fair calibration data

Note: please be careful selecting calibrations in your own work; the newest isn't always the "best", or the right one for your needs.

If you're unsure, please contact me.

If you use fair calibrations in your own work, please cite:

Smith, C., Cummins, D. P., Fredriksen, H.-B., Nicholls, Z., Meinshausen, M., Allen, M., Jenkins, S., Leach, N., Mathison, C., and Partanen, A.-I.: fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections, Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, 2024.

This dataset contains the full data, input scripts and produced output data for the updated-2023 calibration of fair v2.2.2.

The zipfile contains everything, allowing you perform your own analysis. The GitHub version contains enough for "bare bones" reproducibility. In addition, I have tried to provide all of the individual files that would be needed to run a historical run of fair.

fair v2.2.2

Obtainable from https://pypi.org/project/fair/

From the command line:

pip install fair==2.2.2

or

conda install -c conda-forge fair==2.2.2

Calibration v1.5

A 1.6 million member prior and 841 member posterior are implemented.

• 1.6 million prior ensemble
• Climate response calibrated on 49 abrupt-4xCO2 experiments from CMIP6 and sampled using correlated kernel density estimates
• Methane lifetime calibrated on 4 AerChemMIP experiments for 1850 and 2014 (Thornhill et al. 2021a, 2021b). Unlike other variables which are sampled around some prior uncertainty, only the best estimate historical calibration is used. The base (1750) lifetime has been fixed and consistently used across projections
• Carbon cycle uses the parameters from Leach et al. 2021 calibrated for FaIR 2.0.0 using 11 C4MIP models.
• Aerosol cloud interactions depend on SO2, BC and OC, using new calibrations from 13 RFMIP and AerChemMIP models, with the APRP code fixed by Mark Zelinka (Zelinka et al. 2023). Prior is a trapezoidal distribution with vertices at (-2.2, -1.6, -0.4, +0.2) W/m2.
• Aerosol radiation interactions use prior values from AR6 Ch6, with best estimates and uncertainties scaled to create a prior in the range of -0.6 to 0.0 W/m2.
• Ozone uses the same methodology as AR6 (Smith et al. 2021).
• Effective radiative forcing uncertainty follows the distributions in AR6, with asymmetric distributions switched to skew-normal.
• Contrails are excluded from the calibration
• fair version bumped to v2.2.2
• many more individual files included in the calibration output which should make plugging and playing easier

Constraint sets

updated_2023 (v1.5.0)

• 841-member posterior (deliberately chosen).
• Emissions and concentrations from CMIP7 historical (Zeb Nicholls, Jarmo Kikstra et al.)
• Volcanic forcing time series updated to use CMIP7 (from Thomas Aubry and collaborators).
• Solar forcing time series updated to use CMIP7 (Funke et al. 2024).
• Temperature from IGCC 2024 (Forster et al. 2025) (1850-2024, mean of 4 datasets).
• Warming 2004-2023 relative to 1850-1900 range from IGCC 2023 (Forster et al. 2024).
• CO2 concentrations constrained to IGCC estimate for 2023.
• Ocean heat content from IGCC (1971-2020), linear, from IGCC.
• two step constraining procedure used: first RMSE of less than 0.19K, then 8-variable distribution fitting.
• Aerosol ERF, ERFari and ERFaci as in AR6 WG1
• No future warming constraints

Performance relative to AR6 assessed ranges

On request, as Zenodo seems to have removed the ability to format tables in the dataset description.

References

• Forster et al. 2024: https://doi.org/10.5194/essd-16-2625-2024
• Forster et al. 2025: https://doi.org/10.5194/essd-17-2641-2025
• Funke et al. 2024: https://doi.org/10.5194/gmd-17-1217-2024
• Leach et al. 2021: https://doi.org/10.5194/gmd-14-3007-2021
• Smith et al. 2021: https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FGD_Chapter07_SM.pdf
• Thornhill et al. 2021a: https://doi.org/10.5194/acp-21-853-2021
• Thornhill et al. 2021b: https://doi.org/10.5194/acp-21-1105-2021
• Zelinka et al. 2023: https://doi.org/10.5194/acp-23-8879-2023