FaIR
FaIR (the Finite-amplitude Impulse-Response) climate model is a simple climate model, or emulator, useful for producing global mean temperature projections from a wide range of emissions or prescribed forcing scenarios.
Requirements
- python 3.8, 3.9, 3.10, 3.11, 3.12 or 3.13
Installation
From anaconda (recommended)
NEW! from v2.1.4, fair is available on conda-forge:
conda install -c conda-forge fair
Older versions of fair (1.6.2+, 2.1.0-4) can be installed from the chrisroadmap channel:
conda install -c chrisroadmap fair==X.Y.Z
From the Python Package Index
pip install fair
From source
Refer to the documentation
Usage
FaIR can be driven by emissions of greenhouse gases (GHGs) and
short-lived forcers (SLCFs), concentrations of GHGs, or effective
radiative forcing (ERF), with different input methods for different
species possible in the same run. If run concentration-driven, emissions
are back-calculated. Custom GHGs and SLCFs can be defined, and all
components are optional allowing experiments such as pulse-response
analyses to single forcers or gathering up non-CO2 species as an
aggregate forcing.
Examples
The examples directory contains Jupyter notebooks with some simple examples showing how to run FaIR and the standalone energy balance model.
If you want to try this out online, go here.
Important: A note about calibrating and constraining
FaIR is naive. It will run whatever climate scenario and climate configuration you give it. If you violate the laws of physics, FaIR won't stop you. For simple climate models as for complex, garbage in leads to garbage out. More subtle to spot are those analyses with simple climate models where the present day warming (or historical) is wrong or the climate is warming too slowly or too quickly. At least, plot a historical temperature reconstruction over your results and see if it looks right.
We have produced IPCC AR6 Working Group 1 consistent probabilistic ensembles to run with. The calibration data can be obtained here. These parameter sets are calibrated to CMIP6 models, run in a large Monte Carlo ensemble, and constrained based on observed and assessed climate metrics. For an example of how to use this calibration data set with SSP emissions, see this example. If you're writing a paper using FaIR, you should use these. A paper describing this method has been submitted, but for now please cite the Zenodo DOI.
Citation
If you use FaIR in your work, please cite the following references depending on the version:
- v2.0+: Leach, N. J., Jenkins, S., Nicholls, Z., Smith, C. J., Lynch, J., Cain, M., Walsh, T., Wu, B., Tsutsui, J., and Allen, M. R.: FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration, Geosci. Model Dev., 14, 3007--3036, https://doi.org/10.5194/gmd-14-3007-2021, 2021
- v1.1-v1.6: 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., https://doi.org/10.5194/gmd-11-2273-2018, 2018.
- v1.0 (or the concept of the state-dependent impulse-response function for CO2): Millar, R. J., Nicholls, Z. R., Friedlingstein, P., and Allen, M. R.: A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions, Atmos. Chem. Phys., 17, 7213-7228, https://doi.org/10.5194/acp-17-7213-2017, 2017.
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Release files for fair 2.2.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| fair-2.2.4.tar.gz | 65.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| fair-2.2.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 118.3 kB
Release files / fair-2.2.4.tar.gz
| Download URL | fair-2.2.4.tar.gz |
|---|---|
| Size | 65.3 kB |
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| Tags | Python 3 |
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