# Why is there only 2 years of data for net\_sfc\_heating in ACCESS-OM2 RYF9091 runs? **URL:** \ **Category:** Experiments\ **Tags:** python, help, cosima\ **Created:** [26 March 2025 01:34 UTC](https://forum.access-hive.org.au/t/why-is-there-only-2-years-of-data-for-net-sfc-heating-in-access-om2-ryf9091-runs/4350 "2025-03-26T01:34:04Z")\ **Posts on this page:** 7\ **Page:** 1
**Author:** ![sb4233](https://avatars.discourse-cdn.com/v4/letter/s/85f322/32.png) [@sb4233](https://forum.access-hive.org.au/u/sb4233)\ **Post date:** [26 March 2025 01:34 UTC](https://forum.access-hive.org.au/t/why-is-there-only-2-years-of-data-for-net-sfc-heating-in-access-om2-ryf9091-runs/4350/1 "2025-03-26T01:34:04Z")
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`? ![image](https://us1.discourse-cdn.com/flex020/uploads/access1/original/2X/2/237cdabeae18e92bd9b4a349c86508b9e998ea75.png) ---
**Author:** ![dougiesquire](https://sea2.discourse-cdn.com/flex020/user_avatar/forum.access-hive.org.au/dougiesquire/32/16_2.png) [@dougiesquire](https://forum.access-hive.org.au/u/dougiesquire)\ **Post date:** [26 March 2025 11:17 UTC](https://forum.access-hive.org.au/t/why-is-there-only-2-years-of-data-for-net-sfc-heating-in-access-om2-ryf9091-runs/4350/2 "2025-03-26T11:17:29Z")
According to [ACCESS-OM2 Control Experiments](https://forum.access-hive.org.au/t/access-om2-control-experiments/258) I think @AndyHoggANU is responsible for that experiment. Possibly he can recall/help. ---
**Author:** ![AndyHoggANU](https://sea2.discourse-cdn.com/flex020/user_avatar/forum.access-hive.org.au/andyhogganu/32/43_2.png) [@AndyHoggANU](https://forum.access-hive.org.au/u/AndyHoggANU)\ **Post date:** [26 March 2025 21:26 UTC](https://forum.access-hive.org.au/t/why-is-there-only-2-years-of-data-for-net-sfc-heating-in-access-om2-ryf9091-runs/4350/3 "2025-03-26T21:26:22Z")
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: > You’ll see that we define heat flux in MOM5 as: ```auto "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)? ---
**Author:** ![sb4233](https://avatars.discourse-cdn.com/v4/letter/s/85f322/32.png) [@sb4233](https://forum.access-hive.org.au/u/sb4233)\ **Post date:** [26 March 2025 22:07 UTC](https://forum.access-hive.org.au/t/why-is-there-only-2-years-of-data-for-net-sfc-heating-in-access-om2-ryf9091-runs/4350/4 "2025-03-26T22:07:19Z")
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](https://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? ---
**Author:** ![AndyHoggANU](https://sea2.discourse-cdn.com/flex020/user_avatar/forum.access-hive.org.au/andyhogganu/32/43_2.png) [@AndyHoggANU](https://forum.access-hive.org.au/u/AndyHoggANU)\ **Post date:** [27 March 2025 01:21 UTC](https://forum.access-hive.org.au/t/why-is-there-only-2-years-of-data-for-net-sfc-heating-in-access-om2-ryf9091-runs/4350/5 "2025-03-27T01:21:08Z")
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? ---
**Author:** ![Hangyum](https://sea2.discourse-cdn.com/flex020/user_avatar/forum.access-hive.org.au/hangyum/32/561_2.png) [@Hangyum](https://forum.access-hive.org.au/u/Hangyum)\ **Post date:** [27 March 2025 23:21 UTC](https://forum.access-hive.org.au/t/why-is-there-only-2-years-of-data-for-net-sfc-heating-in-access-om2-ryf9091-runs/4350/6 "2025-03-27T23:21:24Z")
Yes, I will start a recipe for the heat budget calculation. ---
**Author:** ![CharlesTurner](https://sea2.discourse-cdn.com/flex020/user_avatar/forum.access-hive.org.au/charlesturner/32/2518_2.png) [@CharlesTurner](https://forum.access-hive.org.au/u/CharlesTurner)\ **Post date:** [30 March 2025 23:15 UTC](https://forum.access-hive.org.au/t/why-is-there-only-2-years-of-data-for-net-sfc-heating-in-access-om2-ryf9091-runs/4350/7 "2025-03-30T23:15:30Z")
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? ```python >>> 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: ```python >>> 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) 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 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 ```