AS3.8

Synthetic halocarbons reach the atmosphere due to a wide range of anthropogenic activities. They are, for example, used as propellants in foam blowing or as cooling agents in refrigeration and air conditioning. Long-lived halocarbons act as strong greenhouse gases. They are responsible for about 11% of the radiative forcing by long-lived greenhouse gases (LLGHGs). In addition, chlorinated or brominated halocarbons contribute to stratospheric ozone depletion. There are only two in situ long-term measurement programs, operated by the Advanced Global Atmospheric Gases Experiment (AGAGE) and the National Oceanic and Atmospheric Administration (NOAA) that monitor the worldwide abundance of halocarbons in the atmosphere. Based on these observations, halocarbon emissions are estimated by top-down box- or inverse modelling approaches on a global to transnational scale. However, to capture regional pollution sources and to validate country-specific bottom-up emission estimates by top-down methods, additional regional-scale measurements are required.

We present the first continuous halocarbon measurements at the Beromünster tall tower, representing the most industrialized and densely populated area of Switzerland, the Swiss Plateau. During one year, high precision, high accuracy atmospheric measurements were performed with the analytical setup of the global AGAGE network. This involves sample pre-concentration at low temperatures (down to -180 ^oC), and analyte separation and detection by gas chromatography and quadrupole mass spectrometry. All halocarbon compound classes of the Montreal and Kyoto Protocols are covered by our measurements. This includes the banned chlorofluorocarbons (CFCs) and halons, the regulated hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs), as well as the recently introduced unregulated hydrochfluoroolefins (HFOs). The results improve our understanding of important source areas in Switzerland, and, for the first time offer the possibility to robustly quantify Swiss national halocarbon emissions with observation-based top-down methods, i.e. the tracer ratio method and Bayesian inverse modeling.

How to cite: Rust, D., Vollmer, M. K., Katharopoulos, I., Henne, S., Hill, M., Emmenegger, L., Zenobi, R., and Reimann, S.: Swiss Emissions of Halogenated Greenhouse Gases derived from Atmospheric Measurements at Beromünster, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6001, https://doi.org/10.5194/egusphere-egu21-6001, 2021.

Achievement of the carbon-neutral society is one of the overarching tasks for the sustainability of humanity. Under the Paris Agreement of the United Nations Framework Convention of Climate Change, it becomes an important task for research community to establish a comprehensive, high-precision, and transparent system of greenhouse gas (GHG) budget estimation. In April 2021, a new task-force project will be launched in Japan to develop a GHG monitoring system, provisionally called the Comprehensive Observation and Modeling for Multi-scale Estimation of Greenhouse Gas budgetS (COMM-EGGS), funded by the Ministry of the Environment, Japan. This is a joint project of National Institute for Environmental Studies, Japan Agency for Marine-Earth Science and Technology, Chiba University, and Meteorological Research Institute. The project is composed of three research components: 1) observation and top-down estimation of GHG budget, 2) evaluation of GHG mitigation with an Earth system model, and 3) bottom-up estimation of GHG budget. These activities cover different spatial scales spanning from major city to national and global emissions, by using ground observatory, aircraft, and satellite observations and fine-mesh atmospheric and surface emission models. In the project, we put emphasis on the Asia-Pacific region, in which a comprehensive GHG monitoring system is deficient to date. Through mutual comparison and validation, we will make attempt to improve confidence of the estimation system for GHG budget verification. Finally, the system aims at contributing to the Global Stocktake of the Paris Agreement by providing scientific evidence for GHG emission reduction.

How to cite: Ito, A., Niwa, Y., Hajima, T., and Saigusa, N.: Development of a multi-scale greenhouse gas budget estimation system in Japan, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1643, https://doi.org/10.5194/egusphere-egu21-1643, 2021.

The world-wide lockdown in response to the COVID-19 pandemic in year 2020 led to economic slowdown and large reduction of fossil fuel CO2 emissions 1,2, but it is unclear how much it would reduce atmospheric CO2 concentration, the main driver of climate change, and whether it can be observed. We estimated that a 7.9% reduction in emissions for 4 months would result in a 0.25 ppm decrease in the Northern Hemisphere CO2, an increment that is within the capability of current CO2 analyzers, but is a few times smaller than natural CO2 variabilities caused by weather and the biosphere such as El Nino. We used a state-of-the-art atmospheric transport model to simulate CO2, driven by a new daily fossil fuel emissions dataset and hourly biospheric fluxes from a carbon cycle model forced with observed climate variability. Our results show a 0.13 ppm decrease in atmospheric column CO2 anomaly averaged over 50S-50N for the period February-April 2020 relative to a 10-year climatology. A similar decrease was observed by the carbon satellite GOSAT3. Using model sensitivity experiments, we further found that COVID, the biosphere and weather contributed 54%, 23%, and 23% respectively. In May 2020, the CO2 anomaly continued to decrease and was 0.36 ppm below climatology, mostly due to the COVID reduction and a biosphere that turned from a relative carbon source to carbon sink, while weather impact fluctuated. This seemingly small change stands out as the largest sub-annual anomaly in the last 10 years. Measurements from global ground stations were analyzed. At city scale, on-road CO2 enhancement measured in Beijing shows reduction of 20-30 ppm, consistent with drastically reduced traffic during the lockdown, while station data suggest that the expected COVID signal of 5-10 ppm was swamped by weather-driven variability on multi-day time scales. The ability of our current carbon monitoring systems in detecting the small and short-lasting COVID signal on the background of fossil fuel CO2 accumulated over the last two centuries is encouraging. The COVID-19 pandemic is an unintended experiment whose impact suggests that to keep atmospheric CO2 at a climate-safe level will require sustained effort of similar magnitude and improved accuracy and expanded spatiotemporal coverage of our monitoring systems.

How to cite: Zeng, N.: Global to local impacts on atmospheric CO2 caused by COVID-19 lockdown, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2978, https://doi.org/10.5194/egusphere-egu21-2978, 2021.

The sharp decrease in emissions caused by the Corona crisis entails the question: where, when, and how strong impacts on observations can be expected? The Icosahedral Nonhydrostatic (ICON) model is online-coupled to the modules for Aerosols and Reactive Trace gases (ART). With the model system ICON-ART run at roughly 13km resolution we determine how CO₂ emission reductions in Germany relate to a reduction in CO₂ concentrations. This varies over several orders of magnitude, depending on the weather related atmospheric transport. We compare this with the emission reduction effect originating from outside Germany. In a case study, we identify locations and times, where either effect reaches a magnitude to be observable at the Integrated Carbon Observation System (ICOS) towers in Germany. In contrast, there are also weather situations, where both contributions (from inside/outside Germany) are negligible with respect to the background variability. Reducing background uncertainty, as foreseen in the CoCO2 project, will allow better disentangling of the national contribution in future. Here we focus on the height dependency of the modelled concentration change with respect to recent anthropogenic emissions. We draw conclusions on measurement and modelling capabilities essential for an integrated greenhouse gas monitoring system for Germany to detect anthropogenic emission reductions.

How to cite: Kaiser-Weiss, A. K., Mamtimin, B., Roth, F., Sunkisala, A., and Förstner, J.: Modelling the Covid-19 impact on CO2 concentrations in Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13521, https://doi.org/10.5194/egusphere-egu21-13521, 2021.

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO₂) emissions. This research first presented near-real-time high-spatial-resolution(0.1°*0.1°) and high-temporal-resolution(daily) gridded estimates of CO₂ emissions for different sectors named Carbon Monitor Gridded Dataset(CMGD). This dataset responds to the growing and urgent need for high-quality, fine-grained CO₂ emission estimates to support global emissions monitoring on the refined spatial scale. CMGD is derived from our Carbon Monitor, a near-real-time daily dataset of global CO₂ emission from fossil fuel and cement production, including detailed information in 6 sectors and main countries. Based on EDGAR v5.0 gridded CO₂ emissions map and other geospatial proxies, we finally constructed CMGD with a high spatial resolution of 0.1 degree. Here, we provided the total emissions of specific countries and analyzed the countries with larger emissions (including the EU). Furthermore, we analyzed the daily emission changes of several typical cities around the world and provided insights on the contributions of various sectors. Through CMGD, we can get a much faster and more fine-grained overview, including timelines that show where and how emissions decreases have corresponded to lockdown measures at the finer spatial scales. The fine-grain and timeliness of CMGD emissions estimates will facilitate more local and adaptive management of CO₂ emissions during both the pandemic recovery and the ongoing energy transition.

How to cite: Dou, X. and Liu, Z.: Carbon Monitor Gridded Dataset (CMGD), a near-real-time high resolution gridded CO2 emission estimates dataset, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4323, https://doi.org/10.5194/egusphere-egu21-4323, 2021.

To understand the Korean Peninsula's carbon dioxide (CO₂) emissions and sinks as well as those of the surrounding region, we used 70 flask-air samples collected during May 2014 to August 2016 at Anmyeondo (AMY; 36.53^∘ N, 126.32^∘ E; 46 m a.s.l.) World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) station, located on the west coast of South Korea, for analysis of observed ¹⁴C in atmospheric CO₂ as a tracer of fossil fuel CO₂ contribution (C_ff). Observed ¹⁴C ∕ C ratios in CO₂ (reported as Δ values) at AMY varied from −59.5 ‰ to 23.1 ‰, with a measurement uncertainty of ±1.8 ‰. The derived mean value C_ff of (9.7±7.8) µmol mol⁻¹ (1σ) is greater than that found in earlier observations from Tae-Ahn Peninsula (TAP; 36.73^∘ N, 126.13^∘ E; 20 m a.s.l., 28 km away from AMY) of (4.4±5.7) µmol mol⁻¹ from 2004 to 2010. The enhancement above background mole fractions of sulfur hexafluoride (Δx(SF₆)) and carbon monoxide (Δx(CO)) correlate strongly with C_ff (r>0.7) and appear to be good proxies for fossil fuel CO₂ at regional and continental scales. Samples originating from the Asian continent had greater Δx(CO) : C_ff(R_CO) values, (29±8) to (36±2) nmol µmol⁻¹, than in Korean Peninsula local air ((8±2) nmol µmol⁻¹). Air masses originating in China showed (1.6±0.4) to (2.0±0.1) times greater R_CO than a bottom-up inventory, suggesting that China's CO emissions are underestimated in the inventory, while observed R_SF6 values are 2–3 times greater than inventories for both China and South Korea. However, R_CO values derived from both inventories and observations have decreased relative to previous studies, indicating that combustion efficiency is increasing in both China and South Korea. Since we confirmed the possibility to verify the bottom-up inventories using our measurement data, it will be presented the Korea IG3IS future plan in this presentation.

How to cite: Lee, H., Dlugokencky, E., Turnbull, J., Lehman, S., Miller, J., Pétron, G., Joo, S., and Kim, Y.-H.: Observations of atmospheric 14CO2 at Anmyeondo GAW station, South Korea: implications for fossil fuel CO2 and emission ratios, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7688, https://doi.org/10.5194/egusphere-egu21-7688, 2021.

Combining updated methodology and data from different sources, we reported the estimates of China's carbon budget, including carbon sources from fossil fuel combustion and industrial process, and carbon sinks from terrestrial and marine systems. China's carbon budgets provide insights on the temporal and spatial distribution of the uptake of atmospheric carbon dioxide, and can be used to evaluate carbon cycle models and to define baselines for supporting China's climate policies and mitigation efforts. So far, we have found that terrestrial carbon sinks of China have increased significantly in the past 70 years with the development of afforestation projects in China. Seas of China have gradually transformed from carbon sources to carbon sinks.

How to cite: Ke, P., Liu, Z., Li, W., Guo, X., Dai, M., Deng, Z., Zhu, B., Guo, R., and Tan, J.: China's Carbon Budget, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5838, https://doi.org/10.5194/egusphere-egu21-5838, 2021.

Climate change mitigation strategies require regional-scale estimations of CO₂ fluxes, but estimates are often tampered with a significant amount of uncertainties due to multiple factors. In the case of the top-down approach, the error associated with the forward transport model is one of the major causes of uncertainty. Current generation global atmospheric transport models are more often simulated at a horizontal resolution of one degree or less, which omits a large number of small-scale processes and creates a significant amount of uncertainty in estimations. Attempts for the estimation of CO₂ fluxes at fine scales over India using the top-down approach is essentially new compared to some other parts of the globe like North America or Europe. The study focuses on implementing an inverse modelling system, by considering high-resolution atmospheric transport and flux distribution, to retrieve the fluxes at spatial scales relevant for ecosystem and policy-making. Using WRF-Chem (GHG) model, we estimate the transport error in CO₂ simulations over India during July and November 2017 which is associated with different processes whose scale is smaller than the resolution of current-generation global transport models. We show that the overall sub-grid variability (or representation error) at the surface can go up to ~10 ppm for the surface CO₂, which is markedly higher than the sampling errors. Total column-averaged CO₂ also shows similar variability in the spatial pattern though the magnitude is less compared to the surface, which indicates the prominence of heterogeneity at surface flux in modulating the entire column variability. Our results show that there exist regional differences in sub-grid scale variability for both surface and column CO₂ with very high magnitude observed at coastal and mountain regions and at emission hot spots. In addition to spatial variability, the sub-grid variability of CO₂ over India exhibits seasonal variations as well. The vertical distribution of sub-grid variability during July shows its association with the monsoon circulations, which is much different than those during November. Our estimates suggest that the terrain heterogeneity alone can explain about 53-63% of the surface representation errors over the domain, which shows the importance of using accurate Digital Elevation Models in the atmospheric transport model simulations. With underlying assumptions, the total flux uncertainty solely due to the unresolved sub-grid scale variations is estimated to be 8.1 to 14.4% of the total NEE. These results will be discussed and presented in the context of our attempts towards estimating the source-sink distribution of CO₂ over India.

How to cite: Thilakan, V., Pillai, D., Gerbig, C., Marshall, J., Ravi, A., Galkowski, M., and Mathew, T. A.: Towards implementing an atmospheric observation-based modelling system for estimating the source-sink distribution of CO2 over India: an assessment of fine-scale CO2 spatiotemporal variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9218, https://doi.org/10.5194/egusphere-egu21-9218, 2021.

High resolution simulations of carbon dioxide, methane and carbon monoxide (CO₂, CH₄ and CO) have been produced as part of the CO₂ Human Emissions (CHE) project in order to assist carbon-cycle research and applications, such as the design of a CO2 Monitoring Verification Support (CO2MVS) capacity in support of the Paris Agreement. This dataset provides realistic variability of the carbon tracers in the atmosphere modulated by the weather and the underlying surface fluxes as shown by comparison with independent observations. It can therefore provide a reference for atmospheric inversion systems that use atmospheric observations from satellites and in situ networks to derive natural surface fluxes and anthropogenic emissions of CO₂, CH₄ and CO. Additional tagged tracers are used to identify the atmospheric enhancements associated with the different surface fluxes. These flux enhancements can shed light into the potential of new satellites to detect the emission signals in the atmosphere. As satellites observe the mean concentration of carbon tracers over a partial/total atmospheric column, the CHE nature run is also used here to assess the contribution of total column variability from different layers in the atmosphere. We find that the variability in the free troposphere is often dominating the variability of the total column for CO2, CH4 and CO, highlighting the role of long-range transport to represent variability of carbon tracers in the atmosphere, as well as the importance of assessing the accuracy of long-range transport in chemical transport models used in atmospheric inversions.

How to cite: Agusti-Panareda, A. and McNorton, J. and the CHE nature run team: The CHE global nature run: A high-resolution simulation providing realistic global carbon weather for the year of the Paris Agreement, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12556, https://doi.org/10.5194/egusphere-egu21-12556, 2021.

Parties to the Paris Agreement agreed to report GHG emissions and removals to the United Nations Framework Convention on Climate Change (UNFCCC), which will evaluate progress toward the NDCs through Global Stocktakes (GSTs) conducted at five-year intervals, the first of which is scheduled in 2023. National emission reports are based on “bottom-up” inventories of emissions or removals, derived from statistics such as the number tons of coal or barrels of oil delivered to the commercial, residential, industrial or transportation sectors or the number of acres of forest converted to agriculture. These methods can provide accurate estimates for fossil fuel emissions, but are somewhat less reliable for tracking changes in emissions from agriculture, forestry and other land use (AFOLU) or rapid changes in emissions due to disturbance events, such as hurricanes, drought, wildfires, or climate change.

CO₂ and CH₄  emissions and removals can also be estimated using high resolution, time-resolved measurements of their concentrations in the atmosphere. These data are analyzed with atmospheric inverse models to derive the flux distribution needed to match the observed atmospheric concentrations in the presence of the winds. These top-down atmospheric inventories complement bottom-up inventories by providing an integrated constraint on emissions from all sources and removals by all sinks. They are less source specific than bottom-up inventories, but are ideal for tracking rapid changes in large emitters or changes in emissions or uptake by forests, crops or the ocean associated with human activities, severe weather or climate change.

The GHG Task Team of the Joint CEOS/CGMS Working Group on Climate has embarked on an ambitious effort to use available ground-based and space based atmospheric measurements of CO₂ and CH₄ to develop a pilot, top-down atmospheric inventory to support the 2023 GST. CO₂ estimates derived from Orbiting Carbon Observatory-2 (OCO-2) data will be combined with surface CO₂ measurements from the World Meteorological Organization (WMO) Global Atmospheric Watch (GAW) and its partners to construct a CO₂ inventory. CH₄ estimates derived from Greenhouse gases Observing SATellite (GOSAT) and the Copernicus Sentinel 5 Precursor (S5P) data will be combined with ground based GHG data to construct a CH₄ inventory. These inventories will be compared with results from a parallel effort within CEOS to produce space-based bottom-up inventories for emissions and removals by AFOLU to provide more source specific constraints on emissions and removals.

With the current measurement and modeling capabilities, these pilot inventories may not improve the results delivered by developed nations, where high-quality bottom-up inventories have been produced for decades. They should have greater value in the developing world, where countries have much less experience and resources for developing inventories and/or a much larger fraction of their emissions come from AFOLU. They are also expected to yield much greater insight into the evolution of the natural carbon cycle as it responds to human activities, extreme weather and climate change. The pilot products prepared for the 2023 Global Stocktake will provide the basis for iterative improvements in the products and their delivery to users for future GSTs.

How to cite: Crisp, D. and Dowell, M. and the CEOS/CGMS WGClimate Greenhouse Gas Task Team: Top-down Atmospheric Inventories of CO2 and CH4 to Support the Global Stocktakes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13535, https://doi.org/10.5194/egusphere-egu21-13535, 2021.

The study aims to quantify the Paris region’s CO and CO₂ emissions from fossil fuel and biogenic CO₂ fluxes during the spring season (March-May) of 2019-2020, based on a network of six ground-based stations. Hourly CO₂ mixing ratio gradients between the station Saclay (SAC), located in the south-west of Paris region and five other sites in the urban area are used to estimate the 5-day mean daytime budgets of the fossil fuel CO₂ emissions and biogenic fluxes. The inversion relies on the transport model simulations using the Weather Research and Forecasting model at 1 km × 1 km horizontal resolution, combined with 1-km fossil fuel CO₂ emissions from the Origins inventory, and biogenic CO₂ fluxes from the VPRM model. The methodology is based on a Lagrangian particle dispersion model (LPDM) approach that could efficiently determine the sensitivity of downwind mixing ratio changes to upwind sources. The inversion adjusts both fossil fuel emissions and VPRM biogenic CO₂ fluxes using tower observations and transport matrix generated from LPDM hourly footprints. The emission map shows noticeable changes in the central Paris region, whereas the biogenic fluxes do not show any noticeable change after inversion. This can happen if the choice of background station is not representative concerning biogenic fluxes.  The inversion could reduce the uncertainty up to 20% for the fossil fuel emission but the biogenic flux uncertainty does not show a significant difference from the prior. In comparison with the 2019 pattern, the rate of increase in fossil fuel emission after inversion was considerably reduced for 2020 (up to 20-30%). The same pattern is observed in the 5-day total flux time series where the magnitude of posterior fluxes falls below prior fluxes except for the first few days of March, before the lockdown period. This aspect is further analysed in the second part of the study. Analysis of hourly mixing ratios generated from prior and posterior fluxes shows that prior mixing ratios increased as a result of large observed CO₂ gradients. A comparison of diurnal mixing ratios generated from prior and posterior fluxes shows that the mixing ratio gradient of all the sites shows a similar pattern, but the direct observations show an offset in the diurnal pattern. The second part of the study aims to quantify the changes in the CO₂ emission pattern over the Paris region during the recent COVID19 lockdown during 2020. Here, a multisystem comparison is carried out for the Lagrangian-based inversion and Eulerian WRF-CO₂ inversion. Both systems capture the effect of lockdown, with a significant reduction in traffic emissions. To improve the inversion and to reduce the uncertainty, the third part of the study uses a gridded CO/CO₂ mole fraction ratio to further constrain anthropogenic CO₂ emissions. Our study shows that It is an added advantage to assimilate CO mixing ratios alongside CO₂ to increase the accuracy of anthropogenic carbon estimates.

How to cite: Krishnankutty, N., Lauvaux, T., Abdallah, C., Lian, J., Ciais, P., Utard, H., and Ramonet, M.: High-resolution inversion of fossil fuel emissions and biogenic fluxes over the Paris region during 2019-2020, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8246, https://doi.org/10.5194/egusphere-egu21-8246, 2021.

How to cite: Karion, A., Yadav, V., Ghosh, S., Mueller, K., Roest, G., Gourdji, S., Lopez-Coto, I., Gurney, K., Parazoo, N., Verhulst, K., Kim, J., Prinzivalli, S., Fain, C., Nehrkorn, T., Mountain, M., Keeling, R., Weiss, R., Duren, R., Miller, C., and Whetstone, J.: The impact of COVID-19 on CO2 emissions in the Los Angeles and Washington DC/Baltimore metropolitan areas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13246, https://doi.org/10.5194/egusphere-egu21-13246, 2021.

To verify the urban fossil fuel carbon dioxide (FFCO₂) flux over the Seoul Capital Area (SCA), we initiated the “Megacity CO₂-Seoul” project in the year 2018. For the project, our research group established CO₂ and XCO₂ ground measurement stations deploying Seoul National University CO₂ Measurement instruments (SNUCO₂M) and EM27/SUN. We also produced 1x1km urban biospheric flux with the CArbon Simulator from Space (CASS) and 1x1km FFCO₂ carbon emission inventory by employing machine learning techniques. The project comprises inverse modeling system using WRF-STILT. Under the Bayesian inverse model framework, we assess FFCO₂ inventory of Seoul, which are generated by the bottom-up approach, by paring the ground CO₂ measurement constraints. This is the first look at the verification of self-developed FFCO₂ inventory of Seoul. We are currently working on the improvement of the WRF-STILT inverse modeling system. In this presentation, we report verification of FFCO₂ emissions in SCA on February 2018. Our estimate reflects that our prior FFCO₂ inventory was overestimated in the comparison with results of the inverse model. Detailed results will be presented at the webinar. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korean government (MSIT) (No. NRF-2019R1A2C3002868).

How to cite: Oh, E., Jeong, S., Kim, Y., Park, H., Park, C., Park, H., Sim, S., Yoon, J., and Kwack, T.: First look at the urban carbon flux inversion system for Megacity CO2-Seoul, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13858, https://doi.org/10.5194/egusphere-egu21-13858, 2021.

Atmospheric Carbon dioxide (CO₂) has reached 150% of its pre-industrial level and has contributed to more than 60% of the global direct radiative forcing from Greenhouse Gases (GHGs). Global fossil fuel CO₂ (CO_2ff) emissions exceeded 38 Gt in 2020 accounting for more than 77% of fossil fuel greenhouse gas emissions. City areas, where gathering more than 55% of the global population, alone contributed to more than 70% of anthropogenic CO_2ff emissions. Proper management of fossil fuel sources designed to achieve the 2.0-degree temperature threshold of the Paris Agreement requires accurate monitoring of emissions from major metropolitan areas globally to track this commitment. Satellite-based inversion is unique among the “top-down” approaches, potentially allowing us to track and monitor fossil fuel emission changes over cities globally. However, its accuracy is still limited by incomplete background information, cloud blockages, aerosol contaminations, and uncertainties in models and priori fluxes.

To evaluate the current potential of space-based quantification techniques, we present the first attempt to monitor long-term changes in CO_2ff emissions based on the OCO-2 satellite measurements over a fast-growing Asian metropolitan area: Lahore, Pakistan. We first examined the OCO-2 data availability at the global scale. About 17% of OCO-2 soundings are marked as high-quality soundings by quality flags over the global 70 most populated cities. Cloud blockage and aerosol contamination are the two main causes of data loss. As an attempt to recover additional retrievals, we evaluated the effectiveness of OCO-2 quality flags at the city level by comparing the satellite/reference ratios derived from three independent methods (WRF-Chem, X-STILT, and Flux cross-sectional integration method), all based on the ODIAC inventory. The satellite/reference ratios of the high-quality tracks better converged across the three methods compared to the all-data tracks with reduced uncertainties in emissions. Thus, we conclude that OCO-2 quality flags are highly relevant to filter unrealistic OCO-2 retrievals even at local scales, although originally designed for global-scale studies. All three methods consistently suggested that the ratio medians are greater than 1, which implies that the ODIAC slightly underestimated the CO_2ff emissions over Lahore. The posterior CO_2ff emission trend was about 734 kt C/year (i.e., an annual 6.7% increase), while the a priori emission ODIAC showed that the trend was about 650 kt C/year (i.e., an annual 5.9% increase). The 10,000 Monte Carlo simulations of the Mann-Kendall upward trend test showed that less than 10% prior uncertainty for 8 tracks (or less than 20% prior uncertainty for 25 tracks) is required to achieve a greater-than-50% trend significant possibility at a 95% confidence level. It implies that the trend is driven by the prior rather than the optimized emissions. The key to improving the role of satellite and model in emission trend detection is to obtain more high-quality tracks near metropolitan areas to achieve significant constraints from X_CO2 retrievals.

How to cite: Lei, R., Feng, S., Danjou, A., Broquet, G., Wu, D., Lin, J., O’Dell, C., and Lauvaux, T.: Satellite-based fossil fuel CO2 emissions detection over metropolitan areas: a multi-model analysis of OCO-2 data over Lahore, Pakistan, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9338, https://doi.org/10.5194/egusphere-egu21-9338, 2021.

The COVID-19 pandemic led to widespread reductions in mobility and induced observable changes in the atmosphere. Recent work has employed novel mobility datasets as a proxy for trace gas emissions from traffic, yet there has been little work evaluating these emission numbers.

We systematically compare mobility datasets from TomTom and Apple to traffic data from local governments in seven diverse urban and rural regions to characterize the magnitude of errors in emissions that result from using those mobility datasets as a proxy for traffic. We observe differences in excess of 60% between these mobility datasets and local traffic data, which result in large errors in emission estimates. These differences are in part driven by the usage of different baselines and the neglect of seasonality, but mainly they are caused by the individual representations of the datasets. The relationship varies strongly depending on time and region and therefore no general functional relationship between mobility data and traffic flow over all regions can be determined. Future work should be cautious when using these mobility metrics for emission estimates. Further, we use the local government data to identify actual emission reductions from traffic in the range of 7-22% in 2020 compared to 2019 for our study regions. Our full analysis is summarized in Gensheimer et al. (2020).

Gensheimer, J., Turner, A., Shekhar, A., Wenzel, A., & Chen, J. (2020). What are different measures of mobility changes telling us about emissions during the COVID-19 pandemic? Earth and Space Science Open Archive, 11. Retrieved from doi: 10.1002/essoar.10504783.1

How to cite: Gensheimer, J., Turner, A. J., Shekhar, A., Wenzel, A., Keutsch, F. N., and Chen, J.: Error assessment of traffic emission estimates using novel mobility datasets., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5419, https://doi.org/10.5194/egusphere-egu21-5419, 2021.

The French-Mexican project Mexico City’s Regional Carbon Impacts (MERCI-CO₂) is building a CO₂ observation network in the Metropolitan Zone of the Valley of Mexico (ZMVM). The project investigates the atmospheric signals generated by the city's emissions on total column and surface measurements, aiming at reducing the uncertainties of CO₂ emissions in ZMVM and evaluating the effects of policies that had been implemented by the city authorities. 

A nested high-resolution atmospheric transport simulation based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is performed to analyze the observed CO₂ mixing ratios during dry and wet seasons over Mexico City and its vicinity. Both anthropogenic emissions (UNAM 1-km fossil fuel emissions) and biogenic fluxes (CASA 5-km simulations) are taken into account. The model configuration, with a horizontal resolution of 1km and using the Single-Layer urban canopy Model (SLUCM), has been evaluated over two weeks in January 2018 using meteorological measurements from 26 stations set by the Air Quality Agency of Mexico City (Secretary of the Environment of Mexico City - SEDEMA). The reconstruction of meteorological conditions in the urban area shows better performances than suburban and mountainous areas. Due to the complex topography, wind speeds in mountain areas are 2-3 m/s over estimated and wind direction simulations in some stations are 90° deflected, especially in southern mountains. 

Two high-precision CO₂ analyzers deployed in urban and rural areas of Mexico City are used to evaluate the WRF CO₂ 1-km simulations. The model reproduced the diurnal cycle of CO₂ mixing ratios at the background station but under-estimates the nighttime accumulation at the urban station. Mean absolute errors of CO₂ concentrations range from 6.5 ppm (background station) to 27.1 ppm (urban station), mostly driven by the elevated nocturnal enhancements (up to 500 ppm at UNAM station). Based on this analysis, we demonstrate the challenges and potential of mesoscale modeling over complex topography, and the potential use of mid-cost sensors to constrain the urban GHG emissions of Mexico City.

How to cite: Xu, Y., Ramonet, M., Lauvaux, T., Lian, J., Bréon, F.-M., Ciais, P., Grutter, M., and Garcia, A.: Interpretation of Atmospheric CO2 Measurements in Mexico City, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15117, https://doi.org/10.5194/egusphere-egu21-15117, 2021.

This study demonstrates the utility of combining Airborne Doppler Wind Lidar measurements and quantitative methane (CH4) retrievals from the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) to estimate CH4 emission rates. In a controlled release experiment, Twin Otter Doppler Wind Lidar (TODWL) observed wind speed and direction agreed closely with sonic anemometer measurements and CH4 emission rates derived from TODWL observations were more accurate than those using the sonic during periods of stable winds. During periods exhibiting rapid shifts in wind speed and direction, estimating emission rates proved more challenging irrespective of the use of model, sonic, or TODWL wind data. Overall, TODWL was able to provide accurate wind measurements and emission rate estimates despite the variable wind conditions and excessive flight level turbulence which impacted near surface measurement density. TODWL observed winds were also used to constrain CH4 emissions at a refinery, landfill, wastewater facility, and dairy digester. At these sites, TODWL wind measurements agreed well with wind observations from nearby meteorological stations, and when combined with quantitative CH4 plume imagery, yielded emission rate estimates that were similar to those obtained using model winds.

How to cite: Thorpe, A., O’Handley, C., Emmitt, G., Decola, P., Hopkins, F., Yadav, V., Guha, A., Newman, S., Herner, J., Falk, M., and Duren, R.: Improved methane emission estimates using AVIRIS-NG and an Airborne Doppler Wind Lidar, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3138, https://doi.org/10.5194/egusphere-egu21-3138, 2021.

Assessment of bottom-up greenhouse gas emissions estimates through independent methods is needed to demonstrate whether reported values are accurate or if bottom-up methodologies need to be refined. Previous studies of measurements of atmospheric methane (CH₄) in London revealed that inventories substantially underestimated the amount of natural gas CH₄ ^1,2. We report atmospheric CH₄ concentrations and δ¹³CH₄ measurements from Imperial College London since early 2018 using a Picarro G2201-i analyser. Measurements from Sept. 2019-Oct. 2020 were compared to the values simulated using the dispersion model NAME coupled with the UK national atmospheric emissions inventory, NAEI, and the global inventory, EDGAR, for emissions outside the UK. Simulations of CH₄ concentration and δ¹³CH₄ values were generated using nested NAME back-trajectories with horizontal spatial resolutions of 2 km, 10 km and 30 km. Observed concentrations were underestimated in the simulations by 22 % for all data, and by 16 % when using only 13:00-17:00 data. There was no correlation between the measured and simulated δ¹³CH₄ values. On average, simulated natural gas mole fractions accounted for 28 % of the CH₄ added by regional emissions, and simulated water sector mole fractions accounted for 32 % of the CH₄added by regional emissions. To estimate the isotopic source signatures for individual pollution events, an algorithm was created for automatically analysing measurement data by using the Keeling plot approach. Nearly 70 % of isotopic source values were higher than -50 ‰, suggesting the primary CH₄ sources in London are natural gas leaks. The model-data comparison of δ¹³CH₄ and Keeling plot results both indicate that emissions due to natural gas leaks in London are being underestimated in the UK NAEI and EDGAR.

¹ Helfter, C. et al. (2016), Atmospheric Chemistry and Physics, 16(16), pp. 10543-10557

² Zazzeri, G. et al. (2017), Scientific Reports, 7(1), pp. 1-13

How to cite: Saboya, E., Zazzeri, G., Graven, H., Manning, A. J., and Michel, S. E.: Continuous CH4 and  d13CH4 measurements in London demonstrate under-reported natural gas leakage, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10995, https://doi.org/10.5194/egusphere-egu21-10995, 2021.

Independent, timely and accurate monitoring of urban CO₂ emissions is important to assess the progress towards the Paris Agreement goals, evaluate the mitigation potential of the implemented actions and support urban planning, policy- and decision-making processes. However, there are several challenges towards achieving comprehensive urban emission monitoring at the required scales, which are mainly related to the complexities in the urban form, the urban function and their interactions with the atmosphere. Cities are highly heterogeneous mosaics of CO₂ sources and sinks. Typically, the main emission sources in an urban neighbourhood are vehicles and buildings, while the contribution of human, plant and soil respiration can be also significant depending on population density and green area fraction. At the same time, urban vegetation acts as carbon sink, mitigating urban emissions locally. This study attempts to unravel the complex urban CO₂ flux dynamics by modelling each component separately (i.e. building emissions, traffic emissions, human metabolism, photosynthetic uptake, plant respiration, soil respiration) based on high resolution geospatial, meteorological and population activity datasets. The case study is the city centre of Basel, Switzerland. The models are calibrated and evaluated using Eddy Covariance measurements of CO₂ flux from two permanent tower sites in the city centre, covering a significant part of the study area. Moreover, an extended field campaign for the measurement of the biogenic components (i.e. photosynthetic uptake, plant respiration, soil respiration) has been active since the summer of 2020, involving regular chamber flux measurements and soil stations across the study area. The study reveals the spatial and temporal complexity of the urban CO₂ flux dynamics both diurnally and seasonally. The relative contribution of each flux component to the seasonal cycle is presented, while the mitigation potential of urban vegetation is evaluated. Cross-comparison between model outputs and Eddy Covariance measurements are discussed in respect to source area variability, airflow complexity in the urban canopy layer and irregular unrecognized emission sources.

How to cite: Stagakis, S., Feigenwinter, C., Zurbriggen, E., Pitacco, A., and Vogt, R.: Spatiotemporal dynamics of CO2 flux in Basel city centre, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9906, https://doi.org/10.5194/egusphere-egu21-9906, 2021.

In 2019, we established the Munich Urban Carbon Column network (MUCCnet) [1] that measures the column-averaged concentration gradients of CO₂, CH₄ and CO using the differential column methodology (DCM, [2]). The network consists of five ground-based FTIR spectrometers (EM27/SUN from Bruker [3]), which are deployed both on the outskirts of Munich and in the city center. The distance between each outer spectrometer and the center station is approximately 10 km. Each spectrometer is protected by one of our fully automated enclosure systems [4], allowing us to run the network permanently. In addition, data are available from three one-month measurement campaigns in Munich between 2017 and 2019, each using five to six spectrometers.

To quantify urban methane emissions, we developed a Bayesian inverse modeling approach that was tested first in Indianapolis using campaign data from 2016 [5]. After adapting the modeling framework to the Munich case, we are able to use the large amount of data gathered by MUCCnet to quantify the methane emissions of the third largest city in Germany in detail. The framework takes the spatially resolved emission inventory TNO-GHGco (1 km x 1 km) as a prior estimate and refines it through the Bayesian inversion of the EM27/SUN observations. Our long-term dataset and continuous operation will provide new insights into Munich’s urban carbon cycle and will allow us to evaluate climate protection measures in the future.

Thanks to the automation, we were also able to continue the measurements during the COVID-19 lockdowns in Germany, resulting in a unique dataset that allows us to verify and improve our model.

[1] Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: Munich permanent urban greenhouse gas column observing network, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-300, accepted, 2020.

[2] Chen, J., Viatte, C., Hedelius, J. K., Jones, T., Franklin, J. E., Parker, H., Gottlieb, E. W., Wennberg, P. O., Dubey, M. K., and Wofsy, S. C.: Differential column measurements using compact solar-tracking spectrometers, Atmos. Chem. Phys., 16, 8479–8498, https://doi.org/10.5194/acp-16-8479-2016, 2016. 

[3] Gisi, M., Hase, F., Dohe, S., Blumenstock, T., Simon, A., and Keens, A.: XCO₂-measurements with a tabletop FTS using solar absorption spectroscopy, Atmos. Meas. Tech., 5, 2969–2980, https://doi.org/10.5194/amt-5-2969-2012, 2012.

[4] Heinle, L. and Chen, J.: Automated enclosure and protection system for compact solar-tracking spectrometers, Atmos. Meas. Tech., 11, 2173–2185, https://doi.org/10.5194/amt-11-2173-2018, 2018.

[5] Jones, T. S., Franklin, J. E., Chen, J., Dietrich, F., Hajny, K. D., Paetzold, J. C., Wenzel, A., Gately, C., Gottlieb, E., Parker, H., Dubey, M., Hase, F., Shepson, P. B., Mielke, L. H., and Wofsy, S. C.: Assessing Urban Methane Emissions using Column Observing Portable FTIR Spectrometers and a Novel Bayesian Inversion Framework, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2020-1262, in review, 2021.

How to cite: Dietrich, F., Chen, J., Wenzel, A., Forstmaier, A., Klappenbach, F., Zhao, X., Nachtigall, N., Altmann, M., Paetzold, J. C., Jones, T., Franklin, J., Luther, A., Kleinscheck, R., Butz, A., and Hase, F.: Urban methane emission estimate using measurements obtained by MUCCnet (Munich Urban Carbon Column network), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12210, https://doi.org/10.5194/egusphere-egu21-12210, 2021.

Mobile measurements of greenhouse gasses are used more often for emission evaluation studies (https://h2020-memo2.eu). Over the last few years, TNO have carried out multiple studies using a van to measure greenhouse gasses mobile (e.g. Hensen et.al., 2018 and Hensen & Scharff, 2001). Evaluation of the campaign data sets, where nitrous oxide (N₂O) is released as a tracer release and meteorological conditions (windspeed and -direction) are measured, has provided a great quantity of information both on the different sources that are investigated as well as on the evaluation method itself. This study examines a subset of the “random” survey datasets that were obtained while driving in the Netherlands. In general, these are single pass plume measurements that can be used to generate a single shot emission estimate as long as the exact location of the source and local meteorological data are known. In order to automatically indicate different sources, it is assumed that different source types emit different mixtures of trace gasses into the atmosphere, which leave behind a typical ‘’fingerprint’’. A combustion source, for instance, might leak methane (CH₄) as well as ethane (C₂H₆) and produce carbon monoxide (CO) and nitric oxide/nitrogen dioxide (NO/NO₂). Farms, on the other hand, produce CH₄, ammonia (NH₃) and potentially N₂O, but in principal no C₂H₆, CO, NO and NO₂. For the mobile measurements of greenhouse gasses the Aerodyne TDLAS instrument was used. This instrument measures CH₄, C₂H₆, N₂O, CO₂, and CO simultaneously and data is stored at a 1 second time resolution. Since December 2020, the MIRO instrument, which measures CH₄, N₂O, CO, NH₃, Sulphur dioxide (SO₂), NO and NO₂ on a 1 second time resolution, was added in the van as well. The expected co-emitted species are then used in an algorithm to automatically categorize the mixture of gas in the observed gas plumes into five different source types (farms, traffic, burning, fossil and wastewater treatment plants) and can be viewed per category in Google Earth. Emission levels are subsequently calculated using the TNO Gaussian model that is used in many of our emission studies (e.g. Hensen et.al., 2019) and calibrated versus N₂O tracer release tests, which can then be compared to emission registration (ER) numbers. In this study, a subset of available datasets will be shown covering a large part of the Netherlands. Different sources were assigned a source category and, if possible, these sources were assigned an emission level. Some of these locations, for instance along major highways, have multiple “hits” in a year. For these sources, an average and standard deviation in the emission level numbers are provided and compared to ER numbers.

References:

Hensen, A., Bulk, W.C.M. van den, Dinther, D. van, 2018. Methaan emissiemetingen aan buiten gebruik gestelde olie- en gaswinningsputten. ECN-E—18-032, Petten.

Hensen, A., Scharff, H., 2001. Methane emission estimates from landfills obtained with dynamic plume measurements. Water, Air and Soil Pollution Kluwer focus1:455-464.

Hensen, A., Velzeboer, I., Frumau, K.F.A., Bulk, W.C.M. van den, Dinther, D. van, 2019. Methane emission measurements of offshore oil and gas platforms, TNO report 2019 R10895, Petten.

How to cite: van Dinther, D., de Bie, S., Velzeboer, I., van den Bulk, P., Frumau, A., and Hensen, A.: Defining sources from mobile gas measurements using their typical “fingerprint”, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15779, https://doi.org/10.5194/egusphere-egu21-15779, 2021.

High-resolution monitoring is the basis for CO₂ emissions tracking and attribution in urban areas. This work is an important step towards an integrated urban CO₂ emissions monitoring system. Three middle-cost nondispersive infrared (NDIR) sensors of 500€ to 3000€ are characterised. Furthermore, CO₂ emissions of large, regional point sources are simulated to analyse their effect on these sensors’ signals.

The three sensors are Vaisala GMP343, Senseair HPP3 and SmartGas FlowEvo CO₂. Their analysis and characterisation is achieved by co-locating them with a Picarro G2401 cavity ringdown spectrometer for 40 days. Co-locating different middle-cost sensors is novel and enables a direct performance comparison. While the HPP3 is the only one to reach a 1 min mean standard deviation under 1 ppm, the GMP343 is the most linear and stable with a drift of 0.03(2) × 10⁻¹ ppm per day and the SmartGas sensor provides the best price-to-performance ratio. For all sensors, precisions (the 1 min mean error’s lower bound) of under 0.8 ppm are determined. In general, temperature stabilisation turns out to be one of the most promising avenues of performance improvement for all sensors.

The sensors’ in-situ measurements are combined with meso-scale meteorological simulations for the Rhine-Neckar region using the Weather Research and Forecasting model (WRF). In two case-studies, simulated excess CO₂ due to large, regional point sources and measured CO₂ concentration are compared. Both simulations show qualitative agreement with the measurements. The differences between measurements and simulation, however, highlight aspects to be refined. These include increasing the horizontal and vertical resolution of the simulation domain as well improving as the parametrisation of the planetary and urban boundary layer.

How to cite: Pilz, L., Vardag, S., Kleinschek, R., Hammer, S., and Butz, A.: Towards an integrated study of urban CO2 emissions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2724, https://doi.org/10.5194/egusphere-egu21-2724, 2021.