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  • 1
    Online Resource
    Online Resource
    Newark :American Geophysical Union,
    Keywords: Atmosphere - Mathematical models. ; Electronic books.
    Description / Table of Contents: Published by the American Geophysical Union as part of the Geophysical Monograph Series.
    Type of Medium: Online Resource
    Pages: 1 online resource (358 pages)
    Edition: 1st ed.
    ISBN: 9781118704394
    Series Statement: Geophysical Monograph Series ; v.200
    Language: English
    Note: COVER -- Title Page -- Contents -- Preface -- Lagrangian Modeling of the Atmosphre: An Introduction -- Section I Turbulent Dispersion: Theory and Parameterization -- Turbulent Dispersion: Theory and Parameterization-Overview -- History of Lagrangian Stochastic Models for Turbulent Dispersion -- Lagrangian Particle Modeling of Dispersion in Light Winds -- "Rogue Velocities" in a Lagrangian Stochastic Model for Idealized Inhomogeneous Turbulence -- How Can We Satisfy the Well-Mixed Criterion in Highly Inhomogeneous Flows? A Practical Approach -- Section II Transport in Geophysical Fluids -- Transport in Geophysical Fluids-Overview -- Out of Flatland: Three-Dimensional Aspects of Lagrangian Transport in Geophysical Fluids -- A Lagrangian Method for Simulating Geophysical Fluids -- Entropy-Based and Static Stability-Based Lagrangian Model Grids -- Moisture Sources and Large-Scale Dynamics Associated With a Flash Flood Event -- The Association Between the North Atlantic Oscillation and the Interannual Variability of the Tropospheric Transport Pathways in Western Europe -- Section III Applications of Lagrangian Modeling: Greenhouse Gases -- Applications of Lagrangian Modeling: Greenhouse Gases-Overview -- Estimating Surface-Air Gas Fluxes by Inverse Dispersion Using a Backward Lagrangian Stochastic Trajectory Model -- Linking Carbon Dioxide Variability at Hateruma Station to East Asia Emissions by Bayesian Inversion -- The Use of a High-Resolution Emission Data Set in a Global Eulerian-Lagrangian Coupled Model -- Toward Assimilation of Observation-Derived Mixing Heights to Improve Atmospheric Tracer Transport Models -- Estimating European Halocarbon Emissions Using Lagrangian Backward Transport Modeling and in Situ Measurements at the Jungfraujo High-Alphine Site -- Section IV Atmospheric Chemistry -- Atmospheric Chemistry in Lagrangian Models-Overview. , Global-Scale Tropospheric Lagrangian Particle Models With Linear Chemistry -- Quantitative Attribution of Processes Affecting Atmospheric Chemical Concentrations by Combining a Time-Reversed Lagrangian Particle Dispersion Model and a Regression Approach -- Section V Operational/Emergency Modeling -- Operational Emergency Preparedness Modeling-Overview -- Operational Volcanic Ash Cloud Modeling: Discussion on Model Inputs, Products, and the Application of Real-Time Probabilistic Forecasting -- A Bayesian Method to Rank Different Model Forecasts of the Same Volcanic Ash Cloud -- Review and Validation of MicroSpray, a Lagrangian Particle Model of Turbulent Dispersion -- Lagrangian Models for Nuclear Studies: Examples and Applications -- AGU Category Index.
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  • 2
    Publication Date: 2024-01-12
    Description: Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGARv6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH4 yr−1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8±55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr−1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27 + UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs.
    Description: Published
    Description: 1197–1268
    Description: OSA2: Evoluzione climatica: effetti e loro mitigazione
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 3
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    Wiley
    In:  In: Lagrangian Modeling of the Atmosphere. Wiley, pp. 225-233. ISBN 978-0-87590-490-0
    Publication Date: 2015-09-09
    Type: Book chapter , NonPeerReviewed
    Format: text
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  • 4
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    Unknown
    PANGAEA
    In:  Supplement to: Hiller, Rebecca V; Bretscher, Daniel; DelSontro, Tonya; Diem, Torsten; Eugster, Werner; Henneberger, Ruth; Hobi, Silas; Hodson, Elke; Imer, Dennis; Kreuzer, Michael; Künzle, Thomas; Merbold, Lutz; Niklaus, Pascal A; Rihm, Beat; Schellenberger, Andreas; Schroth, Martin H; Schubert, Carsten J; Siegrist, Hansruedi; Stieger, Jacqueline; Buchmann, N; Brunner, Dominik (2014): Anthropogenic and natural methane fluxes in Switzerland synthesized within a spatially explicit inventory. Biogeosciences, 11(7), 1941-1959, https://doi.org/10.5194/bg-11-1941-2014
    Publication Date: 2023-09-02
    Description: We present the first high-resolution (500 m × 500 m) gridded methane (CH4) emission inventory for Switzerland, which integrates the national emission totals reported to the United Nations Framework Convention on Climate Change (UNFCCC) and recent CH4 flux studies conducted by research groups across Switzerland. In addition to anthropogenic emissions, we also include natural and semi-natural CH4 fluxes, i.e., emissions from lakes and reservoirs, wetlands, wild animals as well as uptake by forest soils. National CH4 emissions were disaggregated using detailed geostatistical information on source locations and their spatial extent and process- or area-specific emission factors. In Switzerland, the highest CH4 emissions in 2011 originated from the agricultural sector (150 Gg CH4/yr), mainly produced by ruminants and manure management, followed by emissions from waste management (15 Gg CH4/yr) mainly from landfills and the energy sector (12 Gg CH4/yr), which was dominated by emissions from natural gas distribution. Compared to the anthropogenic sources, emissions from natural and semi-natural sources were relatively small (6 Gg CH4/yr), making up only 3 % of the total emissions in Switzerland. CH4 fluxes from agricultural soils were estimated to be not significantly different from zero (between -1.5 and 0 Gg CH4/yr), while forest soils are a CH4 sink (approx. -2.8 Gg CH4/yr), partially offsetting other natural emissions. Estimates of uncertainties are provided for the different sources, including an estimate of spatial disaggregation errors deduced from a comparison with a global (EDGAR v4.2) and a European CH4 inventory (TNO/MACC). This new spatially-explicit emission inventory for Switzerland will provide valuable input for regional scale atmospheric modeling and inverse source estimation.
    Keywords: Switzerland
    Type: Dataset
    Format: application/x-netcdf, 23.8 MBytes
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  • 5
    Publication Date: 2024-02-10
    Keywords: Air chemistry observatory; DATE/TIME; INGOS; Integrated non-CO2 Greenhouse gas Observing System; Ireland; MaceHead; Precision; SPUSO; Sulfur hexafluoride, SF6
    Type: Dataset
    Format: text/tab-separated-values, 7176 data points
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  • 6
    Publication Date: 2024-02-10
    Keywords: Air chemistry observatory; DATE/TIME; INGOS; Integrated non-CO2 Greenhouse gas Observing System; Italy; MteCimone; Pentafluoroethane, HFC-125; Precision; SPUSO
    Type: Dataset
    Format: text/tab-separated-values, 4760 data points
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  • 7
    Publication Date: 2024-02-10
    Keywords: 1,1,1,2-Tetrafluoroethane; Air chemistry observatory; DATE/TIME; INGOS; Integrated non-CO2 Greenhouse gas Observing System; Italy; MteCimone; Precision; SPUSO
    Type: Dataset
    Format: text/tab-separated-values, 4618 data points
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  • 8
    Publication Date: 2024-02-10
    Keywords: File content; File format; File name; File size; INGOS; Integrated non-CO2 Greenhouse gas Observing System; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 20 data points
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  • 9
    Publication Date: 2024-02-10
    Keywords: Air chemistry observatory; DATE/TIME; INGOS; Integrated non-CO2 Greenhouse gas Observing System; Jungfraujoch; Pentafluoroethane, HFC-125; Precision; SPUSO; Switzerland
    Type: Dataset
    Format: text/tab-separated-values, 8808 data points
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  • 10
    Publication Date: 2024-02-10
    Keywords: 1,1,1,2-Tetrafluoroethane; Air chemistry observatory; DATE/TIME; INGOS; Integrated non-CO2 Greenhouse gas Observing System; Jungfraujoch; Precision; SPUSO; Switzerland
    Type: Dataset
    Format: text/tab-separated-values, 8768 data points
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