{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Meridional Heat Transport (MHT)\n", "This notebook calculates the model MHT using three methods based on distinct MOM5 diagnostics. The methods are listed below along with the caveats that come with each one:\n", "\n", "1. `temp_yflux_adv_int_z` (depth-integrated meridional heat transport due to resolved advection): This diagnostic is computed online and is therefore accurate. However, being an estimate of the advective meridional heat transport, it doesn't contain any heat transport due to diffusive parameterizations in the model.\n", "\n", "2. `net_sfc_heating` (net surface heat flux): This method estimates the meridional heat transport and is approximate because of the steady state assumption. Ideally, frazil formation at higher latitudes should be added to the net surface heating variable, but we skip it here as the diagnostic is not available with this experiment.\n", "\n", "3. `ty_trans`, `temp`, `dx` and `dzt` (depth- and zonally-integrated product of meridional transport and temperature): This method is also approximate as it neglects contributions from time-varying correlations between meridional transport and temperature for frequencies faster than the output frequency of the diagnostics themselves.\n", "\n", "Strictly speaking, the above methods compute reference-dependent heat flux at each latitude instead of meridional heat transport as there may be net meridional mass transports for which a unique heat transport cannot be defined. To compute a reference-temperature independent heat transport, the anomalous meridional mass transport needs to be subtracted from the total transport at each latitude (which is quite large in comparison to the anomaly).\n", "\n", "To use this notebook, we need to ensure the above diagnostics are available in the model output. We can check if a variable is available using `cc.querying.get_variables()` function from the COSIMA cookbook.\n", "\n", "**NOTE:** The third method is memory-intensive, so we recommend using at least 128 GB of memory. Alternatively, select a smaller temporal or spatial region of interest.\n", " \n", "Currently this notebook calculates the total (all basins) MHT and it also includes comparisons to a few observational products. Basin-specific MHT can be calculated by defining relevant masks (see for e.g., https://github.com/COSIMA/cosima-recipes/blob/main/DocumentedExamples/Atlantic_IndoPacific_Basin_Overturning_Circulation.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading relevant libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import cosima_cookbook as cc\n", "import numpy as np\n", "import xarray as xr\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start dask cluster." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "
\n", "
\n", "

Client

\n", "

Client-ef795084-7c78-11ee-b86e-000007e6fe80

\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
Connection method: Cluster objectCluster type: distributed.LocalCluster
\n", " Dashboard: /proxy/8787/status\n", "
\n", "\n", " \n", " \n", " \n", "\n", " \n", "
\n", "

Cluster Info

\n", "
\n", "
\n", "
\n", "
\n", "

LocalCluster

\n", "

0daab491

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", "
\n", " Dashboard: /proxy/8787/status\n", " \n", " Workers: 7\n", "
\n", " Total threads: 28\n", " \n", " Total memory: 251.20 GiB\n", "
Status: runningUsing processes: True
\n", "\n", "
\n", " \n", "

Scheduler Info

\n", " \n", "\n", "
\n", "
\n", "
\n", "
\n", "

Scheduler

\n", "

Scheduler-b7b66cc7-2dc7-48fb-965d-8f662d6be263

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " Comm: tcp://127.0.0.1:45899\n", " \n", " Workers: 7\n", "
\n", " Dashboard: /proxy/8787/status\n", " \n", " Total threads: 28\n", "
\n", " Started: Just now\n", " \n", " Total memory: 251.20 GiB\n", "
\n", "
\n", "
\n", "\n", "
\n", " \n", "

Workers

\n", " \n", "\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 0

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:43905\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/36119/status\n", " \n", " Memory: 35.89 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44101\n", "
\n", " Local directory: /jobfs/100038024.gadi-pbs/dask-scratch-space/worker-qv8h64x6\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 1

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:40331\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/46367/status\n", " \n", " Memory: 35.89 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45083\n", "
\n", " Local directory: /jobfs/100038024.gadi-pbs/dask-scratch-space/worker-sml0_3p3\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 2

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:45139\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/40415/status\n", " \n", " Memory: 35.89 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34305\n", "
\n", " Local directory: /jobfs/100038024.gadi-pbs/dask-scratch-space/worker-bqzhni39\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 3

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:40233\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/35697/status\n", " \n", " Memory: 35.89 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41281\n", "
\n", " Local directory: /jobfs/100038024.gadi-pbs/dask-scratch-space/worker-8215tppk\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 4

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:34131\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/38009/status\n", " \n", " Memory: 35.89 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39981\n", "
\n", " Local directory: /jobfs/100038024.gadi-pbs/dask-scratch-space/worker-54fxpgaw\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 5

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:35343\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/34417/status\n", " \n", " Memory: 35.89 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44789\n", "
\n", " Local directory: /jobfs/100038024.gadi-pbs/dask-scratch-space/worker-o3nez__a\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 6

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:41379\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/45607/status\n", " \n", " Memory: 35.89 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41591\n", "
\n", " Local directory: /jobfs/100038024.gadi-pbs/dask-scratch-space/worker-t8ek86yr\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask.distributed import Client\n", "client = Client()\n", "client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading model data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use the COSIMA cookbook to search its database and load `temp_yflux_adv_int_z` to start our calculations. To do this we will need to do the following:\n", "1. Start a COSIMA cookbook session\n", "2. Define the model configuration of interest\n", "3. Define the experiment of interest\n", "4. Define the diagnostic (variable) of interest\n", "\n", "**NOTE:** If you are in doubt about the models, experiments and diagnostics available in the database, check the [Cookbook Tutorial](../Tutorials/COSIMA_CookBook_Tutorial.ipynb) for more information." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start a cookbook session." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [], "source": [ "session = cc.database.create_session()\n", "\n", "#Define experiment of interest\n", "experiment = '025deg_jra55v13_iaf_gmredi6'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now ready to query the database and load the data to start our analysis. We load `temp_yflux_adv_int_z`. For this example, we have chosen to use 6 years of output." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Method 1: using depth-integrated meridional heat transport due to resovled advection" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'temp_yflux_adv_int_z' (time: 72, yu_ocean: 1080,\n",
       "                                          xt_ocean: 1440)>\n",
       "dask.array<concatenate, shape=(72, 1080, 1440), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * yu_ocean  (yu_ocean) float64 -81.02 -80.92 -80.81 ... 89.79 89.89 90.0\n",
       "  * time      (time) datetime64[ns] 2198-01-14T12:00:00 ... 2203-12-14T12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "Attributes:\n",
       "    long_name:      z-integral of cp*rho*dxt*v*temp\n",
       "    units:          Watts\n",
       "    valid_range:    [-1.e+18  1.e+18]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    coordinates:    geolon_t geolat_c\n",
       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i...
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * yu_ocean (yu_ocean) float64 -81.02 -80.92 -80.81 ... 89.79 89.89 90.0\n", " * time (time) datetime64[ns] 2198-01-14T12:00:00 ... 2203-12-14T12:00:00\n", " * xt_ocean (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", "Attributes:\n", " long_name: z-integral of cp*rho*dxt*v*temp\n", " units: Watts\n", " valid_range: [-1.e+18 1.e+18]\n", " cell_methods: time: mean\n", " time_avg_info: average_T1,average_T2,average_DT\n", " coordinates: geolon_t geolat_c\n", " ncfiles: ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..." ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mht_method1 = cc.querying.getvar(experiment, 'temp_yflux_adv_int_z', session, n=3)\n", "mht_method1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Viewing the variable containing our data provides a number of key information, including:\n", "- *Shape of dataset* - In this case the data includes 72 time steps, 1080 steps along the y axis and 1440 along the x axis.\n", "- *Name of coordinates* - Our data includes `time`, `yu_ocean` (i.e., latitude), and `xt_ocean` (i.e., longitude).\n", "- *Values included under each coordinate* - We can see that `time` includes monthly values from 2198-01-14 to 2203-12-14.\n", "- *Metadata* - This information is included under `Attributes` and it includes things like units." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating mean and converting units\n", "From the metadata, we can see that our dataset is in Watts (W), so we will convert them to petawatts (PW)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'temp_yflux_adv_int_z' (time: 72, yu_ocean: 1080,\n",
       "                                          xt_ocean: 1440)>\n",
       "dask.array<mul, shape=(72, 1080, 1440), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * yu_ocean  (yu_ocean) float64 -81.02 -80.92 -80.81 ... 89.79 89.89 90.0\n",
       "  * time      (time) datetime64[ns] 2198-01-14T12:00:00 ... 2203-12-14T12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "Attributes:\n",
       "    units:    PettaWatts
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * yu_ocean (yu_ocean) float64 -81.02 -80.92 -80.81 ... 89.79 89.89 90.0\n", " * time (time) datetime64[ns] 2198-01-14T12:00:00 ... 2203-12-14T12:00:00\n", " * xt_ocean (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", "Attributes:\n", " units: PettaWatts" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert Watts -> PettaWatts\n", "mht_method1 = (mht_method1 * 1e-15).assign_attrs(units='PettaWatts')\n", "mht_method1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and then we compute the mean across `time` and sum over all longitudes." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'temp_yflux_adv_int_z' (yu_ocean: 1080)>\n",
       "dask.array<sum-aggregate, shape=(1080,), dtype=float32, chunksize=(540,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * yu_ocean  (yu_ocean) float64 -81.02 -80.92 -80.81 ... 89.79 89.89 90.0
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * yu_ocean (yu_ocean) float64 -81.02 -80.92 -80.81 ... 89.79 89.89 90.0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mht_method1 = mht_method1.mean('time').sum('xt_ocean')\n", "mht_method1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Method 2: using from the net surface heat flux (assuming steady state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we load the surface heat flux and grid metrics:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "shflux = cc.querying.getvar(experiment, 'net_sfc_heating', session, n=3)\n", "shflux_am = shflux.mean('time').load()\n", "\n", "area = cc.querying.getvar(experiment, 'area_t', session, ncfile=\"ocean_grid.nc\", n=1)\n", "lat = cc.querying.getvar(experiment, 'geolat_t', session, ncfile=\"ocean_grid.nc\", n=1)\n", "latv = cc.querying.getvar(experiment, 'yt_ocean', session, ncfile=\"ocean_grid.nc\", n=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now calculate Meridional Heat Flux (MHF):\n", "\n", "$$\\textrm{MHF} = \\textrm{Cumulative sum of } (\\textrm{SHFLUX} \\times \\textrm{AREA}) \\textrm{ along latitudes}$$\n", "\n", "**Note**: The following cell might take 1-2 min." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 37s, sys: 12.8 s, total: 1min 49s\n", "Wall time: 3min 26s\n" ] } ], "source": [ "%%time\n", "\n", "mhf = xr.zeros_like(latv)\n", "\n", "for i in range(len(latv)):\n", " inds = lat < latv[i]\n", " atmp = area.where(lat < latv[i])\n", " stmp = shflux_am.where(lat < latv[i])\n", " mhf[i] = np.sum(atmp * stmp)\n", "\n", "mht_method2 = mhf + (mhf[0] - mhf[-1]) / 2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'yt_ocean' (yt_ocean: 1080)>\n",
       "array([ 0.11841208,  0.11841208,  0.11841208, ..., -0.11841084,\n",
       "       -0.11841175, -0.11841208])\n",
       "Coordinates:\n",
       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "Attributes:\n",
       "    units:    PettaWatts
" ], "text/plain": [ "\n", "array([ 0.11841208, 0.11841208, 0.11841208, ..., -0.11841084,\n", " -0.11841175, -0.11841208])\n", "Coordinates:\n", " * yt_ocean (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", "Attributes:\n", " units: PettaWatts" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert Watts -> PettaWatts\n", "mht_method2 = (mht_method2 * 1e-15).assign_attrs(units='PettaWatts')\n", "mht_method2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Method 3: Using 3D transport and potential temperature " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This method computes the MHF using meridional transport (or alternatively, meridional velocities mapped on to the transport grid) and potential temperature diagnostics. For this method to work, the net transport across each latitude section must be zero. Then, the MHF can be understood as the product of northward (or southward) flow with the temperature difference between the northward and southward flow. \n", "\n", "We choose an experiment which contains monthly data fields to capture monthly temporal correlations between meridional transport and temperature:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "experiment = '025deg_jra55_ryf9091_gadi'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We interpolate each variable on to $x$-center and $y$-face grid as we are estimating a tracer across a given latitude:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "V = cc.querying.getvar(experiment, 'ty_trans', session, frequency = '1 monthly', n = 3, use_cftime = True)\n", "\n", "θ = cc.querying.getvar(experiment, 'temp', session, frequency = '1 monthly', n = 3, use_cftime = True)\n", "\n", "# convert degK -> degC\n", "θ = (θ - 273.15).assign_attrs(units='degrees C')\n", "\n", "θ = θ.interp(yt_ocean = V.yu_ocean.values, method = \"linear\").rename({'yt_ocean': 'yu_ocean'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The meridional heat transport at a given latitude is then calculated using:\n", "\\begin{equation}\n", " MHF(y, t) = \\rho \\, C_p \\iint \\, v(x,y,z,t) \\, \\theta(x,y,z,t) \\, \\mathrm{d}x \\mathrm{d}z,\n", "\\end{equation}\n", "where we time-average the MHF in the end." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 25s, sys: 5.05 s, total: 1min 30s\n", "Wall time: 2min 10s\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray (yu_ocean: 1080)>\n",
       "array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\n",
       "       -2.0530437e-04, -2.2442040e-05,  0.0000000e+00], dtype=float32)\n",
       "Coordinates:\n",
       "  * yu_ocean  (yu_ocean) float64 -81.02 -80.92 -80.81 ... 89.79 89.89 90.0\n",
       "Attributes:\n",
       "    units:    PettaWatts
" ], "text/plain": [ "\n", "array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " -2.0530437e-04, -2.2442040e-05, 0.0000000e+00], dtype=float32)\n", "Coordinates:\n", " * yu_ocean (yu_ocean) float64 -81.02 -80.92 -80.81 ... 89.79 89.89 90.0\n", "Attributes:\n", " units: PettaWatts" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "\n", "Cp = 3992.10322329649 # heat capacity [J / (kg C)] used by MOM5\n", "\n", "HT = (Cp * V * θ).sum('xt_ocean').sum('st_ocean').mean('time')\n", "\n", "# convert Watts -> PettaWatts\n", "mht_method3 = (HT * 1e-15).assign_attrs(units='PettaWatts').load()\n", "mht_method3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Additional remarks on method 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Strictly speaking, the MHF calculated using the above formula contains a net meridional flow for each latitude, which makes the MHF dependent on the temperature scale. We can ensure zero net transport across a given latitude by subtracting mean flow ($v_m(y,t)$) across that latitude:\n", "\\begin{equation}\n", " v_m(y,t) = \\frac{\\iint v(x,y,z,t) \\, \\mathrm{d}x \\mathrm{d}z}{\\iint \\, \\mathrm{d}x \\mathrm{d}z},\n", "\\end{equation}\n", "where $v(x,y,z,t)$ is the meridional velocity at a given latitude. From this, the no-mean velocity ($v_{nm} (x,y,z,t)$) is given by:\n", "\\begin{equation}\n", " v_{nm} (x,y,z,t) = v(x,y,z,t) - v_m(y,t).\n", "\\end{equation}\n", "\n", "The no-mean velocity (not estimated numerically in this notebook) could then used to evaluate the MHF using:\n", "\\begin{equation}\n", " MHF(y,t) = \\rho C_p\\iint v_{nm}(x,y,z,t)\\, \\theta(x,y,z,t) \\, \\mathrm{d}x \\mathrm{d}z,\n", "\\end{equation}\n", "where we time-average the MHF in the end." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison between model output and observations\n", "Before producing our figure, we compare the model output with observations to check the model accuracy. These observations are derived using various methods, in particular using surface flux observations and method 2 (which assumes a steady state)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read ERBE Period Ocean and Atmospheric Heat Transport\n", "Observations are also available on `gadi`, here we show how to load them to our notebook." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[7.82256e-05,\n", " -0.00548183,\n", " -0.00534277,\n", " -0.00778958,\n", " -0.0153764,\n", " -0.0189109,\n", " -0.0172473,\n", " -0.0321044,\n", " -0.0647783,\n", " -0.104008,\n", " -0.129806,\n", " -0.202605,\n", " -0.297446,\n", " -0.374377,\n", " -0.424618,\n", " -0.464923,\n", " -0.508466,\n", " -0.544651,\n", " -0.56517,\n", " -0.575634,\n", " -0.563567,\n", " -0.51931,\n", " -0.45106,\n", " -0.377069,\n", " -0.310393,\n", " -0.271679,\n", " -0.279999,\n", " -0.329881,\n", " -0.39744,\n", " -0.464742,\n", " -0.515384,\n", " -0.565431,\n", " -0.620221,\n", " -0.678495,\n", " -0.736833,\n", " -0.790937,\n", " -0.843637,\n", " -0.90752,\n", " -0.984516,\n", " -1.05272,\n", " -1.0956,\n", " -1.10212,\n", " -1.05218,\n", " -0.93882,\n", " -0.761357,\n", " -0.502307,\n", " -0.155427,\n", " 0.253559,\n", " 0.666604,\n", " 1.00846,\n", " 1.25254,\n", " 1.42962,\n", " 1.57235,\n", " 1.69269,\n", " 1.78862,\n", " 1.84355,\n", " 1.85095,\n", " 1.82898,\n", " 1.79722,\n", " 1.76105,\n", " 1.71365,\n", " 1.65596,\n", " 1.59826,\n", " 1.53441,\n", " 1.45553,\n", " 1.34517,\n", " 1.20096,\n", " 1.03473,\n", " 0.856831,\n", " 0.704894,\n", " 0.62273,\n", " 0.602973,\n", " 0.60382,\n", " 0.602437,\n", " 0.599438,\n", " 0.588678,\n", " 0.573292,\n", " 0.5557,\n", " 0.518399,\n", " 0.46073,\n", " 0.40554,\n", " 0.361088,\n", " 0.328643,\n", " 0.303888,\n", " 0.271855,\n", " 0.232879,\n", " 0.18562,\n", " 0.146825,\n", " 0.110927,\n", " 0.0737569,\n", " 0.0460509,\n", " 0.0254577,\n", " 0.0100299,\n", " 0.00272631,\n", " 0.000137086]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Path to the file containing observations\n", "filename = '/g/data3/hh5/tmp/cosima/observations/original/MHT/obs_vq_am_estimates.txt'\n", "\n", "#Creating empty variables to store our observations\n", "erbe_mht = []\n", "erbe_lat = []\n", "\n", "#Opening data and saving it to empty variables above\n", "with open(filename) as f:\n", " #Open each line from rows 1 to 96\n", " for line in f.readlines()[1:96]:\n", " #Separating each line to extract data\n", " line = line.strip()\n", " sline = line.split()\n", " #Extracting latitude and MHT and saving to empty variables\n", " erbe_lat.append(float(sline[0]))\n", " erbe_mht.append(float(sline[3]))\n", "\n", "#Checking MHT variables\n", "erbe_mht" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read NCEP and ECMWF Oceanic and Atmospheric Transport Products\n", "\n", "These datasets are available at https://climatedataguide.ucar.edu/climate-data. We use a climatological mean of surface fluxes or vertically integrated total energy divergence for oceanic and atmospheric transports respectively for the period between February 1985 - April 1989." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#Path to the file containing observations\n", "filename = '/g/data3/hh5/tmp/cosima/observations/original/MHT/ANNUAL_TRANSPORTS_1985_1989.ascii'\n", "\n", "#Creating empty variables to store our observations\n", "ncep_g_mht = []\n", "ecwmf_g_mht = []\n", "ncep_g_err = []\n", "ecwmf_g_err = []\n", "ncep_a_mht = []\n", "ecwmf_a_mht = []\n", "ncep_a_err = []\n", "ecwmf_a_err = []\n", "ncep_p_mht = []\n", "ecwmf_p_mht = []\n", "ncep_p_err = []\n", "ecwmf_p_err = []\n", "ncep_i_mht = []\n", "ecwmf_i_mht = []\n", "ncep_i_err = []\n", "ecwmf_i_err = []\n", "ncep_ip_mht = []\n", "ecwmf_ip_mht = []\n", "ncep_ip_err = []\n", "ecwmf_ip_err = []\n", "o_lat = []\n", "\n", "#Opening data and saving it to empty variables above\n", "with open(filename) as f:\n", "#Open each line in file (ignoring the first row)\n", " for line in f.readlines()[1:]:\n", " #Separating each line to extract data\n", " line = line.strip()\n", " sline = line.split()\n", " #Extracting values and saving to correct variable defined above\n", " o_lat.append(float(sline[0])*0.01) # T42 latitudes (north to south)\n", " ncep_g_mht.append(float(sline[4])*0.01) # Residual Ocean Transport - NCEP\n", " ecwmf_g_mht.append(float(sline[5])*0.01)# Residual Ocean Transport - ECWMF\n", " ncep_a_mht.append(float(sline[7])*0.01)# Atlantic Ocean Basin Transport - NCEP\n", " ncep_p_mht.append(float(sline[8])*0.01)# Pacific Ocean Basin Transport - NCEP\n", " ncep_i_mht.append(float(sline[9])*0.01)# Indian Ocean Basin Transport - NCEP\n", " ncep_g_err.append(float(sline[10])*0.01)# Error Bars for NCEP Total Transports\n", " ncep_a_err.append(float(sline[11])*0.01)# Error Bars for NCEP Atlantic Transports \n", " ncep_p_err.append(float(sline[12])*0.01)# Error Bars for NCEP Pacific Transports \n", " ncep_i_err.append(float(sline[13])*0.01)# Error Bars for NCEP Indian Transports \n", " ecwmf_a_mht.append(float(sline[15])*0.01)# Atlantic Ocean Basin Transport - ECWMF\n", " ecwmf_p_mht.append(float(sline[16])*0.01)# Pacific Ocean Basin Transport - ECWMF\n", " ecwmf_i_mht.append(float(sline[17])*0.01)# Indian Ocean Basin Transport - ECWMF\n", " ecwmf_g_err.append(float(sline[18])*0.01)# Error Bars for ECWMF Total Transports\n", " ecwmf_a_err.append(float(sline[19])*0.01)# Error Bars for NCEP Atlantic Transports\n", " ecwmf_p_err.append(float(sline[20])*0.01)# Error Bars for NCEP Pacific Transports\n", " ecwmf_i_err.append(float(sline[21])*0.01)# Error Bars for NCEP Indian Transports\n", "\n", "#Calculating MHT\n", "ncep_ip_mht = [a+b for a, b in zip(ncep_p_mht,ncep_i_mht)]\n", "ecwmf_ip_mht = [a+b for a, b in zip(ecwmf_p_mht,ecwmf_i_mht)]\n", "ncep_ip_err = [max(a, b) for a, b in zip(ncep_p_err, ncep_i_err)]\n", "ecwmf_ip_err = [max(a, b) for a, b in zip(ecwmf_p_err, ecwmf_i_err)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting model outputs against observations\n", "\n", "We plot the global meridional heat transport as calculated from model outputs (blue line) and observations." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12, 6))\n", "ax = fig.add_subplot(1, 1, 1)\n", "\n", "#Plotting MHT from model outputs\n", "mht_method1.plot(ax = ax, color = \"blue\", label = \"ACCESS-OM2-025 (MHF diagnostic)\")\n", "mht_method2.plot(ax = ax, color = \"green\", label = \"ACCESS-OM2-025 (shflux)\")\n", "mht_method3.plot(ax = ax, color = 'orange', label = 'ACCESS-OM2-025 (velocity x temp integral)')\n", "\n", "#Adding observations and error bars for observations\n", "ax.plot(erbe_lat, erbe_mht, 'k--', linewidth=1, label=\"ERBE, JRA-25, NCEP/NCAR and ERA40\")\n", "plt.errorbar(o_lat[::-1], ncep_g_mht[::-1], yerr=ncep_g_err[::-1], c='gray', fmt='s', \n", " markerfacecolor='k', markersize=3, capsize=2, linewidth=1, label=\"NCEP\")\n", "plt.errorbar(o_lat[::-1], ecwmf_g_mht[::-1], yerr=ecwmf_g_err[::-1], c='gray', fmt='D', \n", " markerfacecolor='white', markersize=3, capsize=2, linewidth=1, label=\"ECWMF\")\n", "plt.errorbar( 24, 1.5, yerr=0.3, fmt='o', c='black', markersize=3, capsize=2, linewidth=1,\n", " label=\"Macdonald and Wunsch 1996\")\n", "plt.errorbar(-30, -0.9, yerr=0.3, fmt='o', c='black', markersize=3, capsize=2, linewidth=1)\n", "plt.errorbar( 24, 2.0, yerr=0.3, fmt='x', c='green', markersize=3, capsize=2, linewidth=1,\n", " label=\"Lavin et al. and Bryden et al.\")\n", "plt.errorbar( 24, 1.83, yerr=0.28, fmt='^', c='red', markersize=4, capsize=2, linewidth=1,\n", " label=\"Ganachaud and Wunsch 2003\")\n", "plt.errorbar(-30, -0.6, yerr=0.3, fmt='^', c='red', markersize=4, capsize=2, linewidth=1)\n", "plt.errorbar(-19, -0.8, yerr=0.3, fmt='^', c='red', markersize=4, capsize=2, linewidth=1)\n", "plt.errorbar( 47, 0.6, yerr=0.1, fmt='^', c='red', markersize=4, capsize=2, linewidth=1)\n", "\n", "#Adding legend\n", "plt.legend(frameon=False, fontsize=12)\n", "plt.axhline(y=0, linewidth=1, color='black')\n", "\n", "#Defining plot limits along the y axis\n", "plt.ylim(-2.25, 2.75)\n", "\n", "#Adding titles for figure and axes\n", "plt.title('Global Ocean Meridional Heat Transport', fontsize=18)\n", "plt.xlabel('Latitude')\n", "plt.ylabel('$10^{15}$ Watts');" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:analysis3-23.04] *", "language": "python", "name": "conda-env-analysis3-23.04-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": "3.9.17" } }, "nbformat": 4, "nbformat_minor": 4 }