baltimore decreased by 4-5%/yr between 2018 and 2021.  Mobile laboratory measurements of GHGs and air pollutants such as black carbon with high temporal and spatial resolution reveal a variety of sources in densely populated urban residential areas related to traffic and industry and with implications for environmental justice.  Analysis of long-term monitoring data with clustering of trajectories identified dominant transport pathways and sources in upwind states that likely contribute in a major way to ambient methane concentrations in the Baltimore/Washington area – these include the Marcellus oil and gas plays in Pennsylvania and West Virginia as well as swine production in North Carolina.  Ongoing and future work includes developing a landfill as a testbed for emissions quantification and control and use of carbon and hydrogen isotopes to partition fossil and biogenic emissions and biogenic losses.  The combination of State, federal, and university resources makes for a powerful tool to tackle air quality and climate problems. 

 

How to cite: Dickerson, R., Ren, X., Karion, A., Shepson, P., Stratton, P., Sun, J., Sayatan, S., He, H., and Daley, H.: Greenhouse gas and short-lived pollutants in the Baltimore, MD and Washington, DC area: Coordinated measurements and models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6957, https://doi.org/10.5194/egusphere-egu24-6957, 2024.

17:05–17:15
|
EGU24-5221
|
ECS
|
On-site presentation
Stuart K. Grange, Pascal Rubli, Andrea Fischer, Christoph Hueglin, Nikolai Ponomarev, Dominik Brunner, and Lukas Emmenegger

As a part of the ICOS Cities project, a dense CO2 sensor network was deployed across Zürich city in July 2022 that will remain operational until July 2024. The network comprises 250 NDIR (nondispersive infrared) CO2 sensors from three manufacturers (Senseair, Vaisala, and Licor) at 87 monitoring sites. The sensors can be classified into low- and mid-cost groups (~€500 and ~€7000 respectively). Most mid-cost sensors were installed with rooftop inlets, while most low-cost sensors were deployed near ground level, i.e. near sources of biogenic activities, human respiration, and fossil fuel burning. All data are transferred using LoRaWan and Picarro CRDS (cavity ring-down spectroscopy) gas analysers with traceable reference gases are used for calibration and the assessment of the sensors’ performance before field deployment.
The mid-cost CO2 sensors run on mains power, are placed inside maintenance rooms or measurement cabins, and make use of two calibration gases that are tested daily. After accounting for air pressure, humidity and the reference gas tests, the mid-cost CO2 sensors achieve an accuracy of 1.5 ppm of root mean square error (RMSE) and a mean bias that is within ± 1 ppm when considering hourly means in field conditions. The low-cost sensors are battery-powered and require an initial calibration period to address potential deficiencies with their factory calibration. During field deployment, an algorithm for drift correction is applied that considers meteorological conditions and data provided by the mid-cost sensors in the network. The low-cost sensors achieve a mean RMSE of 15 ppm under field conditions when compared to pseudo-reference time series provided by mid-cost sensors, and on average, they show no systematic bias.
The sensor measurement performance is adequate to resolve site-specific differences and interesting source-sink processes – especially those related to traffic and the biosphere. Mean CO2 dry air mole fractions ranged between 432 and 460 ppm across the network with some sites displaying large CO2 diurnal ranges (up to 70 ppm) due to confinement of biogenic emissions in the very early hours of the morning. The network’s background CO2 is highly variable, indicating that Zürich’s ambient CO2 levels are strongly influenced by regional scale processes as well as emissions and sinks within the city’s boundary. In a first attempt to quantify CO2 emissions, the rooftop sensors are combined with inventory data and simulations of biogenic activity using ICON-ART at a spatial resolution of 600 m. In contrast, the low-cost sensors will be employed in combination with highly-resolved urban emission data and GRAMM/GRAL, a building-resolved transport model. In collaboration with the city government, we expect this to become a long-term, actionable contribution to address urban emissions and the city's net-zero commitment.

How to cite: Grange, S. K., Rubli, P., Fischer, A., Hueglin, C., Ponomarev, N., Brunner, D., and Emmenegger, L.: Design, operation, and insights from Zürich city's mid- and low-cost CO2 sensor network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5221, https://doi.org/10.5194/egusphere-egu24-5221, 2024.

17:15–17:25
|
EGU24-14046
|
ECS
|
On-site presentation
Stijn Naus, Sara Mikaloff-Fletcher, Beata Bukosa, Jocelyn Turnbull, Timothy Hilton, Elizabeth Keller, Stuart Moore, Daemon Kennett, Vanessa Monteiro, Gordon Brailsford, Sally Gray, Rowena Moss, and Sylvia Nichol

Carbon dioxide (CO2) is the single largest contributor to anthropogenic radiative forcing, with 70% of global CO2 emissions originating from urban areas. New Zealand has set ambitious greenhouse gas emission reduction targets, and its largest city, Auckland, will play a key role in achieving those reductions as it houses over 25% of the national population. To meet reduction targets, it is vital to understand current emissions and monitor the impact of implemented policies (e.g., planting trees). For these reasons, we are developing the first observation-constrained, urban-scale emission estimation framework for Auckland. This work is part of the New Zealand CarbonWatch-NZ project that also includes emission estimation at the national scale.

A new and developing atmospheric observation network is operated in and around Auckland to measure CO2, 14CO2, CO, CH4, and COS. The combination of trace gases is useful in distinguishing between source sectors, especially biosphere from anthropogenic fluxes. This is important for Auckland: a green city with a year-round growing season. High-resolution bottom-up emission estimates have been developed specifically for anthropogenic (Mahuika-Auckland) and biospheric (UrbanVPRM) CO2 fluxes in Auckland. We combine bottom-up estimates and atmospheric CO2 observations in an inverse emission estimation framework that includes atmospheric transport simulations with the Lagrangian NAME-III model, driven by meteorological data from the 333-m horizontal resolution Auckland Numerical Weather Prediction model. Use of such high-resolution meteorological data is unique and helps interpret atmospheric measurements in the heterogeneous landscape of Auckland, especially when combined with our high-resolution bottom-up estimates. Finally, we explore the value and difficulties of including the full diurnal cycle of CO2 data. The resulting emission product will be a policy-relevant instrument that can help evaluate and meet New Zealand’s emission reduction targets.

How to cite: Naus, S., Mikaloff-Fletcher, S., Bukosa, B., Turnbull, J., Hilton, T., Keller, E., Moore, S., Kennett, D., Monteiro, V., Brailsford, G., Gray, S., Moss, R., and Nichol, S.: Resolving Auckland’s CO2 budget: urban biosphere, diurnal cycle and constraints from isotopes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14046, https://doi.org/10.5194/egusphere-egu24-14046, 2024.

17:25–17:35
|
EGU24-7288
|
ECS
|
On-site presentation
Ruosi Liang and Yuzhong Zhang

Rice cultivation is one of the dominant anthropogenic methane sources in China and globally. However, it is often challenging to accurately quantify national and regional rice methane emissions. Conventional bottom-up methods often rely on a small number of ground-based flux measurements to derive emission factors or to calibrate process-based models, despite of inherently high heterogeneity in rice methane emission intensities. Satellite observations provide an independent regional-scale constraint on the magnitude of rice methane emissions. We apply atmospheric methane observations from the Tropospheric Monitoring Instrument (TROPOMI) to a high-resolution (0.625° × 0.5°) inversion to estimate monthly methane emissions for 2021 from Heilongjiang province in Northeast China, which is the country’s largest rice province. Our optimal estimate of annual rice methane emissions is 0.89 (0.57 – 1.04) Tg a−1, a factor of 2 or more higher than various bottom-up estimates. The results show that rice methane emissions in Heilongjiang peak during the tillering stage in June, consistent with intermittent flooding as the primary practice of water-regime management. This one-peak seasonality differs from the two-peak pattern in the prior estimate of the inversion (EDGAR v6.0) but agrees with flux measurements taken at a site in the region. Finally, our results are used to evaluate and improve process-based models of rice methane emissions.

How to cite: Liang, R. and Zhang, Y.: Satellite-based monitoring of methane emissions from China's rice hub, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7288, https://doi.org/10.5194/egusphere-egu24-7288, 2024.

17:35–17:45
|
EGU24-12782
|
ECS
|
On-site presentation
Sven Krautwurst, Christian Fruck, Jakob Borchardt, Oke Huhs, Sebastian Wolff, Konstantin Gerilowski, Michał Gałkowski, Mathieu Quatrevalet, John P. Burrows, Christoph Gerbig, Andreas Fix, Hartmut Bösch, and Heinrich Bovensmann

To reduce and mitigate anthropogenic greenhouse gas surface fluxes from industrial sites, their sources must be, firstly, identified or localized and, secondly, accurately quantified. For methane (CH4), the second most important anthropogenic greenhouse gas, the quantification of its diverse emitters is still a challenge. Due to their nature, these emitters can reach dimensions from point sources to hundreds of square kilometres for fossil fuel (gas, oil, coal) exploitation sites or up to several square kilometres in case of waste disposal sites. Although, CH4 emissions from, e.g., waste disposal sites can be computed from activity data combined with landfill models, a high potential for unintended and poorly quantified leakages remain due to, e.g., potential ruptures in the landfill cover. Consequently, the exact localization and quantification of those leakages is a necessary step towards reducing CH4 emissions from waste disposal sites.

To have better knowledge and insights into anthropogenic and natural greenhouse gas emissions, a team of scientists has assembled a comprehensive suite of instruments aboard the German Research aircraft HALO (High Altitude and Long Range Research Aircraft) during the CoMet 2.0 Arctic mission conducted in Canada in August and September 2022. Although the campaign was primarily intended to observe and quantify CH4 and CO2 emissions and disentangle anthropogenic from natural sources at the high northern latitudes of Canada, a test flight over Spain revealed unexpectedly high and still persistent emissions from two landfills in Madrid - Valdemingomez and Pinto, previously also pointed out in an ESA story based on satellite observations. Both were investigated by means of passive and active remote sensing, as well as in situ airborne techniques.

The measurements of the passive airborne remote sensing instrument MAMAP2D-Light, developed at the University of Bremen, delivers atmospheric concentration anomaly maps of CH4 and CO2. Here, its imaging capabilities are used to pin-point the origin of the CH4 emissions across the targeted landfills and to quantify their emissions. MAMAP2D-Light’s concentration maps are combined with highly accurate CH4 column concentration measurements from the integrated-path differential-absorption lidar CHARM-F (CO2 and CH4 Remote Monitoring-Flugzeug), developed by German Aerospace Center (DLR) in Oberpfaffenhofen. Additionally, airborne CH4 in situ mole fractions were measured by the Jena Instrument for Greenhouse Gases (JIG) and supplemented with wind data within the emission plume in order to complement the remote sensing observations.

This contribution will present top-down emission estimates from measurements of all aforementioned instruments, operated quasi-simultaneously, i.e. within a time span of approximate 2 hours, over the targeted area in Madrid in August 2022.

How to cite: Krautwurst, S., Fruck, C., Borchardt, J., Huhs, O., Wolff, S., Gerilowski, K., Gałkowski, M., Quatrevalet, M., Burrows, J. P., Gerbig, C., Fix, A., Bösch, H., and Bovensmann, H.: Pin-pointing and quantifying anthropogenic CH4 emissions from two landfill sites in Madrid, Spain, observed by a combination of passive, active, and in situ airborne measurements during the HALO CoMet 2.0 mission, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12782, https://doi.org/10.5194/egusphere-egu24-12782, 2024.

17:45–17:55
|
EGU24-14036
|
ECS
|
On-site presentation
Afshan Khaleghi, Evelise Bourlon, Athar Omidi, Jordan Stuart, Rebecca Martino, Donya Ghasemi, Chelsea Fougere, Andrea Darlington, Sebastien Ars, Lawson Gillespie, Mathias Goeckede, and David Risk

Canada’s waste sector contributes 23% toward national methane emission totals. Recently Canada committed to a 50% reduction in waste sector methane emissions by 2030 from 2020 levels, as part of its Global Methane Pledge action plan. Achieving this ambitious goal will certainty requires that regulators to be armed with an accurate and measurement-informed understanding of landfill methane emissions. In 2022 we carried out a snapshot methane emissions quantification campaign targeting 125 landfills across Canada, followed in 2023 by more detailed source-level measurements across seasons at selected landfills in various climate zones. Snapshot measurements were carried out by vehicle-based surveying coupled with Gaussian and Lagrangian flux inversion, and aircraft mass balance measurements. Repeating source-based measurements were conducted in 2023 across seasons at 12 landfills for 3 climatic regions using vehicle-based surveys, stationary tripod deployments, and drone measurements of plumes, from on-site and off-site locations. Source-specific flux rates were generated based on triangulation, Lagrangian backtrajectory analysis, and a Gaussian dispersion model, and were assessed for magnitude and temporal variability.  In snapshot measurement campaigns across the country, we saw generally good agreement between aircraft mass balance and truck measurements, with a moderate but consistent low bias in the truck emission rate estimates. Lagrangian methods to derive flux rate were comparable as long as input data was limited to exclude highly enriched onsite samples. Throughout a varied population of landfills across Canada, we found that emission rate estimates from measurement campaigns were generally in-line with operator-submitted values to the Canadian Greenhouse Gas Reporting Program, whereas a First Order Decay model used by the federal government for planning purposes tended to over-estimate landfill emissions. Climate zone was a clear predictor of methane generated per waste in place. In more detailed source studies, we found that numerous features on landfill operations could emit methane, most expected, but some unexpected. Management practice was a strong predictor of whether source types emitted significantly, or not. Meteorology and seasonal changes in climate were also strong predictor of emissions over time. These large-scale studies provide a wealth of data upon which Canada can base regulatory development and will be beneficial to countries with similar waste sector patterns and climates.

How to cite: Khaleghi, A., Bourlon, E., Omidi, A., Stuart, J., Martino, R., Ghasemi, D., Fougere, C., Darlington, A., Ars, S., Gillespie, L., Goeckede, M., and Risk, D.: Mitigating Methane Emissions: A Comprehensive Measurement Study of Canadian Landfills, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14036, https://doi.org/10.5194/egusphere-egu24-14036, 2024.

Posters on site: Fri, 19 Apr, 16:15–18:00 | Hall X5

Display time: Fri, 19 Apr 14:00–Fri, 19 Apr 18:00
Chairperson: Tomohiro Oda
X5.32
|
EGU24-18939
|
ECS
Konstantinos Kissas, Anastasia Gorlenko, Charlotte Scheutz, and Andreas Ibrom

Due to its urgency, curbing greenhouse gas (GHG) emissions as fast as possible is pivotal for all countries. Mitigation measures are being initiated and a need for monitoring verification and reporting (MVR) of their efficacy arises. Currently existing science based MVR strategies are either too fine scale, e.g. traditional eddy covariance, or very coarse scale, e.g., atmospheric model inversion from high precision continental scale concentration fields. Integral methods to estimate GHG budgets of landscapes and cities are in the state of development. One element of such systems are turbulent flux measurements from tall tower platforms. To be able to document the green transition of Denmark in terms of reducing GHG budgets, we proposed a network of tall tower GHG flux measurements covering representative urban and remote landscapes. In the first step of the project, we designed and built a prototype of such system and applied it in a rural area close to the Danish Capital of Copenhagen.

In this presentation, we define criteria for a successful tall tower based GHG flux observation system for MVR of a change in net GHG emissions. We provide a brief overview how we optimized the design to meet these criteria. Finally, we present some key results from the first five months of continuous observation to demonstrate how well we actually met the criteria with our system and conclude on the future prospects of the proposed tall tower GHG observation network.

The results include net fluxes of all major long living GHG (CO2, CH4 and N2O) and two indicator gasses, i.e. carbonyl sulfide (COS) and carbon monoxide (CO). These indicator gases were chosen to represent photosynthesis and to estimate fossil CO2 fluxes from combustion processes. Important results are how accurate the data represent the landscape and what the detection limits for flux estimations of the different GHGs are.

How to cite: Kissas, K., Gorlenko, A., Scheutz, C., and Ibrom, A.: Bridging the gap between plot and continental scale: A landscape scale greenhouse gas observation system based on a tall tower eddy covariance., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18939, https://doi.org/10.5194/egusphere-egu24-18939, 2024.

X5.33
|
EGU24-18491
|
ECS
Judith Tettenborn, Daniel Zavala-Araiza, Daan Stroeken, Hossein Maazallahi, Arjan Hensen, Ilona Velzeboer, Pim van den Bulk, Felix Vogel, Lawson Gillespie, Sebastien Ars, James France, and Thomas Röckmann

Efficient and cost-effective mitigation of methane emissions from local gas distribution systems requires full characterization of leaks across an urban region. Mobile real-time measurements of ambient CH4 provide a fast and effective approach to identify and quantify methane leaks. The objective of such methodologies is to relate emission rates to parameters obtained during mobile measurements. These parameters encompass the maximum methane enhancement detected while crossing a methane plume and the integrated area of the associated peak. The maximum enhancement is currently used for emission quantification in mobile measurements, but was suggested to exhibit inconsistency among various measurement devices. Based on controlled release experiments conducted in four cities (London, Toronto, Rotterdam, and Utrecht), emission estimation methodologies were evaluated. Integrated plume area was found to be a more robust metric across different methane gas analyzer devices than the maximum methane enhancement. A statistical function based on integrated plume area is proposed for more consistent emission estimations when using different instruments. Nevertheless, large temporal variations in CH4 concentration enhancements were observed for the same release rate in line with previous experiments. Evaluation of repeated measurements to address this uncertainty and enable differentiation among various leak sizes was included. This study recommends a minimum of three repeated measurements and an optimal range of 5-7 plume transects for effective emission quantification to prioritize repair actions.

How to cite: Tettenborn, J., Zavala-Araiza, D., Stroeken, D., Maazallahi, H., Hensen, A., Velzeboer, I., van den Bulk, P., Vogel, F., Gillespie, L., Ars, S., France, J., and Röckmann, T.: Improving Consistency in Methane Emission Quantification from the Natural Gas Distribution System across Measurement Devices, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18491, https://doi.org/10.5194/egusphere-egu24-18491, 2024.

X5.34
|
EGU24-16432
Cynthia Randles, Daniel Zavala-Araiza, Marci Baranski, Andrea Calcan, Claudio Cifarelli, Meghan Demeter, James France, Luis Guanter, Itziar Irakulis-Loitxate, Marc Watine-Guiu, Stefan Schwietzke, Manfredi Caltigirone, and Steven Hamburg

UNEP’s International Methane Emissions Observatory (IMEO) was established to provide reliable, public, and policy-relevant data to facilitate actions to reduce methane emissions.  IMEO is collecting and integrating diverse methane emissions data streams that will help to fill gaps in knowledge and refine global understanding of the location and magnitude of emissions across sectors.  Together these data streams – which include satellite remote sensing data, detailed analyses from multi-scale measurement campaigns, bottom-up inventory data, and measurement-based industry reporting – complement one another and provide a fuller characterization of the spatial and temporal variability in emissions than they do individually.  Knowledge of this variability is key to understanding the emissions of different populations of emitters and to identifying key mitigation opportunities for specific populations of emitters.  Such data can also be used as cross-verification points for other estimates of population-scale emissions – such as from inverse modelling or elsewhere reported emissions.  In this work, we will summarize IMEO’s efforts to assemble and integrate spatio-temporally dynamic methane emissions data including insights from measurement campaigns across the world, high-resolution methane emissions data from satellites, and developing standards for company-reported, measurement-based source- and site-level emission from the Oil and Gas Methane Partnership 2.0 (OGMP2.0). 

How to cite: Randles, C., Zavala-Araiza, D., Baranski, M., Calcan, A., Cifarelli, C., Demeter, M., France, J., Guanter, L., Irakulis-Loitxate, I., Watine-Guiu, M., Schwietzke, S., Caltigirone, M., and Hamburg, S.: The International Methane Emissions Observatory (IMEO):  Integration of methane data across scales for policy-relevant results, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16432, https://doi.org/10.5194/egusphere-egu24-16432, 2024.

X5.35
|
EGU24-7039
Evaluation of National Methane Inventories using GOSAT based Methane Fluxes for 2010-2020
(withdrawn)
John Worden, Zhen Qu, Sudhanshu Pandey, and Daniel Jacob
X5.36
|
EGU24-3664
|
ECS
Angel Vara-Vela, Christoffer Karoff, Noelia Rojas Benavente, and Janaina Nascimento

After decades of steady growth, even reaching a growth rate of approximately zero from 2000 to 2006, the atmospheric methane (CH4) has returned to values observed in the second half of the twentieth century, and in recent years it has increased at a faster rate (Palmer et al., 2021). In this context, major initiatives involving the use of satellite-based inversion approaches have been implemented to respond to a growing demand from the climate community. One of this initiatives is the Integrated Methane Inversion (IMI, Varon et al., 2022). IMI is a cloud-based facility developed to infer regional CH4 emissions at 0.25° × 0.3125° resolution, with dynamic boundary conditions from a global archive of smoothed TROPOspheric Monitoring Instrument (TROPOMI) data. Three monthly IMI simulations were conducted over Denmark to estimate CH4 emissions before (June 2018), during (June 2020), and after (June 2021) the COVID-19-related lockdowns. The calculated a posteriori emissions for these periods were 0.579 Tg yr-1, 0.396 Tg yr-1, and 0.553 Tg yr-1, respectively. The approximately 31% emission reduction in June 2020 was almost swiftly reversed in June 2021, with a reduction of emissions in June 2021 by less than 5% compared to the same period in 2018. As many months other than June do not frequently meet the IMI preview configuration (a model feature to rate the quality of a proposed inversion without actually performing the inversion), multi-period simulations are being conducted to characterize CH4 emissions across the country. The new CH4 emissions data set will serve as a benchmark to evaluate the model performance of the Aarhus University Methane Inversion Algorithm (AUMIA, Vara-Vela et al., 2023). Currently under development, AUMIA is a satellite-based tool designed to quantify CH4 emissions over Europe, with a specific focus on anthropogenic activities.

References

Palmer, P. L., Feng, L., Lunt, M. F., Parker, R. J., Bosch, H., Lan, X., Lorente, A., and Borsdorff, T.: The added value of satellite observations of methane for understanding the contemporary methane budget, Philos. T. R. Soc. A., 379, 2210, https://doi.org/10.1098/rsta.2021.0106, 2021.

Vara-Vela, A. L., Karoff, C., Benavente, R. N., and Nascimento, J. P.: Implementation of a satellite- based tool for the quantification of CH4 emissions over Europe (AUMIA v1.0) – Part 1: forward modelling evaluation against near-surface and satellite data, Geosci. Model Dev., 16, 6413-6431, 2023.

Varon, D. J., Jacob, D. J., Sulprizio, M., Estrada, L. A., Downs, W. B., Shen, L., Hancock, S. E., Nesser, H., Qu, Z., Penn, E., Chen, Z., Lu, X., Lorente, A., Tewari, A., and Randles, C. A.: Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high- resolution methane emissions from TROPOMI satellite observations, Geosci. Model Dev., 15, 5787-5805, 2022.

How to cite: Vara-Vela, A., Karoff, C., Rojas Benavente, N., and Nascimento, J.: Quantifying Methane Emissions Using Satellite Data: Integrated Methane Inversion (IMI) Model Application for Denmark, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3664, https://doi.org/10.5194/egusphere-egu24-3664, 2024.

X5.37
|
EGU24-12805
Kevin Gurney, Pawlok Dass, Huilin Sun, anna kato, Lech Gawuc, and Modeste Nematchoua

New global greenhouse gas emission products have emerged in recent years providing emissions estimation at increasing fine space/time scales. Furthermore, these efforts are moving away from traditional forms of proxy linear downscaling and toward the use of machine learning and integration of some forms of “bottom-up” data. In terms of application, there is interest in applying these data products to the city-scale, assisting and supporting mitigation activities in the global urban governance level.

 

However, it is also acknowledged that developing these very high-resolution efforts at the global scale come with particular challenges associated with data availability, method limitations, and data quality variations. Here we use a very high-resolution data product developed in the United States, the Vulcan version 4.0 emissions, as a point of comparison with two of the new global very high-resolution efforts: Climate Trace, and GRACED. The ‘Vulcan Project’ is an effort to compute bottom-up CO2 emissions from fossil fuel combustion (FFCO2) and cement production for the entire USA. Vulcan v4.0 quantifies emissions from 2010 to 2021 for multiple sectors to the point, line, and polygon spatial scale.

 

We use detailed comparison with Vulcan to illuminate and inform aspects of the global efforts that many warrant further investigation or methodological development. We upscale Vulcan to match the resolution of the global data products and aggregate as necessary to isolate sectoral matches. Using statistical analysis techniques we isolate differences that may be systematic and explainable via alternative methodologies and/or data sources. Our aim is to strengthen and improve all high-resolution efforts at multiple scales and recommend where scale limitation may exist.

How to cite: Gurney, K., Dass, P., Sun, H., kato, A., Gawuc, L., and Nematchoua, M.: Comparison of global high-resolution fossil fuel CO2 emissions data products to Vulcan v4.0: sector differences, urban geographies, and methodological guidance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12805, https://doi.org/10.5194/egusphere-egu24-12805, 2024.

X5.38
|
EGU24-5727
Shanna Combley, Argyro Kavvada, Lesley Ott, Kevin Bowman, Manil Maskey, Robert Green, William Irving, Melissa Weitz, Vanda Grubisic, Ariel Stein, James Whetstone, Annmarie Eldering, Erin McDuffie, and Alix Kashdan

The newly established United States Greenhouse Gas Center (U.S. GHG Center) is a multi-agency partnership between the National Aeronautics and Space Administration (NASA), the Environmental Protection Agency (EPA), the National Oceanic and Atmospheric Administration (NOAA) and the National Institute of Standards and Technology (NIST) that aims to accelerate the production and delivery of actionable, trusted greenhouse gas (GHG) information from the federal government and non-public sector to a variety of users through a coordinated data system, reflecting transparency and open source science principles in both data and methods. The US GHG Center acts as an enabler of collaboration with networks of interagency, international, intergovernmental and private sector partners to increase confidence in setting, assessing, and meeting climate change mitigation goals, with a preliminary focus on carbon dioxide and methane. The US GHG Center is also a critical element in the implementation of the “National Strategy to Advance an Integrated US Greenhouse Gas Measurement, Monitoring, and Information System”.  Initial focus areas include 1) Gridded anthropogenic greenhouse gas emissions, 2) Natural sources and sinks, and 3) New Observations for tracking large emission events. The US GHG Center web portal includes a prototype data catalogue, exploratory data analysis capabilities, a collaborative science environment for data analysis and exploration, as well as an interactive visual interface for storytelling. Examples of products currently available on the GHG Center portal include methane and carbon dioxide concentration anomalies and emissions from airborne and space-based instruments, including from NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer in orbit on the International Space Station, EPA’s gridded U.S. anthropogenic methane greenhouse gas inventory data, gridding methodologies and visualizations, NOAA’s Observation Package (ObsPack) data products that bring together atmospheric greenhouse gas observations from a variety of sampling platforms, as well as multi-model land flux and ecosystem exchange estimates. 

How to cite: Combley, S., Kavvada, A., Ott, L., Bowman, K., Maskey, M., Green, R., Irving, W., Weitz, M., Grubisic, V., Stein, A., Whetstone, J., Eldering, A., McDuffie, E., and Kashdan, A.: The U.S. Greenhouse Gas Center: Extending Accessible and Integrated GHG Information from U.S. Government and Non-Public Sources to meet user needs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5727, https://doi.org/10.5194/egusphere-egu24-5727, 2024.

X5.39
|
EGU24-14411
|
ECS
Quantifying CO2 Emissions from Large Point Sources in the Indian Region: A  Data-Driven Approach using Satellite Measurements.
(withdrawn)
Jithin Sukumaran, Dhanyalekshmi Pillai, Vishnu Thilakan, and Monish Deshpande
X5.40
|
EGU24-11669
Mirosław Zimnoch, Michał Gałkowski, and Piotr Sekuła

In order to accurately and precisely estimate anthropogenic greenhouse gas (GHG) emissions at different spatial and temporal scales, independent tools based on atmospheric observations are required as a necessary source of information for mitigation climate change efforts to be successful. Bayesian inversion systems utilizing state-of-the-art atmospheric transport models constitute a key element of anthropogenic emissions monitoring and verification systems, allowing for mathematically-grounded method of assessing emissions based on observed mole fractions.

Poland, the fifth largest economy in the EU, is simultaneously the fourth largest emitter of GHGs in terms of CO2 equivalent, owing primarily to only slowly decreasing reliance on coal for power generation.  Here, we present first results of the developmental inversion framework consisting of the WRF-GHG model run at 5 km spatial resolution over Central Europe, coupled with an analytical system in order to explain total emissions of CO2 for selected months (February and July) over Poland and Germany, the largest emitter of CO2 in Europe, for comparison. We also compare results for both 2018 and 2021 in an attempt to capture changes in emission patterns following the implementation of the various policies both before and after Paris Agreement. We also focus on the ability of the inversion system to capture changes in biogenic and anthropogenic emissions and address challenges stemming from the limited ground-based observation network in Poland.

Furthermore, we also discuss the ability of the system to distinguish emissions on the national, voivodeship (admin level 1) and city scale, thanks to the additional high-resolution simulations and in-situ observations in the city of Kraków.

 

The presented work was funded by the CoCO2 project, which has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 958927 and the "Excellence Initiative - Research University" programme at AGH University of Kraków. We also gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centres: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2022/015860.

How to cite: Zimnoch, M., Gałkowski, M., and Sekuła, P.: Analytical regional inversion system for CO2 fluxes in Poland – first results from CoCO2 project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11669, https://doi.org/10.5194/egusphere-egu24-11669, 2024.

X5.41
|
EGU24-1562
|
ECS
Dylan Geissbühler, Thomas Laemmel, Philip Gautschi, Lukas Wacker, and Sönke Szidat

The RICH (Radiocarbon Inventories of Switzerland) project aims to build the first database and model of the distribution and cycling of 14C at a national scale across the atmosphere, soils, rivers and lakes C pools. The subproject presented here (RICH-Air) will serve to construct complementary monitoring and snapshots approaches of atmospheric 14CO2 measurement in this larger scope.

Radiocarbon measurements of atmospheric CO2 provide unique information on its sources and subsequent transport. It allows the apportionment between biogenic and fossil sources, which are close to the contemporary atmospheric background and 14C-free, respectively. The determination of the fossil CO2 fraction in air samples, can be used to identify fossil fuel emission patterns from a local to a regional scale. These efforts can then be used to plan and enforce future CO2 emissions mitigation steps.

Presented here are preliminary results from investigations regarding the fossil factor in emissions of 3 Swiss cement factories and the urban area of Bern, Switzerland. The radiocarbon content of emissions were studied in multiple ways:

  • Direct and downwind measurement of 14CO2 emissions at cement factories
  • Measurement of 14C content in tree leaves around cement factories and the urban area of Bern

The 14CO2 results show that downwind emissions from cement factories are only accurate if the choice of local background is appropriate. Measured values, both direct and indirect, show that the fossil fraction of emissions is at least of 2/3, which is within the theoretical range for cement production. Also, different facilities seem to have contrasting mean fossil content in their emissions, probably due to their individual fuel mix. Finally, leaf samples show a gradient in 14C values, more depleted closer from the source, both for cement factories or the urban area, which is consistent with previous studies.

How to cite: Geissbühler, D., Laemmel, T., Gautschi, P., Wacker, L., and Szidat, S.: Radiocarbon Inventories of Switzerland (RICH): Investigations into fossil CO2 emissions from cement factories and urban areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1562, https://doi.org/10.5194/egusphere-egu24-1562, 2024.

X5.42
|
EGU24-19004
|
Highlight
Multi-decade evaluation of the UK’s greenhouse gas emissions
(withdrawn)
Matthew Rigby, Alistair Manning, Simon O'Doherty, Kieran Stanley, Anita Ganesan, Joe Pitt, Chris Rennick, Emmal Safi, and Tim Arnold
X5.43
|
EGU24-16466
Rona Thompson, Andreas Stohl, Philippe Peylin, Philippe Ciais, Hartmut Boesch, Tuula Aalto, Antoine Berchet, Maria Kanakidou, Wilfried Winiwarter, Glen Peters, Dmitry Shchepashchenko, Jean-Pierre Chang, Roland Fuss, Ignacio Pisso, Richard Engelen, Almut Arneth, Nina Buchmann, Stefan Reimann, Stephen Platt, and Nalini Krishnankutty

National greenhouse gas inventories (NGHGIs) and Biennial Transparency Reports (BTRs) on emissions and removals are crucial elements of the Paris Agreement and its Global Stocktake. However, NGHGIs are subject to significant uncertainties, owing to uncertain emission factors and/or insufficient activity data, thus there is a need for their independent verification. One method to do this is through atmospheric inversions, which use atmospheric observations in a statistical optimization framework to estimate surface-to-atmosphere fluxes. This method of verification is referred to in the 2006 IPCC Guidelines on national reporting and in their 2019 refinement. However, atmospheric inversions have been hitherto considered too complex and inaccurate at national scales to be widely used for this purpose.

EYE-CLIMA is a Horizon Europe project that aims to develop the atmospheric inversion methodology to a level of readiness where it can be used to support the verification of NGHGIs. The overarching goals are to: i) develop a best practice in atmospheric inverse modelling for estimating emissions at national scale, including a full assessment of the uncertainties, ii) develop the methodology on how to prepare sectorial emission estimates from atmospheric inversions and make these comparable to what is reported in NGHGIs, iii) work together with NGHGI agencies on projects piloting the EYE-CLIMA methodology of emissions verification and iv) develop international best practices for the quality control of NGHGIs. EYE-CLIMA covers CH4, N2O, 5 HFC species, SF6, and the black carbon (BC) aerosol. This presentation will focus on the set-up of the EYE-CLIMA project and provide an overview of the first results in support of NGHGI verification.

How to cite: Thompson, R., Stohl, A., Peylin, P., Ciais, P., Boesch, H., Aalto, T., Berchet, A., Kanakidou, M., Winiwarter, W., Peters, G., Shchepashchenko, D., Chang, J.-P., Fuss, R., Pisso, I., Engelen, R., Arneth, A., Buchmann, N., Reimann, S., Platt, S., and Krishnankutty, N.: EYE-CLIMA: A Horizon Europe project to support national inventories for emissions of climate forcers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16466, https://doi.org/10.5194/egusphere-egu24-16466, 2024.

X5.44
|
EGU24-17381
Sylvia Walter, Anita Ganesan, Marko Scholze, Rona Thompson, and Thomas Röckmann

PARIS, AVENGERS, and EYE-CLIMA represent initiatives funded under the EU Horizon call focused on "Verification and reconciliation of estimates of climate forcers." Drawing expertise from diverse fields such as atmospheric science, ecology, computer science, systems analysis, climate, and emissions reporting, these projects collaborate with the shared objective of refining estimates of greenhouse gas (GHG) emissions through observation-based methodologies. This collaborative effort not only aims to enhance the precision of GHG emission estimates but also facilitates meaningful exchanges with stakeholders involved in policymaking, national greenhouse gas inventories (NGHGIs), government bodies, and non-governmental organizations.

Utilizing atmospheric inversion models, the projects establish connections between surface-atmosphere GHG exchanges and atmospheric concentrations. The emissions estimates derived through this method directly correlate with atmospheric observations, remaining independent of activity data and emission factors. Consequently, this approach supports the independent verification of NGHGIs. In essence, PARIS, AVENGERS, and EYE-CLIMA strive to reconcile emissions information to contribute to the effective implementation of the Paris Agreement. Beyond atmospheric inversion methods, the projects incorporate land-surface models, which simulate the processes governing GHG exchanges between the land surface and atmosphere, along with data-driven models. 

This presentation will provide a comprehensive overview of the three projects, delving into their individual objectives and highlighting the overarching efforts aimed at verifying and reconciling estimates of climate forcers.

How to cite: Walter, S., Ganesan, A., Scholze, M., Thompson, R., and Röckmann, T.: PARIS, AVENGERS, EYE-CLIMA - Verification and reconciliation of estimates of climate forcers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17381, https://doi.org/10.5194/egusphere-egu24-17381, 2024.

X5.45
|
EGU24-19167
|
ECS
Yeon Bae, Sujong Jeong, and Jeongmin Yun

he precise estimation and verification of country-specific net carbon exchange are growing in importance for meeting greenhouse gas reduction targets outlined in the Paris Agreement. In the East Asian region, carbon dioxide emissions account for more than one-third of global emissions; however, the values remain highly uncertain. This study aims to calculate the country-specific Net Carbon Exchange (NCE) for the years 2015–2022 in three East Asian countries—Korea, Japan, and China—using a top-down assessment approach. We utilize the atmospheric inversion system developed based on Geos-Chem adjoint v35j to assimilate OCO-2 XCO2 retrievals, generating net carbon flux. These values compare with national emission inventory data reported to the IPCC and forest growing stock form the National Forest Inventory. We evaluate the national CO2 budgets for the three East Asian countries and analyze the spatial and temporal variations in carbon fluxes.

How to cite: Bae, Y., Jeong, S., and Yun, J.: Evaluating the national CO2 budgets of East Asian countries (2015-2022) using the top-down approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19167, https://doi.org/10.5194/egusphere-egu24-19167, 2024.

X5.46
|
EGU24-18574
Hugo Denier van der Gon, Sophie van Mil, Rianne Dröge, Xinxuan Zhang, and Junjie Wang

Nitrous oxide (N2O) is the third most important long-lived greenhouse gas (GHG) and an important stratospheric ozone-depleting substance. We present the first version of a new high-resolution European emission inventory of anthropogenic direct and indirect N2O emissions. This inventory is developed to achieve one of the special objectives of the AVENGERS project, namely to advance the provision of high-resolution prior emissions by use of innovative activity data. AVENGERS is a Research and Innovation project funded under the Horizon Europe program of the European Union whose objective is to reconcile reported GHG emissions with independent information from atmospheric observations using top-down methods and process-based models, and thereby reduce the most important uncertainties of national emission inventories. To be able to compare national reported emission data against observations and model estimates, these reported emissions need to be available in a gridded form including essential emission characteristics such as emission timing and emission height. Our emission inventory starts from the reporting by European countries in their National Inventory Reports (NIR) to UNFCCC. Emissions are collected at the highest sectoral level. Each (sub)sector is connected to a specific spatial proxy, whereafter a consistent spatial distribution is applied for Europe at a resolution of 0.05 × 0.1 grid resolution (~6x6km). To support inverse modelling over a longer period where measurements are available, the inventory covers the period 2010-2021. Differences in country-specific choices in emission reporting to UNFCCC, e.g., in the waste sector, may lead to inconsistent emission estimates; we provide options for harmonization of these discrepancies. We pay special attention to understanding the role of indirect N2O emissions that may be equivalent to 15-20% of the total anthropogenic N2O emissions. Indirect emissions involve nitrogen that is emitted by anthropogenic activities and/or removed from agricultural soils and animal waste management systems via volatilization, leaching, runoff, or harvest of crop biomass, leading to N2O formation elsewhere. The reported indirect emissions, emitted from natural ecosystems and/or waterbodies are compared against process-model based emission estimates. The ultimate objective of this research is to reduce uncertainties in the key sources of N2O and support the implementation of top-down methods in support of the UNFCCC’s NIR preparation in collaboration between inverse modellers and inventory compilers.

How to cite: Denier van der Gon, H., van Mil, S., Dröge, R., Zhang, X., and Wang, J.: A high-resolution European emission inventory of anthropogenic direct and indirect N2O emissions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18574, https://doi.org/10.5194/egusphere-egu24-18574, 2024.

X5.47
|
EGU24-9996
|
ECS
|
Christopher Lüken-Winkels, Lukas Pilz, Massimo Cassiani, Ignacio Pisso, and Sanam N. Vardag

Urban areas are significant contributors to anthropogenic carbon dioxide (CO2) emissions, responsible for approximately 70% of the total anthropogenic CO2 emissions. In the years to come, it is expected that urban areas will increase their efforts to mitigate CO2 emissions. To independently verify these reductions, atmospheric measurements of CO2 and other tracers can be used within an inversion framework to estimate emissions. While there are some cities which have established measurement networks for this purpose, many urban centers are still lacking the necessary measurement infrastructure for high-resolution inverse modeling. It is an open question how a measurement network should be designed to maximize the information content of the urban emissions. 

In our study, we conduct Observing System Simulation Experiments (OSSEs) to evaluate the potential of different measurement network configurations for the city of Berlin, Germany. The approach involves utilizing meteorological data at a spatial resolution of 1 km, computed using the Weather Research & Forecasting Model (WRF), to model the relationship between emissions and measured concentrations (footprints). The footprints are calculated using the Lagrangian Particle Dispersion Model FLEXPART-WRF. Concentration enhancements of WRF and FLEXPART-WRF are compared throughout a year. 

We assess various in-situ network configurations, considering both preexisting meteorological networks and a gridded approach for potential measurement locations. Using a Bayesian inversion for the prediction of emissions, different subsets of these networks are selected to constrain total emissions as well as anthropogenic and biogenic CO2 fluxes. The tested measurement configurations encompass variations in the number and quality of stations, allowing for the identification of both efficient and effective networks.  

In conclusion, our findings provide insights into the strategic deployment of CO2 measurement networks in Berlin, supporting ongoing efforts to refine greenhouse gas monitoring. The available meteorological data will additionally enable comparable studies for further German metropolitan areas as planned in the German project “Intergiertes Treibhausgas Monitoring System (ITMS)”. 

How to cite: Lüken-Winkels, C., Pilz, L., Cassiani, M., Pisso, I., and Vardag, S. N.: High-resolution inversion of Berlin city emissions – A synthetic study using FLEXPART-WRF for network optimization within ITMS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9996, https://doi.org/10.5194/egusphere-egu24-9996, 2024.

X5.48
|
EGU24-9044
|
ECS
Friedrich Klappenbach, Jia Chen, Ronald C. Cohen, Jonathan Franklin, Taylor Jones, Moritz Makowski, and Seven Wofsy

We developed a novel method to estimate from an observations-time series the upwind distance as well as the emission strength of an unknown source, which releases a gas into the atmosphere (top-down). For this purpose, we used LPDM-modeled particle trajectories to infer the transfer function of the source region. The transfer function that matches the observed enhancement best, identifies the potential source region. In a second step, we infer the source strength using the particle ensemble.

We developed this method with a data set obtained during a six-week campaign in the San Francisco Bay Area. Aim was, to infer greenhouse gas emissions, specifically carbon dioxide and methane, from total column abundances.

At the UC-Berkeley site, one particular instrument recorded a strictly periodic peak-enhancement of approximately 10ppb methane within a consecutive 12-minute interval. Co-emitted species showed no correlation with this pattern. Therefore, we assumed a singular, point, and puff-emitting source of methane.

Due to favorable meteorological conditions, we were able to analyze a total of 14 peaks during a three-hour time-span in the forenoon. We estimated the average emission strength during the emission period to be 1.8+/-0.5 g(CH4)/s (equivalent to 6.48+/-1.80 kg/hr). Although we were unable to identify the source in the field, we concluded that methane ventilation from the natural gas supply, a so-called blow-down, could be a plausible explanation.

How to cite: Klappenbach, F., Chen, J., Cohen, R. C., Franklin, J., Jones, T., Makowski, M., and Wofsy, S.: Novel source localization method from observed peak emissions in time series using LPDM transfer functions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9044, https://doi.org/10.5194/egusphere-egu24-9044, 2024.

X5.49
|
EGU24-8039
|
ECS
Antje Hoheisel, Christian Maurer, Marie D. Mulder, Peter Redl, Stefan Schneider, Jia Chen, Andreas Luther, Bradley Matthews, Andrea Watzinger, Kathiravan Meeran, and Marcus Hirtl

The Austrian Flagship Project “GHG-KIT: Keep it traceable” aims to prototype an Austrian, Earth Observation-Integrated GHG measurement and modelling system, which can support national GHG emission monitoring. Among other aims, the project is working toward an Austrian inverse modelling framework to produce top-down estimates of national and subnational CO2 and CH4 fluxes that are independent of the current bottom-up system of the Austrian GHG inventory.
This conference contribution will present the GHG-KIT progress on inverse modelling of subnational CO2 and CH4 emissions, using Vienna as a case study. Vienna is the most populated city in Austria with around 2 million inhabitants, corresponding to slightly more than a fifth of the total Austrian population. To estimate the GHG emissions in Vienna the inversion framework FLEXINVERT is used. The atmospheric back trajectories for GHG measurements carried out in Vienna are calculated using the Lagrangian dispersion model FLEXPART-WRF, which is driven by WRF meteorology. The a priori fluxes of GHGs, used in FLEXINVERT, are prepared using WRF-GHG and are based on data from the Copernicus Atmosphere Monitoring Service (CAMS), among others. WRF-GHG is a Weather Research and Forecasting (WRF) model version that is coupled with chemistry modules as well as the GHG flux module and considers urban building features. Atmospheric ground-based observations of CO2 and CH4 mole fractions from the Vienna Urban Carbon Laboratory are included in the inverse modelling. These include in-situ observations from a tall-tower, as well as total column measurements at four locations from a 2022 summer campaign performed by the Technical University of Munich. Furthermore, the usability of satellite measurements over Vienna as an additional observation constraint will be investigated. This includes GHGSat measurements from Vienna that are carried out as part of the GHG-KIT project, as well as synthetic or possibly authentic observations from satellite missions (such as CO2M and MethaneSAT) that will be launched during the course of this project or shortly thereafter.

How to cite: Hoheisel, A., Maurer, C., Mulder, M. D., Redl, P., Schneider, S., Chen, J., Luther, A., Matthews, B., Watzinger, A., Meeran, K., and Hirtl, M.: GHG-KIT project: Inverse modelling of Vienna’s CH4 and CO2 emissions using in-situ and remote observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8039, https://doi.org/10.5194/egusphere-egu24-8039, 2024.

X5.50
|
EGU24-8915
|
ECS
Lukas Pilz, Christopher Lüken-Winkels, Michał Gałkowski, David Ho, Fei Chen, and Sanam N. Vardag

Verifying greenhouse gas (GHG) mitigation efforts of governments using atmospheric observations is a task which is rapidly gaining scientific interest and attention. The United Nations Framework Convention on Climate Change (UNFCCC) requires the compilation of National Inventory Reports and recommends augmenting them with observational data. The joint project ITMS (Integriertes Treibhausgas-Monitoringsystem für Deutschland) is Germany's national contribution to the World Meteorological Organization’s Integrated Global Greenhouse Gas Information System (IG3IS). It will establish the scientific basis and methodology for integrating GHG observations into the national emissions inventories. Our focus within the ITMS joint project is to optimize observation strategies for monitoring fossil CO2 emissions in German urban and metropolitan areas using synthetic studies. Focusing on cities is especially relevant as cities are substantial contributors to total anthropogenic CO2 emissions. 

Our study uses the Weather Research and Forecasting model (WRF v4.3.3) with ECMWF-ERA5 as meteorological input and boundary conditions. As a first step, we have optimized model transport such that our results are representative of real-world conditions as much as possible. Within comprehensive sensitivity studies, we have analyzed the optimal model settings for German urban areas. Our sensitivity studies focus on the Rhine-Neckar region and compare 16 different physics configurations of WRF for 4 months of 2020, representative of the four seasons. Modeled meteorological variables were compared against 19 meteorological observation stations operated by the German Weather Service and 2 radiosonde stations. We found the setup using Mellor-Yamada-Janjic boundary layer, Noah MP land surface, Monin-Obukhov surface layer and BEP urban parametrization scheme has the overall best performance for our use case. 

Using the optimal setup for urban areas in WRF, we have generated a year-long, 1km resolved dataset of German metropolitan areas. This dataset contains meteorological and sector specific CO2 enhancement data for the year 2018. These metropolitan areas include the Rhine-Neckar, Berlin, Rhine-Ruhr, Nuremberg and Munich metropolitan areas. We showcase the usefulness of the dataset by comparison to actual observations in cities.

How to cite: Pilz, L., Lüken-Winkels, C., Gałkowski, M., Ho, D., Chen, F., and Vardag, S. N.: High-resolution meteorological CO2 enhancements of German metropolitan areas using WRF, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8915, https://doi.org/10.5194/egusphere-egu24-8915, 2024.

X5.51
|
EGU24-5069
|
ECS
Sojung Sim and Sujong Jeong

The Bayesian inverse method, combined with measurements of atmospheric carbon dioxide (CO2) and a transport model, can serve as an independent verification approach to improve the precision of emission estimates. This study utilized the Bayesian inverse model, along with ground- and space-based measurements, to validate CO2 emissions in Seoul. A Bayesian inverse modeling framework was developed, integrating crucial input data such as anthropogenic CO2 emissions, biogenic CO2 fluxes, atmospheric CO2 measurements, a Lagrangian transport model, and error covariances for both prior emissions and observations. The averages of posterior emissions decreased after the inversion run, with a correction of approximately -8.69%. This suggests that the prior emissions were overestimated. There was an average 9.7% reduction in posterior emission uncertainties compared to prior uncertainties. The most substantial reductions in uncertainty were observed in areas with concentrated observation sites. The performance of the inverse model was thoroughly investigated through sensitivity analysis, encompassing different background representations, prior uncertainty levels, temporal and spatial uncertainties, and observational network configurations. Additionally, we quantified spatiotemporal changes in CO2 emissions due to COVID-19. The abundance of ground and space observations in Seoul provided robust constraints on urban CO2 emissions, allowing for an objective evaluation of the effectiveness of carbon reduction policies.

This work was supported by Korea Environmental Industry & Technology Institute (KEITI) through "Project for developing an observation-based GHG emissions geospatial information map", funded by Korea Ministry of Environment(MOE)(RS-2023-00232066).

How to cite: Sim, S. and Jeong, S.: Development of Bayesian inverse modeling framework to verify CO2 emissions in Seoul, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5069, https://doi.org/10.5194/egusphere-egu24-5069, 2024.

X5.52
|
EGU24-11113
|
ECS
|
Tobias D. Schmitt, Lukas Pilz, Robert Maiwald, Maximilian May, Benedikt A. Löw, Ralph Kleinschek, Julia B. Wietzel, Jonas Kuhn, Stefan Schmitt, Martina Schmidt, Sanam N. Vardag, Frank Hase, David W. T. Griffith, and André Butz

Urban areas are a major and growing contributor to anthropogenic greenhouse gas (GHG) emissions and are thus an important target for emission reduction efforts. However, measurement-based information for planning, implementing, and monitoring such reduction efforts on city scales is rarely available to policymakers and stakeholders. Such monitoring systems typically rely on three key components: measurements of GHG concentrations (or turbulent fluxes), modeling of the atmospheric transport and prior information on the spatial and/or temporal structure of the emissions. The high spatial and temporal heterogeneity of urban areas and their emissions is especially challenging for atmospheric transport models and gridded inventories, which are currently pushed to resolutions of kilometers and below in an effort to accurately represent these effects. However, GHG concentration measurements are often performed by in-situ systems and are thus not necessarily representative for the kilometer scales on which measurements, transport modeling and prior information are typically compiled. This becomes increasingly important with the ever-improving quality of measurements, models, and inventories themselves.

We present a dataset of urban path averaged concentration measurements of CO2 and CH4 and their comparison to co-located in-situ measurements. The path averaged measurements are taken along a 1.55 km long path over the city of Heidelberg, Germany. The observatory utilizes FTIR spectroscopy and is now in continuous operation since February 2023. Analysis of the path averaged and co-located in-situ measurements reveals differences of up to 20 ppm in CO2 for specific wind directions, which are most likely a result of a local atmospheric transport phenomenon. Further, the two measurements show differences in CH4, which are likely a result of different sensitivities to local emissions. Overall, the data indicate a clear but different sensitivity of either measurement approach to localized source patterns. Thus, the dataset enables the assessment of the representativeness of the different measurement approaches and of the performance of atmospheric transport models and emission inventories in the urban environments.

How to cite: Schmitt, T. D., Pilz, L., Maiwald, R., May, M., Löw, B. A., Kleinschek, R., Wietzel, J. B., Kuhn, J., Schmitt, S., Schmidt, M., Vardag, S. N., Hase, F., Griffith, D. W. T., and Butz, A.: A combined dataset of path-averaged and in-situ measurements of greenhouse gases to inform on the sensitivities to localized source patterns and transport effects in the urban atmosphere., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11113, https://doi.org/10.5194/egusphere-egu24-11113, 2024.

X5.53
|
EGU24-22508
Vanda Grubišić, Ariel Stein, Monica Kopacz, and Annarita Mariotti

NOAA has over 50 years of experience monitoring global atmospheric levels of greenhouse gasses (GHG) emitted to the atmosphere by human activities and natural sources. This includes most notably measurements of atmospheric abundances of CO2 and other long-lived GHGs at its Global Greenhouse Gas Reference Network (GGGRN). The goal of GGGRN is to provide measurements of GHGs and their large-scale spatial and temporal distributions as precisely and accurately as possible to be able to determine spatial gradients in GHGs and inform changes in emissions and sinks. Success at meeting this goal requires long-term continuity of measurements and measurement quality. To that end, NOAA is largely responsible for the World Meteorological Organization (WMO) standard for CO2 and several other GHG measurements, providing a solid foundation for well-calibrated global atmospheric GHG measurements. The NOAA carbon monitoring covers both the atmosphere and the oceans and its unique multi-platform approach to observing GHGs, includes the ocean observing capabilities of air-sea carbon fluxes and pCO2 measurements, among other key assets. While these networks have been instrumental in providing a basic understanding of the carbon cycle, there are many gaps that need to be filled over the next decade to assess interannual variability in both natural and anthropogenic sources and sinks of GHGs. This presentation will highlight the latest developments in NOAA carbon monitoring, including the development of new planforms, networks, and tracers for observing atmospheric and ocean GHGs gradients and processes driving these gradients. Innovative research supported by NOAA has leveraged long-term GHG monitoring to accelerate the development of global GHG models, and advanced GHG research and accounting on urban, regional, and global scales. NOAA’s GHG capabilities span measurements, process research, modeling, data assimilation and data products such as the Annual Greenhouse Gas Index (AGGI) as well as future climate projections for IPCC reports and the National Climate Assessment. NOAA uniquely includes research and service capabilities under one roof. Decades of experience in carbon cycle research, including GHG monitoring, modeling and data assimilation provide the ideal foundation to accelerate national and international efforts for carbon measurement, monitoring, reporting and verification (MMRV) in support of climate mitigation.

How to cite: Grubišić, V., Stein, A., Kopacz, M., and Mariotti, A.: NOAA Carbon Monitoring, Research, and Innovation: Long-Standing Foundation to Support Climate Mitigation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22508, https://doi.org/10.5194/egusphere-egu24-22508, 2024.

Posters virtual: Fri, 19 Apr, 14:00–15:45 | vHall X5

Display time: Fri, 19 Apr 08:30–Fri, 19 Apr 18:00
Chairperson: Werner Leo Kutsch
vX5.9
|
EGU24-4825
|
ECS
Inversion and Evaluation of anthropogenic CO2 emissions based on 4Dvar method and the ratio of NOx-to-CO2
(withdrawn after no-show)
Yiwen Hu, Zengliang Zang, Yi Li, Wei You, Lang Liu, and Ning Liu
vX5.10
|
EGU24-13264
|
ECS
A generalized Large Eddy Simulation (LES) based framework for quantifying methane emissions from oil & gas facilities: An alternative to controlled-release field tests
(withdrawn after no-show)
Umair Ismail, Jorge Guerra, Nathan Eichenlaub, David Ball, and Kieran Lynn