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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.