Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5259-2020
https://doi.org/10.5194/gmd-13-5259-2020
Model description paper
 | 
04 Nov 2020
Model description paper |  | 04 Nov 2020

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

Robin D. Lamboll, Zebedee R. J. Nicholls, Jarmo S. Kikstra, Malte Meinshausen, and Joeri Rogelj

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Referee Nomination & Report Request started (24 Aug 2020) by Rolf Sander
RR by Anonymous Referee #2 (02 Sep 2020)
RR by Anonymous Referee #1 (06 Sep 2020)
AR by Svenja Lange on behalf of the Authors (01 Sep 2020)  Author's response
ED: Publish subject to minor revisions (review by editor) (06 Sep 2020) by Rolf Sander
AR by Robin Lamboll on behalf of the Authors (14 Sep 2020)  Author's response    Manuscript
ED: Publish as is (23 Sep 2020) by Rolf Sander
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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.