Accurate and precise atmospheric measurements of greenhouse gas (GHG) concentrations reveal the rapid and unceasing rise of global GHG concentrations due to human activity. The resulting increases in global temperatures, sea-level, glacial retreat, and other negative impacts are clear. In response to this evidence, nations, states, and cities, private enterprises and individuals have been accelerating GHG reduction efforts while meeting the needs of global development. The urgency, complexity and economic implications of GHG reductions demand strategic investment in science-based information for planning and tracking emission reduction policies and actions. In response, the World Meteorological Organization (WMO) Global Atmosphere Watch Program (GAW) and its partners have initiated the development of an Integrated Global Greenhouse Gas Information System (IG3IS). IG3IS combines atmospheric GHG concentration measurements and human-activity data in an inverse modeling framework to help decision-makers take better-informed action to reduce emissions of greenhouse gases and pollutants that reduce air quality. This service is based on existing and successful measurement and analysis methods and use-cases for which the scientific and technical skill is proven or emerging.
This session intends to gather presentations from researchers and decision-makers (user-community) on the development, implementation and use of atmospheric measurement-based “top-down” and data-driven “bottom-up” GHG emission inventory estimates, and the combination of both approaches, explicit in space and time, to deliver actionable emissions information at scales where human activity occurs and emission reduction is most effective. This session will also showcase the new projects and “good-practice” standards of the World Meteorological Organization (WMO) Integrated Global Greenhouse Gas Information System (IG3IS), which is part of WMO’s commitment to science-based services.
vPICO presentations: Fri, 30 Apr
Verification of the nationally reported greenhouse gas (GHG) inventories using inverse modelling and atmospheric observations is considered to be best practice by the United Nations Framework Convention on Climate Change (UNFCCC). It allows for an independent assessment of the nationally reported GHG emissions using a comprehensively different approach to the inventory methods. Significant differences in the emissions estimated using the two approaches are a means of identifying areas worthy of further investigation.
An inversion methodology called Inversion Technique for Emission Modelling (InTEM) has been developed that uses a non-negative least squares minimisation technique to determine the emission magnitude and distribution that most accurately reproduces the observations. By estimating the underlying baseline time series, atmospheric concentrations where the short-term impact of regional pollution has been removed, and by modelling where the air has passed over on route to the observation stations on a regional scale, estimates of UK emissions are made. In this study we use an extensive network of observations with six stations across the UK and six more in neighbouring countries. InTEM uses information from a Lagrangian dispersion model NAME (Numerical Atmospheric dispersion Modelling Environment), driven by three-dimensional, modelled meteorology, to understand how the air mixes during transport from the emission sources to observation points. The InTEM inversion results are submitted annually by the UK as part of their National Inventory Report to the UNFCCC. They are used within the UK inventory team to highlight areas for investigation and have led to significant improvements to the submitted UK inventory. The latest UK comparisons will be shown along with examples of how the inversion results have informed the inventory.
How to cite: Manning, A., Redington, A., O'Doherty, S., Young, D., Say, D., Arnold, T., Rennick, C., Wisher, A., Spain, G., Frumau, A., Forster, G., Stanley, K., Vollmer, M., Reimann, S., Arduini, J., Maione, M., Schuck, T., and Engel, A.: Using emissions derived from atmospheric observations to inform the reported UK inventory, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7486, https://doi.org/10.5194/egusphere-egu21-7486, 2021.
Atmospheric observations of CO2 and other greenhouse gases have been widely used to constrain estimates of terrestrial and oceanic CO2 fluxes through atmospheric inverse modelling. Yet, applying these methods at national scale to verify and improve the National Inventory Report (NIR) and support the Paris agreement remains at the frontier of CO2 science.
We use inverse modelling to estimate New Zealand’s carbon uptake and emissions using atmospheric measurements and model. This effort is part of a five year CarbonWatch-NZ research programme, which aims to develop a complete top-down picture of New Zealand's carbon balance using national inverse modelling and targeted studies of New Zealand’s forest, grassland and urban environments. In addition to quantifying New Zealand’s carbon emissions on a national scale, we also focus on identifying the prevailing processes driving CO2 changes in New Zealand to support climate mitigation.
In an initial study based on the inversion system used in CarbonWatch-NZ, a significantly stronger (30-60 %) sink was found relative to the NIR (Steinkamp et al., 2017), suggesting a strong CO2 uptake in Fiordland, a region covered by indigenous temperate rainforest in New Zealand's South Island. Here, we present new results of CarbonWatch-NZ by expanding the studied time period from 2011-2013 to 2020, expanding our atmospheric observing network from two (Baring Head, 41.41°S, 174.87°E and Lauder, 38.33°S, 176.38°E) to a total of eleven in situ greenhouse gas measurement sites and improving our atmospheric model resolution by roughly a factor of ten (NAME model, 1.5 km).
Our new results suggest that the strong sink observed in 2011-2013 did not diminish, but for recent years we have found an even stronger sink than for before. Additional measurements collected in the Fiordland region (i.e., mixing ratios, CO2 isotopes, carbonyl sulphide) also suggest a stronger CO2 uptake, supporting our inversion results. Both the measurements and inversion results show that the CO2 uptake does not seem to shut down completely during winter time, suggesting that there might be something about this ecosystem that we do not yet understand. This winter uptake signal is also present in independent data collected in and around New Zealand as part of the ATom campaigns (Atmospheric Tomography Mission). Implementing observations from an additional site in the North Island (Maunga Kakaramea, 45.034°S, 169.68°E) has increased the strength of the sink, pointing to additional strong sink region at the top of the North Island.
Kay Steinkamp, Sara E. Mikaloff Fletcher, Gordon Brailsford, Dan Smale, Stuart Moore, Elizabeth D. Keller, W. Troy Baisden, Hitoshi Mukai and Britton B. Stephens, Atmospheric CO2 observations and models suggest strong carbon uptake by forests in New Zealand, Atmospheric Chemistry and Physics, 2017.
How to cite: Bukosa, B., Mikaloff-Fletcher, S., Brailsford, G., Nankivell, C., Smale, D., Keller, E., Turnbull, J., Steinkamp, K., Harvey, M., Sperlich, P., Moss, R., Gray, S., Moore, S., Nichol, S., and Buxton, Z.: CarbonWatchNZ: Regional to National Scale Inverse Modelling of New Zealand’s Carbon Balance, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14323, https://doi.org/10.5194/egusphere-egu21-14323, 2021.
Sulfur hexafluoride (SF6) is a potent greenhouse gas (GHG) that is primarily emitted from electrical circuit breakers and heavy-duty gas-insulated switchgears in electric transmission and distribution equipment, magnesium production and processing, and electronics production. It has a 100-year global warming potential of 23500 and an atmospheric lifetime of 850 (580 - 1400) years. Because of its extremely large global warming potential and long atmospheric lifetime, its emissions, while currently small, have an outsized influence on changing climate over the long term. However, current US emissions of SF6 are uncertain. The US SF6 consumption that was used to estimate SF6 emissions in the US EPA national GHG reporting to the UNFCCC has an uncertainty of 30 – 60%, depending on whether to use the US SF6 supplier reports or user reports. With different inventory methodologies, the national emissions estimates of SF6 from the EDGAR and US EPA’s GHG inventories differ by more than a factor of 4. Here, we will present the first detailed U.S. national and regional emissions of SF6 that were derived from an inverse analysis of an extensive flask-air sampling network from the US NOAA’s Global Greenhouse Gas Reference Network and high-resolution atmospheric transport simulations for 2007 - 2018. We will discuss our atmosphere-based top-down emission estimates in comparison with the existing bottom-up emission inventories, our derived seasonal variation of SF6 emissions, and associated implications regarding each industry’s contribution to emissions and optimal emissions mitigation strategies. Because atmospheric SF6 measurements are also used to assess atmospheric transport errors assuming no biases in SF6 emissions reported by the EDGAR inventory, our analysis also has important implications on limitations in such applications.
How to cite: Hu, L., Montzka, S., Dlugokencky, E., DeCola, P., Ottinger, D., Thoning, K., Dutton, G., Andrews, A., Bogle, S., Roshchanka, V., and Crotwell, A.: Atmosphere-based US emission estimates of SF6 for 2007 - 2018, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7980, https://doi.org/10.5194/egusphere-egu21-7980, 2021.
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.
We report the first national scale estimates of CO2 emissions from fossil fuel combustion and cement production in the US based directly on atmospheric observations, using a dual-tracer inverse modeling framework and CO2 and Δ14CO2measurements obtained primarily from the North American portion of NOAA’s Global Greenhouse Gas Reference Network. The derived US national total for 2010 is 1653±60 TgC/yr, with an uncertainty (2σ) that takes into account random errors associated with atmospheric transport, atmospheric measurements, and specified prior CO2 and 14C fluxes. The atmosphere-derived estimate is significantly (>3σ) larger than US national emissions for 2010 from three global inventories widely-used for CO2 accounting, even after adjustments for emissions that might be sensed by the atmospheric network but which are not included in inventory totals. In contrast, the atmosphere-derived estimate is within 1σ of a similarly adjusted 2010 annual total and 9 of 12 adjusted monthly totals aggregated from the latest release of the high-resolution, US-specific “Vulcan” emissions data product. Here we focus our presentation on determination and reduction of methodological uncertainties and future applications of the method for annual emissions detection and emissions trend detection at scales ranging from the US as a whole to contiguous groups of US states, such as those participating in the Regional Greenhouse Gas Initiative.
How to cite: Lehman, S., Basu, S., Miller, J., Andrews, A., and Sweeney, C.: Quantifying US Fossil Fuel CO2 Emissions Using Precise Measurements of 14C in Atmospheric CO2, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13708, https://doi.org/10.5194/egusphere-egu21-13708, 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.
In 2020, many countries implemented lockdowns to control the spread of the novel coronavirus disease (COVID-19), leading to reported decreases in anthropogenic CO2 emissions based on bottom-up estimates. Some studies reported that the resulting atmospheric CO2 changes were below the detection limit of current observing systems on the ground or in space. We quantify CO2 emissions from Europe’s largest fossil fuel burning power plant before and during lockdown using space-based CO2 observations from NASA’s Orbiting Carbon Observatory (OCO) 2 and 3 missions. The results show clear emission reductions of >20% in April 2020, demonstrating the ability of space-based CO2 observations to quantify emission reductions at the facility level. This research reinforces the value of space-based CO2 data for verifying future CO2 emission reductions expected from climate change mitigation policies and the importance of monitoring emissions at sub-national scales.
How to cite: Nassar, R., Mastrogiacomo, J.-P., Bateman-Hemphill, W., McCracken, C., MacDonald, C., Hill, T., O'Dell, C., Nelson, R., Kiel, M., Pavlick, R., Eldering, A., and Crisp, D.: Space-based detection of CO2 emission reductions due to COVID-19 at Europe's largest fossil fuel power plant and implications for CO2 emission monitoring, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8979, https://doi.org/10.5194/egusphere-egu21-8979, 2021.
Methane emissions associated with human activities contributes significantly to global climate change. China is the world largest methane emitter and the coal mining sector is the largest contributor. Recent atmospheric inversion by Miller et al. using spaceborne column CH4 concentration measurements inferred that emissions in China rose by more than 1.0 Tg CH4 yr−1 from 2010 to 2017 due to the contribution of fossil fuel, especially from coal sector. Here we revisit methane emissions from the coal sector in China by comparing a sectorial bottom up emission inventory (2005-2019) with the results from another ensemble of CH4 inversions using GOSAT satellite data during 2011-2017. During that period, the bottom up inventory gives an average emission of 17.9 Tg CH4 yr-1 and the median of all inversions of 18.6 Tg CH4 yr-1, with a range of [10.8, 25.6] corresponding to the min-max of all inversions and the use of two gridded maps of emissions to separate the coal sector from total emissions in each inversion grid cell. We confirm the upward trend in methane emissions from the coal sector from 2005 to 2019 observed by Miller et al. In addition, we show that trend accelerated after 2016 as consistently found in the bottom-up inventory and top-down inversions approaches. However, during the period of 2010-2017, the bottom-up inventory and top-down inversions showed opposite trends in emissions. Especially during the period of 2014-2016, emissions from coal sector decreased at a rate of 0.8 Tg CH4 yr-1 using bottom up inventory, while emissions from top-down inversions maintained a relatively high growth rate at 0.4 Tg CH4 yr-1. Suggesting possible underestimation of the emission by bottom up inventories. In addition, we estimates the contribution of abandoned mines to the growth of methane emissions from coal sector was around 20%, we also show a COVID-19 pandemic related sharp dip in methane emission from the coal sector in Feb 2020 and rebound since in April 2020 based on the estimation of monthly bottom-up inventory.
How to cite: Cui, D.: Revisiting China’s methane emissions from coal sector, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5259, https://doi.org/10.5194/egusphere-egu21-5259, 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 (Cff). 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 Cff 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 Cff (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) : Cff(RCO) 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 RCO than a bottom-up inventory, suggesting that China's CO emissions are underestimated in the inventory, while observed RSF6 values are 2–3 times greater than inventories for both China and South Korea. However, RCO 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.
The maritime sector is one of the most efficient freight modal options in terms of emissions per tonnage transported per kilometer. However, alongside aviation, it is one of the most challenging transportation sectors to be decarbonized. Among the possible mitigation options are a switch towards less carbon-intensive fuels. However, the adoption of a global strategy towards cleaner fuels is not possible before fully understanding the climate implications throughout their entire life cycle. For such assessment at a global level, reliable and robust emission inventories are necessary. For this purpose, we present a novel bottom-up assessment of emissions of greenhouse gases (GHGs) and aerosols (NOx, SOx, CO, OC, EC and BC) in the maritime sector. Our high-resolution, data-driven emission inventory comprises a baseline of emissions for the year 2017, in which the global fleet has a fuel mix of heavy-fuel oil (HFO) and marine diesel oil (MDO). In addition, we present three scenarios in which the global fleet runs in its entirety with one of the potential fuel substitutes; i) Low-Sulphur diesel, ii) Liquefied-natural gas (LNG), and iii) Ammonia.
These emission inventories are developed through the use of the state-of-the-art MariTEAM model, which combines ship satellite data (AIS), historical weather data, and individual ship information in its emissions calculations. Additionally, the emissions resulting from the fuel production and processing life cycles are included and presented geospatially, resulting in a full ‘well-to-wake’ emission inventory. The spatiotemporal inventories for the alternative scenarios reveal that technology used in the fuel production, the weather, and heavy traffic regions all have a significant environmental impact on the overall emissions, both globally and regionally, highlighting the importance of measuring and modelling this correctly. Results show that a full transition towards LNG could achieve a reduction in terms of global warming potential (GWP100) of 21% and, in the case of ammonia, around 88%. The emission inventories also allow us to estimate the global annual efficiency ratio for each alternative fuel combining upstream and downstream emissions, indicating the need for more comprehensive metrics for designing appropriate policies aiming at net-zero emissions by 2100.
How to cite: Kramel, D., Muri, H., Kim, Y., Lonka, R., Nielsen, J. B., Ringvold, A. L., Bouman, E. A., Steen, S., and Strømman, A. H.: A novel bottom-up global ship emission inventory for conventional and alternative fuels in a well-to-wake approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10144, https://doi.org/10.5194/egusphere-egu21-10144, 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.
The greenhouse gas (GHG) emissions from cities account for more than 70% of the global emissions. Over the past decades, GHG-dedicated space-based instruments, such as Japan’s Greenhouse gases Observing SATellite (GOSAT) (2009-), GOSAT-2 (2018-), NASA’s Orbiting Carbon Observatory-2 (OCO-2) (2014-), and OCO-3 (2019-), have collected the increased amount of the GHG data on the global scale, especially over urban areas. Such data have provided new opportunities to explore ways to study urban emissions, and they will also play a key role in monitoring the progress of subnational climate mitigation efforts towards the Paris Climate Agreement goal.
Here we present the first high-resolution multi-species (CO2 and NO2) observations from Japanese passenger aircrafts, which should further enhance our ability to quantify GHG emissions in combination with data collected from existing ground-based stations and satellites. Our multi-species observations should also provide direct technical and scientific implications to the planned future space missions, such as Japan’s Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW) and ESA’s CO2 Monitoring Mission (CO2M), which also plan to measure CO2 and NO2 with a special focus on monitoring GHG emissions.
We designed and developed a carry-on luggage sized imaging spectrometer to collect high-resolution (a few handed m to a few thousand m) CO2 and NO2 concentration data during domestic passenger flights. We conducted our first observation during the flight between Tokyo and Fukuoka in October 2020. The two-hour flight allowed us to collect sounding data ranging from 130°E to 140°E in longitude and 33.5°N to 36°N in latitude. The data were being collected every 0.5 sec in nominal and were created up to 5M soundings during the single flight. The obtained data depicted spatial patterns of CO2and NO2 concentrations over the cities and industrial areas, with some notable differences from ones seen from existing satellite observations. We compared our data to other data, such as emission inventories, and satellite observations of CO2, NO2, and nighttime lights, in order to further characterize the observed spatial gradient and patterns.
In our presentation, we will also discuss the unique utility of our new aircraft observation and its potential contribution to GHG emission monitoring and the upcoming Global Stocktakes (GST) with an expanded observation coverage and frequency.
How to cite: Suto, H., Kuze, A., Oda, T., Kataoka, F., Matsumoto, A., Mori, S., Shiomi, K., Kosaki, S., Kaku, T., Yoshida, J., Nakamura, Y., and Tsubakihara, Y.: Monitoring the nations’ climate mitigation progress using multi-species observations from Japanese passenger aircrafts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13404, https://doi.org/10.5194/egusphere-egu21-13404, 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.
Estimation of greenhouse gas emissions from atmospheric measurement-based "top-down" methods is complicated by strong and uncertain fluxes from natural systems, for example carbon dioxide (CO2) sources and sinks from the terrestrial biosphere. Additional tracers such as radiocarbon are promising for disentangling the different emission contributions from human activity and natural systems. However, many open questions remain about how different uncertainties in the modeling and observation of these tracers influence the emission estimates.
Here we assess the potential benefits of using radiocarbon observations to constrain global fossil fuel emissions in a Carbon Cycle Fossil Fuel Data Assimilation System (CCFFDAS). We performed sensitivity experiments to quantify how uncertainties in the observations and models affect the uncertainties in the derived emissions, including different prior assumptions about natural and anthropogenic CO2 fluxes and varying observation networks. Further, we demonstrate how radiocarbon observations can complement the existing CO2 observation network.
How to cite: Chen, H. W., Scholze, M., Kaminski, T., Vossbeck, M., Rayner, P., and Karstens, U.: Assessment of radiocarbon observations for constraining fossil fuel emissions in a comprehensive Carbon Cycle Fossil Fuel Data Assimilation System, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13258, https://doi.org/10.5194/egusphere-egu21-13258, 2021.
Rapid regional changes in anthropogenic emissions in response to the COVID-19 pandemic have underscored the contribution of fossil fuel (FF) emission uncertainty to regional carbon budgets. Typical methods for spatially-explicit FF emissions are dependent on national reporting, which can incur substantial latencies. However, the concomitant changes in short-lived pollutants from common emission sources point to opportunities to develop independent low-latency estimates of fossil fuel emissions and to better understand anthropogenic processes. Here we combine state-of-the-art Multiple Model Multi Constituent chemical data assimilation system (MOMO-Chem) with bottom-up FF emissions to repartition the net carbon fluxes from the NASA Carbon Monitoring System Flux (CMS-Flux) project. To that end, we implement a novel Kalman filtering algorithm that predicts emission ratio co-evolution of air quality (AQ) and carbon species. Based upon top-down estimates of AQ emissions, FF CO2 emissions and uncertainties can be rapidly determined. We show overall good agreement between predicted FF fluxes and the latest bottom-up inventories. These data are in turn used to interpret the decadal evolution of CMS-Flux net carbon exchange. This approach is an important step in quantifying both regional fossil fuel and natural carbon fluxes contributions to the atmospheric CO2 growth rate.
How to cite: Bowman, K., Miyazaki, K., Liu, J., and Bloom, A.: Co-evolution of carbon cycle and air quality fluxes constrained by CMS-Flux and MOMO-Chem assimilation systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14067, https://doi.org/10.5194/egusphere-egu21-14067, 2021.
The Paris Agreement foresees to establish a transparency framework that builds upon inventory-based national greenhouse gas emission reports, complemented by independent emission estimates derived from atmospheric measurements through inverse modelling. The capability of such a Monitoring and Verification Support (MVS) capacity to constrain fossil fuel emissions to a sufficient extent has not yet been assessed. The CO2 Monitoring Mission (CO2M), planned as a constellation of satellites measuring column-integrated atmospheric CO2 concentration (XCO2), is expected to become a key component of an MVS capacity.
Here we present a CCFFDAS that operates at the resolution of the CO2M sensor, i.e. 2km by 2km, over a 200 km by 200 km region around Berlin. It combines models of sectorial fossil fuel CO2 emissions and biospheric fluxes with the Community Multiscale Air Quality model (coupled to a model of the plume rise from large power plants) as observation operator for XCO2 and tropospheric column NO2 measurements. Inflow from the domain boundaries is treated as extra unknown to be solved for by the CCFFDAS, which also includes prior information on the process model parameters. We discuss the sensitivities (Jacobian matrix) of simulated XCO2 and NO2 troposheric columns with respect to a) emissions from power plants, b) emissions from the surface and c) the lateral inflow and quantify the respective contributions to the observed signal. The Jacobian representation of the complete modelling chain allows us to evaluate data sets of simulated random and systematic CO2M errors in terms of posterior uncertainties in sectorial fossil fuel emissions. We provide assessments of XCO2 alone and in combination with NO2 on the posterior uncertainty in sectorial fossil fuel emissions for two 1-day study periods, one in winter and one in summer. We quantify the added value of the observations for emissions at a single point, at the 2km by 2km scale, at the scale of Berlin districts, and for Berlin and further cities in our domain. This means the assessments include temporal and spatial scales typically not covered by inventories. Further, we quantify the effect of better information of atmospheric aerosol, provided by a multi-angular polarimeter (MAP) onboard CO2M, on the posterior uncertainties.
The assessments differentiate the fossil fuel CO2 emissions into two sectors, an energy generation sector (power plants) and the complement, which we call “other sector”. We find that XCO2 measurements alone provide a powerful constraint on emissions from larger power plants and a constraint on emissions from the other sector that increases when aggregated to larger spatial scales. The MAP improves the impact of the CO2M measurements for all power plants and for the other sector on all spatial scales. Over our study domain, the impact of the MAP is particularly high in winter. NO2 measurements provide a powerful additional constraint on the emissions from power plants and from the other sector.
How to cite: Kaminski, T., Scholze, M., Rayner, P., Houweling, S., Voßbeck, M., Silver, J., Lama, S., Buchwitz, M., Reuter, M., Knorr, W., Chen, H., Kuhlmann, G., Brunner, D., Dellaert, S., Denier van der Gon, H., Super, I., Löscher, A., and Meijer, Y.: Assessing the constraint of the CO2 monitoring mission on fossil fuel emissions from power plants and a city in a regional carbon cycle fossil fuel data assimilation system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16139, https://doi.org/10.5194/egusphere-egu21-16139, 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.
Recent studies have shown that methane emissions are underestimated by inventories in many US urban areas. This has important implications for climate change mitigation policy at the city, state and national level. Uncertainty in both the spatial distribution and sectoral allocation of urban emissions can limit the ability of policy makers to develop well-targeted emission reductions strategies. Top-down emission estimates based on atmospheric greenhouse gas measurements can help to improve inventories and better inform policy decisions.
This presentation builds on previous work estimating methane emissions from New York City and the wider urban area based on measurements taken during nine research flights. We used an ensemble of dispersion model runs in a Bayesian inverse modelling framework to derive posterior emission estimates. Prior emissions were taken from three coarse-resolution inventories based on spatially disaggregated national totals. The most recent version of EDGAR (v5) and the gridded EPA inventory both required upscaling by more than a factor of two to be consistent with our measurements.
Here, we construct a high-resolution methane emission prior using a combination of spatial proxies and reported emissions for various sectors. We present preliminary results evaluating the ability of this new prior to represent the magnitude and spatial distribution of emissions, through comparison with both the measured data and results obtained using coarser resolution inventories.
How to cite: Pitt, J., Lopez-Coto, I., Hajny, K., Tomlin, J., Kaeser, R., Jayarathne, T., Stirm, B., Floerchinger, C., Loughner, C., Commane, R., Gately, C., Hutyra, L., Gurney, K., Roest, G., Liang, J., Karion, A., Whetstone, J., and Shepson, P.: Development of a high-resolution prior for inverse modelling of New York City methane emissions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13828, https://doi.org/10.5194/egusphere-egu21-13828, 2021.
Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. Our analysis uses three lines of evidence with increasing model dependency. The first detects the timing of emissions declines using the variability in atmospheric CO2 observations, the second assesses the continuation of reduced emissions using CO2 enhancements, and the third employs an inverse model to estimate the relative emissions changes in 2020 compared to 2018 and 2019. Emissions declines began in mid-March in both cities. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales, a proxy for vehicular emissions. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines, while the remainder of the emissions reduction remains unexplained. To help diagnose such observed changes in emissions, more reliable, publicly available emission information from all significant sectors needs to be made available. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing urban emissions patterns that can empower cities to course-correct mitigation activities more efficiently.
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.
Assessing progress towards greenhouse gas mitigation targets in recent legislation requires reliable, precise methods for emissions quantification. Top-down approaches can provide a complementary assessment to the bottom-up inventories typically used by cities.
In this work we have performed a series of 9 winter aircraft measurement flights downwind of New York City in 2018 – 2020. We use dispersion modeling driven by publicly available meteorological products to calculate footprints relevant to the flight data. To calculate modeled emissions, we combine these footprints with four CO2 inventories (ODIAC, EDGAR, ACES, and Vulcan) using a spatially explicit scaling factor approach. We show that we can isolate the emissions from two areas of interest, New York City and the New York-Newark urban area, by using the fraction of modeled enhancements originating in said areas of interest as weighting functions. We then calculate a scaling factor that optimizes agreement with measurements for each flight. Here we discuss this technique and the posterior emissions for both areas of interest as compared to inversion analyses for the same areas. We also quantify the variability across the ensemble including multiple meteorological products, scaling factor calculation methods, and mixing parameterizations across all inventories and flight days.
How to cite: Hajny, K., Floerchinger, C., Pitt, J., Lopez-Coto, I., Tomlin, J., Kaeser, R., Stirm, B., Jayarathne, T., Gately, C., Sargent, M., Gurney, K., Roest, G., Turner, A., Hutyra, L., Shepson, P., and Wofsy, S.: Application of a Spatially Explicit Scaling Factor Method on CO2 Emissions From New York, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13737, https://doi.org/10.5194/egusphere-egu21-13737, 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.
Major cities such as London are increasingly becoming targets for reducing greenhouse gas emissions by policy makers. This is due in part to their higher rate of emissions compared to more rural areas, but also due to the political powers of city level government. To ensure that emission reduction policies are successful, policy makers require accurate knowledge of how emissions change over time.
The London Greenhouse Gas Project aims to provide top-down emission estimates for London, adding a London measurement network to expand upon the UK’s existing national top-down measurement infrastructure. The national network has proved useful in contributing to the UK’s national emission reports, and the new local network will provide useful data targeted to London’s policy makers.
A series of in-situ atmospheric concentration instruments are being installed across the city and will be used to estimate London’s emissions of methane initially, with carbon dioxide emissions to follow. A medium-density urban network provides challenges in instrument calibration and siting, as well as the development of new modelling approaches to capture the urban environment and link the measurements to policy-relevant emissions estimates. There are also opportunities to link with remote observations of London, including satellite and ground-based FTIR instruments. We present considerations of setting up the new network, and results from the initial instrument installation and model development.
How to cite: Hoare, D., Jones, R. L., Fan, S., Harris, N., Ferracci, V., Carruthers, D., Stidworthy, A., Forsyth, E., and Rigby, M.: Inferring London’s Methane Emissions from Atmospheric Measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10016, https://doi.org/10.5194/egusphere-egu21-10016, 2021.
Urban areas are a large source of carbon dioxide (CO2) to the atmosphere. Cities are seeking solutions to reduce the CO2 emissions and to achieve carbon neutrality. Thus, there is a growing interest in maximizing the carbon sinks of urban vegetation and soil. Current knowledge on the carbon sinks is mainly based on data from non-urban environments. In the cities, environmental controls of carbon flows are different compared to the surroundings: temperatures are higher and water cycles altered compared to non-urban areas, green areas are managed (e.g. mowed and irrigated), and trees typically have very limited space for their roots but less competition at the canopy-level. In order to reduce uncertainties particularly in observation based urban carbon emission estimation, biogenic fluxes and their behaviour need to be correctly described/presented.
In the CarboCity project (Urban green space solutions in carbon neutral cities; 2019–2023), we aim to achieve a thorough understanding of atmosphere-plant-soil carbon dynamics in urban areas, and to find the best practices for designing the green areas to maximise their carbon sinks and stocks. In Helsinki, Finland, we have three sites in the footprint area of the SMEAR III ICOS station (SMEAR – Station for Measuring Earth surface-Atmosphere Relations; ICOS – Integrated Carbon Observation System): a botanical garden, a small urban forest, and a street site. The measurements were started in 2020, and include photosynthesis and fluorescence of trees (Tilia cordata Mill., T. × europaea L., Betula pendula Roth) and soil respiration, together with several supporting measurements (e.g. air and soil temperature, relative humidity, soil water content, sap flow, LAI). Ecosystem-level CO2 exchange over the whole area of all three sites is measured at the SMEAR III ICOS station. Since late 2020, we are measuring also carbonyl sulphide exchange at the neighbourhood scale, which is used as a proxy for GPP. In addition to the measurements in Helsinki, we will use measured data from London, Minneapolis-Saint Paul, Beijing and São Paolo – cities that differ in the climate regions, vegetation types, and management styles of their green areas. Furthermore, the measurements will be used to parameterise land surface model SUEWS (Surface Urban Energy and Water balance Scheme), soil carbon model Yasso and dynamic land-surface models.
How to cite: Vainio, E., Kulmala, L., Fruhauf, Y., Soininen, J., Havu, M., Thum, T., and Järvi, L.: CarboCity – Solving biogenic carbon cycle in urban environments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14647, https://doi.org/10.5194/egusphere-egu21-14647, 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 (CO2ff) 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 CO2ff 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 CO2ff 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 CO2ff emissions over Lahore. The posterior CO2ff 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 XCO2 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.
On March 11th , 2020, the World Health Organization (WHO) characterized the COVID-19 respiratory disease caused by the coronavirus (SARS-CoV-2) as a world wide pandemic which led to a massive slowdown in anthropogenic activity as people attempted to "shelter in place". In response to this slowdown NOAA's Global Monitoring Lab (GML), in collaboration with the National Institute of Standards and Technology (NIST), University of Michigan, University of Maryland, Stony Brook University and NOAA's Chemical Science and Atmospheric Resource Laboratories, launched a campaign to measure CO2, CH4, and CO emissions from five major cities along the northeast corridor of the US (Washington, D.C., Baltimore, MD, Philadelphia, PA, New York, NY, and Boston, MA). The month-long campaign which lasted from April 16 to May 16 of 2020 mirrored a campaign that was completed exactly two years prior in April and May of 2018 and which enabled direct comparison of CO2, CH4, CO emissions from these five cities before and during SARS-CoV-2.
In this work, we used a Bayesian multi-resolution tiered inversion framework to quantify the CO2, CH4 and CO emissions from these urban areas. We used the HYSPLIT atmospheric transport and dispersion model to calculate the sensitivity of our aircraft observations to surface fluxes (footprints) using three meteorological drivers (NAM, ERA5 and a custom WRF); using three driver models allowed us to account for uncertainties in the transport. To account for biospheric influences on atmospheric CO2, we used a year-specific VPRM simulation that allowed us to isolate the fossil-fuel contribution and solve for it alone. In addition, we also solved for total CO2 and show that not accounting for biogenic activity in lower latitude urban areas could have led to an overestimation of the observed reduction due to biogenic flux differences between the two years.
Results show that systematic reductions in CO2 and CO emissions for the five urban areas occurred in April 2020 with signs of recovery in May 2020, which had larger emissions than April 2020. The observed reductions and evolution are consistent with bottom-up estimations based on mobility metrics, which showed the lowest mobility in April with progressive recovery in May. Fuel use from tax records indicates similar reductions. In addition, we show that changes are not homogeneous in space within the urban metropolitan areas and that CO2 and CO emissions reductions are collocated, showing the largest drops in urban centers and roads. While CO2 and CO estimated reductions and evolution are systematic in all cities, CH4 does not show a clear reduction or consistent pattern among cities during the COVID-19 lock-downs. In fact, all the measured changes for CH4 were lower than the standard errors of the differences, implying that the observed changes in CH4 are not significant. Last, we note that since the same prior emissions, constant in time, were used in all the inversions, the anomalous decrease in posterior emissions and subsequent recovery in CO2 and CO observed during the COVID-19 lock-down period are driven by the atmospheric observations and not by temporal changes in the prior emissions.
How to cite: Lopez-Coto, I., Sweeney, C., Plant, G., McKain, K., Ren, X., Karion, A., Kort, E., McDonald, B., Gourdji, S., Miller, J., Dickerson, R., Shepson, P., Roest, G., Gurney, K., Stein, A., and Whetstone, J.: Reduction in GHG emissions in the U.S. North East Corridor due to COVID-19 lockdowns as measured by the East Coast Outflow Experiment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16040, https://doi.org/10.5194/egusphere-egu21-16040, 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.
In order to infer greenhouse gas emissions from a source region, several top-down approaches can confirm or constrain the existing emission inventories. In this work an adopted version of a Bayesian inversion framework  will be presented. Methane emissions are derived from the column concentrations measured with six EM27/SUN FTIR spectrometers using ground based direct sunlight spectroscopy. The measurement campaign was carried out in the San Francisco Bay Area in 2016.
The framework uses the STILT generated footprints, which represent the surface-interaction of an air-parcel on its trajectory to the measurement site and thus describe the sensitivity of the measured concentration at a certain location to its surrounding source emissions. The dot product of the footprint matrix with a gridded emission inventory matrix results in expected concentration enhancements at the measurement site as a prior estimate. Here, we use the 1km-gridded local methane inventory by the Bay Area Air Quality Management District (BAAQMD).
Due to the long-term stability of methane, the air parcel holds a non-zero background concentration, which is not negligible. This poses a major challenge in the inversion. The existing Bayesian framework constrains a background concentration as well as a scaling factor for the inventory from the measurements. Within the existing framework, the assumption is made that all instruments eventually experience the same, time dependent background concentration. This assumption holds well for flat terrain with undisturbed wind-fields.
However, in the presence of complex topography, such as San Francisco Bay Area, the background source regions may differ significantly for the individual measurement sites. Here, we present an approach to account for differing background concentrations seen by multiple measurement sites:
The adopted inversion allows to have individual background concentrations for each measurement site. This is strongly constrained by background covariances, which represent the background in common with the remaining measurement sites. These covariances are calculated from the STILT trajectories.
 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: Klappenbach, F., Chen, J., Wenzel, A., Forstmaier, A., Dietrich, F., Zhao, X., Jones, T., Franklin, J., Wofsy, S., Frey, M., Hase, F., Hedelius, J., Wennberg, P., Cohen, R., and Fischer, M.: Methane emission estimate using ground based remote sensing in complex terrain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15406, https://doi.org/10.5194/egusphere-egu21-15406, 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)  that measures the column-averaged concentration gradients of CO2, CH4 and CO using the differential column methodology (DCM, ). The network consists of five ground-based FTIR spectrometers (EM27/SUN from Bruker ), 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 , 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 . 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.
 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.
 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.
 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.
 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.
 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.
The Vienna Urban Carbon Laboratory (VUCL) has begun testing in situ measurement-based options for monitoring local carbon dioxide (CO2) and methane (CH4) emissions in Austria’s capital city. Building upon the groundwork of the CarboWien project, VUCL extends and expands the current tall-tower eddy covariance flux system and will furthermore conduct campaigns to measure carbon isotopes and isofluxes, as well as upwind-downwind gradients in total column CO2 and CH4 mixing ratios. The project, which runs between 2021 and 2024 and is funded by the Vienna Science and Technology Fund (WWTF), will be implemented by a collaboration between the University of Natural Resources and Life Sciences Vienna (BOKU), the Technical University of Munich (TUM), the Environment Agency Austria (EAA) and A1 Telekom Austria AG (A1). In addition to contributing to international research into measurement-based greenhouse gas emissions monitoring, the multi-method approach provides an opportunity to demonstrate measurement-based emissions monitoring options directly to Vienna’s civil servants responsible for climate change mitigation action in the city. Continuous local stakeholder engagement over the project duration is therefore planned.
This conference contribution to the WMO-IG3IS session at vEGU21 will allow VUCL to be introduced to relevant scientists and stakeholders in the international community. Given the recent project start (01 Feb 2021), the foreseen discussions on the project’s planned implementation will provide an important and timely input into VUCL. Finally, initial VUCL results will be presented together with data from the preceding CarboWien project (2018-2020) to show how the measured CO2 fluxes in Vienna have been impacted by the lockdown restrictions due to the COVID-19 pandemic.
How to cite: Matthews, B., Watzinger, A., Chen, J., Schume, H., Sanden, H., Dietrich, F., and Leitner, S.: Introducing the Vienna Urban Carbon Laboratory (VUCL), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12964, https://doi.org/10.5194/egusphere-egu21-12964, 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.
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.
Most of the world population leaves in urbanized areas, and this is expected to expand rapidly in the next decades. Cities and their industrial facilities are estimated to emit more than 70% of fossil fuel CO2. Still, these estimates, mostly based on bottom-up emission inventories, need to be verified at the city scale. Atmospheric top-down approaches are a tool of choice in this sense. They rely mostly on continuous atmospheric CO2 measurements inside and outside of the studied urbanized area to catch the urban plume and its variability (either from in-situ, remote sensing or airborne instrumentation), on the use of emission tracers such as carbon monoxide and black carbon for combustion processes, of volatile organic compounds and of carbon isotopes to distangle the contribution of natural, modern and fossil fluxes, on mass balance approaches which needs measurements of the atmospheric boundary layer height, and on direct and inverse modeling frameworks. Furthermore, as they represent the main anthropogenic CO2 emission sector, cities and industrial facilities are strategic places where actions on mitigating CO2 emissions should be undertaken in priority.
The Aix-Marseille metropolis (AMm), located in the south-east of France, is the second most populated area of France (1.8 M inhabitants). It is also much industrialized, and is located in the SUD-PACA region, which is strongly exposed to the risks of Climate Change. Since 2017, two top-down research projects have been funded by the LABEX OT-MED (AMC project, 2016-2019) and by the French National Research Agency ANR (COoL-AMmetropolis project, 2020-2024) to fullfill the following objectives : 1/ assessing the spatio-temporal variability of atmospheric CO2 in the AMm area ; 2/ characterizing the different sources and sinks that control CO2 through the use of tracers and carbon isotopes ; 3/ verifying independently the high-resolved CO2 emission inventory delivered by the regional air quality agency ATMOSUD ; 4/ developing a direct modeling framework, facing challenges such as the complex AMm topography, coastal boundary layer dynamics, and some specific meteorological features that are mistral and land/sea breezes ; and 5/ developing scenarios to the horizon 2035 for mitigating AMm CO2 emissions and find the most effective way to integrate vertuous scenarios, defined in interaction with stakeholders, into legal and urban planning schemes, tools, charters or practices. A synthesis of the results obtained until now from these two projects will be presented.
How to cite: Xueref-Remy, I., Nathan, B., Milne, M., Lelandais, L., Riandet, A., Lauvaux, T., Chen, H., Palstra, S., Scherren, B., Armengaud, A., Blanc, P.-E., Turnbull, J., Lambert, M.-L., Hernandez, F., Masson, V., Yohia, C., and Nicault, A.: Towards improving current estimates of CO2 emissions and sinks in the Aix-Marseille metropolis area, France, and developing virtuous CO2 mitigation scenarios in link with local stakeholders and socio-economic actors., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12629, https://doi.org/10.5194/egusphere-egu21-12629, 2021.
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