xgcm

xgcm is a python packge for working with the datasets produced by numerical General Circulation Models (GCMs) and similar gridded datasets that are amenable to finite volume analysis. In these datasets, different variables are located at different positions with respect to a volume or area element (e.g. cell center, cell face, etc.) xgcm solves the problem of how to interpolate and difference these variables from one position to another.

Official Documents

xgcm: General Circulation Model Postprocessing with xarray — xgcm 0.2.0+44.gcac66af documentation

MITgcm Example

Tutorials

PyVideo.org · Xgcm: Analyzing General Circulation Models in Python | SciPy 2019 | Ryan Abernathey

Examples

ECCOv4

ECCOv4 budgets

code:python

{'axis': 'X', 'c_grid_axis_shift': -0.5}),

{'axis': 'Y', 'c_grid_axis_shift': -0.5})

},

)

grid=xgcm.Grid(ds,periodic=False)

print(grid)

Metric-aware diagnostics on curvilinear grids (xgcm integration)

For C-grid ocean models (e.g., MOM, NEMO, MITgcm), using raw differences can produce physically inconsistent gradients. Pair xarray with grid-aware operators.

code:python

import xgcm

grid = xgcm.Grid(ds, metrics={

})

# Relative vorticity ζ = dv/dx - du/dy

Why it’s new in practice: more workflows now keep metric tensors as first-class variables, enabling consistent energetics and vorticity budgets without regridding.

Combine xarray with xgcm to run grid-aware (e.g., C-grid) flux-form diagnostics safely

By delegating staggered-grid bookkeeping and metric terms (dx, dy, dz, areas) to xgcm.Grid, you can compute vorticity/divergence/transports more robustly and with fewer silent sign/offset bugs.

code:python

import xarray as xr

from xgcm import Grid

ds = ds.chunk({"time": 30, "y": 800, "x": 800})

grid = Grid(

ds,

coords={

"X": {"center": "x", "left": "x_u"},

"Y": {"center": "y", "left": "y_v"},

"Z": {"center": "z", "left": "z_w"},

},

metrics={

},

periodic=False,

)

# Example: horizontal divergence (conceptual; adapt names/coords to your dataset)

See also