{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### **Description**\n", "\n", "Recipe showing how to extract the Overturning in the Subpolar North Atlantic (OSNAP) trans-basin hydrographic section using annual-mean outputs from the National Oceanography Centre Near-Present-Day global eORCA1 configuration of NEMO forced using ERA-5 climatologically adjusted atmospheric forcing from 1976-2024.\n", "\n", "For more details on this model configuration and the available outputs, users can explore the Near-Present-Day documentation [here](https://noc-msm.github.io/NOC_Near_Present_Day/).\n", "\n", "---\n", "### **Background**\n", "The Overturning in the Subpolar North Atlantic Program (OSNAP) is an international program designed to provide a continuous record of the full-water column, trans-basin fluxes of heat, mass and freshwater in the subpolar North Atlantic.\n", "\n", "The OSNAP observing system comprises of two trans-basin arrays: extending from southern Labrador to the southwestern tip of Greenland across the Labrador Sea (OSNAP West), and from the southeastern tip of Greenland to Scotland (OSNAP East).\n", "\n", "The diapycnal overturning stream function is used to characterise the strength and structure of the AMOC in density-space (e.g., $\\sigma_{0}$ or $\\sigma_{2}$) across OSNAP and can be defined at time $t$ as follows:\n", "\n", "$$\\Psi_{\\sigma}(\\sigma, t) = \\int_{\\sigma_{min}}^{\\sigma} \\int_{x_w}^{x_e} v(x, \\sigma, t) \\ dx \\ d\\sigma$$\n", "\n", "where the velocity field $v(x, \\sigma, t)$ normal to OSNAP is first integrated zonally between the western $x_w$ and eastern $x_e$ boundaries of the basin, before being accumulated from the sea surface $\\sigma_{min}$ to a specified isopycnal $\\sigma$.\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# -- Import required packages -- #\n", "import gsw\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "\n", "from nemo_cookbook import NEMODataTree\n", "\n", "xr.set_options(display_style=\"text\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Using Dask**\n", "\n", "**Optional: Connect Client to Dask Local Cluster to run analysis in parallel.**\n", "\n", "Note that, although using Dask is not strictly necessary for this simple example using eORCA1, if we wanted to generalise this recipe to eORCA025 or eORCA12 outputs, using Dask would be essential to avoid unnecessary slow calculations using only a single process." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# -- Initialise Dask Local Cluster -- #\n", "import dask\n", "from dask.distributed import Client, LocalCluster\n", "\n", "# Update temporary directory for Dask workers:\n", "dask.config.set({'temporary_directory': '/dssgfs01/working/otooth/Diagnostics/nemo_cookbook/recipes/',\n", " 'local_directory': '/dssgfs01/working/otooth/Diagnostics/nemo_cookbook/recipes/'\n", " })\n", "\n", "# Create Local Cluster:\n", "cluster = LocalCluster(n_workers=5, threads_per_worker=2, memory_limit='8GB')\n", "client = Client(cluster)\n", "client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Accessing NEMO Model Data**\n", "**Let's begin by loading the grid variables for our eORCA1 NEMO model from the [JASMIN Object Store](https://help.jasmin.ac.uk/docs/short-term-project-storage/using-the-jasmin-object-store/)**. \n", "\n", "**Alternatively, you can replace the ``domain_filepath`` below with a file path to your domain_cfg.nc file and read this with xarray's ``open_dataset()`` function.**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 709MB\n",
       "Dimensions:       (y: 331, x: 360, nav_lev: 75)\n",
       "Coordinates:\n",
       "  * nav_lev       (nav_lev) int64 600B 0 1 2 3 4 5 6 7 ... 68 69 70 71 72 73 74\n",
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       "Data variables: (12/49)\n",
       "    atlmsk        (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    bathy_metry   (y, x) float32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    bottom_level  (y, x) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    e1f           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    e1t           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    e1u           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    ...            ...\n",
       "    top_level     (y, x) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    umask         (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "    umaskutil     (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    vmask         (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "    vmaskutil     (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    wmask         (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CfgName:    UNKNOWN\n",
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       "    Iperio:     1\n",
       "    Jperio:     0\n",
       "    NFold:      1\n",
       "    NFtype:     F\n",
       "    VertCoord:  zps\n",
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       "    file_name:  mesh_mask.nc\n",
       "    TimeStamp:  01/03/2025 22:19:49 -0000

" ], "text/plain": [ " Size: 709MB\n", "Dimensions: (y: 331, x: 360, nav_lev: 75)\n", "Coordinates:\n", " * nav_lev (nav_lev) int64 600B 0 1 2 3 4 5 6 7 ... 68 69 70 71 72 73 74\n", " * x (x) int64 3kB 0 1 2 3 4 5 6 7 ... 353 354 355 356 357 358 359\n", " * y (y) int64 3kB 0 1 2 3 4 5 6 7 ... 324 325 326 327 328 329 330\n", "Data variables: (12/49)\n", " atlmsk (y, x) int8 119kB dask.array\n", " bathy_metry (y, x) float32 477kB dask.array\n", " bottom_level (y, x) int32 477kB dask.array\n", " e1f (y, x) float64 953kB dask.array\n", " e1t (y, x) float64 953kB dask.array\n", " e1u (y, x) float64 953kB dask.array\n", " ... ...\n", " top_level (y, x) int32 477kB dask.array\n", " umask (nav_lev, y, x) int8 9MB dask.array\n", " umaskutil (y, x) int8 119kB dask.array\n", " vmask (nav_lev, y, x) int8 9MB dask.array\n", " vmaskutil (y, x) int8 119kB dask.array\n", " wmask (nav_lev, y, x) int8 9MB dask.array\n", "Attributes:\n", " CfgName: UNKNOWN\n", " CfgIndex: -999\n", " Iperio: 1\n", " Jperio: 0\n", " NFold: 1\n", " NFtype: F\n", " VertCoord: zps\n", " IsfCav: 0\n", " file_name: mesh_mask.nc\n", " TimeStamp: 01/03/2025 22:19:49 -0000" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define directory path to ancillary files:\n", "domain_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/domain_cfg\"\n", "\n", "# Open eORCA1 NEMO model domain_cfg:\n", "ds_domain = xr.open_zarr(domain_url, consolidated=True, chunks={})\n", "\n", "ds_domain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, we need to open the conservative temperature and absolute salinity variables stored at T-points as a single dataset.**\n", "\n", "**Here, for simplicity, we will only consider the time-period from 2014-2023**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 9GB\n",
       "Dimensions:                (time_counter: 10, y: 331, x: 360, deptht: 75,\n",
       "                            axis_nbounds: 2)\n",
       "Coordinates:\n",
       "  * deptht                 (deptht) float32 300B 0.5058 1.556 ... 5.902e+03\n",
       "    nav_lat                (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    nav_lon                (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    time_centered          (time_counter) datetime64[ns] 80B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * time_counter           (time_counter) datetime64[ns] 80B 2014-07-02T12:00...\n",
       "Dimensions without coordinates: y, x, axis_nbounds\n",
       "Data variables: (12/75)\n",
       "    berg_latent_heat_flux  (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    deptht_bounds          (deptht, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
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       "    evs                    (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ficeberg               (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ...                     ...\n",
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       "    zos                    (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    zossq                  (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    sigma0                 (time_counter, deptht, y, x) float64 715MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    name:         OUTPUT/eORCA1_1y_grid_T\n",
       "    description:  ocean T grid variables\n",
       "    title:        ocean T grid variables\n",
       "    Conventions:  CF-1.6\n",
       "    timeStamp:    2024-Dec-09 10:28:01 GMT\n",
       "    uuid:         89824b34-5d44-4531-8344-fe1ce39fbb7b

" ], "text/plain": [ " Size: 9GB\n", "Dimensions: (time_counter: 10, y: 331, x: 360, deptht: 75,\n", " axis_nbounds: 2)\n", "Coordinates:\n", " * deptht (deptht) float32 300B 0.5058 1.556 ... 5.902e+03\n", " nav_lat (y, x) float64 953kB dask.array\n", " nav_lon (y, x) float64 953kB dask.array\n", " time_centered (time_counter) datetime64[ns] 80B dask.array\n", " * time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00...\n", "Dimensions without coordinates: y, x, axis_nbounds\n", "Data variables: (12/75)\n", " berg_latent_heat_flux (time_counter, y, x) float32 5MB dask.array\n", " deptht_bounds (deptht, axis_nbounds) float32 600B dask.array\n", " e3t (time_counter, deptht, y, x) float32 357MB dask.array\n", " empmr (time_counter, y, x) float32 5MB dask.array\n", " evs (time_counter, y, x) float32 5MB dask.array\n", " ficeberg (time_counter, y, x) float32 5MB dask.array\n", " ... ...\n", " vohfcisf (time_counter, deptht, y, x) float32 357MB dask.array\n", " vohflisf (time_counter, deptht, y, x) float32 357MB dask.array\n", " vowflisf (time_counter, deptht, y, x) float32 357MB dask.array\n", " zos (time_counter, y, x) float32 5MB dask.array\n", " zossq (time_counter, y, x) float32 5MB dask.array\n", " sigma0 (time_counter, deptht, y, x) float64 715MB dask.array\n", "Attributes:\n", " name: OUTPUT/eORCA1_1y_grid_T\n", " description: ocean T grid variables\n", " title: ocean T grid variables\n", " Conventions: CF-1.6\n", " timeStamp: 2024-Dec-09 10:28:01 GMT\n", " uuid: 89824b34-5d44-4531-8344-fe1ce39fbb7b" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define directory path to model output files:\n", "gridT_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/T1y\"\n", "\n", "# Construct NEMO model T-grid dataset, including conservative temperature (deg C) and absolute salinity (g/kg) variables:\n", "ds_gridT = xr.open_zarr(gridT_url, consolidated=True, chunks={}).sel(time_counter=slice(\"2014-01\", \"2023-12\"))\n", "\n", "# Calculate potential density anomaly referenced to the sea surface (kg/m3):\n", "ds_gridT['sigma0'] = gsw.density.sigma0(CT=ds_gridT['thetao_con'], SA=ds_gridT['so_abs'])\n", "ds_gridT['sigma0'].name = 'sigma0'\n", "\n", "ds_gridT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, we need to import the zonal & meridional velocities and vertical grid cell thicknesses stored at U- & V-points, respectively.**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 2GB\n",
       "Dimensions:               (depthu: 75, axis_nbounds: 2, time_counter: 10,\n",
       "                           y: 331, x: 360)\n",
       "Coordinates:\n",
       "  * depthu                (depthu) float32 300B 0.5058 1.556 ... 5.902e+03\n",
       "    nav_lat               (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    nav_lon               (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    time_centered         (time_counter) datetime64[ns] 80B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * time_counter          (time_counter) datetime64[ns] 80B 2014-07-02T12:00:...\n",
       "Dimensions without coordinates: axis_nbounds, y, x\n",
       "Data variables: (12/14)\n",
       "    depthu_bounds         (depthu, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "    e3u                   (time_counter, depthu, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    hfx                   (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfx_adv               (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfx_diff              (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    sozosatr              (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 160B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    u2o                   (time_counter, depthu, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    umo                   (time_counter, depthu, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    umo_vint              (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    uo                    (time_counter, depthu, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    uo_eiv                (time_counter, depthu, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    name:         OUTPUT/eORCA1_1y_grid_U\n",
       "    description:  ocean U grid variables\n",
       "    title:        ocean U grid variables\n",
       "    Conventions:  CF-1.6\n",
       "    timeStamp:    2024-Dec-09 10:18:04 GMT\n",
       "    uuid:         8156ec68-1106-4f56-9f8c-d8798e1bd32f

" ], "text/plain": [ " Size: 2GB\n", "Dimensions: (depthu: 75, axis_nbounds: 2, time_counter: 10,\n", " y: 331, x: 360)\n", "Coordinates:\n", " * depthu (depthu) float32 300B 0.5058 1.556 ... 5.902e+03\n", " nav_lat (y, x) float64 953kB dask.array\n", " nav_lon (y, x) float64 953kB dask.array\n", " time_centered (time_counter) datetime64[ns] 80B dask.array\n", " * time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00:...\n", "Dimensions without coordinates: axis_nbounds, y, x\n", "Data variables: (12/14)\n", " depthu_bounds (depthu, axis_nbounds) float32 600B dask.array\n", " e3u (time_counter, depthu, y, x) float32 357MB dask.array\n", " hfx (time_counter, y, x) float32 5MB dask.array\n", " hfx_adv (time_counter, y, x) float32 5MB dask.array\n", " hfx_diff (time_counter, y, x) float32 5MB dask.array\n", " sozosatr (time_counter, y, x) float32 5MB dask.array\n", " ... ...\n", " time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 160B dask.array\n", " u2o (time_counter, depthu, y, x) float32 357MB dask.array\n", " umo (time_counter, depthu, y, x) float32 357MB dask.array\n", " umo_vint (time_counter, y, x) float32 5MB dask.array\n", " uo (time_counter, depthu, y, x) float32 357MB dask.array\n", " uo_eiv (time_counter, depthu, y, x) float32 357MB dask.array\n", "Attributes:\n", " name: OUTPUT/eORCA1_1y_grid_U\n", " description: ocean U grid variables\n", " title: ocean U grid variables\n", " Conventions: CF-1.6\n", " timeStamp: 2024-Dec-09 10:18:04 GMT\n", " uuid: 8156ec68-1106-4f56-9f8c-d8798e1bd32f" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define directory path to model output files:\n", "gridU_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/U1y\"\n", "\n", "# Construct NEMO model U-grid dataset, including zonal velocities (m/s) and vertical grid cell thicknesses (m):\n", "ds_gridU = xr.open_zarr(gridU_url, consolidated=True, chunks={}).sel(time_counter=slice(\"2014-01\", \"2023-12\"))\n", "\n", "ds_gridU" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 2GB\n",
       "Dimensions:               (depthv: 75, axis_nbounds: 2, time_counter: 10,\n",
       "                           y: 331, x: 360)\n",
       "Coordinates:\n",
       "  * depthv                (depthv) float32 300B 0.5058 1.556 ... 5.902e+03\n",
       "    nav_lat               (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    nav_lon               (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    time_centered         (time_counter) datetime64[ns] 80B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * time_counter          (time_counter) datetime64[ns] 80B 2014-07-02T12:00:...\n",
       "Dimensions without coordinates: axis_nbounds, y, x\n",
       "Data variables: (12/13)\n",
       "    depthv_bounds         (depthv, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "    e3v                   (time_counter, depthv, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    hfy                   (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfy_adv               (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfy_diff              (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    somesatr              (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 160B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 160B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    v2o                   (time_counter, depthv, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vmo                   (time_counter, depthv, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vo                    (time_counter, depthv, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vo_eiv                (time_counter, depthv, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    name:         OUTPUT/eORCA1_1y_grid_V\n",
       "    description:  ocean V grid variables\n",
       "    title:        ocean V grid variables\n",
       "    Conventions:  CF-1.6\n",
       "    timeStamp:    2024-Dec-09 10:18:04 GMT\n",
       "    uuid:         b77cb501-a564-4451-8908-d66fe201e54a

" ], "text/plain": [ " Size: 2GB\n", "Dimensions: (depthv: 75, axis_nbounds: 2, time_counter: 10,\n", " y: 331, x: 360)\n", "Coordinates:\n", " * depthv (depthv) float32 300B 0.5058 1.556 ... 5.902e+03\n", " nav_lat (y, x) float64 953kB dask.array\n", " nav_lon (y, x) float64 953kB dask.array\n", " time_centered (time_counter) datetime64[ns] 80B dask.array\n", " * time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00:...\n", "Dimensions without coordinates: axis_nbounds, y, x\n", "Data variables: (12/13)\n", " depthv_bounds (depthv, axis_nbounds) float32 600B dask.array\n", " e3v (time_counter, depthv, y, x) float32 357MB dask.array\n", " hfy (time_counter, y, x) float32 5MB dask.array\n", " hfy_adv (time_counter, y, x) float32 5MB dask.array\n", " hfy_diff (time_counter, y, x) float32 5MB dask.array\n", " somesatr (time_counter, y, x) float32 5MB dask.array\n", " ... ...\n", " time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 160B dask.array\n", " time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 160B dask.array\n", " v2o (time_counter, depthv, y, x) float32 357MB dask.array\n", " vmo (time_counter, depthv, y, x) float32 357MB dask.array\n", " vo (time_counter, depthv, y, x) float32 357MB dask.array\n", " vo_eiv (time_counter, depthv, y, x) float32 357MB dask.array\n", "Attributes:\n", " name: OUTPUT/eORCA1_1y_grid_V\n", " description: ocean V grid variables\n", " title: ocean V grid variables\n", " Conventions: CF-1.6\n", " timeStamp: 2024-Dec-09 10:18:04 GMT\n", " uuid: b77cb501-a564-4451-8908-d66fe201e54a" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define directory path to model output files:\n", "gridV_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/V1y\"\n", "\n", "# Construct NEMO model V-grid dataset, including meridional velocities (m/s) and vertical grid cell thicknesses (m):\n", "ds_gridV = xr.open_zarr(gridV_url, consolidated=True, chunks={}).sel(time_counter=slice(\"2014-01\", \"2023-12\"))\n", "\n", "ds_gridV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Creating a NEMODataTree**\n", "\n", "**Next, let's create a NEMODataTree to store our domain and T, U & V-grid variables for the eORCA1 model.**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.DataTree 'NEMO model'>\n",
       "Group: /\n",
       "│   Dimensions:               (time_counter: 10, axis_nbounds: 2)\n",
       "│   Coordinates:\n",
       "│       time_centered         (time_counter) datetime64[ns] 80B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│     * time_counter          (time_counter) datetime64[ns] 80B 2014-07-02T12:00:...\n",
       "│   Dimensions without coordinates: axis_nbounds\n",
       "│   Data variables:\n",
       "│       time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 160B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│       time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 160B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       nftype:   F\n",
       "│       iperio:   True\n",
       "├── Group: /gridT\n",
       "│       Dimensions:                (time_counter: 10, j: 331, i: 360, k: 75,\n",
       "│                                   axis_nbounds: 2)\n",
       "│       Coordinates:\n",
       "│         * deptht                 (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n",
       "│           time_centered          (time_counter) datetime64[ns] 80B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│           gphit                  (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           glamt                  (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│         * k                      (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n",
       "│         * j                      (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331\n",
       "│         * i                      (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360\n",
       "│       Dimensions without coordinates: axis_nbounds\n",
       "│       Data variables: (12/81)\n",
       "│           berg_latent_heat_flux  (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           deptht_bounds          (k, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "│           e3t                    (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           empmr                  (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           evs                    (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ficeberg               (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ...                     ...\n",
       "│           e1t                    (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           e2t                    (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           top_level              (j, i) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           bottom_level           (j, i) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           tmask                  (k, j, i) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "│           tmaskutil              (j, i) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           nftype:   F\n",
       "│           iperio:   True\n",
       "├── Group: /gridU\n",
       "│       Dimensions:               (k: 75, axis_nbounds: 2, time_counter: 10, j: 331,\n",
       "│                                  i: 360)\n",
       "│       Coordinates:\n",
       "│         * depthu                (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n",
       "│           time_centered         (time_counter) datetime64[ns] 80B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│           gphiu                 (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           glamu                 (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│         * k                     (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n",
       "│         * j                     (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331\n",
       "│         * i                     (i) float64 3kB 1.5 2.5 3.5 4.5 ... 358.5 359.5 360.5\n",
       "│       Dimensions without coordinates: axis_nbounds\n",
       "│       Data variables: (12/18)\n",
       "│           depthu_bounds         (k, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "│           e3u                   (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           hfx                   (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfx_adv               (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfx_diff              (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           sozosatr              (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ...                    ...\n",
       "│           uo                    (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           uo_eiv                (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           e1u                   (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           e2u                   (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           umask                 (k, j, i) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "│           umaskutil             (j, i) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           name:         OUTPUT/eORCA1_1y_grid_U\n",
       "│           description:  ocean U grid variables\n",
       "│           title:        ocean U grid variables\n",
       "│           Conventions:  CF-1.6\n",
       "│           timeStamp:    2024-Dec-09 10:18:04 GMT\n",
       "│           uuid:         8156ec68-1106-4f56-9f8c-d8798e1bd32f\n",
       "│           nftype:       F\n",
       "│           iperio:       True\n",
       "├── Group: /gridV\n",
       "│       Dimensions:               (k: 75, axis_nbounds: 2, time_counter: 10, j: 331,\n",
       "│                                  i: 360)\n",
       "│       Coordinates:\n",
       "│         * depthv                (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n",
       "│           time_centered         (time_counter) datetime64[ns] 80B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│           gphiv                 (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           glamv                 (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│         * k                     (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n",
       "│         * j                     (j) float64 3kB 1.5 2.5 3.5 4.5 ... 329.5 330.5 331.5\n",
       "│         * i                     (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360\n",
       "│       Dimensions without coordinates: axis_nbounds\n",
       "│       Data variables: (12/17)\n",
       "│           depthv_bounds         (k, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "│           e3v                   (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           hfy                   (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfy_adv               (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfy_diff              (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           somesatr              (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ...                    ...\n",
       "│           vo                    (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           vo_eiv                (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           e1v                   (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           e2v                   (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           vmask                 (k, j, i) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "│           vmaskutil             (j, i) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           name:         OUTPUT/eORCA1_1y_grid_V\n",
       "│           description:  ocean V grid variables\n",
       "│           title:        ocean V grid variables\n",
       "│           Conventions:  CF-1.6\n",
       "│           timeStamp:    2024-Dec-09 10:18:04 GMT\n",
       "│           uuid:         b77cb501-a564-4451-8908-d66fe201e54a\n",
       "│           nftype:       F\n",
       "│           iperio:       True\n",
       "├── Group: /gridW\n",
       "│       Dimensions:       (j: 331, i: 360, k: 75)\n",
       "│       Coordinates:\n",
       "│           gphit         (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           glamt         (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│         * k             (k) float64 600B 0.5 1.5 2.5 3.5 4.5 ... 71.5 72.5 73.5 74.5\n",
       "│         * j             (j) int64 3kB 1 2 3 4 5 6 7 8 ... 325 326 327 328 329 330 331\n",
       "│         * i             (i) int64 3kB 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360\n",
       "│       Data variables:\n",
       "│           e1t           (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           e2t           (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           wmask         (k, j, i) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "│           wmaskutil     (j, i) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           nftype:   F\n",
       "│           iperio:   True\n",
       "└── Group: /gridF\n",
       "        Dimensions:       (j: 331, i: 360, k: 75)\n",
       "        Coordinates:\n",
       "            gphif         (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "            glamf         (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "          * k             (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n",
       "          * j             (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n",
       "          * i             (i) float64 3kB 1.5 2.5 3.5 4.5 ... 357.5 358.5 359.5 360.5\n",
       "        Data variables:\n",
       "            e1f           (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "            e2f           (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "            fmask         (k, j, i) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "            fmaskutil     (j, i) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "        Attributes:\n",
       "            nftype:   F\n",
       "            iperio:   True

" ], "text/plain": [ "\n", "Group: /\n", "│ Dimensions: (time_counter: 10, axis_nbounds: 2)\n", "│ Coordinates:\n", "│ time_centered (time_counter) datetime64[ns] 80B dask.array\n", "│ * time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00:...\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables:\n", "│ time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 160B dask.array\n", "│ time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 160B dask.array\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridT\n", "│ Dimensions: (time_counter: 10, j: 331, i: 360, k: 75,\n", "│ axis_nbounds: 2)\n", "│ Coordinates:\n", "│ * deptht (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n", "│ time_centered (time_counter) datetime64[ns] 80B dask.array\n", "│ gphit (j, i) float64 953kB dask.array\n", "│ glamt (j, i) float64 953kB dask.array\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n", "│ * j (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331\n", "│ * i (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables: (12/81)\n", "│ berg_latent_heat_flux (time_counter, j, i) float32 5MB dask.array\n", "│ deptht_bounds (k, axis_nbounds) float32 600B dask.array\n", "│ e3t (time_counter, k, j, i) float32 357MB dask.array\n", "│ empmr (time_counter, j, i) float32 5MB dask.array\n", "│ evs (time_counter, j, i) float32 5MB dask.array\n", "│ ficeberg (time_counter, j, i) float32 5MB dask.array\n", "│ ... ...\n", "│ e1t (j, i) float64 953kB dask.array\n", "│ e2t (j, i) float64 953kB dask.array\n", "│ top_level (j, i) int32 477kB dask.array\n", "│ bottom_level (j, i) int32 477kB dask.array\n", "│ tmask (k, j, i) int8 9MB dask.array\n", "│ tmaskutil (j, i) int8 119kB dask.array\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridU\n", "│ Dimensions: (k: 75, axis_nbounds: 2, time_counter: 10, j: 331,\n", "│ i: 360)\n", "│ Coordinates:\n", "│ * depthu (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n", "│ time_centered (time_counter) datetime64[ns] 80B dask.array\n", "│ gphiu (j, i) float64 953kB dask.array\n", "│ glamu (j, i) float64 953kB dask.array\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n", "│ * j (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331\n", "│ * i (i) float64 3kB 1.5 2.5 3.5 4.5 ... 358.5 359.5 360.5\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables: (12/18)\n", "│ depthu_bounds (k, axis_nbounds) float32 600B dask.array\n", "│ e3u (time_counter, k, j, i) float32 357MB dask.array\n", "│ hfx (time_counter, j, i) float32 5MB dask.array\n", "│ hfx_adv (time_counter, j, i) float32 5MB dask.array\n", "│ hfx_diff (time_counter, j, i) float32 5MB dask.array\n", "│ sozosatr (time_counter, j, i) float32 5MB dask.array\n", "│ ... ...\n", "│ uo (time_counter, k, j, i) float32 357MB dask.array\n", "│ uo_eiv (time_counter, k, j, i) float32 357MB dask.array\n", "│ e1u (j, i) float64 953kB dask.array\n", "│ e2u (j, i) float64 953kB dask.array\n", "│ umask (k, j, i) int8 9MB dask.array\n", "│ umaskutil (j, i) int8 119kB dask.array\n", "│ Attributes:\n", "│ name: OUTPUT/eORCA1_1y_grid_U\n", "│ description: ocean U grid variables\n", "│ title: ocean U grid variables\n", "│ Conventions: CF-1.6\n", "│ timeStamp: 2024-Dec-09 10:18:04 GMT\n", "│ uuid: 8156ec68-1106-4f56-9f8c-d8798e1bd32f\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridV\n", "│ Dimensions: (k: 75, axis_nbounds: 2, time_counter: 10, j: 331,\n", "│ i: 360)\n", "│ Coordinates:\n", "│ * depthv (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n", "│ time_centered (time_counter) datetime64[ns] 80B dask.array\n", "│ gphiv (j, i) float64 953kB dask.array\n", "│ glamv (j, i) float64 953kB dask.array\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n", "│ * j (j) float64 3kB 1.5 2.5 3.5 4.5 ... 329.5 330.5 331.5\n", "│ * i (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables: (12/17)\n", "│ depthv_bounds (k, axis_nbounds) float32 600B dask.array\n", "│ e3v (time_counter, k, j, i) float32 357MB dask.array\n", "│ hfy (time_counter, j, i) float32 5MB dask.array\n", "│ hfy_adv (time_counter, j, i) float32 5MB dask.array\n", "│ hfy_diff (time_counter, j, i) float32 5MB dask.array\n", "│ somesatr (time_counter, j, i) float32 5MB dask.array\n", "│ ... ...\n", "│ vo (time_counter, k, j, i) float32 357MB dask.array\n", "│ vo_eiv (time_counter, k, j, i) float32 357MB dask.array\n", "│ e1v (j, i) float64 953kB dask.array\n", "│ e2v (j, i) float64 953kB dask.array\n", "│ vmask (k, j, i) int8 9MB dask.array\n", "│ vmaskutil (j, i) int8 119kB dask.array\n", "│ Attributes:\n", "│ name: OUTPUT/eORCA1_1y_grid_V\n", "│ description: ocean V grid variables\n", "│ title: ocean V grid variables\n", "│ Conventions: CF-1.6\n", "│ timeStamp: 2024-Dec-09 10:18:04 GMT\n", "│ uuid: b77cb501-a564-4451-8908-d66fe201e54a\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridW\n", "│ Dimensions: (j: 331, i: 360, k: 75)\n", "│ Coordinates:\n", "│ gphit (j, i) float64 953kB dask.array\n", "│ glamt (j, i) float64 953kB dask.array\n", "│ * k (k) float64 600B 0.5 1.5 2.5 3.5 4.5 ... 71.5 72.5 73.5 74.5\n", "│ * j (j) int64 3kB 1 2 3 4 5 6 7 8 ... 325 326 327 328 329 330 331\n", "│ * i (i) int64 3kB 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360\n", "│ Data variables:\n", "│ e1t (j, i) float64 953kB dask.array\n", "│ e2t (j, i) float64 953kB dask.array\n", "│ wmask (k, j, i) int8 9MB dask.array\n", "│ wmaskutil (j, i) int8 119kB dask.array\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: True\n", "└── Group: /gridF\n", " Dimensions: (j: 331, i: 360, k: 75)\n", " Coordinates:\n", " gphif (j, i) float64 953kB dask.array\n", " glamf (j, i) float64 953kB dask.array\n", " * k (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n", " * j (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n", " * i (i) float64 3kB 1.5 2.5 3.5 4.5 ... 357.5 358.5 359.5 360.5\n", " Data variables:\n", " e1f (j, i) float64 953kB dask.array\n", " e2f (j, i) float64 953kB dask.array\n", " fmask (k, j, i) int8 9MB dask.array\n", " fmaskutil (j, i) int8 119kB dask.array\n", " Attributes:\n", " nftype: F\n", " iperio: True" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define dictionary of grid datasets defining eORCA1 parent model domain with no child/grand-child nests:\n", "datasets = {\"parent\":\n", " {\"domain\": ds_domain,\n", " \"gridT\": ds_gridT,\n", " \"gridU\": ds_gridU,\n", " \"gridV\": ds_gridV\n", " }\n", " }\n", "\n", "# Initialise a new NEMODataTree whose parent domain is zonally periodic & north-folding on F-points:\n", "nemo = NEMODataTree.from_datasets(datasets=datasets, iperio=True, nftype=\"F\", read_mask=True)\n", "\n", "nemo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Preparing OSNAP Coordinates**\n", "\n", "**Next, we will prepare geographical (lat, lon) coordinates defining the Overturning in the Subpolar North Atlantic (OSNAP) array from the [JASMIN Object Store](https://help.jasmin.ac.uk/docs/short-term-project-storage/using-the-jasmin-object-store/).**\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Define S3 URL to OSNAP gridded observational data in JASMIN Object Store:\n", "url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/OSNAP/OSNAP_Gridded_TSV_201408_202006_2023\"\n", "ds_osnap = xr.open_zarr(url, consolidated=True, chunks={})\n", "\n", "# Define OSNAP section coordinates (adding final land point -> UK):\n", "lon_osnap = np.concatenate([ds_osnap['LONGITUDE'].values, np.array([-4.0])])\n", "lat_osnap = np.concatenate([ds_osnap['LATITUDE'].values, np.array([56.0])])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Extracting the OSNAP array as a continuous hydrographic section**\n", "\n", "**Now, let's use the `extract_section()` function to extract the OSNAP array from our NEMO model output.**\n", "\n", "**We need to provide the names of the zonal & meridional velocity variables (`uv_vars=[\"uo\", \"vo\"]`) and any scalar variables (e.g. potential density `vars=sigma0`) to extract along the secton.**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 1MB\n",
       "Dimensions:       (bdy: 68, time_counter: 10, k: 75)\n",
       "Coordinates:\n",
       "  * time_counter  (time_counter) datetime64[ns] 80B 2014-07-02T12:00:00 ... 2...\n",
       "  * k             (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n",
       "  * bdy           (bdy) int64 544B 0 1 2 3 4 5 6 7 8 ... 60 61 62 63 64 65 66 67\n",
       "    glamb         (bdy) float64 544B -56.74 -55.72 -54.7 ... -5.497 -4.411\n",
       "    gphib         (bdy) float64 544B 51.89 51.96 52.03 ... 56.5 56.16 56.08\n",
       "    depthb        (k, bdy) float64 41kB 0.5058 0.5058 ... 5.902e+03 5.902e+03\n",
       "Data variables:\n",
       "    i_bdy         (bdy) float64 544B 232.0 233.0 234.0 ... 279.5 280.0 281.0\n",
       "    j_bdy         (bdy) float64 544B 263.5 263.5 263.5 ... 270.0 269.5 269.5\n",
       "    flux_type     (bdy) <U1 272B 'V' 'V' 'V' 'U' 'V' 'V' ... 'V' 'V' 'U' 'V' 'V'\n",
       "    flux_dir      (bdy) int64 544B 1 1 1 -1 1 1 1 1 -1 1 ... 1 1 1 1 1 1 1 1 1 1\n",
       "    velocity      (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>\n",
       "    bmask         (k, bdy) float64 41kB dask.array<chunksize=(75, 68), meta=np.ndarray>\n",
       "    e1b           (bdy) float64 544B dask.array<chunksize=(68,), meta=np.ndarray>\n",
       "    e3b           (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>\n",
       "    sigma0        (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>

" ], "text/plain": [ " Size: 1MB\n", "Dimensions: (bdy: 68, time_counter: 10, k: 75)\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00:00 ... 2...\n", " * k (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n", " * bdy (bdy) int64 544B 0 1 2 3 4 5 6 7 8 ... 60 61 62 63 64 65 66 67\n", " glamb (bdy) float64 544B -56.74 -55.72 -54.7 ... -5.497 -4.411\n", " gphib (bdy) float64 544B 51.89 51.96 52.03 ... 56.5 56.16 56.08\n", " depthb (k, bdy) float64 41kB 0.5058 0.5058 ... 5.902e+03 5.902e+03\n", "Data variables:\n", " i_bdy (bdy) float64 544B 232.0 233.0 234.0 ... 279.5 280.0 281.0\n", " j_bdy (bdy) float64 544B 263.5 263.5 263.5 ... 270.0 269.5 269.5\n", " flux_type (bdy) \n", " bmask (k, bdy) float64 41kB dask.array\n", " e1b (bdy) float64 544B dask.array\n", " e3b (time_counter, k, bdy) float64 408kB dask.array\n", " sigma0 (time_counter, k, bdy) float64 408kB dask.array" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Extract the OSNAP section from the NEMO model data:\n", "ds_osnap = nemo.extract_section(lon_section=lon_osnap,\n", " lat_section=lat_osnap,\n", " uv_vars=[\"uo\", \"vo\"],\n", " vars=[\"sigma0\"],\n", " dom=\".\",\n", " )\n", "\n", "ds_osnap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualising properties along the OSNAP array**\n", "\n", "**Next, let's plot the time-mean potential density along the OSNAP array:**" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4000.0, 0.0)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Plot the time-mean velocity along the OSNAP section -- #\n", "plt.figure(figsize=(10, 4))\n", "\n", "# Plot time-mean velocity across OSNAP section:\n", "ds_osnap['velocity'].mean(dim='time_counter').plot(y='depthb', yincrease=False, cbar_kwargs={\"label\": \"Velocity [ms$^{-1}$]\"})\n", "\n", "# Plot OSNAP West - East reference line:\n", "plt.axvline(x=ds_osnap['bdy'].where(ds_osnap['glamb'] <= -44).max(), color='k', lw=2, ls='--')\n", "\n", "# Axes labels:\n", "plt.title(\"eORCA1 JRA55v1: Velocity across OSNAP Section (2014-2023)\", fontdict={'size':12, 'weight':'bold'})\n", "plt.xlabel(\"Boundary Index\", fontdict={'size':11, 'weight':'bold'})\n", "plt.ylabel(\"Depth [m]\", fontdict={'size':11, 'weight':'bold'})\n", "plt.ylim([4000, 0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Calculating Meridional Overturning Stream Functions along the OSNAP array**\n", "\n", "**Finally, let's calculate the meridional overturning stream function in potential density coordinates along the OSNAP array using the `.compute_binned_statistic()` function:**" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from nemo_cookbook.stats import compute_binned_statistic\n", "\n", "# Calculate volume transport in Sv:\n", "ds_osnap['volume_transport'] = 1E-6 * (ds_osnap['velocity'] * ds_osnap['e1b'] * ds_osnap['e3b'])\n", "\n", "# Define potential density bins:\n", "sigma0_bins = np.arange(21, 28.2, 0.01)\n", "\n", "# Compute Total OSNAP diapycnal overturning stream function:\n", "ds_osnap['moc_total'] = compute_binned_statistic(vars=[ds_osnap['sigma0']],\n", " values=ds_osnap['volume_transport'],\n", " keep_dims=['time_counter'],\n", " bins=[sigma0_bins],\n", " statistic='nansum',\n", " mask=None\n", " ).cumsum(dim='sigma0_bins')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, let's calculate the meridional overturning stream functions for the OSNAP East and OSNAP West arrays separately.**" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 2MB\n",
       "Dimensions:           (bdy: 68, time_counter: 10, k: 75, sigma0_bins: 719)\n",
       "Coordinates:\n",
       "  * time_counter      (time_counter) datetime64[ns] 80B 2014-07-02T12:00:00 ....\n",
       "  * k                 (k) int64 600B 1 2 3 4 5 6 7 8 ... 68 69 70 71 72 73 74 75\n",
       "  * bdy               (bdy) int64 544B 0 1 2 3 4 5 6 7 ... 61 62 63 64 65 66 67\n",
       "    glamb             (bdy) float64 544B -56.74 -55.72 -54.7 ... -5.497 -4.411\n",
       "    gphib             (bdy) float64 544B 51.89 51.96 52.03 ... 56.5 56.16 56.08\n",
       "    depthb            (k, bdy) float64 41kB 0.5058 0.5058 ... 5.902e+03\n",
       "  * sigma0_bins       (sigma0_bins) float64 6kB 21.01 21.02 ... 28.18 28.19\n",
       "Data variables: (12/13)\n",
       "    i_bdy             (bdy) float64 544B 232.0 233.0 234.0 ... 279.5 280.0 281.0\n",
       "    j_bdy             (bdy) float64 544B 263.5 263.5 263.5 ... 270.0 269.5 269.5\n",
       "    flux_type         (bdy) <U1 272B 'V' 'V' 'V' 'U' 'V' ... 'V' 'V' 'U' 'V' 'V'\n",
       "    flux_dir          (bdy) int64 544B 1 1 1 -1 1 1 1 1 -1 ... 1 1 1 1 1 1 1 1 1\n",
       "    velocity          (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>\n",
       "    bmask             (k, bdy) float64 41kB dask.array<chunksize=(75, 68), meta=np.ndarray>\n",
       "    ...                ...\n",
       "    e3b               (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>\n",
       "    sigma0            (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>\n",
       "    volume_transport  (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>\n",
       "    moc_total         (time_counter, sigma0_bins) float64 58kB dask.array<chunksize=(10, 719), meta=np.ndarray>\n",
       "    moc_east          (time_counter, sigma0_bins) float64 58kB dask.array<chunksize=(10, 719), meta=np.ndarray>\n",
       "    moc_west          (time_counter, sigma0_bins) float64 58kB dask.array<chunksize=(10, 719), meta=np.ndarray>

" ], "text/plain": [ " Size: 2MB\n", "Dimensions: (bdy: 68, time_counter: 10, k: 75, sigma0_bins: 719)\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00:00 ....\n", " * k (k) int64 600B 1 2 3 4 5 6 7 8 ... 68 69 70 71 72 73 74 75\n", " * bdy (bdy) int64 544B 0 1 2 3 4 5 6 7 ... 61 62 63 64 65 66 67\n", " glamb (bdy) float64 544B -56.74 -55.72 -54.7 ... -5.497 -4.411\n", " gphib (bdy) float64 544B 51.89 51.96 52.03 ... 56.5 56.16 56.08\n", " depthb (k, bdy) float64 41kB 0.5058 0.5058 ... 5.902e+03\n", " * sigma0_bins (sigma0_bins) float64 6kB 21.01 21.02 ... 28.18 28.19\n", "Data variables: (12/13)\n", " i_bdy (bdy) float64 544B 232.0 233.0 234.0 ... 279.5 280.0 281.0\n", " j_bdy (bdy) float64 544B 263.5 263.5 263.5 ... 270.0 269.5 269.5\n", " flux_type (bdy) \n", " bmask (k, bdy) float64 41kB dask.array\n", " ... ...\n", " e3b (time_counter, k, bdy) float64 408kB dask.array\n", " sigma0 (time_counter, k, bdy) float64 408kB dask.array\n", " volume_transport (time_counter, k, bdy) float64 408kB dask.array\n", " moc_total (time_counter, sigma0_bins) float64 58kB dask.array\n", " moc_east (time_counter, sigma0_bins) float64 58kB dask.array\n", " moc_west (time_counter, sigma0_bins) float64 58kB dask.array" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Determine index to split OSNAP West & OSNAP East sections:\n", "station_OWest_OEast = ds_osnap['bdy'].where(ds_osnap['glamb'] <= -44).max()\n", "\n", "# OSNAP East diapycnal overturning stream function:\n", "mask_OEast = ds_osnap['bdy'] >= station_OWest_OEast\n", "ds_osnap['moc_east'] = compute_binned_statistic(vars=[ds_osnap['sigma0']],\n", " values=ds_osnap['volume_transport'],\n", " keep_dims=['time_counter'],\n", " bins=[sigma0_bins],\n", " statistic='nansum',\n", " mask=mask_OEast\n", " ).cumsum(dim='sigma0_bins')\n", "\n", "# OSNAP West diapycnal overturning stream function:\n", "mask_OWest = ds_osnap['bdy'] < station_OWest_OEast\n", "ds_osnap['moc_west'] = compute_binned_statistic(vars=[ds_osnap['sigma0']],\n", " values=ds_osnap['volume_transport'],\n", " keep_dims=['time_counter'],\n", " bins=[sigma0_bins],\n", " statistic='nansum',\n", " mask=mask_OWest\n", " ).cumsum(dim='sigma0_bins')\n", "\n", "ds_osnap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Notice that the resulting Dataset includes dask arrays, so we haven't actually computed the diapycnal overturning yet. To do this, we need to call the ``.compute()`` method:**" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" ] } ], "source": [ "ds_osnap = ds_osnap.compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualising the time-mean diapycnal overturning stream functions**\n", "\n", "**Finally, let's visualise the results by plotting the time-mean OSNAP overturning stream functions in potential density-coordinates:**" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot time-mean diapycnal overturning stream functions along the OSNAP section:\n", "plt.figure(figsize=(4, 7))\n", "plt.grid(True, lw=1, color='0.5', alpha=0.3)\n", "\n", "# Plot Osnap Total, OSNAP East & OSNAP West diapycnal overturning stream functions:\n", "ds_osnap['moc_total'].mean(dim='time_counter').plot(yincrease=False, y='sigma0_bins', label='Total', lw=3)\n", "ds_osnap['moc_east'].mean(dim='time_counter').plot(yincrease=False, y='sigma0_bins', label='OEast', lw=2)\n", "ds_osnap['moc_west'].mean(dim='time_counter').plot(yincrease=False, y='sigma0_bins', label='OWest', lw=2)\n", "\n", "# Axes labels:\n", "plt.title(\"eORCA1 JRA55v1:\\nOSNAP AMOC$_{\\\\sigma_{0}}$ (2014-2023)\", fontdict={\"size\": 12, \"weight\": \"bold\"})\n", "plt.xlabel(\"$\\\\Psi_{\\\\sigma_0}$ (Sv)\", fontdict={\"size\": 10, \"weight\": \"bold\"})\n", "plt.ylabel(\"Potential Density $\\\\sigma_0$ (kg m$^{-3}$)\", fontdict={\"size\": 10, \"weight\": \"bold\"})\n", "plt.ylim([28.2, 24.5])\n", "plt.legend()" ] } ], "metadata": { "kernelspec": { "display_name": "dev", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.12" } }, "nbformat": 4, "nbformat_minor": 2 }