add_TCO_60S60N to default lat-lon variables

[chengzhuzhang]

Description

• Closes [Bug]: lat-lon TCO calculations use inconsistent areas #878

Checklist

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• My changes generate no new warnings
• Any dependent changes have been merged and published in downstream modules

If applicable:

• New and existing unit tests pass with my changes (locally and CI/CD build)
• I have added tests that prove my fix is effective or that my feature works
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• I have noted that this is a breaking change for a major release (fix or feature that would cause existing functionality to not work as expected)

[chengzhuzhang]

@tangq based on our discussion, I added a new entry to the default lat-lon variable configuration file. This way both global TCO and 60S60N TCO that is consistent to observation record are created. Here is an example run. https://web.lcrc.anl.gov/public/e3sm/diagnostic_output/zhang40/tco/viewer/lat_lon/index.html Notice that all metrics from the diff plots matches except that mean is -2.67 vs -2.68. I suspect the difference was caused by the way how region was subset is slightly different. I'm leaving a note here and later I can check if xarray/xcdat version of e3sm_diags v3 will present the same issue.

[chengzhuzhang]

The CI/CD tests are having a bunch of Numpy 2.0 related error: e.g. https://github.com/E3SM-Project/e3sm_diags/actions/runs/11564567326/job/32189994750

I think the tests start to pick up numpy 2.0.2. and cdms3.1.5 build 23 (py39h4a6e4dc_23). @xylar there seem to be remaining issues with numpy 2 compatibility for cdms..

[xylar]

@chengzhuzhang, okay, please explicitly constrain to numpy<2.0 for now. Hopefully, the logs will persistent long enough for me to use them for more patching. (What fun!)

[xylar]

All errors seem to be the same and from a single line in cdms2. Conda-forge (via Azure) isn't able to build packages right now but I will patch and make a new build as soon as Azure is back up.

[xylar]

conda-forge/cdms2-feedstock#96

[xylar]

I ran the e3sm_diags pytest locally with a local build of the patched cdms2 package and it passed, so I think we'll be good once I can get that one little patch in.

[xylar]

We should be able to try CI again here in an hour or so to bring in a version of cdms2 with the remaining numpy 2.0 fix.

[tangq]

@chengzhuzhang , the figures look good. The changes are straightforward - Adding TCO_60S60N as a new variable. I wouldn't worry too much about the small difference in averages (-2.67 vs -2.68).

[xylar]

Shoot! Clearly my local testing didn't catch all the numpy 2.0 issues. I'll try to figure out why and keep patching.

[chengzhuzhang]

@xylar thank you for taking on this task!! And good thing that these CI/CD testing are in place to help inform the health of the patched builds. ..

[chengzhuzhang]

@chengzhuzhang , the figures look good. The changes are straightforward - Adding TCO_60S60N as a new variable. I wouldn't worry too much about the small difference in averages (-2.67 vs -2.68).

Thank you for reviewing, @tangq !

[xylar]

@chengzhuzhang, I'm now able to reproduce the numpy errors locally. I'm re-testing with yet another patched cdms2 (conda-forge/cdms2-feedstock#97). I don't want to have to do another cycle so I will keep working locally until tests pass. Sorry for all the noise!

[chengzhuzhang]

@chengzhuzhang, I'm now able to reproduce the numpy errors locally. I'm re-testing with yet another patched cdms2 (conda-forge/cdms2-feedstock#97). I don't want to have to do another cycle so I will keep working locally until tests pass. Sorry for all the noise!

No worries! Hope there aren't too many troubles patching.

[xylar]

@chengzhuzhang, we're finally good here, regarding numpy 2.0. I wouldn't be surprised if more thorough e3sm_diags or other testing reveals some edge cases, though. Keep me posted.

[chengzhuzhang]

@chengzhuzhang, we're finally good here, regarding numpy 2.0. I wouldn't be surprised if more thorough e3sm_diags or other testing reveals some edge cases, though. Keep me posted.

Awesome! Thank you. Will let you know if more cases are found.