From b4cc3b5374c2e55df3fe35f57ff6e3ea8229ee39 Mon Sep 17 00:00:00 2001 From: minghangli-uni <24727729+minghangli-uni@users.noreply.github.com> Date: Tue, 9 Sep 2025 08:57:42 +1000 Subject: [PATCH 1/2] Convert Barotropic_Streamfunction.ipynb to use MOM6 only Co-authored-by: Ed Doddridge --- 02-Appetisers/Barotropic_Streamfunction.ipynb | 1462 ++++++----------- 1 file changed, 534 insertions(+), 928 deletions(-) diff --git a/02-Appetisers/Barotropic_Streamfunction.ipynb b/02-Appetisers/Barotropic_Streamfunction.ipynb index 773341e6..1db32156 100644 --- a/02-Appetisers/Barotropic_Streamfunction.ipynb +++ b/02-Appetisers/Barotropic_Streamfunction.ipynb @@ -2,12 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "14b764a5-2679-43e1-9f37-04f781fa01d1", - "metadata": { - "tags": [] - }, + "id": "b5853c5b-5a7c-4c54-baaa-1e6848125fb4", + "metadata": {}, "source": [ - "# Barotropic Streamfunction\n", + "# Barotropic Streamfunction (MOM6-only)\n", + " \n", + "This recipe demonstrates how to compute and plot the barotropic streamfunction ($\\psi$) from MOM6 output.\n", + "\n", + "The workflow is,\n", + "1. Load the MOM6 depth-integrated zonal mass transport (`umo_2d`).\n", + "2. Convert it to volume transport in Sverdrups (`Sv`).\n", + "3. Integrate meridionally (cumulative sum along latitude) to obtain the barotropic streamfunction ($\\psi$).\n", + "4. Plot a circumpolar map with a circular boundary and land mask.\n", + "\n", + "## Physical background\n", "\n", "The barotropic streamfunction ($\\psi$) is obtained from the integration of the velocity field starting from a physical boundary at which we know the transport is zero. The difference between to streamlines is a measure of the transport between them. \n", "\n", @@ -17,47 +25,54 @@ "\\psi = \\int_{y_{\\rm Antarctica}}^{y} U \\, \\mathrm{d}y ,\n", "$$\n", "\n", - "where $U = \\int u \\, \\mathrm{dz}$ is the depth-integrated $u$-velocity." + "where $U = \\int u \\, \\mathrm{dz}$ is the depth-integrated $u$-velocity.\n", + "\n", + "### MOM6 settings\n", + "\n", + "```python\n", + "expt = \"panant-01-zstar-v13\"\n", + "transport_var = \"umo_2d\" # zonal mass transport\n", + "bathy_var = \"deptho\" # bathymetry\n", + "```\n", + "with,\n", + "- coordinates: `yh` (latitude) and `xh` (longitude)\n", + "- land mask: `deptho` is NaN over land.\n", + "\n", + "### MOM5 settings\n", + "To adapt this recipe for MOM5 output, update the variables as follows,\n", + "```python\n", + "expt = \"01deg_jra55v13_ryf9091\"\n", + "transport_var = \"tx_trans_int_z\" # zonal mass transport\n", + "bathy_var = \"ht\" # bathymetry\n", + "```\n", + "with,\n", + "- coordinates: `yt_ocean` (latitude) and `xt_ocean` (longitude)\n", + "- land mask: `ht` is NaN over land." ] }, { "cell_type": "code", - "execution_count": 40, - "id": "577aa03f-c391-4009-a759-5dd361c83c09", + "execution_count": 1, + "id": "73aeb9e5-5d66-40b5-8cb7-4fd3f62b3fc5", "metadata": {}, "outputs": [], "source": [ + "import warnings\n", "import numpy as np\n", "import xarray as xr\n", - "\n", - "import cf_xarray as cfxr\n", - "import cf_xarray.units\n", - "import pint_xarray\n", - "from pint import application_registry as ureg\n", - "\n", "import matplotlib.path as mpath\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import cmocean\n", - "\n", "import dask.distributed as dask\n", - "import warnings\n", "import intake\n", - "warnings.simplefilter(action = 'ignore', category = UserWarning)" - ] - }, - { - "cell_type": "markdown", - "id": "cf8f3cd7-1add-41bb-91c8-12fae6376e07", - "metadata": {}, - "source": [ - "Initialise a dask client" + "warnings.simplefilter(action=\"ignore\", category=UserWarning)" ] }, { "cell_type": "code", - "execution_count": 41, - "id": "2f25a245-9737-4444-8c53-0b91ec9a5c06", + "execution_count": 2, + "id": "61285a8a-1b9e-4021-a92d-8bd7b5d00cc9", "metadata": {}, "outputs": [ { @@ -67,7 +82,7 @@ "
\n", "
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Client

\n", - "

Client-0f9aeeb7-6e76-11f0-b572-000003affe80

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Client-2abf206f-974a-11f0-bfe0-000003d9fe80

\n", " \n", "\n", " \n", @@ -80,7 +95,7 @@ " \n", " \n", " \n", " \n", " \n", @@ -89,7 +104,7 @@ "
\n", - " Dashboard: /proxy/46589/status\n", + " Dashboard: /proxy/8787/status\n", "
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LocalCluster

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f73d5de6

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Scheduler

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Scheduler-b86aeabc-5fde-4ad7-bcbf-d2b008f45ce9

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Scheduler-ec7c6d58-290b-4e48-ac60-91801db0c43c

\n", "
\n", - " Dashboard: /proxy/46589/status\n", + " Dashboard: /proxy/8787/status\n", " \n", - " Workers: 7\n", + " Workers: 2\n", "
\n", - " Total threads: 7\n", + " Total threads: 2\n", " \n", - " Total memory: 32.00 GiB\n", + " Total memory: 9.00 GiB\n", "
\n", " \n", " \n", " \n", " \n", " \n", "
\n", - " Comm: tcp://127.0.0.1:38291\n", + " Comm: tcp://127.0.0.1:34303\n", " \n", " Workers: 0 \n", @@ -151,7 +166,7 @@ "
\n", - " Dashboard: /proxy/46589/status\n", + " Dashboard: /proxy/8787/status\n", " \n", " Total threads: 0\n", @@ -185,7 +200,7 @@ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -230,187 +245,7 @@ "
\n", - " Comm: tcp://127.0.0.1:40935\n", + " Comm: tcp://127.0.0.1:46023\n", " \n", " Total threads: 1\n", @@ -193,21 +208,21 @@ "
\n", - " Dashboard: /proxy/44323/status\n", + " Dashboard: /proxy/45657/status\n", " \n", - " Memory: 4.57 GiB\n", + " Memory: 4.50 GiB\n", "
\n", - " Nanny: tcp://127.0.0.1:35041\n", + " Nanny: tcp://127.0.0.1:41641\n", "
\n", - " Local directory: /jobfs/146290599.gadi-pbs/dask-scratch-space/worker-9xelmbx5\n", + " Local directory: /jobfs/150387427.gadi-pbs/dask-scratch-space/worker-8lfujkzp\n", "
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\n", - " Comm: tcp://127.0.0.1:40989\n", - " \n", - " Total threads: 1\n", - "
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\n", - " Nanny: tcp://127.0.0.1:39225\n", - "
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\n", - " Comm: tcp://127.0.0.1:44025\n", + " Comm: tcp://127.0.0.1:41553\n", " \n", " Total threads: 1\n", @@ -418,66 +253,21 @@ "
\n", - " Dashboard: /proxy/44993/status\n", + " Dashboard: /proxy/37601/status\n", " \n", - " Memory: 4.57 GiB\n", + " Memory: 4.50 GiB\n", "
\n", - " Nanny: tcp://127.0.0.1:41777\n", + " Nanny: tcp://127.0.0.1:35279\n", "
\n", - " Local directory: /jobfs/146290599.gadi-pbs/dask-scratch-space/worker-z1go0tk3\n", - "
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Worker: 6

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" \n", + " \n", " \n", " \n", "
\n", - " Comm: tcp://127.0.0.1:41625\n", - " \n", - " Total threads: 1\n", - "
\n", - " Dashboard: /proxy/42361/status\n", - " \n", - " Memory: 4.57 GiB\n", - "
\n", - " Nanny: tcp://127.0.0.1:37725\n", - "
\n", - " Local directory: /jobfs/146290599.gadi-pbs/dask-scratch-space/worker-yktmp_zs\n", + " Local directory: /jobfs/150387427.gadi-pbs/dask-scratch-space/worker-w_ha264x\n", "
01deg_jra55_ryf_Control{ACCESS-OM2-01}{0.1° ACCESS-OM2 repeat year forcing control run for the simulations performed in Huguenin et al. (2024, GRL)}{seaIce, ocean}{ocean, seaIce}{1mon, fx}{mlt_onset_m, vvel_m, average_T1, total_ocean_hflux_evap, u, total_volume_seawater, geolon_c, Tsfc_m, hi_m, mass_pmepr_on_nrho, uarea, ty_trans_nrho, blkmask, total_ocean_fprec, frazil_3d_int_z, t...{geolon_t, sss_m, total_ocean_sens_heat, aice_m, temp_advection, neutral, strength_m, fswup_m, temp_eta_smooth_on_nrho, time, time_bnds, scalar_axis, evap, strairx_m, total_net_sfc_heating, eta_gl...
01deg_jra55_ryf_ENFull{ACCESS-OM2}{0.1° ACCESS-OM2 El Níño run for the simulations performed in Huguenin et al. (2024, GRL)}{seaIce, ocean}{ocean, seaIce}{1mon, fx}{mlt_onset_m, vvel_m, average_T1, total_ocean_hflux_evap, u, total_volume_seawater, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, time, wt, area_u, sfc_hflux_from_run...{geolon_t, sss_m, total_ocean_sens_heat, aice_m, temp_advection, neutral, strength_m, fswup_m, time, time_bnds, scalar_axis, evap, strairx_m, total_net_sfc_heating, eta_global, sw_edges_ocean, pot...
01deg_jra55_ryf_LNFull{ACCESS-OM2}{0.1° ACCESS-OM2 La Níña run for the simulations performed in Huguenin et al. (2024, GRL)}{seaIce, ocean}{ocean, seaIce}{1mon, fx}{mlt_onset_m, vvel_m, average_T1, total_ocean_hflux_evap, u, total_volume_seawater, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, time, wt, area_u, sfc_hflux_from_run...{geolon_t, sss_m, total_ocean_sens_heat, aice_m, temp_advection, neutral, strength_m, fswup_m, time, time_bnds, scalar_axis, evap, strairx_m, total_net_sfc_heating, eta_global, sw_edges_ocean, pot...
01deg_jra55v13_ryf9091{ACCESS-OM2-01}{0.1 degree ACCESS-OM2 global model configuration with JRA55-do v1.3 RYF9091 repeat year forcing (May 1990 to Apr 1991)}{seaIce, ocean}{1day, fx, 3mon, 3hr, 1mon}{mlt_onset_m, wfimelt, vvel_m, average_T1, total_ocean_hflux_evap, u, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, surface_temp, time, pbot_t, fprec, wt, area_u, sfc...{ocean, seaIce}{1mon, 3hr, 1day, 3mon, fx}{yu_ocean_sub02, geolon_t, sss_m, total_ocean_sens_heat, aice_m, temp_advection, neutral, strength_m, fswup_m, rho, time, yt_ocean_sub01, scalar_axis, evap, strairx_m, total_net_sfc_heating, eta_g...
01deg_jra55v13_ryf9091_easterlies_down10{ACCESS-OM2-01}{0.1 degree ACCESS-OM2 global model configuration with JRA55-do v1.3 RYF9091 repeat year forcing (May 1990 to Apr 1991) and zonal/meridional wind speed around Antarctica decreased by 10%.}{seaIce, ocean}{1day, 1mon, fx}{mlt_onset_m, wfimelt, vvel_m, average_T1, total_ocean_hflux_evap, u, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, surface_temp, time, pbot_t, fprec, wt, area_u, sfc...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sss_m, total_ocean_sens_heat, aice_m, neutral, strength_m, fswup_m, time, scalar_axis, evap, strairx_m, eta_global, sw_edges_ocean, potrho, pot_rho_0, TLON, total_ocean_river, average_T...
01deg_jra55v13_ryf9091_easterlies_up10{ACCESS-OM2-01}{0.1 degree ACCESS-OM2 global model configuration with JRA55-do v1.3 RYF9091 repeat year forcing (May 1990 to Apr 1991) and zonal/meridional wind speed around Antarctica increased by 10%.}{seaIce, ocean}{1day, 1mon, fx}{mlt_onset_m, wfimelt, vvel_m, average_T1, total_ocean_hflux_evap, u, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, surface_temp, time, pbot_t, fprec, wt, area_u, sfc...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sss_m, total_ocean_sens_heat, aice_m, neutral, strength_m, fswup_m, time, scalar_axis, evap, strairx_m, eta_global, sw_edges_ocean, potrho, pot_rho_0, TLON, total_ocean_river, average_T...
01deg_jra55v13_ryf9091_easterlies_up10_meridional{ACCESS-OM2-01}{0.1 degree ACCESS-OM2 global model configuration with JRA55-do v1.3 RYF9091 repeat year forcing (May 1990 to Apr 1991) and meridional wind speed around Antarctica increased by 10%.}{seaIce, ocean}{1day, 1mon, fx}{mlt_onset_m, wfimelt, vvel_m, average_T1, total_ocean_hflux_evap, u, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, surface_temp, time, pbot_t, fprec, wt, area_u, sfc...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sss_m, total_ocean_sens_heat, aice_m, neutral, strength_m, fswup_m, time, scalar_axis, evap, strairx_m, eta_global, sw_edges_ocean, potrho, pot_rho_0, TLON, total_ocean_river, average_T...
01deg_jra55v13_ryf9091_easterlies_up10_zonal{ACCESS-OM2-01}{0.1 degree ACCESS-OM2 global model configuration with JRA55-do v1.3 RYF9091 repeat year forcing (May 1990 to Apr 1991) and zonal wind speed around Antarctica increased by 10%.}{seaIce, ocean}{1day, 1mon, fx}{mlt_onset_m, wfimelt, vvel_m, average_T1, total_ocean_hflux_evap, u, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, surface_temp, time, pbot_t, fprec, wt, area_u, sfc...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sss_m, total_ocean_sens_heat, aice_m, neutral, strength_m, fswup_m, time, scalar_axis, evap, strairx_m, eta_global, sw_edges_ocean, potrho, pot_rho_0, TLON, total_ocean_river, average_T...
01deg_jra55v13_ryf9091_qian_wthmp{ACCESS-OM2}{Future perturbations with wind, thermal and meltwater forcing, branching off 01deg_jra55v13_ryf9091, as described in Li et al. 2023, https://www.nature.com/articles/s41586-023-05762-w}{seaIce, ocean}{ocean, seaIce}{1mon, fx}{mlt_onset_m, wfimelt, vvel_m, average_T1, total_ocean_hflux_evap, u, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, surface_temp, time, pbot_t, fprec, wt, area_u, sfc...{geolon_t, sss_m, total_ocean_sens_heat, aice_m, temp_advection, strength_m, fswup_m, time, scalar_axis, evap, strairx_m, eta_global, sw_edges_ocean, potrho, pot_rho_0, TLON, total_ocean_river, av...
01deg_jra55v13_ryf9091_qian_wthp{ACCESS-OM2}{Future perturbation with wind and thermal forcing, branching off 01deg_jra55v13_ryf9091, as described in Li et al. 2023, https://www.nature.com/articles/s41586-023-05762-w}{seaIce, ocean}{ocean, seaIce}{1mon, fx}{mlt_onset_m, wfimelt, vvel_m, average_T1, total_ocean_hflux_evap, u, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, surface_temp, time, pbot_t, fprec, wt, area_u, sfc...{geolon_t, sss_m, total_ocean_sens_heat, aice_m, temp_advection, strength_m, fswup_m, time, scalar_axis, evap, strairx_m, eta_global, sw_edges_ocean, potrho, pot_rho_0, TLON, total_ocean_river, av...
01deg_jra55v13_ryf9091_weddell_down2{ACCESS-OM2-01}{Weddell Sea decreased meltwater perturbation experiment, branched off 01deg_jra55v13_ryf9091. }{seaIce, ocean}{1day, 1mon, fx}{mlt_onset_m, wfimelt, vvel_m, average_T1, total_ocean_hflux_evap, u, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, surface_temp, time, pbot_t, fprec, wt, area_u, sfc...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sss_m, total_ocean_sens_heat, aice_m, neutral, strength_m, fswup_m, time, scalar_axis, evap, strairx_m, eta_global, sw_edges_ocean, potrho, pot_rho_0, TLON, total_ocean_river, average_T...
01deg_jra55v13_ryf9091_weddell_up1{ACCESS-OM2-01}{Weddell Sea increased meltwater perturbation experiment, branched off 01deg_jra55v13_ryf9091. }{seaIce, ocean}{1day, 1mon, fx}{mlt_onset_m, wfimelt, vvel_m, average_T1, total_ocean_hflux_evap, u, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, surface_temp, time, pbot_t, fprec, wt, area_u, sfc...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sss_m, total_ocean_sens_heat, aice_m, neutral, strength_m, fswup_m, time, scalar_axis, evap, strairx_m, eta_global, sw_edges_ocean, potrho, pot_rho_0, TLON, total_ocean_river, average_T...
01deg_jra55v140_iaf{ACCESS-OM2-01}{Cycle 1 of 0.1 degree ACCESS-OM2 global model configuration with JRA55-do v1.4.0 OMIP2 interannual forcing}{seaIce, ocean}{1day, 1mon, fx}{bottom_temp, frzmlt_m, wfimelt, vvel_m, total_ocean_hflux_evap, average_T1, u, geolon_c, Tsfc_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, surface_temp, time, pbot_t, mh_flux, fpr...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, total_ocean_sens_heat, frazil, aice_m, uvel, neutral, strength_m, fswup_m, time, vvel, scalar_axis, evap, eta_nonbouss, hs, total_net_sfc_heating, strairx_m, eta_global, sw_edges_ocean,...
01deg_jra55v140_iaf_cycle2{ACCESS-OM2-01}{Cycle 2 of 0.1 degree ACCESS-OM2 global model configuration with JRA55-do v1.4.0 OMIP2 interannual forcing}{seaIce, ocean}{1day, 1mon, fx}{surface_pot_temp_min, bottom_temp, frzmlt_m, daidtt, wfimelt, vvel_m, total_ocean_hflux_evap, average_T1, u, geolon_c, Tsfc_m, surface_pot_temp_max, hi_m, uarea, blkmask, total_ocean_fprec, frazi...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, total_ocean_sens_heat, frazil, surface_temp_max, dvirdgdt_m, aice_m, VGRDi, uvel, neutral, strength_m, fswup_m, time, vvel, scalar_axis, evap, eta_nonbouss, hs, total_net_sfc_heating, s...
01deg_jra55v140_iaf_cycle3{ACCESS-OM2-01}{Cycle 3 of 0.1 degree ACCESS-OM2 global model configuration with JRA55-do v1.4.0 OMIP2 interannual forcing}{seaIce, ocean}{1day, 1mon, fx}{surface_pot_temp_min, bottom_temp, frzmlt_m, daidtt, wfimelt, vvel_m, total_ocean_hflux_evap, average_T1, u, geolon_c, Tsfc_m, surface_pot_temp_max, hi_m, uarea, blkmask, total_ocean_fprec, frazi...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, total_ocean_sens_heat, frazil, dvirdgdt_m, aice_m, VGRDi, uvel, neutral, strength_m, fswup_m, time, vvel, scalar_axis, evap, eta_nonbouss, hs, total_net_sfc_heating, strairx_m, eta_glob...
01deg_jra55v140_iaf_cycle4{ACCESS-OM2-01}{Cycle 4 of 0.1 degree ACCESS-OM2 global model configuration with JRA55-do v1.4.0 OMIP2 interannual forcing}{seaIce, ocean}{1day, 6hr, fx, 3hr, 1mon}{surface_pot_temp_min, wfimelt, vvel_m, average_T1, surface_pot_temp_max, uarea, src05, det, time, mh_flux, adic_yflux_adv, dvidtt_m, grid_yu_ocean, NCAT, uvel, npp3d, total_aco2_flux, evap_heat, ...{ocean, seaIce}{1mon, 3hr, 1day, 6hr, fx}{dvirdgdt_m, uvel, fNO_ai_m, radbio_intmld, strocny_m, frzmlt, TLON, no3_int100, phy_intmld, grid_xu_ocean, ke_tot, ht, Tinz, river, vvel_h, sea_level_sq, caco3, alidf_ai_m, fswabs_ai_m, fsurf_ai_...
01deg_jra55v140_iaf_cycle4_jra55v150_extension{ACCESS-OM2-01}{Extensions of cycle 4 of 0.1 degree ACCESS-OM2 + WOMBAT BGC global model configuration with JRA55-do v1.5.0 and v1.5.0.1 interannual forcing}{seaIce, ocean}{subhr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, average_T1, surface_pot_temp_max, uarea, src05, det, time, mh_flux, adic_yflux_adv, dvidtt_m, grid_yu_ocean, NCAT, uvel, npp3d, total_aco2_flux, evap_heat, ...{ocean, seaIce}{subhr, 1mon, fx, 1day}{dvirdgdt_m, uvel, fNO_ai_m, radbio_intmld, strocny_m, frzmlt, TLON, no3_int100, phy_intmld, grid_xu_ocean, ke_tot, ht, river, sea_level_sq, caco3, alidf_ai_m, fswabs_ai_m, fsurf_ai_m, fsalt_m, dv...
01deg_jra55v150_iaf_cycle1{ACCESS-OM2}{Cycle 1 of 0.1 degree ACCESS-OM2 global model configuration with JRA55-do v1.5.0 OMIP2 interannual forcing}{seaIce, ocean}{1day, 1mon, fx}{wfimelt, average_T1, u, geolon_c, hi_m, blkmask, frazil_3d_int_z, time, pbot_t, mh_flux, fprec, sfc_hflux_from_runoff, grid_yu_ocean, kmu, v, dzt, TLAT, temp_yflux_adv_int_z, yt_ocean, dyu, evap_...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, aice_m, neutral, time, time_bnds, eta_nonbouss, evap, potrho, TLON, average_T2, sfc_salt_flux_restore, ty_trans_submeso, ht, river, potrho_edges, ULON, neutralrho_edges, sea_level_sq, a...
025deg_era5_iaf{ACCESS-OM2}{0.25 degree ACCESS-OM2 global model configuration with ERA5 interannual\\nforcing (1980-2021)}{seaIce, ocean}{1day, 1mon, fx}{surface_pot_temp_min, bottom_temp, frzmlt_m, fsens_ai_m, wfimelt, vvel_m, Tair_m, total_ocean_hflux_evap, average_T1, temp_yflux_ndiffuse_int_z, u, geolon_c, Tsfc_m, surface_pot_temp_max, hi_m, f...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sice_m, total_ocean_sens_heat, frazil, aice_m, temp_yflux_submeso_int_z, uvel, neutral, strength_m, fswup_m, flwup_ai_m, time, vvel, time_bnds, scalar_axis, eta_nonbouss, evap, hs, tota...
025deg_era5_ryf{ACCESS-OM2}{0.25 degree ACCESS-OM2 global model configuration with ERA5 RYF9091 repeat\\nyear forcing (May 1990 to Apr 1991)}{seaIce, ocean}{1day, 1mon, fx}{surface_pot_temp_min, frzmlt_m, fsens_ai_m, wfimelt, vvel_m, Tair_m, total_ocean_hflux_evap, average_T1, u, temp_yflux_ndiffuse_int_z, geolon_c, Tsfc_m, surface_pot_temp_max, hi_m, fsurfn_ai_m, u...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sice_m, total_ocean_sens_heat, frazil, aice_m, temp_yflux_submeso_int_z, uvel, neutral, strength_m, fswup_m, flwup_ai_m, time, vvel, time_bnds, scalar_axis, evap, eta_nonbouss, hs, tota...
025deg_jra55_iaf_era5comparison{ACCESS-OM2}{0.25 degree ACCESS-OM2 global model configuration with JRA55-do v1.5.0\\ninterannual forcing (1980-2019)}{seaIce, ocean}{1day, 1mon, fx}{surface_pot_temp_min, bottom_temp, frzmlt_m, fsens_ai_m, wfimelt, vvel_m, Tair_m, total_ocean_hflux_evap, average_T1, temp_yflux_ndiffuse_int_z, u, geolon_c, Tsfc_m, surface_pot_temp_max, hi_m, f...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sice_m, total_ocean_sens_heat, frazil, aice_m, temp_yflux_submeso_int_z, uvel, neutral, strength_m, fswup_m, flwup_ai_m, time, vvel, time_bnds, scalar_axis, eta_nonbouss, evap, hs, tota...
025deg_jra55_iaf_omip2_cycle1{ACCESS-OM2}{Cycle 1/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, strcory_m, time, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fswabs_ai_m, tota...{ocean, seaIce}{1yr, 1mon, fx, 1day}{dvirdgdt_m, uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, sw_heat, alidf_ai_m, fswabs_ai_m, fsurf_ai_m, fsalt_m, dvidtt_m, divu_m, temp_xflu...
025deg_jra55_iaf_omip2_cycle2{ACCESS-OM2}{Cycle 1/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, strcory_m, time, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fswabs_ai_m, tota...{ocean, seaIce}{1yr, 1mon, fx, 1day}{dvirdgdt_m, uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, sw_heat, alidf_ai_m, fswabs_ai_m, fsurf_ai_m, fsalt_m, dvidtt_m, divu_m, temp_xflu...
025deg_jra55_iaf_omip2_cycle3{ACCESS-OM2}{Cycle 3/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, strcory_m, time, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fswabs_ai_m, tota...{ocean, seaIce}{1yr, 1mon, fx, 1day}{dvirdgdt_m, uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, sw_heat, alidf_ai_m, fswabs_ai_m, fsurf_ai_m, fsalt_m, dvidtt_m, divu_m, temp_xflu...
025deg_jra55_iaf_omip2_cycle4{ACCESS-OM2}{Cycle 4/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, strcory_m, time, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fswabs_ai_m, tota...{ocean, seaIce}{1yr, 1mon, fx, 1day}{dvirdgdt_m, uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, sw_heat, alidf_ai_m, fswabs_ai_m, fsurf_ai_m, fsalt_m, dvidtt_m, divu_m, temp_xflu...
025deg_jra55_iaf_omip2_cycle5{ACCESS-OM2}{Cycle 5/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, strcory_m, time, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fswabs_ai_m, tota...{ocean, seaIce}{1yr, 1mon, fx, 1day}{dvirdgdt_m, uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, sw_heat, alidf_ai_m, fswabs_ai_m, fsurf_ai_m, fsalt_m, dvidtt_m, divu_m, temp_xflu...
025deg_jra55_iaf_omip2_cycle6{ACCESS-OM2}{Cycle 6/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{seaIce, ocean}{1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, strcory_m, time, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fswabs_ai_m, tota...{ocean, seaIce}{1mon, fx, 1day}{dvirdgdt_m, uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, sw_heat, alidf_ai_m, fswabs_ai_m, fsurf_ai_m, fsalt_m, dvidtt_m, divu_m, temp_xflu...
025deg_jra55_ryf9091_gadi{ACCESS-OM2}{0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.3 RYF9091 repeat year forcing (May 1990 to Apr 1991)}{seaIce, ocean}{1yr, 1mon, fx}{frzmlt_m, mlt_onset_m, fsens_ai_m, vvel_m, Tair_m, total_ocean_hflux_evap, average_T1, u, geolon_c, Tsfc_m, fsurfn_ai_m, hi_m, uarea, blkmask, total_ocean_fprec, frazil_3d_int_z, strcory_m, time,...{ocean, seaIce}{1mon, fx, 1yr}{geolon_t, sice_m, total_ocean_sens_heat, sss_m, aice_m, strength_m, fswup_m, flwup_ai_m, time, scalar_axis, strairx_m, strocny_m, eta_global, sw_edges_ocean, potrho, pot_rho_0, TLON, total_ocean_...
025deg_jra55_ryf_era5comparison{ACCESS-OM2}{0.25 degree ACCESS-OM2 global model configuration with JRA55-do v1.4.0\\nRYF9091 repeat year forcing (May 1990 to Apr 1991)}{seaIce, ocean}{1day, 1mon, fx}{surface_pot_temp_min, bottom_temp, frzmlt_m, fsens_ai_m, wfimelt, vvel_m, Tair_m, total_ocean_hflux_evap, average_T1, u, temp_yflux_ndiffuse_int_z, geolon_c, Tsfc_m, surface_pot_temp_max, hi_m, f...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sice_m, total_ocean_sens_heat, frazil, aice_m, temp_yflux_submeso_int_z, uvel, neutral, strength_m, fswup_m, flwup_ai_m, time, vvel, scalar_axis, evap, eta_nonbouss, hs, total_net_sfc_h...
1deg_era5_iaf{ACCESS-OM2}{1 degree ACCESS-OM2 global model configuration with ERA5 interannual\\nforcing (1960-2019)}{seaIce, ocean}{1day, 1mon, fx}{surface_pot_temp_min, bottom_temp, frzmlt_m, fsens_ai_m, wfimelt, vvel_m, Tair_m, total_ocean_hflux_evap, average_T1, u, temp_yflux_ndiffuse_int_z, geolon_c, Tsfc_m, surface_pot_temp_max, hi_m, f...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sice_m, total_ocean_sens_heat, frazil, aice_m, temp_yflux_submeso_int_z, uvel, neutral, strength_m, fswup_m, flwup_ai_m, time, vvel, time_bnds, scalar_axis, evap, eta_nonbouss, hs, tota...
1deg_era5_ryf{ACCESS-OM2}{1 degree ACCESS-OM2 global model configuration with ERA5 RYF9091 repeat\\nyear forcing (May 1990 to Apr 1991)}{seaIce, ocean}{1day, 1mon, fx}{surface_pot_temp_min, frzmlt_m, fsens_ai_m, wfimelt, vvel_m, Tair_m, total_ocean_hflux_evap, average_T1, temp_yflux_ndiffuse_int_z, u, geolon_c, Tsfc_m, surface_pot_temp_max, hi_m, fsurfn_ai_m, u...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sice_m, total_ocean_sens_heat, frazil, aice_m, temp_yflux_submeso_int_z, uvel, strength_m, fswup_m, flwup_ai_m, time, vvel, time_bnds, scalar_axis, eta_nonbouss, evap, hs, total_net_sfc...
1deg_jra55_iaf_era5comparison{ACCESS-OM2}{1 degree ACCESS-OM2 global model configuration with JRA55-do v1.4.0\\ninterannual forcing (1960-2019)}{seaIce, ocean}{1day, 1mon, fx}{surface_pot_temp_min, bottom_temp, frzmlt_m, fsens_ai_m, wfimelt, vvel_m, Tair_m, total_ocean_hflux_evap, average_T1, u, temp_yflux_ndiffuse_int_z, geolon_c, Tsfc_m, surface_pot_temp_max, hi_m, f...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sice_m, total_ocean_sens_heat, frazil, aice_m, temp_yflux_submeso_int_z, uvel, neutral, strength_m, fswup_m, flwup_ai_m, time, vvel, scalar_axis, evap, eta_nonbouss, hs, total_net_sfc_h...
1deg_jra55_iaf_omip2_cycle1{ACCESS-OM2}{Cycle 1/6 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, det, strcory_m, time, fgo2_raw, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fs...{ocean, seaIce}{1yr, 1mon, fx, 1day}{uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, caco3, sw_heat, alidf_ai_m, fswabs_ai_m, fsalt_m, dvidtt_m, fgco2_raw, divu_m, temp_xflux_ndif...
1deg_jra55_iaf_omip2_cycle2{ACCESS-OM2}{Cycle 2/6 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, det, strcory_m, time, fgo2_raw, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fs...{ocean, seaIce}{1yr, 1mon, fx, 1day}{uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, caco3, sw_heat, alidf_ai_m, fswabs_ai_m, fsalt_m, dvidtt_m, fgco2_raw, divu_m, temp_xflux_ndif...
1deg_jra55_iaf_omip2_cycle3{ACCESS-OM2}{Cycle 3/6 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, det, strcory_m, time, fgo2_raw, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fs...{ocean, seaIce}{1yr, 1mon, fx, 1day}{uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, caco3, sw_heat, alidf_ai_m, fswabs_ai_m, fsalt_m, dvidtt_m, fgco2_raw, divu_m, temp_xflux_ndif...
1deg_jra55_iaf_omip2_cycle4{ACCESS-OM2}{Cycle 4/6 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, det, strcory_m, time, fgo2_raw, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fs...{ocean, seaIce}{1yr, 1mon, fx, 1day}{uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, caco3, sw_heat, alidf_ai_m, fswabs_ai_m, fsalt_m, dvidtt_m, fgco2_raw, divu_m, temp_xflux_ndif...
1deg_jra55_iaf_omip2_cycle5{ACCESS-OM2}{Cycle 5/6 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, det, strcory_m, time, fgo2_raw, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fs...{ocean, seaIce}{1yr, 1mon, fx, 1day}{uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, caco3, sw_heat, alidf_ai_m, fswabs_ai_m, fsalt_m, dvidtt_m, fgco2_raw, divu_m, temp_xflux_ndif...
1deg_jra55_iaf_omip2_cycle6{ACCESS-OM2}{Cycle 6/6 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{surface_pot_temp_min, wfimelt, vvel_m, Tair_m, average_T1, surface_pot_temp_max, uarea, det, strcory_m, time, fgo2_raw, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fs...{ocean, seaIce}{1yr, 1mon, fx, 1day}{uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, caco3, sw_heat, alidf_ai_m, fswabs_ai_m, fsalt_m, dvidtt_m, fgco2_raw, divu_m, temp_xflux_ndif...
1deg_jra55_iaf_omip2spunup_cycle1{ACCESS-OM2}{Cycle 1/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{wfimelt, vvel_m, Tair_m, average_T1, uarea, det, strcory_m, time, fgo2_raw, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fswabs_ai_m, total_ocean_hflux_prec, temp_xflu...{ocean, seaIce}{1yr, 1mon, fx, 1day}{uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, caco3, sw_heat, alidf_ai_m, fswabs_ai_m, fsalt_m, dvidtt_m, fgco2_raw, divu_m, temp_xflux_ndif...
1deg_jra55_iaf_omip2spunup_cycle10{ACCESS-OM2}{Cycle 10/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle11{ACCESS-OM2}{Cycle 11/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle12{ACCESS-OM2}{Cycle 12/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle13{ACCESS-OM2}{Cycle 13/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle14{ACCESS-OM2}{Cycle 14/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle15{ACCESS-OM2}{Cycle 15/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle16{ACCESS-OM2}{Cycle 16/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle17{ACCESS-OM2}{Cycle 17/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle18{ACCESS-OM2}{Cycle 18/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle19{ACCESS-OM2}{Cycle 19/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle2{ACCESS-OM2}{Cycle 2/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{wfimelt, vvel_m, Tair_m, average_T1, uarea, det, strcory_m, time, fgo2_raw, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fswabs_ai_m, total_ocean_hflux_prec, temp_xflu...{ocean, seaIce}{1yr, 1mon, fx, 1day}{uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, caco3, sw_heat, alidf_ai_m, fswabs_ai_m, fsalt_m, dvidtt_m, fgco2_raw, divu_m, temp_xflux_ndif...
1deg_jra55_iaf_omip2spunup_cycle20{ACCESS-OM2}{Cycle 20/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle21{ACCESS-OM2}{Cycle 21/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle22{ACCESS-OM2}{Cycle 22/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle23{ACCESS-OM2}{Cycle 23/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle24{ACCESS-OM2}{Cycle 24/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle25{ACCESS-OM2}{Cycle 25/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle26{ACCESS-OM2}{Cycle 26/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle27{ACCESS-OM2}{Cycle 27/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle28{ACCESS-OM2}{Cycle 28/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle29{ACCESS-OM2}{Cycle 29/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle3{ACCESS-OM2}{Cycle 3/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{wfimelt, vvel_m, Tair_m, average_T1, uarea, det, strcory_m, time, fgo2_raw, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fswabs_ai_m, total_ocean_hflux_prec, temp_xflu...{ocean, seaIce}{1yr, 1mon, fx, 1day}{uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, caco3, sw_heat, alidf_ai_m, fswabs_ai_m, fsalt_m, dvidtt_m, fgco2_raw, divu_m, temp_xflux_ndif...
1deg_jra55_iaf_omip2spunup_cycle30{ACCESS-OM2}{Cycle 30/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle31{ACCESS-OM2}{Cycle 31/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle32{ACCESS-OM2}{Cycle 32/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle33{ACCESS-OM2}{Cycle 33/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle34{ACCESS-OM2}{Cycle 34/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{bottom_temp, salt_rhodzt, dissicos_raw, wfimelt, temp_xflux_sigma, total_ocean_hflux_evap, average_T1, u, total_volume_seawater, temp_yflux_ndiffuse_int_z, geolon_c, hi_m, uarea, blkmask, salt_in...{ocean, seaIce}{1yr, 1mon, fx, 1day}{geolon_t, salt_eta_smooth, total_ocean_sens_heat, aice_m, temp_yflux_submeso_int_z, hblt_max, alk, temp_advection, neutral, vsq, ULAT, fswup_m, rho, time, sss_sq, aiso_bih, scalar_axis, evap, tot...
1deg_jra55_iaf_omip2spunup_cycle35{ACCESS-OM2}{Cycle 35/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{bottom_temp, salt_rhodzt, dissicos_raw, wfimelt, temp_xflux_sigma, total_ocean_hflux_evap, average_T1, u, total_volume_seawater, temp_yflux_ndiffuse_int_z, geolon_c, hi_m, uarea, blkmask, salt_in...{ocean, seaIce}{1yr, 1mon, fx, 1day}{geolon_t, salt_eta_smooth, total_ocean_sens_heat, aice_m, temp_yflux_submeso_int_z, hblt_max, alk, temp_advection, neutral, vsq, ULAT, fswup_m, rho, time, sss_sq, aiso_bih, scalar_axis, evap, tot...
1deg_jra55_iaf_omip2spunup_cycle36{ACCESS-OM2}{Cycle 36/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{bottom_temp, salt_rhodzt, dissicos_raw, wfimelt, temp_xflux_sigma, total_ocean_hflux_evap, average_T1, u, total_volume_seawater, temp_yflux_ndiffuse_int_z, geolon_c, hi_m, uarea, blkmask, salt_in...{ocean, seaIce}{1yr, 1mon, fx, 1day}{geolon_t, salt_eta_smooth, total_ocean_sens_heat, aice_m, temp_yflux_submeso_int_z, hblt_max, alk, temp_advection, neutral, vsq, ULAT, fswup_m, rho, time, sss_sq, aiso_bih, scalar_axis, evap, tot...
1deg_jra55_iaf_omip2spunup_cycle37{ACCESS-OM2}{Cycle 37/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{bottom_temp, salt_rhodzt, dissicos_raw, wfimelt, temp_xflux_sigma, total_ocean_hflux_evap, average_T1, u, total_volume_seawater, temp_yflux_ndiffuse_int_z, geolon_c, hi_m, uarea, blkmask, salt_in...{ocean, seaIce}{1yr, 1mon, fx, 1day}{geolon_t, salt_eta_smooth, total_ocean_sens_heat, aice_m, temp_yflux_submeso_int_z, hblt_max, alk, temp_advection, neutral, vsq, ULAT, fswup_m, rho, time, sss_sq, aiso_bih, scalar_axis, evap, tot...
1deg_jra55_iaf_omip2spunup_cycle38{ACCESS-OM2}{Cycle 38/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{bottom_temp, salt_rhodzt, dissicos_raw, wfimelt, temp_xflux_sigma, total_ocean_hflux_evap, average_T1, u, total_volume_seawater, temp_yflux_ndiffuse_int_z, geolon_c, hi_m, uarea, blkmask, salt_in...{ocean, seaIce}{1yr, 1mon, fx, 1day}{geolon_t, salt_eta_smooth, total_ocean_sens_heat, aice_m, temp_yflux_submeso_int_z, hblt_max, alk, temp_advection, neutral, vsq, ULAT, fswup_m, rho, time, sss_sq, aiso_bih, scalar_axis, evap, tot...
1deg_jra55_iaf_omip2spunup_cycle39{ACCESS-OM2}{Cycle 39/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{bottom_temp, salt_rhodzt, dissicos_raw, wfimelt, temp_xflux_sigma, total_ocean_hflux_evap, average_T1, u, total_volume_seawater, temp_yflux_ndiffuse_int_z, geolon_c, hi_m, uarea, blkmask, salt_in...{ocean, seaIce}{1yr, 1mon, fx, 1day}{geolon_t, salt_eta_smooth, total_ocean_sens_heat, aice_m, temp_yflux_submeso_int_z, hblt_max, alk, temp_advection, neutral, vsq, ULAT, fswup_m, rho, time, sss_sq, aiso_bih, scalar_axis, evap, tot...
1deg_jra55_iaf_omip2spunup_cycle4{ACCESS-OM2}{Cycle 4/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{wfimelt, vvel_m, Tair_m, average_T1, uarea, det, strcory_m, time, fgo2_raw, mh_flux, dvidtt_m, fcondtopn_ai_m, grid_yu_ocean, NCAT, uvel, evap_heat, fswabs_ai_m, total_ocean_hflux_prec, temp_xflu...{ocean, seaIce}{1yr, 1mon, fx, 1day}{uvel, strocny_m, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, diff_cbt_s, sea_level_sq, caco3, sw_heat, alidf_ai_m, fswabs_ai_m, fsalt_m, dvidtt_m, fgco2_raw, divu_m, temp_xflux_ndif...
1deg_jra55_iaf_omip2spunup_cycle40{ACCESS-OM2}{Cycle 40/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle41{ACCESS-OM2}{Cycle 41/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle42{ACCESS-OM2}{Cycle 42/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle43{ACCESS-OM2}{Cycle 43/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle44{ACCESS-OM2}{Cycle 44/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle45{ACCESS-OM2}{Cycle 45/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle5{ACCESS-OM2}{Cycle 5/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{bottom_temp, salt_rhodzt, dissicos_raw, wfimelt, temp_xflux_sigma, total_ocean_hflux_evap, average_T1, u, total_volume_seawater, temp_yflux_ndiffuse_int_z, geolon_c, hi_m, uarea, blkmask, salt_in...{ocean, seaIce}{1yr, 1mon, fx, 1day}{geolon_t, salt_eta_smooth, total_ocean_sens_heat, aice_m, temp_yflux_submeso_int_z, hblt_max, alk, temp_advection, neutral, vsq, ULAT, fswup_m, rho, time, sss_sq, aiso_bih, scalar_axis, evap, tot...
1deg_jra55_iaf_omip2spunup_cycle6{ACCESS-OM2}{Cycle 6/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1day, 1mon, fx}{bottom_temp, salt_rhodzt, dissicos_raw, wfimelt, temp_xflux_sigma, total_ocean_hflux_evap, average_T1, u, total_volume_seawater, temp_yflux_ndiffuse_int_z, geolon_c, hi_m, uarea, blkmask, salt_in...{ocean, seaIce}{1yr, 1mon, fx, 1day}{geolon_t, salt_eta_smooth, total_ocean_sens_heat, aice_m, temp_yflux_submeso_int_z, hblt_max, alk, temp_advection, neutral, vsq, ULAT, fswup_m, rho, time, sss_sq, aiso_bih, scalar_axis, evap, tot...
1deg_jra55_iaf_omip2spunup_cycle7{ACCESS-OM2}{Cycle 7/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon, 1day}{frzmlt_m, fsens_ai_m, vvel_m, Tair_m, average_T1, total_volume_seawater, Tsfc_m, fsurfn_ai_m, hi_m, uarea, blkmask, phy, det, strcory_m, time, dvidtt_m, frazil, fcondtopn_ai_m, NCAT, sss, uvel, T...{ocean, seaIce}{1day, 1mon, 1yr}{sice_m, frazil, aice_m, alk, uvel, strength_m, fswup_m, flwup_ai_m, time, vvel, scalar_axis, strairx_m, hs, strocny_m, eta_global, frzmlt, TLON, average_T2, vvel_m, ULON, HTN, total_mass_seawater...
1deg_jra55_iaf_omip2spunup_cycle8{ACCESS-OM2}{Cycle 8/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_iaf_omip2spunup_cycle9{ACCESS-OM2}{Cycle 9/45 of 1 degree ACCESS-OM2-BGC global configuration with JRA55-do v1.4 OMIP2 spunup interannual forcing (1958-2018)}{seaIce, ocean}{1yr, 1mon}{uatm_m, age_global, salt, mld, ULAT, zoo, sst, alidr_ai_m, dyt, alvdr_ai_m, average_T1, total_volume_seawater, vatm_m, temp_surface_ave, hi_m, ANGLE, time_bounds, o2, xt_ocean, uarea, blkmask, HT...{ocean, seaIce}{1mon, 1yr}{aice_m, alk, yt_ocean, fswup_m, st_ocean, nv, no3, ANGLET, tarea, phy, uatm_m, time_bounds, uarea, time, hi_m, st_edges_ocean, scalar_axis, fe, alidr_ai_m, eta_global, aicen_m, average_DT, NCAT, ...
1deg_jra55_ryf9091_gadi{ACCESS-OM2}{1 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.3 RYF9091 repeat year forcing (May 1990 to Apr 1991)}{seaIce, ocean}{1yr, 1mon, fx}{temp_vdiffuse_diff_cbt_kppicon_on_nrho, frzmlt_m, temp_vdiffuse_k33_on_nrho, mlt_onset_m, temp_vdiffuse_diff_cbt_kppbl_on_nrho, fsens_ai_m, vvel_m, Tair_m, average_T1, total_ocean_hflux_evap, u, ...{ocean, seaIce}{1mon, fx, 1yr}{geolon_t, sice_m, total_ocean_sens_heat, sss_m, temp_yflux_adv_on_nrho, aice_m, temp_advection, temp_yflux_gm_on_nrho, neutral, temp_advection_on_nrho, strength_m, fswup_m, temp_eta_smooth_on_nrh...
1deg_jra55v14_ryf{ACCESS-OM2}{1 degree ACCESS-OM2 global model configuration with JRA55-do v1.4.0 RYF9091\\nrepeat year forcing (May 1990 to Apr 1991)}{seaIce, ocean}{1day, 1mon, fx}{surface_pot_temp_min, frzmlt_m, fsens_ai_m, wfimelt, vvel_m, Tair_m, total_ocean_hflux_evap, average_T1, temp_yflux_ndiffuse_int_z, u, geolon_c, Tsfc_m, surface_pot_temp_max, hi_m, fsurfn_ai_m, u...{ocean, seaIce}{1mon, fx, 1day}{geolon_t, sice_m, total_ocean_sens_heat, frazil, aice_m, temp_yflux_submeso_int_z, uvel, strength_m, fswup_m, flwup_ai_m, time, vvel, scalar_axis, eta_nonbouss, evap, hs, total_net_sfc_heating, s...
HI_CN_05{ACCESS-ESM1-5}{Historical run using same configuration as CMIP6 ACCESS-ESM1.5 historical r1i1p1f1, but with phosphorus limitation disabled within CASA-CNP}{seaIce, ocean, atmos}{1day, 6hr, 1yr, 3hr, 1mon}{fld_s30i215, fld_s03i258, wfimelt, fld_s33i001, average_T1, fld_s03i100, fld_s00i104, fld_s03i875, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, fld_s03i920, det, fld_s00i509,...{atmos, ocean, seaIce}{1mon, 3hr, 1day, 1yr, 6hr}{fld_s03i876, fld_s00i113, frazil_2d, uvel, fld_s30i225, stf10, conv_rho_ud_t, fld_s02i295, TLON, fld_s03i870, grid_xu_ocean, ke_tot, fld_s03i898, ht, ty_trans_rho_gm, fld_s03i812, river, fld_s00i...
HI_C_05_r1{ACCESS-ESM1-5}{Historical run using same configuration as CMIP6 ACCESS-ESM1.5 historical r1i1p1f1, but with nitrogen and phosphorus limitations disabled within CASA-CNP}{seaIce, ocean, atmos}{1yr, 1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, fld_s33i001, average_T1, fld_s03i100, fld_s00i104, fld_s03i875, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, fld_s03i920, det, fld_s00i509,...{atmos, ocean, seaIce}{1yr, 1mon, 1day}{fld_s03i876, fld_s00i113, frazil_2d, uvel, fld_s30i225, stf10, conv_rho_ud_t, fld_s02i295, TLON, fld_s03i870, grid_xu_ocean, ke_tot, fld_s03i898, ht, ty_trans_rho_gm, fld_s03i812, river, fld_s00i...
HI_nl_C_05_r1{ACCESS-ESM1-5}{Historical run using same configuration as CMIP6 ACCESS-ESM1.5 historical r1i1p1f1, but with nitrogen and phosphorus limitations disabled within CASA-CNP, and land-use change disabled}{seaIce, ocean, atmos}{1yr, 1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, fld_s33i001, average_T1, fld_s03i100, fld_s00i104, fld_s03i875, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, fld_s03i920, det, fld_s00i509,...{atmos, ocean, seaIce}{1yr, 1mon, 1day}{fld_s03i876, fld_s00i113, frazil_2d, uvel, fld_s30i225, stf10, conv_rho_ud_t, fld_s02i295, TLON, fld_s03i870, grid_xu_ocean, ke_tot, fld_s03i898, ht, ty_trans_rho_gm, fld_s03i812, river, fld_s00i...
HI_noluc_CN_05{ACCESS-ESM1-5}{Historical run using same configuration as CMIP6 ACCESS-ESM1.5 historical r1i1p1f1, but with phosphorus limitation disabled within CASA-CNP, and land-use change disabled}{seaIce, ocean, atmos}{1day, 6hr, 1yr, 3hr, 1mon}{fld_s30i215, fld_s03i258, wfimelt, fld_s33i001, average_T1, fld_s03i100, fld_s00i104, fld_s03i875, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, fld_s03i920, det, fld_s00i509,...{atmos, ocean, seaIce}{1mon, 3hr, 1day, 1yr, 6hr}{fld_s03i876, fld_s00i113, frazil_2d, uvel, fld_s30i225, stf10, conv_rho_ud_t, fld_s02i295, TLON, fld_s03i870, grid_xu_ocean, ke_tot, fld_s03i898, ht, ty_trans_rho_gm, fld_s03i812, river, fld_s00i...
OM4_025.JRA_RYF{SIS2, MOM6}{0.25 degree GFDL-OM4 (MOM6+SIS2) global model configuration under 1990-1991 JRA55-do repeat year forcing.}{seaIce, ocean}{1yr, 1day, 1mon, fx}{average_T1, heat_content_massout, geolon_c, thkcello, hfibthermds, ficeberg, LwLatSens, wet, time, wfo, vmo, areacello_cv, zi, dxCv, rsdo, yq, yTe, heat_content_cond, scalar_axis, siconc, average...{ocean, seaIce}{1yr, 1mon, fx, 1day}{rlntds, pbo, vo, wet_u, friver, geolat_u, so_xyave, sob, tosmin, ficeberg, pso, areacello, time, time_bnds, scalar_axis, heat_content_massout, uh, dyCu, T_adx, geolon, average_T2, sftof, thetaoga...
PI_GWL_B2035{ACCESS-ESM1-5}{Climate stabilization run at different global warming levels with zero C02 emissions and pre-industrial aerosols, starting in 2035 }{seaIce, ocean, atmos}{1yr, 1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, fld_s33i001, average_T1, fld_s03i100, fld_s00i104, fld_s03i875, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, fld_s03i920, det, fld_s00i509,...{atmos, ocean, seaIce}{1yr, 1mon, 1day}{fld_s03i876, fld_s00i113, frazil_2d, uvel, fld_s30i225, stf10, conv_rho_ud_t, fld_s02i295, TLON, fld_s03i870, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, fld_s03i812, river, fld_s00i116, fld_s03i...
PI_GWL_B2040{ACCESS-ESM1-5}{Climate stabilization run at different global warming levels with zero C02 emissions and pre-industrial aerosols, starting in 2040}{seaIce, ocean, atmos}{1yr, 1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, fld_s33i001, average_T1, fld_s03i100, fld_s00i104, fld_s03i875, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, fld_s03i920, det, fld_s00i509,...{atmos, ocean, seaIce}{1yr, 1mon, 1day}{fld_s03i876, fld_s00i113, frazil_2d, uvel, fld_s30i225, stf10, conv_rho_ud_t, fld_s02i295, TLON, fld_s03i870, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, fld_s03i812, river, fld_s00i116, fld_s03i...
PI_GWL_B2045{ACCESS-ESM1-5}{Climate stabilization run at different global warming levels with zero C02 emissions and pre-industrial aerosols, starting in 2045}{seaIce, ocean, atmos}{1yr, 1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, fld_s33i001, average_T1, fld_s03i100, fld_s00i104, fld_s03i875, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, fld_s03i920, det, fld_s00i509,...{atmos, ocean, seaIce}{1yr, 1mon, 1day}{fld_s03i876, fld_s00i113, frazil_2d, uvel, fld_s30i225, stf10, conv_rho_ud_t, fld_s02i295, TLON, fld_s03i870, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, fld_s03i812, river, fld_s00i116, fld_s03i...
PI_GWL_B2050{ACCESS-ESM1-5}{Climate stabilization run at different global warming levels with zero C02 emissions and pre-industrial aerosols, starting in 2050}{seaIce, ocean, atmos}{1yr, 1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, fld_s33i001, average_T1, fld_s03i100, fld_s00i104, fld_s03i875, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, fld_s03i920, det, fld_s00i509,...{atmos, ocean, seaIce}{1yr, 1mon, 1day}{fld_s03i876, fld_s00i113, frazil_2d, uvel, fld_s30i225, stf10, conv_rho_ud_t, fld_s02i295, TLON, fld_s03i870, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, fld_s03i812, river, fld_s00i116, fld_s03i...
PI_GWL_B2055{ACCESS-ESM1-5}{Climate stabilization run at different global warming levels with zero C02 emissions and pre-industrial aerosols, starting in 2055}{seaIce, ocean, atmos}{1yr, 1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, fld_s33i001, average_T1, fld_s03i100, fld_s00i104, fld_s03i875, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, fld_s03i920, det, fld_s00i509,...{atmos, ocean, seaIce}{1yr, 1mon, 1day}{fld_s03i876, fld_s00i113, frazil_2d, uvel, fld_s30i225, stf10, conv_rho_ud_t, fld_s02i295, TLON, fld_s03i870, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, fld_s03i812, river, fld_s00i116, fld_s03i...
PI_GWL_B2060{ACCESS-ESM1-5}{Climate stabilization run at different global warming levels with zero C02 emissions and pre-industrial aerosols, starting in 2060}{seaIce, ocean, atmos}{1yr, 1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, fld_s33i001, average_T1, fld_s03i100, fld_s00i104, fld_s03i875, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, fld_s03i920, det, fld_s00i509,...{atmos, ocean, seaIce}{1yr, 1mon, 1day}{fld_s03i876, fld_s00i113, frazil_2d, uvel, fld_s30i225, stf10, conv_rho_ud_t, fld_s02i295, TLON, fld_s03i870, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, fld_s03i812, river, fld_s00i116, fld_s03i...
WOA-13{2013 World Ocean Atlas (WOA-13), regridded to various model grids.}{ocean}{fx}{temp, GRID_Y_T, salt, ZT, time, GRID_X_T}{GRID_X_T, ZT, temp, salt, GRID_Y_T, time}
barpa_py18{BARPA-R1-NN, BARPA-C, BARPA-R}{BARPA-R, BARPA-C, BARPA-R1-NN}{Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)}{none}{1day, subhr, 6hr, fx, 3hr, 1hr, 1mon}{va250m, coltotdrym, snw, mrfsos, hus850, MUEL, ta1000, wa500, ua500, rldscs, ta1500m, twisomax, zg10, ta850, sfcWind10minmean, hus1000, rsdsdir, wap400, rsutcs, va900, tasmin, hus950, va400, STPl...{1mon, 3hr, 6hr, 1day, subhr, 1hr, fx}{wap800, va100m, wa950, wap150, hus750, va750, hus950, wap10, va850, zg400, ta100, ztp, MLCIN, helicity, ua20, hus50, MLLCL, rsut, hus500, twisomax, ua200, helicitymax, hus600, hus700, DCP, wap70,...
bx944{ACCESS-CM2}{Standard CMIP6 historical simulation, control experiment for by473 pacemaker experiment (948d8676-2c56-49db-8ea1-b80572b074c8)}{seaIce, ocean, atmos}{1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, average_T1, iage, temp_yflux_gm, sipr, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, sidmassmelttop, fld_s00i509, time, fld_s30i429, grid_yu...{atmos, ocean, seaIce}{1mon, 1day}{frazil_2d, uvel, fld_s30i225, fld_s38i437, sig2, fld_s03i328, fld_s30i298, conv_rho_ud_t, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, fld_s34i071, fld_s03i812, river, fld_s17i257, f...
by473{ACCESS-CM2}{Pacemaker variation of CMIP6 historical simulation, Topical Atlantic region replaced with fixed SSTs from observations}{seaIce, ocean, atmos}{1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, average_T1, iage, temp_yflux_gm, sipr, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, sidmassmelttop, fld_s00i509, time, fld_s30i429, grid_yu...{atmos, ocean, seaIce}{1mon, 1day}{frazil_2d, uvel, fld_s30i225, fld_s38i437, sig2, fld_s03i328, fld_s30i298, conv_rho_ud_t, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, fld_s34i071, fld_s03i812, river, fld_s17i257, f...
by578{ACCESS-CM2}{Pacemaker variation of CMIP6 ssp245 simulation with Tropical Atlantic region replaced with fixed SSTs from observations}{seaIce, ocean, atmos}{1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, average_T1, iage, temp_yflux_gm, sipr, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, sidmassmelttop, fld_s00i509, time, fld_s30i429, grid_yu...{atmos, ocean, seaIce}{1mon, 1day}{frazil_2d, uvel, fld_s30i225, fld_s38i437, sig2, fld_s03i328, fld_s30i298, conv_rho_ud_t, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, fld_s34i071, fld_s03i812, river, fld_s17i257, f...
by647{ACCESS-CM2}{Standard CMIP6 ssp245 simulation, control experiment for by578 pacemaker experiment (1fd9e682-d393-4b17-a9cd-934c3a48a1f8)}{seaIce, ocean, atmos}{1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, average_T1, iage, temp_yflux_gm, sipr, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, sidmassmelttop, fld_s00i509, time, fld_s30i429, grid_yu...{atmos, ocean, seaIce}{1mon, 1day}{frazil_2d, uvel, fld_s30i225, fld_s38i437, sig2, fld_s03i328, fld_s30i298, conv_rho_ud_t, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, fld_s34i071, fld_s03i812, river, fld_s17i257, f...
bz687{ACCESS-CM2}{ACCESS-CM2 CMIP6 with 1 degree ocean. Present day atmospheric forcing with 1985-2014 mean GHG, aerosol emissions etc.}{seaIce, ocean, atmos}{1day, 1mon}{fld_s30i215, fld_s03i258, wfimelt, average_T1, iage, temp_yflux_gm, sipr, uarea, temp_merid_flux_gyre_arctic, temp_merid_flux_gyre_pacific, sidmassmelttop, fld_s00i509, time, fld_s30i429, grid_yu...{atmos, ocean, seaIce}{1mon, 1day}{frazil_2d, uvel, fld_s30i225, fld_s38i437, sig2, fld_s03i328, fld_s30i298, conv_rho_ud_t, frzmlt, TLON, grid_xu_ocean, ke_tot, ht, ty_trans_rho_gm, fld_s34i071, fld_s03i812, river, fld_s17i257, f...
cj877{ACCESS-CM2}{ACCESS-CM2 with COSIMA OM2 0.25 degree ocean configuration. Present day atmospheric forcing with 1985-2014 mean GHG, aerosol emissions etc.}{seaIce, ocean, atmos}{1day, 1mon, fx}{surface_pot_temp_min, fld_s30i215, fld_s03i258, wfimelt, average_T1, iage, surface_pot_temp_max, sipr, uarea, sidmassmelttop, fld_s00i509, time, mh_flux, fld_s30i429, grid_yu_ocean, fld_s30i202, ...{atmos, ocean, seaIce}{1mon, fx, 1day}{uvel, fld_s30i225, fld_s38i437, sig2, fld_s03i328, fld_s30i298, frzmlt, TLON, ke_tot, ht, ty_trans_rho_gm, fld_s34i071, fld_s03i812, river, fld_s17i257, fld_s03i821, sea_level_sq, fld_s03i814, ap...
cmip-forcing-qv56{ERA-5, CEDS-CMIP-2025-04-18, UofMD-landState-GCAM-ssp434-2-1-f, UCLA-1-0-2-decreasing, ImperialCollege-REMIND-MAGPIE-ssp534os-1-0, NCAR-CCMI-ssp434-1-0, AVISO-1-0, Aura-MLS-v04-2, UReading-CCMI-s...{ImperialCollege-1-1, UoM-GCAM4-ssp434-1-2-0, MRI-JRA55-do-1-4-0, UReading-CCMI-ssp119-1-0, CERES-EBAF_Surface, NCAR-CCMI-ssp245-2-0, UofMD-landState-high-2-1-h, CESM2-ssp585-1-0, CCSM4-rcp85-1-0,...{Earth System Grid (ESGF) Reference Datasets for Climate Model Analysis/Forcing}{ocean, landIce, seaIce, none, land, atmos}{1day, fx, 1yr, 3hr, 1hr, 1mon}{NH3-percentage-DEFO-em-biomassburning, CO-em-anthro, C8H10smoothedpercentageBORF, mask4resto-amv, VOC-em-AIR-anthro, C2H6S-percentage-DEFO-em-biomassburning, Higher-Alkanes-percentage-SAVA-em-bio...{none, landIce, atmos, land, ocean, seaIce}{1mon, 3hr, 1day, 1yr, 1hr, fx}{CO2-em-biomassburning, COsmoothedpercentageAGRI, VOC01-alcohols-em-speciated-VOC-anthro, HOCH2CHOpercentageSAVA, C3H8percentageTEMF, CH3COCHOpercentageDEFO, NOx-percentage-SAVA-em-biomassburning,...
cmip5_al33{MIROC4h, CCLM4-8-17-CLM3-5, WRF331F, COSMO-crCLIM-v1-1, RACMO21P, MPI-ESM-P, GISS-E2-H-CC, ALADIN52, GFDL-ESM2G, HadGEM2-ES, CCLM5-0-2, RegCM4-6, GFDL-HIRAM-C180, GFDL-CM3, MIROC5, ALADIN53, CCLM...{RACMO22E, CESM1-CAM5, CanAM4, WRF331F, GEOS-5, CFSv2-2011, CCLM5-0-15, WRF341I, CESM1-WACCM, GFDL-HIRAM-C360, RegCM4-4, fio-esm, CNRM-CM5-2, NorESM1-ME, CESM1-BGC, HadGEM2-AO, bcc-csm1-1, ALADIN5...{Replicated CMIP5-era datasets catalogued by NCI}{ocnBgchem, aerosol, ocean, landIce, seaIce, none, land, atmos}{1day, subhr, 6hr, fx, 1yr, 3hr, 1mon}{wetsoa, wmo, reffclic, cSoilSlow, cfc11, tnt, ta850, frc, lwsnl, fco2antt, rsutcs, grFrazil, transix, wfo, phyn, va400, fbddtdife, tnsclicd, rsd, ra, intpcalcite, fbddtdisi, nh4, difvho, va300, v...{none, landIce, atmos, land, ocnBgchem, ocean, aerosol, seaIce}{1mon, 3hr, 1day, subhr, 1yr, 6hr, fx}{hfxdiff, masscello, calc, tnhusmp, va, hfsithermds, dems, ficeberg, zo2min, phydiaz, hus600, hus700, prveg, intpbsi, ta850, epsi100, rsdcs, tnsccw, grLateral, concnh4, basin, tnhusa, co3satcalc, ...
cmip5_rr3{CSIRO-Mk3L-1-2, UNSW-WRF360L, ACCESS1-0, CSIRO-CCAM-2008, CSIRO-Mk3-6-0, UNSW-WRF360K, UQ-DES-CCAM, CSIRO-CCAM-1704, ACCESS1-3, CSIRO-CCAM, UNSW-WRF360J, BOM-SDMa-NRM}{CSIRO-Mk3L-1-2, ACCESS1-0, CSIRO-CCAM-1704, CSIRO-Mk3-6-0, UNSW-WRF360K, CSIRO-CCAM, UQ-DES-CCAM, ACCESS1-3, UNSW-WRF360L, UNSW-WRF360J, BOM-SDMa-NRM, CSIRO-CCAM-2008}{Australian CMIP5-era datasets catalogued by NCI}{aerosol, ocean, landIce, seaIce, none, land, atmos}{1day, 6hr, fx, 3mon, 3hr, 1hr, 1mon}{u500, snw, hus850, ua500, wmo, emiso2, thkcello, msftyyzba, rldscs, tasmax-bc, pr-bc, ta850, landCoverFrac, omldamax, u850, mfo, wetbc, pfull, rsutcs, evspsblveg, tasmin, grFrazil, loadoa, transi...{none, landIce, aerosol, atmos, land, ocean, seaIce}{1mon, 3hr, 1day, 3mon, 6hr, fx, 1hr}{pbo, vo, hfxdiff, friver, masscello, cldncl, va, va850, omlmax, zg400, hus, loadss, hfxba, pso, vsi, loadpoa, concdust, areacello, mrsofc, ta, divice, concdms, drydust, rsut, sconcsoa, ua200, str...
cmip6_fs38{ACCESS-OM2, ACCESS-CM2, ACCESS-OM2-025, ACCESS-ESM1-5}{ACCESS-ESM1-5, ACCESS-CM2, ACCESS-OM2-025, ACCESS-OM2}{Australian CMIP6-era datasets catalogued by NCI}{ocnBgchem, aerosol, ocean, landIce, seaIce, land, atmos}{1day, 6hr, fx, 1yr, 3hr, 1mon}{treeFracNdlEvg, nVeg, wmo, ocontemppadvect, cSoilSlow, sipr, rsutcs, sltovovrt, treeFracNdlDcd, sidmassmelttop, dfeos, wfo, prbigthetao, ra, difvho, vas, sifllatstop, ci, tauu, agessc, cProduct, ...{landIce, aerosol, atmos, land, ocnBgchem, ocean, seaIce}{1mon, 3hr, 1day, 1yr, 6hr, fx}{rlntds, masscello, va, rv850, mmraerh2o, hursmin, nwdFracLut, sivols, siextents, basin, po4os, wap, orog, mc, ocontemppadvect, rh, nLitter, thetao, epc100, sftgif, prc, pctisccp, sftlf, mmroa, hf...
cmip6_oi10{MPI-ESM1-2-LR, CNRM-ESM2-1, ACCESS-OM2-025, E3SM-1-0, E3SM-1-1-ECA, CESM1-CAM5-SE-HR, INM-CM4-8, ICON-ESM-LR, NorESM1-F, NorCPM1, CESM2, CAMS-CSM1-0, TaiESM1, GISS-E2-2-G, FGOALS-g3, CESM2-FV2, U...{EC-Earth3, IPSL-CM5A2-INCA, NorCPM1, E3SM-2-0, CNRM-CM6-1, UKESM1-1-LL, AWI-ESM-1-1-LR, GISS-E2-1-G-CC, EC-Earth3-Veg-LR, ECMWF-IFS-HR, HadGEM3-GC31-MM, EC-Earth3-CC, MIROC-ES2H, GFDL-OM4p5B, Tai...{Replicated CMIP6-era datasets catalogued by NCI}{ocnBgchem, aerosol, ocean, atmosChem, seaIce, landIce, land, atmos}{1day, subhr, 6hr, fx, 1yr, 3hr, 1hr, 1mon}{treeFracNdlEvg, nVeg, wmo, cSoilSlow, cfc11, ta850, lwsnl, fco2antt, rsutcs, sltovovrt, sidmassmelttop, treeFracNdlDcd, dfeos, wfo, fLulccProductLut, ra, vas, tauu, fNAnthDisturb, cProduct, laiLu...{landIce, atmosChem, atmos, land, ocnBgchem, ocean, aerosol, seaIce}{1mon, 3hr, 1day, subhr, 1yr, 6hr, fx, 1hr}{masscello, va, rv850, ficeberg, nStem, hursmin, prveg, ta850, fracOutLut, opottemprmadvect, basin, po4os, ch4, fDeforestToAtmos, sisali, wap, mlotstsq, ph, orog, cfc11, rh, nLitter, phos, thetao,...
cordex_ig45{CNRM-CM6-1-HR, GISS-E2-1-G, ACCESS-CM2, NorESM2-MM, MPI-ESM1-2-LR, GFDL-ESM4, ERA5, EC-Earth3, ACCESS-ESM1-5, CMCC-ESM2, FGOALS-g3, MRI-ESM2-0}{ACCESS-ESM1-5, CMCC-ESM2, MRI-ESM2-0, EC-Earth3, MPI-ESM1-2-LR, CNRM-CM6-1-HR, ACCESS-CM2, GFDL-ESM4, FGOALS-g3, GISS-E2-1-G, ERA5, NorESM2-MM}{20km regional projections for CORDEX-CMIP6 from the Queensland Future Climate Science Program}{none}{1day, 1mon, fx, 1hr}{snw, hus850, ta1000, ua500, ta850, hus1000, tasmin, va400, cll, orog, zmla, sfcWind, hus700, zg250, sund, va300, vas, va700, tauu, ua700, hus500, clt, uas, rlds, rsut, zg200, mrro, tauv, tas, rsd...{1hr, 1mon, fx, 1day}{va100m, va850, zg400, rsut, hus500, ua200, hus600, hus700, rsdt, va400, ta850, ua250, tasmax, hus250, va600, prhmax, va1000, psl, zg925, ta925, clh, ua400, snw, cll, sftlaf, orog, mrros, hus200, ...
era5_rt52{era5, era5-preliminary, era5-1, era5t, era5-derived}{era5t, era5-preliminary, era5-derived, era5, era5-1}{ERA5 fifth generation model reanalysis of global climate from ECMWF}{none}{1day, 1mon, 1hr}{tclw, Rainf, msdrswrfcs, stl3, Wind, mdww, u, tisr, es, msr, acwh, msl, vikee, msdwuvrf, mtnlwrfcs, msdwlwrfcs, flsr, viozn, msqs, vioze, mssror, mlssr, csf, mror, Snowf, mdts, z, swvl1, sdfor, 1...{1hr, 1mon, 1day}{vo, mcpr, o3, bfi, metss, licd, tcw, ltlt, slhf, lict, istl2, tcc, msnswrfcs, lshf, tcwv, lsm, w, dndzn, mwp1, 10si, ci, mdts, msdwswrf, vithen, msdrswrf, msmr, tciw, msr, mser, mtnswrfcs, strdc,...
esmvaltool-obs-ct11{CALIPSO-GOCCP, GISTEMP, ESACCI-LANDCOVER, REGEN, Duveiller2018, PATMOS-x, CT2019, Kadow2020, JRA-25, NCEP-NCAR-R1, AIRS-2-0, TCOM-N2O, AIRS, ESACCI-OZONE, JRA-55, GCP2018, ESACCI-SST, MODIS-1-0, ...{ERA-Interim-Land, CALIOP, ESRL, CFSR, OSI-450-sh, CRU, AGCD, ISCCP-FH, HadCRUT5, ISCCP, NOAA-ERSSTv3b, GHCN-CAMS, BerkeleyEarth, Landschuetzer2020, ghgcci, CERES-EBAF, CowtanWay, AIRS-2-0, MODIS-...{Replicated observational datasets for ESMValTool CT11}{aerosol, ocean, landIce, none, land, atmos}{1yr, 1day, 1mon, fx}{ch4, chl, sm, clhcalipso, rldscs, burntArea, rsutcs, tasmin, lwp, husNobs, rsdscs, orog, clrcalipso, cllcalipso, husStderr, sfcWind, od550aerStderr, cl, vas, alb, nbp, dpco2, taStderr, Omon, tauu...{none, landIce, aerosol, atmos, land, ocean}{1yr, 1mon, fx, 1day}{cltStderr, va, spco2, dos, hus, clrcalipso, areacello, ta, rsut, tdps, talk, rsdt, tasa, tasmax, clw, tsn, prwErr, ch4, shrubFrac, psl, wap, burntArea, od550lt1aer, ph, cltisccp, baresoilFrac, do...
narclim2_zz63{UKESM1-0-LL, NorESM2-MM, MPI-ESM1-2-HR, ACCESS-ESM1-5, EC-Earth3-Veg}{ACCESS-ESM1-5, UKESM1-0-LL, MPI-ESM1-2-HR, EC-Earth3-Veg, NorESM2-MM}{NARCliM2.0 climate pojections, downscaled from ACCESS-ESM1-5 over Australasia at ~18km resolution.}{atmos}{1day, fx, 1yr, 3hr, 1hr, 1mon}{FD, va250m, snw, mrfsos, FFDI, hus850, R20mm, ta1000, wa500, TXgt50p, ua500, rldscs, vegFrac, ta850, hus1000, rsdsdir, DTR, rsutcs, tasmin, va400, HDDheat18, rsdscs, orog, zmla, sfcWind, hus700, ...{1mon, 3hr, 1day, 1yr, 1hr, fx}{va100m, hus750, va750, SPI03, va850, zg400, hus50m, ta100, TXgt50p, HWA, DTR, R10mm, SDII, rsut, hus500, ua200, hus600, hus700, rsdt, va400, mrfsos, ta850, TN90p, ua250, tasmax, hus250, va200m, v...
panant-0025-zstar-ACCESSyr2{SIS2, MOM6}{0.025 degree (MOM6+SIS2) Pan-Antarctic regional model configuration under 1990-1991 JRA55-do repeat year forcing.}{seaIce, ocean}{1day, 1mon, fx}{salt_flux_added, average_T1, geolon_c, wet, time, wfo, vmo, areacello_cv, dxCv, yq, yTe, yB, xB, siconc, average_T2, rhopot2, dxt, xh, agessc, geolat_c, geolat, umo_2d, wet_u, uo, FA_X, volcello,...{ocean, seaIce}{1mon, fx, 1day}{vo, wet_u, geolat_u, net_melt, sob, areacello, time, time_bnds, dyCu, T_adx, geolon, average_T2, FA_X, salt_flux_added, yq, rho2_l, rho2_i, tob, dxCv, yTe, z_l_sub01, yT, geolat_c, thetao, geolon...
panant-005-zstar-ACCESSyr2{SIS2, MOM6}{0.05 degree (MOM6+SIS2) Pan-Antarctic regional model configuration under 1990-1991 JRA55-do repeat year forcing.}{seaIce, ocean}{1day, 1mon, fx}{salt_flux_added, average_T1, geolon_c, wet, time, wfo, vmo, areacello_cv, dxCv, yq, yTe, yB, xB, siconc, average_T2, rhopot2, dxt, xh, agessc, geolat_c, geolat, umo_2d, wet_u, uo, FA_X, volcello,...{ocean, seaIce}{1mon, fx, 1day}{vo, wet_u, geolat_u, net_melt, sob, areacello, time, time_bnds, dyCu, T_adx, geolon, average_T2, FA_X, salt_flux_added, yq, rho2_l, rho2_i, tob, dxCv, yTe, z_l_sub01, yT, geolat_c, thetao, geolon...
panant-01-hycom1-v13{SIS2, MOM6}{0.1 degree (MOM6+SIS2) Pan-Antarctic regional model configuration under 1990-1991 JRA55-do repeat year forcing with a hybrid (HYCOM1) vertical coordinate..}{seaIce, ocean}{1day, 1mon, fx}{rho2_i, wet_u, wet_c, dyt, yT, average_T1, uo, u, geolon_c, zl, z_i, tauvo, h, z_l, volcello, so, vmo_2d, sithick, wet, rho2_l, dyCu, sob, dyCv, vo, geolat_u, geolon_v, time, umo, wfo, time_bnds,...{ocean, seaIce}{1mon, fx, 1day}{vo, wet_u, geolat_v, friver, umo_2d, thetao, vmo, geolat_u, geolon_v, sithick, xT, mlotst, sob, wet, nv, zos, wet_c, wfo, tauvo, time, z_i, geolat, areacello_bu, time_bnds, areacello, areacello_c...
panant-01-zstar-ACCESSyr2{SIS2, MOM6}{0.1 degree (MOM6+SIS2) Pan-Antarctic regional model configuration under 1990-1991 JRA55-do repeat year forcing.}{seaIce, ocean}{1day, 1mon, fx}{salt_flux_added, Kd_shear, intz_diffu_2d, average_T1, geolon_c, taux, IY_TRANS, hf_dudt_2d, rvxu, dudt, wet, Kd_BBL, time, wfo, vmo, areacello_cv, dxCv, CAv, intz_CAu_2d, hf_dvdt_2d, yq, yTe, yB,...{ocean, seaIce}{1mon, fx, 1day}{vo, wet_u, geolat_u, net_melt, T_adx_2d, Kd_BBL, IY_TRANS, sob, dudt, Kd_shear, areacello, time, time_bnds, hf_dvdt_2d, col_height, Kd_ePBL, T_adx, dyCu, geolon, average_T2, intz_gKEv_2d, salt_fl...
panant-01-zstar-v13{SIS2, MOM6}{0.1 degree (MOM6+SIS2) Pan-Antarctic regional model configuration under 1990-1991 JRA55-do repeat year forcing.}{seaIce, ocean}{1day, 1mon, fx}{intz_diffu_2d, average_T1, geolon_c, taux, hf_dudt_2d, wet, time, wfo, vmo, areacello_cv, dxCv, intz_CAu_2d, hf_dvdt_2d, yq, yTe, siconc, average_T2, rhopot2, tauy, dxt, xh, taux_bot, geolat_c, g...{ocean, seaIce}{1mon, fx, 1day}{vo, wet_u, friver, geolat_u, sob, areacello, time, time_bnds, col_height, hf_dvdt_2d, dyCu, geolon, average_T2, intz_gKEv_2d, yq, rho2_l, rho2_i, tob, PRCmE, dxCv, yTe, yT, intz_CAu_2d, geolat_c,...
rcm_ccam_hq89{ACCESS-CM2, NorESM2-MM, CNRM-ESM2-1, ERA5, CESM2, ACCESS-ESM1-5, EC-Earth3, CMCC-ESM2}{ACCESS-ESM1-5, CMCC-ESM2, EC-Earth3, CESM2, ACCESS-CM2, CNRM-ESM2-1, ERA5, NorESM2-MM}{CMIP6 Regional Climate Model Data from CCAM for Australian Climate Service}{none}{1day, 6hr, fx, 1hr, 1mon}{va250m, snw, mrfsos, hus850, ta1000, wa500, ua500, ta850, hus1000, rsdsdir, tasmin, va400, cll, orog, wsgsmax, zmla, sfcWind, hus700, zg250, ua250m, wa300, sund, va300, vas, ua200m, va700, tauu, ...{1mon, 6hr, 1day, 1hr, fx}{va100m, va850, zg400, rsut, hus500, ua200, hus600, hus700, rsdt, va400, mrfsos, ta850, ua250, tasmax, hus250, va200m, va600, prhmax, va1000, z0, psl, zg925, wa1000, ta925, clh, ua400, snw, wa850,...
shackleton_v4_jk72{Shackleton/Denman Ice Shelf-ocean model application built with ROMSIceShelf}{seaIce}{5day}{M3nudg, Tnudg, hc, u, Vtransform, LtracerSrc, Tobc_in, s_w, rdrg2, v, svstr, LnudgeM2CLM, m, xl, Lm2CLM, nRST, y_psi, x_rho, lon_psi, FSobc_out, lat_rho, LnudgeM3CLM, mask_rho, M2obc_in, FSobc_in...{rho, ndefHIS, w, LwSrc, dtfast, Tobc_out, Zob, zice, Tcline, x_rho, LsshCLM, rdrg, nl_tnu2, sustr, lon_psi, Tnudg, lon_u, u, nAVG, AKv, LnudgeTCLM, ocean_time, rho0, theta_s, Znudg, ntsAVG, LuvSp...
\n", @@ -1545,311 +1327,116 @@ "cat" ] }, - { - "cell_type": "markdown", - "id": "9ab44e8f-b16f-405c-b67d-b5702281a412", - "metadata": {}, - "source": [ - "The dictionary below specifies experiment, start and ending times for each model we can use (MOM5 or MOM6). \n", - "\n", - "If you want a different experiment, or a different time period, change the necessary values.\n", - "\n", - "(We refer to the [tutorial](https://cosima-recipes.readthedocs.io/en/latest/Tutorials/Using_Explorer_tools.html) for more details on how to explore the available experiments and available output variables that exist in the cookbook database.)" - ] - }, { "cell_type": "code", - "execution_count": 43, - "id": "bc296c6e-70b7-4a89-8400-257563ad3d3b", + "execution_count": 4, + "id": "c01e897c-a078-4122-83c5-d11079ee85d4", "metadata": {}, "outputs": [], "source": [ - "model_args = {\"mom5\": {\"expt\": \"01deg_jra55v13_ryf9091\",\n", - " \"data_var\": \"tx_trans_int_z\",\n", - " \"bathy_var\" : \"ht\",\n", - " \"start_date\": \"203[5-9].*|204[0-9].*\",\n", - " \"frequency\" : \"1mon\",\n", - " },\n", - "\n", - " \"mom6\": {\"expt\": \"panant-01-zstar-v13\",\n", - " \"data_var\": \"umo_2d\",\n", - " \"bathy_var\": \"deptho\",\n", - " \"start_date\": \"203[5-9].*|204[0-9].*\",\n", - " \"frequency\" : \"1mon\",\n", - " }\n", - "}\n", - "\n", - "# It shouldn't be necessary to use these, but we keep them around to show how to select a time range in xarray\n", - "start_time = \"2035-01-01\"\n", - "end_time = \"2050-01-01\"" - ] - }, - { - "cell_type": "markdown", - "id": "3aabdfc7-530f-4095-a679-3029bd3b1d3c", - "metadata": {}, - "source": [ - "## Functions to load data\n", - "\n", - "The functions below will load the necessary data, and calculate the barotropic streamfunction" + "expt = \"panant-01-zstar-v13\"\n", + "transport_var = \"umo_2d\"\n", + "bathy_var = \"deptho\"\n", + "freq = \"1mon\"\n", + "start_date_regex = r\"203[5-9].*|204[0-9].*\"\n", + "start_date = \"2035-01-01\"\n", + "end_date = \"2050-01-01\"\n", + "rho0 = 1035 #kg/m3\n", + "lat = \"yh\"\n", + "lon = \"xq\"" ] }, { "cell_type": "code", - "execution_count": 44, - "id": "e806aeca-ecfe-4cc6-805b-aad30f5d6858", + "execution_count": 5, + "id": "b628cedb-85d3-4bdc-86a8-8d1c3a105bcb", "metadata": {}, "outputs": [], "source": [ - "def load_zonal_transport(model):\n", - " \"\"\"Load the zonal volume transport from ``model`` (either 'mom5' or 'mom6').\"\"\"\n", - "\n", - " # the reference density\n", - " ρ0 = 1035 * ureg.kilogram / ureg.meter**3\n", - "\n", - " experiment = model_args[model][\"expt\"]\n", - " start_date = model_args[model][\"start_date\"]\n", - " var = model_args[model][\"data_var\"]\n", - " freq = model_args[model][\"frequency\"]\n", + "def load_mom6_zonal_mass_transport(transport_var: str, rho0: int|float) -> xr.DataArray:\n", + " \"\"\"\n", + " Load mom6 zonal mass transport, and convert it to Sverdrups\n", + " \"\"\"\n", + " mass_transport = (\n", + " cat[expt]\n", + " .search(variable=transport_var,\n", + " start_date=start_date_regex,\n", + " frequency=freq)\n", + " .to_dask(xarray_open_kwargs={\n", + " \"chunks\":{\"time\": 3},\n", + " \"decode_timedelta\": False})[transport_var]\n", + " .sel(time=slice(start_date, end_date))\n", + " )\n", "\n", - " mass_transport = cat[experiment].search(\n", - " variable=var, start_date=start_date,frequency=freq\n", - " ).to_dask(xarray_open_kwargs={ \"chunks\" : { \"time\" : 3}, \"decode_timedelta\" : False }\n", - " )[var] # Index into var to get a DataArray for the variable of interest from the dataset intake loads\n", - " \n", - " # ensure we get the time-slice we wanted\n", - " mass_transport = mass_transport.sel(time = slice(start_time, end_time))\n", - " \n", - " # use pint to properly deal with units and unit conversions\n", - " mass_transport = mass_transport.pint.quantify()\n", - " \n", - " volume_transport = mass_transport / ρ0\n", - " volume_transport = volume_transport.pint.to('sverdrup') # convert units to Sv\n", - " volume_transport.attrs['units'] = volume_transport.pint.units\n", + " # convert to volume transport (m^3/s)\n", + " volume_transport = mass_transport / rho0\n", "\n", + " # convert to Sverdrups\n", + " volume_transport = volume_transport / 1e6\n", + " volume_transport.attrs[\"units\"] = \"Sv\"\n", " return volume_transport\n", "\n", - "def calculate_streamfunction(model):\n", - " \"\"\"Compute the streamfunction psi for ``model`` (either 'mom5' or 'mom6').\"\"\"\n", + "def get_land_mask(bathy_var: str)-> xr.DataArray:\n", + " \"\"\"\n", + " 1 = ocean, Nan = land\n", + " \"\"\"\n", + " mask = (\n", + " cat[expt]\n", + " .search(variable=bathy_var)\n", + " .to_dask(xarray_open_kwargs={\n", + " \"chunks\":{\"time\": 3}})[bathy_var]\n", + " )\n", + " return xr.where(np.isnan(mask), 1, np.nan).rename(\"land_mask\")\n", "\n", - " volume_transport = load_zonal_transport(model)\n", - "\n", - " psi = volume_transport.cf.cumsum('latitude')\n", - " \n", - " psi = psi.rename('psi')\n", - " psi.attrs['Standard name'] = 'Barotropic streamfunction'\n", - " psi.attrs['units'] = psi.pint.units\n", - " psi.pint.quantify()\n", - " \n", + "def calculate_streamfunction(transport_var, rho0, lat) -> xr.DataArray:\n", + " \"\"\"\n", + " Compute barotropic streamfunction (Sv)\n", + " \"\"\"\n", + " volume_transport = load_mom6_zonal_mass_transport(transport_var, rho0)\n", + " psi = volume_transport.cumsum(dim=lat)\n", + " psi.name = \"psi\"\n", + " psi.attrs[\"standard_name\"] = \"Barotropic streamfunction\"\n", + " psi.attrs[\"units\"] = \"Sv\"\n", " return psi\n", "\n", - "def get_land_mask(model):\n", - " experiment = model_args[model][\"expt\"]\n", - " var = model_args[model][\"bathy_var\"]\n", - " \n", - " bathymetry = cat[experiment].search(\n", - " variable=var\n", - " ).to_dask(xarray_open_kwargs={ \"chunks\" : { \"time\" : 3} }\n", - " )[var] # Index into var to get a DataArray for the variable of interest from the dataset intake loads\n", - "\n", - "\n", - " land_mask = xr.where(np.isnan(bathymetry), 1, np.nan)\n", - " land_mask = land_mask.rename('land_mask')\n", - " \n", - " return land_mask" - ] - }, - { - "cell_type": "markdown", - "id": "a83553a0-76b6-4f97-bd5c-5b4aac17a6cd", - "metadata": {}, - "source": [ - "## Calculate the barotropic streamfunction and its time-mean" - ] - }, - { - "cell_type": "markdown", - "id": "4062e83e-7641-4dac-a7e0-0e395957257f", - "metadata": {}, - "source": [ - "Now we compute the streamfunction using `calculate_streamfunction()`." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "79258bef-4223-4a05-a8fa-64eb15e98573", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "psi = {}\n", - "psi['mom5'] = calculate_streamfunction('mom5')\n", - "psi['mom6'] = calculate_streamfunction('mom6')" - ] - }, - { - "cell_type": "markdown", - "id": "377d1f18-04ad-4711-a4a4-cfd6450057e7", - "metadata": {}, - "source": [ - "Calculate the time-mean of the streamfunction." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "b79ad7c5-c9c8-426e-8994-7661700c8e12", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-08-01 11:23:47,915 - distributed.protocol.pickle - ERROR - Failed to serialize .\n", - "Traceback (most recent call last):\n", - " File \"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.06/lib/python3.11/site-packages/distributed/protocol/pickle.py\", line 60, in dumps\n", - " result = pickle.dumps(x, **dump_kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "_pickle.PicklingError: Can't pickle : it's not the same object as pint.Quantity\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.06/lib/python3.11/site-packages/distributed/protocol/pickle.py\", line 65, in dumps\n", - " pickler.dump(x)\n", - "_pickle.PicklingError: Can't pickle : it's not the same object as pint.Quantity\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.06/lib/python3.11/site-packages/distributed/protocol/pickle.py\", line 77, in dumps\n", - " result = cloudpickle.dumps(x, **dump_kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.06/lib/python3.11/site-packages/cloudpickle/cloudpickle.py\", line 1537, in dumps\n", - " cp.dump(obj)\n", - " File \"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.06/lib/python3.11/site-packages/cloudpickle/cloudpickle.py\", line 1303, in dump\n", - " return super().dump(obj)\n", - " ^^^^^^^^^^^^^^^^^\n", - "TypeError: cannot pickle 'weakref.ReferenceType' object\n", - "2025-08-01 11:23:54,629 - distributed.protocol.pickle - ERROR - Failed to serialize .\n", - "Traceback (most recent call last):\n", - " File \"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.06/lib/python3.11/site-packages/distributed/protocol/pickle.py\", line 60, in dumps\n", - " result = pickle.dumps(x, **dump_kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "_pickle.PicklingError: Can't pickle : it's not the same object as pint.Quantity\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.06/lib/python3.11/site-packages/distributed/protocol/pickle.py\", line 65, in dumps\n", - " pickler.dump(x)\n", - "_pickle.PicklingError: Can't pickle : it's not the same object as pint.Quantity\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.06/lib/python3.11/site-packages/distributed/protocol/pickle.py\", line 77, in dumps\n", - " result = cloudpickle.dumps(x, **dump_kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.06/lib/python3.11/site-packages/cloudpickle/cloudpickle.py\", line 1537, in dumps\n", - " cp.dump(obj)\n", - " File \"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.06/lib/python3.11/site-packages/cloudpickle/cloudpickle.py\", line 1303, in dump\n", - " return super().dump(obj)\n", - " ^^^^^^^^^^^^^^^^^\n", - "TypeError: cannot pickle 'weakref.ReferenceType' object\n" - ] - } - ], - "source": [ - "psi_mean = {}\n", - "\n", - "psi_mean['mom5'] = psi['mom5'].cf.mean('time').load()\n", - "psi_mean['mom6'] = psi['mom6'].cf.mean('time').load()" - ] - }, - { - "cell_type": "markdown", - "id": "5b7dc7c6-132d-4159-98d8-c9e2926d0301", - "metadata": {}, - "source": [ - "### Don't worry about these warnings!" - ] - }, - { - "cell_type": "markdown", - "id": "1abf4658-bc2b-4543-9f78-608cbf0cf413", - "metadata": { - "tags": [] - }, - "source": [ - "## Let's plot\n", - "\n", - "We define a nice plotting method using the model's land mask as land..." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "b934157a-4417-444d-9345-39b8dd65970f", - "metadata": {}, - "outputs": [], - "source": [ - "land_mask = {}\n", - "land_mask['mom5'] = get_land_mask('mom5')\n", - "land_mask['mom6'] = get_land_mask('mom6')" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "7513963d-f43c-46ef-8aa8-d152a391a9e9", - "metadata": {}, - "outputs": [], - "source": [ "def circumpolar_map():\n", - " fig = plt.figure(figsize = (12, 8))\n", - " ax = plt.axes(projection = ccrs.SouthPolarStereo())\n", - " ax.set_extent([-180, 180, -80, -40], crs = ccrs.PlateCarree())\n", - " ax.set_facecolor('lightgrey')\n", + " \"\"\"\n", + " Set up South Polar Stereo map with circular boundary.\n", + " \"\"\"\n", + " fig = plt.figure(figsize=(12, 8))\n", + " ax = plt.axes(projection=ccrs.SouthPolarStereo())\n", + " ax.set_extent([-180, 180, -80, -40], crs=ccrs.PlateCarree())\n", + " ax.set_facecolor(\"lightgrey\")\n", + "\n", " # Map the plot boundaries to a circle\n", " theta = np.linspace(0, 2 * np.pi, 100)\n", " center, radius = [0.5, 0.5], 0.5\n", " verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", " circle = mpath.Path(verts * radius + center)\n", - " ax.set_boundary(circle, transform = ax.transAxes)\n", + " ax.set_boundary(circle, transform=ax.transAxes)\n", "\n", " return fig, ax" ] }, - { - "cell_type": "markdown", - "id": "db94c03b-06b9-4de1-a1cb-65468504d9b1", - "metadata": {}, - "source": [ - "... and finally, it's time to visualise the streamfunction!" - ] - }, { "cell_type": "code", - "execution_count": 50, - "id": "1f41d8d6-b763-4f6b-8ba0-8ef7f9f5f5d9", + "execution_count": 6, + "id": "740c798a-3e89-4335-86e2-9219ebf2fa84", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.05/lib/python3.11/site-packages/intake_esm/core.py:259: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " records = grouped.get_group(internal_key).to_dict(orient='records')\n", + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.05/lib/python3.11/site-packages/intake_esm/core.py:259: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " records = grouped.get_group(internal_key).to_dict(orient='records')\n" + ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1859,32 +1446,51 @@ } ], "source": [ - "levels = np.arange(-50, 150, 10) # levels used in contour plots\n", + "psi = calculate_streamfunction(transport_var, rho0, lat)\n", + "psi_mean = psi.mean(dim=\"time\").load()\n", + "land_mask = get_land_mask(bathy_var)\n", "\n", - "for model in ['mom5', 'mom6']:\n", + "fig, ax = circumpolar_map()\n", + "levels = np.arange(-50, 150, 10) # levels used in contour plots\n", "\n", - " fig, ax = circumpolar_map()\n", + "psi_mean.plot.contourf(\n", + " ax=ax,\n", + " x=lon,\n", + " y=lat,\n", + " transform=ccrs.PlateCarree(),\n", + " levels=levels,\n", + " extend=\"both\",\n", + " cmap=cmocean.cm.speed,\n", + " cbar_kwargs={\"label\": r\"$\\psi$ (Sv)\"},\n", + ")\n", "\n", - " psi_mean[model].cf.plot.contourf(ax = ax,\n", - " x = 'longitude',\n", - " y = 'latitude', \n", - " transform = ccrs.PlateCarree(),\n", - " levels = levels,\n", - " extend = 'both',\n", - " cmap = cmocean.cm.speed,\n", - " cbar_kwargs = {'label': '$\\psi$ (Sv)'})\n", - " land_mask[model].plot.contourf(ax = ax, colors = 'lightgrey', add_colorbar = False, \n", - " zorder = 2, transform = ccrs.PlateCarree())\n", + "land_mask.plot.contourf(\n", + " ax=ax,\n", + " colors=\"lightgrey\",\n", + " add_colorbar=False,\n", + " zorder=2,\n", + " transform=ccrs.PlateCarree(),\n", + ")\n", "\n", - " plt.title('Barotropic streamfunction from ' + model)" + "plt.title(\"Barotropic streamfunction (MOM6)\")\n", + "plt.tight_layout()\n", + "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf97e1b0-9847-47a1-b823-38daed57860d", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:analysis3-25.05]", "language": "python", - "name": "python3" + "name": "conda-env-analysis3-25.05-py" }, "language_info": { "codemirror_mode": { @@ -1896,7 +1502,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.13" + "version": "3.11.12" } }, "nbformat": 4, From 718aa241abb6083074e5a0210d0a63a043556ab6 Mon Sep 17 00:00:00 2001 From: minghangli-uni <24727729+minghangli-uni@users.noreply.github.com> Date: Tue, 9 Sep 2025 09:07:09 +1000 Subject: [PATCH 2/2] Add Minghang Li to .zenodo.json --- .zenodo.json | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/.zenodo.json b/.zenodo.json index c5072753..1f79f129 100644 --- a/.zenodo.json +++ b/.zenodo.json @@ -247,6 +247,12 @@ "name": "Day, Noah", "orcid": "0000-0003-4176-7956", "type":"ProjectMember" + }, + { + "affiliation": "Australian National University, Australia's Climate Simulator (ACCESS-NRI)", + "name": "Li, Minghang", + "orcid": "0000-0002-6167-9999", + "type":"ProjectMember" } ], "keywords": [