Articles | Volume 13, issue 11
Geosci. Model Dev., 13, 5259–5275, 2020
https://doi.org/10.5194/gmd-13-5259-2020
Geosci. Model Dev., 13, 5259–5275, 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 et al.

Viewed

Total article views: 2,236 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,613 571 52 2,236 54 39
  • HTML: 1,613
  • PDF: 571
  • XML: 52
  • Total: 2,236
  • BibTeX: 54
  • EndNote: 39
Views and downloads (calculated since 28 May 2020)
Cumulative views and downloads (calculated since 28 May 2020)

Viewed (geographical distribution)

Total article views: 1,954 (including HTML, PDF, and XML) Thereof 1,934 with geography defined and 20 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 08 Aug 2022
Download
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.