{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### **Description**\n", "\n", "This recipe shows how to calculate statistics for regional sub-domains using monthly-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", "---" ] }, { "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 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 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 url to domain_cfg ancillary file:\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 will import the sea surface temperature and sea surface salinity stored at T-points in a single dataset.**\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 467GB\n",
       "Dimensions:                (time_counter: 577, 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] 5kB dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * time_counter           (time_counter) datetime64[ns] 5kB 1976-01-16T12:00...\n",
       "Dimensions without coordinates: y, x, axis_nbounds\n",
       "Data variables: (12/74)\n",
       "    berg_latent_heat_flux  (time_counter, y, x) float32 275MB 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",
       "    e3t                    (time_counter, deptht, y, x) float32 21GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    empmr                  (time_counter, y, x) float32 275MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    evs                    (time_counter, y, x) float32 275MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ficeberg               (time_counter, y, x) float32 275MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ...                     ...\n",
       "    ttrd_qns_li            (time_counter, y, x) float32 275MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    vohfcisf               (time_counter, deptht, y, x) float32 21GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vohflisf               (time_counter, deptht, y, x) float32 21GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vowflisf               (time_counter, deptht, y, x) float32 21GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    zos                    (time_counter, y, x) float32 275MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    zossq                  (time_counter, y, x) float32 275MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    name:         OUTPUT/eORCA1_1m_grid_T\n",
       "    description:  ocean T grid variables\n",
       "    title:        ocean T grid variables\n",
       "    Conventions:  CF-1.6\n",
       "    timeStamp:    2024-Dec-09 10:18:50 GMT\n",
       "    uuid:         d4f9020a-a621-4a95-81da-0b477f67ed38

" ], "text/plain": [ " Size: 467GB\n", "Dimensions: (time_counter: 577, 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] 5kB dask.array\n", " * time_counter (time_counter) datetime64[ns] 5kB 1976-01-16T12:00...\n", "Dimensions without coordinates: y, x, axis_nbounds\n", "Data variables: (12/74)\n", " berg_latent_heat_flux (time_counter, y, x) float32 275MB dask.array\n", " deptht_bounds (deptht, axis_nbounds) float32 600B dask.array\n", " e3t (time_counter, deptht, y, x) float32 21GB dask.array\n", " empmr (time_counter, y, x) float32 275MB dask.array\n", " evs (time_counter, y, x) float32 275MB dask.array\n", " ficeberg (time_counter, y, x) float32 275MB dask.array\n", " ... ...\n", " ttrd_qns_li (time_counter, y, x) float32 275MB dask.array\n", " vohfcisf (time_counter, deptht, y, x) float32 21GB dask.array\n", " vohflisf (time_counter, deptht, y, x) float32 21GB dask.array\n", " vowflisf (time_counter, deptht, y, x) float32 21GB dask.array\n", " zos (time_counter, y, x) float32 275MB dask.array\n", " zossq (time_counter, y, x) float32 275MB dask.array\n", "Attributes:\n", " name: OUTPUT/eORCA1_1m_grid_T\n", " description: ocean T grid variables\n", " title: ocean T grid variables\n", " Conventions: CF-1.6\n", " timeStamp: 2024-Dec-09 10:18:50 GMT\n", " uuid: d4f9020a-a621-4a95-81da-0b477f67ed38" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define url to T-grid NEMO model output files:\n", "gridT_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/T1m\"\n", "\n", "# Open NEMO model grid dataset, including vertical grid cell thicknesses (m) and meridional velocities (m/s):\n", "ds_gridT = xr.open_zarr(gridT_url, consolidated=True, chunks={})\n", "\n", "ds_gridT" ] }, { "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: 577, axis_nbounds: 2)\n",
       "│   Coordinates:\n",
       "│       time_centered         (time_counter) datetime64[ns] 5kB dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│     * time_counter          (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:...\n",
       "│   Dimensions without coordinates: axis_nbounds\n",
       "│   Data variables:\n",
       "│       time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 9kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│       time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 9kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       nftype:   F\n",
       "│       iperio:   True\n",
       "├── Group: /gridT\n",
       "│       Dimensions:                (time_counter: 577, 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] 5kB 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: (12/80)\n",
       "│           berg_latent_heat_flux  (time_counter, j, i) float32 275MB 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 21GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           empmr                  (time_counter, j, i) float32 275MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           evs                    (time_counter, j, i) float32 275MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ficeberg               (time_counter, j, i) float32 275MB 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:       (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:       (j: 331, i: 360, k: 75)\n",
       "│       Coordinates:\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 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) int64 3kB 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360\n",
       "│       Data variables:\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",
       "│           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

<xarray.DataTree 'NEMO model'>\n",
       "Group: /\n",
       "│   Dimensions:               (time_counter: 577, axis_nbounds: 2)\n",
       "│   Coordinates:\n",
       "│       time_centered         (time_counter) datetime64[ns] 5kB dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│     * time_counter          (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:...\n",
       "│   Dimensions without coordinates: axis_nbounds\n",
       "│   Data variables:\n",
       "│       time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 9kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│       time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 9kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       nftype:   F\n",
       "│       iperio:   True\n",
       "├── Group: /gridT\n",
       "│       Dimensions:                (time_counter: 577, j: 83, i: 90, 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] 5kB dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│           gphit                  (j, i) float64 60kB 20.54 20.54 20.55 ... 71.07 70.74\n",
       "│           glamt                  (j, i) float64 60kB -79.5 -78.5 -77.5 ... 31.15 32.2\n",
       "│         * k                      (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n",
       "│         * j                      (j) int64 664B 223 224 225 226 ... 302 303 304 305\n",
       "│         * i                      (i) int64 720B 208 209 210 211 ... 294 295 296 297\n",
       "│       Dimensions without coordinates: axis_nbounds\n",
       "│       Data variables: (12/80)\n",
       "│           berg_latent_heat_flux  (time_counter, j, i) float32 17MB dask.array<chunksize=(1, 83, 90), meta=np.ndarray>\n",
       "│           deptht_bounds          (k, axis_nbounds, j, i) float32 4MB dask.array<chunksize=(25, 2, 83, 90), meta=np.ndarray>\n",
       "│           e3t                    (time_counter, k, j, i) float32 1GB dask.array<chunksize=(1, 25, 83, 90), meta=np.ndarray>\n",
       "│           empmr                  (time_counter, j, i) float32 17MB dask.array<chunksize=(1, 83, 90), meta=np.ndarray>\n",
       "│           evs                    (time_counter, j, i) float32 17MB dask.array<chunksize=(1, 83, 90), meta=np.ndarray>\n",
       "│           ficeberg               (time_counter, j, i) float32 17MB dask.array<chunksize=(1, 83, 90), meta=np.ndarray>\n",
       "│           ...                     ...\n",
       "│           e1t                    (j, i) float64 60kB dask.array<chunksize=(83, 90), meta=np.ndarray>\n",
       "│           e2t                    (j, i) float64 60kB dask.array<chunksize=(83, 90), meta=np.ndarray>\n",
       "│           top_level              (j, i) int32 30kB dask.array<chunksize=(83, 90), meta=np.ndarray>\n",
       "│           bottom_level           (j, i) int32 30kB dask.array<chunksize=(83, 90), meta=np.ndarray>\n",
       "│           tmask                  (k, j, i) float32 2MB dask.array<chunksize=(75, 83, 90), meta=np.ndarray>\n",
       "│           tmaskutil              (j, i) float32 30kB dask.array<chunksize=(83, 90), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           nftype:   F\n",
       "│           iperio:   False\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:       (j: 331, i: 360, k: 75)\n",
       "│       Coordinates:\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 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) int64 3kB 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360\n",
       "│       Data variables:\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",
       "│           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: 577, axis_nbounds: 2)\n", "│ Coordinates:\n", "│ time_centered (time_counter) datetime64[ns] 5kB dask.array\n", "│ * time_counter (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:...\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables:\n", "│ time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 9kB dask.array\n", "│ time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 9kB dask.array\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridT\n", "│ Dimensions: (time_counter: 577, j: 83, i: 90, 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] 5kB dask.array\n", "│ gphit (j, i) float64 60kB 20.54 20.54 20.55 ... 71.07 70.74\n", "│ glamt (j, i) float64 60kB -79.5 -78.5 -77.5 ... 31.15 32.2\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n", "│ * j (j) int64 664B 223 224 225 226 ... 302 303 304 305\n", "│ * i (i) int64 720B 208 209 210 211 ... 294 295 296 297\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables: (12/80)\n", "│ berg_latent_heat_flux (time_counter, j, i) float32 17MB dask.array\n", "│ deptht_bounds (k, axis_nbounds, j, i) float32 4MB dask.array\n", "│ e3t (time_counter, k, j, i) float32 1GB dask.array\n", "│ empmr (time_counter, j, i) float32 17MB dask.array\n", "│ evs (time_counter, j, i) float32 17MB dask.array\n", "│ ficeberg (time_counter, j, i) float32 17MB dask.array\n", "│ ... ...\n", "│ e1t (j, i) float64 60kB dask.array\n", "│ e2t (j, i) float64 60kB dask.array\n", "│ top_level (j, i) int32 30kB dask.array\n", "│ bottom_level (j, i) int32 30kB dask.array\n", "│ tmask (k, j, i) float32 2MB dask.array\n", "│ tmaskutil (j, i) float32 30kB dask.array\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: False\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: (j: 331, i: 360, k: 75)\n", "│ Coordinates:\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 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) int64 3kB 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360\n", "│ Data variables:\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", "│ 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": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define bounding box (lon_min, lon_max, lat_min, lat_max)\n", "bbox = (-80, 10, 20, 70)\n", "\n", "# Clip eORCA1 model T-grid to bounding box:\n", "nemo_clipped = nemo.clip_grid(grid='gridT', bbox=bbox)\n", "\n", "nemo_clipped" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Plotting time-mean sea surface temperature for the regional subdomain -- #\n", "nemo_clipped['gridT/tos_con'].mean(dim='time_counter').plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Defining a Regional Sub-Domain using a Polygon**\n", "\n", "**Next, let's define a more complex regional sub-domain by constructing a mask using a polygon.**\n", "\n", "**Since we have already clipped the T-grid of our NEMODataTree parent domain, we will define a polygon comprised of longitude-latitude coordinates within this region.**\n", "\n", "**We will use the Overturning in the Subpolar North Atlantic Program (OSNAP) observational array coordinates made available via the [JASMIN Object Store](https://help.jasmin.ac.uk/docs/short-term-project-storage/using-the-jasmin-object-store/) to construct a polygon enclosing the North Atlantic subpolar gyre**." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Open OSNAP gridded observations dataset: \n", "ds_osnap = xr.open_zarr(\"https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/OSNAP/OSNAP_Gridded_TSV_201408_202006_2023\")\n", "\n", "# Define a closed polygon which includes both the OSNAP West & East arrays:\n", "lon_poly = np.concatenate([ds_osnap['LONGITUDE'].values, np.array([ds_osnap['LONGITUDE'][-1], ds_osnap['LONGITUDE'][0]])])\n", "lat_poly = np.concatenate([ds_osnap['LATITUDE'].values, np.array([ds_osnap['LATITUDE'][0], ds_osnap['LATITUDE'][0]])])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now we have defined our polygon, we can use the `mask_with_polygon()` method to return the boolean mask classifying whether NEMO model grid points are inside (True) or outside (False) the polygon**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/z2/j_dr250s42x34hk63_rp4bm80000gq/T/ipykernel_65138/336358065.py:6: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.\n", " plt.pcolormesh(nemo_clipped['gridT/glamt'], nemo_clipped['gridT/gphit'], nemo_clipped['gridT/tos_con'].mean(dim='time_counter'), cmap='RdBu_r')\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Masking T-grid using polygon coordinates:\n", "mask_spg = nemo_clipped.mask_with_polygon(grid='gridT', lon_poly=lon_poly, lat_poly=lat_poly)\n", "\n", "# -- Plotting SPG polygon sub-domain -- #\n", "plt.figure()\n", "plt.pcolormesh(nemo_clipped['gridT/glamt'], nemo_clipped['gridT/gphit'], nemo_clipped['gridT/tos_con'].mean(dim='time_counter'), cmap='RdBu_r')\n", "plt.plot(lon_poly, lat_poly, color='0.1', lw=2)\n", "plt.colorbar(label='Sea Surface Temperature (°C)')\n", "\n", "# Axes labels:\n", "plt.xlabel('Longitude', fontdict={'size':12, 'weight':'bold'})\n", "plt.ylabel('Latitude', fontdict={'size':12, 'weight':'bold'})\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/z2/j_dr250s42x34hk63_rp4bm80000gq/T/ipykernel_65138/1564322244.py:3: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.\n", " plt.pcolormesh(nemo_clipped['gridT/glamt'],\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Plotting time-mean sea surface temperature for the SPG polygon -- #\n", "plt.figure()\n", "plt.pcolormesh(nemo_clipped['gridT/glamt'],\n", " nemo_clipped['gridT/gphit'],\n", " nemo_clipped['gridT/tos_con'].mean(dim='time_counter').where(mask_spg),\n", " cmap='RdBu_r'\n", " )\n", "plt.plot(lon_poly, lat_poly, color='0.1', lw=2)\n", "plt.colorbar(label='Sea Surface Temperature (°C)')\n", "\n", "# Axes labels:\n", "plt.title('eORCA1-JRA55v1:\\nTime-Mean SST for SPG Region', fontdict={'size':12, 'weight':'bold'})\n", "plt.xlabel('Longitude', fontdict={'size':12, 'weight':'bold'})\n", "plt.xlim([-70, 5])\n", "plt.ylabel('Latitude', fontdict={'size':12, 'weight':'bold'})\n", "plt.ylim([40, 70])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Calculating statistics for a Regional Sub-Domain**\n", "\n", "**Finally, let's use our North Atlantic subpolar gyre polygon to calculate statistics for this regional sub-domain of the eORCA1 model.**\n", "\n", "**Given a closed polygon, we can use the `masked_statistic()` method to calculate statistics of a specified variable in the masked sub-domain**" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Calculating the area weighted-mean sea surface temperature in the SPG region:\n", "sst_wmean = nemo_clipped[\"gridT/tos_con\"].masked_statistic(lon_poly=lon_poly,\n", " lat_poly=lat_poly,\n", " statistic=\"weighted_mean\",\n", " dims=[\"i\", \"j\"]\n", " )\n", "\n", "sst_wmean = sst_wmean.compute()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Sea Surface Temperature (°C)')" ] }, "execution_count": 13, "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", "# Plotting the area weighted-mean sea surface temperature time series for the SPG region:\n", "sst_wmean.plot(lw=1, color='0.1', alpha=0.3)\n", "sst_wmean.rolling(time_counter=12, center=True).mean().plot(lw=3, color='0.1')\n", "\n", "# Axes labels:\n", "plt.title('eORCA1 JRA55v1:\\nWeighted Mean SST for SPG Region', fontdict={'size':12, 'weight':'bold'})\n", "plt.xlabel('Time', fontdict={'size':12, 'weight':'bold'})\n", "plt.ylabel('Sea Surface Temperature (°C)', fontdict={'size':12, 'weight':'bold'})" ] } ], "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 }