Data available: 0.1° 1958-2018 ACCESS-OM2 IAF run

Announcement:

25TB of model output data from a 1958-2018 spinup run with COSIMA’s ACCESS-OM2-01 0.1-degree global coupled ocean – sea ice model is now available for anyone to use (see conditions below) on this path at NCI:

/g/data/ik11/outputs/access-om2-01/01deg_jra55v140_iaf

You will need to be a member of the ik11 group for access – apply at https://my.nci.org.au/mancini/project-search if needed.

The 01deg_jra55v140_iaf spinup was run under interannually-varying JRA55-do v1.4.0 forcing from 1 Jan 1958 to 31 Dec 2018, starting from rest with World Ocean Atlas 2013 v2 climatological temperature and salinity. The run configuration is based on that used for Kiss et al. (2020) but has many improvements which will be documented soon.

There are many outputs available for the entire run, with additional outputs also enabled for the later years (see below for details).
MOM5 ocean model outputs are saved under self-explanatory filenames in
/g/data/ik11/outputs/access-om2-01/01deg_jra55v140_iaf/output*/ocean/*.nc
and CICE5 outputs are in
/g/data/ik11/outputs/access-om2-01/01deg_jra55v140_iaf/output*/ice/OUTPUT/*.nc
(there are too many files to list with ls, so narrow it down, e.g. by including the year, e.g. *2000*.nc)
We recommend using the COSIMA Cookbook to access and analyse this data: https://github.com/COSIMA/cosima-cookbook.

In addition, from 1 Jan 1987 to 31 Dec 2018 we have daily-mean 3d temp, salt, u, v and wt data. However, this is currently stored at
/scratch/x77/aek156/access-om2/archive/01deg_jra55v140_iaf/output*/ocean/ocean-3d-*-1-daily*.nc
and not yet available on /g/data/ik11 (to access this you will need to be a member of project x77 – apply here). It amounts to 51TB and we are considering ways to reduce this storage requirement, for example by restricting the geographical or depth or time range, reducing numerical precision or vertical resolution, and/or averaging over longer time intervals. If you have an interest in this daily 3d data please let us know what form of data reduction is compatible with your needs.

Annual restarts (on 1 Jan each year) are also available at
/g/data/ik11/outputs/access-om2-01/01deg_jra55v140_iaf
for anyone who may wish to re-run a segment with different diagnostics or branch off a perturbation experiment.
Summary details of each submitted run are tabulated (and searchable) here and the model configuration used for the spinup is here.

Conditions of use:
We request that users of this or other COSIMA model code or output data:

    1. consider citing Kiss et al. (2020) [doi.org/10.5194/gmd-13-401-2020]
    2. include an acknowledgement such as the following:
      The authors thank the Consortium for Ocean-Sea Ice Modelling in Australia (COSIMA; www.cosima.org.au), for making the ACCESS-OM2 suite of models available at github.com/COSIMA/access-om2. Model runs were undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government.
    3. let us know of any publications which use these models or data so we can add them to our list

Details of model outputs available at /g/data/ik11/outputs/access-om2-01/01deg_jra55v140_iaf
You may find this partial list of diagnostics useful for decoding the MOM diagnostic names.

  • 1 Jan 1958 to 31 Dec 2018
    • MOM ocean data
      • Daily mean 2d bottom_temp, frazil_3d_int_z, mld, pme_river, sea_level, sfc_hflux_coupler, sfc_hflux_from_runoff, sfc_hflux_pme, surface_salt, surface_temp
      • Monthly mean 3d age_global, buoyfreq2_wt, diff_cbt_t, dzt, pot_rho_0, pot_rho_2, pot_temp, salt, temp_xflux_adv, temp_yflux_adv, temp, tx_trans, ty_trans_nrho_submeso, ty_trans_rho, ty_trans_submeso, ty_trans, u, v, vert_pv, wt
      • Monthly mean 2d bmf_u, bmf_v, ekman_we, eta_nonbouss, evap_heat, evap, fprec_melt_heat, fprec, frazil_3d_int_z, lprec, lw_heat, melt, mh_flux, mld, net_sfc_heating, pbot_t, pme_net, pme_river, river, runoff, sea_level_sq, sea_level, sens_heat, sfc_hflux_coupler, sfc_hflux_from_runoff, sfc_hflux_pme, sfc_salt_flux_coupler, sfc_salt_flux_ice, sfc_salt_flux_restore, surface_salt, surface_temp, swflx, tau_x, tau_y, temp_int_rhodz, temp_xflux_adv_int_z, temp_yflux_adv_int_z, tx_trans_int_z, ty_trans_int_z, wfiform, wfimelt
      • Monthly mean squared 3d u, v
      • Monthly max 2d mld
      • Monthly min 2d surface_temp
      • Daily snapshot scalar eta_global, ke_tot, pe_tot, rhoave, salt_global_ave, salt_surface_ave, temp_global_ave, temp_surface_ave, total_net_sfc_heating, total_ocean_evap_heat, total_ocean_evap, total_ocean_fprec_melt_heat, total_ocean_fprec, total_ocean_heat, total_ocean_hflux_coupler, total_ocean_hflux_evap, total_ocean_hflux_prec, total_ocean_lprec, total_ocean_lw_heat, total_ocean_melt, total_ocean_mh_flux, total_ocean_pme_river, total_ocean_river_heat, total_ocean_river, total_ocean_runoff_heat, total_ocean_runoff, total_ocean_salt, total_ocean_sens_heat, total_ocean_sfc_salt_flux_coupler, total_ocean_swflx_vis, total_ocean_swflx
    • CICE sea ice data
      • Daily mean 2d aice, congel, dvidtd, dvidtt, frazil, frzmlt, hi, hs, snoice, uvel, vvel
      • Monthly mean 2d aice, alvl, ardg, congel, daidtd, daidtt, divu, dvidtd, dvidtt, flatn_ai, fmeltt_ai, frazil, frzmlt, fsalt, fsalt_ai, hi, hs, iage, opening, shear, snoice, strairx, strairy, strength, tsfc, uvel, vvel
  • 1 Jan 1987 to 31 Dec 2018 only
    • MOM ocean data
      • monthly mean 3d bih_fric_u, bih_fric_v, u_dot_grad_vert_pv
      • daily mean 3d salt, temp, u, v, wt (not yet available on ik11 – see above)
    • CICE sea ice data
      • daily mean 2d aicen, vicen
  • 1 Jan 2012 to 31 Dec 2018 only
    • MOM ocean data
      • monthly snapshot 2d sea_level
      • monthly snapshot 3d salt, temp, u, v, vert_pv and vorticity_z

cheers
Andrew

SAMx paper submitted

The Southern Ocean has accounted for the vast majority of the global ocean heat uptake since the early 2000s. The atmospheric winds over the Southern Ocean play a leading role in its ability to uptake heat, by way of driving much of the Southern Ocean circulation. Observations of these winds indicate that they have been steadily changing over the past few decades, and hence, so too is the Southern Ocean heat uptake. However, despite recent research efforts, the details of the Southern Ocean’s response to these changing winds remain uncertain.

In a recently submitted paper, we introduce a novel methodology to examine the Southern Ocean’s response to changing winds. We perform numerical simulations with the COSIMA model suite at all three resolutions and driven by realistic atmospheric forcing conditions. Our novel approach requires us to characterise the dynamical differences between our various realistic forcing conditions, which we do so with a new simple diagnostic. This new diagnostic proves robust in predicting the rapid response of the Southern Ocean circulation to the changing winds.

Response of the Southern Ocean overturning circulation to extreme Southern Annular Mode conditions“; Stewart, Hogg, England & Waugh, Submitted to Geophysical Research Letters.

Figure: a) Three-monthly SAM index calculated from the JRA55-do dataset; solid bars indicate the Austral summer quarters (September-February, inclusive). The four SAMx periods are shaded, as well as the RYF9091. For comparison, the equivalent observation station-based SAM index of Marshall (2003) is included in black. b) Zonal average zonal winds for the SAMx and RYF9091 periods, with the location of the respective peaks indicated by the circles. Also included is the 1980–2010 average of the JRA55-do dataset (dashed cyan), and all remaining May–April periods between 1980–2015 coloured blue–red by their respective SAM index. c) Timeseries of the zonal average temperatures between 200–500m depth for 45–50S (upper) and 55–60S (lower); these latitudinal bands are selected to lie either side of the zonal wind speed maximum evident in (b). Distributions of the Year 5 temperature anomalies from the ACCESS-OM2-01 for (d) SAM+9899, (e) SAM+1011, (f) SAM-9192 and (g) SAM-0203 simulations. The black and dashed green contours represent the SAMx and RYF9091 isopycnals at 0.5kg/m3 intervals, respectively, with the cyan and dashed magenta contours representing the respective mixed-layer depths.

Updated COSIMA Cookbook default database

The COSIMA Cookbook is the recommended, and supported, method for finding and accessing COSIMA datasets.

Currently COSIMA datasets are located in temporary storage under the hh5 project on the /g/data filesystem at NCI. The default COSIMA Cookbook database (/g/data/hh5/tmp/cosima/database/access-om2.db) indexes data in this location.

The COSIMA datasets are being moved to a new project, ik11: dedicated storage provided by an ARC LIEF grant. As part of this transition the default database will change to:

/g/data/ik11/databases/cosima_master.db

and will index all data in /g/data/ik11/outputs/. The database is updated daily.

This change will take place from Wednesday the 1st of July. To access the old database pass an argument to create_session:

session = cc.database.create_session(db='/g/data/hh5/tmp/cosima/database/access-om2.db')

or set the COSIMA_COOKBOOK_DB environment variable, e.g. for bash

export set COSIMA_COOKBOOK_DB=/g/data/hh5/tmp/cosima/database/access-om2.db

In the same way the new ik11 database can be accessed by using the path to it (/g/data/ik11/databases/cosima_master.db) in the same manner as above.