Tuesday, 24 January 2017: 10:45 AM
Conference Center: Chelan 5 (Washington State Convention Center )
Global climate is a complex emergent property of the rich interactions between simpler components of the climate system. We build scientific understanding of this system by breaking it down into component process models (e.g. radiation, large-scale dynamics, boundary layer turbulence), understanding each components, and putting them back together. Hands-on experience and freedom to tinker with climate models (whether simple or complex) is invaluable for building physical understanding. CLIMLAB is an open-ended, object-oriented software engine for interactive, process-oriented climate modeling. With CLIMLAB you can interactively mix and match model components, or combine simpler process models together into a more comprehensive model. It was created in part to support classroom activities, using hands-on modeling to teach fundamentals of climate science at both undergraduate and graduate levels, but also has applications in climate research. CLIMLAB is written in Python and ties in with the rich ecosystem of open-source scientific Python tools for numerics and graphics. Using CLIMLAB requires some basic Python coding skills, which we consider to be an educational asset given the exploding popularity of Python in STEM fields. The Jupyter Notebook format provides an elegant medium for distributing interactive example code. I will give an overview of the current capabilities of CLIMLAB, the curriculum we have developed thus far, and plans for the future.
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