Installation

Installing pre-built binaries with conda (Mac OSX, OSX-ARM64, and Linux)

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 and Linux. Some binaries for earlier versions are available for Windows but this is not currently supported.

Installing into a self-contained conda environment

To avoid issues with package conflicts, it’s often best to work in self-contained environments. This example installs climlab and jupyter along with all their dependencies in a fresh environment:

conda create --name climlab-test --channel conda-forge climlab jupyter
conda activate climlab-test

Installing on Google Colab

The following code will install climlab and its dependencies on Google Colab:

!pip install -q condacolab
import condacolab
condacolab.install()
!conda install -c conda-forge climlab

Installing from source

You can clone the source code repository with:

git clone https://github.com/climlab/climlab.git

and from the climlab directory, do:

python -m pip install . --no-deps -vv

Please see Contributing to CLIMLAB for more details.

About the compiled Fortran components

Climlab itself is pure Python and should work on any system. As of version 0.8.0, all the Fortran code has been moved into external companion packages:

climlab-rrtmg

climlab-cam3-radiation

climlab-emanuel-convection

climlab-sbm-convection

If you install climlab via conda-forge, these pre-compiled dependencies will be installed automatically.

It is possible to install and run climlab without the compiled dependencies. In this case you should then find that you can still:

import climlab

and use parts of the package that don’t depend on compiled code. You will see warning messages about the missing components.

Source Code

Stables releases as well as the current development version can be found on github:

Stable Releases

Development Version

Dependencies

These are handled automatically if you install with conda.

Python (currently testing on versions 3.10, 3.11, 3.12, 3.13)
numpy
scipy
pooch (for remote data access and caching)
xarray (for data handling)

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.