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Fix derivation for sea ice extent (siextent) #2648

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Description

The PR fixes some small issues with the derivation script for sea ice extent, i.e.

  • corrected unit
  • changed list of required variables so the script can be applied to both, CMIP5 and CMIP6 data
  • added/updated some comments

This fix is required for calculating and plotting a correct seasonal cycle of Arctic/Antarctic sea ice extent for REF: ESMValGroup/ESMValTool#3891

@@ -48,16 +49,19 @@ def calculate(cubes):
sic = cubes.extract_cube(Constraint(name="sic"))
except iris.exceptions.ConstraintMismatchError:
try:
sic = cubes.extract_cube(Constraint(name="siconca"))
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If I remember correctly, siconca was added because some models were missing siconc, but I am not sure if that is still the case.

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That is right. In any case, I think it does not hurt to also try "siconca", so I would prefer to keep this here if that's fine.

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Well in that case, it looks like it needs to be re-added because tests are failing due to siconca not being required anymore.

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I justed updated that part a bit: 0c8beb9
I cannot get this preprocessor working for CMIP5 data, though, when adding {"short_name": "siconca", "optional": "true"},. Alternatively, I can remove the "siconca" part. Any advice would be highly appreciated...

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@sloosvel sloosvel Feb 21, 2025

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I tested with the latest commit 1eaa6f0 loading CMIP5 sic data, CMIP6 sic data and CMIP6 data that only has siconca available and it worked finding the data that is needed for each project:

datasets:

        - {dataset: GISS-E2-1-H, grid: gr} # CMIP6 sic data 
        - {dataset: GISS-E2-1-H, exp: piControl, grid: gn, timerange: '3180/3180'} # CMIP6 siconca data
        - {dataset: GISS-E2-H-CC, project: CMIP5, ensemble: r1i1p1, mip: OImon} # CMIP5 sic data

diagnostics:

  test:
    variables:
      siextent:
        project: CMIP6
        mip: SImon
        timerange: '2000/2000'
        derive: true
        exp: historical
        ensemble: r1i1p1f1
    scripts: null

Let me know if it works for you

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Thank you for looking into this, @sloosvel! I tried this and I don't think we found the optimum solution yet. If I add a CMIP6 dataset that provides both, siconc and siconca (e.g. MPI-ESM1-2-LR), I run into a shape error, e.g.

ValueError: Chunks do not add up to shape. Got chunks=((96,), (192,)), shape=(220, 256)

Also, our current solution does not support to process any observationally-based data (e.g. projects OBS, OBS6, ana4mips, obs4MIPs, native5, etc.). Here are some examples for observationally-based datasets that I tried:

  - {dataset: ESACCI-SEAICE, project: OBS6, tier: 2, type: sat, version: L4-SICONC-RE-SSMI-12.5kmEASE2-fv3.0-NH,
     supplementary_variables: [{short_name: areacello, mip: Ofx}]}
  - {dataset: HadISST, project: OBS, tier: 2, type: reanaly, version: '1', mip: OImon}
  - {dataset: CFSR, project: ana4mips, tier: 1, type: reanalysis, mip: OImon}

So maybe checking for if project == 'CMIP6' or project == 'OBS6' is enough and a plain else for all other cases in the required function? But then, there is still the shape problem.

Do you have an idea what we could do? I didn't expect this to be so complicated...

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