{ "cells": [ { "cell_type": "markdown", "id": "65fe6a4e-f751-40c3-80ed-12c66e728784", "metadata": {}, "source": [ "# Temperature-Salinity diagram using MOM6 output" ] }, { "cell_type": "markdown", "id": "35b13488-c6b8-40be-8432-3df28b390664", "metadata": {}, "source": [ "This notebook shows how to plot a temperature-salinity diagram which is weighted by volume using xhistogram. \n", "\n", "The notebook is written using output from MOM6. If you want to use output from MOM5 the relevant diagnostics are as follows;\n", "- Temperature: `temp` (in MOM6 this is `thetao`)\n", "- Salinity: `salt` (in MOM6 this is `so`)\n", " \n", "In MOM6, there is a tracer cell volume diagnostic (`volcello`). There is no output diagnostic equivalent in MOM5, however you can calculate the tracer cell volume by multiplying the tracer cell area (`area_t`) by the tracer cell thickness (`dzt`). \n", "\n", "Note that the coordinate (lat and lon) names also differ between MOM6 and MOM5. In MOM6, the tracer longitude coordinate is labelled `xh`, the tracer latitude coordinate is labelled `yh`, and the tracer vertical coordinate is `z_l`. In MOM5, the tracer longitude coordinate is labelled `xt_ocean`, the tracer latitude coordinate is labelled `yt_ocean`, and the tracer vertical coordinate is `st_ocean`. \n", "\n", "**Requirements**: The conda/analysis3 (or later) module on ARE. A session with 4 cores is sufficient for this example but more cores will be needed for larger datasets. \n", "\n", "Firstly, we load all required modules and start a client." ] }, { "cell_type": "code", "execution_count": 1, "id": "14189095-05e3-4e8f-b5dc-648498e5b783", "metadata": {}, "outputs": [], "source": [ "# analysis libraries\n", "import xarray as xr\n", "import numpy as np\n", "import gsw\n", "from xhistogram.xarray import histogram as xhistogram\n", "\n", "# intake and Dask\n", "import intake\n", "catalog = intake.cat.access_nri\n", "from dask.distributed import Client\n", "\n", "# plotting libaries\n", "import matplotlib.pyplot as plt\n", "import cmocean.cm as cmo\n", "import matplotlib.colors as colors" ] }, { "cell_type": "code", "execution_count": 2, "id": "96030807-8749-4e3f-83b8-72d625e92c8e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Client

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Client-b444da82-8df6-11f0-99fe-00000187fe80

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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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5497102f

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Status: runningUsing processes: True
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Scheduler Info

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Scheduler

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Scheduler-4d5b3061-e30f-42fa-a167-3ad329bf057f

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Workers

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Worker: 0

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Worker: 1

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Worker: 2

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Worker: 3

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Worker: 4

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Worker: 5

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Worker: 6

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Worker: 7

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Worker: 8

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Worker: 9

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Worker: 10

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Worker: 11

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Worker: 12

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Worker: 13

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Worker: 14

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Worker: 15

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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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Worker: 20

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Worker: 21

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Worker: 22

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Worker: 23

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\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://127.0.0.1:39059\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38145/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45745\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-izawqr1q\n", "
\n", "
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Worker: 24

\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://127.0.0.1:45663\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/45149/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41295\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-7hiii8df\n", "
\n", "
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Worker: 25

\n", "
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\n", " Comm: tcp://127.0.0.1:34631\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33767/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46155\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-r38mr4xs\n", "
\n", "
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Worker: 26

\n", "
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\n", " Comm: tcp://127.0.0.1:35131\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/46383/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46409\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-5l5d3xbl\n", "
\n", "
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Worker: 27

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\n", " Comm: tcp://127.0.0.1:35697\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38915/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46077\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-i86vb2ki\n", "
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Worker: 28

\n", "
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\n", " Comm: tcp://127.0.0.1:38509\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36659/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34855\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-s6o9k8kx\n", "
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Worker: 29

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\n", " Comm: tcp://127.0.0.1:42339\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/32917/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38189\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-4psniwjt\n", "
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Worker: 30

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\n", " Comm: tcp://127.0.0.1:34401\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37665/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42157\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-2d1h4mgf\n", "
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Worker: 31

\n", "
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\n", " Comm: tcp://127.0.0.1:35765\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34187/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42119\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-tnmqa2mn\n", "
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Worker: 32

\n", "
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\n", " Comm: tcp://127.0.0.1:41835\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33205/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:43703\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-phun_nqr\n", "
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Worker: 33

\n", "
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\n", " Comm: tcp://127.0.0.1:38103\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43687/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38797\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-fsuelkkn\n", "
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Worker: 34

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\n", " Comm: tcp://127.0.0.1:45901\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/42083/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46803\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-z7xmkm1_\n", "
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Worker: 35

\n", "
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\n", " Comm: tcp://127.0.0.1:43177\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/39737/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42981\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-w4r_8dhu\n", "
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Worker: 36

\n", "
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\n", " Comm: tcp://127.0.0.1:37723\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/39473/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38867\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-98sa90fq\n", "
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Worker: 37

\n", "
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\n", " Comm: tcp://127.0.0.1:38771\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38335/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35951\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-j06_e980\n", "
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Worker: 38

\n", "
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\n", " Comm: tcp://127.0.0.1:41091\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38165/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40837\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-6mjfy0bg\n", "
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Worker: 39

\n", "
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\n", " Comm: tcp://127.0.0.1:41691\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34587/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38739\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-ihxp8zr1\n", "
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Worker: 40

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\n", " Comm: tcp://127.0.0.1:43285\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44939/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:37073\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-__yoya3w\n", "
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Worker: 41

\n", "
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\n", " Comm: tcp://127.0.0.1:38019\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33715/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35695\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-8u7b2be8\n", "
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Worker: 42

\n", "
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\n", " Comm: tcp://127.0.0.1:45117\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38063/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:43929\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-67rppl7k\n", "
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Worker: 43

\n", "
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\n", " Comm: tcp://127.0.0.1:39683\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:42503\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-c_d6ir17\n", "
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Worker: 44

\n", "
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\n", " Comm: tcp://127.0.0.1:38371\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40599/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46389\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-3zjsyyyd\n", "
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Worker: 45

\n", "
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\n", " Comm: tcp://127.0.0.1:38641\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34415/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39893\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-g822fnjk\n", "
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Worker: 46

\n", "
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\n", " Comm: tcp://127.0.0.1:43519\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37531/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33019\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-4kh9t5gm\n", "
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Worker: 47

\n", "
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\n", " Comm: tcp://127.0.0.1:39033\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/35439/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42027\n", "
\n", " Local directory: /jobfs/149304494.gadi-pbs/dask-scratch-space/worker-fdgu3ok_\n", "
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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": "markdown", "id": "fc82535c-a04a-43d4-8703-73a0d96270bf", "metadata": { "tags": [] }, "source": [ "## Load data" ] }, { "cell_type": "markdown", "id": "899d0501-ed19-49ca-bfe0-b21bd8528eb2", "metadata": {}, "source": [ "Choose an experiment of any resolution. Here, only 1 year of the MOM6 `panant-01-zstar-v13` experiment is selected; if you want to use a longer time period, you might need more resources!" ] }, { "cell_type": "code", "execution_count": 3, "id": "b3fc0fe0-7b35-49eb-b18a-c34819a59a6d", "metadata": {}, "outputs": [], "source": [ "experiment = \"panant-01-zstar-v13\"\n", "start_time = \"1991-01-01\"\n", "end_time = \"1991-12-31\"" ] }, { "cell_type": "markdown", "id": "c098e4d0-3e8f-4558-b0ae-33418a611ef6", "metadata": {}, "source": [ "### Load the data" ] }, { "cell_type": "markdown", "id": "06f4a900-f686-4e89-b27c-8e2c1b65895f", "metadata": {}, "source": [ "We define a function below to load output (temperature or salinity) on which we will compute the histogram for the T-S diagram." ] }, { "cell_type": "code", "execution_count": 4, "id": "b2f8c24c-63ae-4fdf-803e-6899e1e728e1", "metadata": {}, "outputs": [], "source": [ "def load_data(variable, frequency='1mon', file_id=None, start_time=None, end_time=None):\n", " \n", " '''\n", " variable: string defining the variable to load (e.g. `thetao` or `so`)\n", " '''\n", "\n", " catalog_subset = catalog[experiment]\n", " variable_search = catalog_subset.search(variable=variable, frequency=frequency)\n", " if file_id is not None:\n", " variable_search = variable_search.search(file_id=file_id)\n", " \n", " darray = variable_search.to_dask(xarray_open_kwargs={\"decode_timedelta\": False})\n", "\n", " darray = darray.get(variable)\n", " \n", " if 'time' in darray.coords:\n", " darray = darray.sel(time=slice(start_time, end_time))\n", " \n", " return darray\n", " " ] }, { "cell_type": "markdown", "id": "cdf24727-d22c-44ea-b68e-2f9ac353faac", "metadata": {}, "source": [ "Now load the data using this function. " ] }, { "cell_type": "code", "execution_count": 5, "id": "dea3e68c-1c4e-4b7a-ad5e-d0e7552d30d2", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/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/analysis3-25.08/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/analysis3-25.08/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": [ "salt = load_data('so', start_time=start_time, end_time=end_time)\n", "temperature = load_data('thetao', start_time=start_time, end_time=end_time)\n", "cell_volume = load_data('volcello', start_time=start_time, end_time=end_time, file_id='ocean.1mon.nv:2.xh:3600.xq:3601.yh:845.yq:846.z_i:76.z_l:75')" ] }, { "cell_type": "markdown", "id": "93a52197-7ee9-4ec8-8e4f-718733eeca16", "metadata": {}, "source": [ "### Subset the data regionally" ] }, { "cell_type": "markdown", "id": "73fb00bb-5c21-4ffd-9e70-d1f1a92fd950", "metadata": {}, "source": [ "Select the region and coordinates of meridional section for the temperature-salinity diagram." ] }, { "cell_type": "code", "execution_count": 6, "id": "d8d8b6a0-6ede-4afb-9a78-942a2efecafe", "metadata": {}, "outputs": [], "source": [ "longitude = -25 # longitude of meridional section\n", "latitude_range = slice(-90, -37) # latitude range of section" ] }, { "cell_type": "markdown", "id": "ee50ce22-49fb-4bcb-be05-b1061d2b2434", "metadata": {}, "source": [ "And we subset the previously loaded data using this function. " ] }, { "cell_type": "code", "execution_count": 7, "id": "b71d3ec2-b86a-4d8f-aee2-b404ec7d89cf", "metadata": {}, "outputs": [], "source": [ "salt = salt.sel(xh=longitude, method='nearest').sel(yh=latitude_range)\n", "temperature = temperature.sel(xh=longitude, method='nearest').sel(yh=latitude_range)\n", "cell_volume = cell_volume.sel(xh=longitude, method='nearest').sel(yh=latitude_range)" ] }, { "cell_type": "markdown", "id": "59692951-4bdf-477d-855e-f8c8877de8c5", "metadata": {}, "source": [ "### Convert Temperature and Salinity units" ] }, { "cell_type": "markdown", "id": "87a093e5-5ea4-40fc-a16f-d0e4dc9e0120", "metadata": {}, "source": [ "Now we need to convert from potential temperature to conservative temperature and from practical salinity to absolute salinity. If adapting this to MOM5 output, make sure you check the temperature and salinity definitions/units - they may be different than for MOM6! To learn more about the different types of salinity and temperature measurements, and how to convert between them, visit the `GSW` toolbox: https://teos-10.github.io/GSW-Python/intro.html" ] }, { "cell_type": "code", "execution_count": 8, "id": "c119bb2b-5577-44af-a412-e50c5d5d164b", "metadata": {}, "outputs": [], "source": [ "# Calculate ocean pressure (gsw assumes ocean depth is positive upwards, \n", "# hence the negative applied here)\n", "pressure = gsw.p_from_z(-salt.z_l, salt.yh)\n", "\n", "# This converts pratical salinity (psu) to absolute salinity (g/kg)\n", "SA = gsw.SA_from_SP(salt, pressure, salt.xh, salt.yh)\n", "SA.attrs = {'units': 'Absolute Salinity (g/kg)'}\n", "\n", "# This converts potential temperature (deg C) to conservative temperature (deg C)\n", "CT = gsw.CT_from_pt(salt, temperature)\n", "CT = CT.rename('thetao')\n", "CT.attrs = {'units': 'Conservative temperature (°C)'}" ] }, { "cell_type": "markdown", "id": "be615281-0956-43c1-8465-b87a07035592", "metadata": {}, "source": [ "Now load the converted data into memory. Note: You don't need to load the data into memory to run the rest of the script but it can make subsequent calculations faster. However, if you choose to load more data (e.g. a larger region or longer timeframe) then this step might cause the kernel to die. " ] }, { "cell_type": "code", "execution_count": 9, "id": "e1dde387-f837-4403-89e7-e72db849899c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 17.40 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": [ "CT = CT.compute()\n", "SA = SA.compute()" ] }, { "cell_type": "markdown", "id": "04b4931a-6c8f-4796-9b3c-4468fab74cbb", "metadata": {}, "source": [ "### Compute T-S histogram" ] }, { "cell_type": "markdown", "id": "8fc17024-5ec9-439b-a026-9b3bd0c78a4b", "metadata": {}, "source": [ "Now define the function that computes the temperature and salinity bins for the T-S histogram. " ] }, { "cell_type": "code", "execution_count": 10, "id": "a2de26fb-a206-4966-8111-3d4aaddb6b34", "metadata": {}, "outputs": [], "source": [ "def compute_TS_bins(salt, temperature, volume):\n", " \n", " temp_bins = np.arange(np.floor(temperature.min().values)-0.5, np.ceil(temperature.max().values)+0.6, 0.5)\n", " salt_bins = np.arange(np.floor(salt.min().values)-0.1, np.ceil(salt.max().values)+0.11, 0.1)\n", " \n", " # To create density contours in the T-S diagram\n", " temp_bins_mesh, salt_bins_mesh = np.meshgrid(temp_bins, salt_bins)\n", " TS_density = gsw.density.sigma2(salt_bins_mesh, temp_bins_mesh)\n", " \n", " # Create the 2D histogram array containing the temperature and salinity values, \n", " # weighted by grid cell volume\n", " TS_histogram = xhistogram(temperature, salt, bins=(temp_bins, salt_bins), weights=volume)\n", " TS_histogram = TS_histogram.where(TS_histogram != 0).compute()\n", " \n", " return TS_histogram, TS_density, salt_bins_mesh, temp_bins_mesh " ] }, { "cell_type": "markdown", "id": "63931c59-cd17-425b-a09e-80f4b54c260c", "metadata": {}, "source": [ "Calculate the histogram. " ] }, { "cell_type": "code", "execution_count": 11, "id": "3678092f-40bd-4e50-8aac-bdfa61f42d10", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 19.73 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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 8.66 s, sys: 482 ms, total: 9.14 s\n", "Wall time: 9.34 s\n" ] } ], "source": [ "%%time\n", "TS_histogram, TS_density, salt_bins_mesh, temp_bins_mesh = compute_TS_bins(SA, CT, cell_volume)" ] }, { "cell_type": "markdown", "id": "53057aca-b03a-499d-b02f-8c88f30bf566", "metadata": {}, "source": [ "### Plot the histogram" ] }, { "cell_type": "markdown", "id": "343f3812-6959-4c2d-94ba-dfb6780309a1", "metadata": {}, "source": [ "Now we can plot this T-S histogram!" ] }, { "cell_type": "code", "execution_count": 12, "id": "1f29e537-015b-41e8-9d49-a7ab651ff18c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['font.size']=14\n", "plt.figure(figsize=(9, 8))\n", "\n", "# normalize the colorbar to the min and maximum values of the histogram \n", "# using a LogNormal scale\n", "norm=colors.LogNorm(vmin=TS_histogram.min().values, vmax=TS_histogram.max().values)\n", "\n", "# Plot (shade) the TS histogram data\n", "TS_histogram.plot(cmap=cmo.dense, norm=norm, cbar_kwargs=dict(label='volume (m$^{3}$)'))\n", "\n", "# Add the density contours\n", "cs = plt.contour(salt_bins_mesh, temp_bins_mesh, TS_density, \n", " colors='silver', linewidths=1, \n", " levels=np.arange(np.floor(TS_density.min()), np.ceil(TS_density.max()), .5))\n", "plt.clabel(cs, inline=True)\n", "\n", "plt.xlabel(SA.units)\n", "plt.ylabel(CT.units)\n", "plt.title(\"T-S weighted histogram from MOM6\");\n", "\n", "plt.xlim([32.9, 35.7])\n", "plt.yticks(np.arange(-2, 23, 2))\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:analysis3-25.08] *", "language": "python", "name": "conda-env-analysis3-25.08-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 }