FAIR

Finite Amplitude Impulse-Response simple climate-carbon-cycle model.
https://github.com/oms-netzero/fair

Category: Climate Change
Sub Category: Earth and Climate Modeling

Keywords from Contributors

climate-model climate climate-change scenarios data-package magicc-model emissions git-scraping frictionlessdata

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Repository metadata

Finite-amplitude Impulse Response simple climate model

Host: GitHub
URL: https://github.com/oms-netzero/fair
Owner: OMS-NetZero
License: apache-2.0
Created: 2017-06-26T06:18:37.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2025-03-04T08:29:30.000Z (about 2 months ago)
Last Synced: 2025-04-10T06:04:44.724Z (18 days ago)
Language: Python
Homepage: https://docs.fairmodel.net
Size: 58.7 MB
Stars: 135
Watchers: 7
Forks: 71
Open Issues: 21
Releases: 36
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.rst
- License: LICENSE

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.

Owner metadata

Name: OMS-NetZero
Login: OMS-NetZero
Email:
Kind: organization
Description:
Website:
Location:
Twitter:
Company:
Icon url: https://avatars.githubusercontent.com/u/26712044?v=4
Repositories: 4
Last ynced at: 2024-03-19T08:00:26.597Z
Profile URL: https://github.com/OMS-NetZero

GitHub Events

Total

Create event: 7
Release event: 2
Issues event: 7
Watch event: 13
Delete event: 4
Member event: 1
Issue comment event: 8
Push event: 8
Pull request event: 5
Fork event: 11

Last Year

Create event: 7
Release event: 2
Issues event: 7
Watch event: 13
Delete event: 4
Member event: 1
Issue comment event: 8
Push event: 8
Pull request event: 5
Fork event: 11

Committers metadata

Last synced: 7 days ago

Total Commits: 560
Total Committers: 6
Avg Commits per committer: 93.333
Development Distribution Score (DDS): 0.089

Commits in past year: 74
Committers in past year: 1
Avg Commits per committer in past year: 74.0
Development Distribution Score (DDS) in past year: 0.0

Name	Email	Commits
Chris Smith	c**p@g**m	510
Zebedee Nicholls	z**s@c**g	23
Robert Gieseke	r**e@p**e	21
James Penn	j**s@j**k	3
Sally	s**e@g**m	2
Robert Gieseke	r**g@w**e	1

Committer domains:

Issue and Pull Request metadata

Last synced: 2 days ago

Total issues: 73
Total pull requests: 104
Average time to close issues: about 1 year
Average time to close pull requests: 2 months
Total issue authors: 15
Total pull request authors: 8
Average comments per issue: 1.97
Average comments per pull request: 1.73
Merged pull request: 93
Bot issues: 0
Bot pull requests: 0

Past year issues: 8
Past year pull requests: 6
Past year average time to close issues: about 1 month
Past year average time to close pull requests: about 10 hours
Past year issue authors: 2
Past year pull request authors: 1
Past year average comments per issue: 0.5
Past year average comments per pull request: 0.83
Past year merged pull request: 6
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/oms-netzero/fair

Top Issue Authors

chrisroadmap (42)
znicholls (9)
rgieseke (6)
TeddyHornsby (2)
aliss77777 (2)
Hans-PeterH (2)
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milankl (1)
saraarriz (1)
duncanwp (1)
FrankErrickson (1)
colemanmr (1)
dyrehaugen (1)
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michellecain (1)

Top Pull Request Authors

chrisroadmap (75)
rgieseke (13)
znicholls (8)
JGBroadbent (3)
SallyDa (2)
nsfmc (1)
jamesp (1)
chrisdwells (1)

Top Issue Labels

bug (6)
repository-set-up (5)
enhancement (3)
automation (3)
v2.0 (2)
carbon-cycle (1)
docs (1)

Top Pull Request Labels

automation (1)

Package metadata

Total packages: 1
Total downloads:
- pypi: 3,701 last-month
Total docker downloads: 278
Total dependent packages: 3
Total dependent repositories: 13
Total versions: 41
Total maintainers: 4

pypi.org: fair

Finite-amplitude Impulse Response (FaIR) simple climate model

Homepage: https://github.com/OMS-NetZero/FAIR
Documentation: https://fair.readthedocs.io/
Licenses: Apache 2.0
Latest release: 2.2.2 (published 5 months ago)
Last Synced: 2025-04-26T14:03:36.527Z (2 days ago)
Versions: 41
Dependent Packages: 3
Dependent Repositories: 13
Downloads: 3,701 Last month
Docker Downloads: 278
Rankings:
- Dependent repos count: 4.067%
- Forks count: 5.635%
- Downloads: 5.966%
- Average: 6.05%
- Stargazers count: 7.274%
- Dependent packages count: 7.306%
Maintainers (4)

Dependencies

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Score: 15.13566511657962

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