FaIR simple climate modelThe FaIR model converts emissions of greenhouse gases and short lived climate forcers to a concentration and radiative forcing time series, and from there to a global temperature anomaly. Natural forcing from solar variability and volcanoes can be supplied externally. It is written in Python, is compatible with Python 2 and Python 3, and is available on PyPI and at GitHub.
Simple models are needed because full complexity Earth system models that are used in climate change projections such as those used by the IPCC are expensive to run. FaIR is designed to emulate the behaviour of more complex models. The input parameters to FaIR can then be varied to assess the responses in the full range of uncertainty to the carbon cycle, radiative forcing and temperature response.
The carbon cycle component of FaIR is based on a modified four time-constant impulse response function that simulates the behaviour of complex earth system models remarkably well. It was developed by Richard Millar, Zeb Nicholls and Myles Allen at Oxford University. This is version 1.0 of FaIR, described in Millar et al., 2017.
For a full assessment of future emissions scenarios, non-CO2 gases, and short lived forcers such as aerosols and tropospheric ozone precursors should be included. The FaIR model was extended to include these emissions. The latest version is 1.3.
FaIR is in ongoing development in order to make the model more flexible, easy to use, and to keep up to date with the latest science.
- fair-scm.org: very simplified online interactive version showing impacts of varying parameters (work in progress!)
- Interactive iPython Binder showing basic usage examples
- FaIR GitHub project
- PyPI install
- User documentation
Questions, bug reports or feature requests? Raise an issue.
If you use FaIR in your work, please use the below reference:
Smith, C. J., Forster, P. M., Allen, M., Leach, N., Millar, R. J., Passerello, G. A., and Regayre, L. A.: FAIR v1.3: a simple emissions-based impulse response and carbon cycle model, Geosci. Model Dev., 11, 2273-2297, https://doi.org/10.5194/gmd-11-2273-2018, 2018.