diff --git a/03-Mains/Heaving_Water_mass_transformation_decomposition.ipynb b/03-Mains/Heaving_Water_mass_transformation_decomposition.ipynb new file mode 100644 index 00000000..8fe297a4 --- /dev/null +++ b/03-Mains/Heaving_Water_mass_transformation_decomposition.ipynb @@ -0,0 +1,1863 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "530bbfdf-3175-44b5-ba76-92c9bba1c685", + "metadata": {}, + "source": [ + "# Isopycnal heaving and along isopycnal change decomposition\n", + "This notebook is to show how we can decompose temperature (or other scalar, e.g., salinity, age tracer) anomalies into isopycnal heaving and changes along isopycnals (water mass transformation)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "19b556e3-bf3a-42e8-ade2-dc3018789a09", + "metadata": {}, + "outputs": [], + "source": [ + "# load modules\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import xarray as xr\n", + "import cf_xarray\n", + "import cartopy.crs as ccrs\n", + "import cmocean as cm\n", + "import logging\n", + "from dask.distributed import Client\n", + "import matplotlib.path as mpath\n", + "import cartopy.feature as cft\n", + "import xgcm\n", + "from xgcm import Grid\n", + "import gsw\n", + "from xhistogram.xarray import histogram\n", + "import intake" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2a86b46b-f295-4771-a3d8-f1e75095826c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Worker: 16

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Worker: 17

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Worker: 18

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Worker: 19

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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Start a cluster with multiple cores\n", + "client = Client(threads_per_worker = 1)\n", + "client" + ] + }, + { + "cell_type": "markdown", + "id": "a58ea0e7-ad97-4077-89af-c8ec9540825c", + "metadata": {}, + "source": [ + "## Step 1: Load dataset\n", + "To calculate the temperature change along the isopycnal, we will need both temperature(x,y,depth) and density(x,y,depth), to get tempertaure(x,y,density)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4c44c423-1fee-486d-ad1b-5c6dc998b332", + "metadata": {}, + "outputs": [], + "source": [ + "catalog = intake.cat.access_nri\n", + "experiment_control = '01deg_jra55v13_ryf9091' # Control experiment\n", + "experiment_pertubation = '01deg_jra55v13_ryf9091_qian_wthp' # Perturbation experiment" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a898d760-0554-42b1-bc51-824966420737", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " records = grouped.get_group(internal_key).to_dict(orient='records')\n", + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " records = grouped.get_group(internal_key).to_dict(orient='records')\n" + ] + } + ], + "source": [ + "# Load Control conservative temperature and practical salinity\n", + "temp_ctrl = catalog[experiment_control].search(variable=\"temp\",frequency='1mon').to_dask() \n", + "temp_ctrl = temp_ctrl.temp\n", + "\n", + "salt_ctrl = catalog[experiment_control].search(variable=\"salt\",frequency='1mon').to_dask() \n", + "salt_ctrl = salt_ctrl.salt" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "03c8e3d7-f7ac-435b-adc1-aae7108ee8d6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " records = grouped.get_group(internal_key).to_dict(orient='records')\n", + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " records = grouped.get_group(internal_key).to_dict(orient='records')\n" + ] + } + ], + "source": [ + "# Load Perturbation conservative temperature and practical salinity\n", + "temp_pert = catalog[experiment_pertubation].search(variable=\"temp\",frequency='1mon').to_dask() \n", + "temp_pert = temp_pert.temp\n", + "\n", + "salt_pert = catalog[experiment_pertubation].search(variable=\"salt\",frequency='1mon').to_dask() \n", + "salt_pert = salt_pert.salt" + ] + }, + { + "cell_type": "markdown", + "id": "79a5dbdb-9fac-4401-9961-3c3df90a7a25", + "metadata": {}, + "source": [ + "## Step 2: Get the potential density by using gsw" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5e2239f8-7d00-45f3-aad2-ef2b1853500a", + "metadata": {}, + "outputs": [], + "source": [ + "# The time period you want to look at\n", + "start_time = '2150-01-01'\n", + "end_time = '2150-12-31'\n", + "\n", + "# the domain you want to look at\n", + "lat_slice = slice(-90,-50)\n", + "lon = -130" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8c07c20b-b99c-424e-a332-c733ef8dc586", + "metadata": {}, + "outputs": [], + "source": [ + "# function to select the time period and domain you want for your variables\n", + "def subset_by_time_and_space(var):\n", + " var_domain = var.sel(time = slice(start_time,end_time)).sel(xt_ocean = lon, method = 'nearest').sel(yt_ocean = lat_slice).mean('time').load()\n", + "\n", + " return var_domain" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "29d6fa8d-652f-4f31-9f0b-8e221039f268", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 70.64 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n", + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 70.64 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n", + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 17.36 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n", + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 17.36 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# output for temporal averaged variables in the Southern Ocean\n", + "temp_SO_ctrl = subset_by_time_and_space(temp_ctrl)\n", + "salt_SO_ctrl = subset_by_time_and_space(salt_ctrl)\n", + "\n", + "temp_SO_pert = subset_by_time_and_space(temp_pert)\n", + "salt_SO_pert = subset_by_time_and_space(salt_pert)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5580d4b8-1dac-4987-9809-ecbe64876566", + "metadata": {}, + "outputs": [], + "source": [ + "# Use temperature and salinity to get the potential density you want, here I use the potential density referenced to 1000 m depth.\n", + "depth_rho = 1000 \n", + "\n", + "def pot_rho(temp,salt,depth_rho):\n", + " # practical salinity to absolute salinity\n", + " salt_ab = gsw.conversions.SA_from_SP(salt, salt.st_ocean, salt.xt_ocean, salt.yt_ocean)\n", + " \n", + " # transfer to potential density you want\n", + " rho = gsw.density.rho(salt_ab, temp-273.15, depth_rho)\n", + " \n", + "\n", + " return rho" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6818335e-4763-47c7-8f66-4e01ddbb4d56", + "metadata": {}, + "outputs": [], + "source": [ + "# get the density variables\n", + "rho_SO_ctrl = pot_rho(temp_SO_ctrl,salt_SO_ctrl,depth_rho)\n", + "rho_SO_pert = pot_rho(temp_SO_pert,salt_SO_pert,depth_rho)" + ] + }, + { + "cell_type": "markdown", + "id": "435284a7-2e73-4b2e-a0eb-37e6670d7bd9", + "metadata": {}, + "source": [ + "## Step 3: Calculate the temperature change along the isopycnal by using xgcm, and the residual is the temperature change due to isopycnal heaving." + ] + }, + { + "cell_type": "markdown", + "id": "ffb10a48-ceaa-48b1-966c-cfef281b7f12", + "metadata": {}, + "source": [ + "First you need a isoopycnal bin to transfer from temp(y,depth) to temp(y,density)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9fbd0f07-6527-413c-aacb-eab3a79a17b6", + "metadata": {}, + "outputs": [], + "source": [ + "# Create your isopycnal bins, to reduce your computing time, they could be non-linear.\n", + "bin1 = np.arange(1020, 1025, 0.1)\n", + "bin2 = np.arange(1025, 1035, 0.02)\n", + "# Concatenate the two non-linear bins\n", + "isopycnal_bins = np.concatenate((bin1, bin2))" + ] + }, + { + "cell_type": "markdown", + "id": "a3d73d6f-f40e-490c-9eb3-1c08d096a42a", + "metadata": {}, + "source": [ + "Also, to transfer isopycnal temperature changes temp_anomaly(y,density) back to temp_anomaly(y,depth), we will need a depth grid depth(x,y,z)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "bff7fdff-5c00-4cc0-99b1-55c68897a88f", + "metadata": {}, + "outputs": [], + "source": [ + "# the depth and lat coordinate in the model\n", + "st_ocean = temp_SO_ctrl.st_ocean.values\n", + "yt_ocean = temp_SO_ctrl.yt_ocean.values\n", + "# get the depth(y,depth)\n", + "depth = np.zeros((len(st_ocean),len(yt_ocean)))\n", + "for j in range(len(yt_ocean)):\n", + " depth[:,j] = st_ocean\n", + "\n", + "depth = xr.DataArray(depth, dims = ['st_ocean','yt_ocean'], coords = [temp_SO_ctrl.st_ocean, temp_SO_ctrl.yt_ocean])" + ] + }, + { + "cell_type": "markdown", + "id": "1db1ef46-38dd-494a-80b4-f73808e46708", + "metadata": {}, + "source": [ + "Now all good to go. Let's calculate the temperature change along the isopycnal by using gsw. And the residual (total temperature change minus temperature change along the isopycnal) is the isopycnal heaving part." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "dd02dcb4-f707-4fb1-a8ef-6550dd9d5683", + "metadata": {}, + "outputs": [], + "source": [ + "# Function to calculate the temperature change along the isopycnal\n", + "def isopycnal_temp_change(var_ctrl, var_pert, rho_ctrl, rho_pert):\n", + " # get the grid dataset you want to transfer\n", + " ds = xr.Dataset({'var_ctrl': var_ctrl, 'rho_ctrl': rho_ctrl,\n", + " 'var_pert': var_pert, 'rho_pert': rho_pert})\n", + " ds.coords['st_ocean'].attrs.update(axis='Z')\n", + " ds.coords['yt_ocean'].attrs.update(axis='Y')\n", + " grid = Grid(ds, periodic=False)\n", + " # transfer vars from depth coord to isopycnal bins\n", + " var_ctrl_rho = grid.transform(ds.var_ctrl, 'Z', isopycnal_bins, target_data=ds.rho_ctrl,method='linear')\n", + " var_ctrl_rho = var_ctrl_rho.rename({'rho_ctrl': 'pot_rho_1'})\n", + " var_pert_rho = grid.transform(ds.var_pert, 'Z', isopycnal_bins, target_data=ds.rho_pert,method='linear')\n", + " var_pert_rho = var_pert_rho.rename({'rho_pert': 'pot_rho_1'})\n", + " # Create depth(t,pot_rho,y), here we use the averaged density field between two experiments\n", + " ds_depth = xr.Dataset({'pot_rho_1': (rho_ctrl+rho_pert)/2, 'depth': depth})\n", + " ds_depth.coords['st_ocean'].attrs.update(axis='Z')\n", + " ds_depth.coords['yt_ocean'].attrs.update(axis='Y')\n", + " grid = Grid(ds_depth, periodic=False)\n", + " depth_rho = grid.transform(ds_depth.depth, 'Z', isopycnal_bins, target_data=ds_depth.pot_rho_1,method='linear')\n", + " # Compute the anomaly in density coords, then transfer anomaly back to depth coords\n", + " var_ano_rho = var_pert_rho-var_ctrl_rho\n", + " ds_ano = xr.Dataset({'depth': depth_rho, 'var_ano': var_ano_rho})\n", + " ds_ano.coords['yt_ocean'].attrs.update(axis='Y')\n", + " ds_ano.coords['pot_rho_1'].attrs.update(axis='Pot_rho_1')\n", + " grid = Grid(ds_ano, periodic=False)\n", + " depth_bin = np.concatenate((np.arange(0,200.,0.02), np.arange(200.,6000.,10.)))\n", + " var_ano_depth = grid.transform(ds_ano.var_ano, 'Pot_rho_1', depth_bin, target_data=ds_ano.depth,method='linear')\n", + " # Make the coordinates and dimensions in the new anomaly consistent to the total anomaly\n", + " var_ano_depth = var_ano_depth.transpose('depth','yt_ocean')\n", + " var_ano_depth = var_ano_depth.interp(depth = st_ocean, method = 'linear')\n", + " var_ano_depth = var_ano_depth.rename({'depth':'st_ocean'})\n", + "\n", + " return [var_ctrl_rho,var_pert_rho,var_ano_rho,var_ano_depth,depth_rho] " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9af34a94-1ef8-40c0-91a7-1c4f31b81e1a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/numba/np/ufunc/gufunc.py:261: RuntimeWarning: invalid value encountered in _interp_1d_linear\n", + " return self.ufunc(*args, **kwargs)\n", + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/numba/np/ufunc/gufunc.py:261: RuntimeWarning: invalid value encountered in _interp_1d_linear\n", + " return self.ufunc(*args, **kwargs)\n", + "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/numba/np/ufunc/gufunc.py:261: RuntimeWarning: invalid value encountered in _interp_1d_linear\n", + " return self.ufunc(*args, **kwargs)\n" + ] + } + ], + "source": [ + "# get the values\n", + "temp_ctrl_rho,temp_pert_rho,temp_ano_rho,temp_ano_depth,depth_rho = isopycnal_temp_change(temp_SO_ctrl, temp_SO_pert, rho_SO_ctrl, rho_SO_pert)" + ] + }, + { + "cell_type": "markdown", + "id": "4141f09d-36bf-46e2-8350-a9ef5a144076", + "metadata": {}, + "source": [ + "## Step 4: Plot the total temperature change, temperature change along the isopycnal and temperature change due to heaving. " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "81ceec84-8493-41cb-9eea-34c514c3aa08", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(18,4),dpi = 300)\n", + "\n", + "ax1 = plt.subplot(1,3,1)\n", + "\n", + "p1 = (temp_SO_pert-temp_SO_ctrl).plot(vmin=-0.5,vmax=0.5, cmap = cm.cm.balance,add_colorbar = False)\n", + "ax1.invert_yaxis()\n", + "ax1.set_xlim(-75,-50)\n", + "ax1.set_xlabel('Latitude ˚N')\n", + "ax1.set_ylabel('Depth (m)')\n", + "ax1.set_title('Total temperature anomaly')\n", + "\n", + "ax2 = plt.subplot(1,3,2)\n", + "(temp_ano_depth).plot(vmin=-0.5,vmax=0.5, cmap = cm.cm.balance,add_colorbar = False)\n", + "ax2.invert_yaxis()\n", + "ax2.set_xlim(-75,-50)\n", + "ax2.set_xlabel('Latitude ˚N')\n", + "ax2.set_ylabel('Depth (m)')\n", + "ax2.set_title('Isopycnal temperature change')\n", + "\n", + "ax3 = plt.subplot(1,3,3)\n", + "(temp_SO_pert-temp_SO_ctrl-temp_ano_depth).plot(vmin=-0.5,vmax=0.5, cmap = cm.cm.balance,add_colorbar = False)\n", + "ax3.invert_yaxis()\n", + "ax3.set_xlim(-75,-50)\n", + "ax3.set_xlabel('Latitude (˚N)')\n", + "ax3.set_ylabel('Depth (m)')\n", + "ax3.set_title('Temperature change by isopycnal heaving')\n", + "\n", + "ax_cb1 = plt.axes([0.93, 0.15, 0.01, 0.7])\n", + "cb = plt.colorbar(p1, cax=ax_cb1, orientation='vertical',extend='both')\n", + "cb.ax.set_ylabel('Temperature anomaly (˚C)')\n", + "cb.ax.yaxis.set_label_position('right')\n", + "cb.ax.yaxis.set_ticks_position('right')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84878162-f03b-4027-9d25-5f8e84246b1f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:analysis3-25.09]", + "language": "python", + "name": "conda-env-analysis3-25.09-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}