A Computational Approach to Climate Science Education with CLIMLAB

CLIMLAB is a Python-based toolkit for interactive, process-oriented climate modeling for use in education and research. It is motivated by the need for simpler tools and more reproducible workflows with which to "fill in the gaps" between blackboard-level theory and the results of comprehensive climate models. With CLIMLAB you can interactively mix and match physical model components, or combine simpler process models together into a more comprehensive model. In the classroom, I use CLIMLAB to put models in the hands of students (at both undergraduate and graduate levels), and emphasize a hierarchical, process-oriented approach to understanding the key emergent properties of the climate system. CLIMLAB is equally a tool for climate research, where the same needs exist for more robust, process-based understanding and reproducible computational results.

I will give an overview of CLIMLAB and an update on recent developments, including:

• a full-featured, well-documented, interactive implementation of the RRTMG radiation model
• packaging with conda-forge for compiler-free (and hassle-free!) installation on Mac, Windows and Linux
• interfacing with xarray for i/o and graphics with gridded model data
• a rich and growing collection of examples and self-computing lecture notes in Jupyter notebook format