{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### **Description**\n", "\n", "This recipe shows how to calculate Ocean Heat Transport (OHT) 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", "Ocean Heat Transport (OHT) is a essential contribution to the Earth's energy balance, and can be defined as a function of latitude $\\phi$ at time $t$ as follows:\n", "\n", "$$OHT(\\phi, t) = \\rho_o c_p \\int_{x_w}^{x_e} \\int_{-H}^{\\eta} v(\\lambda, \\phi, z, t) \\ \\Theta (\\lambda, \\phi, z, t) \\ dz \\ dx$$\n", "\n", "where the product of the meridional velocity $v(\\lambda, \\phi, z, t)$ and conservative temperature $\\Theta (\\lambda, \\phi, z, t)$ is integrated both vertically from the sea surface $\\eta$ to the seafloor $-H$ and zonally between the western $x_w$ and eastern $x_e$ boundaries of the basin. The reference density is $\\rho_o$ and a constant ocean model heat capacity is $c_p^o$. \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 NEMO model from the [JASMIN Object Store](https://help.jasmin.ac.uk/docs/short-term-project-storage/using-the-jasmin-object-store/)**. \n", "\n", "**Alternatively, you can replace the ``domain_filepath`` below with a file path to your domain_cfg.nc file and read this with xarray's ``open_dataset()`` function.**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 709MB\n",
       "Dimensions:       (y: 331, x: 360, nav_lev: 75)\n",
       "Coordinates:\n",
       "  * nav_lev       (nav_lev) int64 600B 0 1 2 3 4 5 6 7 ... 68 69 70 71 72 73 74\n",
       "  * x             (x) int64 3kB 0 1 2 3 4 5 6 7 ... 353 354 355 356 357 358 359\n",
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       "Data variables: (12/49)\n",
       "    atlmsk        (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    bathy_metry   (y, x) float32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    bottom_level  (y, x) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    e1f           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    e1t           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    e1u           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    ...            ...\n",
       "    top_level     (y, x) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    umask         (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "    umaskutil     (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    vmask         (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "    vmaskutil     (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    wmask         (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CfgName:    UNKNOWN\n",
       "    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 conservative temperature stored at T-points in a single dataset.**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 28GB\n",
       "Dimensions:                (time_counter: 34, y: 331, x: 360, deptht: 75,\n",
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       "  * 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",
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       "Dimensions without coordinates: y, x, axis_nbounds\n",
       "Data variables: (12/74)\n",
       "    berg_latent_heat_flux  (time_counter, y, x) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    deptht_bounds          (deptht, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
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       "    ficeberg               (time_counter, y, x) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ...                     ...\n",
       "    ttrd_qns_li            (time_counter, y, x) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
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       "    vowflisf               (time_counter, deptht, y, x) float32 1GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    zos                    (time_counter, y, x) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    zossq                  (time_counter, y, x) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    name:         OUTPUT/eORCA1_1y_grid_T\n",
       "    description:  ocean T grid variables\n",
       "    title:        ocean T grid variables\n",
       "    Conventions:  CF-1.6\n",
       "    timeStamp:    2024-Dec-09 10:28:01 GMT\n",
       "    uuid:         89824b34-5d44-4531-8344-fe1ce39fbb7b

" ], "text/plain": [ " Size: 28GB\n", "Dimensions: (time_counter: 34, 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] 272B dask.array\n", " * time_counter (time_counter) datetime64[ns] 272B 1990-07-02T12:0...\n", "Dimensions without coordinates: y, x, axis_nbounds\n", "Data variables: (12/74)\n", " berg_latent_heat_flux (time_counter, y, x) float32 16MB dask.array\n", " deptht_bounds (deptht, axis_nbounds) float32 600B dask.array\n", " e3t (time_counter, deptht, y, x) float32 1GB dask.array\n", " empmr (time_counter, y, x) float32 16MB dask.array\n", " evs (time_counter, y, x) float32 16MB dask.array\n", " ficeberg (time_counter, y, x) float32 16MB dask.array\n", " ... ...\n", " ttrd_qns_li (time_counter, y, x) float32 16MB dask.array\n", " vohfcisf (time_counter, deptht, y, x) float32 1GB dask.array\n", " vohflisf (time_counter, deptht, y, x) float32 1GB dask.array\n", " vowflisf (time_counter, deptht, y, x) float32 1GB dask.array\n", " zos (time_counter, y, x) float32 16MB dask.array\n", " zossq (time_counter, y, x) float32 16MB dask.array\n", "Attributes:\n", " name: OUTPUT/eORCA1_1y_grid_T\n", " description: ocean T grid variables\n", " title: ocean T grid variables\n", " Conventions: CF-1.6\n", " timeStamp: 2024-Dec-09 10:28:01 GMT\n", " uuid: 89824b34-5d44-4531-8344-fe1ce39fbb7b" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define directory path to model output files:\n", "gridT_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/T1y\"\n", "\n", "# Construct NEMO model T-grid dataset, including conservative temperature (degC) and absolute salinity (g/kg):\n", "ds_gridT = xr.open_zarr(gridT_url, consolidated=True, chunks={}).sel(time_counter=slice(\"1990-01\", \"2023-12\"))\n", "\n", "ds_gridT" ] }, { "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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 6GB\n",
       "Dimensions:               (depthv: 75, axis_nbounds: 2, time_counter: 34,\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] 272B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * time_counter          (time_counter) datetime64[ns] 272B 1990-07-02T12:00...\n",
       "Dimensions without coordinates: axis_nbounds, y, x\n",
       "Data variables: (12/13)\n",
       "    depthv_bounds         (depthv, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "    e3v                   (time_counter, depthv, y, x) float32 1GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    hfy                   (time_counter, y, x) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfy_adv               (time_counter, y, x) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfy_diff              (time_counter, y, x) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    somesatr              (time_counter, y, x) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 544B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 544B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    v2o                   (time_counter, depthv, y, x) float32 1GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vmo                   (time_counter, depthv, y, x) float32 1GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vo                    (time_counter, depthv, y, x) float32 1GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vo_eiv                (time_counter, depthv, y, x) float32 1GB 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: 6GB\n", "Dimensions: (depthv: 75, axis_nbounds: 2, time_counter: 34,\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] 272B dask.array\n", " * time_counter (time_counter) datetime64[ns] 272B 1990-07-02T12:00...\n", "Dimensions without coordinates: axis_nbounds, y, x\n", "Data variables: (12/13)\n", " depthv_bounds (depthv, axis_nbounds) float32 600B dask.array\n", " e3v (time_counter, depthv, y, x) float32 1GB dask.array\n", " hfy (time_counter, y, x) float32 16MB dask.array\n", " hfy_adv (time_counter, y, x) float32 16MB dask.array\n", " hfy_diff (time_counter, y, x) float32 16MB dask.array\n", " somesatr (time_counter, y, x) float32 16MB dask.array\n", " ... ...\n", " time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 544B dask.array\n", " time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 544B dask.array\n", " v2o (time_counter, depthv, y, x) float32 1GB dask.array\n", " vmo (time_counter, depthv, y, x) float32 1GB dask.array\n", " vo (time_counter, depthv, y, x) float32 1GB dask.array\n", " vo_eiv (time_counter, depthv, y, x) float32 1GB 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": 5, "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={}).sel(time_counter=slice(\"1990-01\", \"2023-12\"))\n", "\n", "ds_gridV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Creating a NEMODataTree**\n", "\n", "**Next, let's create a NEMODataTree to store our domain and T- & V-grid variables for the eORCA1 model.**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.DataTree 'NEMO model'>\n",
       "Group: /\n",
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       "│   Coordinates:\n",
       "│       time_centered         (time_counter) datetime64[ns] 272B dask.array<chunksize=(1,), meta=np.ndarray>\n",
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       "│       time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 544B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│       time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 544B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       nftype:   F\n",
       "│       iperio:   True\n",
       "├── Group: /gridT\n",
       "│       Dimensions:                (time_counter: 34, j: 331, i: 360, k: 75,\n",
       "│                                   axis_nbounds: 2)\n",
       "│       Coordinates:\n",
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       "│           time_centered          (time_counter) datetime64[ns] 272B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│           gphit                  (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
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       "│       Dimensions without coordinates: axis_nbounds\n",
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       "│           berg_latent_heat_flux  (time_counter, j, i) float32 16MB 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 1GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           empmr                  (time_counter, j, i) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           evs                    (time_counter, j, i) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ficeberg               (time_counter, j, i) float32 16MB 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:               (k: 75, axis_nbounds: 2, time_counter: 34, 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] 272B 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 1GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           hfy                   (time_counter, j, i) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfy_adv               (time_counter, j, i) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfy_diff              (time_counter, j, i) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           somesatr              (time_counter, j, i) float32 16MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ...                    ...\n",
       "│           vo                    (time_counter, k, j, i) float32 1GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           vo_eiv                (time_counter, k, j, i) float32 1GB 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: 34, axis_nbounds: 2)\n", "│ Coordinates:\n", "│ time_centered (time_counter) datetime64[ns] 272B dask.array\n", "│ * time_counter (time_counter) datetime64[ns] 272B 1990-07-02T12:00...\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables:\n", "│ time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 544B dask.array\n", "│ time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 544B dask.array\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridT\n", "│ Dimensions: (time_counter: 34, 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] 272B dask.array\n", "│ gphit (j, i) float64 953kB dask.array\n", "│ glamt (j, i) float64 953kB dask.array\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n", "│ * j (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331\n", "│ * i (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables: (12/80)\n", "│ berg_latent_heat_flux (time_counter, j, i) float32 16MB dask.array\n", "│ deptht_bounds (k, axis_nbounds) float32 600B dask.array\n", "│ e3t (time_counter, k, j, i) float32 1GB dask.array\n", "│ empmr (time_counter, j, i) float32 16MB dask.array\n", "│ evs (time_counter, j, i) float32 16MB dask.array\n", "│ ficeberg (time_counter, j, i) float32 16MB dask.array\n", "│ ... ...\n", "│ e1t (j, i) float64 953kB dask.array\n", "│ e2t (j, i) float64 953kB dask.array\n", "│ top_level (j, i) int32 477kB dask.array\n", "│ bottom_level (j, i) int32 477kB dask.array\n", "│ tmask (k, j, i) int8 9MB dask.array\n", "│ tmaskutil (j, i) int8 119kB dask.array\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridU\n", "│ Dimensions: (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: 34, 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] 272B 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 1GB dask.array\n", "│ hfy (time_counter, j, i) float32 16MB dask.array\n", "│ hfy_adv (time_counter, j, i) float32 16MB dask.array\n", "│ hfy_diff (time_counter, j, i) float32 16MB dask.array\n", "│ somesatr (time_counter, j, i) float32 16MB dask.array\n", "│ ... ...\n", "│ vo (time_counter, k, j, i) float32 1GB dask.array\n", "│ vo_eiv (time_counter, k, j, i) float32 1GB 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": 6, "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, \"gridT\": ds_gridT, \"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 Ocean Heat Transport**\n", "**Now we have constructed our `NEMODataTree`, let's calculate the global Ocean Heat Transport (OHT).**\n", "\n", "**Note, we can also pass an ocean basin mask to `.integral()` to decompose the OHT by basin**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "

\n", " \n", "

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

\n", "\n", "

\n", " NEMODataArray 'integral_ik(None)'\n", "

├─ Domain: '.'

\n", "

├─ Grid Path: 'gridV'

\n", "

└─ Grid Type: 'V'

\n", "

\n", " \n", "

<xarray.DataArray 'integral_ik(None)' (time_counter: 34, j: 331)> Size: 90kB\n",
       "dask.array<sum-aggregate, shape=(34, 331), dtype=float64, chunksize=(1, 331), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time_counter   (time_counter) datetime64[ns] 272B 1990-07-02T12:00:00 ......\n",
       "  * j              (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n",
       "    time_centered  (time_counter) datetime64[ns] 272B dask.array<chunksize=(1,), meta=np.ndarray>

\n", " \n", " " ], "text/plain": [ "\n", " \n", "\n", " Size: 90kB\n", "dask.array\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 272B 1990-07-02T12:00:00 ......\n", " * j (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n", " time_centered (time_counter) datetime64[ns] 272B dask.array" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Reference density [kg m-3]\n", "rho0 = 1025\n", "# Reference specific heat capacity [J kg-1 C-1]\n", "cp0 = 3994\n", "\n", "# Note, we need to interpolate conservative temperature from T-points to V-points to align with meridional velocity:\n", "MHT = (rho0 * cp0 * nemo[\"gridT/thetao_con\"].interp_to(to='V') * nemo[\"gridV/vo\"]).integral(dims=[\"i\", \"k\"])\n", "\n", "MHT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Notice that the resulting DataArray includes a dask array, so we haven't actually computed the diapycnal overturning yet. To do this, we need to call the ``.compute()`` method:**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "MHT = MHT.compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualising the time-mean global Ocean Heat Transport (OHT)**\n", "\n", "**Finally, let's visualise the results by plotting the time-mean global Ocean Heat Transport as a function of NEMO model j coordinate:**" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Ocean Heat Transport (PW)')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 4))\n", "plt.grid(visible=True, linestyle='--', linewidth=0.5, color='0.1', alpha=0.5)\n", "\n", "# Plot time-mean meridional heat transport for the Atlantic Ocean in Petawatts (1 PW = 10^15 W):\n", "(1E-15 * MHT.mean(dim=\"time_counter\")).plot(linewidth=3, color='0.1')\n", "\n", "# Axes labels:\n", "plt.title('eORCA1-JRA55v1: Global Ocean Heat Transport', fontdict={'size':12, 'weight':'bold'})\n", "plt.xlabel('NEMO Model $j$ Coordinate', fontdict={'size':11, 'weight':'bold'})\n", "plt.ylabel('Ocean Heat Transport (PW)', fontdict={'size':11, '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 }