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tinaok/ESGF_kerchunk_nodata.ipynb

Created November 19, 2022 15:03
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Loading file to xarray with kerchunk catalogue (from a http server), got no data ( no error message?, or no automatic re-try?) Raw
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ESGF_kerchunk_nodata.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "50e1c60f-117b-400b-b16f-9bfe5bec7f3d",
"metadata": {},
"source": [
"# This notebook shows that loading xarray based on kerchunke'd catalogue may sometime get NaN, probably due to http server not responding.\n",
"\n",
"I would like to know how we can make kerchunk (?) or xarray (?) can re-try and get repeat accesing untill we get the data, or show err. \n",
"\n",
"Untill i 'count' the value, i didn't see that data was not loaded.."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "17d1628d-d4dc-4320-8da3-ec83db4de159",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n"
]
}
],
"source": [
"import os\n",
"import kerchunk.hdf\n",
"import fsspec\n",
"#import json\n",
"import xarray as xr\n",
"import gcsfs\n",
"from dask_gateway import Gateway\n",
"gateway = Gateway()\n",
"# Clean dask cluster you've spawend before\n",
"#\n",
"from dask.distributed import Client\n",
"clusters=gateway.list_clusters()\n",
"print(clusters )\n",
"for cluster in clusters :\n",
" cluster= gateway.connect(cluster.name)\n",
" print(cluster)\n",
" client = Client(cluster)\n",
" client.close()\n",
" cluster.shutdown()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "34f8eb69-5bfb-4932-8853-af4cd3b00202",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-6ee07301-67fc-11ed-9044-06c6ae6a539c</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
"\n",
" <tr>\n",
" \n",
" <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
" <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_gateway.GatewayCluster</td>\n",
" \n",
" </tr>\n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"/services/dask-gateway/clusters/prod.634f4c0be66946a5902216271ad57d16/status\" target=\"_blank\">/services/dask-gateway/clusters/prod.634f4c0be66946a5902216271ad57d16/status</a>\n",
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" \n",
"\n",
" </table>\n",
"\n",
" \n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
" <div style='background-color: #f2f2f2; display: inline-block; padding: 10px; border: 1px solid #999999;'>\n",
" <h3>GatewayCluster</h3>\n",
" <ul>\n",
" <li><b>Name: </b>prod.634f4c0be66946a5902216271ad57d16\n",
" <li><b>Dashboard: </b><a href='/services/dask-gateway/clusters/prod.634f4c0be66946a5902216271ad57d16/status' target='_blank'>/services/dask-gateway/clusters/prod.634f4c0be66946a5902216271ad57d16/status</a>\n",
" </ul>\n",
"</div>\n",
"\n",
" </details>\n",
" \n",
"\n",
" </div>\n",
"</div>"
],
"text/plain": [
"<Client: 'tls://10.8.15.3:8786' processes=0 threads=0, memory=0 B>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cluster = gateway.new_cluster(worker_memory=8, worker_cores=2)\n",
"cluster.adapt(minimum=4,maximum=40)\n",
"#cluster.scale(20)\n",
"cluster\n",
"from distributed import Client\n",
"client = Client(cluster)\n",
"client"
]
},
{
"cell_type": "markdown",
"id": "e32ead46-34be-4a6a-8cc8-06f442222b9d",
"metadata": {},
"source": [
"## Reproducing the err\n",
"This happend when using CMIP6 data provided by ESGF server. \n",
"Below two cells shows how to get http_url (non opendap server download_url) "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1ee44b12-e075-4a67-872c-d5a25dd3dcb1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"pip install esgf-pyclient"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4e66f90d-4a3e-4280-92d0-84da886d9fa7",
"metadata": {},
"outputs": [],
"source": [
"from pyesgf.search import SearchConnection\n",
"\n",
"server={\n",
" 'de':'https://esgf-data.dkrz.de/esg-search',\n",
" 'llnl':'http://esgf-node.llnl.gov/esg-search',\n",
" 'fr':'https://esgf-data.ipsl.upmc.fr/esg-search',\n",
" 'uk':'https://esgf.ceda.ac.uk/esg-search'}\n",
"\n",
"server=server['llnl']\n",
"models=['CMCC-CM2-SR5']\n",
"variables=['thetao','so']\n",
"\n",
"def create_urls(server,model,variable_id):\n",
" conn = SearchConnection(server, distrib=False)\n",
" activity_id='OMIP'\n",
" experiment_id='omip2'\n",
" project='CMIP6'\n",
" from_timestamp = \"1958-01-01T00:00:00Z\"\n",
" to_timestamp= \"2022-01-01T00:00:00Z\"\n",
" facets='project,source_id,variable,experiment_id,frequency,from_timestamp,to_timestamp'\n",
" print(server,model,variable_id,'start new_context')\n",
" ctx = conn.new_context(\n",
" facets=facets,\n",
" project=project,\n",
" source_id=model,\n",
" experiment_id=experiment_id,\n",
" variable=variable_id,\n",
" frequency='mon',\n",
" from_timestamp = from_timestamp,\n",
" to_timestamp= to_timestamp \n",
" )\n",
" result = ctx.search()[0]\n",
" files = result.file_context().search()\n",
"\n",
" opendap=[f.opendap_url for f in files]\n",
" download_urls=[f.download_url for f in files] \n",
" return download_urls\n",
"\n",
"model=models[0]\n",
"variables=['thetao']\n",
"var_urls={var: create_urls(server,model,var) for var in variables}\n",
"\n",
"url=var_urls['thetao'][-1]\n",
"url"
]
},
{
"cell_type": "markdown",
"id": "c5756e4c-c91f-41c3-9d50-8108ecc97588",
"metadata": {},
"source": [
"### We'll use folloing url (5.4G of NetCDF file) to reproduce the error. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5e4c1367-87f5-47a6-a83d-406f233785d9",
"metadata": {},
"outputs": [],
"source": [
"url='http://esgf-data1.llnl.gov/thredds/fileServer/css03_data/CMIP6/OMIP/CMCC/CMCC-CM2-SR5/omip2/r1i1p1f1/Omon/thetao/gn/v20200226/thetao_Omon_CMCC-CM2-SR5_omip2_r1i1p1f1_gn_195801-201812.nc'"
]
},
{
"cell_type": "markdown",
"id": "e23a9924-3071-4352-a5b3-3ed03f2a4dbc",
"metadata": {
"tags": []
},
"source": [
"### Create kerchunk catalogue from the url "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c08004df-d97b-4c89-8e9c-6294fc6242a2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 5.93 s, sys: 982 ms, total: 6.91 s\n",
"Wall time: 2min 13s\n"
]
}
],
"source": [
"%%time\n",
"with fsspec.open(url) as inf:\n",
" info_http = kerchunk.hdf.SingleHdf5ToZarr(inf, url, inline_threshold=100).translate()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "c0b98a68-dbe4-4b9a-917a-77b3e12076ef",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 153 ms, sys: 4.73 ms, total: 158 ms\n",
"Wall time: 733 ms\n"
]
}
],
"source": [
"%%time\n",
"ds=xr.open_dataset(\n",
" \"reference://\", engine=\"zarr\",\n",
" backend_kwargs={\n",
" \"storage_options\": {\n",
" \"fo\":info_http,\n",
" },\n",
" \"consolidated\": False\n",
" } ,chunks={} \n",
" ).sel(time =slice('1960','2003'))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "8af75f13-17ae-4c5c-992a-d356b40c2b5b",
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [
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".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (i: 360, j: 291, lev: 50, bnds: 2, time: 528,\n",
" vertices: 4)\n",
"Coordinates:\n",
" * i (i) float64 0.0 1.0 2.0 3.0 ... 356.0 357.0 358.0 359.0\n",
" * j (j) float64 0.0 1.0 2.0 3.0 ... 287.0 288.0 289.0 290.0\n",
" latitude (j, i) float64 dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;\n",
" * lev (lev) float64 0.5126 1.621 2.858 ... 5.498e+03 5.904e+03\n",
" longitude (j, i) float64 dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;\n",
" * time (time) object 1960-01-16 12:00:00 ... 2003-12-16 12:0...\n",
"Dimensions without coordinates: bnds, vertices\n",
"Data variables:\n",
" lev_bnds (lev, bnds) float64 dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;\n",
" thetao (time, lev, j, i) float32 dask.array&lt;chunksize=(1, 25, 146, 180), meta=np.ndarray&gt;\n",
" time_bnds (time, bnds) object dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
" vertices_latitude (j, i, vertices) float64 dask.array&lt;chunksize=(291, 360, 2), meta=np.ndarray&gt;\n",
" vertices_longitude (j, i, vertices) float64 dask.array&lt;chunksize=(291, 360, 2), meta=np.ndarray&gt;\n",
"Attributes: (12/39)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: OMIP\n",
" cmor_version: 3.5.0\n",
" comment: Ocean initial conditions: WOA 2013 T &amp; S; ocean at...\n",
" contact: Pier Giuseppe Fogli ([email protected])\n",
" creation_date: 2020-02-19T13:19:38Z\n",
" ... ...\n",
" table_id: Omon\n",
" table_info: Creation Date:(15 January 2020) MD5:bc48740ff90dcf...\n",
" title: CMCC-CM2-SR5 output prepared for CMIP6\n",
" tracking_id: hdl:21.14100/1b7c2d37-0a5c-43c0-8a3b-d534e03c401f\n",
" variable_id: thetao\n",
" variant_label: r1i1p1f1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2a87db82-5055-4e5e-8a7a-f4aa88e68360' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2a87db82-5055-4e5e-8a7a-f4aa88e68360' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>i</span>: 360</li><li><span class='xr-has-index'>j</span>: 291</li><li><span class='xr-has-index'>lev</span>: 50</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>time</span>: 528</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f3506377-a0b5-4363-91ad-f3b46a60a576' class='xr-section-summary-in' type='checkbox' checked><label for='section-f3506377-a0b5-4363-91ad-f3b46a60a576' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 357.0 358.0 359.0</div><input id='attrs-68b0fc26-bcbe-441c-9304-492e52efb162' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68b0fc26-bcbe-441c-9304-492e52efb162' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79958640-46ef-4a98-b37e-aa9cca3c6752' class='xr-var-data-in' type='checkbox'><label for='data-79958640-46ef-4a98-b37e-aa9cca3c6752' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>first spatial index for variables stored on an unstructured grid</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([ 0., 1., 2., ..., 357., 358., 359.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 288.0 289.0 290.0</div><input id='attrs-ffe681c5-64b9-48d0-b386-b821aa29beef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffe681c5-64b9-48d0-b386-b821aa29beef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7e37fba-ce39-46a1-899a-4bb329b2ac46' class='xr-var-data-in' type='checkbox'><label for='data-c7e37fba-ce39-46a1-899a-4bb329b2ac46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>second spatial index for variables stored on an unstructured grid</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([ 0., 1., 2., ..., 288., 289., 290.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;</div><input id='attrs-183aff40-742c-473d-b670-1e8c23d81f66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-183aff40-742c-473d-b670-1e8c23d81f66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2b28377-35be-43b6-a429-6798d3b21ff7' class='xr-var-data-in' type='checkbox'><label for='data-a2b28377-35be-43b6-a429-6798d3b21ff7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>vertices_latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 818.44 kiB </td>\n",
" <td> 818.44 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (291, 360) </td>\n",
" <td> (291, 360) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 2 Graph Layers </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"170\" height=\"147\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"97\" x2=\"120\" y2=\"97\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"97\" style=\"stroke-width:2\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"97\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,97.0 0.0,97.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"117.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
" <text x=\"140.000000\" y=\"48.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,48.500000)\">291</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5126 1.621 ... 5.904e+03</div><input id='attrs-99d5e8a9-8b6b-482a-ba58-b59ac333a6db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99d5e8a9-8b6b-482a-ba58-b59ac333a6db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71b58e78-2cbc-40bd-a749-2c4dc041106f' class='xr-var-data-in' type='checkbox'><label for='data-71b58e78-2cbc-40bd-a749-2c4dc041106f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>long_name :</span></dt><dd>ocean depth coordinate</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.126340e-01, 1.621015e+00, 2.858431e+00, 4.250513e+00, 5.827960e+00,\n",
" 7.627532e+00, 9.693230e+00, 1.207770e+01, 1.484391e+01, 1.806713e+01,\n",
" 2.183723e+01, 2.626152e+01, 3.146791e+01, 3.760874e+01, 4.486517e+01,\n",
" 5.345229e+01, 6.362492e+01, 7.568428e+01, 8.998535e+01, 1.069451e+02,\n",
" 1.270512e+02, 1.508713e+02, 1.790624e+02, 2.123794e+02, 2.516826e+02,\n",
" 2.979431e+02, 3.522440e+02, 4.157769e+02, 4.898312e+02, 5.757748e+02,\n",
" 6.750254e+02, 7.890108e+02, 9.191196e+02, 1.066644e+03, 1.232717e+03,\n",
" 1.418255e+03, 1.623902e+03, 1.849991e+03, 2.096521e+03, 2.363159e+03,\n",
" 2.649263e+03, 2.953915e+03, 3.275983e+03, 3.614175e+03, 3.967101e+03,\n",
" 4.333334e+03, 4.711457e+03, 5.100101e+03, 5.497977e+03, 5.903893e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;</div><input id='attrs-4b3c020c-119a-4916-8141-b8ea95f6c61e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b3c020c-119a-4916-8141-b8ea95f6c61e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6278b44f-88f5-4397-b52b-444142ee1f16' class='xr-var-data-in' type='checkbox'><label for='data-6278b44f-88f5-4397-b52b-444142ee1f16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>vertices_longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 818.44 kiB </td>\n",
" <td> 818.44 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (291, 360) </td>\n",
" <td> (291, 360) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 2 Graph Layers </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"170\" height=\"147\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"97\" x2=\"120\" y2=\"97\" style=\"stroke-width:2\" />\n",
"\n",
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"97\" style=\"stroke-width:2\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"97\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,97.0 0.0,97.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"117.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
" <text x=\"140.000000\" y=\"48.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,48.500000)\">291</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1960-01-16 12:00:00 ... 2003-12-...</div><input id='attrs-2f10e85b-e7f9-4528-9c54-5a1d7e26eb4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f10e85b-e7f9-4528-9c54-5a1d7e26eb4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da32959c-25e1-4a65-b12a-c82caaf93532' class='xr-var-data-in' type='checkbox'><label for='data-da32959c-25e1-4a65-b12a-c82caaf93532' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1960, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(1960, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(1960, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
" ...,\n",
" cftime.DatetimeNoLeap(2003, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(2003, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(2003, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
" dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d415975-ac17-4b4e-b010-0d5d361f6edf' class='xr-section-summary-in' type='checkbox' checked><label for='section-0d415975-ac17-4b4e-b010-0d5d361f6edf' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lev_bnds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;</div><input id='attrs-47dc7193-ad24-4d5b-ae7f-d9d717dbdd39' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47dc7193-ad24-4d5b-ae7f-d9d717dbdd39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-102279d0-445b-4957-bfe5-bd77f5bcf90b' class='xr-var-data-in' type='checkbox'><label for='data-102279d0-445b-4957-bfe5-bd77f5bcf90b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 800 B </td>\n",
" <td> 800 B </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (50, 2) </td>\n",
" <td> (50, 2) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 2 Graph Layers </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"83\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
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"\n",
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"</svg>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c153913b-1567-477d-91e9-41260285aeb7' class='xr-section-summary-in' type='checkbox' ><label for='section-c153913b-1567-477d-91e9-41260285aeb7' class='xr-section-summary' >Attributes: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>OMIP</dd><dt><span>cmor_version :</span></dt><dd>3.5.0</dd><dt><span>comment :</span></dt><dd>Ocean initial conditions: WOA 2013 T &amp; S; ocean at rest;\n",
"Sea Ice initial conditions: restart from a previous OMIP2 experiment;\n",
"Forcing dataset: JRA55-do v1.4.0;\n",
"Forcing dataset temporal coverage: 1958-2018;\n",
"Forcing cycle length: 61 years;\n",
"Total forcing cycles: 6 (366 years);\n",
"Initial year of the simulation: 1653;\n",
"Final year of the simulation: 2018</dd><dt><span>contact :</span></dt><dd>Pier Giuseppe Fogli ([email protected])</dd><dt><span>creation_date :</span></dt><dd>2020-02-19T13:19:38Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.31</dd><dt><span>experiment :</span></dt><dd>OMIP experiment forced by JRA55-do atmospheric data set and initialized with observed physical and biogeochemical ocean data</dd><dt><span>experiment_id :</span></dt><dd>omip2</dd><dt><span>external_variables :</span></dt><dd>areacello volcello</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CMCC.CMCC-CM2-SR5.omip2.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>standard</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-02-19T13:19:38Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy</dd><dt><span>institution_id :</span></dt><dd>CMCC</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CMCC is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>no parent</dd><dt><span>parent_experiment_id :</span></dt><dd>no parent</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>source :</span></dt><dd>CMCC-CM2-SR5 (2016): \n",
"aerosol: MAM3\n",
"atmos: CAM5.3 (1deg; 288 x 192 longitude/latitude; 30 levels; top at ~2 hPa)\n",
"atmosChem: none\n",
"land: CLM4.5 (BGC mode)\n",
"landIce: none\n",
"ocean: NEMO3.6 (ORCA1 tripolar primarly 1 deg lat/lon with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 50 vertical levels; top grid cell 0-1 m)\n",
"ocnBgchem: none\n",
"seaIce: CICE4.0</dd><dt><span>source_id :</span></dt><dd>CMCC-CM2-SR5</dd><dt><span>source_type :</span></dt><dd>OGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Omon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(15 January 2020) MD5:bc48740ff90dcf937150006b75c0f518</dd><dt><span>title :</span></dt><dd>CMCC-CM2-SR5 output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/1b7c2d37-0a5c-43c0-8a3b-d534e03c401f</dd><dt><span>variable_id :</span></dt><dd>thetao</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (i: 360, j: 291, lev: 50, bnds: 2, time: 528,\n",
" vertices: 4)\n",
"Coordinates:\n",
" * i (i) float64 0.0 1.0 2.0 3.0 ... 356.0 357.0 358.0 359.0\n",
" * j (j) float64 0.0 1.0 2.0 3.0 ... 287.0 288.0 289.0 290.0\n",
" latitude (j, i) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
" * lev (lev) float64 0.5126 1.621 2.858 ... 5.498e+03 5.904e+03\n",
" longitude (j, i) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
" * time (time) object 1960-01-16 12:00:00 ... 2003-12-16 12:0...\n",
"Dimensions without coordinates: bnds, vertices\n",
"Data variables:\n",
" lev_bnds (lev, bnds) float64 dask.array<chunksize=(50, 2), meta=np.ndarray>\n",
" thetao (time, lev, j, i) float32 dask.array<chunksize=(1, 25, 146, 180), meta=np.ndarray>\n",
" time_bnds (time, bnds) object dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
" vertices_latitude (j, i, vertices) float64 dask.array<chunksize=(291, 360, 2), meta=np.ndarray>\n",
" vertices_longitude (j, i, vertices) float64 dask.array<chunksize=(291, 360, 2), meta=np.ndarray>\n",
"Attributes: (12/39)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: OMIP\n",
" cmor_version: 3.5.0\n",
" comment: Ocean initial conditions: WOA 2013 T & S; ocean at...\n",
" contact: Pier Giuseppe Fogli ([email protected])\n",
" creation_date: 2020-02-19T13:19:38Z\n",
" ... ...\n",
" table_id: Omon\n",
" table_info: Creation Date:(15 January 2020) MD5:bc48740ff90dcf...\n",
" title: CMCC-CM2-SR5 output prepared for CMIP6\n",
" tracking_id: hdl:21.14100/1b7c2d37-0a5c-43c0-8a3b-d534e03c401f\n",
" variable_id: thetao\n",
" variant_label: r1i1p1f1"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds"
]
},
{
"cell_type": "markdown",
"id": "d2d5dfa0-4831-44fc-8f49-39dbf164677a",
"metadata": {},
"source": [
"### Plot to verify if all data are there. "
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "2d742c56-c22a-49a1-81cf-497d9b0703b4",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fc7052b6fd0>]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds.thetao.count(dim=['lev','j','i']).plot()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "ea147299-c4d4-4466-8468-f3c7eea2da5a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fc70f9834f0>]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds.thetao.count(dim=['lev','j','i']).plot()"
]
},
{
"cell_type": "markdown",
"id": "b167e9fa-4a4c-465c-a43a-4ee1df408d5f",
"metadata": {},
"source": [
"### For each time coordinate, we have same amount of data (Shown in the cells below. ) , but as shown in the above plots, we miss data, and each plot (each access to the 'url') we do not get the same amoutn of data back. \n"
]
},
{
"cell_type": "markdown",
"id": "8d15f442-ccea-4efc-a9ce-2f6edefdb2bc",
"metadata": {
"tags": []
},
"source": [
"## By copying the same NetCDF data to local s3 bucket we do not have above problem, and we will see in the plot, the same number of data points for each time coordinate. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a87e5630-1f19-4783-9848-d2aa86a0b52b",
"metadata": {},
"outputs": [],
"source": [
"pip install wget"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "4a6db3fa-c909-45a4-844d-6eef06153aac",
"metadata": {},
"outputs": [],
"source": [
"fs = gcsfs.GCSFileSystem()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d1ef75e-e654-4136-a292-18a055539061",
"metadata": {},
"outputs": [],
"source": [
"wget_file = wget.download(url)\n",
"fs.rm(url_local)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e8643355-c401-4631-ab5f-67cf05ec03a7",
"metadata": {},
"outputs": [],
"source": [
"url_local=os.environ['PANGEO_SCRATCH']+'/nc/'+url.rsplit('/')[-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "682780c3-d6e8-4f3c-80a5-d26df0d06e01",
"metadata": {},
"outputs": [],
"source": [
"fs.put(wget_file,url_local)\n",
"fs.invalidate_cache()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "32b36be5-070d-4ca2-954f-76ddc29824dd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['pangeo-integration-te-3eea-prod-scratch-bucket/tinaok/nc/thetao_Omon_CMCC-CM2-SR5_omip2_r1i1p1f1_gn_195801-201812.nc']"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fs.ls(url_local)"
]
},
{
"cell_type": "markdown",
"id": "01787f30-2699-4b1c-a3a3-3ce0f739081e",
"metadata": {
"tags": []
},
"source": [
"### Create kerchunk catalogue"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "64e8bee2-b504-4260-8272-19ad46a00932",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 5.77 s, sys: 953 ms, total: 6.72 s\n",
"Wall time: 53.5 s\n"
]
}
],
"source": [
"%%time\n",
"with fs.open(url_local) as inf:\n",
" info_local = kerchunk.hdf.SingleHdf5ToZarr(inf, url_local, inline_threshold=100).translate()"
]
},
{
"cell_type": "markdown",
"id": "303d82f9-44c7-4db5-8a61-01a28b2122b2",
"metadata": {
"tags": []
},
"source": [
"### Load from local netcdf file"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "71da9269-3756-43d2-bf83-c8c91f1c682b",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 180 ms, sys: 2.49 ms, total: 182 ms\n",
"Wall time: 424 ms\n"
]
}
],
"source": [
"%%time\n",
"ds_local=xr.open_dataset(\n",
" \"reference://\", engine=\"zarr\",\n",
" backend_kwargs={\n",
" \"storage_options\": {\n",
" \"fo\":info_local,\n",
" },\n",
" \"consolidated\": False\n",
" } ,chunks={} \n",
" ).sel(time =slice('1960','2003'))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "a2d39b47-b8d7-4007-9fd7-5aa750822a9d",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (i: 360, j: 291, lev: 50, bnds: 2, time: 528,\n",
" vertices: 4)\n",
"Coordinates:\n",
" * i (i) float64 0.0 1.0 2.0 3.0 ... 356.0 357.0 358.0 359.0\n",
" * j (j) float64 0.0 1.0 2.0 3.0 ... 287.0 288.0 289.0 290.0\n",
" latitude (j, i) float64 dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;\n",
" * lev (lev) float64 0.5126 1.621 2.858 ... 5.498e+03 5.904e+03\n",
" longitude (j, i) float64 dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;\n",
" * time (time) object 1960-01-16 12:00:00 ... 2003-12-16 12:0...\n",
"Dimensions without coordinates: bnds, vertices\n",
"Data variables:\n",
" lev_bnds (lev, bnds) float64 dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;\n",
" thetao (time, lev, j, i) float32 dask.array&lt;chunksize=(1, 25, 146, 180), meta=np.ndarray&gt;\n",
" time_bnds (time, bnds) object dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
" vertices_latitude (j, i, vertices) float64 dask.array&lt;chunksize=(291, 360, 2), meta=np.ndarray&gt;\n",
" vertices_longitude (j, i, vertices) float64 dask.array&lt;chunksize=(291, 360, 2), meta=np.ndarray&gt;\n",
"Attributes: (12/39)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: OMIP\n",
" cmor_version: 3.5.0\n",
" comment: Ocean initial conditions: WOA 2013 T &amp; S; ocean at...\n",
" contact: Pier Giuseppe Fogli ([email protected])\n",
" creation_date: 2020-02-19T13:19:38Z\n",
" ... ...\n",
" table_id: Omon\n",
" table_info: Creation Date:(15 January 2020) MD5:bc48740ff90dcf...\n",
" title: CMCC-CM2-SR5 output prepared for CMIP6\n",
" tracking_id: hdl:21.14100/1b7c2d37-0a5c-43c0-8a3b-d534e03c401f\n",
" variable_id: thetao\n",
" variant_label: r1i1p1f1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6b624580-af97-462c-9dc2-ffd1bea61afb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b624580-af97-462c-9dc2-ffd1bea61afb' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>i</span>: 360</li><li><span class='xr-has-index'>j</span>: 291</li><li><span class='xr-has-index'>lev</span>: 50</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>time</span>: 528</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-635edb2c-2d75-43f1-9048-337972d838a5' class='xr-section-summary-in' type='checkbox' checked><label for='section-635edb2c-2d75-43f1-9048-337972d838a5' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 357.0 358.0 359.0</div><input id='attrs-f5b9c8cf-8181-4320-b162-c92e743fa5a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5b9c8cf-8181-4320-b162-c92e743fa5a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb998d74-d0e9-4baa-bdf1-35020fbeae1e' class='xr-var-data-in' type='checkbox'><label for='data-eb998d74-d0e9-4baa-bdf1-35020fbeae1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>first spatial index for variables stored on an unstructured grid</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([ 0., 1., 2., ..., 357., 358., 359.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 288.0 289.0 290.0</div><input id='attrs-c69e0d5b-91ca-4fa5-a4d7-eb4ae5ce1535' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c69e0d5b-91ca-4fa5-a4d7-eb4ae5ce1535' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e83c3fda-c666-4ba6-b301-554aa9a50398' class='xr-var-data-in' type='checkbox'><label for='data-e83c3fda-c666-4ba6-b301-554aa9a50398' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>second spatial index for variables stored on an unstructured grid</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([ 0., 1., 2., ..., 288., 289., 290.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;</div><input id='attrs-3513e28f-7d55-4193-894c-ad09d87a6564' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3513e28f-7d55-4193-894c-ad09d87a6564' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a09ab75a-d4d3-4da8-8fb5-c5f79c4661f0' class='xr-var-data-in' type='checkbox'><label for='data-a09ab75a-d4d3-4da8-8fb5-c5f79c4661f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>vertices_latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 818.44 kiB </td>\n",
" <td> 818.44 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (291, 360) </td>\n",
" <td> (291, 360) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 2 Graph Layers </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"170\" height=\"147\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"97\" style=\"stroke-width:2\" />\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5126 1.621 ... 5.904e+03</div><input id='attrs-abb35628-3ebd-4403-9091-561206ea0808' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abb35628-3ebd-4403-9091-561206ea0808' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f14e1c9-0963-46ca-a500-f895daa29568' class='xr-var-data-in' type='checkbox'><label for='data-4f14e1c9-0963-46ca-a500-f895daa29568' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>long_name :</span></dt><dd>ocean depth coordinate</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.126340e-01, 1.621015e+00, 2.858431e+00, 4.250513e+00, 5.827960e+00,\n",
" 7.627532e+00, 9.693230e+00, 1.207770e+01, 1.484391e+01, 1.806713e+01,\n",
" 2.183723e+01, 2.626152e+01, 3.146791e+01, 3.760874e+01, 4.486517e+01,\n",
" 5.345229e+01, 6.362492e+01, 7.568428e+01, 8.998535e+01, 1.069451e+02,\n",
" 1.270512e+02, 1.508713e+02, 1.790624e+02, 2.123794e+02, 2.516826e+02,\n",
" 2.979431e+02, 3.522440e+02, 4.157769e+02, 4.898312e+02, 5.757748e+02,\n",
" 6.750254e+02, 7.890108e+02, 9.191196e+02, 1.066644e+03, 1.232717e+03,\n",
" 1.418255e+03, 1.623902e+03, 1.849991e+03, 2.096521e+03, 2.363159e+03,\n",
" 2.649263e+03, 2.953915e+03, 3.275983e+03, 3.614175e+03, 3.967101e+03,\n",
" 4.333334e+03, 4.711457e+03, 5.100101e+03, 5.497977e+03, 5.903893e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(291, 360), meta=np.ndarray&gt;</div><input id='attrs-f1cd2c76-b307-41b6-bcee-9448e38219c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1cd2c76-b307-41b6-bcee-9448e38219c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a43fc1bb-ffd1-4d79-bd51-4cc9047313c6' class='xr-var-data-in' type='checkbox'><label for='data-a43fc1bb-ffd1-4d79-bd51-4cc9047313c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>vertices_longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 818.44 kiB </td>\n",
" <td> 818.44 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (291, 360) </td>\n",
" <td> (291, 360) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 2 Graph Layers </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"170\" height=\"147\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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" <text x=\"60.000000\" y=\"117.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1960-01-16 12:00:00 ... 2003-12-...</div><input id='attrs-28838919-9069-4bba-a224-b02405a632d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28838919-9069-4bba-a224-b02405a632d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b795bc9-0afd-4bec-adf7-30b664cc86a3' class='xr-var-data-in' type='checkbox'><label for='data-8b795bc9-0afd-4bec-adf7-30b664cc86a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1960, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(1960, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(1960, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
" ...,\n",
" cftime.DatetimeNoLeap(2003, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(2003, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeNoLeap(2003, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
" dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46468b5a-6bac-4293-8a01-da39fefcd81a' class='xr-section-summary-in' type='checkbox' checked><label for='section-46468b5a-6bac-4293-8a01-da39fefcd81a' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lev_bnds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;</div><input id='attrs-7a43aa16-8e16-4303-a7cb-969a754f56d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a43aa16-8e16-4303-a7cb-969a754f56d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86436d87-174c-4fb0-a51c-f5edbbaae605' class='xr-var-data-in' type='checkbox'><label for='data-86436d87-174c-4fb0-a51c-f5edbbaae605' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 800 B </td>\n",
" <td> 800 B </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (50, 2) </td>\n",
" <td> (50, 2) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 2 Graph Layers </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"83\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
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"</svg>\n",
" </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thetao</span></div><div class='xr-var-dims'>(time, lev, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 25, 146, 180), meta=np.ndarray&gt;</div><input id='attrs-8edc2681-eec8-498d-a562-2aa64b2df4d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8edc2681-eec8-498d-a562-2aa64b2df4d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71c6b795-86a4-450a-b2ab-d5e8ef125656' class='xr-var-data-in' type='checkbox'><label for='data-71c6b795-86a4-450a-b2ab-d5e8ef125656' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Diagnostic should be contributed even for models using conservative temperature as prognostic field.</dd><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 10.30 GiB </td>\n",
" <td> 2.51 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (528, 50, 291, 360) </td>\n",
" <td> (1, 25, 146, 180) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 3 Graph Layers </td>\n",
" <td> 4224 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float32 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
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"\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertices_latitude</span></div><div class='xr-var-dims'>(j, i, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(291, 360, 2), meta=np.ndarray&gt;</div><input id='attrs-e481ca27-579c-4e85-8335-9b5f30d9ec6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e481ca27-579c-4e85-8335-9b5f30d9ec6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b92c3e5e-fba0-410f-b941-e922a64ee18d' class='xr-var-data-in' type='checkbox'><label for='data-b92c3e5e-fba0-410f-b941-e922a64ee18d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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" </td>\n",
" </tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f155d879-f61f-43b3-bf4c-b0b4e8216313' class='xr-section-summary-in' type='checkbox' ><label for='section-f155d879-f61f-43b3-bf4c-b0b4e8216313' class='xr-section-summary' >Attributes: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>OMIP</dd><dt><span>cmor_version :</span></dt><dd>3.5.0</dd><dt><span>comment :</span></dt><dd>Ocean initial conditions: WOA 2013 T &amp; S; ocean at rest;\n",
"Sea Ice initial conditions: restart from a previous OMIP2 experiment;\n",
"Forcing dataset: JRA55-do v1.4.0;\n",
"Forcing dataset temporal coverage: 1958-2018;\n",
"Forcing cycle length: 61 years;\n",
"Total forcing cycles: 6 (366 years);\n",
"Initial year of the simulation: 1653;\n",
"Final year of the simulation: 2018</dd><dt><span>contact :</span></dt><dd>Pier Giuseppe Fogli ([email protected])</dd><dt><span>creation_date :</span></dt><dd>2020-02-19T13:19:38Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.31</dd><dt><span>experiment :</span></dt><dd>OMIP experiment forced by JRA55-do atmospheric data set and initialized with observed physical and biogeochemical ocean data</dd><dt><span>experiment_id :</span></dt><dd>omip2</dd><dt><span>external_variables :</span></dt><dd>areacello volcello</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CMCC.CMCC-CM2-SR5.omip2.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>standard</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-02-19T13:19:38Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy</dd><dt><span>institution_id :</span></dt><dd>CMCC</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CMCC is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>no parent</dd><dt><span>parent_experiment_id :</span></dt><dd>no parent</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>source :</span></dt><dd>CMCC-CM2-SR5 (2016): \n",
"aerosol: MAM3\n",
"atmos: CAM5.3 (1deg; 288 x 192 longitude/latitude; 30 levels; top at ~2 hPa)\n",
"atmosChem: none\n",
"land: CLM4.5 (BGC mode)\n",
"landIce: none\n",
"ocean: NEMO3.6 (ORCA1 tripolar primarly 1 deg lat/lon with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 50 vertical levels; top grid cell 0-1 m)\n",
"ocnBgchem: none\n",
"seaIce: CICE4.0</dd><dt><span>source_id :</span></dt><dd>CMCC-CM2-SR5</dd><dt><span>source_type :</span></dt><dd>OGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Omon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(15 January 2020) MD5:bc48740ff90dcf937150006b75c0f518</dd><dt><span>title :</span></dt><dd>CMCC-CM2-SR5 output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/1b7c2d37-0a5c-43c0-8a3b-d534e03c401f</dd><dt><span>variable_id :</span></dt><dd>thetao</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (i: 360, j: 291, lev: 50, bnds: 2, time: 528,\n",
" vertices: 4)\n",
"Coordinates:\n",
" * i (i) float64 0.0 1.0 2.0 3.0 ... 356.0 357.0 358.0 359.0\n",
" * j (j) float64 0.0 1.0 2.0 3.0 ... 287.0 288.0 289.0 290.0\n",
" latitude (j, i) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
" * lev (lev) float64 0.5126 1.621 2.858 ... 5.498e+03 5.904e+03\n",
" longitude (j, i) float64 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
" * time (time) object 1960-01-16 12:00:00 ... 2003-12-16 12:0...\n",
"Dimensions without coordinates: bnds, vertices\n",
"Data variables:\n",
" lev_bnds (lev, bnds) float64 dask.array<chunksize=(50, 2), meta=np.ndarray>\n",
" thetao (time, lev, j, i) float32 dask.array<chunksize=(1, 25, 146, 180), meta=np.ndarray>\n",
" time_bnds (time, bnds) object dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
" vertices_latitude (j, i, vertices) float64 dask.array<chunksize=(291, 360, 2), meta=np.ndarray>\n",
" vertices_longitude (j, i, vertices) float64 dask.array<chunksize=(291, 360, 2), meta=np.ndarray>\n",
"Attributes: (12/39)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: OMIP\n",
" cmor_version: 3.5.0\n",
" comment: Ocean initial conditions: WOA 2013 T & S; ocean at...\n",
" contact: Pier Giuseppe Fogli ([email protected])\n",
" creation_date: 2020-02-19T13:19:38Z\n",
" ... ...\n",
" table_id: Omon\n",
" table_info: Creation Date:(15 January 2020) MD5:bc48740ff90dcf...\n",
" title: CMCC-CM2-SR5 output prepared for CMIP6\n",
" tracking_id: hdl:21.14100/1b7c2d37-0a5c-43c0-8a3b-d534e03c401f\n",
" variable_id: thetao\n",
" variant_label: r1i1p1f1"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_local"
]
},
{
"cell_type": "markdown",
"id": "9d39a6b2-0a03-4d3a-a008-ae689d308079",
"metadata": {
"tags": []
},
"source": [
"### Plot to verify if all data are there. "
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "485775e2-b8cc-419c-84e8-a2b06d031136",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fc704b4df70>]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds_local.thetao.count(dim=['lev','j','i']).plot()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "a4c706d1-b83f-4995-86a8-e60a8ce76022",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fc706ba75e0>]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds_local.thetao.count(dim=['lev','j','i']).plot()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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