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Research data keyboard_double_arrow_right Dataset 2022Publisher:DataverseNL Authors: Koretsky, Zahar; Hernández Serrano, Pedro; Adekunle, Seun; Dumontier, Michel;doi: 10.34894/q80que
Article Abstract To better allocate funds in the new EU research framework programme Horizon Europe, an assessment of current and past efforts is crucial. In this paper we develop and apply a multi-method qualitative and computational approach to provide a catalogue of climate crisis mitigation technologies on the EU level between 2014 and 2020. Using the approach, we observed no public EU-level funding for multiple technologies prioritised by the EU, such as low-carbon production and use of cement and chemicals, electric battery, and a number of industrial decarbonisation processes. We observed a rising trend in the funding of solar power and onshore wind, the adjacent to them power-to-X technology, as well as recycling. At the same time, the shares of funding into fuel cell, biofuel, demand-side energy management, microgrids, and waste management show a decline trend. With note of the exploratory character of the present paper, we propose that the EU Horizon 2020 funding of clean technologies only partially reflected the expectations of key institutionalised EU actors due to the existence of many non-funded prioritised technologies.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Minx, Jan C.; Lamb, William F.; Andrew, Robbie M.; Canadell, Josep G.; Crippa, Monica; Döbbeling, Niklas; Forster, Piers; Guizzardi, Diego; Olivier, Jos; Pongratz, Julia; Reisinger, Andy; Rigby, Matthew; Peters, Glen; Saunois, Marielle; Smith, Steven J.; Solazzo, Efisio; Tian, Hanqin;Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Voldoire, Aurore;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.HighResMIP.CNRM-CERFACS.CNRM-CM6-1-HR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-CM6-1-HR climate model, released in 2017, includes the following components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T359; Gaussian Reduced with 181724 grid points in total distributed over 360 latitude circles (with 720 grid points per latitude circle between 32.2degN and 32.2degS reducing to 18 grid points per latitude circle at 89.6degN and 89.6degS); 91 levels; top level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA025, tripolar primarily 1/4deg; 1442 x 1050 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Voldoire, Aurore;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CNRM-CERFACS.CNRM-CM6-1-HR.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-CM6-1-HR climate model, released in 2017, includes the following components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T359; Gaussian Reduced with 181724 grid points in total distributed over 360 latitude circles (with 720 grid points per latitude circle between 32.2degN and 32.2degS reducing to 18 grid points per latitude circle at 89.6degN and 89.6degS); 91 levels; top level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA025, tripolar primarily 1/4deg; 1442 x 1050 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Voldoire, Aurore;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.HighResMIP.CNRM-CERFACS.CNRM-CM6-1-HR.control-1950' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-CM6-1-HR climate model, released in 2017, includes the following components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T359; Gaussian Reduced with 181724 grid points in total distributed over 360 latitude circles (with 720 grid points per latitude circle between 32.2degN and 32.2degS reducing to 18 grid points per latitude circle at 89.6degN and 89.6degS); 91 levels; top level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA025, tripolar primarily 1/4deg; 1442 x 1050 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Lovato, Tomas; Peano, Daniele;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CMCC.CMCC-CM2-SR5.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CMCC-CM2-SR5 climate model, released in 2016, includes the following components: aerosol: MAM3, atmos: CAM5.3 (1deg; 288 x 192 longitude/latitude; 30 levels; top at ~2 hPa), land: CLM4.5 (BGC mode), ocean: NEMO3.6 (ORCA1 tripolar primarly 1 deg lat/lon with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 50 vertical levels; top grid cell 0-1 m), seaIce: CICE4.0. The model was run by the Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy (CMCC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Kalt, Gerald; Mayer, Andreas; Haberl, Helmut; Kaufmann, Lisa; Lauk, Christian; Matej, Sarah; Theurl, Michaela C.; Erb, Karl-Heinz;The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use. The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions. The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)". Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information. This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019Publisher:Elsevier BV Authors: Patrizia Simeoni; Gellio Ciotti; Antonella Meneghetti; Mattia Cottes;Abstract To achieve the EU climate and energy objectives, a transition towards a future sustainable energy system is needed. The integration of the huge potential for industrial waste heat recovery into smart energy system represents a main opportunity to accomplish these goals. To successfully implement this strategy, all the several stakeholders' conflicting objectives should be considered. In this paper an evolutionary multi-objective optimization model is developed to perform a sustainability evaluation of an energy system involving an industrial facility as the waste heat source and the neighbourhood as district heating network end users. An Italian case study of heat recovery from a steel casting facility shows how the model allows to properly select the district heating network set of users to fully exploit the available waste energy. Design directions such as the thermal energy storage capacity can be also provided. Moreover, the model enables the analysis of the trade-off between the stakeholders’ different perspectives, allowing to identify possible win-win solutions for both the industrial sector and the citizenship.
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For further information contact us at helpdesk@openaire.euAccess Routesbronze 43 citations 43 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Part of book or chapter of book , Other literature type 2017 ItalyPublisher:Informa UK Limited Fausto Guzzetti; Monia Santini; Stefano Luigi Gariano; Guido Rianna; Luciano Picarelli; Paola Mercogliano; Paolo Tommasi; Luca Comegna;Changes in the main weather features are becoming perceivable in Italy. In the last years, the average temperature and the intensity of rainstorms are increasing. Such phenomena could lead to changes in the earth-atmosphere interaction. Accounting for the high vulnerability of the Italian territory, an obvious consequence would concern the effects on the hydro-geological hazards, including landslides and floods. As a matter of fact, the higher frequency of high magnitude flood and landslide events represent an alarm bell. It is necessary to explore with all available instruments the consequences of the potential climate changes. Since quantitative predictions are not possible, the only way to run is depicting rational scenarios for the most vulnerable contexts adopting a multidisciplinary approach. Based on projections of potential climate changes in the 21st century, the report examines their potential impact in the context of the Italian peninsula. After a general overview about typical slope responses to weather changes, some quantitative scenarios have been depicted for representative geomorphological contexts. The last part of the paper examines changes in the expected risk, based on the characteristics of the Italian territory and its vulnerability, looking at the initiatives to undertake for its mitigation.
CNR ExploRA arrow_drop_down https://doi.org/10.1201/978131...Part of book or chapter of book . 2017 . Peer-reviewedData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1201/9781315387789-5&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu11 citations 11 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert CNR ExploRA arrow_drop_down https://doi.org/10.1201/978131...Part of book or chapter of book . 2017 . Peer-reviewedData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2020Publisher:Copernicus GmbH Funded by:EC | METLAKE, EC | VERIFY, EC | IMBALANCE-P +4 projectsEC| METLAKE ,EC| VERIFY ,EC| IMBALANCE-P ,EC| CHE ,RCN| Integrated Carbon Observation System (ICOS)-Norway and Ocean Thematic Centre (OTC) ,EC| VISUALMEDIA ,AKA| Novel soil management practices - key for sustainable bioeconomy and climate change mitigation -SOMPA / Consortium: SOMPAAna Maria Roxana Petrescu; Chunjing Qiu; Philippe Ciais; Rona L. Thompson; Philippe Peylin; Matthew J. McGrath; Efisio Solazzo; Greet Janssens‐Maenhout; Francesco N. Tubiello; P. Bergamaschi; D. Brunner; Glen P. Peters; L. Höglund-Isaksson; Pierre Regnier; Ronny Lauerwald; David Bastviken; Aki Tsuruta; Wilfried Winiwarter; Prabir K. Patra; Matthias Kuhnert; Gabriel D. Orregioni; Monica Crippa; Marielle Saunois; Lucia Perugini; Tiina Markkanen; Tuula Aalto; Christine Groot Zwaaftink; Yuanzhi Yao; Chris Wilson; Giulia Conchedda; Dirk Günther; Adrian Leip; Pete Smith; Jean‐Matthieu Haussaire; Antti Leppänen; Alistair J. Manning; Joe McNorton; Patrick Brockmann; A.J. Dolman;Abstract. 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+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. 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 NGHGI 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 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates 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 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 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 NGHGI and inversions and account for 5.2 Tg CH4 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 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−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 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU 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 EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).
https://doi.org/10.5... arrow_drop_down https://doi.org/10.5194/essd-2...Article . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert https://doi.org/10.5... arrow_drop_down https://doi.org/10.5194/essd-2...Article . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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Research data keyboard_double_arrow_right Dataset 2022Publisher:DataverseNL Authors: Koretsky, Zahar; Hernández Serrano, Pedro; Adekunle, Seun; Dumontier, Michel;doi: 10.34894/q80que
Article Abstract To better allocate funds in the new EU research framework programme Horizon Europe, an assessment of current and past efforts is crucial. In this paper we develop and apply a multi-method qualitative and computational approach to provide a catalogue of climate crisis mitigation technologies on the EU level between 2014 and 2020. Using the approach, we observed no public EU-level funding for multiple technologies prioritised by the EU, such as low-carbon production and use of cement and chemicals, electric battery, and a number of industrial decarbonisation processes. We observed a rising trend in the funding of solar power and onshore wind, the adjacent to them power-to-X technology, as well as recycling. At the same time, the shares of funding into fuel cell, biofuel, demand-side energy management, microgrids, and waste management show a decline trend. With note of the exploratory character of the present paper, we propose that the EU Horizon 2020 funding of clean technologies only partially reflected the expectations of key institutionalised EU actors due to the existence of many non-funded prioritised technologies.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Minx, Jan C.; Lamb, William F.; Andrew, Robbie M.; Canadell, Josep G.; Crippa, Monica; Döbbeling, Niklas; Forster, Piers; Guizzardi, Diego; Olivier, Jos; Pongratz, Julia; Reisinger, Andy; Rigby, Matthew; Peters, Glen; Saunois, Marielle; Smith, Steven J.; Solazzo, Efisio; Tian, Hanqin;Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.
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visibility 3Kvisibility views 3,130 download downloads 1,221 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Voldoire, Aurore;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.HighResMIP.CNRM-CERFACS.CNRM-CM6-1-HR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-CM6-1-HR climate model, released in 2017, includes the following components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T359; Gaussian Reduced with 181724 grid points in total distributed over 360 latitude circles (with 720 grid points per latitude circle between 32.2degN and 32.2degS reducing to 18 grid points per latitude circle at 89.6degN and 89.6degS); 91 levels; top level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA025, tripolar primarily 1/4deg; 1442 x 1050 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Voldoire, Aurore;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CNRM-CERFACS.CNRM-CM6-1-HR.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-CM6-1-HR climate model, released in 2017, includes the following components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T359; Gaussian Reduced with 181724 grid points in total distributed over 360 latitude circles (with 720 grid points per latitude circle between 32.2degN and 32.2degS reducing to 18 grid points per latitude circle at 89.6degN and 89.6degS); 91 levels; top level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA025, tripolar primarily 1/4deg; 1442 x 1050 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Voldoire, Aurore;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.HighResMIP.CNRM-CERFACS.CNRM-CM6-1-HR.control-1950' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-CM6-1-HR climate model, released in 2017, includes the following components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T359; Gaussian Reduced with 181724 grid points in total distributed over 360 latitude circles (with 720 grid points per latitude circle between 32.2degN and 32.2degS reducing to 18 grid points per latitude circle at 89.6degN and 89.6degS); 91 levels; top level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA025, tripolar primarily 1/4deg; 1442 x 1050 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Lovato, Tomas; Peano, Daniele;Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CMCC.CMCC-CM2-SR5.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CMCC-CM2-SR5 climate model, released in 2016, includes the following components: aerosol: MAM3, atmos: CAM5.3 (1deg; 288 x 192 longitude/latitude; 30 levels; top at ~2 hPa), land: CLM4.5 (BGC mode), ocean: NEMO3.6 (ORCA1 tripolar primarly 1 deg lat/lon with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 50 vertical levels; top grid cell 0-1 m), seaIce: CICE4.0. The model was run by the Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy (CMCC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Kalt, Gerald; Mayer, Andreas; Haberl, Helmut; Kaufmann, Lisa; Lauk, Christian; Matej, Sarah; Theurl, Michaela C.; Erb, Karl-Heinz;The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use. The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions. The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)". Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information. This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.4965052&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 133visibility views 133 download downloads 25 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.4965052&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019Publisher:Elsevier BV Authors: Patrizia Simeoni; Gellio Ciotti; Antonella Meneghetti; Mattia Cottes;Abstract To achieve the EU climate and energy objectives, a transition towards a future sustainable energy system is needed. The integration of the huge potential for industrial waste heat recovery into smart energy system represents a main opportunity to accomplish these goals. To successfully implement this strategy, all the several stakeholders' conflicting objectives should be considered. In this paper an evolutionary multi-objective optimization model is developed to perform a sustainability evaluation of an energy system involving an industrial facility as the waste heat source and the neighbourhood as district heating network end users. An Italian case study of heat recovery from a steel casting facility shows how the model allows to properly select the district heating network set of users to fully exploit the available waste energy. Design directions such as the thermal energy storage capacity can be also provided. Moreover, the model enables the analysis of the trade-off between the stakeholders’ different perspectives, allowing to identify possible win-win solutions for both the industrial sector and the citizenship.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2019.03.104&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 43 citations 43 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2019.03.104&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Part of book or chapter of book , Other literature type 2017 ItalyPublisher:Informa UK Limited Fausto Guzzetti; Monia Santini; Stefano Luigi Gariano; Guido Rianna; Luciano Picarelli; Paola Mercogliano; Paolo Tommasi; Luca Comegna;Changes in the main weather features are becoming perceivable in Italy. In the last years, the average temperature and the intensity of rainstorms are increasing. Such phenomena could lead to changes in the earth-atmosphere interaction. Accounting for the high vulnerability of the Italian territory, an obvious consequence would concern the effects on the hydro-geological hazards, including landslides and floods. As a matter of fact, the higher frequency of high magnitude flood and landslide events represent an alarm bell. It is necessary to explore with all available instruments the consequences of the potential climate changes. Since quantitative predictions are not possible, the only way to run is depicting rational scenarios for the most vulnerable contexts adopting a multidisciplinary approach. Based on projections of potential climate changes in the 21st century, the report examines their potential impact in the context of the Italian peninsula. After a general overview about typical slope responses to weather changes, some quantitative scenarios have been depicted for representative geomorphological contexts. The last part of the paper examines changes in the expected risk, based on the characteristics of the Italian territory and its vulnerability, looking at the initiatives to undertake for its mitigation.
CNR ExploRA arrow_drop_down https://doi.org/10.1201/978131...Part of book or chapter of book . 2017 . Peer-reviewedData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1201/9781315387789-5&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu11 citations 11 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert CNR ExploRA arrow_drop_down https://doi.org/10.1201/978131...Part of book or chapter of book . 2017 . Peer-reviewedData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1201/9781315387789-5&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2020Publisher:Copernicus GmbH Funded by:EC | METLAKE, EC | VERIFY, EC | IMBALANCE-P +4 projectsEC| METLAKE ,EC| VERIFY ,EC| IMBALANCE-P ,EC| CHE ,RCN| Integrated Carbon Observation System (ICOS)-Norway and Ocean Thematic Centre (OTC) ,EC| VISUALMEDIA ,AKA| Novel soil management practices - key for sustainable bioeconomy and climate change mitigation -SOMPA / Consortium: SOMPAAna Maria Roxana Petrescu; Chunjing Qiu; Philippe Ciais; Rona L. Thompson; Philippe Peylin; Matthew J. McGrath; Efisio Solazzo; Greet Janssens‐Maenhout; Francesco N. Tubiello; P. Bergamaschi; D. Brunner; Glen P. Peters; L. Höglund-Isaksson; Pierre Regnier; Ronny Lauerwald; David Bastviken; Aki Tsuruta; Wilfried Winiwarter; Prabir K. Patra; Matthias Kuhnert; Gabriel D. Orregioni; Monica Crippa; Marielle Saunois; Lucia Perugini; Tiina Markkanen; Tuula Aalto; Christine Groot Zwaaftink; Yuanzhi Yao; Chris Wilson; Giulia Conchedda; Dirk Günther; Adrian Leip; Pete Smith; Jean‐Matthieu Haussaire; Antti Leppänen; Alistair J. Manning; Joe McNorton; Patrick Brockmann; A.J. Dolman;Abstract. 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+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. 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 NGHGI 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 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates 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 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 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 NGHGI and inversions and account for 5.2 Tg CH4 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 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−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 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU 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 EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).
https://doi.org/10.5... arrow_drop_down https://doi.org/10.5194/essd-2...Article . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/essd-2020-367&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert https://doi.org/10.5... arrow_drop_down https://doi.org/10.5194/essd-2...Article . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/essd-2020-367&type=result"></script>'); --> </script>
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