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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "\n", "![pavics_climindices_3.png](attachment:140c61cf-9226-415b-a6dc-a71c958530d7.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "This third tutorial will demonstate PAVICS tools for calculating climate indicators, again accessing Ouranos' cb-oura-1.0 ensemble. PAVICS incorporates the xclim library which essentially has two layers for the calculation of indicators. The first `xclim.indices` is a core layer and contains the various algorithms and calculation logic, as well as any potential unit conversions. A second layer consists of `xclim.core.indicator.Indicator` instances that essentially perform the same computations found in `xclim.indices`, but also run a number of health checks on input data and assign attributes to the output arrays. Indicators are split into realms (`atmos`, `land`, `seaIce`), according to the variables they operate on. \n", "\n", "This tutorial uses the`xclim.atmos` module outlining steps for:\n", "\n", "* [Climate indicator calculation](#climcalc)\n", "* [Missing data options](#missing)\n", "* [French language metadata fields](#french)\n", "\n", "Advanced topics:\n", "\n", "* [Simple multiprocessing of an ensemble](#multiproc)\n", "\n", "We re-use part of the data-access and subset tutorials to select a dataset from cb-oura-1.0 datasets from the PAVICS THREDDS server.\n", "\n", "
<xarray.Dataset>\n", "Dimensions: (lat: 18, lon: 44, time: 55115)\n", "Coordinates:\n", " * lat (lat) float32 49.29 49.21 49.12 49.04 ... 48.12 48.04 47.96 47.87\n", " * lon (lon) float32 -67.71 -67.63 -67.55 -67.46 ... -64.3 -64.21 -64.13\n", " * time (time) object 1950-01-01 00:00:00 ... 2100-12-31 00:00:00\n", "Data variables:\n", " tasmin (time, lat, lon) float32 dask.array<chunksize=(256, 18, 44), meta=np.ndarray>\n", " tasmax (time, lat, lon) float32 dask.array<chunksize=(256, 18, 44), meta=np.ndarray>\n", " pr (time, lat, lon) float32 dask.array<chunksize=(256, 18, 44), meta=np.ndarray>\n", " crs int64 1\n", "Attributes: (12/27)\n", " Conventions: CF-1.5\n", " title: Ouranos standard ensemble of bias-adjusted cl...\n", " history: 2011-06-01T01:08:07Z CMOR rewrote data to com...\n", " institution: Ouranos Consortium on Regional Climatology an...\n", " source: NorESM1-M 2011 atmosphere: CAM-Oslo (CAM4-Os...\n", " driving_experiment: historical,rcp85\n", " ... ...\n", " modeling_realm: atmos\n", " target_dataset: CANADA : ANUSPLIN interpolated Canada daily 3...\n", " target_dataset_references: CANADA : https://doi.org/10.1175/2011BAMS3132...\n", " driving_institution: Norwegian Climate Centre\n", " driving_institute_id: NCC\n", " crs: EPSG:4326
<xarray.DataArray 'tx_days_above' (time: 151, lat: 18, lon: 44)>\n", "dask.array<where, shape=(151, 18, 44), dtype=float64, chunksize=(1, 18, 44), chunktype=numpy.ndarray>\n", "Coordinates:\n", " * lat (lat) float32 49.29 49.21 49.12 49.04 ... 48.12 48.04 47.96 47.87\n", " * lon (lon) float32 -67.71 -67.63 -67.55 -67.46 ... -64.3 -64.21 -64.13\n", " * time (time) object 1950-01-01 00:00:00 ... 2100-01-01 00:00:00\n", "Attributes:\n", " units: days\n", " cell_methods: time: sum over days\n", " history: [2023-05-26 13:20:33] tx_days_above: TX_DAYS_ABOVE(tasma...\n", " standard_name: number_of_days_with_air_temperature_above_threshold\n", " long_name: The number of days with maximum temperature above 27 degc\n", " description: Annual number of days where daily maximum temperature ex...\n", " long_name_fr: Nombre de jours ayant une température maximale quotidien...\n", " description_fr: Nombre annuel de jours où la température maximale quotid...