Preprints
https://doi.org/10.5194/egusphere-2022-802
https://doi.org/10.5194/egusphere-2022-802
 
25 Aug 2022
25 Aug 2022

Pathfinder v1.0: a Bayesian-inferred simple carbon-climate model to explore climate change scenarios

Thomas Bossy1,2,, Thomas Gasser1,, and Philippe Ciais2 Thomas Bossy et al.
  • 1International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
  • 2Laboratoire des Sciences du Climat et de l’Environnement (LSCE), Gif-sur-Yvette, France
  • These authors contributed equally to this work.

Abstract. The Pathfinder model was developed to fill a perceived gap within the range of existing simple climate models. Pathfinder is a compilation of existing formulations describing the climate and carbon cycle systems, chosen for their balance between mathematical simplicity and physical accuracy. The resulting model is simple enough to be used with Bayesian inference algorithms for calibration, which enables assimilation of the latest data from complex Earth system models and the IPCC 6th assessment report, as well as a yearly update based on observations of global temperature and atmospheric CO2. The model’s simplicity also enables coupling with integrated assessment models and their optimization algorithms, or running the model in a backward temperature-driven fashion. In spite of this simplicity, the model accurately reproduces behaviours and results from complex models – including uncertainty ranges – when ran following standardized diagnostic experiments. Pathfinder is open-source, and this is its first comprehensive description.

Journal article(s) based on this preprint

12 Dec 2022
Pathfinder v1.0.1: a Bayesian-inferred simple carbon–climate model to explore climate change scenarios
Thomas Bossy, Thomas Gasser, and Philippe Ciais
Geosci. Model Dev., 15, 8831–8868, https://doi.org/10.5194/gmd-15-8831-2022,https://doi.org/10.5194/gmd-15-8831-2022, 2022
Short summary

Thomas Bossy et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2022-802', Juan Antonio Añel, 21 Sep 2022
    • AC1: 'Reply on CEC1', Thomas Bossy, 21 Sep 2022
  • RC1: 'Comment on egusphere-2022-802', Anonymous Referee #1, 22 Sep 2022
    • AC2: 'Reply on RC1', Thomas Bossy, 20 Oct 2022
  • RC2: 'Comment on egusphere-2022-802', Ian Enting, 29 Sep 2022
    • AC3: 'Reply on RC2', Thomas Bossy, 20 Oct 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2022-802', Juan Antonio Añel, 21 Sep 2022
    • AC1: 'Reply on CEC1', Thomas Bossy, 21 Sep 2022
  • RC1: 'Comment on egusphere-2022-802', Anonymous Referee #1, 22 Sep 2022
    • AC2: 'Reply on RC1', Thomas Bossy, 20 Oct 2022
  • RC2: 'Comment on egusphere-2022-802', Ian Enting, 29 Sep 2022
    • AC3: 'Reply on RC2', Thomas Bossy, 20 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Thomas Bossy on behalf of the Authors (24 Oct 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (07 Nov 2022) by Marko Scholze
RR by Anonymous Referee #1 (17 Nov 2022)
ED: Publish as is (18 Nov 2022) by Marko Scholze

Journal article(s) based on this preprint

12 Dec 2022
Pathfinder v1.0.1: a Bayesian-inferred simple carbon–climate model to explore climate change scenarios
Thomas Bossy, Thomas Gasser, and Philippe Ciais
Geosci. Model Dev., 15, 8831–8868, https://doi.org/10.5194/gmd-15-8831-2022,https://doi.org/10.5194/gmd-15-8831-2022, 2022
Short summary

Thomas Bossy et al.

Model code and software

Pathfinder: v1.0 Thomas Gasser, Thomas Bossy https://zenodo.org/record/7003849#.Yvzol3ZBy5c

Thomas Bossy et al.

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Short summary
We developed a new simple climate model, designed to fill a perceived gap within the existing simple climate models by fulfilling three key requirements: calibration using Bayesian inference, possibility of coupling with integrated assessment models, and capacity to explore climate scenarios compatible with limiting climate impacts. Here, we describe the model, its calibration using the latest data from complex CMIP6 models and the IPCC AR6, and we assess its performance.