{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### **Description**\n", "\n", "This recipe shows how to calculate the barotropic stream function for the North Atlantic Ocean using annual-mean outputs from the National Oceanography Centre Near-Present-Day global eORCA1 configuration of NEMO forced using JRA55-do 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 barotropic stream function is routinely used to characterise the large-scale ocean circulation $\\psi_{xy}$ and can be defined using the meridional velocity field at time $t$ as follows:\n", "\n", "$$\\Psi_{xy}(\\lambda, \\phi, t) = \\int_{x_w}^{x} \\int_{-H}^{\\eta} v(\\lambda, \\phi, z, t) \\ dz \\ dx$$\n", "\n", "where the meridional velocity $v(\\lambda, \\phi, z, t)$ is first integrated from the sea surface $\\eta$ to the seafloor $-H$ before being accumulated zonally from the western ($x_w$) to the eastern ($x_e$) boundary of our model domain.\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 cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "import matplotlib.pyplot as plt\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 os\n", "\n", "import dask\n", "from dask.distributed import Client, LocalCluster\n", "\n", "# Update temporary directory for Dask workers:\n", "dask.config.set({'temporary_directory': f\"{os.getcwd()}/dask_tmp\",\n", " 'local_directory': f\"{os.getcwd()}/dask_tmp\"\n", " })\n", "\n", "# Create Local Cluster:\n", "cluster = LocalCluster(n_workers=4, threads_per_worker=3, memory_limit='5GB')\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 JRA-55 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": 9, "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",
       "  * 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<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",
       "    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

" ], "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": 9, "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 import the meridional velocity and vertical grid cell thicknesses stored at V-points in a single dataset.**\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 9GB\n",
       "Dimensions:               (depthv: 75, axis_nbounds: 2, time_counter: 49,\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] 392B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * time_counter          (time_counter) datetime64[ns] 392B 1976-07-02 ... 2...\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 2GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    hfy                   (time_counter, y, x) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfy_adv               (time_counter, y, x) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfy_diff              (time_counter, y, x) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    somesatr              (time_counter, y, x) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 784B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 784B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    v2o                   (time_counter, depthv, y, x) float32 2GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vmo                   (time_counter, depthv, y, x) float32 2GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vo                    (time_counter, depthv, y, x) float32 2GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vo_eiv                (time_counter, depthv, y, x) float32 2GB 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: 9GB\n", "Dimensions: (depthv: 75, axis_nbounds: 2, time_counter: 49,\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] 392B dask.array\n", " * time_counter (time_counter) datetime64[ns] 392B 1976-07-02 ... 2...\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 2GB dask.array\n", " hfy (time_counter, y, x) float32 23MB dask.array\n", " hfy_adv (time_counter, y, x) float32 23MB dask.array\n", " hfy_diff (time_counter, y, x) float32 23MB dask.array\n", " somesatr (time_counter, y, x) float32 23MB dask.array\n", " ... ...\n", " time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 784B dask.array\n", " time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 784B dask.array\n", " v2o (time_counter, depthv, y, x) float32 2GB dask.array\n", " vmo (time_counter, depthv, y, x) float32 2GB dask.array\n", " vo (time_counter, depthv, y, x) float32 2GB dask.array\n", " vo_eiv (time_counter, depthv, y, x) float32 2GB 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": 3, "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 vertical grid cell thicknesses (m) and meridional velocities (m/s):\n", "ds_gridV = xr.open_zarr(gridV_url, consolidated=True, chunks={})\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 V-grid variables for the eORCA1 model.**\n", "\n", "**Since all land-sea masks expected by `NEMODataTree` are included in `ds_domain`, let's specify `read_mask = True` to read rather than calculate land-sea masks when constructing our `NEMODataTree`.**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.DataTree 'NEMO model'>\n",
       "Group: /\n",
       "│   Dimensions:               (time_counter: 49, axis_nbounds: 2)\n",
       "│   Coordinates:\n",
       "│       time_centered         (time_counter) datetime64[ns] 392B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│     * time_counter          (time_counter) datetime64[ns] 392B 1976-07-02 ... 2...\n",
       "│   Dimensions without coordinates: axis_nbounds\n",
       "│   Data variables:\n",
       "│       time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 784B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│       time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 784B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       nftype:   F\n",
       "│       iperio:   True\n",
       "├── Group: /gridT\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) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\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",
       "│           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:       (j: 331, i: 360, k: 75)\n",
       "│       Coordinates:\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 8 9 ... 68 69 70 71 72 73 74 75\n",
       "│         * j             (j) int64 3kB 1 2 3 4 5 6 7 8 ... 325 326 327 328 329 330 331\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",
       "│           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",
       "│           nftype:   F\n",
       "│           iperio:   True\n",
       "├── Group: /gridV\n",
       "│       Dimensions:               (k: 75, axis_nbounds: 2, time_counter: 49, 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] 392B 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 2GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           hfy                   (time_counter, j, i) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfy_adv               (time_counter, j, i) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfy_diff              (time_counter, j, i) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           somesatr              (time_counter, j, i) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ...                    ...\n",
       "│           vo                    (time_counter, k, j, i) float32 2GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           vo_eiv                (time_counter, k, j, i) float32 2GB 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: 49, axis_nbounds: 2)\n", "│ Coordinates:\n", "│ time_centered (time_counter) datetime64[ns] 392B dask.array\n", "│ * time_counter (time_counter) datetime64[ns] 392B 1976-07-02 ... 2...\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables:\n", "│ time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 784B dask.array\n", "│ time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 784B dask.array\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridT\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) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\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", "│ 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: (j: 331, i: 360, k: 75)\n", "│ Coordinates:\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 8 9 ... 68 69 70 71 72 73 74 75\n", "│ * j (j) int64 3kB 1 2 3 4 5 6 7 8 ... 325 326 327 328 329 330 331\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", "│ 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", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridV\n", "│ Dimensions: (k: 75, axis_nbounds: 2, time_counter: 49, 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] 392B 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 2GB dask.array\n", "│ hfy (time_counter, j, i) float32 23MB dask.array\n", "│ hfy_adv (time_counter, j, i) float32 23MB dask.array\n", "│ hfy_diff (time_counter, j, i) float32 23MB dask.array\n", "│ somesatr (time_counter, j, i) float32 23MB dask.array\n", "│ ... ...\n", "│ vo (time_counter, k, j, i) float32 2GB dask.array\n", "│ vo_eiv (time_counter, k, j, i) float32 2GB 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": 4, "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\": {\"domain\": ds_domain, \"gridV\": ds_gridV}}\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": [ "### **Calculating the Barotropic Stream Function**\n", "**Now we have constructed our `NEMODataTree`, let's calculate the time-mean barotropic stream function.**\n", "\n", "$$\\psi_{xy}(t) = \\sum_{i^{*}=1}^{i} \\sum_{k} (e_{1v} . e_{3v}(t) . v(t))$$\n", "\n", "In this example, our eORCA1 model uses $z^{*}$ vertical coordinates, so we will use the `integral()` method to perform integration along the $i$ and $k$ dimensions of the NEMO model grid.\n", "\n", "The resulting 2-dimensional barotropic stream function represents the depth-integrated volume transport in the Atlantic Ocean." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "

\n", " \n", "

\n", " \n", " NEMODataTree 'NEMO model'\n", "

\n", "\n", "

\n", " NEMODataArray 'integral_ik(vo)'\n", "

├─ Domain: '.'

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├─ Grid Path: 'gridV'

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└─ Grid Type: 'V'

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\n", " \n", "

<xarray.DataArray 'integral_ik(vo)' (time_counter: 49, j: 331, i: 360)> Size: 47MB\n",
       "dask.array<nancumsum, shape=(49, 331, 360), dtype=float64, chunksize=(1, 331, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time_counter   (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\n",
       "    time_centered  (time_counter) datetime64[ns] 392B 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",
       "  * j              (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n",
       "  * i              (i) int64 3kB 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360

\n", " \n", " " ], "text/plain": [ "\n", " \n", "\n", " Size: 47MB\n", "dask.array\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\n", " time_centered (time_counter) datetime64[ns] 392B dask.array\n", " gphiv (j, i) float64 953kB dask.array\n", " glamv (j, i) float64 953kB dask.array\n", " * j (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n", " * i (i) int64 3kB 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define Atlantic Ocean basin mask:\n", "atlmask = ds_domain['atlmsk'].rename({\"x\":\"i\", \"y\":\"j\"}).astype(bool)\n", "# Assign (i,j) coordinates of V-points:\n", "atlmask['i'] = atlmask['i'] + 1\n", "atlmask['j'] = atlmask['j'] + 1.5\n", "\n", "# Compute barotropic stream function:\n", "bsf_atl = nemo[\"gridV/vo\"].integral(dims=[\"i\", \"k\"], cum_dims=[\"i\"], dir=\"+1\", mask=atlmask)\n", "\n", "bsf_atl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Notice that the resulting `NEMODataArray` includes a dask array, so we haven't actually computed the barotropic stream function yet.**\n", "\n", "**To do this, we need to call the ``.compute()`` method to produce the result as an `xarray.DataArray`:**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.DataArray 'integral_ik(vo)' (time_counter: 49, j: 331, i: 360)> Size: 47MB\n",
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       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], shape=(49, 331, 360))\n",
       "Coordinates:\n",
       "  * time_counter   (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\n",
       "    time_centered  (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\n",
       "    gphiv          (j, i) float64 953kB -84.16 -84.16 -84.16 ... 0.0 0.0 0.0\n",
       "    glamv          (j, i) float64 953kB 73.5 74.5 75.5 76.5 ... 0.0 0.0 0.0 0.0\n",
       "  * j              (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n",
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" ], "text/plain": [ " Size: 47MB\n", "array([[[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan]],\n", "\n", " [[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan]],\n", "\n", " [[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", "...\n", " ...,\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan]],\n", "\n", " [[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan]],\n", "\n", " [[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan]]], shape=(49, 331, 360))\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\n", " time_centered (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\n", " gphiv (j, i) float64 953kB -84.16 -84.16 -84.16 ... 0.0 0.0 0.0\n", " glamv (j, i) float64 953kB 73.5 74.5 75.5 76.5 ... 0.0 0.0 0.0 0.0\n", " * j (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n", " * i (i) int64 3kB 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute barotropic stream function in Sverdrups [1 Sv = 1E6 m3/s] applying NEMO land-sea mask:\n", "bsf_atl = 1E-6 * bsf_atl.masked.compute()\n", "\n", "bsf_atl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualising the time-mean barotropic stream function**\n", "\n", "**Finally, let's visualise the results by plotting the time-mean barotropic stream function for the North Atlantic basin:**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Create a Figure with an orthographic (globe) projection -- #\n", "fig = plt.figure(figsize=(7, 7))\n", "proj = ccrs.Orthographic(central_longitude=-25, central_latitude=35)\n", "ax = plt.axes(projection=proj)\n", "ax.coastlines(resolution='110m', linewidth=0.8)\n", "ax.add_feature(cfeature.LAND, facecolor='0.1', edgecolor='0.1', linewidth=0.2, zorder=4)\n", "ax.gridlines(draw_labels=False, dms=True, x_inline=False, y_inline=False)\n", "\n", "# Plot eORCA1 JRA55v1 time-mean barotropic stream function:\n", "c_parent = ax.pcolormesh(bsf_atl['glamv'], bsf_atl['gphiv'], bsf_atl.mean(dim='time_counter'),\n", " transform=ccrs.PlateCarree(),\n", " cmap=\"RdBu_r\", shading=\"auto\",\n", " vmin=-30, vmax=30,\n", " zorder=1)\n", "\n", "# Plot eORCA1 JRA55v1 time-mean barotropic stream function contours:\n", "plt.contour(bsf_atl['glamv'], bsf_atl['gphiv'], bsf_atl.mean(dim='time_counter'),\n", " levels=8, colors='k', linewidths=1,\n", " transform=ccrs.PlateCarree(),\n", " zorder=2)\n", "\n", "# Add colorbar with label:\n", "cb = plt.colorbar(c_parent, orientation=\"horizontal\", pad=0.05, shrink=0.7)\n", "cb.set_label(\"Barotropic Stream Function [Sv]\", fontsize=11)\n" ] } ], "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 }