climlab

Python package for process-oriented climate modeling

Author

Brian E. J. Rose

Department of Atmospheric and Environmental Sciences

University at Albany

[email protected]

About climlab

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, interacting processes. climlab defines a base class called Process, which can contain an arbitrarily complex tree of sub-processes (each also some sub-class of 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.

Currently, 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

Installation

Installing pre-built binaries with conda (Mac OSX, Linux, and Windows)

By far the simplest and recommended way to install climlab is using conda (which is the wonderful package manager that comes with Anaconda Python).

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:

import climlab

and use most of the package. You will see warning messages about the missing components.

Links

• HTML documentation: http://climlab.readthedocs.io/en/latest/intro.html
• Issue tracker: http://github.com/brian-rose/climlab/issues
• Source code: http://github.com/brian-rose/climlab
• JOSS meta-paper: https://doi.org/10.21105/joss.00659

Dependencies

These are handled automatically if you install with conda.

Required

• Python 2.7, 3.5, 3.6, 3.7 (as of version 0.7.1)
• numpy
• scipy
• xarray
• attrdict

Recommended for full functionality

• numba (used for acceleration of some components)
• pytest (to run the automated tests, important if you are developing new code)

Anaconda Python is highly recommended and will provide everything you need. See "Installing pre-built binaries with conda" above.

Documentation and Examples

Full user manual is available here.

The directory climlab/courseware/ also contains a collection of Jupyter notebooks (*.ipynb) used for teaching some basics of climate science, and documenting use of the climlab package.

These are self-describing, and should all run out-of-the-box once the package is installed, e.g:

jupyter notebook Insolation.ipynb

Release history

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.

Details:

• Removed climlab.utils.attr_dict.AttrDict and replaced with AttrDict package (a new dependency)
• Added xarray input and output capabilities for climlab.solar.insolation.daily_insolation()
• climlab.solar.orbital.OrbitalTable and climlab.solar.orbital.long.OrbitalTable now return xarray.Dataset objects containing the orbital data.
• The 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=None for 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.

Details:

• climlab.convection.ConvectiveAdjustement now allows non-constant critical lapse rates, stored in input parameter adj_lapse_rate.

  • 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 climlab.radiation.SimpleAbsorbedShortwave class
  • Diffusion and its diagnostics now handled by climlab.dynamics.MeridionalHeatDiffusion class.
  • Diffusivity can be altered at any time by the user, e.g. during timestepping
  • Diffusivity input value K in class climlab.dynamics.MeridionalDiffusion is now specified in physical units of m2/s instead of (1/s). This is consistent with its parent class climlab.dynamics.Diffusion.

• A new class climlab.dynamics.MeridionalMoistDiffusion for the moist EBM (diffusion down moist static energy gradient)
• Tests that require compiled code are now marked with pytest.mark.compiled for 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 xarray.DataArray and xarray.Dataset objects)

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

License

This code is freely available under the MIT license. See the accompanying LICENSE file.

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Support

Development of 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.