{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### **Description**\n", "\n", "This recipe shows how to calculate the Atlantic Meridional Overturning Circulation (AMOC) stream function in depth-coordinates 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 vertical overturning stream function is routinely used to characterise the strength and structure of the AMOC in depth-space as a function of latitude $\\phi$ and can be defined at time $t$ as follows:\n", "\n", "$$\\Psi_{z}(\\phi, z, t) = \\int_{z}^{\\eta} \\int_{x_w}^{x_e} v(\\lambda, \\phi, z, t) \\ dx \\ dz$$\n", "\n", "where the meridional velocity $v(\\lambda, \\phi, z, t)$ is first integrated zonally between the western $x_w$ and eastern $x_e$ boundaries of the basin before being accumulated vertically from the\n", "sea surface $\\eta$ to a specified depth $z(\\lambda, \\phi)$ (decreasing downward).\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 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": 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",
       "  * 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": 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 import the meridional velocity and vertical grid cell thicknesses stored at V-points in a single dataset.**" ] }, { "cell_type": "code", "execution_count": 4, "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": 4, "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.**" ] }, { "cell_type": "code", "execution_count": 5, "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define dictionary of grid datasets defining eORCA1 parent model domain with no child/grand-child nests:\n", "# Note: domain_cfg z-dimension is expected to be named 'nav_lev'.\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 AMOC vertical overturning stream function**\n", "**Now we have constructed our `NEMODataTree`, let's calculate the vertical overturning stream function.**\n", "\n", "**In this example, our eORCA1 model uses $z^{*}$ vertical coordinates, so using `integral()`, which supports integration along the $i$, $j$, $k$ dimensions of the NEMO model grid, is appropriate. However, this would not be the case if using a NEMO model with geopotential / terrain-following coordinates.**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "

\n", " \n", "

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

\n", "\n", "

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

├─ Domain: '.'

\n", "

├─ Grid Path: 'gridV'

\n", "

└─ Grid Type: 'V'

\n", "

\n", " \n", "

<xarray.DataArray 'integral_ik(vo)' (time_counter: 49, k: 75, j: 331)> Size: 10MB\n",
       "dask.array<nancumsum, shape=(49, 75, 331), dtype=float64, chunksize=(1, 25, 331), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time_counter   (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\n",
       "  * depthv         (k) float32 300B 0.5058 1.556 2.668 ... 5.698e+03 5.902e+03\n",
       "    time_centered  (time_counter) datetime64[ns] 392B dask.array<chunksize=(1,), 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", " \n", " " ], "text/plain": [ "\n", " \n", "\n", " Size: 10MB\n", "dask.array\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\n", " * depthv (k) float32 300B 0.5058 1.556 2.668 ... 5.698e+03 5.902e+03\n", " time_centered (time_counter) datetime64[ns] 392B 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" ] }, "execution_count": 6, "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 vertical meridional overturning stream function:\n", "moc_z_atl = nemo['gridV/vo'].integral(dims=[\"i\", \"k\"], \n", " cum_dims=[\"k\"],\n", " dir=\"+1\",\n", " mask=atlmask\n", " )\n", "\n", "moc_z_atl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Notice that the resulting DataArray includes a dask array, so we haven't actually computed the vertical overturning yet. To do this, we need to call the ``.compute()`` method:**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

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       "  * time_counter   (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\n",
       "  * depthv         (k) float32 300B 0.5058 1.556 2.668 ... 5.698e+03 5.902e+03\n",
       "    time_centered  (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\n",
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" ], "text/plain": [ " Size: 10MB\n", "array([[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -5.93256017e-03, -5.98465019e-03, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -1.23823036e-02, -1.24593425e-02, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -1.93505099e-02, -1.93588036e-02, 0.00000000e+00],\n", " ...,\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -9.30921961e-01, -9.31773471e-01, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -9.30921961e-01, -9.31773471e-01, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -9.30921961e-01, -9.31773471e-01, 0.00000000e+00]],\n", "\n", " [[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -9.31946621e-03, -9.62942149e-03, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -1.87225095e-02, -1.92967880e-02, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -2.81349402e-02, -2.89098083e-02, 0.00000000e+00],\n", "...\n", " -8.73345777e-01, -8.73976184e-01, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -8.73345777e-01, -8.73976184e-01, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -8.73345777e-01, -8.73976184e-01, 0.00000000e+00]],\n", "\n", " [[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", " ...,\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]],\n", " shape=(49, 75, 331))\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\n", " * depthv (k) float32 300B 0.5058 1.556 2.668 ... 5.698e+03 5.902e+03\n", " time_centered (time_counter) datetime64[ns] 392B 1976-07-02 ... 2024-07-02\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" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute vertical overturning stream function in Sverdrups [1 Sv = 1E6 m3/s]:\n", "moc_z_atl = 1E-6 * moc_z_atl.compute()\n", "\n", "moc_z_atl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualising the time-mean AMOC vertical overturning stream function**\n", "\n", "**Finally, let's visualise the results by plotting the time-mean Atlantic Meridional Overturning stream function in depth-coordinates:**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Depth $z$ (m)')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 4))\n", "\n", "# Plot time-mean vertical meridional overturning stream function:\n", "(moc_z_atl\n", " .mean(dim='time_counter')\n", " .plot(y='depthv', yincrease=False, cbar_kwargs={\"label\": \"$\\\\psi_{z}(j, z)$ [Sv]\"})\n", ")\n", "\n", "# Axes labels:\n", "plt.title('eORCA1-JRA55v1: AMOC$_{z}$', fontdict={'size':12, 'weight':'bold'})\n", "plt.xlabel('NEMO Model $j$ Coordinate', fontdict={'size':11, 'weight':'bold'})\n", "plt.ylabel('Depth $z$ (m)', fontdict={'size':11, 'weight':'bold'})\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 }