scholarly journals Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling

2015 ◽  
Vol 15 (24) ◽  
pp. 35417-35484
Author(s):  
S. Henne ◽  
D. Brunner ◽  
B. Oney ◽  
M. Leuenberger ◽  
W. Eugster ◽  
...  
Keyword(s):  
Natural Gas ◽  
Greenhouse Gas ◽  
Urban Areas ◽  
Transport Model ◽  
Regional Scale ◽  
Inverse Modelling ◽  
Bottom Up ◽  
Climate Agreements ◽  
Ch4 Emissions ◽  
The North

Abstract. Atmospheric inverse modelling has the potential to provide observation-based estimates of greenhouse gas emissions at the country scale, thereby allowing for an independent validation of national emission inventories. Here, we present a regional scale inverse modelling study to quantify the emissions of methane (CH4) from Switzerland, making use of the newly established CarboCount-CH measurement network and a high resolution Lagrangian transport model. Overall we estimate national CH4 emissions to be 196 ± 18 Gg yr−1 for the year 2013 (1σ uncertainty). This result is in close agreement with the recently revised "bottom-up" estimate of 206 ± 33 Gg yr−1 published by the Swiss Federal Office for the Environment as part of the Swiss Greenhouse Gas Inventory (SGHGI). Results from sensitivity inversions using alternative prior emissions, covariance settings, baseline treatments, two different inverse algorithms (Bayesian and extended Kalman Filter), and two different transport models confirms the robustness and independent character of our estimate. According to the latest "bottom-up" inventory the main CH4 source categories in Switzerland are agriculture (78 %), waste handling (15 %) and natural gas distribution and combustion (6 %). The spatial distribution and seasonal variability of our posterior emissions suggest an overestimation of agricultural CH4 emissions by 10 to 20 % in the most recent national inventory, which is likely due to an overestimation of emissions from manure handling. Urban areas do not appear as emission hotspots in our posterior results suggesting that leakages from natural gas disribution are only a minor source of CH4 in Switzerland. This is consistent with rather low emissions of 8.4 Gg yr−1 reported by the SGHGI but inconsistent with the much higher value of 32 Gg yr−1 implied by the EDGARv4.2 inventory for this sector. Increased CH4 emissions (up to 30 % compared to the prior) were deduced for the north-eastern parts of Switzerland. This feature was common to most sensitivity inversions, which rules out an artefact of the transport model and the inversion system. However, it was not possible to assign an unambiguous source process to the region. The observations of the CarboCount-CH network provided invaluable and independent information for the validation of the national bottom-up inventory. Similar systems need to be sustained to provide independent monitoring of future climate agreements.

scholarly journals Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling

2016 ◽  
Vol 16 (6) ◽  
pp. 3683-3710 ◽  
Author(s):  
Stephan Henne ◽  
Dominik Brunner ◽  
Brian Oney ◽  
Markus Leuenberger ◽  
Werner Eugster ◽  
...  
Keyword(s):  
Natural Gas ◽  
Greenhouse Gas ◽  
Urban Areas ◽  
Large Scale ◽  
Transport Model ◽  
Regional Scale ◽  
Inverse Modelling ◽  
Gas Distribution ◽  
Bottom Up ◽  
Ch4 Emissions

Abstract. Atmospheric inverse modelling has the potential to provide observation-based estimates of greenhouse gas emissions at the country scale, thereby allowing for an independent validation of national emission inventories. Here, we present a regional-scale inverse modelling study to quantify the emissions of methane (CH4) from Switzerland, making use of the newly established CarboCount-CH measurement network and a high-resolution Lagrangian transport model. In our reference inversion, prior emissions were taken from the "bottom-up" Swiss Greenhouse Gas Inventory (SGHGI) as published by the Swiss Federal Office for the Environment in 2014 for the year 2012. Overall we estimate national CH4 emissions to be 196 ± 18 Gg yr−1 for the year 2013 (1σ uncertainty). This result is in close agreement with the recently revised SGHGI estimate of 206 ± 33 Gg yr−1 as reported in 2015 for the year 2012. Results from sensitivity inversions using alternative prior emissions, uncertainty covariance settings, large-scale background mole fractions, two different inverse algorithms (Bayesian and extended Kalman filter), and two different transport models confirm the robustness and independent character of our estimate. According to the latest SGHGI estimate the main CH4 source categories in Switzerland are agriculture (78 %), waste handling (15 %) and natural gas distribution and combustion (6 %). The spatial distribution and seasonal variability of our posterior emissions suggest an overestimation of agricultural CH4 emissions by 10 to 20 % in the most recent SGHGI, which is likely due to an overestimation of emissions from manure handling. Urban areas do not appear as emission hotspots in our posterior results, suggesting that leakages from natural gas distribution are only a minor source of CH4 in Switzerland. This is consistent with rather low emissions of 8.4 Gg yr−1 reported by the SGHGI but inconsistent with the much higher value of 32 Gg yr−1 implied by the EDGARv4.2 inventory for this sector. Increased CH4 emissions (up to 30 % compared to the prior) were deduced for the north-eastern parts of Switzerland. This feature was common to most sensitivity inversions, which is a strong indicator that it is a real feature and not an artefact of the transport model and the inversion system. However, it was not possible to assign an unambiguous source process to the region. The observations of the CarboCount-CH network provided invaluable and independent information for the validation of the national bottom-up inventory. Similar systems need to be sustained to provide independent monitoring of future climate agreements.

scholarly journals Assessing Urban Methane Emissions using Column Observing Portable FTIR Spectrometers and a Novel Bayesian Inversion Framework

2021 ◽  
Author(s):  
Taylor S. Jones ◽  
Jonathan E. Franklin ◽  
Jia Chen ◽  
Florian Dietrich ◽  
Kristian D. Hajny ◽  
...  
Keyword(s):  
Natural Gas ◽  
Urban Areas ◽  
Transport Model ◽  
Methane Emissions ◽  
Surface Fluxes ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Bottom Up ◽  
The City ◽  
Total Column

Abstract. Cities represent a large and concentrated portion of global greenhouse gas emissions, including methane. Quantifying methane emissions from urban areas is difficult, and inventories made using bottom-up accounting methods often differ greatly from top-down estimates generated from atmospheric observations. Emissions from leaks in natural gas infrastructure are difficult to predict, and are therefore poorly constrained in bottom-up inventories. Natural gas infrastructure leaks and emissions from end uses can be spread throughout the city, and this diffuse source can represent a significant fraction of a city's total emissions. We investigated diffuse methane emissions of the city of Indianapolis, USA during a field campaign in May of 2016. A network of five portable solar-tracking Fourier transform infrared (FTIR) spectrometers was deployed throughout the city. These instruments measure the mole fraction of methane in a total column of air, giving them sensitivity to larger areas of the city than in situ sensors at the surface. We present an innovative inversion method to link these total column concentrations to surface fluxes. This method combines a Lagrangian transport model with a Bayesian inversion framework to estimate surface emissions and their uncertainties, together with determining the concentrations of methane in the air flowing into the city. Variations exceeding 10 ppb were observed in the inflowing air on a typical day, somewhat larger than the enhancements due to urban emissions (

scholarly journals Influence of the North Atlantic oscillation on the atmospheric levels of benzo[a]pyrene over Europe

2021 ◽  
Author(s):  
Pedro Jiménez-Guerrero ◽  
Nuno Ratola
Keyword(s):  
Spatial Pattern ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Transport Model ◽  
Regional Scale ◽  
The South ◽  
Climatic Fluctuations ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

AbstractThe atmospheric concentration of persistent organic pollutants (and of polycyclic aromatic hydrocarbons, PAHs, in particular) is closely related to climate change and climatic fluctuations, which are likely to influence contaminant’s transport pathways and transfer processes. Predicting how climate variability alters PAHs concentrations in the atmosphere still poses an exceptional challenge. In this sense, the main objective of this contribution is to assess the relationship between the North Atlantic Oscillation (NAO) index and the mean concentration of benzo[a]pyrene (BaP, the most studied PAH congener) in a domain covering Europe, with an emphasis on the effect of regional-scale processes. A numerical simulation for a present climate period of 30 years was performed using a regional chemistry transport model with a 25 km spatial resolution (horizontal), higher than those commonly applied. The results show an important seasonal behaviour, with a remarkable spatial pattern of difference between the north and the south of the domain. In winter, higher BaP ground levels are found during the NAO+ phase for the Mediterranean basin, while the spatial pattern of this feature (higher BaP levels during NAO+ phases) moves northwards in summer. These results show deviations up to and sometimes over 100% in the BaP mean concentrations, but statistically significant signals (p<0.1) of lower changes (20–40% variations in the signal) are found for the north of the domain in winter and for the south in summer.

scholarly journals The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM-MACH-GHG v.137-reg

2019 ◽  
Author(s):  
Jinwoong Kim ◽  
Saroja Polavarapu ◽  
Douglas Chan ◽  
Michael Neish
Keyword(s):  
Greenhouse Gas ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Forecast Error ◽  
Regional Scale ◽  
The United States ◽  
Regional Model ◽  
Boreal Summer ◽  
Chemical Transport Model ◽  
Atmospheric Transport Model

Abstract. In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high spatio-temporal resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km × 10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations, based on comparisons to surface and aircraft observations. In addition, reduced bias and standard deviation of forecast error in boreal summer are obtained by the regional model. Better representation of model topography in the regional model reduces transport and representation errors significantly compared to the global model, especially in regions of complex topography, as revealed by the more precise and detailed structure of the CO2 diurnal cycle produced at observation sites and in model space. The new regional model will form the basis of a flux inversion system that estimates regional scale fluxes of GHGs over Canada.

scholarly journals The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM–MACH–GHG v.137-reg

2020 ◽  
Vol 13 (1) ◽  
pp. 269-295
Author(s):  
Jinwoong Kim ◽  
Saroja M. Polavarapu ◽  
Douglas Chan ◽  
Michael Neish
Keyword(s):  
Greenhouse Gas ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Forecast Error ◽  
Regional Scale ◽  
The United States ◽  
Regional Model ◽  
Boreal Summer ◽  
Spatiotemporal Resolution ◽  
Atmospheric Transport Model

Abstract. In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high-spatiotemporal-resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km×10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower-resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate the high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations based on comparisons to surface and aircraft observations. In addition, the bias and standard deviation of forecast error in boreal summer are reduced by the regional model. Better representation of model topography in the regional model results in improved simulation of the CO2 diurnal cycle compared to the global model at Walnut Grove, California. The new regional model will form the basis of a flux inversion system that estimates regional-scale fluxes of GHGs over Canada.

Methane Estimates in the Northeastern US using Continuous Measurements from a Regional Tower Network

2020 ◽  
Author(s):  
Kimberly Mueller ◽  
Subhomoy Ghosh ◽  
Anna Karion ◽  
Sharon Gourdji ◽  
Israel Lopez-Coto ◽  
...  
Keyword(s):  
Natural Gas ◽  
Urban Areas ◽  
The United States ◽  
Ch4 Emission ◽  
Urban Centers ◽  
Ch4 Emissions ◽  
The Us ◽  
Spatio Temporal ◽  
Northeast Us

&lt;p&gt;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&amp;#8217;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.&amp;#160; 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.&amp;#160; 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.&lt;/p&gt;

The influence of transport model resolution on the inverse modelling of synthetic greenhouse gas emissions in Switzerland

2020 ◽  
Author(s):  
Ioannis Katharopoulos ◽  
Dominique Rust ◽  
Martin Vollmer ◽  
Dominik Brunner ◽  
Stefan Reimann ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Transport Model ◽  
Stratospheric Ozone ◽  
Inverse Modelling ◽  
Model Resolution ◽  
Gas Emissions ◽  
Stratospheric Ozone Layer

&lt;p&gt;Climate change is one of the biggest challenges of the modern era. Halocarbons contribute already about 14% to current anthropogenic radiative forcing, and their future impact may become significantly larger due to their long atmospheric lifetimes and continued and increasing usage. In addition to their influence on climate change, chlorine and bromine-containing halocarbons are the main drivers of the destruction of the stratospheric ozone layer. Therefore, observing their atmospheric abundance and quantifying their sources is critical for predicting the related future impact on climate change and on the recovery of the stratospheric ozone layer.&lt;/p&gt;&lt;p&gt;Regional scale atmospheric inverse modelling can provide observation-based estimates of greenhouse gas emissions at a country scale and, hence, makes valuable information available to policy makers when reviewing emission mitigation strategies and confirming the countries' pledges for emission reduction. Considering that inverse modelling relies on accurate atmospheric transport modelling any advances to the latter are of key importance. The main objective of this work is to characterize and improve the Lagrangian particle dispersion model (LPDM) FLEXPART-COSMO at kilometer-scale resolution and to provide estimates of Swiss halocarbon emissions by integrating newly available halocarbon observations from the Swiss Plateau at the Berom&amp;#252;nster tall tower. The transport model is offline coupled with the regional numerical weather prediction model (NWP) COSMO. Previous inverse modelling results for Swiss greenhouse gases are based on a model resolution of 7 km x 7 km. Here, we utilize higher resolution (1 km x 1 km) operational COSMO analysis fields to drive FLEXPART and compare these to the previous results.&lt;/p&gt;&lt;p&gt;The higher resolution simulations exhibit increased three-dimensional dispersion, leading to a general underestimation of observed tracer concentration at the receptor location and when compared to the coarse model results. The concentration discrepancies due to dispersion between the two model versions cannot be explained by the parameters utilized in FLEPXART&amp;#8217;s turbulence parameterization, (Obhukov length, surface momentum and heat fluxes, atmospheric boundary layer heights, and horizontal and vertical wind speeds), since a direct comparison of these parameters between the different model versions showed no significant differences. The latter suggests that the dispersion differences may originate from a duplication of turbulent transport, on the one hand, covered by the high resolution grid of the Eulerian model and, on the other hand, diagnosed by FLEXPART's turbulence scheme. In an attempt to reconcile FLEXPART-COSMO&amp;#8217;s turbulence scheme at high resolution, we introduced additional scaling parameters based on analysis of simulated mole fraction deviations depending on stability regime. In addition, we used FLEXPART-COSMO source sensitivities in a Bayesian inversion to obtain optimized emission estimates. Inversions for both the high and low resolution models were carried out in order&amp;#160;to quantify the impact of model resolution on posterior emissions and estimate about the uncertainties of these emissions.&amp;#160;&amp;#160;&lt;/p&gt;

scholarly journals Top-down estimates of European CH<sub>4</sub> and N<sub>2</sub>O emissions based on four different inverse models

2015 ◽  
Vol 15 (2) ◽  
pp. 715-736 ◽  
Author(s):  
P. Bergamaschi ◽  
M. Corazza ◽  
U. Karstens ◽  
M. Athanassiadou ◽  
R. L. Thompson ◽  
...  
Keyword(s):  
Eastern Europe ◽  
Inverse Modelling ◽  
N2o Emissions ◽  
Emission Inventories ◽  
Bottom Up ◽  
Uncertainty Range ◽  
Ch4 Emissions ◽  
Inverse Models ◽  
Ch4 And N2o ◽  
North Western

Abstract. European CH4 and N2O emissions are estimated for 2006 and 2007 using four inverse modelling systems, based on different global and regional Eulerian and Lagrangian transport models. This ensemble approach is designed to provide more realistic estimates of the overall uncertainties in the derived emissions, which is particularly important for verifying bottom-up emission inventories. We use continuous observations from 10 European stations (including 5 tall towers) for CH4 and 9 continuous stations for N2O, complemented by additional European and global discrete air sampling sites. The available observations mainly constrain CH4 and N2O emissions from north-western and eastern Europe. The inversions are strongly driven by the observations and the derived total emissions of larger countries show little dependence on the emission inventories used a priori. Three inverse models yield 26–56% higher total CH4 emissions from north-western and eastern Europe compared to bottom-up emissions reported to the UNFCCC, while one model is close to the UNFCCC values. In contrast, the inverse modelling estimates of European N2O emissions are in general close to the UNFCCC values, with the overall range from all models being much smaller than the UNFCCC uncertainty range for most countries. Our analysis suggests that the reported uncertainties for CH4 emissions might be underestimated, while those for N2O emissions are likely overestimated.

scholarly journals Inverse modelling of European CH<sub>4</sub> emissions during 2006–2012 using different inverse models and reassessed atmospheric observations

2017 ◽  
Author(s):  
Peter Bergamaschi ◽  
Ute Karstens ◽  
Alistair J. Manning ◽  
Marielle Saunois ◽  
Aki Tsuruta ◽  
...  
Keyword(s):  
A Priori ◽  
Lower Troposphere ◽  
Inverse Modelling ◽  
Top Down ◽  
Data Set ◽  
Bottom Up ◽  
Ch4 Emissions ◽  
In Situ Data ◽  
Inverse Models ◽  
The Impact

Abstract. We present inverse modelling (top-down) estimates of European methane (CH4) emissions for 2006–2012 based on a new quality-controlled and harmonized in-situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.7 (20.2–29.7) Tg CH4 yr−1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006–2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom-up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr−1 (2006) to 18.8 Tg CH4 yr−1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) total wetland emissions of 4.3 (2.3–8.2) CH4 yr−1 from EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. This analysis identifies regional biases for several models at the aircraft profile sites in France, Hungary and Poland.

scholarly journals Top-down estimates of benzene and toluene emissions in Pearl River Delta and Hong Kong, China

2015 ◽  
Vol 15 (17) ◽  
pp. 24839-24870 ◽  
Author(s):  
X. Fang ◽  
M. Shao ◽  
A. Stohl ◽  
Q. Zhang ◽  
J. Zheng ◽  
...  
Keyword(s):  
Hong Kong ◽  
Urban Areas ◽  
Pearl River Delta ◽  
Transport Model ◽  
Measurement Data ◽  
River Delta ◽  
Rural Site ◽  
Pearl River ◽  
Top Down ◽  
Bottom Up

Abstract. Benzene (C6H6) and toluene (C7H8) are toxic to humans and the environment. They are also important precursors of ground-level ozone and secondary organic aerosols and contribute substantially to severe air pollution in urban areas in China. Discrepancies exist between different bottom-up inventories for benzene and toluene emissions in Pearl River Delta (PRD) and Hong Kong (HK), which are emission hot spots in China. This study provides top-down estimates of benzene and toluene emissions in PRD and HK using atmospheric measurement data from a rural site in the area, Heshan, an atmospheric transport model and an inverse modeling method. The model simulations captured the measured mixing ratios during most pollution episodes. For PRD and HK, the benzene emissions estimated in this study for 2010 were 44 (12–75) Gg yr−1 and 5 (2–7) Gg yr−1 for PRD and HK, respectively, and the toluene emissions were 131 (44–218) Gg yr−1 and 6 (2–9) Gg yr−1, respectively. Temporal and spatial differences between the inversion estimate and four different bottom-up emission estimates are discussed, and it is proposed that more observations at different sites are urgently needed to better constrain benzene and toluene (and other air pollutants) emissions in PRD and HK in the future.

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