(This talk was given at the AMS conference in Denver 2023)

Staggered grids such as Arakawa grids are ubiquitous in climate models. Analysis and post-processing tools using finite-volume operators must respect staggering to get correct results. This presents a challenge for users of General Circulation Model (GCM) data, whose analysis routines must follow the idiosyncrasies of particular GCM grids.

The xGCM package [1] is designed to solve this problem, by extending xarray’s data model with information about the grid. It encodes the positions of variables along each axis of the grid, so that operations can respect differences in staggering between variables. It also encodes the topology of the grid, understanding how the spherical Earth is divided into different regions in various models.

XGCM has recently been upgraded by introducing the concept of “Grid Ufuncs”, which are analogous to numpy ufuncs but grid-aware. Users can define their own grid ufuncs, then apply them to their data. We hope that this extensible model can be built upon by scientists who work post-processing all types of climate models.

We present an overview of xGCM and its capabilities, before showing an example of using it on a multi-TB scale oceanographic dataset.

January 11, 2023