xgcm: General Circulation Model Postprocessing with xarray¶
xgcm aims to become a general purpose tool for processing GCM output in python. xgcm is built on top of xarray, which provides most of the core data model, indexing, and (via dask) parallel, out-of-core array computation. On top of this, xgcm adds an understanding of the finite volume Arakawa Grids commonly used in ocean and atmospheric models, differential and integral operators suited to these grids, and a toolbox of common analysis functions.
xgcm was motivated by the rapid growth in the numerical resolution of ocean, atmosphere, and climate models. While highly parallel supercomputers can now easily generate tera- and petascale datasets, common post-processing workflows struggle with these volumes. Furthermore, we believe that a flexible, evolving, open-source, python-based framework for GCM analysis will enhance the productivity of the field as a whole, accelerating the rate of discovery in climate science.