{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### **Description**\n", "\n", "This recipe shows how to calculate the Atlantic Meridional Overturning Circulation (AMOC) stream function in potential density-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 diapycnal overturning stream function is routinely used to characterise the strength and structure of the AMOC in density-space (e.g., $\\sigma_{0}$ or $\\sigma{2}$) as a function of latitude $\\phi$ and can be defined at time $t$ as follows:\n", "\n", "$$\\Psi_{\\sigma_{0}}(\\phi, \\sigma_{0}, t) = \\int_{x_w}^{x_e} \\int_{z(\\lambda, \\phi, \\sigma_{0})}^{\\eta} v(\\lambda, \\phi, z', t) \\ dz' \\ dx$$\n", "\n", "where the meridional velocity $v(\\lambda, \\phi, z, t)$ is first accumulated vertically from the sea surface $\\eta$ to a specified isopycnal depth $z(\\lambda, \\phi, \\sigma_{0})$ (decreasing downward) before being integrated zonally between the western $x_w$ and eastern $x_e$ boundaries of the basin.\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# -- Import required packages -- #\n", "import gsw\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "\n", "from nemo_cookbook import NEMODataTree\n", "\n", "xr.set_options(display_style=\"text\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Using Dask**\n", "\n", "**Optional: Connect Client to Dask Local Cluster to run analysis in parallel.**\n", "\n", "Note that, although using Dask is not strictly necessary for this simple example using eORCA1, if we wanted to generalise this recipe to eORCA025 or eORCA12 outputs, using Dask would be essential to avoid unnecessary slow calculations using only a single process." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# -- Initialise Dask Local Cluster -- #\n", "import 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",
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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 and absolute salinity stored at T-points and calculate the potential density referenced to the sea surface.**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 42GB\n",
       "Dimensions:                (time_counter: 48, 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] 384B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * time_counter           (time_counter) datetime64[ns] 384B 1976-07-02 ... ...\n",
       "Dimensions without coordinates: y, x, axis_nbounds\n",
       "Data variables: (12/75)\n",
       "    berg_latent_heat_flux  (time_counter, y, x) float32 23MB 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 2GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    empmr                  (time_counter, y, x) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    evs                    (time_counter, y, x) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ficeberg               (time_counter, y, x) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ...                     ...\n",
       "    vohfcisf               (time_counter, deptht, y, x) float32 2GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
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       "    zos                    (time_counter, y, x) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    zossq                  (time_counter, y, x) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    sigma0                 (time_counter, deptht, y, x) float64 3GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    name:         OUTPUT/eORCA1_1y_grid_T\n",
       "    description:  ocean T grid variables\n",
       "    title:        ocean T grid variables\n",
       "    Conventions:  CF-1.6\n",
       "    timeStamp:    2024-Dec-09 10:28:01 GMT\n",
       "    uuid:         89824b34-5d44-4531-8344-fe1ce39fbb7b

" ], "text/plain": [ " Size: 42GB\n", "Dimensions: (time_counter: 48, 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] 384B dask.array\n", " * time_counter (time_counter) datetime64[ns] 384B 1976-07-02 ... ...\n", "Dimensions without coordinates: y, x, axis_nbounds\n", "Data variables: (12/75)\n", " berg_latent_heat_flux (time_counter, y, x) float32 23MB dask.array\n", " deptht_bounds (deptht, axis_nbounds) float32 600B dask.array\n", " e3t (time_counter, deptht, y, x) float32 2GB dask.array\n", " empmr (time_counter, y, x) float32 23MB dask.array\n", " evs (time_counter, y, x) float32 23MB dask.array\n", " ficeberg (time_counter, y, x) float32 23MB dask.array\n", " ... ...\n", " vohfcisf (time_counter, deptht, y, x) float32 2GB dask.array\n", " vohflisf (time_counter, deptht, y, x) float32 2GB dask.array\n", " vowflisf (time_counter, deptht, y, x) float32 2GB dask.array\n", " zos (time_counter, y, x) float32 23MB dask.array\n", " zossq (time_counter, y, x) float32 23MB dask.array\n", " sigma0 (time_counter, deptht, y, x) float64 3GB 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(\"1976-01\", \"2023-12\"))\n", "\n", "# Calculate potential density anomaly referenced to the sea surface (kg/m3):\n", "ds_gridT['sigma0'] = gsw.density.sigma0(CT=ds_gridT['thetao_con'], SA=ds_gridT['so_abs'])\n", "ds_gridT['sigma0'].name = 'sigma0'\n", "\n", "ds_gridT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, we need to import the 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: 9GB\n",
       "Dimensions:               (depthv: 75, axis_nbounds: 2, time_counter: 48,\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] 384B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * time_counter          (time_counter) datetime64[ns] 384B 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] 768B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 768B 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: 48,\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] 384B dask.array\n", " * time_counter (time_counter) datetime64[ns] 384B 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] 768B dask.array\n", " time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 768B 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": 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(\"1976-01\", \"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",
       "│   Dimensions:               (time_counter: 48, axis_nbounds: 2)\n",
       "│   Coordinates:\n",
       "│       time_centered         (time_counter) datetime64[ns] 384B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│     * time_counter          (time_counter) datetime64[ns] 384B 1976-07-02 ... 2...\n",
       "│   Dimensions without coordinates: axis_nbounds\n",
       "│   Data variables:\n",
       "│       time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 768B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│       time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 768B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       nftype:   F\n",
       "│       iperio:   True\n",
       "├── Group: /gridT\n",
       "│       Dimensions:                (time_counter: 48, j: 331, i: 360, k: 75,\n",
       "│                                   axis_nbounds: 2)\n",
       "│       Coordinates:\n",
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       "│           time_centered          (time_counter) datetime64[ns] 384B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│           gphit                  (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           glamt                  (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│         * k                      (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n",
       "│         * j                      (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331\n",
       "│         * i                      (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360\n",
       "│       Dimensions without coordinates: axis_nbounds\n",
       "│       Data variables: (12/81)\n",
       "│           berg_latent_heat_flux  (time_counter, j, i) float32 23MB 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 2GB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           empmr                  (time_counter, j, i) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           evs                    (time_counter, j, i) float32 23MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ficeberg               (time_counter, j, i) float32 23MB 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: 48, 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] 384B 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: 48, axis_nbounds: 2)\n", "│ Coordinates:\n", "│ time_centered (time_counter) datetime64[ns] 384B dask.array\n", "│ * time_counter (time_counter) datetime64[ns] 384B 1976-07-02 ... 2...\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables:\n", "│ time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 768B dask.array\n", "│ time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 768B dask.array\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridT\n", "│ Dimensions: (time_counter: 48, 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] 384B dask.array\n", "│ gphit (j, i) float64 953kB dask.array\n", "│ glamt (j, i) float64 953kB dask.array\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n", "│ * j (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331\n", "│ * i (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables: (12/81)\n", "│ berg_latent_heat_flux (time_counter, j, i) float32 23MB dask.array\n", "│ deptht_bounds (k, axis_nbounds) float32 600B dask.array\n", "│ e3t (time_counter, k, j, i) float32 2GB dask.array\n", "│ empmr (time_counter, j, i) float32 23MB dask.array\n", "│ evs (time_counter, j, i) float32 23MB dask.array\n", "│ ficeberg (time_counter, j, i) float32 23MB 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: 48, 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] 384B 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": 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 the AMOC diapycnal overturning stream function**\n", "**Now we have constructed our `NEMODataTree`, let's calculate the diapycnal overturning stream function.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "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", "# Add meridional volume transport [m3/s] to NEMO V-grid - meridional velocity [m/s] * area of meridional grid cell face [m2]:\n", "nemo['gridV']['volume_transport'] = (nemo['gridV']['vo'] * nemo.cell_area(grid='gridV', dim='j'))\n", "\n", "# Linearly interpolate potential density from NEMO T-grid to V-grid:\n", "nemo['gridV/sigma0'] = nemo['gridT/sigma0'].interp_to(to='V')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.DataArray 'volume_transport' (time_counter: 48, j: 331, sigma0_bins: 699)> Size: 89MB\n",
       "dask.array<reshape, shape=(48, 331, 699), dtype=float64, chunksize=(48, 331, 699), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time_counter  (time_counter) datetime64[ns] 384B 1976-07-02 ... 2023-07-0...\n",
       "  * j             (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n",
       "  * sigma0_bins   (sigma0_bins) float64 6kB 22.01 22.02 22.03 ... 28.98 28.99

" ], "text/plain": [ " Size: 89MB\n", "dask.array\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 384B 1976-07-02 ... 2023-07-0...\n", " * j (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n", " * sigma0_bins (sigma0_bins) float64 6kB 22.01 22.02 22.03 ... 28.98 28.99" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define potential density bins [kg /m3]:\n", "sigma0_bins = np.arange(22, 29, 0.01)\n", "\n", "# Compute meridional volume transport in latitude-potential density coords:\n", "vt_sigma0_atl = nemo.binned_statistic(grid=\"gridV\",\n", " vars=[\"sigma0\"],\n", " values=\"volume_transport\",\n", " keep_dims=[\"time_counter\", \"j\"],\n", " bins=[sigma0_bins],\n", " statistic=\"nansum\",\n", " mask=atlmask\n", " )\n", "\n", "vt_sigma0_atl" ] }, { "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": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" ] }, { "data": { "text/html": [ "

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       "  * sigma0_bins   (sigma0_bins) float64 6kB 22.01 22.02 22.03 ... 28.98 28.99

" ], "text/plain": [ " Size: 89MB\n", "array([[[ 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", " -9.30921961e-01, -9.30921961e-01, -9.30921961e-01],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -9.45353998e-01, -9.45353998e-01, -9.45353998e-01],\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", "...\n", " -9.51691358e-01, -9.51691358e-01, -9.51691358e-01],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -9.57064748e-01, -9.57064748e-01, -9.57064748e-01],\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", " ...,\n", " [-1.24457492e-03, -1.24457492e-03, -1.24457492e-03, ...,\n", " -9.02882356e-01, -9.02882356e-01, -9.02882356e-01],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -9.11390452e-01, -9.11390452e-01, -9.11390452e-01],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]],\n", " shape=(48, 331, 699))\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 384B 1976-07-02 ... 2023-07-0...\n", " * j (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n", " * sigma0_bins (sigma0_bins) float64 6kB 22.01 22.02 22.03 ... 28.98 28.99" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute diapycnal overturning stream function in Sverdrups [1 Sv = 1E6 m3/s]:\n", "# Here, we accumulate diapycnal volume transports from the lightest to the densest\n", "# isopycnal surface.\n", "moc_sigma0_atl = 1E-6 * vt_sigma0_atl.cumsum(dim='sigma0_bins').compute()\n", "moc_sigma0_atl.name = 'moc_sigma0_atl'\n", "\n", "moc_sigma0_atl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualising the time-mean AMOC diapycnal overturning stream function**\n", "\n", "**Finally, let's visualise the results by plotting the time-mean Atlantic Meridional Overturning stream function in potential density-coordinates:**" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:8: SyntaxWarning: invalid escape sequence '\\p'\n", "<>:8: SyntaxWarning: invalid escape sequence '\\p'\n", "/var/folders/z2/j_dr250s42x34hk63_rp4bm80000gq/T/ipykernel_62322/944999173.py:8: SyntaxWarning: invalid escape sequence '\\p'\n", " cbar_kwargs={\"label\": \"$\\\\psi_{\\\\sigma_{0}}(\\phi, \\\\sigma_{0})$ [Sv]\"}\n" ] }, { "data": { "text/plain": [ "Text(0, 0.5, 'Potential Density $\\\\sigma_{0}$ (kg m$^{-3}$)')" ] }, "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", "\n", "# Plot time-mean diapycnal overturning stream function:\n", "(moc_sigma0_atl\n", " .mean(dim='time_counter')\n", " .plot(y='sigma0_bins',\n", " yincrease=False,\n", " cbar_kwargs={\"label\": \"$\\\\psi_{\\\\sigma_{0}}(\\phi, \\\\sigma_{0})$ [Sv]\"}\n", " )\n", " )\n", "\n", "# Axes labels:\n", "plt.title('eORCA1-JRA55v1: AMOC$_{\\\\sigma_{0}}$', fontdict={'size':12, 'weight':'bold'})\n", "plt.xlabel('NEMO Model $j$ Coordinate', fontdict={'size':11, 'weight':'bold'})\n", "plt.ylabel('Potential Density $\\\\sigma_{0}$ (kg m$^{-3}$)', 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 }