{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### **Description**\n", "\n", "This recipe shows how to calculate the surface-forced water mass transformation 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 surface-forced diapycnal water mass transformation is used to quantify the volume flux across isopycnal outcrops due to surface buoyancy fluxes and can be defined at time $t$ as follows:\n", "\n", "First, we computing the surface density flux due to the fluxes of heat $Q_H$ (W m-2) and freshwater $Q_{FW}$ (kg m-2 s-1) at the sea surface following\n", "Speer and Tziperman (1992):\n", "\n", "$$f(\\lambda, \\phi, t) = -\\frac{\\alpha}{c_{p}} Q_{H}(\\lambda, \\phi, t) + \\beta \\frac{S(\\lambda, \\phi, t)}{1 - S(\\lambda, \\phi, t)} Q_{FW}(\\lambda, \\phi, t)$$\n", "\n", "where $\\alpha$ is the thermal expansion coefficient, $\\beta$ is the haline contraction coefficient, $c_p$ is the specific heat capacity of seawater and $S$ is the sea surface salinity. Notably, a positive surface density flux (i.e., $f(\\lambda, \\phi, t)$ > 0 kg m−2 s−1) represents an increase in sea surface density.\n", "\n", "We then calculate the surface-forced diapycnal water mass transformation $H(\\sigma^{*}, t)$ across an outcropping isopycnal surface by integrating the surface density flux over the area of each surface density outcrop $\\sigma^{*}$:\n", "\n", "$$H(\\sigma^{*}, t) = \\frac{1}{\\Delta \\sigma} \\int \\int f(\\lambda, \\phi, t) \\ \\Pi(\\sigma^{*}(\\lambda, \\phi, t)) \\ \\ dx \\ dy$$\n", "\n", "where the $\\Pi(\\sigma^{*}(\\lambda, \\phi, t))$ operator is 1 when $|\\sigma^{*}(\\lambda, \\phi, t) - \\sigma| \\leq \\frac{\\Delta \\sigma}{2}$ and 0 elsewhere.\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 pandas as pd\n", "import xarray as xr\n", "\n", "import nemo_cookbook as ncb\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 IHO World Seas polygons**\n", "**Let's begin by loading the polygons defining the IHO World Seas regions**. \n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

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	ID	Name	MRGID	Longitudes	Latitudes
0	0	Rio de La Plata	4325	[[-54.943023652717045, -54.978746687192626, -5...	[[-34.947906883078645, -34.97439280639835, -35...
1	1	Bass Strait	4366	[[149.90464234356938, 149.9049998519617, 149.9...	[[-37.54324781853184, -37.54805552943908, -37....
2	2	Great Australian Bight	4276	[[143.53250818354263, 143.54855731580784, 143....	[[-38.855345058560204, -38.89580867390668, -38...
3	3	Tasman Sea	4365	[[159.03333000000018, 159.03983414634163, 159....	[[-29.999999999999986, -30.043495934959335, -3...
4	4	Mozambique Channel	4261	[[43.38217926066437, 43.426910578414024, 43.47...	[[-11.370205640977488, -11.374667237992885, -1...
...	...	...	...	...	...
96	96	Laccadive Sea	4269	[[79.19056582495296, 79.20560240772511, 79.205...	[[9.28162992038466, 9.280296445224849, 9.28018...
97	97	Skagerrak	2379	[[10.66160702664314, 10.662945270907699, 10.66...	[[59.91287636715083, 59.910394549469004, 59.90...
98	98	Norwegian Sea	2353	[[16.72106314339885, 16.78890655530549, 16.856...	[[76.5645473926769, 76.50603497544391, 76.4475...
99	99	Ligurian Sea	3363	[[9.834412487214706, 9.835301503777828, 9.8349...	[[44.0485148685363, 44.04729517147973, 44.0472...
100	100	Gulf of Guinea	4286	[[8.975150969002156, 8.974166710829394, 8.9742...	[[-0.935921075698605, -0.9360325715092586, -0....

\n", "

101 rows × 5 columns

\n", "

" ], "text/plain": [ " ID Name MRGID \\\n", "0 0 Rio de La Plata 4325 \n", "1 1 Bass Strait 4366 \n", "2 2 Great Australian Bight 4276 \n", "3 3 Tasman Sea 4365 \n", "4 4 Mozambique Channel 4261 \n", ".. ... ... ... \n", "96 96 Laccadive Sea 4269 \n", "97 97 Skagerrak 2379 \n", "98 98 Norwegian Sea 2353 \n", "99 99 Ligurian Sea 3363 \n", "100 100 Gulf of Guinea 4286 \n", "\n", " Longitudes \\\n", "0 [[-54.943023652717045, -54.978746687192626, -5... \n", "1 [[149.90464234356938, 149.9049998519617, 149.9... \n", "2 [[143.53250818354263, 143.54855731580784, 143.... \n", "3 [[159.03333000000018, 159.03983414634163, 159.... \n", "4 [[43.38217926066437, 43.426910578414024, 43.47... \n", ".. ... \n", "96 [[79.19056582495296, 79.20560240772511, 79.205... \n", "97 [[10.66160702664314, 10.662945270907699, 10.66... \n", "98 [[16.72106314339885, 16.78890655530549, 16.856... \n", "99 [[9.834412487214706, 9.835301503777828, 9.8349... \n", "100 [[8.975150969002156, 8.974166710829394, 8.9742... \n", "\n", " Latitudes \n", "0 [[-34.947906883078645, -34.97439280639835, -35... \n", "1 [[-37.54324781853184, -37.54805552943908, -37.... \n", "2 [[-38.855345058560204, -38.89580867390668, -38... \n", "3 [[-29.999999999999986, -30.043495934959335, -3... \n", "4 [[-11.370205640977488, -11.374667237992885, -1... \n", ".. ... \n", "96 [[9.28162992038466, 9.280296445224849, 9.28018... \n", "97 [[59.91287636715083, 59.910394549469004, 59.90... \n", "98 [[76.5645473926769, 76.50603497544391, 76.4475... \n", "99 [[44.0485148685363, 44.04729517147973, 44.0472... \n", "100 [[-0.935921075698605, -0.9360325715092586, -0.... \n", "\n", "[101 rows x 5 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Open IHO World Seas polygons from GitHub as Pandas DataFrame:\n", "filepaths = ncb.examples.get_filepaths(\"IHO\")\n", "\n", "df_IHO_World_Seas = pd.read_parquet(filepaths['IHO_World_Seas_v3_polygons.parquet'])\n", "df_IHO_World_Seas" ] }, { "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": 13, "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": 13, "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 sea surface temperature and salinity and net surface heat and freshwater fluxes stored at T-points in a single dataset.**" ] }, { "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 directory path to model output files:\n", "gridT_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/T1m\"\n", "\n", "# Construct NEMO model T-grid dataset, including sea surface temperature (degC) and sea surface salinity (g/kg):\n", "ds_gridT = xr.open_zarr(gridT_url, consolidated=True, chunks={})\n", "\n", "ds_gridT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, let's calculate the surface potential density anomaly referenced to the sea surface alongside the thermal expansion and haline contraction coefficients of seawater.**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Calculate potential density anomaly referenced to the sea surface (kg/m3):\n", "ds_gridT['sigma0'] = gsw.density.sigma0(CT=ds_gridT['tos_con'], SA=ds_gridT['sos_abs'])\n", "ds_gridT['sigma0'].name = 'sigma0'\n", "\n", "# Calculate thermal expansion coefficient at sea surface:\n", "ds_gridT['alpha'] = gsw.density.alpha(SA=ds_gridT['sos_abs'], CT=ds_gridT['tos_con'], p=0)\n", "ds_gridT['alpha'].name = 'alpha'\n", "\n", "# Calculate haline contraction coefficient at sea surface:\n", "ds_gridT['beta'] = gsw.density.beta(SA=ds_gridT['sos_abs'], CT=ds_gridT['tos_con'], p=0)\n", "ds_gridT['beta'].name = 'beta'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Creating a NEMODataTree**\n", "\n", "**Using our outputs, let's create a NEMODataTree to store our domain and T-grid variables for the eORCA1 model.**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.DataTree 'NEMO model'>\n",
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       "│     * 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",
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       "│   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",
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       "│           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: (12/83)\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

" ], "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: 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\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: (12/83)\n", "│ berg_latent_heat_flux (time_counter, j, i) float32 275MB dask.array\n", "│ deptht_bounds (k, axis_nbounds) float32 600B dask.array\n", "│ e3t (time_counter, k, j, i) float32 21GB dask.array\n", "│ empmr (time_counter, j, i) float32 275MB dask.array\n", "│ evs (time_counter, j, i) float32 275MB dask.array\n", "│ ficeberg (time_counter, j, i) float32 275MB 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: (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 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}}\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": [ "**Now, we will use the IHO World Seas polygons to extract the Labrador Sea from the eORCA1 model domain.**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Define IHO World Seas Labrador Sea polygon:\n", "lon_poly = df_IHO_World_Seas[df_IHO_World_Seas['Name'] == 'Labrador Sea']['Longitudes'].item()[0]\n", "lat_poly = df_IHO_World_Seas[df_IHO_World_Seas['Name'] == 'Labrador Sea']['Latitudes'].item()[0]\n", "\n", "# Define boolean mask for Labrador Sea:\n", "mask = nemo.mask_with_polygon(grid='gridT', lon_poly=lon_poly, lat_poly=lat_poly)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/z2/j_dr250s42x34hk63_rp4bm80000gq/T/ipykernel_65804/148761668.py:4: 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['gridT']['glamt'], nemo['gridT']['gphit'], nemo['gridT']['tos_con'].where(mask).mean(dim='time_counter'), cmap='RdBu_r')\n" ] }, { "data": { "text/plain": [ "(45.0, 65.0)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Plot time-mean sea surface temperature (°C) in the Labrador Sea, with IHO World Seas polygon overlaid -- #\n", "plt.figure(figsize=(8, 6))\n", "\n", "plt.pcolormesh(nemo['gridT']['glamt'], nemo['gridT']['gphit'], nemo['gridT']['tos_con'].where(mask).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.title(\"eORCA1 JRA55v1:\\nLabrador Sea Mean Sea Surface Temperature\", fontdict={\"size\": 12, \"weight\": \"bold\"})\n", "plt.xlabel('Longitude', fontdict={\"size\": 10, \"weight\": \"bold\"})\n", "plt.ylabel('Latitude', fontdict={\"size\": 10, \"weight\": \"bold\"})\n", "plt.xlim([-80, 0])\n", "plt.ylim([45, 65])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Calculating surface-forced water mass transformation**\n", "\n", "**Now we have constructed our `NEMODataTree`, let's calculate the diapycnal surface-forced water mass trasformation from the surface density flux due to surface heat and freshwater fluxes.**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Define specific heat capacity of sea water [J kg-1 K-1]:\n", "cp0 = 3991.86795711963\n", "\n", "# Add sea surface density fluxes due to heat and freshwater fluxes to T-grid of NEMODataTree:\n", "nemo['gridT']['f_hf'] = -(nemo['gridT']['alpha'] / cp0) * nemo['gridT']['hfds']\n", "nemo['gridT']['f_fw'] = nemo['gridT']['beta'] * (nemo['gridT']['sos_abs'] / (1 - nemo['gridT']['sos_abs'])) * nemo['gridT']['sowaflup']" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.DataArray 'sfwmt' (time_counter: 577, sigma0_bins: 140)> Size: 646kB\n",
       "dask.array<reshape, shape=(577, 140), dtype=float64, chunksize=(577, 140), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time_counter  (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:00 ... 2...\n",
       "  * sigma0_bins   (sigma0_bins) float64 1kB 22.02 22.08 22.12 ... 28.93 28.98

" ], "text/plain": [ " Size: 646kB\n", "dask.array\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:00 ... 2...\n", " * sigma0_bins (sigma0_bins) float64 1kB 22.02 22.08 22.12 ... 28.93 28.98" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define potential density bins [kg m-3]:\n", "sigma0_bins = np.arange(22, 29.05, 0.05)\n", "dsigma0 = 0.05\n", "\n", "# Compute surface forced water mass transformation across each surface T-grid cell [m3 s-1]:\n", "nemo['gridT']['sfwmt'] = (1 / dsigma0) * (nemo['gridT']['f_hf'] + nemo['gridT']['f_fw']) * nemo.cell_area(grid='gridT', dim='k')\n", "\n", "# Compute total surface-force water mass transformation in discrete potential density coords:\n", "sfwmt_sigma0_atl = nemo.binned_statistic(grid=\"gridT\",\n", " vars=[\"sigma0\"],\n", " values=\"sfwmt\",\n", " keep_dims=[\"time_counter\"],\n", " bins=[sigma0_bins],\n", " statistic=\"nansum\",\n", " mask=mask\n", " )\n", "\n", "sfwmt_sigma0_atl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Notice that the resulting DataArray includes a dask array, so we haven't actually computed the surface-forced water mass transformation yet. To do this, we need to call the ``.compute()`` method:**" ] }, { "cell_type": "code", "execution_count": 11, "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": [ "

<xarray.DataArray 'sfwmt_sigma0_atl' (time_counter: 577, sigma0_bins: 140)> Size: 646kB\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], shape=(577, 140))\n",
       "Coordinates:\n",
       "  * time_counter  (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:00 ... 2...\n",
       "  * sigma0_bins   (sigma0_bins) float64 1kB 22.02 22.08 22.12 ... 28.93 28.98

" ], "text/plain": [ " Size: 646kB\n", "array([[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan]], shape=(577, 140))\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:00 ... 2...\n", " * sigma0_bins (sigma0_bins) float64 1kB 22.02 22.08 22.12 ... 28.93 28.98" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute diapycnal surface-forced water mass transformation in Sverdrups [1 Sv = 1E6 m3/s]:\n", "sfwmt_sigma0_atl = (sfwmt_sigma0_atl / 1E6).compute()\n", "sfwmt_sigma0_atl.name = 'sfwmt_sigma0_atl'\n", "\n", "sfwmt_sigma0_atl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualising the seasonal cycle of the surface-forced diapycnal overturning stream function**\n", "\n", "**Finally, let's visualise the results by plotting the seasonal cycle of the surface-forced overturning stream function in potential density-coordinates for the Atlantic Ocean:**" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(28.0, 22.0)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Plot Seasonal Cycle of Labrador Sea SFWMT -- #\n", "plt.figure(figsize=(8, 4))\n", "sfwmt_sigma0_atl.groupby('time_counter.month').mean().plot(y='sigma0_bins', yincrease=False, cbar_kwargs={\"label\": \"SFWMT [Sv]\"})\n", "\n", "# Axis labels:\n", "plt.title('eORCA1 JRA55v1: Labrador Sea\\nSurface-Forced Water Mass Transformation', fontdict={'size':12, 'weight':'bold'})\n", "plt.xticks(ticks=np.arange(1, 13, 2), labels=['Jan', 'Mar', 'May', 'Jul', 'Sep', 'Nov'])\n", "plt.xlabel('Month', fontdict={'size':11, 'weight':'bold'})\n", "plt.ylabel('Potential Density [kg m$^{-3}$]', fontdict={'size':11, 'weight':'bold'})\n", "plt.ylim([28, 22])" ] } ], "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 }