Displays

The need for climate action in cities is becoming more and more critical. As such, systems that quantify local greenhouse gas (GHG) emissions to evaluate mitigation measures are growing in importance and are set to undergo increasing levels of scrutiny. Within the CarboWien project, the University of Natural Resources and Life Sciences, Vienna, the Environment Agency Austria and the telecommunications company A1 Telekom Austria AG are currently collaborating to investigate the potential of a tall tower eddy covariance station to support carbon dioxide (CO₂) emissions monitoring in Vienna. Due to the tall tower approach (144 m measurement height) the measured turbulent fluxes are representative of net emissions from much of the city area. If maintained in the near- to medium‑term, this facility could provide an additional, independent instrument with which local climate change action can be continuously evaluated.

This conference contribution will present results from the measurement campaign so far (2018-2019). In addition to discussing the early-indicator function of these data and the scope for improving emissions inventories, the presentation will demonstrate how these measurements can be directly used to evaluate local mitigation measures. In particular, analyses of the 30-minute fluxes against local activity/proxy data will show how the performance of measures seeking to reduce CO₂ emissions from road traffic and space heating can be inferred.

How to cite: Matthews, B. and Schume, H.: Tall tower eddy covariance as a tool for evaluating climate change mitigation in Vienna, Austria, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4411, https://doi.org/10.5194/egusphere-egu2020-4411, 2020.

Urban areas are rapidly growing and are acknowledged to dominate greenhouse gas (GHG) emissions to the Earth’s atmosphere. They are also emerging as centers of climate mitigation leadership and innovation. However, fundamental quantitative analysis of urban GHG emissions beyond individual city case studies remains challenging due to a lack of comprehensive, quantitative, methodologically consistent emissions data, raising barriers to both scientific and policy progress. Here we present the first such analysis across the entire US urban landscape, answering a series of fundamental questions about emissions responsibility, emissions drivers and emissions integrity. We find that urbanized areas in the U.S. account for 68.1% of total U.S. fossil fuel carbon dioxide (CO₂) emissions. Were they counted as a single country, the 5 largest urban emitters in the US would rank as the 8^th largest country on the planet; the top 20 US cities as the 5^th largest. In contrast to their dominant overall proportion, per capita FFCO2 emissions in urbanized areas of the US are 7% less than the country as a whole, particularly for onroad gasoline emissions (-12.3%).

Contrary to previous findings, we find that emissions grow slower than urban population growth in Eastern US cities, particularly for larger urban centers. The Western US, by contrast, shows emissions growing proportionately with population. Much of the difference between Eastern versus Western cities is determined by the onroad emissions sector. This finding, in particular, suggests that “bigger is better” when considering GHG emissions and U.S. urban population growth.

Finally we find large and persistent differences between the results presented here and 57 self-reported urban inventories. The mean difference between the self-reported inventories and the analysis here is -24% (mean absolute difference: 44.3%) with the majority of self-reported values lower than quantified in this study.

How to cite: Gurney, K., Liang, J., Roest, G., and Song, Y.: The CO2 Emissions of US Cities: Status, Dynamics, and Comparisons, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1643, https://doi.org/10.5194/egusphere-egu2020-1643, 2020.

Methane emissions monitoring is rapidly expanding with increasing coverage of surface, airborne, and satellite instruments. However, no single methane instrument or observing strategy can both close emission budgets and pinpoint point sources on regional to global scales. Instead, we present a multi-tiered data analytics system that synthesizes information across various instruments into a single analytic framework. We highlight an example in Los Angeles, where we combine surface measurements from the Los Angeles megacities project, mountaintop measurements from the CLARS-FTS instrument, airborne AVIRIS-NG point source emission estimates, and TROPOMI total column retrievals into a single analytic framework. Surface, mountaintop, and satellite measurements are assimilated into a methane flux inverse model to constrain basin-wide emissions and pinpoint sub-basin methane hotspots. We show an example of a large urban landfill, whose anomalous emissions were detected by the inverse system, and validated using AVIRIS-NG methane plume maps. This general approach of quantifying both methane area and point source emissions is an avenue not just for closing regional to global scale budgets, but also for understanding which emission sources dominate the budget (i.e., so called methane super-emitters). We finally show how this multi-tiered analytic framework can be improved with future satellite missions, and present examples of unexpectedly large methane emissions that were detected by a new generation of satellite imaging spectrometers.

How to cite: Cusworth, D., Duren, R., Thorpe, A., Stavros, N., Bue, B., Tapella, R., Yadav, V., and Miller, C.: A multi-tiered methane analytic framework for constraining budgets, point source attribution, and anomalous event detection, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6281, https://doi.org/10.5194/egusphere-egu2020-6281, 2020.

For the past five years, we have been measuring greenhouse gases CO₂, and CH₄ along with a suite of pollutants related to photochemical smog (O₃, NO₂, VOCs, CO) and particulate matter (SO₂ (sulfate precursor), & aerosol optical properties) from a research aircraft.  These complement a network of tower-based monitors and provide input to a variety of models used to determine emissions.  Initial findings include identification of landfills and leakage from the natural gas delivery system as major local sources of CH₄, as well as substantial upwind sources such as oil and gas operations in the Marcellus shale play.  Quantification of emissions and flux is complicated by uncertainties in background concentrations and mesoscale dynamics.  Comparison of short-lived species has shed light on the efficiency of combustion and pollution control as well as the temperature dependence of emissions.  Ratios of CO:CO₂, for example, are consistent with emissions inventories and verify the high efficiency catalytic converters. 

How to cite: Dickerson, R., Canty, T., Ren, X., Salawitch, R., Shepson, P., Lopez Coto, I., and Whetstone, J.: Observations of greenhouse gas and short-lived pollutants in the Baltimore Washington area: Quantification and mitigation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11032, https://doi.org/10.5194/egusphere-egu2020-11032, 2020.

The Grater Tokyo Area is the most populated (38 million) metropolitan area in the world. To capture fossil fuel carbon dioxide (CO₂) emissions from the Grater Tokyo Area, we performed ground-based atmospheric observations for measuring concentrations of CO₂, radiocarbon in CO₂ (¹⁴CO₂), oxygen (O₂) and carbon monoxide (CO) at Tokyo Skytree (TST, with high altitude (250m) inlet) and Yoyogi (YYG, turbulentCO₂ flux measurement site located in resident area) in Tokyo and at National Institute for Environmental Studies (NIES, suburb/rural area) in Ibaraki, Japan. The ¹⁴CO₂ measurement was used for separating the fossil fuel CO₂ emissions from the biotic emissions. Results from ¹⁴CO₂ measurements showed that a ratio of fossil fuel-derived CO₂ to the variation of CO₂ concentrations was 71% in average for winter both at TST and YYG but varied from 44% to 92%, indicating significant contribution of biotic CO₂ in Tokyo. The O₂:CO₂ exchange ratio (oxidation ratio, OR) was used for the partitioning of CO₂ into emissions from gas fuels and gasoline. We observed larger OR in winter than in summer (due to both wintertime increases of fossil fuel combustion and summertime terrestrial biospheric activities) at TST and YYG and larger OR in the morning and late evening in winter due to increase of gas fuel combustion at YYG. We showed that the O₂ concentrations might be also used as a proxy for continuous monitoring of fossil fuel CO₂ content by assuming typical ratio of gas fuels and gasoline combustions. The presenter will introduce the related projects including development of building/road-scale dynamic CO₂ mapping and grid-based CO₂ emission inventory with high special resolution in Tokyo.

How to cite: Terao, Y., Tohjima, Y., Ishidoya, S., Ouchi, M., Osonoi, Y., Mukai, H., Machida, T., Sugawara, H., Kaneyasu, N., and Niwa, Y.: Atmospheric observations of CO2, 14CO2 and O2 concentrations to capture fossil fuel CO2 emissions from the Greater Tokyo Area, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12366, https://doi.org/10.5194/egusphere-egu2020-12366, 2020.

Most of the global population leaves in cities, expected to expand rapidly in the next decades. Cities and their industrial facilities are estimated to release more than 70% of fossil fuel CO2, although these estimates need to be verified at the city scale. Furthermore, cities are undergoing higher temperatures than their surrounding rural areas due to the Urban Heat Island (UHI) which also directly influence some CO2 fluxes (for example from buildings domestic heating, car air-conditioning, urban and rural vegetation uptake). Cities are thus strategic places where actions on mitigating CO2 emissions and also on lowering down atmospheric temperature elevation should be undertaken in priority.

The ANR COoL-AMmetropolis project focuses on characterizing and mitigating CO2 emissions and UHI in the Aix-Marseille metropolis (AMm), of which the new governance entity is the “Metropole Aix-Marseille-Provence" (noted AMPM). AMm is the second most populated area of France (1.8 M inhabitants), is much industrialized, and is located in the PACA region strongly exposed to the risks of Climate Change. The  objectives of the project are : (1) verifying and improving the spatio-temporal distribution of the AMm FFCO2 emissions estimates and quantifying their current contribution against natural fluxes, (2) characterizing the  variability of the UHI and atmospheric CO2 at the diurnal, synoptic and seasonal scales in the AMm area, and modeling UHI and CO2 sources and sinks interactions  at the local to the AMm scale; and (3) defining and evaluating the benefits of development scenarios of the AMm urban ecosystem to the horizon 2035 for mitigating both CO2 emissions and UHI, at the different scales, and find the most effective way to integrate the vertuous scenarios, defined in  interaction with stakeholders, into legal and urban planning schemes, tools, charters or practices.

To reach these objectives, a multidisciplinary Consortium made of 5 main partners (IMBE, CNRM, LIEU, AtmoSud, UMS Pytheas) and 6 non-funded partners (LSCE, INRA/URM, ESPACE, MIO, DTN, GNZ New-Zeeland) is proposed, ensuring complementarity between atmospheric physicists, urbanists, territorial jurists, emission stockcounters and AMm socio-economic actors with privileged links with local/regional stakeholders. Through its expertise and the organisation of annual seminars, GREC-SUD (sub-contract.) will reinforce these interactions.

The project is organized in 4 workpackages. WP0 is dedicated to the project coordination. WP1 is assigned to the collection and analyzes of CO2 and UHI observations, and WP2 to the development and assessment of the CO2 and UHI modelling framework. WP1 and WP2 will feed WP3, dedicated to the role of the several levels in the AMPM in the governance for the urban adaptation strategies on the UHI and CO2 issues. It relies on legal documents analyses, multi-indicators evaluation of scenarios, and a strategy of ensuring regular interactions between the research community, local stakeholders & civil society throughout the full project duration.

The ANR COoL-AMmetropolis is funded for 4 years, starting on January 2020.

How to cite: Xueref-Remy, I., Riandet, A., Lelandais, L., Nathan, B., Milne, M., Masson, V., Lambert, M.-L., Armengaud, A., Turnbull, J., Yohia, C., Nicault, A., Lauvaux, T., Piazzola, J., Lac, C., Hedde, T., Robert, S., Simioni, G., Cramer, W., and Bondeau, A.: COoL-AMmetropolis : towards establishing virtuous greenhouse gas emission mitigation scenarios for 2035 in the Aix-Marseille metropolis area (France) through atmospheric top-down technics and social sciences methods in interaction with local stakeholders., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5930, https://doi.org/10.5194/egusphere-egu2020-5930, 2020.

Mexico City (MC) is the home of 21.2M people, 19% of the country's population. The MC urban area has intense emissions of pollutants and greenhouse gases, which accumulate in the overlying air-shed due to the location of the city in a high-altitude basin surrounded by mountains. Local and national authorities have engaged into aggressive emission reduction strategies. The Mexican-French collaborative project, MERCI-CO2, aims to develop atmospheric CO2 measurements that will enable, with the support of atmospheric inversion, to verify the effectiveness of CO2 emission reductions taken by the city authorities. The MERCI-CO2 combines high-precision analysers and low-cost sensors for surface measurements with total column observations up- and down-wind of MC. In addition to the long-term infrastructure currently deployed, an intensive campaign in the spring 2020 will produce an unprecedented data set. For this campaign we will deploy during one month six EM27 spectrometers for total column CO2, CH4 and CO observations; two high-precision analyzer at fixed position and one on board a car for transect measurements; and ten low-cost CO2 sensors which will be setup at air quality stations from the local city network measuring CO, NOx and O3. The dense network will be deployed before, during and after the Eastern vacation period in early April. During this week the traffic, which represents about 70% of CO2 emissions, will be significantly reduced. The atmosphere will be analyzed with a high-resolution transport model to infer the reduction of the surface emissions. This result will be compared to the reduction of the traffic inferred from car counting statistics, and bottom-up estimates. The EM27 instruments will be moved around a large landfill, in order to measure the CH4 enhancement due to this installation, and estimate its emission. The waste sector represent by far the largest CH4 contributor (about 90%) in Mexico, and remains subject to large uncertainties.

How to cite: Ramonet, M., Taquet, N., and Grutter, M. and the MERCI-CO2: Intensive CO2 and CH4 measurement campaign at Mexico-City, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20364, https://doi.org/10.5194/egusphere-egu2020-20364, 2020.

The time for action on the Paris Agreement is upon us, requiring all signatory countries to have a robust reporting and accounting system that is transparent, accurate, complete, consistent and comparable (through the Enhanced Transparency Framework), with a periodical review of the collective achievement of the 2°C temperature goal (the Global Stocktake). The research community is therefore called to reinforce databases and methodologies to improve national greenhouse gas inventory estimates, especially for developing countries that are subject to new reporting obligations, but also to define a comparable scientific “benchmark" to assess the achievement of the Paris Agreement goal.

Despite the key role of science in the process, often research communities working on emission statistics have approached the problem of climate change through different angles and by using terminologies, metrics, rules and approaches (e.g. spatial and temporal scales) that do not always match with those used by the inventory communities. Within the VERIFY project (Horizon 2020, grant agreement No 776810) a networking between the two communities (research and inventory) has been established. The discussion between them highlighted the importance of a continue exchange to increase the mutual understanding of needs, terms, rules, procedures and guidelines in use, especially those adopted under the UNFCCC and Paris Agreement process.

The presentation will therefore guide the researchers through the monitoring, reporting and verification frameworks under the UNFCCC and Paris Agreement, identifying how and where science production can assist the inventory communities in improving greenhouse gasses estimations and verification systems. Land Use, Land-Use Change and Forestry is the most complicate sector to deal with because of intricacy of flux attribution (that can be both anthropogenic and non-anthropogenic) and methodological complexity, affected also by common misunderstandings in the use of terminologies and different definitions. 

On the basis of the available literature and the outcomes of the work undertaken under VERIFY project, we provide an analysis on the possible critical issues and main misunderstanding that could arise, identifying options on how to solve them.

How to cite: Perugini, L., Pellis, G., Grassi, G., Ciais, P., Dolman, H., House, J. I., Peters, G., Smith, P., Günter, D., and Peylin, P.: From science to policy: how can research community contribute to the reporting and verification needs under the Paris Agreement?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8683, https://doi.org/10.5194/egusphere-egu2020-8683, 2020.

Estimates of the net CO₂ flux at a continental scale are essential to building up confidence in the global carbon budget. In this study, we present the assimilation of the satellite data from the Orbiting Carbon Observatory-2 (OCO-2) (land nadir and glint data) to estimate the Australian CO₂ surface fluxes for 2015. We used the Community Multiscale Air Quality (CMAQ) model and a four-dimensional variational scheme. Our preliminary results suggest that Australia was a slight carbon sink during 2015 of -0.15 +- 0.11 PgC y^-1 compared to the prior estimate of 0.13 +- 0.55 PgC y^-1. The monthly seasonal cycle shows there was not a good agreement between the prior and posterior fluxes in 2015. Our monthly posterior estimates suggest that from May to August, Australia was a sink of CO₂ and that from October to December, it was a source of CO₂ compared to the prior estimates, which showed an opposite sign. To understand these results more deeply, we aggregated the CO₂ surface fluxes into six categories using Land Cover Type Product the Moderate Resolution Imaging Spectroradiometer (MODIS) and divided them into two areas (north and south). Our posterior fluxes aggregated in the southern and northern Australia indicates that most of the uptake of CO₂ is driven by grasses and cereal crops. Grasses and cereal crops in these two regions represent -0.11 +- 0.027 and -0.06 +- 0.05 PgC/y respectively. In the southern region, the monthly time series of this category shows that this uptake occurs mainly from June to September, whereas in the north, it occurs from January to March. We evaluate our posterior CO₂ concentration against The Total Carbon Column Observing Network (TCCON) and in-situ measurements.  We use the TCCON stations from Darwin, Wollongong, and Lauder (in New Zealand). Amongst the in-situ measurements, we considered stations located at Gunn Point (near Darwin), Cape Grim (in Tasmania) and Iron Bark and Burncluith (in Queensland). Analysis of the monthly biases indicates that CO₂ concentration simulated by posterior fluxes are in better agreement with TCCON data compared to in-situ measurements. In general, monthly mean biases in TCCON Darwin are improved by almost 70 per cent. Lauder and Wollongong stations are strongly affected by ocean fluxes which have small prior uncertainty in this inversion. Biases are hence not much improved here. We verify this by relating bias to wind direction. If the winds come from the ocean, fluxes over Australia are less constrained by OCO-2 data. Biases against in situ data are generally not improved by assimilation, suggesting either problems with the transport model or an inability for OCO-2 data to constrain fluxes at scales relevant to these measurements.

How to cite: Villalobos, Y., Rayner, P., Thomas, S., and Silver, J.: Was Australia a sink or source of carbon dioxide in 2015? Data assimilation using OCO-2 satellite data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6432, https://doi.org/10.5194/egusphere-egu2020-6432, 2020.

Under the COP Paris Agreement, countries need to prepare their GHG inventories with emissions by source and removals by sinks. In order to meet the UNFCCC quality standards, those inventories should be transparent, accurate, comparable, consistent and complete. For the LULUCF sector, emissions are a result from a change in one of the five IPCC carbon pools (e.g. aboveground biomass, etc.). The change in the carbon stock is not easily directly measured, but usually estimated using proxies of land area and area change and the average carbon stocks in the area. Countries encounter several challenges when collecting forestry and land use data information on land related to the inherent complexity of the measurement and monitoring of LULUCF sector and limited by their institutional arrangements. The REDD+ program of the United Nations has a long history of supporting developing countries on setting up the forest (and land use) monitoring system which has supported several countries to produce regular data and make it publicly available, even using web-geoportals. In this paper, we list the challenges of forestry and land data collection and demonstrate the potential leading role of REDD+ countries in the context of reporting regular GHG estimates for the LULUCF sector and the preparation of GHG baselines for the NDC progress reporting under the Paris Agreement, also in light with the recent developments in the COP25.

Key terms: Institutional arrangements, institutional memory, data management systems, legal instruments, sustainability, national forest monitoring system, LULUCF reporting, regular monitoring of land use data, preparation of land use change data. Data portals for increased transparency and stakeholder involvement. Targeted finance for data measurements at different agencies involved in the GHG inventory

How to cite: Jonckheere, I. and Mertens, E.: On forest monitoring and reporting in developing countries: lessons learnt and way forward , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21917, https://doi.org/10.5194/egusphere-egu2020-21917, 2020.

The Paris Agreement was a watershed moment in providing a framework to address the mitigation of climate change.  The Global Stocktake is a bi-decadal process to assess progress in greenhouse gas emission reductions in light of climate feedbacks and response.   However, the relationship between emission commitments and concentration requirements is confounded by complex natural biogeochemical processes potentially modulated by climate feedbacks.  We investigate the prospects and challenges of mediating between emissions and concentrations through the NASA Carbon Monitoring System Flux (CMS-Flux) project, which is an inverse modeling and data assimilation system that ingests a suite of observations including the Orbital Carbon Observatory (OCO-2) and state-of-the-art biomass change maps across the carbon cycle to attribute atmospheric carbon variability to anthropogenic and biogeochemical processes. We decompose the spatial drivers of CO2 accumulation since the beginning of the decade into component fluxes and emissions in the context of the historic 2010 and 2015 El Ninos, which had a tremendous influence on the CO2 growth rate.  These processes reshuffle the primary contributors of CO2 growth at Stocktake time scales that must be reconciled with Nationally Determine Contributions and concentration targets.  Based on these findings, we investigate how systems such as CMS-Flux can harness the carbon constellation to fill a vital gap between policy needs and scientific assessment needed for the Stocktake.

How to cite: Bowman, K., Liu, J., Bloom, A., Saatchi, S., Xu, L., Miyazaki, K., Lee, M., Menemenlis, D., Carroll, D., and Schimel, D.: From concentrations requirements to emission committments: prospects and challenges for the Global Stocktake, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6202, https://doi.org/10.5194/egusphere-egu2020-6202, 2020.

The Paris Agreement establishes 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, 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 provide an assessment of the potential of a Carbon Cycle Fossil Fuel Data Assimilation System using synthetic XCO2 and other observations to constrain fossil fuel CO₂ emissions for an exemplary 1-week period in 2008. We find that the system can provide useful weekly estimates of country-scale fossil fuel emissions independent of national inventories.  When extrapolated from the weekly to the annual scale, uncertainties in emissions are comparable to uncertainties in inventories, so that estimates from inventories and from the MVS capacity can be used for mutual verification. 

We further demonstrate an alternative, synergistic mode of operation, which delivers a best emission estimate through assimilation of the inventory information as an additional data stream.  We show the sensitivity of the results to the setup of the CCFFDAS and to various aspects of the data streams that are assimilated, including assessments of surface networks.

How to cite: Scholze, M., Kaminski, T., Rayner, P., Vossbeck, M., Buchwitz, M., Reuter, M., Knorr, W., Chen, H., Agusti-Panareda, A., Löscher, A., and Mejer, Y.: Assessments of in situ and remotely sensed CO2 observations in a Carbon Cycle Fossil Fuel Data Assimilation System to estimate fossil fuel emissions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8356, https://doi.org/10.5194/egusphere-egu2020-8356, 2020.

The UK NERC MOYA Global Methane Budget consortium (2016-2020) tracks the changing methane burden, with time series measurement of methane and its isotopes at remote sites, field campaigns in the Arctic, Europe, and Tropics, and through modelling studies. The methane rise that began in 2007 and accelerated in 2014 has continued, as apparently has the isotopic shift to lighter, more C-12 rich values (Nisbet et al. 2019, GBC).

MOYA flight campaigns in S. America (Bolivian Amazonia) and Africa (including ZWAMPS, an aircraft campaign over Upper Congo wetlands around Lake Bangweulu, Zambia), have shown significant tropical emissions from wetlands, cattle and fires. Isotopic values of emissions from wetlands and cattle show strong C4 plant input. Fire is a major source, including biomass burning of seasonal C4 grassland and also of C3 leaf litter in wooded savanna. MOYA campaigns have also identified widespread and significant urban and rural air pollution in tropical Africa from crop waste and urban waste fires, including plastic burning.

MOYA’s work has identified strong opportunities for reducing anthropogenic emissions, and highlights the need for better emissions quantification in tropical nations. Mitigation is feasible not only in northern nations, for example drastically cutting fossil fuel emissions, but also is urgently necessary in tropical nations, where much better inventory information is urgently needed. Natural sources such as wetlands are intractable to mitigation, and emissions are likely to increase, with climate warming feeding warming. Cost-effective low technology actions in tropical nations, such as covering landfills with soil, and reducing waste fires, would have significant impact on emissions. Emission reduction from landfills, sewage, and waste fires, especially around the rapidly growing tropical megacities would also bring significant health benefit by cutting air pollution.

Sharp near-future reductions in anthropogenic methane emissions are indeed possible (Nisbet et al. Rev Geophys. 2020), and are probably inexpensive compared to other ways of decarbonation, but cutting methane will need strong action, including determined effort from tropical nations.

How to cite: Nisbet, E. G., Lowry, D., Fisher, R. E., France, J. L., Allen, G., and Lee, J.: The changing global methane budget. NERC’s MOYA, ZWAMPS and methane reduction projects, and the need for better tropical information and mitigation., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9151, https://doi.org/10.5194/egusphere-egu2020-9151, 2020.

We prototype an Observing System Simulation Experiment (OSSE) system for studying an optimal carbon dioxide (CO2) monitoring network in Osaka city, one of the populated cities in Japan (population: 8.8 million).  In the first phase of our project, we built a multi-resolution, spatially-explicit fossil fuel CO2 emissions model to better quantify CO2 emissions with an updated information and detailed geospatial information.  In the second phase, we coupled the emission model to the WRF Chem model, and developed an OSSE capability to study an optimal CO2 observation network for Osaka.  After completing an evaluation of the meteorological fields and emission fields, we have started simulating atmospheric CO2 concentration using possible emission scenarios and examined the emission change detectability by an imaginary ground-based observation networks.  We started from existing observational sites for air quality monitoring sites and the selected suitable sites based on how much useful signals can be obtained.  In order to fully examine the detectability of CO2 emission changes in the presence of potential strong local and inflow biospheric CO2 contributions, we included biospheric fluxes calculated from the BEAMS model.  We have also attempted to calculate the cost for establishing the observational sites.  Our ultimate goal is to help decision makers to design an effective observation network given their emission reduction target as well as the budget constrain.

How to cite: Hayashida, T., Oda, T., Machimura, T., Matsui, T., Kuze, A., Suto, H., Shiomi, K., Kataoka, F., Fukui, T., Terao, Y., Nishihashi, M., Murakami, K., Sasai, T., Saito, M., and Tanimoto, H.: Simulation experiments for studying an optimal carbon dioxide monitoring network for Osaka, Japan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7767, https://doi.org/10.5194/egusphere-egu2020-7767, 2020.

The NASA Orbiting Carbon Observatory-3 (OCO-3) was launched on May 4, 2019 to the International Space Station and has been taking measurements since August. OCO-3, like its predecessor OCO-2, makes hyperspectral measurements of reflected sunlight in three near-infrared bands. However, one of the unique features of OCO-3 is its ability to scan large contiguous areas on the order of 80 km by 80 km using a pointing mirror assembly. This capability, known as snapshot area mapping (SAM) mode, is being used to look at cities, forests, volcanos, and multiple other areas that are of interest to the carbon dioxide (CO₂) and solar-induced chlorophyll fluorescence (SIF) scientific communities. For example, OCO-3 can measure column-mean CO₂ (XCO₂) over the entire Los Angeles, CA basin during the span of only two minutes. With several hundred SAMs having been collected so far and upwards of 25 possible per day, there is a wealth of data to investigate for scientific features and for any potential instrument biases. Additionally, this type of dense sampling will be a proof-of-concept for multiple future wide-swath CO₂ missions. Here, we present several OCO-3 SAM mode measurements and discuss interesting features, XCO₂ results, and future mission plans.

How to cite: Nelson, R. R., Eldering, A., Kurosu, T., Kiel, M., Fisher, B., Pavlick, R., Spiers, G., Rosenberg, R., Crisp, D., O'Dell, C., Somkuti, P., Taylor, T., Kort, E., Oda, T., Nassar, R., and Lauvaux, T.: OCO-3 Snapshot Area Mapping Mode: Early Results, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10116, https://doi.org/10.5194/egusphere-egu2020-10116, 2020.

As atmospheric carbon dioxide (CO₂) levels continue to rise, a global effort to mitigate greenhouse gas (GHG) emissions is underway. Urban domains, which are responsible for more than 70% of global anthropogenic CO₂ emissions, are emerging as leaders in mitigation policy and planning – especially in the United States of America (US), which has formally withdrawn from the Paris Agreement. However, cities face obstacles in developing comprehensive and spatially explicit GHG inventories to inform specific actions and goals. The Vulcan emission product provides highly resolved Scope 1 fossil fuel CO₂ (FFCO₂) emissions in space and time for the entire US, while the Hestia emission products utilize even more granular spatiotemporal data within four US urban domains. Here, we present results from Hestia for Baltimore – a colonial-era city on the Atlantic Coast of the US. Scope 1 FFCO₂ emissions are dominated by energy consumption in buildings, onroad vehicle emissions, and industrial point sources. Large, systematic differences exist between Hestia and Baltimore’s self-reported GHG inventory, which follows the Global Protocol for Community-scale Greenhouse Gas Emission Inventories (GPC). These differences include entire sectors being omitted from emissions reporting due to a determination of ownership (e.g. Scope 1 vs. Scope 3), data gaps and limitations, and a conflation of Scope 1 and Scope 2 electricity production emissions. Urban planning may be better informed by utilizing additional data sources on fuel and energy consumption – especially fuel and energy that are not provided by a centralized utility – to develop comprehensive GHG emission estimates.

How to cite: Roest, G., Gurney, K., Miller, S., and Liang, J.: Informing urban greenhouse gas quantification and mitigation using high-resolution CO2 emissions: a case study in Baltimore, USA, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11578, https://doi.org/10.5194/egusphere-egu2020-11578, 2020.

To effectively mitigate climate change, it is indispensable to know the locations of the emission sources and their respective emission strength. As the majority of greenhouse gases (GHG) such as carbon dioxide (CO₂), methane (CH₄) and carbon monoxide (CO) are generated in cities, our focus lies in the emission determination of urban areas. For this reason, we established a fully-automated sensor network in Munich, Germany to permanently measure GHGs.

Our permanent network is based on the differential column measurement principle [1] and measures the city emissions using five FTIR spectrometer systems (EM27/SUN from Bruker [2]). For these spectrometers we built a self-developed enclosure system and equipped them with several sensors (e.g. computer vision based solar radiation sensor) to measure the column-averaged concentrations of CO₂, CH₄ and CO in a fully-automated way. The difference between the column amounts inside and outside of the city reflects the pollutants abundance generated in the city. Four stations are placed at the city outskirts to capture the inflow/outflow column amounts in arbitrary wind conditions. One inner-city station, which has already been operating successfully since 2016 [3], is serving as a permanent downwind site for half of the city.

With the help of atmospheric transport models, combined with a Bayesian inverse modelling approach, those concentration differences are transferred into spatially resolved emission estimates of the city. After testing the network in two campaigns (2017 and 2018), the network is finally long-term operating since summer 2019 and continuously measures the GHG concentrations in Munich. We will show both the hardware achievements and first measurement and emission results after ten month of operation.

[1] Chen et al.: Differential column measurements using compact solar-tracking spectrometers. Atmos. Chem. Phys., 16: 8479–8498, 2016.

[2] Gisi et al.: XCO2-measurements with a tabletop FTS using solar absorption spectroscopy, Atmos. Meas. Tech., 5, 2969-2980, 2012

[3] Heinle and Chen: Automated Enclosure and Protection System for Compact Solar-Tracking Spectrometers, Atmos. Meas. Tech., 11, 2173-2185, 2018

How to cite: Dietrich, F., Chen, J., Voggenreiter, B., and Zhao, X.: Greenhouse Gas Emission Estimate Using a Fully-automated Permanent Sensor Network in Munich, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19237, https://doi.org/10.5194/egusphere-egu2020-19237, 2020.

Monitoring CO₂ emissions originating from urban areas has become a necessity to support sustainable urban planning strategies and climate change mitigation efforts. Integrative decision support, where net effects of various emission/sink components are considered and compared, is now an increasingly relevant part of urban planning processes. The current emission inventories rely on indirect approaches that use fuel and electricity consumption statistics for determining CO₂ emissions. The consistency of such approaches is questionable and they usually neglect the contribution of the biogenic components of the urban carbon cycle (i.e. vegetation, soil). Moreover, their spatial and temporal scales are restricted because consumption statistics are often available in coarse spatial scales (national, provincial/state, municipal) and usually scaled down using proxy data (e.g. population density) to city-scale annual estimates. The diFUME project (https://mcr.unibas.ch/difume/) is developing a methodology for mapping and monitoring the actual urban CO₂ flux at optimum spatial and temporal scales, meaningful for urban design decisions. The goal is to develop, apply and evaluate independent models, capable to estimate all the different components of the urban carbon cycle (i.e. building emissions, traffic emissions, human metabolism, photosynthetic uptake, plant respiration, soil respiration), combining mainly Eddy Covariance (EC) with Earth Observation (EO) data. EC provides continuous in-situ measurements of CO₂ flux at the local scale. Processing, analysis and interpretation of urban EC measurements is challenging due to the inherent spatial complexity of CO₂ source and sink configurations of the urban structure. The diFUME methodology is using multiple EO datasets to achieve multi-scale monitoring of urban cover, morphology and vegetation phenology in order to characterize the urban source/sink configurations and parameterize turbulent flux source area models. Such combination of EC and EO provides enhanced interpretation of the measured CO₂ flux, analysis of its controlling factors and therefore the potential of fine scale mapping and monitoring. The diFUME methodology is being developed and applied in the city of Basel, exploiting the available long-term database (> 15 years) of urban EC measurements. The first results highlight the potential of EO-derived geospatial data to interpret the complexity of urban EC measurements. Seasonal and land cover related trends in the EC-measured CO₂ flux are recognized, while the use of environmental, census and mobility datasets are increasing the interpretation capabilities and the modelling potential of the urban CO₂ flux patterns.

How to cite: Stagakis, S., Feigenwinter, C., and Vogt, R.: Urban carbon dioxide flux monitoring using Eddy Covariance and Earth Observation: An introduction to diFUME project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19498, https://doi.org/10.5194/egusphere-egu2020-19498, 2020.

In the past decade, there has been a scientific focus on improving the accuracy and precision of methane (CH4) emission estimates in the United States, with much effort targeting oil and natural gas producing basins. Yet, regional CH4 emissions and their attribution to specific sources continue to have significant associated uncertainties. Recent urban work using aircraft observations have suggested that CH4 emissions are not well characterized in major cities along the U.S. East Coast; discrepancies have been attributed to an under-estimation of fugitive emissions from the distribution of natural gas. However, much of regional and urban research has involved the use of aircraft campaigns that can only provide a spatio-temporal snapshot of the CH4 emission landscape. As such, the annual representation and the seasonal variability of emissions remain largely unknown. To further investigate CH4 emissions, we present preliminary CH4 emissions estimates in the Northeastern US as part of NIST’s Northeast Corridor (NEC) testbed project using a regional inversion framework. This area encompasses over 20% of the US and contains many of the dominant CH4 emissions sources important at both regional and local scales.  The atmospheric inversion can estimate sub-monthly 0.1-degree emissions using observations from a regional network of up to 37 in-situ towers; some towers are in non-urban areas while others are in cities or suburban areas. The inversion uses different emission products to help provide a prior constraint within the inversion including anthropogenic emissions from both the EDGAR v42 for the year 2008 and the US EPA for the year 2012, and natural wetland CH4 emissions from the WetCHARTs ensemble mean for the year 2010. Results include the comparison of synthetic model simulated CH4 concentrations (i.e., convolutions of the emission products with WRF-STILT footprints + background) to mole-fractions measured at the regional in-situ sites. The comparison provides an indication as to how well our prior understanding of emissions and incoming air flow matches the atmospheric signatures due to the underlying CH4 sources.  We also present a preliminary set of CH4 fluxes for a selected number of urban centers and discuss challenges estimating highly-resolved methane emissions using high-frequency in-situ observations for a regional domain (e.g. few constraints, skewness in underlying fluxes, representing incoming background, etc.). Overall, this work provides the basis for a year-long inversion that will yields regional CH4 emissions over the Northeast US with a focus on Eastern urban areas.

How to cite: Mueller, K., Ghosh, S., Karion, A., Gourdji, S., Lopez-Coto, I., and Murray, L.: Methane Estimates in the Northeastern US using Continuous Measurements from a Regional Tower Network, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19798, https://doi.org/10.5194/egusphere-egu2020-19798, 2020.

Carbon emissions related to fossil-fuel use are particularly localized, with urban areas being the dominant contributor responsible for more than 70% of global emissions. In the future, the share of the urban population is expected to continue to rise, leading to further increased focusing of fossil-fuel related emissions in urban areas. Cities are also the focal point of many political decisions on mitigating and stabilization of emissions, often setting more ambitious targets than national governments (e.g. C40 cities). For example, the Mayor of London has set the ambitious target for London to be a zero-carbon city by 2050. If we want to devise robust, well-informed climate change mitigation policies, we need a much better understanding of the carbon budget for cities and the nature of the diverse emission sources underpinned by new approaches that allow verifying and optimizing city carbon emissions and their trends.

New satellite observations of CO₂ from missions such as OCO-3, MicroCarb and CO2M, especially when used in conjunction with ground-based sensors networks provide a powerful novel capability for evaluating and eventually improving existing CO2 emission inventories. We will set up a measurement network up-and downwind of London using portable greenhouse gas (CO₂, CH₄, CO) column sensors (Bruker EM27/SUN) together with UV/VIS DOAS spectrometers (NO₂), which will be operated for extended time periods thanks to automatization of the sensors. The data acquired from the network will not only allow us to critically assess the quality of satellite observations over urban environments, but also to derive data-driven emission estimates using a measurement-modelling framework. In this presentation we will discuss the setup of the experiment, give a description of the sensors, and show some first observations obtained with the sensors.

How to cite: Humpage, N., Boesch, H., Ramsay, R., Gray, A., Gillespie, J., Woodwark, J., and Williams, M.: London Carbon Emission Experiment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21177, https://doi.org/10.5194/egusphere-egu2020-21177, 2020.

Many of the global and regional gridded emission inventories used in atmospheric are based on downscaling techniques.  Regardless of their limitations compared to locally-constructed mechanistic emission inventories, such gridded datasets will keep a key role of transferring the information reported as emission inventories into science-based emission verification support (EVS) systems.  Given the use of inverse modeling in the EVS systems, characterizing errors and biases associated with the downscaled emission field is critical in order to obtain robust verification results.  However, such error characterization is often challenging due to the lack of objective metrics.  

     This study compares downscaled emissions from the ODIAC global high-resolution dataset to values taken from the reported inventories and from other independent emission products with the intent of assessing the validity (e.g., error, bias, or accuracy ) of downscaled emissions databases at different policy relevant scales.  ODIAC is based on its flagship high-resolution emission downscaling using satellite-observed nighttime lights (NTL) and point source information.  The sole use of the NTL proxy for diffuse emissions has limitations.  However, that provides a good opportunity to solely evaluate the performance of NTL as an emission proxy.  It is now relatively straightforward to create detailed, high-resolution emission maps due to the advancements in geospatial modeling.  However, such geospatial modeling techniques, which combine multiple pieces of information from different sources, are often neither validated nor even carefully evaluated. 

     As commonly done in previous emission uncertainty studies, we use the differences and agreements as a proxy for errors and improvements.  We collect emission information reported at policy relevant scales, such as state/province/prefecture, cities and facility level (only for point sources).  We also use locally-constructed fine-grained emission inventories as a quasi-truth for the emission distribution.  We also assess the performance of NASA’s Black Marble NTL product suites as a new emission proxy in relation to current the ODIAC proxy that is based on older NTL datasets.  We also look at how these emission differences translate into atmospheric concentration differences using high-resolution WRF simulations. 

     Based on results from the comparison, we identify and discuss the challenges and limitations in the use of downscaled emissions in carbon monitoring at different policy-relevant scales, especially at the city level, and propose possible ways to overcome some of the challenges and provide emission fields that are useful for both science and policy applications.   

How to cite: Oda, T., Bun, R., Román, M., Wang, Z., Shrestha, R., Lauvaux, T., Richao, C., Saito, M., Nishihashi, M., Terao, Y., Maksyutov, S., Matthews, B., Anderl, M., Ott, L., and Pawson, S.: Downscaling fossil-fuel CO2 emissions to policy relevant scales: Current errors and biases, expected improvements, and future perspectives , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10996, https://doi.org/10.5194/egusphere-egu2020-10996, 2020.

Efforts to reduce greenhouse gas (ghg) emissions require support by independent monitoring. The inverse modeling emission quantification approach, based on measurements of atmospheric ghg mixing ratios, promises objective ghg flux estimates consistent across country borders. Yet, ghg flux quantification on national scales and below is impeded both by the sparsity of atmospheric data and uncertainties in atmospheric ghg transport modeling. To overcome these challenges, the EU supports two concept studies for ghg monitoring satellites via the H2020 projects CHE (CO2M satellite) and SCARBO. Both systems aim at vast coverage and high accuracy and precision. Within these projects, we developed a variant of the CarbonTracker Europe inverse model (van der Laan-Luijkx et al., 2017) that uses WRF-GHG (Beck et al., 2011) to model atmospheric transport (CTDAS-WRF). In this presentation, we first introduce how the versatility of WRF-Chem and modular structure of CTDAS enables our model to estimate ghg fluxes across scales, from point sources to integrated continental fluxes. Next, we used our new model to demonstrate the potential skill of the proposed SCARBO satellite constellation for reducing uncertainties of national-scale CO2 fluxes, focusing on aerosol-induced errors. We demonstrate that this concept has the potential to greatly improve upon existing CO2 monitoring systems because of its unprecedented coverage. Lastly, we outline our plans for using CTDAS-WRF to assess the skill of the proposed CO2M monitoring system to estimate city-scale CO2 emissions.

References:
Beck, V., et al.: The WRF Greenhouse Gas Model (WRF-GHG) Technical Report, [online] Available from: https://www.bgc-jena.mpg.de/bgc-systems/pmwiki2/uploads/Download/Wrf-ghg/WRF-GHG_Techn_Report.pdf, 2011.
van der Laan-Luijkx, I. T., et al.: The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: Implementation and global carbon balance 2001-2015, Geosci. Model Dev., 10(7), 2785–2800, doi:10.5194/gmd-10-2785-2017, 2017.

Acknowledgements:
This work has received funding from the European Union’s H2020 research and innovation programme under grant agreement No 769032 (SCARBO) and 776186 (CHE).

How to cite: Reum, F., Florentie, L., Peters, W., Dogniaux, M., Crevoisier, C., Sic, B., and Houweling, S.: Performance of upcoming CO2 monitoring satellites in the new high-resolution inverse model CTDAS-WRF, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19293, https://doi.org/10.5194/egusphere-egu2020-19293, 2020.

Globally, emissions of long-lived non-CO₂ greenhouse gases (GHG; methane, nitrous oxide and halogenated compounds) account for approximately 30 % of the radiative forcing of all anthropogenic GHG emissions. In industrialised countries, ‘bottom-up’ estimates come with relatively large uncertainties for anthropogenic non-CO₂ GHGs when compared with those of anthropogenic CO₂. 'Top-down' methods on the country scale offer an independent support tool to reduce these uncertainties and detect biases in emissions reported to the UNFCCC. Based on atmospheric concentration observations these tools are also able to detect the effectiveness of emission mitigation measures on the long term.

Since 2012 the Swiss national inventory reporting (NIR) contains an appendix on 'top-down' studies for selected halogenated compound. Subsequently, this appendix was extended to include methane and nitrous oxide. Here, we present these updated (2020 submission) regional-scale (~300 x 200 km²) atmospheric inversion studies for non-CO₂ GHG emission estimates in Switzerland, making use of observations on the Swiss Plateau (Beromünster tall tower) as well as the neighbouring mountain-top sites Jungfraujoch and Schauinsland.

We report spatially and temporally resolved Swiss emissions for CH₄ (2013-2019), N₂O (2017-2019) and total Swiss emissions for hydrofluorocarbons (HFCs) and SF₆ (2009-2019) based on a Bayesian inversion system and a tracer ratio method, respectively. Both approaches make use of transport simulations applying the high-resolution (7 x 7 km²) Lagrangian particle dispersion model (FLEXPART-COSMO). We compare these 'top-down' estimates to the 'bottom-up' results reported by Switzerland to the UNFCCC. Although we find good agreement between the two estimates for some species (CH₄, N₂O), emissions of other compounds (e.g., considerably lower 'top-down' estimates for HFC-134a) show larger discrepancies. Potential reasons for the disagreements are discussed. Currently, our 'top-down' information is only used for comparative purposes and does not feed back into the 'bottom-up' inventory.

How to cite: Henne, S., Vollmer, M. K., Steinbacher, M., Leuenberger, M., Meinhardt, F., Mohn, J., Emmenegger, L., Brunner, D., and Reimann, S.: Top-down Support of Swiss non-CO2 Greenhouse Gas Emissions Reporting to UNFCCC , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17176, https://doi.org/10.5194/egusphere-egu2020-17176, 2020.

We present first results on atmospheric abundances and inferred emissions of the previously undetected ozone depleting hydrochlorofluorocarbon HCFC-132b (1,2-dichloro-1,1-difluoroethane). In addition we report significant updates on observations and inferred emissions for HCFC-133a (2-chloro-1,1,1-trifluoroethane) and HCFC-31 (chlorofluoromethane). All three compounds are Ozone Depleting Substances (ODSs) and their productions are regulated under the Montreal Protocol on Substances that Deplete the Ozone Layer. However, they are not known as end-user products from which potential emissions to the atmosphere could occur. Rather, we hypothesize that the compounds are emitted as byproducts during the production of hydrofluorocarbons (HFCs). If this holds true, then the phase-out regulations of the Protocol do not apply to them, nevertheless the Protocol's overarching Vienna Convention encourages the parties to minimize such ODS byproduct emissions.

In-situ fully intercalibrated high-precision measurements of the recently discovered HCFC-132b have been made for several years at the stations of the Advanced Global Atmospheric Gases Experiment (AGAGE) and are complemented with measurements from archived air samples (1978 – present) of the Cape Grim Air Archive. Based on these measurements we reconstruct global HCFC-132b trends showing its first appearance in the atmosphere in the late 1990s, followed by a general growth in the atmosphere to current globally-averaged mole fractions of approx. 0.13 ppt (picomol mol^-1). Global emissions, which are derived from these observations using the AGAGE 12-box model, show a general increase to approx. 1 Gg yr^-1 in 2019. Observation-based top-down regional emission estimates for the East-Asian region, as derived from a Bayesian inversion with the FLEXPART Lagrangian model, can explain all of the global emissions within the uncertainties of the method. Half of these emissions are allocated to Eastern China, a region where enhanced emissions for other ODSs were previously found. Emissions from Europe are comparably insignificant, but an analysis of the source locations supports the hypothesis that HCFC-132b emissions are a byproduct from HFC production. In addition to HCFC-132b, we present significant updates on observations of HCFC-133a and HCFC-31. HCFC-133a measurements are now fully integrated into the AGAGE network and provide a wealth of atmospheric observations. Similar to HCFC-132b, we show, for example, that abundances and global emissions of these two compounds have generally increased over the last few years.

How to cite: Vollmer, M. and the AGAGE (Advanced Global Atmospheric Gases Experiment) Team: Global and regional emission estimates for three ozone-depleting hydrochlorofluoro-carbons (HCFCs) with no known end-uses, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19458, https://doi.org/10.5194/egusphere-egu2020-19458, 2020.

The N₂O emission factors (EF) in river networks remains a major source of uncertainty due to limited data availability. This study integrated three years of multiple stable isotope (¹⁵N-NO₃^-/¹⁸O-NO₃^- and ²H-H₂O/¹⁸O-H₂O) and hydrochemistry measurements for river water and groundwater to evaluate the effects of hydrological and biogeochemical processes on riverine N₂O emission factors in the Yongan watershed (2474 km²) of subtropical eastern China. The EF in groundwater (0.00195 ± 0.00146) was about one magnitude higher than that in surface water (0.00038 ± 0.00020). The N₂O EF displayed seasonal and spatial variability in surface water and groundwater. The emission factors in surface water showed negative relationship with N levels and positive relationship with dissolved organic carbon: DIN (C:N) ratio. In contrast, N₂O EF in groundwater showed positive relationship with N level and negative relationship with DO concentration, implying quite different processes undergoing in surface water and groundwater. The ²H-H₂O/¹⁸O-H₂O information suggested high base flow contribution (~70%) to rivers, implying the potential N₂O contribution from groundwater to riverine N₂O. Information from ¹⁵N-NO₃^- and ¹⁸O-NO₃^- indicated that N₂O in groundwater were regulated by nitrification and denitrification, while N₂O in river networks was mainly derived from nitrification and may be also regulated by hydrological processes. The strong positive relationship between riverine N₂O concentrations and that in groundwater may indicate the potential high contribution of groundwater N₂O to surface water. This study highlights the importance of combining multiple isotope tracers and hydrochemistry to assess the riverine N₂O dynamics, as well as the necessity to consider the potential impact from groundwater N₂O contribution during the determination of riverine N₂O emission factors in rivers with high groundwater recharge.

How to cite: Hu, M., Dahlgren, R., and Chen, D.: Assessment of hydrological and biogeochemical effects on N2O emission factors in river networks of eastern China based on long-term study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12362, https://doi.org/10.5194/egusphere-egu2020-12362, 2020.

The CO2 Human Emission (CHE) project is an European initiative bringing together a consortium of 22 European partners to build a prototype global CO₂ source inversion system that can provide policy-relevant information on the spatiotemporal characteristics of anthropogenic CO₂ emissions. This prototype shall evolve toward a new Copernicus CO₂ service, which will provide a Monitoring and Verification Support (MVS) capacity that can address the challenge of the global stocktake (GST) devised under the Paris Agreement. The global inversion system will build on existing operational infrastructures (CAMS, C3S) at the European Centre for Medium-range Weather Forecast (ECMWF) to exploit ground-based measurements as well as space-based observations from current and future satellite missions (e.g., Sentinel 5p and CO2M). We will present ongoing efforts at ECMWF to develop a source inversion capability in the current operational Integrated Forecasting System (IFS), which will serve as the basis for the future global CO₂ inversion prototype. Preliminary results will be discussed, that include model transport error estimations based on Monte-Carlo ensemble simulations as well as the first chemical source optimization experiments performed with the IFS 4D-Var system.

How to cite: Bousserez, N., McNorton, J., Ades, M., Agusti-Panareda, A., Balsamo, G., Choulga, M., Engelen, R., Flemming, J., Innes, A., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., and Barre, J.: Towards an European operational monitoring capacity for CO2 emissions: the CO2 Human Emission project at ECMWF, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18741, https://doi.org/10.5194/egusphere-egu2020-18741, 2020.

Asia, as one of the world’s largest greenhouse gas (GHG) emitters, has a responsibility to play an important role to turn the goals of Paris Agreement into reality. Urgent needs in Earth observations for GHGs are to reduce uncertainties in their source and sink estimations and to identify current knowledge gaps and requirement for further international collaboration. Estimating anthropogenic and natural emissions based on observations for GHGs has a great potential for providing additional sources of information that can support estimating the impacts of mitigation actions. Discussions will be focused on current status and challenges from Japan's relevant GHG observation and analysis to improve up-to-date analysis systems and data coverage particularly in Asia–Oceania for better estimation of the distribution of anthropogenic and natural sinks and sources with sufficient accuracy.

How to cite: Saigusa, N., Machida, T., Nakaoka, S., Matsunaga, T., Tanimoto, H., Niwa, Y., Terao, Y., and Ito, A.: Greenhouse Gas Analyzing Platform using Ground Sites, Aircraft, Ships, and Satellite-based Data: Japan's Contribution to the Paris Agreement, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8714, https://doi.org/10.5194/egusphere-egu2020-8714, 2020.

Regional flux estimates over Europe have been calculated using the two-step inverse system of the Jena CarboScope Regional inversion (CSR) to estimate the annual CO₂ budgets for recent years, in cooperation with the research project VERIFY. The CSR system assimilates observational datasets of CO₂ mixing ratio provided by the Integrated Carbon Observation System (ICOS) across the European domain to optimize Net Ecosystem Exchange (NEE) fluxes computed from biosphere models at a spatial resolution of 0.25 degree. Ocean fluxes are assumed to be constant over time. Fossil fuel emissions are obtained from EDGAR_v4.3 and updated based on British Petroleum (BP) statistics. Therefore, only biosphere-atmosphere exchange fluxes are considered to be optimized against the atmospheric data.

In this study we focus on the impact of using a-priori fluxes from different biosphere and ocean models on the annual CO₂ budget of posterior fluxes. Results calculated using the Vegetation and Photosynthesis Respiration Model (VPRM) and Simple Biosphere/Carnegie-Ames Stanford Approach (SiBCASA) models show a consistent posterior interannual variability, largely independent of which prior fluxes are used, even though those prior fluxes show considerable differences on annual scales.

How to cite: Munassar, S., Gerbig, C., Koch, F.-T., and Rödenbeck, C.: Impact of using various prior flux models on the posterior NEE derived from the Jena Carboscope regional inversion system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5567, https://doi.org/10.5194/egusphere-egu2020-5567, 2020.

 In this study, surface carbon dioxide (CO₂) flux was estimated over East Asia using the inverse modeling approach. Two CO₂ mole fraction datasets observed from South Korea (Anmyeon-do (AMY) and Gosan (GSN)), along with ObsPack observation data package, were additionally assimilated in the CarbonTracker system, and the characteristics of the estimated surface CO₂ flux was analyzed over ten years. To see the impact of the inclusion of the two observation datasets in the Korean Peninsula, the other experiment which only assimilated the ObsPack data was conducted and used for comparison.

 The result showed that by including two more datasets in the data assimilation process, the surface CO₂ flux absorption was slightly enhanced in summer and the surface CO₂ flux emission was weakened in late autumn and spring. This characteristic was shown particularly in Eurasian boreal and Eurasian temperate regions. Validation was done using independent observations from surface and aircraft (Comprehensive Observation Network for Trace gases by Airliner; CONTRAIL), and it showed smaller root mean square error (RMSE) values and bigger uncertainty reduction effect with the experiment which additionally assimilated two Korean observation datasets.

 Meanwhile, the estimated biosphere CO₂ flux from the CarbonTracker was compared with Land Use, Land Use Change and Forest (LULUCF) sector CO₂ emission (or absorption) from the national greenhouse gases emission inventory (NIR). In case of South Korea, the observation density (number of observation sites or number of assimilated data on the area of the region) seemed to be related to some statistic parameters between inventory and CarbonTracker result. More results from model-inventory comparison using other data will be presented in the meeting.

Acknowledgements

 This study was supported by the Korea Meteorological Administration Research and Development Program under grant KMI2018-03712 and a National Research Foundation of Korea (NRF) grant funded by the South Korean government (Ministry of Science and ICT) (Grant 2017R1E1A1A03070968). The authors thank Andrew R. Jacobson for providing the CarbonTracker used for this study.

How to cite: Cho, M. and Kim, H. M.: Estimation of surface CO2 flux in East Asia and comparison with the national greenhouse gases emission inventory, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13819, https://doi.org/10.5194/egusphere-egu2020-13819, 2020.

West Siberia contributes a large fraction of Russian methane emissions, with both natural emissions from peatlands and anthropogenic emissions by oil and gas industries. To quantify anthropogenic emissions with atmospheric observations and inventories, we must better understand the natural wetland emissions.  We combine high-resolution wetland mapping based on Landsat data for whole West Siberian lowland with a database of in situ flux measurements to derive bottom-up wetland emission estimates. We use a global high-resolution methane flux inversion based on a Lagrangian-Eulerian coupled tracer transport model to estimate methane emissions in West Siberia using atmospheric methane data collected at the Siberian GHG monitoring network JR-STATION, ZOTTO, data by the global in situ network and GOSAT satellite observations. High-resolution prior fluxes were prepared for anthropogenic emissions (EDGAR), biomass burning (GFAS), and wetlands (VISIT). A global high-resolution wetland emission dataset was constructed using 0.5-degree monthly emission data simulated by the VISIT model and wetland area fraction map by the Global Lake and Wetlands Database (GLWD). We estimate biweekly flux corrections to prior flux fields for 2010 to 2015. The inverse model optimizes corrections to two categories of fluxes: anthropogenic and natural (wetlands). Based on fitting the model simulations to the observations, the inverse model provides upward corrections to West Siberian anthropogenic emissions in winter and wetland emissions in summer. The use of high-resolution atmospheric transport in the flux inversion, when compared to low-resolution transport modeling, enables a better fit to observations in winter, when anthropogenic emissions dominate variability of the near-surface methane concentration. We estimate 15% higher anthropogenic emissions than EDGAR v.4.3.2 inventory for whole Russia, with most of the correction attributed to West Siberia and the European part of Russia. Comparison of the inversion estimates with the bottom-up wetland emission inventory for West Siberia suggests a need to adjust the wetland emissions to match observed north-south gradient of emissions with higher emissions in the southern taiga zone.

How to cite: Maksyutov, S., Sasakawa, M., Janardanan, R., Wang, F., Tsuruta, A., Terentieva, I., Sabrekov, A., Glagolev, M., Machida, T., Arshinov, M., Davydov, D., Krasnov, O., Belan, B., Dlugokencky, E., Lavric, J. V., Ito, A., Janssens-Maenhout, G., Kaiser, J., Yoshida, Y., and Matsunaga, T.: Natural and anthropogenic methane emissions in West Siberia estimated using a wetland inventory, GOSAT and a regional tower network, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16416, https://doi.org/10.5194/egusphere-egu2020-16416, 2020.

The general public, industry and regulators seek information about both intentional and unintentional greenhouse gas (GHG)  emissions from energy wells. Minimizing these emissions may be one of the easiest steps to reaching reduction targets. ẟ¹³C is a common tool used to assess sources of atmospheric methane. Here we report and map the isotopic composition of 1280 production gases from energy wells in the Western Canada Sedimentary Basin (WCSB), which mark the ẟ¹³C of downstream GHG emissions in the production and transmission network. The WCSB is a worldwide recognized hydrocarbon producer, with more than 450,000 energy wells drilled only in Alberta. Produced methane ẟ¹³C ranges from -70‰ (VPDB – biogenic source) to -23‰ (VPDB – over mature shale) averaging, -47.2‰. Many of the currently producing, shut-in and abandoned wells also emit fugitive gas through surface casings (SCVF) and soil/ground migration (GM).  Their ẟ¹³C of the fugitive gases usually indicates a shallower source than the production target (average SCVF ẟ¹³Cmethane= - 55.6 ‰, GM ẟ¹³Cmethane= - 58.0 ‰, and average SCVF ẟ¹³CO₂= - 55.6 ‰, GM ẟ¹³ ẟ¹³CO₂= -15.8 ‰). Mapping (isoscapes) of isotope values from 2800 SCVF, and 1800 GM gases sampled across WCSB, show that geology and topography constrain the source of leaks. The spatial distribution and wide range of ẟ¹³C of fugitive methane across the WCSB provide insights and data to climate modellers seeking to attribute atmospheric methane sources but is also relevant for mitigation of emissions as well as informing regulators.

How to cite: Muehlenbachs, K. and Gonzalez Arismendi, G.: Isotopic fingerprinting of fugitive methane and CO2 from the Western Canada Sedimentary Basin (WCSB): Data documentation and impact , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12037, https://doi.org/10.5194/egusphere-egu2020-12037, 2020.

Because East Asia is the third-largest source region of CO₂ after North America and Europe, there is a need to estimate surface CO₂ fluxes accurately over East Asia. Nevertheless, since the number of surface CO₂ observations in East Asia is relatively small compared to that in North America and Europe, the estimation of surface CO₂ fluxes in East Asia has relatively large uncertainties. To supplement sparse surface CO₂ observations, satellite observations can be used.

In this study, the column-averaged dry-air mole fraction (XCO₂) concentration data from the Greenhouse gas Observing SATellite (GOSAT) Project was used to estimate the surface CO₂ fluxes in East Asia. CarbonTracker developed by Earth System Research Laboratory was used as an inverse modeling system. To assimilate GOSAT XCO₂ data in CarbonTracker, the observation operator for GOSAT XCO₂ data was developed. To determine the appropriate Model-Data-Mismatch (MDM) for GOSAT XCO₂ data, a sensitivity test was conducted. The experiment assimilating GOSAT data showed lower BIAS and RMSE than that without assimilating GOSAT data. In addition, the experiment using 2 ppm MDM for GOSAT data showed lower BIAS and RMSE than that using 3 ppm MDM.

The surface CO₂ fluxes over East Asia from the experiments with and without GOSAT data were also compared. By assimilating GOSAT observations, the absorption of surface CO₂ fluxes in the ocean became strong and that in land became weaker. Especially, the absorption of surface CO₂ fluxes in the Eurasian Boreal region became much weaker than in other regions. The uncertainty reduction was also the largest in the Eurasian Boreal region where the surface CO₂ observations are sparse.

Therefore GOSAT XCO₂ data have a profound impact on estimating the surface CO₂ fluxes in East Asia where the surface observations are insufficient.

Acknowledgments

This study was supported by the Korea Meteorological Administration Research and Development Program under grant KMI2018-03712 and a National Research Foundation of Korea (NRF) grant funded by the South Korean government (Ministry of Science and ICT) (Grant 2017R1E1A1A03070968). The authors thank Andrew R. Jacobson for providing the CarbonTracker and JAXA/NIES/MOE for providing GOSAT data.

How to cite: Seo, M.-G. and Kim, H. M.: Assimilation of GOSAT XCO2 data to optimize surface CO2 flux in East Asia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20825, https://doi.org/10.5194/egusphere-egu2020-20825, 2020.

With an increasing network of atmospheric stations that produce a constant data stream, top-down inverse transport modelling of GHGs in a quasi-operational way becomes feasible. The CarboScope-Regional inversion system embeds the regional inversion, within a global inversion using the two-step approach. The regional inversion consists of Lagrangian mesoscale transport from STILT, prior fluxes from the diagnostic VPRM biosphere model, and anthropogenic emissions from a combination of EDGAR v4.3 with the annually updated BP statistical report. Regional ocean fluxes were derived from the CarboScope ocean flux product based on SOCATv2019 data. The inversion uses atmospheric observations from 44 stations to infer biosphere-atmosphere exchange. The regional domain covers most of Europe (33 – 73N, 15W – 35E) with a spatial resolution of 0.25 degree for fluxes and 0.5 degree for flux corrections inferred by the inversion (i.e. the state space).
One of the critical parameters is the assumed uncertainty of the observations, and the major contribution to this is the model-data mismatch error, or representation error. Within CarboScope-Regional, this model-data mismatch error is specified differently for different station types, such as tall towers, mountain or coastal stations, etc. To evaluate the validity and appropriateness of these assumed uncertainties, a leave-one-out cross-validation is applied for a single year, using all stations except one for the inversion, and comparing posterior concentrations predicted for the omitted station with the observed concentrations. Results of this cross-validation will be presented separately for the different station types, and will be used to evaluate the magnitude of the assumed model-data mismatch errors.

How to cite: Koch, F.-T., Munas, S., Roedenbeck, C., and Gerbig, C.: Evaluation of model-data mismatch errors in the CarboScope-Regional Inversion System, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8480, https://doi.org/10.5194/egusphere-egu2020-8480, 2020.

Rice cultivation has high water consumption and emits large quantities of greenhouse gases. Therefore, rice fields provide great potential to mitigate GHG emissions by modifications to cultivation practices or external inputs. Previous studies showed differences for impacts of alternated wetting and drying (AWD) practices for above-ground and below-ground biomass, which might have long term impacts on soil organic carbon stocks. The objective of this study is to parameterise and evaluate the model ECOSSE for rice simulations based on data from an Italian rice test site where the effects of different water management practices and 12 common European cultivars, on yield and GHG emissions, were investigated. Special focus is on the differences of the impacts on the greenhouse gas emissions for AWD and continuous flooding (CF). The model is calibrated and tested for field measurements and is used for model experiments to explore climate change impacts and long-term effects. Long term carbon storage is of particular interest since it is a suitable mitigation strategy. As experiments showed different impacts of management practices on the below ground biomass, long term model experiments are used to estimate impacts on SOC of the different practices. The measurements also allow an analysis of the impacts of different cultivars and the uncertainty of model approaches using a single data set for calibration.

How to cite: Kuhnert, M., Oliver, V., Volante, A., Monaco, S., Teh, Y. A., and Smith, P.: Simulations of greenhouse gas emissions and soil organic carbon with ECOSSE for a rice field in Northern Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9409, https://doi.org/10.5194/egusphere-egu2020-9409, 2020.

Agriculture is a significant source of greenhouse gas (GHG) emissions in Europe. Croplands contribute to this source, but these contributions are difficult to estimate, as the influencing factors are complex. Human management actions are even more important than environmental drivers for agricultural emissions, but spatially-explicit datasets are scarce. This causes a high uncertainty for GHG emission simulations. We simulated GHG emissions (2010-2015) for selected crops (wheat and maize) in Europe with the biogeochemical model platform SPATIAL ECOSSE, using spatially-explicit management data from the model CAPRI used by JRC and model approaches based on Waha et al. (2012) to derive spatial management data (grid maps) for EU27. First results reveal that emissions estimates are highly sensitive to soil organic carbon (SOC), which results in hotspots of GHG emissions in northern Europe where SOC content is high. This effect is stronger for wheat than for maize. The first results show changes in SOC ranging from 374 --456 g C m² yr^-1 and 317 to 399 C m² yr^-1 across Europe (EU27) for wheat and maize, respectively, which are larger than the values reported in previous studies (e.g., 299 g C m² yr^-1 by Ciais et al., 2010).

Ciais, P., Wattenbach, M., Vuichard, N., Smith, P., Piao, S.L., Don, A., Luyssaert, S.,Janssens, I., Bondeau, A., Dechow, R., Leip, A., Smith, P., Beer, C., van der Werf,G.R., Gervois, S., Van Oost, K., Tomelleri, E., Freibauer, A., Schulze, E.D., 2010. The European greenhouse gas balance. Part 2: croplands. Global Change Biology 16,1409–1428.

Waha, K., van Bussel, L. G. J., Müller, C., & Bondeau, A. (2012). Climate-driven simulation of global crop sowing dates. Global Ecology and Biogeography, 21(2), 247–259. https://doi.org/10.1111/j.1466-8238.2011.00678.x

How to cite: Kuhnert, M., Martin, M., Leip, A., McGrath, M., and Smith, P.: Greenhouse gas emissions of European croplands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9469, https://doi.org/10.5194/egusphere-egu2020-9469, 2020.

Petrochemical fuel usage abuse has caused the depletion of oil reservoirs and increasing environmental problems such as greenhouse gas emission and global warming. Therefore, it is necessary to develop greener and sustainable alternatives. Carbon dioxide is the main contributor to the global warming crisis. Biomass energy has received the most attention in many integrated assessment model studies and the latest IPCC reports. Among various existing carbon capture technologies, microalgae-based biological carbon capture is one of the promising and lower energy consumption technologies.

Microalgae rise as 3rd generation bioenergy feedstock due to its attractive higher carbon dioxide fixation efficiency, higher biomass productivity, and relatively easy pretreatment processes for various biofuel extractions. Besides, microalgae have a low demand for water quality and soil fertility compared to traditional energy plants. It means growing microalgae on the marginal land (non-fertile land that is not suitable for agriculture) could be a promising agent for bioenergy production and CO₂ mitigation.

The study is aimed to evaluate the potential energy production from microalgae on marginal land. We combined geospatial data with climate, soil, and terrain to estimate the marginal land of each country. By using Williams and Laurens’ model (2010), we calculated the annual microalgae areal biomass yields for different latitudes and evaluated annual potential energy production from microalgae on marginal land. It is estimated that microalgae may generate up to 67.9 billion tons of coal equivalent of potential energy per year on the total marginal land. By replacing fossil fuels, there will be emission reduction potential 290.6 billion tons of carbon dioxide.

How to cite: Zhang, Q.: World’s potential energy production from microalgae on marginal land, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12574, https://doi.org/10.5194/egusphere-egu2020-12574, 2020.

Global Forest Watch (GFW) provides a global map of forest loss derived from LANDSAT satellite imagery, providing a tool for monitoring global forest change. In managed forests, GFW mainly provides information on commercial logging. This study is part of the INVENT project which aims to improve the National Forest Inventory based estimates of Carbon stock changes in forests reported to the UNFCCC. The purpose of this study is to evaluate the feasibility of using the GFW database to detect carbon stock changes in forest stands, using LiDAR (Light Detecting and Range) data and the stand wise forest database maintained by the State Forest Service (SFS) as additional data sources.

Only those forest loss areas from GFW database, which were detected according to the national LiDAR survey, were selected for data processing, thus obtaining 3D forest information prior to felling. Information on species composition and number of trees per hectare in the forest was obtained from the SFS stand wise forest database. Living biomass estimates were then calculated for each GFW pixel. For pixels outside the SFS stand wise forest database, living biomass values were determined by extrapolation. The average estimated live biomass per forest loss pixel in GFW database is 6792 kg.

Keywords: ERA-GAS INVENT, living biomass, carbon stock.

How to cite: Ivanovs, J., Lazdins, A., and Bardule, A.: Reduction of uncertainties in greenhouse gas accounting using Global Forest Watch forest loss database, LiDAR and stand wise inventory data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21308, https://doi.org/10.5194/egusphere-egu2020-21308, 2020.

Abstract. Calculation of land use and land use change matrix is one of the key elements for the national greenhouse gas (GHG) inventory in land use, land use change and forestry (LULUCF) sector. Main purpose of the land use and land use change matrix is to present comprehensive and harmonized land use and land use change information nationwide over certain time period. Information on land use and land use changes is further used to calculate other parameters important for determination of carbon stock changes and GHG emissions like the stock changes of living and dead biomass, as well as basic information on applied management measures. Aim of this study is to improve methodology for development and maintenance of land use and land use change matrix in the national GHG inventory system using geospatial data information of National forest inventory (NFI) and auxiliary data sources. Creation of land use and land use change matrix is performed in semi-automated way by using GIS tools, which eliminates possible impurities of reported data and have made the calculation process less time consuming than before. New calculation method takes into account present land use data from NFI and land use data from two previous NFI cycles, considerably reducing uncertainty of the estimates, and takes into account land management practices which may alter the land use category in long-term. Auxiliary data, like national land parcel information systems (LPIS), has been introduced to increase certainty, consistency and accuracy for determination of final land-use category. Year-by-year land use change extent detection is carried out by using linear interpolation and extrapolation method is used for the consecutive years for which NFI data are not available.

Key words: ERA-GAS INVENT, land use and land use changes, national forest inventory, greenhouse gas inventory.

How to cite: Krumsteds, L. L., Ivanovs, J., Lazdins, A., and Melniks, R.: Development of land use matrix using geospatial data of National forest inventory, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21388, https://doi.org/10.5194/egusphere-egu2020-21388, 2020.

During the past 15 years, our research group was developing the Biome-BGCMAg (formerly known as Biome-BGCMuSo) biogeochemical model to improve its ability to simulate carbon and water cycle in different ecosystems, with options for managed croplands, grasslands, and forests. We made various model improvements based on the results of model validation and benchmarking. Our goal is to have a model that is suitable for estimating and predicting greenhouse gas fluxes of different ecosystems at various scales under changing management and climate conditions.

The current, most recent model is called Biome-BGCMAg which is a process-based, biogeochemical model that simulates the storage and flux of water, carbon, and nitrogen in the soil-plant-atmosphere system. Biome-BGCMAg was derived from the widely known Biome-BGC v4.1.1 model developed by the Numerical Terradynamic Simulation Group (NTSG), University of Montana, USA. One of the most important model developments is the implementation of a multilayer soil module with water, carbon, nitrogen, and soil organic matter profiles. We implemented drought and anoxic soil state-related plant mortality. Alternative calculation methods for various processes were implemented to support possible algorithm ensemble modelling approach. Optional dynamic allocation algorithm was introduced using predefined phenophases based on growing degree day method. We implemented optional temperature dependence of allocation and possible assimilation downregulation as a function of temperature. Nitrogen budget simulation was improved. Furthermore, human intervention modules were developed to simulate cropland management (e.g. planting, harvest, ploughing, and application of fertilizers) and forest thinning. Dynamic whole plant mortality was implemented in the model to enable more realistic simulation of forest stand development. Last (but not least) conditional management (irrigation and mowing) was introduced to analyze the effect of different management strategies in the future. We started to build a sophisticated R based software to increase the visibility of the model and enable its use by the wider scientific community.

In our first attempt to simulate national scale greenhouse gas budget with Biome-BGCMAg 2.0, we executed the model at 10 x 10 km spatial resolution for Hungary, using eco-physiological parameterization and prescribed management for maize, winter wheat, forests and grassland. The first results revealed that the spatial pattern of net primary production and crop yield is not represented well by the model. Based on the first experiences we introduced new features within Biome-BGCMAg 2.1 that address soil water deficit related photosynthesis down-regulation. Missing stomatal conductance effect on C4 photosynthesis was also addressed by the new developments. 

How to cite: Hidy, D., Fodor, N., Hollós, R., and Barcza, Z.: Building a modelling framework to simulate ecosystem processes under changing climate: the long road from Biome-BGC to Biome-BGCMAg, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21688, https://doi.org/10.5194/egusphere-egu2020-21688, 2020.

Road transport emissions are among the primary causes of poor air quality in cities. Typically, activity data about road transport is based on point-wise automatic traffic measurements or traffic modelling environments like VISSUM. However, such methods do not provide complete spatial patterns of emissions that are needed for air quality modelling. On the other hand, modern smartphone applications, which are used by drivers to navigate and inform about road hazards, might provide a full spatial pattern of road traffic.

We will present preliminary results of road transport emission estimates based on the application of GPS-based smartphone data. The datasets describe average speed and number of users for every road part in Poland, including both major and minor roads. The data is based on the Open Street Maps road geometry and includes more than 4.5 million road segments describing 840 thousand km of roads.

How to cite: Szymankiewicz, K., Gawuc, L., and Soliwoda, M.: Potential of GPS-based smartphone application data for country-wide emission estimation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21374, https://doi.org/10.5194/egusphere-egu2020-21374, 2020.

Emission inventories of greenhouse gases built up from international statistics of human-related activities and emission factors (often referred to as ‘bottom-up’ inventories) are at the core of emission trend analysis to inform policy actions and scientific applications, to support climate negotiation and pledges for mitigation options.

Increasingly gaining importance is the quantification of the inherent uncertainty of these inventories that could allow moving towards a verification system in support of the enhanced transparency framework of the Paris Agreement, in particular the global stocktakes. Recently, two H2020 projects – CHE (CO2 Human Emissions) and VERIFY – are focusing on this sensible aspect. This paper produces an unprecedented propagation of uncertainty applied to emissions of CO₂, CH₄ and N₂O, impinging in both projects. Starting from the human emission estimates of the Emission Database for Global Atmospheric Research (EDGAR), which encompasses historic and sectoral emissions from all world countries and using the error propagation method, uncertainties of the CO₂, CH₄ and N₂O emissions were computed per sector and country.

The devised methodology applies uncertainty stemming from statistics of human activity and emission factors using the guidelines of Intergovernmental Panel on Climate Change (IPCC 2006). The analysis takes into consideration the accuracy of emission estimates for developed versus developing countries, correlation arising from sector aggregation, and includes an ad-hoc treatment for specific sources and country specific emission factors. The results of emissions and their uncertainties are available for all world countries and all IPCC/EDGAR sectors, and for each country, the share of the total uncertainty each sector is responsible for, is identified.

Our results show that world-wide CO₂, CH₄ and N₂O emissions lies in a confidence range of 5%, 33% and in excess of 100%, respectively. The sectors most responsible for such uncertainty depend strongly on the statistical infrastructure of the country but we observe in general that few sectors with smaller emission total are contributing to a large proportion of the total uncertainty.

This global uncertainty assessment aims at contributing to the European initiative of the CO2 Monitoring Task Force, building up an operational greenhouse gas monitoring and verification support capacity.

How to cite: Solazzo, E., Bergamaschi, P., Choulga, M., Oreggioni, G., Muntean, M., Crippa, M., and Janssens-Maenhout, G.: Uncertainty analysis of greenhouse gases emission: application to the EDGAR inventory, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5633, https://doi.org/10.5194/egusphere-egu2020-5633, 2020.

The CO2 Human Emissions (CHE) project has been tasked by the European Commission to prepare the development of a European capacity to monitor anthropogenic CO2 emissions. The monitoring of fossil fuel CO2 emissions has to come with a sufficiently low uncertainty in order to be useful for policymakers. In this context, the main approaches to estimate fossil fuel emissions, apart from bottom-up inventories, are based on inverse transport
modeling either on its own or within a coupled carbon cycle fossil fuel data assimilation system. Both approaches make use of atmospheric CO2 and other tracers (e.g., CO and NOx) and rely on the availability of prior fossil fuel CO2 emission estimates and uncertainties (as well as biogenic fluxes for the transport inverse modeling). For a robust estimate of the uncertainty, information from different sources needs to be brought together.
A methodology to calculate yearly and monthly anthropogenic CO2 emission uncertainties based on IPCC guidelines (2006 IPCC Guidelines for National Greenhouse Gas Inventories + its 2019 Refinements) has been developed. Emission uncertainties are calculated for all world countries, under the assumption of two categories of world countries, depending on whether the country’s statistical infrastructure is well or less developed. For well-developed statistical infrastructure, emission uncertainties are lower, while less developed statistical infrastructure countries have higher emission uncertainties. A sensitivity analysis is investigating the impact of the well or less developed infrastructure assumption for several countries on the global emission uncertainty. Sensitivity experiments with different anthropogenic CO2 sources distributions, as well as the first results on using these prior anthropogenic CO2 uncertainties in ensemble perturbation runs will be presented.

How to cite: Choulga, M., Janssens-Maenhout, G., Balsamo, G., McNorton, J., Solazzo, E., Bousserez, N., and Agusti-Panareda, A.: Anthropogenic CO2 emission uncertainties, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10560, https://doi.org/10.5194/egusphere-egu2020-10560, 2020.

CO₂ was the largest part of anthropogenic greenhouse gas (GHGs) caused remarkable changes in climate and earth system. In response to this situation, global mitigation efforts, especially sectoral and cross-sectoral, have been taken while meeting the needs of global development. Understanding the sectoral structures and emissions in different countries and regions in the period of emission quick growth and industrial transferred among the world after 1970 could suggest effective efforts to avoid misleading mitigation pathway and could support decision-makers to select efficient strategies for different countries and sectors.

Using CO₂ emission data form GHG emission inventory EDGAR (The Emissions Database for Global Atmospheric Research), we identified the major emission pattern of different regions by counted the largest sectoral emission on each grid, which suggests the spatial distribution of sectoral emission. We also identified the high emission regions in the world by selecting grids where emission higher than the global mean plus 2 times stand deviation after logarithm transform, which those regions contributed more than 80% of global emission in every year since 1970. Then, we counted the largest sectoral emission on each grid in the high emission regions to indicate the main contribute sectors. We analyzed those two types of sectoral emissions changes in space and time that representing the spatial distribution pattern and the highest emission sources at different times.

Our study shown emission by transport sector contribute a major part in space after the compliment of transport infrastructure construction, which emission transfer from manufacturing to transport sector. It has three different types of countries of completed time, for countries like the USA, transport sector dominant the distribution in space since the 1970s, for countries like the UK and France, the major sectoral emission in space was building sector before 1990, then was replaced by transport sector, for other countries have not finished yet. Our study also revealed high emission regions that occurred in megacities and at the place where power industries locate and its area has increased. However, sectoral emissions shown different both in time and space. For the USA and Europe, the main emission sectors in high emission regions transferred from power industry and manufacturing sector to building sector before 1990, especially sector in megacities transferred from manufacturing to building sector with the area of high emission regions increased. For the region in the east of China, the main emission sectors in high emission regions were power industry and manufacturing sector, which experienced quick growth between 1980 to 1990 and cities in there became the world manufacture center. In conclusion, during sharply increased emission since 1970, the role of industrial transfers was transfer emissions from some sectors to another region in another country, and emissions from other sectors replaced those transferred emissions.

How to cite: Li, H. and Ye, Y.: Economic sectoral transfer could not help to global CO2 mitigation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12506, https://doi.org/10.5194/egusphere-egu2020-12506, 2020.