A ship-based methodology for high precision atmospheric oxygen measurements and its application in the Southern Ocean region

A method for achieving continuous high precision measurements of atmospheric O-2 is presented based on a commercially available fuel-cell instrument, (Sable Systems, Oxzilla FC-II) with a precision of 7 per meg (approximately equivalent to 1.2 ppm) for a 6-min measurement. The Oxzilla was deployed on two voyages in the Western Pacific sector of the Southern Ocean, in February 2003 and in April 2004, making these the second set of continuous O-2 measurements ever made from a ship. The results show significant temporal variation in O-2, in the order of +/- 10 per meg over 6-hourly time intervals, and substantial spatial variation. Data from both voyages show an O-2 maximum centred on 50 degrees S, which is most likely to be the result of biologically driven O-2 outgassing in the region of subtropical convergence around New Zealand, and a decreasing O-2 trend towards Antarctica. O-2 from the ship-based measurements is elevated compared with measurements from the Scripps Institution of Oceanography flask-sampling network, and the O-2 maximum is also not captured in the network observations. This preliminary study shows that ship-based continuous measurements are a valuable addition to current fixed site sampling programmes for the understanding of ocean-atmosphere O-2 exchange processes. [References: 39

A complete rethink is needed on how greenhouse gas emissions are quantified for national reporting

The 2015 Conference of the Parties (COP21) in Paris has for the first time agreed that both developed and developing countries need to reduce greenhouse gas (GHG) emissions to maintain a global average temperature ‘well below’ 2°C and aim to limit the increase to less than 1.5°C above pre-industrial temperatures. This requires more ambitious emission reduction targets and an increased level of cooperation and transparency between countries. With the start of the second Kyoto Commitment period in 2013, and the 2015 Paris Agreement, it is, therefore, timely to reconsider how GHG emissions are determined and verified

Acceleration of global N₂O emissions seen from two decades of atmospheric inversion

Nitrous oxide (N2O) is the third most important long-lived GHG and an important stratospheric ozone depleting substance. Agricultural practices and the use of N-fertilizers have greatly enhanced emissions of N2O. Here, we present estimates of N2O emissions determined from three global atmospheric inversion frameworks during the period 1998–2016. We find that global N2O emissions increased substantially from 2009 and at a faster rate than estimated by the IPCC emission factor approach. The regions of East Asia and South America made the largest contributions to the global increase. From the inversion-based emissions, we estimate a global emission factor of 2.3 ± 0.6%, which is significantly larger than the IPCC Tier-1 default for combined direct and indirect emissions of 1.375%. The larger emission factor and accelerating emission increase found from the inversions suggest that N2O emission may have a nonlinear response at global and regional scales with high levels of N-input

Polymorphisms at codons 108 and 189 in murine PrP play distinct roles in the control of scrapie incubation time

Rona Barron - ORCID: 0000-0003-4512-9177 https://orcid.org/0000-0003-4512-9177Item not available from this repository.Susceptibility to transmissible spongiform encephalopathies (TSEs) is associated strongly with PrP polymorphisms in humans, sheep and rodents. In mice, scrapie incubation time is controlled by polymorphisms at PrP codons 108 (leucine or phenylalanine) and 189 (threonine or valine), but the precise role of each polymorphism in the control of disease is unknown. The L108F and T189V polymorphisms are present in distinct structural regions of PrP and thus provide an excellent model with which to investigate the role of PrP structure and gene variation in TSEs. Two unique lines of transgenic mice, in which 108F and 189V have been targeted separately into the endogenous murine Prnp a gene, have been produced. TSE inoculation of inbred lines of mice expressing all allelic combinations at codons 108 and 189 has revealed a complex relationship between PrP allele and incubation time. It has been established that both codons 108 and 189 control TSE incubation time, and that each polymorphism plays a distinct role in the disease process. Comparison of ME7 incubation times in mouse lines that are heterozygous at both codons has also identified a previously unrecognized intramolecular interaction between PrP codons 108 and 189.https://doi.org/10.1099/vir.0.80525-086pubpub

The consolidated European synthesis of CO2emissions and removals for the European Union and United Kingdom : 1990-2018

Acknowledgements FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Philippe Ciais acknowledges the support of the European Research Council Synergy project SyG-2013-610028 IMBALANCE-P and from the ANR CLAND Convergence Institute. We acknowledge the work of the entire EDGAR group (Marilena Muntean, Diego Guizzardi, Edwin Schaaf and Jos Olivier). We acknowledge Stephen Sitch and the authors of the DGVMs TRENDY v7 ensemble models for providing us with the data. Financial support This research has been supported by the H2020 European Research Council (grant no. 776810).Peer reviewedPublisher PD

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom : 1990-2017

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 C UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 TgCH(4) yr (-1) (EDGAR v5.0) and 19.0 TgCH(4) yr(-1) (GAINS), consistent with the NGHGI estimates of 18.9 +/- 1.7 TgCH(4) yr(-1). The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 TgCH(4) yr(-1). Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 TgCH(4) yr(-1)) and surface network (24.4 TgCH(4) yr (-1)). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 TgCH(4) yr(-1). For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 TgN(2)Oyr(-1), respectively, agreeing with the NGHGI data (0.9 0.6 TgN(2)Oyr(-1)). Over the same period, the average of the three total TD global and regional inversions was 1.3 +/- 0.4 and 1.3 +/- 0.1 TgN(2)Oyr(-1), respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU CUK scale and at the national scale.Peer reviewe

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom : 1990-2019

Funding Information: We thank Aurélie Paquirissamy, Géraud Moulas and the ARTTIC team for the great managerial support offered during the project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Annual, gap-filled and harmonized NGHGI uncertainty estimates for the EU and its member states were provided by the EU GHG inventory team (European Environment Agency and its European Topic Centre on Climate change mitigation). Most top-down inverse simulations referred to in this paper rely for the derivation of optimized flux fields on observational data provided by surface stations that are part of networks like ICOS (datasets: 10.18160/P7E9-EKEA , Integrated Non-CO Observing System, 2018a, and 10.18160/B3Q6-JKA0 , Integrated Non-CO Observing System, 2018b), AGAGE, NOAA (Obspack Globalview CH: 10.25925/20221001 , Schuldt et al., 2017), CSIRO and/or WMO GAW. We thank all station PIs and their organizations for providing these valuable datasets. We acknowledge the work of other members of the EDGAR group (Edwin Schaaf, Jos Olivier) and the outstanding scientific contribution to the VERIFY project of Peter Bergamaschi. Timo Vesala thanks ICOS-Finland, University of Helsinki. The TM5-CAMS inversions are available from https://atmosphere.copernicus.eu (last access: June 2022); Arjo Segers acknowledges support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission (grant no. CAMS2_55). This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810). Ronny Lauerwald received support from the CLand Convergence Institute. Prabir Patra received support from the Environment Research and Technology Development Fund (grant no. JPMEERF20182002) of the Environmental Restoration and Conservation Agency of Japan. Pierre Regnier received financial support from the H2020 project ESM2025 – Earth System Models for the Future (grant no. 101003536). David Basviken received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (METLAKE, grant no. 725546). Greet Janssens-Maenhout received support from the European Union's Horizon 2020 research and innovation program (CoCO, grant no. 958927). Tuula Aalto received support from the Finnish Academy (grants nos. 351311 and 345531). Sönke Zhaele received support from the ERC consolidator grant QUINCY (grant no. 647204).Peer reviewedPublisher PD