Python package for process-oriented climate modeling¶
climlab is a flexible engine for process-oriented climate modeling.
It is based on a very general concept of a model as a collection of individual,
climlab defines a base class called
can contain an arbitrarily complex tree of sub-processes (each also some
Process). Every climate process (radiative, dynamical,
physical, turbulent, convective, chemical, etc.) can be simulated as a stand-alone
process model given appropriate input, or as a sub-process of a more complex model.
New classes of model can easily be defined and run interactively by putting together an
appropriate collection of sub-processes.
climlab has out-of-the-box support and documented examples for
- Radiative and radiative-convective column models, with various radiation schemes:
- RRTMG (a widely used radiative transfer code)
- CAM3 (from the NCAR GCM)
- Grey Gas
- Simplified band-averaged models (4 bands each in longwave and shortwave)
- Convection schemes:
- Emanuel moist convection scheme
- Hard convective adjustment (to constant lapse rate or to moist adiabat)
- Diffusion solvers for moist and dry Energy Balance Models
- Flexible insolation including: - Seasonal and annual-mean models - Arbitrary orbital parameters
- Boundary layer scheme including sensible and latent heat fluxes
- Arbitrary combinations of the above, for example:
- 2D latitude-pressure models with radiation, horizontally-varying diffusion, and fixed relative humidity
Installing pre-built binaries with conda (Mac OSX, Linux, and Windows)¶
You can install
climlab and all its dependencies with:
conda install -c conda-forge climlab
Or (recommended) add
conda-forge to your conda channels with:
conda config --add channels conda-forge
and then simply do:
conda install climlab
Binaries are available for OSX, Linux, and Windows.
You may need to update your
numpy if you are using are using a version prior to 1.11
Installing from source¶
If you do not use conda, you can install
climlab from source with:
pip install climlab
(which will download the latest stable release from the pypi repository and trigger the build process.)
Alternatively, clone the source code repository with:
git clone https://github.com/brian-rose/climlab.git
and, from the
climlab directory, do:
python setup.py install
You will need a Fortran compiler on your system. The build has been tested with both gcc/gfortran and ifort (Linux)
Installing from source without a Fortran compiler¶
Many parts of
climlab are written in pure Python and should work on any system.
Fortran builds are necessary for the RRTMG and CAM3 radiation schemes and for the Emanuel convection scheme.
If you follow the instructions for installing from source (above) without a valid Fortran compiler,
you should find that you can still:
and use most of the package. You will see warning messages about the missing components.
These are handled automatically if you install with conda.
- Python 2.7, 3.5, 3.6, 3.7 (as of version 0.7.1)
Documentation and Examples¶
Full user manual is available here.
climlab/courseware/ also contains a collection of Jupyter notebooks (
*.ipynb) used for teaching some basics of climate science, and documenting use of the
These are self-describing, and should all run out-of-the-box once the package is installed, e.g:
jupyter notebook Insolation.ipynb
- Version 0.7.1 (released January 2019)
Deeper xarray integration, include one breaking change to
climlab.solar.orbital.OrbitalTable, Python 3.7 compatibility, and minor enhancements.
climlab.utils.attr_dict.AttrDictand replaced with AttrDict package (a new dependency)
xarrayinput and output capabilities for
xarray.Datasetobjects containing the orbital data.
lookup_parameter()method was removed in favor of using built-in xarray interpolation.
- New class
climlab.process.ExternalForcing()for arbitrary externally defined tendencies for state variables.
- New input option
ozone_file=Nonefor radiation components, sets ozone to zero.
- Tested on Python 3.7. Builds will be available through conda-forge.
- Version 0.7.0 (released July 2018)
New functionality, improved documentation, and a few breaking changes to the API.
Major new functionality includes convective adjustment to the moist adiabat and moist EBMs with diffusion on moist static energy gradients.
climlab.convection.ConvectiveAdjustementnow allows non-constant critical lapse rates, stored in input parameter
- New switches to implement automatic adjustment to dry and moist adiabats (pseudoadiabat)
climlab.EBM()and its daughter classes are significantly reorganized to better respect CLIMLAB principles:
- Essentially all the computations are done by subprocesses
- SW radiation is now handled by
- Diffusion and its diagnostics now handled by
- Diffusivity can be altered at any time by the user, e.g. during timestepping
- Diffusivity input value
climlab.dynamics.MeridionalDiffusionis now specified in physical units of m2/s instead of (1/s). This is consistent with its parent class
- A new class
climlab.dynamics.MeridionalMoistDiffusionfor the moist EBM (diffusion down moist static energy gradient)
- Tests that require compiled code are now marked with
pytest.mark.compiledfor easy exclusion during local development
Under-the-hood changes include
- Internal changes to the timestepping; the
compute()method of every subprocess is now called explicitly.
compute()now always returns tendency dictionaries
- Version 0.6.5 (released April 2018)
- Some improved documentation, associated with publication of a meta-description paper in JOSS.
- Version 0.6.4 (released February 2018)
- Some bug fixes and a new
climlab.couple()method to simplify creating complete models from components.
- Version 0.6.3 (released February 2018)
- Under-the-hood improvements to the Fortran builds which enable successful builds on a wider variety of platforms (incluing Windows/Python3).
- Version 0.6.2 (released February 2018)
- Introduces the Emanuel moist convection scheme, support for asynchonous coupling, and internal optimzations.
- Version 0.6.1 (released January 2018)
- Provides basic integration with xarray
(convenience methods for converting climlab objects into
- Version 0.6.0 (released December 2017)
- Provides full Python 3 compatibility, updated documentation, and minor enhancements and bug fixes.
- Version 0.5.5 (released early April 2017)
- Finally provides easy binary distrbution with conda
- Version 0.5.2 (released late March 2017)
- Many under-the-hood improvements to the build procedure, which should make it much easier to get climlab installed on user machines. Binary distribution with conda is coming soon!
- Version 0.5 (released March 2017)
- Bug fixes and full functionality for the RRTMG radiation module, an improved common API for all radiation modules, and better documentation.
- Version 0.4.2 (released January 2017)
- Introduces the RRTMG radiation scheme, a much-improved build process for the Fortran extension, and numerous enhancements and simplifications to the API.
- Version 0.4 (released October 2016)
- Includes comprehensive documentation, an automated test suite, support for latitude-longitude grids, and numerous small enhancements and bug fixes.
- Version 0.3 (released February 2016)
- Includes many internal changes and some backwards-incompatible changes (hopefully simplifications) to the public API. It also includes the CAM3 radiation module.
- Version 0.2 (released January 2015)
The package and its API was completely redesigned around a truly object-oriented modeling framework in January 2015.
It was used extensively for a graduate-level climate modeling course in Spring 2015: http://www.atmos.albany.edu/facstaff/brose/classes/ATM623_Spring2015/
Many more examples are found in the online lecture notes for that course: http://nbviewer.jupyter.org/github/brian-rose/ClimateModeling_courseware/blob/master/index.ipynb
- Version 0.1
The first versions of the code and notebooks were originally developed in winter / spring 2014 in support of an undergraduate course at the University at Albany.
See the original course webpage at http://www.atmos.albany.edu/facstaff/brose/classes/ENV480_Spring2014/
The documentation was first created by Moritz Kreuzer (Potsdam Institut for Climate Impact Research) as part of a thesis project in Spring 2016.
Contact and Bug Reports¶
Users are strongly encouraged to submit bug reports and feature requests on github at https://github.com/brian-rose/climlab
This code is freely available under the MIT license. See the accompanying LICENSE file.
climlab is partially supported by the National Science Foundation under award AGS-1455071 to Brian Rose.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.