Time-varying forcing scalings

This minor release allows for two additional features:

• a time-varying historical scaling factor for effective radiative forcing (set scale to a 2D array; using scale=None gives the default scaling factor as an array of ones)
• the ability to specify the radiative efficiencies for each aerosol and tropospheric ozone precursor separately with the b_aero and b_tro3 options in the call to fair_scm.

There is also a very minor bug fix compared to v1.2.2, which relates to the setup.py file pointing to the wrong README.

Improved aerosol/ozone relationships, varying ECS/TCR, and other changes

Welcome to the version 1.2.1 release! Several improvements to the non-CO₂ forcing have been made compared to v1.1. Additionally ECS and TCR can now also be time-varying.

• Natural emissions of CH₄ and N₂O are fully backed-out of the RCP scenarios so that the historical concentrations match precisely up to 2005. This is desirable, because published concentration datasets (e.g. from Meinshausen et al.) represent our best estimate of the evolution of greenhouse gases and are likely to exhibit much less uncertainty than any emissions input dataset.
• Tropospheric ozone forcing relationships from Stevenson et al., 2013 have been implemented in preference to the old regression-based treatment. Also included is the effect of temperature on ozone forcing.
• Direct aerosol forcing relationships (treated as ERFari in FAIR) from Myhre et al., 2013 have been implemented in preference to the old regression-based treatment.
• Indirect aerosol forcing relationships (treated as ERFaci) are also implemented. We use a logarithmic dependence of aerosol indirect forcing on emissions. This is inspired by the simple model of Stevens 2015 based on a least-squares curve fit from the global aerosol indirect model of Ghan et al., 2013. The Ghan model emulates the results of several climate models.
• The option to scale aerosol forcing to the AR5 best estimate of -0.45W/m² for ERFari and -0.45W/m² for ERFaci given 2011 emissions is included as an option (true by default). Use scaleAerosolAR5=False in the call to fair_scm to turn off this behaviour.
• Aerosols and tropospheric ozone include an option to ensure that the forcing from 1765 to 1850 follows a trajectory between zero and the 1850 value. Anthropogenic emissions were non-zero in 1750/1765 (Skeie et al., 2011) but the RCPs start from zero. Using RCP (zero-based) values leads to forcings that are too large in 1850 relative to 1765. We therefore assume the Skeie emissions are the true "pre-industrial". If the option is turned off (set fixPre1850RCP=False in the call to fair_scm), or if the date of the simulation is after 1850, then zero emissions will result in a negative tropospheric ozone / positive aerosol forcing. This is by design.
• An option to include a time-varying ECS and TCR has been included by specifying tcrecs as a 2D array.
• Default options to fair_scm have been changed to reflect (mostly) the ensemble setup in the GMD paper, including inclusion of natural emissions, solar and volcanic forcing, and useMultigas=True by default.
• The code is identical to v1.2. A small change to the metadata was needed for compatibility with PyPI.

Dependency fixes and SCEN file support

This micro-release enables compatibility with slightly older versions of numpy (1.11+) compared to the previous release which would only work with numpy 1.13+. This would particularly affect users running with Enthought Canopy. The setup.py script has been modified to check that the user has compatible versions of numpy and scipy.

Functionality added to read in MAGICC SCEN files directly.

Python 3 compatibility and RCPs included in package

The previous versions 1.1 and 1.1.1 contained a few minor omissions which have been rectified in this micro release, including:

• Python 3 compatibility
• Inclusion of RCP emission files in PyPI package
• Ensuring true division when using Python 2
• Continuous integration
• automatic version tracking with versioneer, so that now the version tags on GitHub and PyPI should agree exactly!

Thanks to Robert Gieseke for doing quite a lot of the work in getting this repository up to scratch.

There are no science changes in this micro release.

Emissions-based, multi-species FAIR simple climate model

This release allows the user to specify all greenhouse gas emissions, aerosol and ozone precursors given in the RCPs for CMIP5 (http://www.pik-potsdam.de/~mmalte/rcps/). It is back-compatible with FAIR v1.0, and can still be run in CO2-only mode.

FAIR provides GHG concentrations for 31 species, radiative forcing for 13 separate forcing components, and integrates these into temperature change since the pre-industrial era.

For further details please see the reference publication: https://www.geosci-model-dev-discuss.net/gmd-2017-266/

Original (carbon cycle and non-CO2 radiative forcing)

Original FAIR release with carbon-cycle and non-CO2 drivers provided by a radiative forcing timeseries