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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-138
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-138
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 28 May 2020

Submitted as: model description paper | 28 May 2020

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A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Silicone v1.0.0: an open-source Python package for inferring missing emissions data for climate change research

Robin D. Lamboll1, Zebedee R. J. Nicholls2,3, Jarmo S. Kikstra4, Malte Meinshausen2,3, and Joeri Rogelj1 Robin D. Lamboll et al.
  • 1Grantham Institute for Climate Change and the Environment, Imperial College London, UK
  • 2Australian-German Climate and Energy College, The University of Melbourne, Parkville, Victoria, Australia
  • 3School of Earth Sciences, The University of Melbourne, Parkville, Victoria, Australia
  • 4International Institute for Applied Systems Analysis, Laxenburg, Austria

Abstract. Integrated assessment models (IAMs) project future anthropogenic emissions for input into climate models. However, the full list of climate-relevant emissions is lengthy and most IAMs do not model all of them. Here we present silicone, an open-source Python package which infers anthropogenic emissions of missing species based on other known emissions. For example, it can infer nitrous oxide emissions in one scenario based on carbon dioxide emissions from that scenario plus the relationship between nitrous oxide and carbon dioxide emissions in other scenarios. This broadens the range of IAMs available for exploring projections of future climate change. Silicone forms part of the open-source pipeline for assessments of the climate implications of IAMs by the IAM consortium (IAMC). A variety of infilling options are outlined and their suitability for different cases are discussed. The code and notebooks explaining details of the package and how to use it are available from the GitHub repository, https://github.com/GranthamImperial/silicone. There is an additional repository showing uses of the code to complement existing research at https://github.com/GranthamImperial/silicone_examples.

Robin D. Lamboll et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Robin D. Lamboll et al.

Model code and software

Silicone GitHub R. D. Lamboll, Z.Nicholls, and J. Kikstra https://doi.org/10.5281/zenodo.3822259

Silicone examples GitHub R. D. Lamboll https://doi.org/10.5281/zenodo.3822152

Robin D. Lamboll et al.

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Latest update: 19 Oct 2020
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Short summary
Many models project how human activity can lead to more or less climate change, but most of these models do not project all climate-relevant emissions, potentially biasing climate projections. This paper outlines a Python package called Silicone which can add missing emissions in a flexible yet high-throughput manner. It does this 'infilling' based on more complete literature projections. It facilitates a more complete understanding of the climate impact of alternative emission pathways.
Many models project how human activity can lead to more or less climate change, but most of...
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