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2 results for “WGIII”
Demonstration record for discoverable IPCC WGIII data
<p>This is a record used to demonstrate the concept of discoverable data for IPCC AR7 WGIII.</p>
Infiller database for silicone: IPCC AR6 WGIII version
<p><strong>Download and license information</strong></p> <p>The data is available for download at the <a href="https://data.ece.iiasa.ac.at/ar6/#/downloads">AR6 Scenario Explorer</a>.</p> <p>Details about the be found in the <a href="https://data.ece.iiasa.ac.at/ar6/#/license">license section</a> of the AR6 Scenario Explorer .</p> <p><strong>About the data set</strong></p> <p>To fill in emissions not reported for scenarios in their submission to the database, we use a large set of harmonized AR6 global emissions pathways for inferring pathways based on the relationships between concurrent species development over time observed in the larger set.</p> <p>Infilling ensures that all relevant anthropogenic emissions are included in each climate run for each scenario. This makes the climate assessment of alternative scenarios more comparable and reduces the risk of a biased climate assessment, because not all climatically active emission species are reported by all IAMs. The infilling methods used are from an open-source Python software package called ‘<a href="https://github.com/GranthamImperial/silicone">silicone</a>’ (Lamboll et al. 2020)</p> <p>This file is the harmonized emissions database that was used as "infiller database" for the IPCC AR6 WGIII report on the Mitigation of Climate Change, using data from the chapter on <em>Mitigation Pathways Compatible with Long-Term Goals </em>(Riahi and Schaeffer et al. 2022)<em> </em>as available in the AR6 Scenarios Database (Byers et al. 2022).</p> <p><strong>References</strong></p> <p>Edward Byers, Volker Krey, Elmar Kriegler, Keywan Riahi, Roberto Schaeffer, Jarmo Kikstra, Robin Lamboll, Zebedee Nicholls, Marit Sanstad, Chris Smith, Kaj-Ivar van der Wijst, Franck Lecocq, Joana Portugal-Pereira, Yamina Saheb, Anders Strømann, Harald Winkler, Cornelia Auer, Elina Brutschin, Claire Lepault, Eduardo Müller-Casseres, Matthew Gidden, Daniel Huppmann, Peter Kolp, Giacomo Marangoni, Michaela Werning, Katherine Calvin, Celine Guivarch, Tomoko Hasegawa, Glen Peters, Julia Steinberger, Massimo Tavoni, Detlef von Vuuren, Piers Forster, Jared Lewis, Malte Meinshausen, Joeri Rogelj, Bjorn Samset, Ragnhild Skeie, Alaa Al Khourdajie.<br> <em>AR6 Scenarios Database hosted by IIASA</em><br> International Institute for Applied Systems Analysis, 2022.<br> doi: <a href="https://doi.org/10.5281/zenodo.5886912">10.5281/zenodo.5886912</a> | url: <a href="https://data.ene.iiasa.ac.at/ar6">data.ene.iiasa.ac.at/ar6/</a></p> <p>Keywan Riahi, Roberto Schaeffer, et al.<br> <em>Mitigation Pathways Compatible with Long-Term Goals</em>, in "Mitigation of Climate Change".<br> Intergovernmental Panel on Climate Change, Geneva, 2022.<br> url: <a href="https://www.ipcc.ch/report/sixth-assessment-report-working-group-3/">Sixth Assessment Report Working Group III</a></p> <p>Lamboll, R.D., Nicholls, Z.R., Kikstra, J.S., Meinshausen, M. and<br> Rogelj, J., 2020. Silicone v1. 0.0: an open-source Python package for<br> inferring missing emissions data for climate change research. <em>Geoscientific Model Development</em>, <em>13</em>(11), pp.5259-5275.</p>
ScienceDex guides
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OpenNeuro
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