{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Depth-time Hovmoller plot\n", "\n", "This recipe shows how to calculate a depth-time Hovmoller plot of 1-year anomaly of globally-averaged of conservative temperature and practical salinity from ACCESS-OM2 between Jan 1989 and Dec 2018." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import xarray as xr\n", "import cf_xarray as cfxr\n", "import intake\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "from matplotlib.gridspec import GridSpec\n", "from matplotlib import ticker\n", "import cmocean.cm as cm\n", "\n", "from dask.distributed import Client" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Client

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Client-007bc949-8a1c-11ef-bf82-0000008cfe80

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Connection method: Cluster objectCluster type: distributed.LocalCluster
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Cluster Info

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LocalCluster

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f8fa9aea

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\n", " Dashboard: /proxy/8787/status\n", " \n", " Workers: 48\n", "
\n", " Total threads: 48\n", " \n", " Total memory: 188.56 GiB\n", "
Status: runningUsing processes: True
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Scheduler Info

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Scheduler

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Scheduler-01f85851-adcc-40b4-a1c7-0ddde083432b

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\n", " Comm: tcp://127.0.0.1:46165\n", " \n", " Workers: 48\n", "
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\n", " Started: Just now\n", " \n", " Total memory: 188.56 GiB\n", "
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Workers

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Worker: 0

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\n", " Comm: tcp://127.0.0.1:34597\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41281/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42705\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-aah3cxuv\n", "
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Worker: 1

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\n", " Comm: tcp://127.0.0.1:42711\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/45281/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:37063\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-xtvy0wq2\n", "
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Worker: 2

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\n", " Comm: tcp://127.0.0.1:38195\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44757/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44595\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-i55om_hi\n", "
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Worker: 3

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\n", " Comm: tcp://127.0.0.1:33047\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36495/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33947\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-gfb9zek8\n", "
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Worker: 4

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\n", " Comm: tcp://127.0.0.1:40579\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43293/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35391\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-58xbdpno\n", "
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Worker: 5

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\n", " Comm: tcp://127.0.0.1:37283\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43651/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:37199\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-q7v48mpm\n", "
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Worker: 6

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\n", " Comm: tcp://127.0.0.1:44365\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:41615\n", "
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Worker: 7

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\n", " Comm: tcp://127.0.0.1:37899\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:38381\n", "
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Worker: 8

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\n", " Comm: tcp://127.0.0.1:33303\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:43119\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-9lr61fqu\n", "
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Worker: 9

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\n", " Comm: tcp://127.0.0.1:43839\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37771/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46599\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-6w_fd03o\n", "
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Worker: 10

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\n", " Comm: tcp://127.0.0.1:41051\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/35977/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33615\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-cajmbtz7\n", "
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Worker: 11

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\n", " Comm: tcp://127.0.0.1:41837\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/46321/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42325\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-4n8l05tg\n", "
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Worker: 12

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\n", " Comm: tcp://127.0.0.1:36785\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36775/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39811\n", "
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Worker: 13

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\n", " Comm: tcp://127.0.0.1:37391\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34955/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38251\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-1gbj3vno\n", "
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Worker: 14

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\n", " Comm: tcp://127.0.0.1:41367\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37349/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:36321\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-9rsmw0m0\n", "
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Worker: 15

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\n", " Comm: tcp://127.0.0.1:42419\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43657/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34671\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-ja0i1sw9\n", "
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Worker: 16

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\n", " Comm: tcp://127.0.0.1:45351\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33509/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33977\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-seav5bro\n", "
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Worker: 17

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\n", " Comm: tcp://127.0.0.1:46201\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43193/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33355\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-3lf6l6er\n", "
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Worker: 18

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\n", " Comm: tcp://127.0.0.1:34967\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33013/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:43841\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-le58b2mw\n", "
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Worker: 19

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\n", " Comm: tcp://127.0.0.1:44157\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37705/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40039\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-ixpz66dp\n", "
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Worker: 20

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\n", " Comm: tcp://127.0.0.1:46799\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43959/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40313\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-7us28f4q\n", "
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Worker: 21

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\n", " Comm: tcp://127.0.0.1:32953\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34787/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39109\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-yas3qwde\n", "
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Worker: 22

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\n", " Comm: tcp://127.0.0.1:45285\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40777/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40067\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-9dxff4r6\n", "
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Worker: 23

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\n", " Comm: tcp://127.0.0.1:35255\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34215/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35459\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-w03v_vx1\n", "
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Worker: 24

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\n", " Comm: tcp://127.0.0.1:46747\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/35315/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33881\n", "
\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-axat4v2t\n", "
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Worker: 25

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\n", " Comm: tcp://127.0.0.1:46847\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:45191\n", "
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Worker: 26

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\n", " Nanny: tcp://127.0.0.1:46411\n", "
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Worker: 27

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\n", " Nanny: tcp://127.0.0.1:38387\n", "
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Worker: 28

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Worker: 29

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Worker: 30

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\n", " Local directory: /jobfs/126791498.gadi-pbs/dask-scratch-space/worker-5dxq455m\n", "
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Worker: 31

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Worker: 32

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Worker: 33

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Worker: 34

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Worker: 35

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Worker: 36

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Worker: 37

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Worker: 38

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Worker: 39

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Worker: 40

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Worker: 41

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Worker: 42

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Worker: 43

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Worker: 44

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Worker: 45

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Worker: 46

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Worker: 47

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" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client = Client(threads_per_worker = 1)\n", "client" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "catalog = intake.cat.access_nri\n", "experiment = '1deg_jra55_iaf_omip2_cycle1' # 1-deg experiment" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def load_var(experiment, variable, frequency, start_time=None, end_time=None):\n", "\n", " cat_subset = catalog.search(name = experiment)\n", "\n", " var = cat_subset[experiment].search(\n", " variable = variable, frequency = frequency, variable_cell_methods='time: mean'\n", " ).to_dask(xarray_open_kwargs = dict(use_cftime=True,chunks={}),\n", " xarray_combine_by_coords_kwargs = dict(compat=\"override\", data_vars=\"minimal\", coords=\"minimal\")\n", " )[variable]\n", "\n", " var = var.sel(time=slice(start_time, end_time))\n", "\n", " return var" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loading the variables" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "temperature = load_var(experiment, 'temp', '1mon')\n", "salinity = load_var(experiment, 'salt', '1mon')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute anomalies relative to the first year (assuming monthly output here)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "temperature_anomaly = temperature - temperature.isel(time=slice(0, 12)).cf.mean('time')\n", "salinity_anomaly = salinity - salinity.isel(time=slice(0, 12)).cf.mean('time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we load cell area (denoted as $a(x,y,z)$) to construct the total ocean area as a function of depth, $A$, namely\n", "$$ A(z) = \\sum_x \\sum_y a(x,y,z)$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We load `dxt` and `dyt` and compute a masked version of cell area; we also use a slight hack to divide temperature by itself and thereby get a 3-dimensional cell area mask that is needed to create $A(z)$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "cat_subset = catalog.search(name = experiment)\n", "\n", "dxt = cat_subset[experiment].search(variable = 'dxt', frequency = 'fx', path=\".*output000.*\").to_dask()['dxt']\n", "dyt = cat_subset[experiment].search(variable = 'dyt', frequency = 'fx', path=\".*output000.*\").to_dask()['dyt']\n", "cell_area = dxt * dyt\n", "\n", "## Make a mask to get vertical variation of area\n", "temp1 = temperature.isel(time=0)\n", "cell_mask = temp1 / temp1\n", "\n", "total_area = (cell_area * cell_mask).cf.sum({'longitude', 'latitude'}).load()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, the mean temperature at each time level can then be computed as \n", "$$T(z,t) = \\frac{\\sum_x \\sum_y a(x,y,z) \\, \\tilde{\\theta}(x,y,z,t)}{A(z)}$$\n", "where $T$ is the globally average temperature and $\\tilde{\\theta}$ is the anomaly of the conservative temperature." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "### Temperature hovmoller\n", "temperature_hov = (cell_area * temperature_anomaly).cf.sum({'longitude', 'latitude'}) / total_area\n", "temperature_hov = temperature_hov.compute()\n", "\n", "### Salinity hovmoller\n", "salinity_hov = (cell_area * salinity_anomaly).cf.sum({'longitude', 'latitude'}) / total_area\n", "salinity_hov = salinity_hov.compute()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def plot_hovmoller(fsize = 14, date_format = mdates.DateFormatter('%Y')):\n", " \n", " # Set figures properties\n", " plt.rcParams['font.size'] = fsize\n", " plt.rcParams['xtick.labelsize'] = fsize-2\n", " plt.rcParams['ytick.labelsize'] = fsize-2\n", " \n", " fig = plt.figure(figsize = (10, 6))\n", " grid = GridSpec(100, 100)\n", " \n", " ax = [fig.add_subplot(grid[:30, :30]), fig.add_subplot(grid[:30, 33:63]),\n", " fig.add_subplot(grid[32:, :30]), fig.add_subplot(grid[32:, 33:63])]\n", " \n", " for i in range(len(ax)):\n", " ax[i].xaxis.set_major_formatter(date_format)\n", " ax[i].tick_params(axis='x', labelrotation=45)\n", " if i != 0 and i != 2:\n", " ax[i].set_yticklabels([]) \n", "\n", " return fig, ax" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plot_hovmoller(fsize = 14)\n", "\n", "levels_temperature = np.arange(-0.3, 0.31, 0.01)\n", "\n", "cf_temp = temperature_hov.cf.plot(ax = ax[0],\n", " levels = levels_temperature,\n", " x = 'time',\n", " y = 'vertical',\n", " add_colorbar = False,\n", " label = None,\n", " cmap = cm.balance)\n", "\n", "temperature_hov.cf.plot(ax = ax[2],\n", " levels = levels_temperature,\n", " x = 'time',\n", " y = 'vertical',\n", " add_colorbar = False,\n", " label = None,\n", " cmap = cm.balance)\n", "\n", "levels_salinity = np.arange(-0.03, 0.031, 0.001)\n", "\n", "cf_salt = salinity_hov.cf.plot(ax = ax[1],\n", " levels = levels_salinity,\n", " x = 'time',\n", " y = 'vertical',\n", " add_colorbar = False,\n", " label = None,\n", " cmap = cm.curl)\n", "\n", "salinity_hov.cf.plot(ax = ax[3],\n", " levels = levels_salinity,\n", " x = 'time',\n", " y = 'vertical',\n", " add_colorbar = False,\n", " label = None,\n", " cmap = cm.curl)\n", "\n", "## Beautification details\n", "for i in range(len(ax)):\n", " if i < 2:\n", " ax[i].set_ylim(500, 0)\n", " ax[i].set_xticklabels([])\n", " else:\n", " ax[i].set_xlabel(\"\")\n", " ax[i].set_ylim(5000, 500)\n", "ax[0].set_ylabel(\"Depth [m]\")\n", "ax[1].set_ylabel(\"\")\n", "ax[2].set_ylabel(\"Depth [m]\")\n", "ax[3].set_ylabel(\"\")\n", "\n", "# Colorbars\n", "bar = plt.axes([0.11, 0.99, 0.25, 0.02])\n", "cbar_1 = plt.colorbar(cf_temp, cax = bar, orientation = 'horizontal', extend='both', format= '%.2f') \n", "cbar_1.set_label(\"Temperature [$\\degree$C]\")\n", "\n", "bar = plt.axes([0.37, 0.99, 0.25, 0.02])\n", "cbar_2 = plt.colorbar(cf_salt, cax = bar, orientation = 'horizontal', extend='both', format= '%.2f') \n", "cbar_2.set_label(\"Salinity [psu]\")\n", "\n", "for cbar in [cbar_1, cbar_2]:\n", " tick_locator = ticker.MaxNLocator(nbins=3) ## The ticker needs to called within the loop\n", " cbar.locator = tick_locator\n", " cbar.update_ticks()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:analysis3-24.07] *", "language": "python", "name": "conda-env-analysis3-24.07-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": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 4 }