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

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          Abstract

          <p><strong>Abstract.</strong> 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 (CH<sub>4</sub>) 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 CH<sub>4</sub> emissions to be 196 ± 18 Gg yr<sup>−1</sup> for the year 2013 (1<i>σ</i> uncertainty). This result is in close agreement with the recently revised SGHGI estimate of 206 ± 33 Gg yr<sup>−1</sup> 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 CH<sub>4</sub> 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 CH<sub>4</sub> 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 CH<sub>4</sub> in Switzerland. This is consistent with rather low emissions of 8.4 Gg yr<sup>−1</sup> reported by the SGHGI but inconsistent with the much higher value of 32 Gg yr<sup>−1</sup> implied by the EDGARv4.2 inventory for this sector. Increased CH<sub>4</sub> 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.</p>

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            Tropospheric ozone and black carbon (BC) contribute to both degraded air quality and global warming. We considered ~400 emission control measures to reduce these pollutants by using current technology and experience. We identified 14 measures targeting methane and BC emissions that reduce projected global mean warming ~0.5°C by 2050. This strategy avoids 0.7 to 4.7 million annual premature deaths from outdoor air pollution and increases annual crop yields by 30 to 135 million metric tons due to ozone reductions in 2030 and beyond. Benefits of methane emissions reductions are valued at $700 to $5000 per metric ton, which is well above typical marginal abatement costs (less than $250). The selected controls target different sources and influence climate on shorter time scales than those of carbon dioxide-reduction measures. Implementing both substantially reduces the risks of crossing the 2°C threshold.
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              Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities

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                Author and article information

                Journal
                Atmospheric Chemistry and Physics
                Atmos. Chem. Phys.
                Copernicus GmbH
                1680-7324
                2016
                March 21 2016
                : 16
                : 6
                : 3683-3710
                Article
                10.5194/acp-16-3683-2016
                934a9bd1-e31b-410d-9052-473e539432c0
                © 2016

                https://creativecommons.org/licenses/by/3.0/

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