Good to know!

This section presents general knowledge about climate data and their usage, including aspects about temporal horizons, data gaps, ensemble datasets use, uncertainty considerations, data formats and access.

General information about Climate and Weather data

• Understand the usage of the latest (historical) data for planning for the next two years (relevance of well representing today’s climate)

Temporal horizons and predictability

A contribution from expert Louis-Philippe!?

Prediction Types

Data Gaps

*TODO** Put this in a separate section!

• Scenarios post 2100 (Results from Anne-Sophie)
• Horizon 2-20 years (IESO)

Ensemble Datasets

• ensemble reduction techniques summarized (Marco’s presentation at CEATI!)

Uncertainties

Data formats

A section about the standard netCDF format for climate data.

A simple Example of accessing data on PAVICS

To access ERA5-Land data hosted on PAVICS via a Python script, locate the the OpenDAP Data URL of the dataset by clicking on the THREADS Catalog link and selecting the ERA5-Land dataset. Using the Xarray Python Library the dataset can be easily read. The output from the following code allows to interactively browse the content of the dataset:

import xarray as xr

# the OpenDAP URL for hourly ERA5-Land data
url = "https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/datasets/reanalyses/day_ERA5-Land_NAM.ncml"

# open the dataset
ds = xr.open_dataset(url, chunks={'time': -1, 'lat': 50, 'lon': 50}, decode_timedelta=False)
print("The ERA5-Land dataset:")
ds

Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.
Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.
The ERA5-Land dataset:

<xarray.Dataset>
Dimensions:  (lat: 800, lon: 1700, time: 26298)
Coordinates:
  * lat      (lat) float32 10.0 10.1 10.2 10.3 10.4 ... 89.5 89.6 89.7 89.8 89.9
  * lon      (lon) float32 -179.9 -179.8 -179.7 -179.6 ... -10.2 -10.1 -10.0
  * time     (time) datetime64[ns] 1950-01-01 1950-01-02 ... 2021-12-31
Data variables:
    tas      (time, lat, lon) float32 dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>
    tasmin   (time, lat, lon) float32 dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>
    tasmax   (time, lat, lon) float32 dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>
    pr       (time, lat, lon) float32 dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>
    prsn     (time, lat, lon) float32 dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>
Attributes: (12/26)
    Conventions:          CF-1.8
    cell_methods:         time: mean (interval: 1 day)
    data_specs_version:   00.00.07
    domain:               NAM
    format:               netcdf
    frequency:            day
    ...                   ...
    dataset_description:  https://www.ecmwf.int/en/era5-land
    license_type:         permissive
    license:              Please acknowledge the use of ERA5-Land as stated i...
    attribution:          Contains modified Copernicus Climate Change Service...
    citation:             Muñoz Sabater, J., (2021): ERA5-Land hourly data fr...
    doi:                  https://doi.org/10.24381/cds.e2161bac

xarray.Dataset

• Dimensions:
  • lat: 800
  • lon: 1700
  • time: 26298
• Coordinates: (3)
  • lat
    (lat)
    float32
    10.0 10.1 10.2 ... 89.7 89.8 89.9

    long_name :

    latitude

    units :

    degrees_north

    array([10. , 10.1, 10.2, ..., 89.7, 89.8, 89.9], dtype=float32)

  • lon
    (lon)
    float32
    -179.9 -179.8 ... -10.1 -10.0

    long_name :

    longitude

    units :

    degrees_east

    array([-179.9, -179.8, -179.7, ...,  -10.2,  -10.1,  -10. ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1950-01-01 ... 2021-12-31

    array(['1950-01-01T00:00:00.000000000', '1950-01-02T00:00:00.000000000',
           '1950-01-03T00:00:00.000000000', ..., '2021-12-29T00:00:00.000000000',
           '2021-12-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (5)
  • tas
    (time, lat, lon)
    float32
    dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>

    long_name :

    2 metre temperature

    original_variable :

    t2m

    standard_name :

    air_temperature

    units :

    K

    cell_methods :

    time: point

    _ChunkSizes :

    [366 50 50]

    Array Chunk Bytes 133.24 GiB 250.80 MiB Shape (26298, 800, 1700) (26298, 50, 50) Dask graph 544 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tasmin
    (time, lat, lon)
    float32
    dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>

    long_name :

    2 metre temperature

    original_variable :

    t2m

    standard_name :

    air_temperature

    units :

    K

    cell_methods :

    time: point

    _ChunkSizes :

    [366 50 50]

    Array Chunk Bytes 133.24 GiB 250.80 MiB Shape (26298, 800, 1700) (26298, 50, 50) Dask graph 544 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tasmax
    (time, lat, lon)
    float32
    dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>

    long_name :

    2 metre temperature

    original_variable :

    t2m

    standard_name :

    air_temperature

    units :

    K

    cell_methods :

    time: point

    _ChunkSizes :

    [366 50 50]

    Array Chunk Bytes 133.24 GiB 250.80 MiB Shape (26298, 800, 1700) (26298, 50, 50) Dask graph 544 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pr
    (time, lat, lon)
    float32
    dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>

    long_name :

    Precipitation

    cell_methods :

    time: mean (interval: 1 hour)

    comments :

    Converted from Total Precipitation using a density of 1000 kg/m³.

    original_long_name :

    Total Precipitation

    original_variable :

    tp

    standard_name :

    precipitation_flux

    units :

    kg m-2 s-1

    _ChunkSizes :

    [366 50 50]

    Array Chunk Bytes 133.24 GiB 250.80 MiB Shape (26298, 800, 1700) (26298, 50, 50) Dask graph 544 chunks in 2 graph layers Data type float32 numpy.ndarray

  • prsn
    (time, lat, lon)
    float32
    dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>

    long_name :

    Snowfall flux

    cell_methods :

    time: mean (interval: 1 hour)

    comments :

    Converted from Snowfall (water equivalent) using a density of 1000 kg/m³.

    original_long_name :

    Snowfall

    original_variable :

    sf

    standard_name :

    snowfall_flux

    units :

    kg m-2 s-1

    _ChunkSizes :

    [366 50 50]

    Array Chunk Bytes 133.24 GiB 250.80 MiB Shape (26298, 800, 1700) (26298, 50, 50) Dask graph 544 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Float64Index([              10.0, 10.100000381469727, 10.199999809265137,
                  10.300000190734863, 10.399999618530273,               10.5,
                  10.600000381469727, 10.699999809265137, 10.800000190734863,
                  10.899999618530273,
                  ...
                                89.0,   89.0999984741211,  89.19999694824219,
                   89.30000305175781,   89.4000015258789,               89.5,
                    89.5999984741211,  89.69999694824219,  89.80000305175781,
                    89.9000015258789],
                 dtype='float64', name='lat', length=800))

  • lon
    PandasIndex

    PandasIndex(Float64Index([-179.89999389648438,  -179.8000030517578,  -179.6999969482422,
                  -179.60000610351562,              -179.5, -179.39999389648438,
                   -179.3000030517578,  -179.1999969482422, -179.10000610351562,
                               -179.0,
                  ...
                  -10.899999618530273, -10.800000190734863, -10.699999809265137,
                  -10.600000381469727,               -10.5, -10.399999618530273,
                  -10.300000190734863, -10.199999809265137, -10.100000381469727,
                                -10.0],
                 dtype='float64', name='lon', length=1700))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1950-01-01', '1950-01-02', '1950-01-03', '1950-01-04',
                   '1950-01-05', '1950-01-06', '1950-01-07', '1950-01-08',
                   '1950-01-09', '1950-01-10',
                   ...
                   '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
                   '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
                   '2021-12-30', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=26298, freq=None))

• Attributes: (26)

  Conventions :

  CF-1.8

  cell_methods :

  time: mean (interval: 1 day)

  data_specs_version :

  00.00.07

  domain :

  NAM

  format :

  netcdf

  frequency :

  day

  history :

  [2022-07-16 11:22:42.650045] Converted from original data to zarr with modified metadata for CF-like compliance.

  institution :

  ECMWF

  processing_level :

  raw

  project :

  era5-land

  realm :

  atmos

  source :

  ERA5-Land

  table_date :

  13 June 2022

  type :

  reanalysis

  version :

  v2022.07.15

  title :

  ERA5-Land : daily

  institute :

  European Centre for Medium-Range Weather Forecasts

  institute_id :

  ECMWF

  dataset_id :

  ERA5-Land

  abstract :

  ERA5-Land provides hourly high resolution information of surface variables. The data is a replay of the land component of the ERA5 climate reanalysis with a finer spatial resolution: ~9km grid spacing. ERA5-Land includes information about uncertainties for all variables at reduced spatial and temporal resolutions. The model used in the production of ERA5-Land is the tiled ECMWF Scheme for Surface Exchanges over Land incorporating land surface hydrology (H-TESSEL).

  dataset_description :

  https://www.ecmwf.int/en/era5-land

  license_type :

  permissive

  license :

  Please acknowledge the use of ERA5-Land as stated in the Copernicus C3S/CAMS License agreement http://apps.ecmwf.int/datasets/licences/copernicus/

  attribution :

  Contains modified Copernicus Climate Change Service Information 2022. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus Information or Data it contains.

  citation :

  Muñoz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 2022.06.07)

  doi :

  https://doi.org/10.24381/cds.e2161bac

A quicklook visualization of a day in the data is shown below.

ds.tas.sel(time="2019-01-16").plot()

(ToDo?): Access a climate simulation ensemble