add H2OLNZ in zonal mean 2d stratosphere

[chengzhuzhang]

Description

• Closes #867

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 This PR add H2OLNZ to pressure latitude plots focusing stratosphere. Here is an example testing with v3_historical_0051. Let me know if you'd like further change.

[chengzhuzhang]

@tangq Thank you for reviewing. For quickly check other v3.historical runs. You could try source my build of e3sm_diags on LCRC with the command:

source /home/ac.zhang40/y/etc/profile.d/conda.sh; conda activate edv290

The command to generate the H2OLNZ plots is:

e3sm_diags zonal_mean_2d_stratosphere --no_viewer --reference_data_path '/lcrc/group/e3sm/diagnostics/observations/Atm/climatology' --test_data_path '/lcrc/group/e3sm2/ac.zhang40/E3SMv3/v3.LR.historical_0618/post/atm/180x360_aave/clim/30yr' --results_dir '/lcrc/group/acme/public_html/diagnostic_output/zhang40/h2olnz_only_1' --case_id 'ERA5' --run_type 'model_vs_obs' --sets 'zonal_mean_2d_stratosphere' --variables 'H2OLNZ' --seasons 'ANN' --regions 'global' --regrid_tool 'esmf' --regrid_method 'conservative' --plevs '1.0' '1.6681005372000588' '2.7825594022071245' '4.641588833612778' '7.742636826811269' '12.91549665014884' '21.544346900318832' '35.93813663804626' '59.94842503189409' '100.0' --plot_log_plevs --multiprocessing --num_workers '1' --main_title 'H2OLNZ ANN' --backend 'mpl' --output_format 'png' --canvas_size_w '1212' --canvas_size_h '1628' --figsize '8.5' '11.0' --dpi '150' --arrows --contour_levels '0.001' '0.0015' '0.002' '0.0025' '0.003' '0.0035' '0.004' '0.0045' '0.005' --test_name 'v3.LR.historical_0051' --test_colormap 'cet_rainbow.rgb' --ref_name 'ERA5' --reference_name 'ERA5 Reanalysis' --reference_colormap 'cet_rainbow.rgb' --diff_title 'Model - Observations' --diff_colormap 'diverging_bwr.rgb' --diff_levels '-0.001' '-0.00075' '-0.0005' '-0.00025' '-5e-05' '5e-05' '0.00025' '0.0005' '0.00075' '0.001' --granulate 'variables' 'seasons' 'regions' --selectors 'sets' 'seasons'

Just to replace the test_data_path to where the data is stored, and the result_dir to your own path. You could adjust the contour levels to fine tune for the paper.

Alternatively, since I already have the environment set up. I can quickly generate the same set for the v3 runs of interests, just let me know.

[chengzhuzhang]

The CI/CD tests passed after LCRC maintenance yesterday. Merging.

[tangq]

Hi @chengzhuzhang , I didn't catch this earlier. The H2OLNZ plot and the Q plot use different units. This makes it harder to compare them.

Can we change it to the same unit? I'd suggest that we change it all to match the Q plot, i.e., ppm and absolute difference. Thanks.

[chengzhuzhang]

@tangq, Just to clarify for the stratosphere focused plots, you'd like to use ppm for both H2OLNZ and Q. In the codes we have:https://github.com/E3SM-Project/e3sm_diags/blob/6b0a24575d3dc5ad76a06952f85ebb188f9d8f20/e3sm_diags/driver/zonal_mean_2d_driver.py#L162-L173

If it is the case, I can add the logic to convert H2OLNZ for zonal_mean_2d_stratosphere set, in addition to Q.

[tangq]

@chengzhuzhang , that's right. I suggest we use ppm by volumn for these plots.

Is my understanding correct that the code you pointed out multiplies Q (in g/kg) by 1000 to get Q in ppm? If so, it is ppm by weight. We need to add the molecular weight ratio to get the ppm by volume, which is the typical unit in this case.

If mv1_p is in g/kg, mv1_p = mv1_p * 28.97 / 18.0 * 1000.0 converts the unit to ppm by volume.