Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8831-2022
https://doi.org/10.5194/gmd-15-8831-2022
Model description paper
 | 
12 Dec 2022
Model description paper |  | 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

Related authors

Global atmospheric inversion of the anthropogenic NH3 emissions over 2019–2022 using the LMDZ-INCA chemistry transport model and the IASI NH3 observations
Pramod Kumar, Grégoire Broquet, Didier Hauglustaine, Maureen Beaudor, Lieven Clarisse, Martin Van Damme, Pierre Coheur, Anne Cozic, Bo Zheng, Beatriz Revilla Romero, Antony Delavois, and Philippe Ciais
Atmos. Chem. Phys., 25, 12379–12407, https://doi.org/10.5194/acp-25-12379-2025,https://doi.org/10.5194/acp-25-12379-2025, 2025
Short summary
Representing dynamic grass density in the land surface model ORCHIDEE r9010
Siqing Xu, Sebastiaan Luyssaert, Yves Balkanski, Philippe Ciais, Nicolas Viovy, Liang Wan, and Jean Sciare
EGUsphere, https://doi.org/10.5194/egusphere-2025-3382,https://doi.org/10.5194/egusphere-2025-3382, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Representing high-latitude deep carbon in the pre-industrial state of the ORCHIDEE-MICT land surface model (r8704)
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev., 18, 6043–6062, https://doi.org/10.5194/gmd-18-6043-2025,https://doi.org/10.5194/gmd-18-6043-2025, 2025
Short summary
A fused canopy height map of Italy (2004–2024) from spaceborne and airborne LiDAR, and Landsat via deep learning and Bayesian averaging
Yang Su, Nikola Besic, Xianglin Zhang, Yidi Xu, Saverio Francini, Giovanni D'Amico, Gherardo Chirici, Martin Schwartz, Ibrahim Fayad, Sarah Brood, Agnes Pellissier-tanon, Ke Yu, Haotian Chen, Songchao Chen, Alexandre d'Aspremont, and Philippe Ciais
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-378,https://doi.org/10.5194/essd-2025-378, 2025
Preprint under review for ESSD
Short summary
High spatiotemporal resolution traffic CO2 emission maps derived from Floating Car Data (FCD) for 20 European cities (2023)
Qinren Shi, Philippe Ciais, Nicolas Megel, Xavier Bonnemaizon, Rohith Teja Mittakola, Richard Engelen, and Chuanlong Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-458,https://doi.org/10.5194/essd-2025-458, 2025
Preprint under review for ESSD
Short summary

Cited articles

Armour, K. C.: Energy budget constraints on climate sensitivity in light of inconstant climate feedbacks, Nat. Clim. Change, 7, 331–335, 2017. a
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020. a, b, c, d, e, f
Bayes, T.: LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S, Philos. T. Roy. Soc. Lond., 53, 370–418, 1763. a
Bernie, D., Lowe, J., Tyrrell, T., and Legge, O.: Influence of mitigation policy on ocean acidification, Geophys. Res. Lett., 37, 1–5, https://doi.org/10.1029/2010GL043181, 2010. a
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D.: Variational inference: A review for statisticians, J. Am. Stat. Assoc., 112, 859–877, 2017. a
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

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, the possibility of coupling with integrated assessment models, and the capacity to explore climate scenarios compatible with limiting climate impacts. Here, we describe the model and its calibration using the latest data from complex CMIP6 models and the IPCC AR6, and we assess its performance.
Share