{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eecd1d2c-6b93-428e-8a60-5800274da618",
   "metadata": {},
   "source": [
    "## ESA CCI Toolbox Vector Data Cube Access\n",
    "\n",
    "The ESA CCI Toolbox also provides access to data that is provided in the form of vector data cubes. These can be thought of as a hybrid between data cubes and vector data. It is a data cube where the spatial locations are not identified through a structured grid, but distinct geometries.\n",
    "\n",
    "To run this Notebook, make sure the ESA CCI Toolbox is setup correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb14d9ac-8491-410d-a836-c554dae785c7",
   "metadata": {},
   "source": [
    "For this notebook, we start as before, by opening the standard `esa-cci` data store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8835695-30f4-49aa-9a83-bd83f66db59e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from xcube.core.store import new_data_store\n",
    "\n",
    "cci_store = new_data_store('esa-cci')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f9dd64-758d-4c2e-8793-d34b7e2e3bb2",
   "metadata": {},
   "source": [
    "The data in question are ICESHEETS and SEALEVEL datasets. We can find the vector data cubes by specifying their data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07789554-f0be-47b0-941b-ec4c8825134e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.1-2.greenland_gmb_mass_trends',\n",
       " 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.1-3.greenland_gmb_mass_trends',\n",
       " 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.1-4.greenland_gmb_mass_trends',\n",
       " 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.1-5.greenland_gmb_mass_trends',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.ASA',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.BENGUELA',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.CARIBBEAN',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.GULFSTREAM',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.HUMBOLDT',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.MED_SEA',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.NE_ATL',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.N_INDIAN',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.SE_AFRICA',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.SE_ASIA',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.S_AUSTRALIA',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.WAFRICA',\n",
       " 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.r1']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptors = cci_store.search_data(\n",
    "    data_type=\"vectordatacube\",\n",
    ")\n",
    "[descriptor.data_id for descriptor in descriptors]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e173a52c-e49c-4d8a-9980-7a56191fe853",
   "metadata": {},
   "source": [
    "We have a closer look at the first sea level dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "053694e7-f5e8-439e-838a-cee50bbc9573",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "attrs": {
        "abstract": "This dataset contains  a 17-year-long (January 2002 to December 2019 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of: Northeast Atlantic,  Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia,  Australia and North and South America.  Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. \n\nThis dataset has been derived from a new version of the ESA SL_cci+  dataset of coastal sea level anomalies which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.\n\nThis large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 756 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span. \n\nThe main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.\n\nThe product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.\n\nThis dataset is v2.2 of the data and is a copy of the v2.2 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#98856).  \n\nThe dataset should be cited as: \tCazenave Anny, Gouzenes Yvan, Birol Florence, Legér Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, Niño Fernando, Legeais Jean François, Oelsmann Julius, Benveniste Jérôme (2022). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE.  https://doi.org/10.17882/74354\n\nIn addition,it would be appreciated that the following work(s) be cited too, when using this dataset in a publication :\n\n - Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean François, Oelsmann Julius, Restano Marco, Benveniste Jérôme (2022). Sea level along the world’s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z\n\n -  Benveniste Jérôme, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean François, Léger Fabien, Niño Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002–2018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w",
        "catalog_url": "https://catalogue.ceda.ac.uk/uuid/90049a6555d1480bb5ce9637051dede8",
        "cci_project": "SEALEVEL",
        "comment": "These data were produced at LEGOS as part of the ESA SL_CCI+ project.",
        "data_type": "MSLTR",
        "ecv": "SEALEVEL",
        "history": "2022-12-20 generated by X-TRACK v.1.06",
        "platform_id": "multi-platform",
        "processing_level": "IND",
        "product_string": "MERGED",
        "product_version": "2-2",
        "project": "Sea Level Climate Change Initiative – European Space Agency",
        "publication_date": "2023-08-03T16:51:27",
        "references": "https://climate.esa.int/en/projects/sea-level/data/",
        "sensor_id": "multi-sensor",
        "source": "Jason-1 GDR-E, Jason-2 GDR-D, Jason-3 GDR-D, RADS 4.0, ALES",
        "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2019, v2.2",
        "uuid": "90049a6555d1480bb5ce9637051dede8"
       },
       "bbox": [
        -180,
        -90,
        180,
        90
       ],
       "coords": {
        "geometry": {
         "attrs": {
          "chunk_sizes": [
           50
          ],
          "data_type": "object",
          "dimensions": [
           "nbpoints"
          ],
          "file_chunk_sizes": [
           50
          ],
          "file_dimensions": [
           "nbpoints"
          ],
          "long_name": "geometry",
          "shape": [
           2066
          ],
          "size": 2066,
          "standard_name": "geometry"
         },
         "dims": [
          "nbpoints"
         ],
         "dtype": "object",
         "name": "geometry"
        }
       },
       "crs": "WGS84",
       "data_id": "esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.ASA",
       "data_type": "vectordatacube",
       "data_vars": {
        "distance_to_coast": {
         "attrs": {
          "chunk_sizes": 50,
          "comment": "Distance along track to a reference point at the coast ",
          "data_type": "float32",
          "dimensions": [
           "nbpoints"
          ],
          "distance_to_coast_max": 18446700000000000000,
          "distance_to_coast_min": 3299.2,
          "file_chunk_sizes": 50,
          "file_dimensions": [
           "nbpoints"
          ],
          "fill_value": 18446740000000000000,
          "long_name": "Distance to GSHHS 1.3 coastline",
          "orig_data_type": "float32",
          "shape": [
           2066
          ],
          "size": 2066,
          "units": "m"
         },
         "dims": [
          "nbpoints"
         ],
         "dtype": "float32",
         "name": "distance_to_coast"
        },
        "local_sla_trend": {
         "attrs": {
          "chunk_sizes": 50,
          "comment": "Sea level trends computed from X-TRACK/ALES monthly sea level anomalies between 2002-01-01 and 2019-12-31",
          "data_type": "float32",
          "dimensions": [
           "nbpoints"
          ],
          "file_chunk_sizes": 50,
          "file_dimensions": [
           "nbpoints"
          ],
          "fill_value": 18446740000000000000,
          "long_name": "Geographical distribution of sea level trends",
          "orig_data_type": "float32",
          "shape": [
           2066
          ],
          "size": 2066,
          "standard_name": "tendency_of_sea_surface_height_above_sea_level",
          "units": "mm/year"
         },
         "dims": [
          "nbpoints"
         ],
         "dtype": "float32",
         "name": "local_sla_trend"
        },
        "local_sla_trend_error": {
         "attrs": {
          "chunk_sizes": 50,
          "data_type": "float32",
          "dimensions": [
           "nbpoints"
          ],
          "file_chunk_sizes": 50,
          "file_dimensions": [
           "nbpoints"
          ],
          "fill_value": 18446740000000000000,
          "long_name": "Geographical distribution of sea level trends errors",
          "orig_data_type": "float32",
          "shape": [
           2066
          ],
          "size": 2066,
          "units": "mm/year"
         },
         "dims": [
          "nbpoints"
         ],
         "dtype": "float32",
         "name": "local_sla_trend_error"
        },
        "sla": {
         "attrs": {
          "chunk_sizes": [
           50,
           216
          ],
          "comment": "The sla are monthly averaged and annual and semi-annual cycles are removed. sla = altitude of satellite - 20 Hz Ku band ALES corrected altimeter range  (Passaro et al. 2014) - altimeter ionospheric correction on Ku band  (From dual-frequency altimeter range measurements) - model dry tropospheric correction (From ECMWF model) - GPD+ wet tropospheric correction (Fernandes et al. 2016) - sea state bias correction in Ku band (ALES retracking, Passaro et al. 2014) -  solid earth tide height (From RADS, tide potential model, Cartwright and Taylor 1971,  Cartwright and Eden 1973) - geocentric ocean tide (FES 2014 from RADS, Carrere et al. 2012) - geocentric pole tide height (Wahr 1985) - Atmospheric correction (From RADS, Carrere and Lyard 2003)  - X-TRACK mean sea surface (Birol et al. 2017). Each corrective term is edited following Birol et al. 2017.",
          "data_type": "float32",
          "dimensions": [
           "nbpoints",
           "nbmonth"
          ],
          "file_chunk_sizes": [
           50,
           216
          ],
          "file_dimensions": [
           "nbpoints",
           "nbmonth"
          ],
          "fill_value": 18446740000000000000,
          "orig_data_type": "float32",
          "shape": [
           2066,
           216
          ],
          "size": 2282,
          "standard_name": "sea_surface_height_above_mean_sea_level",
          "units": "m"
         },
         "dims": [
          "nbpoints",
          "nbmonth"
         ],
         "dtype": "float32",
         "name": "sla"
        }
       },
       "dims": {
        "nbmonth": 216,
        "nbpoints": 2066
       },
       "open_params_schema": {
        "additionalProperties": false,
        "properties": {
         "normalize_data": {
          "default": true,
          "type": "boolean"
         },
         "variable_names": {
          "items": {
           "enum": [
            "distance_to_coast",
            "local_sla_trend",
            "local_sla_trend_error",
            "sla"
           ],
           "type": "string"
          },
          "type": "array"
         }
        },
        "type": "object"
       },
       "time_period": "1M",
       "time_range": [
        "2002-01-01",
        "2019-12-31"
       ]
      },
      "text/plain": [
       "<xcube_cci.vdcaccess.VectorDataCubeDescriptor at 0x7c12e3a03a80>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptors[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53507a23-45cb-4742-b9d8-90cd2b748984",
   "metadata": {},
   "source": [
    "Now, we can open the dataset to see what the vector data cube looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "197f8ff6-46f0-490f-a1a2-4539cc47fa92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1f1f1f;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\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; Size: 2MB\n",
       "Dimensions:                (nbpoints: 2066, nbmonth: 216, bnds: 2)\n",
       "Coordinates:\n",
       "  * geometry               (nbpoints) object 17kB POINT (-67.60308074951172 -...\n",
       "  * nbmonth                (nbmonth) datetime64[ns] 2kB 2002-01-16T12:00:00 ....\n",
       "    nbmonth_bnds           (nbmonth, bnds) datetime64[ns] 3kB dask.array&lt;chunksize=(216, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nbpoints, bnds\n",
       "Data variables:\n",
       "    distance_to_coast      (nbpoints) float32 8kB dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    lat                    float64 8B ...\n",
       "    local_sla_trend        (nbpoints) float32 8kB dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    local_sla_trend_error  (nbpoints) float32 8kB dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    lon                    float64 8B ...\n",
       "    sla                    (nbpoints, nbmonth) float32 2MB dask.array&lt;chunksize=(50, 216), meta=np.ndarray&gt;\n",
       "Indexes:\n",
       "    geometry  GeometryIndex (crs=None)\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    title:                   esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi...\n",
       "    date_created:            2025-12-04T11:41:39.087709\n",
       "    processing_level:        IND\n",
       "    time_coverage_start:     2002-01-01T00:00:00\n",
       "    time_coverage_end:       2020-01-01T00:00:00\n",
       "    time_coverage_duration:  P6574DT0H0M0S\n",
       "    history:                 [{&#x27;program&#x27;: &#x27;xcube_cci.chunkstore.CciChunkStore...</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-62d4ac6e-82b8-41db-989a-852cc1764090' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-62d4ac6e-82b8-41db-989a-852cc1764090' 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'>nbpoints</span>: 2066</li><li><span class='xr-has-index'>nbmonth</span>: 216</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d2999f2c-c576-487a-bffd-92df4860e295' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d2999f2c-c576-487a-bffd-92df4860e295' class='xr-section-summary' >Coordinates: <span>(3)</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'>geometry</span></div><div class='xr-var-dims'>(nbpoints)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>POINT (-67.60308074951172 -49.39...</div><input id='attrs-271a9105-1c3c-427d-aff1-4376b1de5b0e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-271a9105-1c3c-427d-aff1-4376b1de5b0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0baa1f90-718e-495e-8232-3fa8c47af46e' class='xr-var-data-in' type='checkbox'><label for='data-0baa1f90-718e-495e-8232-3fa8c47af46e' 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'><pre>array([&lt;POINT (-67.603 -49.397)&gt;, &lt;POINT (-67.601 -49.4)&gt;,\n",
       "       &lt;POINT (-67.598 -49.402)&gt;, ..., &lt;POINT (-34.747 -7.808)&gt;,\n",
       "       &lt;POINT (-34.746 -7.81)&gt;, &lt;POINT (-34.745 -7.813)&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>nbmonth</span></div><div class='xr-var-dims'>(nbmonth)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2002-01-16T12:00:00 ... 2019-12-...</div><input id='attrs-93567fa0-bbf5-4bab-85f3-6611c062bb4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93567fa0-bbf5-4bab-85f3-6611c062bb4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d78b2a9-c708-4993-b8d2-be0c5de8dcda' class='xr-var-data-in' type='checkbox'><label for='data-5d78b2a9-c708-4993-b8d2-be0c5de8dcda' 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>standard_name :</span></dt><dd>nbmonth</dd><dt><span>bounds :</span></dt><dd>nbmonth_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-01-16T12:00:00.000000000&#x27;, &#x27;2002-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-03-16T12:00:00.000000000&#x27;, ..., &#x27;2019-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-16T00:00:00.000000000&#x27;, &#x27;2019-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nbmonth_bnds</span></div><div class='xr-var-dims'>(nbmonth, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(216, 2), meta=np.ndarray&gt;</div><input id='attrs-dae2b5b2-6523-42c8-a897-2632ccb213c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dae2b5b2-6523-42c8-a897-2632ccb213c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00c7bc28-4d13-4ec7-8b33-cb1dc50569f0' class='xr-var-data-in' type='checkbox'><label for='data-00c7bc28-4d13-4ec7-8b33-cb1dc50569f0' 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>standard_name :</span></dt><dd>nbmonth_bnds</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\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> 3.38 kiB </td>\n",
       "                        <td> 3.38 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (216, 2) </td>\n",
       "                        <td> (216, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">216</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-20c88a84-cf36-4e77-bbcf-9a7fd0aee075' class='xr-section-summary-in' type='checkbox'  checked><label for='section-20c88a84-cf36-4e77-bbcf-9a7fd0aee075' class='xr-section-summary' >Data variables: <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>distance_to_coast</span></div><div class='xr-var-dims'>(nbpoints)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-8db6639f-69ae-4c2a-b9b2-1390680e8600' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8db6639f-69ae-4c2a-b9b2-1390680e8600' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a51acd9b-a3c9-4be7-8fcd-5799230595f5' class='xr-var-data-in' type='checkbox'><label for='data-a51acd9b-a3c9-4be7-8fcd-5799230595f5' 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>Distance to GSHHS 1.3 coastline</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>distance_to_coast_min :</span></dt><dd>3299.2</dd><dt><span>distance_to_coast_max :</span></dt><dd>1.84467e+19</dd><dt><span>comment :</span></dt><dd>Distance along track to a reference point at the coast </dd><dt><span>orig_data_type :</span></dt><dd>float32</dd><dt><span>fill_value :</span></dt><dd>1.844674e+19</dd><dt><span>size :</span></dt><dd>2066</dd><dt><span>shape :</span></dt><dd>[2066]</dd><dt><span>chunk_sizes :</span></dt><dd>[50]</dd><dt><span>file_chunk_sizes :</span></dt><dd>[50]</dd><dt><span>data_type :</span></dt><dd>float32</dd><dt><span>dimensions :</span></dt><dd>[&#x27;nbpoints&#x27;]</dd><dt><span>file_dimensions :</span></dt><dd>[&#x27;nbpoints&#x27;]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\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> 8.07 kiB </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2066,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 42 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" 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=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n",
       "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
       "  <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"25\" />\n",
       "  <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"25\" />\n",
       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
       "  <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
       "  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n",
       "  <line x1=\"55\" y1=\"0\" x2=\"55\" y2=\"25\" />\n",
       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
       "  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n",
       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"25\" />\n",
       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2066</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d204ab7d-d6f5-4053-9da7-bc6bfcf5ceb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d204ab7d-d6f5-4053-9da7-bc6bfcf5ceb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd3fd06f-4b44-438c-b1d6-0ae021c9c806' class='xr-var-data-in' type='checkbox'><label for='data-cd3fd06f-4b44-438c-b1d6-0ae021c9c806' 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>shape :</span></dt><dd>[]</dd><dt><span>size :</span></dt><dd>1</dd><dt><span>chunk_sizes :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>local_sla_trend</span></div><div class='xr-var-dims'>(nbpoints)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-c5a4dd9e-e62f-4718-976e-0c4c84f9ad22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5a4dd9e-e62f-4718-976e-0c4c84f9ad22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38a8cc56-4e17-405d-a9bc-ab049b118b42' class='xr-var-data-in' type='checkbox'><label for='data-38a8cc56-4e17-405d-a9bc-ab049b118b42' 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>Geographical distribution of sea level trends</dd><dt><span>standard_name :</span></dt><dd>tendency_of_sea_surface_height_above_sea_level</dd><dt><span>units :</span></dt><dd>mm/year</dd><dt><span>comment :</span></dt><dd>Sea level trends computed from X-TRACK/ALES monthly sea level anomalies between 2002-01-01 and 2019-12-31</dd><dt><span>orig_data_type :</span></dt><dd>float32</dd><dt><span>fill_value :</span></dt><dd>1.844674e+19</dd><dt><span>size :</span></dt><dd>2066</dd><dt><span>shape :</span></dt><dd>[2066]</dd><dt><span>chunk_sizes :</span></dt><dd>[50]</dd><dt><span>file_chunk_sizes :</span></dt><dd>[50]</dd><dt><span>data_type :</span></dt><dd>float32</dd><dt><span>dimensions :</span></dt><dd>[&#x27;nbpoints&#x27;]</dd><dt><span>file_dimensions :</span></dt><dd>[&#x27;nbpoints&#x27;]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\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> 8.07 kiB </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2066,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 42 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" 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=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n",
       "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
       "  <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"25\" />\n",
       "  <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"25\" />\n",
       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
       "  <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
       "  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n",
       "  <line x1=\"55\" y1=\"0\" x2=\"55\" y2=\"25\" />\n",
       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
       "  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n",
       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"25\" />\n",
       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2066</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>local_sla_trend_error</span></div><div class='xr-var-dims'>(nbpoints)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-3045dc40-8a9f-4bfb-94f3-ada0db96d902' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3045dc40-8a9f-4bfb-94f3-ada0db96d902' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84c67583-3497-4708-9a31-cb68cf1f371b' class='xr-var-data-in' type='checkbox'><label for='data-84c67583-3497-4708-9a31-cb68cf1f371b' 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>Geographical distribution of sea level trends errors</dd><dt><span>units :</span></dt><dd>mm/year</dd><dt><span>orig_data_type :</span></dt><dd>float32</dd><dt><span>fill_value :</span></dt><dd>1.844674e+19</dd><dt><span>size :</span></dt><dd>2066</dd><dt><span>shape :</span></dt><dd>[2066]</dd><dt><span>chunk_sizes :</span></dt><dd>[50]</dd><dt><span>file_chunk_sizes :</span></dt><dd>[50]</dd><dt><span>data_type :</span></dt><dd>float32</dd><dt><span>dimensions :</span></dt><dd>[&#x27;nbpoints&#x27;]</dd><dt><span>file_dimensions :</span></dt><dd>[&#x27;nbpoints&#x27;]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\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> 8.07 kiB </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2066,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 42 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" 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=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n",
       "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
       "  <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"25\" />\n",
       "  <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"25\" />\n",
       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
       "  <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
       "  <line x1=\"49\" y1=\"0\" x2=\"49\" y2=\"25\" />\n",
       "  <line x1=\"55\" y1=\"0\" x2=\"55\" y2=\"25\" />\n",
       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
       "  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n",
       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"25\" />\n",
       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2066</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49e8866a-0ac0-4408-8a72-5618245a7bb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49e8866a-0ac0-4408-8a72-5618245a7bb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b2caa3f-9313-494f-b5e7-42f6af74d788' class='xr-var-data-in' type='checkbox'><label for='data-1b2caa3f-9313-494f-b5e7-42f6af74d788' 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>shape :</span></dt><dd>[]</dd><dt><span>size :</span></dt><dd>1</dd><dt><span>chunk_sizes :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sla</span></div><div class='xr-var-dims'>(nbpoints, nbmonth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 216), meta=np.ndarray&gt;</div><input id='attrs-64dea29e-17ba-45d0-99ce-95d9665473c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64dea29e-17ba-45d0-99ce-95d9665473c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c51568e6-35a2-44d8-9e23-de976bcafdb2' class='xr-var-data-in' type='checkbox'><label for='data-c51568e6-35a2-44d8-9e23-de976bcafdb2' 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>m</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_mean_sea_level</dd><dt><span>comment :</span></dt><dd>The sla are monthly averaged and annual and semi-annual cycles are removed. sla = altitude of satellite - 20 Hz Ku band ALES corrected altimeter range  (Passaro et al. 2014) - altimeter ionospheric correction on Ku band  (From dual-frequency altimeter range measurements) - model dry tropospheric correction (From ECMWF model) - GPD+ wet tropospheric correction (Fernandes et al. 2016) - sea state bias correction in Ku band (ALES retracking, Passaro et al. 2014) -  solid earth tide height (From RADS, tide potential model, Cartwright and Taylor 1971,  Cartwright and Eden 1973) - geocentric ocean tide (FES 2014 from RADS, Carrere et al. 2012) - geocentric pole tide height (Wahr 1985) - Atmospheric correction (From RADS, Carrere and Lyard 2003)  - X-TRACK mean sea surface (Birol et al. 2017). Each corrective term is edited following Birol et al. 2017.</dd><dt><span>orig_data_type :</span></dt><dd>float32</dd><dt><span>fill_value :</span></dt><dd>1.844674e+19</dd><dt><span>size :</span></dt><dd>446256</dd><dt><span>shape :</span></dt><dd>[2066, 216]</dd><dt><span>chunk_sizes :</span></dt><dd>[50, 216]</dd><dt><span>file_chunk_sizes :</span></dt><dd>[50, 216]</dd><dt><span>data_type :</span></dt><dd>float32</dd><dt><span>dimensions :</span></dt><dd>[&#x27;nbpoints&#x27;, &#x27;nbmonth&#x27;]</dd><dt><span>file_dimensions :</span></dt><dd>[&#x27;nbpoints&#x27;, &#x27;nbmonth&#x27;]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\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> 1.70 MiB </td>\n",
       "                        <td> 42.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2066, 216) </td>\n",
       "                        <td> (50, 216) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 42 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"5\" x2=\"38\" y2=\"5\" />\n",
       "  <line x1=\"0\" y1=\"11\" x2=\"38\" y2=\"11\" />\n",
       "  <line x1=\"0\" y1=\"17\" x2=\"38\" y2=\"17\" />\n",
       "  <line x1=\"0\" y1=\"23\" x2=\"38\" y2=\"23\" />\n",
       "  <line x1=\"0\" y1=\"31\" x2=\"38\" y2=\"31\" />\n",
       "  <line x1=\"0\" y1=\"37\" x2=\"38\" y2=\"37\" />\n",
       "  <line x1=\"0\" y1=\"43\" x2=\"38\" y2=\"43\" />\n",
       "  <line x1=\"0\" y1=\"49\" x2=\"38\" y2=\"49\" />\n",
       "  <line x1=\"0\" y1=\"55\" x2=\"38\" y2=\"55\" />\n",
       "  <line x1=\"0\" y1=\"63\" x2=\"38\" y2=\"63\" />\n",
       "  <line x1=\"0\" y1=\"69\" x2=\"38\" y2=\"69\" />\n",
       "  <line x1=\"0\" y1=\"75\" x2=\"38\" y2=\"75\" />\n",
       "  <line x1=\"0\" y1=\"81\" x2=\"38\" y2=\"81\" />\n",
       "  <line x1=\"0\" y1=\"87\" x2=\"38\" y2=\"87\" />\n",
       "  <line x1=\"0\" y1=\"95\" x2=\"38\" y2=\"95\" />\n",
       "  <line x1=\"0\" y1=\"101\" x2=\"38\" y2=\"101\" />\n",
       "  <line x1=\"0\" y1=\"107\" x2=\"38\" y2=\"107\" />\n",
       "  <line x1=\"0\" y1=\"113\" x2=\"38\" y2=\"113\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 38.841967566754825,0.0 38.841967566754825,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"19.420984\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >216</text>\n",
       "  <text x=\"58.841968\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,58.841968,60.000000)\">2066</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-40d9b932-7414-4ea9-904f-17238922f4ce' class='xr-section-summary-in' type='checkbox'  ><label for='section-40d9b932-7414-4ea9-904f-17238922f4ce' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>nbmonth</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-dfaa121d-ed8d-48b0-b9dd-9c24846cffb6' class='xr-index-data-in' type='checkbox'/><label for='index-dfaa121d-ed8d-48b0-b9dd-9c24846cffb6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2002-01-16 12:00:00&#x27;, &#x27;2002-02-15 00:00:00&#x27;,\n",
       "               &#x27;2002-03-16 12:00:00&#x27;, &#x27;2002-04-16 00:00:00&#x27;,\n",
       "               &#x27;2002-05-16 12:00:00&#x27;, &#x27;2002-06-16 00:00:00&#x27;,\n",
       "               &#x27;2002-07-16 12:00:00&#x27;, &#x27;2002-08-16 12:00:00&#x27;,\n",
       "               &#x27;2002-09-16 00:00:00&#x27;, &#x27;2002-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2019-03-16 12:00:00&#x27;, &#x27;2019-04-16 00:00:00&#x27;,\n",
       "               &#x27;2019-05-16 12:00:00&#x27;, &#x27;2019-06-16 00:00:00&#x27;,\n",
       "               &#x27;2019-07-16 12:00:00&#x27;, &#x27;2019-08-16 12:00:00&#x27;,\n",
       "               &#x27;2019-09-16 00:00:00&#x27;, &#x27;2019-10-16 12:00:00&#x27;,\n",
       "               &#x27;2019-11-16 00:00:00&#x27;, &#x27;2019-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;nbmonth&#x27;, length=216, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>geometry</div></div><div class='xr-index-preview'>GeometryIndex (crs=None)</div><input type='checkbox' disabled/><label></label><input id='index-207afe4e-fd91-482d-8bd9-d2b6d977e8ad' class='xr-index-data-in' type='checkbox'/><label for='index-207afe4e-fd91-482d-8bd9-d2b6d977e8ad' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>GeometryIndex(\n",
       "    [&lt;POINT (-67.603 -49.397)&gt;\n",
       "     &lt;POINT (-67.601 -49.4)&gt;\n",
       "     &lt;POINT (-67.598 -49.402)&gt;\n",
       "     &lt;POINT (-67.596 -49.404)&gt;\n",
       "     ...\n",
       "     &lt;POINT (-34.748 -7.805)&gt;\n",
       "     &lt;POINT (-34.747 -7.808)&gt;\n",
       "     &lt;POINT (-34.746 -7.81)&gt;\n",
       "     &lt;POINT (-34.745 -7.813)&gt;],\n",
       "    crs=None)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-586dc4e0-18b1-424a-b687-5ff2513df7f3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-586dc4e0-18b1-424a-b687-5ff2513df7f3' class='xr-section-summary' >Attributes: <span>(8)</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</dd><dt><span>title :</span></dt><dd>esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.ASA</dd><dt><span>date_created :</span></dt><dd>2025-12-04T11:41:39.087709</dd><dt><span>processing_level :</span></dt><dd>IND</dd><dt><span>time_coverage_start :</span></dt><dd>2002-01-01T00:00:00</dd><dt><span>time_coverage_end :</span></dt><dd>2020-01-01T00:00:00</dd><dt><span>time_coverage_duration :</span></dt><dd>P6574DT0H0M0S</dd><dt><span>history :</span></dt><dd>[{&#x27;program&#x27;: &#x27;xcube_cci.chunkstore.CciChunkStore&#x27;, &#x27;cube_params&#x27;: {&#x27;time_range&#x27;: [&#x27;2002-01-01T00:00:00&#x27;, &#x27;2019-12-31T23:59:59&#x27;], &#x27;variable_names&#x27;: [&#x27;distance_to_coast&#x27;, &#x27;lat&#x27;, &#x27;lon&#x27;, &#x27;local_sla_trend&#x27;, &#x27;local_sla_trend_error&#x27;, &#x27;sla&#x27;]}}]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2MB\n",
       "Dimensions:                (nbpoints: 2066, nbmonth: 216, bnds: 2)\n",
       "Coordinates:\n",
       "  * geometry               (nbpoints) object 17kB POINT (-67.60308074951172 -...\n",
       "  * nbmonth                (nbmonth) datetime64[ns] 2kB 2002-01-16T12:00:00 ....\n",
       "    nbmonth_bnds           (nbmonth, bnds) datetime64[ns] 3kB dask.array<chunksize=(216, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: nbpoints, bnds\n",
       "Data variables:\n",
       "    distance_to_coast      (nbpoints) float32 8kB dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    lat                    float64 8B ...\n",
       "    local_sla_trend        (nbpoints) float32 8kB dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    local_sla_trend_error  (nbpoints) float32 8kB dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    lon                    float64 8B ...\n",
       "    sla                    (nbpoints, nbmonth) float32 2MB dask.array<chunksize=(50, 216), meta=np.ndarray>\n",
       "Indexes:\n",
       "    geometry  GeometryIndex (crs=None)\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    title:                   esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi...\n",
       "    date_created:            2025-12-04T11:41:39.087709\n",
       "    processing_level:        IND\n",
       "    time_coverage_start:     2002-01-01T00:00:00\n",
       "    time_coverage_end:       2020-01-01T00:00:00\n",
       "    time_coverage_duration:  P6574DT0H0M0S\n",
       "    history:                 [{'program': 'xcube_cci.chunkstore.CciChunkStore..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl_ds = cci_store.open_data(\n",
    "    \"esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.ASA\"\n",
    ")\n",
    "\n",
    "sl_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d556a0-1081-44f6-932c-1e1a4517942d",
   "metadata": {},
   "source": [
    "We see that the resulting dataset has a geometry coordinate which consists of single points. We may plot a variable which only has a single geometric dimension like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4797e2ee-c115-4fc1-8d3e-a88a4073c036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "gdf = gpd.GeoDataFrame(sl_ds[[\"geometry\", \"local_sla_trend\"]].to_dataframe())\n",
    "gdf.plot(column=\"local_sla_trend\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1621de4b-9c8c-414d-a86a-2e80907715cd",
   "metadata": {},
   "source": [
    "We may also plot variables which have values along the temporal dimension. We can do so like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1552cd4e-e850-47f9-b923-24d61b02e99f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf2 = gpd.GeoDataFrame(sl_ds[[\"geometry\", \"sla\", \"nbmonth\"]].to_dataframe())\n",
    "gdf2.xs(\"2019-09-16 00:00:00\", level=\"nbmonth\").plot(column=\"sla\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2faf687a-3f4f-48bf-86ca-ad5b0710b637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sl_ds = cci_store.open_data(\n",
    "    \"esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.ASA\"\n",
    ")\n",
    "gdf = gpd.GeoDataFrame(sl_ds[[\"geometry\", \"local_sla_trend\"]].to_dataframe())\n",
    "gdf.plot(column=\"local_sla_trend\")"
   ]
  }
 ],
 "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": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}