A Documented Example on how to use `xgcm` with cosima output is wanted

[navidcy]

That's coming out from the discussion in Cosima workshop 2022.

cc @janjaapmeijer

[rbeucher]

Maybe relevant?

https://earth-env-data-science.github.io/lectures/working_with_gcm_data.html

[adele-morrison]

Agreed this would be good.

We currently have:

• a Regridding notebook using xesmf to do horizontal regridding. Though I've failed to get this to work when I've tried previously. Do we want both xesmf and xgcm horizontal regridding methods? Possibly we could also combine these with the Model Resolution Comparison notebook (i.e. interpolate the different model resolution grids onto each other so bias maps could be produced)? We could rename this notebook Horizontal Regridding perhaps, given that vertical regridding is dealt with in a separate notebook?
• a Binning Transformation from Depth to Potential Density notebook that (amongst other methods) uses xgcm to do vertical conservative regridding onto isopycnal layers. I think this is sufficient - no more needed here?

[aekiss]

@adele157 re. your first point, I thought xesmf and xgcm are designed for different purposes: xesmf for fast regridding between different grids, and xgcm for Arakawa-grid-aware calculations (div, grad, curl, etc) on a given grid, and also vertical coordinate transformation. As far as I can see xgcm doesn't do horizontal regridding (happy to be corrected, but even if it can I expect xesmf would be much faster at high resolution).

[navidcy]

xesmf actually regrids the variables and interpolates to some other grid of your liking

xgcm simply takes into account that some of your variables live on a slightly different locations on the same grid than others, e.g. u lives elsewhere from T, and it gives you the opportunity to compute quantities like ∂x( u T ) without you having to think which variable you need to interpolate first onto which grid before you take the product and how do you take the derivative and what not.

That is,

• xgcm does interpolate but only within cells to bring the variable from the u-grid to the t-grid and things like that....
• xesmf can be used to interpolate a 1/10 tripolar grid onto a 1/2 degree lat-log grid!

Is this clarifying enough?

[aekiss]

Yep, thanks, that's what I thought! So we need both xesmf and xgcm.

[navidcy]

I made a regridding one with xesmf at some point: let's revisit that --> https://cosima-recipes.readthedocs.io/en/latest/documented_examples/Regridding.html#gallery-documented-examples-regridding-ipynb

[aekiss]

Thanks, I've used xesmf a lot, but not xgcm. I'd just been confused by Adele's suggestion of xgcm for horizontal regridding, but it turns out that's not a thing.

[adele-morrison]

Sorry for confusion and thanks for clarification. I’ve only used xgcm before in the vertical (where it does do regridding) not the horizontal.

On Mon, Jan 23, 2023 at 12:18 PM, Andrew Kiss @.***> wrote:

Thanks, I've used xesmf a lot, but not xgcm. I'd just been confused by Adele's suggestion of xgcm for horizontal regridding, but it turns out that's not a thing.

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[Thomas-Moore-Creative]

[ waves ] Hi folks. I'm a 100% COSIMA Cookbook noob - with all of 1 hour under my belt.

I'm wondering if there is a CC "key" to pull in all the grid_spec details for a particular model configuration?

[navidcy]

like the grid specs that xgcm requires? no there isn't... it could be something that we add after tomorrow.

but there are few examples in the recipes that use xgcm; see, e.g., the relative vorticity example or the vertical coords transformation example.

[aekiss]

There have been some proposals to store grid info in the cookbook database, but none implemented as yet - e.g. see https://github.com/COSIMA/cosima-cookbook/issues/191 and https://github.com/COSIMA/cosima-recipes/issues/190

[navidcy]

xgcm now works with grid metrics (I tried a million times to make it work back in the day with no success)

folder = '/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ocean/'
grid = xr.open_mfdataset(folder + 'ocean_grid.nc', combine='by_coords')

ds = xr.merge([u, v, grid])
ds.coords['xt_ocean'].attrs.update(axis='X')
ds.coords['xu_ocean'].attrs.update(axis='X', c_grid_axis_shift=0.5)
ds.coords['yt_ocean'].attrs.update(axis='Y')
ds.coords['yu_ocean'].attrs.update(axis='Y', c_grid_axis_shift=0.5)

metrics = {
    ('X',): ['dxt', 'dxu'], # X distances
    ('Y',): ['dyt', 'dyu'], # Y distances
    ('X', 'Y'): ['area_t', 'area_u'] # Areas
}

grid = xgcm.Grid(ds, periodic=['X'], metrics=metrics)

ζ_xgcm = grid.interp(grid.derivative(v, 'X'), 'Y', boundary='extend') - grid.interp(grid.derivative(u, 'Y'), 'X')
ζ_xgcm

We need to update the relative vorticity example. Another job for the Hackathon v2.0.

[navidcy]

151 is definitely related here (if we'll update the relative vorticity example)