Why is there only 2 years of data for net_sfc_heating in ACCESS-OM2 RYF9091 runs?

sb4233 · 26 March 2025 01:34

I am looking at the output variables from access_01deg_jra55v13_ryf9091 runs for a heat budget analysis and I am accessing the data using cosima-cookbook.

I noticed that net_sfc_heating has only ~2 years (1900-01-01 to 1902-10-01) of data available while other variables are available for a longer period of time.

I understand that the runs are essentially representing the same calendar year (i.e., if neglecting model internal variability) but is there a specific reason for the limited output for net_sfc_heating?

dougiesquire · 26 March 2025 11:17

According to ACCESS-OM2 Control Experiments I think @AndyHoggANU is responsible for that experiment. Possibly he can recall/help.

AndyHoggANU · 26 March 2025 21:26

Hi Sugata, I can’t remember specifically why we made those changes - that run was kicked off about 6 years ago - but I do recall that we don’t usually use net_sfc_heating as the heat flux diagnostic, as it’s inaccurate. If you look at this recipe:

github.com/COSIMA/cosima-recipes

Recipes/Surface_Water_Mass_Transformation.ipynb

main

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Surface Water-Mass Transformation\n",
    "\n",
    "In this recipe, we compute surface water-mass transformation rates (both in the net and partitioned into contributions from heat and salt fluxes) for the southern ocean, south of 60S.\n",
    "\n",
    "This recipe is model-agnostic. However, as diagnostics used for the surface water mass transformation calculation considerably differ between models. You should read the diagnostics in the `get_variables` function and add/remove according to your needs. An embedded dictionary automatically identifies the correct forcing, once the user points out which ocean model is being analyzed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Defining surface water-mass transformation\n",
    "\n",
    "The surface water-mass transformation framework described here follows [Newsom *et al* (2016)](https://journals.ametsoc.org/doi/full/10.1175/JCLI-D-15-0513.1) and [Abernathey *et al* (2016)](https://www.nature.com/articles/ngeo2749). Surface water-mass transformation may be defined as the volume flux into a given density class ($\\sigma$) from lighter density classes ($\\sigma'<\\sigma$) due to surface buoyancy forcing. Integrated over a region of the ocean surface, this volume flux can be expressed as,\n",

This file has been truncated. show original

You’ll see that we define heat flux in MOM5 as:

"heat_flux":             ["sfc_hflux_coupler", "sfc_hflux_from_runoff", "frazil_3d_int_z", "sfc_hflux_pme"]

That is, we add those 4 components. I would give that a go.

(Note that I tried to quickly test this with the intake catalog and had errors loading the data – @CharlesTurner might be able to cross-check if he gets the same errors?)

Also, I would consider using a newer run - possibly one of the IAF cycles - to look at this diagnostic (depending on what you need it for)?

sb4233 · 26 March 2025 22:07

Thank you!! this is very helpful.

I have been trying to look at the heat budget terms over Indonesian seas based on mom-ocean.github.io/assets/pdfs/ESM2M_heat_budget.pdf. Now that I checked this doc carefully it does mention net surface heat flux = radiative + turbulent + mass heat + frazil (and subsequent variable names in section 1.3.3).

The reason I didn’t use the IAF runs is because not all the variables, required for closing the heat budget, are saved. Just the main ones like temp_xflux_adv , temp_yflux_adv etc. Please let me know if I am wrong.

So I had no choice but to use the RYF runs, which has all the heat budget variables available.

Also just curious to know, why ‘temp_zflux_adv’ is not saved in any of the ACCESS runs?

AndyHoggANU · 27 March 2025 01:21

I think cycle4 might have some monthly temperature fluxes saved. Will try to check it out.

In the meantime, based on your questions it seems like we need a cosima-recipe for the heat budget calculation (I had a quick look, but I can’t see an existing recipe). Maybe @Hangyum has something he could post which might be a starting point?

Hangyum · 27 March 2025 23:21

Yes, I will start a recipe for the heat budget calculation.

CharlesTurner · 30 March 2025 23:15

Sorry Andy, only just looking at this now.

I’ve had a look at the ESM Datastore for the catalog, and it looks like for whatever reason, the model stopped writing net_sfc_heating out at that time step?

>>> import intake

>>> cat = intake.cat.access_nri
>>> esm_ds = cat['01deg_jra55v13_ryf9091']
>>> esm_ds.search(variable='net_sfc_heating').df.path

0    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output000/ocean/ocean_month.nc
1    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output001/ocean/ocean_month.nc
2    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output002/ocean/ocean_month.nc
3    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output003/ocean/ocean_month.nc
4    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output004/ocean/ocean_month.nc
5    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output005/ocean/ocean_month.nc
6    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output006/ocean/ocean_month.nc
7    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output007/ocean/ocean_month.nc
Name: path, dtype: object

# Did something funny happen with ocean_month files?

>>> esm_ds.search(file_id='ocean_month').df.path.head(20)

0     /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output000/ocean/ocean_month.nc
1     /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output001/ocean/ocean_month.nc
2     /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output002/ocean/ocean_month.nc
3     /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output003/ocean/ocean_month.nc
4     /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output004/ocean/ocean_month.nc
5     /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output005/ocean/ocean_month.nc
6     /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output006/ocean/ocean_month.nc
7     /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output007/ocean/ocean_month.nc
8     /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output008/ocean/ocean_month.nc
9     /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output009/ocean/ocean_month.nc
10    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output010/ocean/ocean_month.nc
11    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output011/ocean/ocean_month.nc
12    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output012/ocean/ocean_month.nc
13    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output013/ocean/ocean_month.nc
14    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output014/ocean/ocean_month.nc
15    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output015/ocean/ocean_month.nc
16    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output016/ocean/ocean_month.nc
17    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output017/ocean/ocean_month.nc
18    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output018/ocean/ocean_month.nc
19    /g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output019/ocean/ocean_month.nc
Name: path, dtype: object

# Nope - so let's look at the files manually (ie. is this an intake problem)

>>> import xarray as xr

>>> ds1 = xr.open_dataset("/g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output007/ocean/ocean_month.nc")
>>> vars1 = [v for v in ds1.variables]
>>> ds2 = xr.open_dataset("/g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output008/ocean/ocean_month.nc")
>>> vars2 = [v for v in ds.variables]

>>> set(vars1) - set(vars2)
{'net_sfc_heating'}

So it looks like net_sfc_heating output was turned off to me?

I can load that data just fine though:

>>> from dask.distributed import Client
>>> client = Client(threads_per_worker=1)
>>> esm_ds.search(variable='net_sfc_heating').to_dask()
/g/data/xp65/public/apps/med_conda/envs/analysis3-25.02/lib/python3.11/site-packages/intake_esm/source.py:82: FutureWarning: In a future version of xarray decode_timedelta will default to False rather than None. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  ds = xr.open_dataset(url, **xarray_open_kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-25.02/lib/python3.11/site-packages/intake_esm/source.py:82: FutureWarning: In a future version of xarray decode_timedelta will default to False rather than None. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  ds = xr.open_dataset(url, **xarray_open_kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-25.02/lib/python3.11/site-packages/intake_esm/source.py:82: FutureWarning: In a future version of xarray decode_timedelta will default to False rather than None. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  ds = xr.open_dataset(url, **xarray_open_kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-25.02/lib/python3.11/site-packages/intake_esm/source.py:82: FutureWarning: In a future version of xarray decode_timedelta will default to False rather than None. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  ds = xr.open_dataset(url, **xarray_open_kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-25.02/lib/python3.11/site-packages/intake_esm/source.py:82: FutureWarning: In a future version of xarray decode_timedelta will default to False rather than None. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  ds = xr.open_dataset(url, **xarray_open_kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-25.02/lib/python3.11/site-packages/intake_esm/source.py:82: FutureWarning: In a future version of xarray decode_timedelta will default to False rather than None. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  ds = xr.open_dataset(url, **xarray_open_kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-25.02/lib/python3.11/site-packages/intake_esm/source.py:82: FutureWarning: In a future version of xarray decode_timedelta will default to False rather than None. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  ds = xr.open_dataset(url, **xarray_open_kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-25.02/lib/python3.11/site-packages/intake_esm/source.py:82: FutureWarning: In a future version of xarray decode_timedelta will default to False rather than None. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  ds = xr.open_dataset(url, **xarray_open_kwargs)
<xarray.Dataset> Size: 1GB
Dimensions:          (time: 33, yt_ocean: 2700, xt_ocean: 3600)
Coordinates:
  * xt_ocean         (xt_ocean) float64 29kB -279.9 -279.8 ... 79.85 79.95
  * yt_ocean         (yt_ocean) float64 22kB -81.11 -81.07 ... 89.94 89.98
  * time             (time) object 264B 1900-01-16 12:00:00 ... 1902-09-16 00...
Data variables:
    net_sfc_heating  (time, yt_ocean, xt_ocean) float32 1GB dask.array<chunksize=(1, 675, 900), meta=np.ndarray>
Attributes: (12/16)
    filename:                                 ocean_month.nc
    title:                                    ACCESS-OM2-01
    grid_type:                                mosaic
    grid_tile:                                1
    intake_esm_vars:                          ['net_sfc_heating']
    intake_esm_attrs:filename:                ocean_month.nc
    ...                                       ...
    intake_esm_attrs:variable_standard_name:  ,,,,,,sea_surface_height_above_...
    intake_esm_attrs:variable_cell_methods:   ,,,,,,time: mean,time: mean,tim...
    intake_esm_attrs:variable_units:          degrees_E,degrees_N,days since ...
    intake_esm_attrs:realm:                   ocean
    intake_esm_attrs:_data_format_:           netcdf
    intake_esm_dataset_key:                   ocean_month.1mon

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Why is there only 2 years of data for net_sfc_heating in ACCESS-OM2 RYF9091 runs?

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