FaIR calibration data

This dataset contains the full data, input scripts and produced output data for the AR6-consistent calibration of FaIRv2.1.3.

The zipfile contains everything, allowing you perform your own analysis. The GitHub version contains enough for "bare bones" reproducibility, including downloading of external datasets and generation of intermediate files. 

Four CSV files of the constrained parameter set, the chemical sensitivies to methane lifetime, and the scale factors to use for LAPSI and land use are provided. This may be all that is required to run your own simulations without dipping into the ZIP file (though note particularly in this instance that running with the correct emissions and forcing data is critical). The best example available might be from https://github.com/chrisroadmap/fair-calibrate/blob/main/input/fair-2.1.3/v1.4/all-2022/constraining/05_constrained-ssp-projections.py.

FaIR v2.1.3

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

From the command line:

pip install fair==2.1.3

Calibration v1.4

A slightly bigger prior and slightly smaller posterior are implemented. ERFari distributions are changed to bring them closer in line with the intent of AR6 WG1 Ch6. ERFaci prior is made slightly wider and non-uniform. Contrails are excluded from the calibration since few IAM scenarios provide detailed enough information to assess their future forcing (some kind of aviation activity indicator or proxy such as emissions from the sector would be needed).

• 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. 2021b).
• Effective radaitive forcing uncertainty follows the distributions in AR6, with asymmetric distributions switched to skew-normal.
• Volcanic forcing time series updated to 2022 (from IGCC).
• Contrails are excluded from the calibration

all-2022 (v1.4.1)

• 841-member posterior (deliberately chosen).
• Emissions are from several observational and proxy datasets updated to 2022 (Global Carbon Project, PRIMAP-Hist, Global Fire Emissions Database, Community Emissions Data System), and harmonized to run scenarios post-2022.
• Temperature from IGCC (Forster et al. 2023) (1850-2022, mean of 4 datasets).
• Warming 2003-2022 relative to 1850-1900 range from IGCC.
• CO2 concentrations constrained to IGCC estimate for 2022.
• Ocean heat content from IGCC (1971-2020), linear.
• two step constraining procedure used: first RMSE of less than 0.17K (up from 0.16), 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. 2023: https://doi.org/10.5194/essd-15-2295-2023
• Leach et al. 2021: https://doi.org/10.5194/gmd-14-3007-2021
• Smith et al. 2021a: https://doi.org/10.1029/2020JD033622
• Smith et al. 2021b: 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