The Sound of Climate Change

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Data

In this example, we use the NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), which provides gridded monthly surface temperature anomalies since 1880.

import xarray as xr

data_path = "data/NOAAGlobalTemp_v5.0.0_gridded_s188001_e202212_c20230108T133308.nc"
ds = xr.open_dataset(data_path)
ds

<xarray.Dataset>
Dimensions:  (time: 1716, lat: 36, lon: 72, z: 1)
Coordinates:
  * time     (time) datetime64[ns] 1880-01-01 1880-02-01 ... 2022-12-01
  * lat      (lat) float32 -87.5 -82.5 -77.5 -72.5 -67.5 ... 72.5 77.5 82.5 87.5
  * lon      (lon) float32 2.5 7.5 12.5 17.5 22.5 ... 342.5 347.5 352.5 357.5
  * z        (z) float32 0.0
Data variables:
    anom     (time, z, lat, lon) float32 ...
Attributes: (12/66)
    Conventions:                     CF-1.6, ACDD-1.3
    title:                           NOAA Merged Land Ocean Global Surface Te...
    summary:                         NOAAGlobalTemp is a merged land-ocean su...
    institution:                     DOC/NOAA/NESDIS/National Centers for Env...
    id:                               gov.noaa.ncdc:C00934
    naming_authority:                 gov.noaa.ncei
    ...                              ...
    time_coverage_duration:          P143Y0M
    references:                      Vose, R. S., et al., 2012: NOAAs merged ...
    climatology:                     Climatology is based on 1971-2000 monthl...
    acknowledgment:                  The NOAA Global Surface Temperature Data...
    date_modified:                   2023-01-08T18:33:09Z
    date_issued:                     2023-01-08T18:33:09Z

xarray.Dataset

• Dimensions:
  • time: 1716
  • lat: 36
  • lon: 72
  • z: 1
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1880-01-01 ... 2022-12-01

    long_name :

    reference time of global temperature anomalies

    standard_name :

    time

    coverage_content_type :

    coordinate

    axis :

    T

    array(['1880-01-01T00:00:00.000000000', '1880-02-01T00:00:00.000000000',
           '1880-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lat
    (lat)
    float32
    -87.5 -82.5 -77.5 ... 82.5 87.5

    long_name :

    Latitude

    standard_name :

    latitude

    coverage_content_type :

    coordinate

    units :

    degrees_north

    grids :

    Uniform grid from -87.5 to 87.5 by 5

    valid_min :

    -87.5

    valid_max :

    87.5

    axis :

    Y

    _CoordinateAxisType :

    Lat

    coordinate_defines :

    center

    array([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,
           -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,
            12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,
            62.5,  67.5,  72.5,  77.5,  82.5,  87.5], dtype=float32)

  • lon
    (lon)
    float32
    2.5 7.5 12.5 ... 347.5 352.5 357.5

    long_name :

    Longitude

    standard_name :

    longitude

    coverage_content_type :

    coordinate

    units :

    degrees_east

    grids :

    Uniform grid from 2.5 to 357.5 by 5

    axis :

    X

    valid_min :

    2.5

    valid_max :

    357.5

    _CoordinateAxisType :

    Lon

    coordinate_defines :

    center

    array([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,
            52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,
           102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,
           152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,
           202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5, 247.5,
           252.5, 257.5, 262.5, 267.5, 272.5, 277.5, 282.5, 287.5, 292.5, 297.5,
           302.5, 307.5, 312.5, 317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5,
           352.5, 357.5], dtype=float32)

  • z
    (z)
    float32
    0.0

    long_name :

    height of surface temperature

    standard_name :

    height

    coverage_content_type :

    coordinate

    geospatial_bounds_vertical_crs :

    EPSG:5703

    units :

    meters

    positive :

    up

    valid_min :

    0.0

    valid_max :

    0.0

    axis :

    Z

    _CoordinateAxisType :

    Height

    comment :

    Actual measurement depth of in situ sea surface temperature varies from 0.2 to 10 m, but corrected to the nominal depth of buoy at 0.2 m; for air temperature over land is 2m.

    array([0.], dtype=float32)

• Data variables: (1)
  • anom
    (time, z, lat, lon)
    float32
    ...

    long_name :

    Global Temperature Anomalies

    standard_name :

    surface_temperature_anomaly

    coverage_content_type :

    physicalMeasurement

    units :

    degrees C

    valid_min :

    -40.0

    valid_max :

    40.0

    [4447872 values with dtype=float32]

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1880-01-01', '1880-02-01', '1880-03-01', '1880-04-01',
                   '1880-05-01', '1880-06-01', '1880-07-01', '1880-08-01',
                   '1880-09-01', '1880-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=1716, freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,
           -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,
            12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,
            62.5,  67.5,  72.5,  77.5,  82.5,  87.5],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,
            52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,
           102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,
           152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,
           202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5, 247.5,
           252.5, 257.5, 262.5, 267.5, 272.5, 277.5, 282.5, 287.5, 292.5, 297.5,
           302.5, 307.5, 312.5, 317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5,
           352.5, 357.5],
          dtype='float32', name='lon'))

  • z
    PandasIndex

    PandasIndex(Index([0.0], dtype='float32', name='z'))

• Attributes: (66)

  Conventions :

  CF-1.6, ACDD-1.3

  title :

  NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)

  summary :

  NOAAGlobalTemp is a merged land-ocean surface temperature analysis, formerly abbreviated as MLOST. The SST dataset is the Extended Reconstructed Sea Surface Temperature (ERSST) and the land surface air temperature dataset is from the Global Historical Climatology Network Monthly (GHCN-M) database.

  institution :

  DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)

  id :

  gov.noaa.ncdc:C00934

  naming_authority :

  gov.noaa.ncei

  project :

  NOAAGlobalTemp

  platform :

  Ships, moored buoys, surface drifting buoys, Argo floats, and weather stations

  instrument :

  Conventional thermometers

  history :

  Version 5.0.0, blending ERSST V5 and GHCN-M V4.

  comment :

  Merged land ocean surface temperature anomalies.

  product_version :

  5.0.0

  source :

  NOAAGlobalTemp combines a global sea surface temperature with a global land surface air temperature data

  ncei_template_version :

  NCEI_NetCDF_Grid_Template_v2.0

  Metadata_Conventions :

  Unidata Dataset Discovery v1.0

  license :

  These data are available for use without restriction.

  creator_name :

  Boyin Huang, Xungang Yin, Ph.D

  creator_email :

  Boyin.Huang@noaa.gov, Xungang.Yin@noaa.gov

  creator_type :

  group

  creator_institution :

  US DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)

  contributor_name :

  Huai-min Zhang, Ph.D

  contributor_role :

  Chief, NOAA/NESDIS/NCEI/CCOG/OSB Ocean Surface Section

  publisher_name :

  NCEI

  publisher_institution :

  NCEI

  publisher_url :

  https://www.ncdc.noaa.gov/

  publisher_email :

  ncei.info@noaa.gov

  publisher_type :

  institution

  keywords :

  Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature, Earth Science > Atmosphere > Atmospheric Temperature > Surface Temperature > Air Temperature

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  standard_name_vocabulary :

  Climate and Forecast (CF) Standard Name Table (Version 46, 25 July 2017)

  date_created :

  2023-01-08T18:33:09Z

  necdf_version_id :

  4.2.1.1

  netcdf_creator_name :

  Yongsheng Zhang, Ph.D

  netcdf_creator_email :

  yongsheng.zhang@noaa.gov

  dataset_citation_product :

  NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)

  dataset_citation_version :

  5.0.0

  dataset_citation_institution :

  NOAA/NESDIS/NCEI, Asheville, NC, USA

  dataset_citation_url :

  https://doi.org/10.25921/9qth-2p70

  metadata_link :

  https://doi.org/10.25921/9qth-2p70

  cdm_data_type :

  Grid

  grid_mapping_name :

  latitude_longitude

  processing_level :

  NOAA Processing Level 4

  geospatial_lon_min :

  2.5

  geospatial_lon_max :

  357.5

  geospatial_lon_units :

  degrees_east

  geospatial_lon_resolution :

  5.0 degree

  geospatial_lat_min :

  -87.5

  geospatial_lat_max :

  87.5

  geospatial_lat_units :

  degrees_north

  geospatial_lat_resolution :

  5.0 degree

  geospatial_bounds :

  POLYGON ((2.5 -87.5, 2.5 87.5, 357.5 87.5, 357.5 -87.5, 2.5 -87.5))

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_vertical_min :

  0.0

  geospatial_vertical_max :

  0.0

  geospatial_vertical_units :

  meters

  geospatial_vertical_resolution :

  0.f

  geospatial_vertical_positive :

  up

  time_coverage_start :

  1880-01

  time_coverage_end :

  2022-12

  time_coverage_resolution :

  P1M

  time_coverage_duration :

  P143Y0M

  references :

  Vose, R. S., et al., 2012: NOAAs merged land-ocean surface temperature analysis. Bulletin of the American Meteorological Society, 93, 1677-1685. doi: 10.1175/BAMS-D-11-00241.1. Huang, B., Peter W. Thorne, et. al, 2017: Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5), Upgrades, validations, and intercomparisons. J. Climate, 30, 8179-8205. doi: 10.1175/JCLI-D-16-0836.1

  climatology :

  Climatology is based on 1971-2000 monthly climatology

  acknowledgment :

  The NOAA Global Surface Temperature Dataset is provided by the NOAA National Centers for Environmental Information(NCEI)

  date_modified :

  2023-01-08T18:33:09Z

  date_issued :

  2023-01-08T18:33:09Z

To perform our Sonification, we need to first reduce the dimensionality of our data into a one-dimensional time series.

We can achieve this by taking the global average of each grid cell over our desired time dimension (in our case, years)

ds_yearly = ds.groupby('time.year').mean()
anom = ds_yearly['anom'].mean(skipna=True, dim=['lat', 'lon', 'z'])
anom

<xarray.DataArray 'anom' (year: 143)>
array([-0.3943866 , -0.41972142, -0.4036848 , -0.48530814, -0.57469773,
       -0.5889563 , -0.57268155, -0.5909827 , -0.44562352, -0.38252443,
       -0.63903975, -0.52612305, -0.570924  , -0.59950167, -0.5950165 ,
       -0.5180988 , -0.41718104, -0.44207883, -0.56131285, -0.4545371 ,
       -0.4238661 , -0.46986705, -0.60135543, -0.66082263, -0.72585166,
       -0.5568018 , -0.4936252 , -0.6894805 , -0.72841936, -0.75785506,
       -0.7034879 , -0.75667304, -0.6826453 , -0.65288305, -0.4602562 ,
       -0.43005922, -0.6172296 , -0.71297073, -0.59762526, -0.6004663 ,
       -0.59456426, -0.47546577, -0.5666871 , -0.56307   , -0.5301119 ,
       -0.47914082, -0.38606668, -0.4893744 , -0.4795832 , -0.6426879 ,
       -0.4171341 , -0.38618568, -0.42179072, -0.59395295, -0.39917687,
       -0.45938993, -0.40029082, -0.29080656, -0.2633328 , -0.27344295,
       -0.16464116, -0.11016758, -0.20841345, -0.13299076,  0.01565617,
       -0.13738923, -0.37518796, -0.30405325, -0.35113192, -0.35852575,
       -0.4490882 , -0.32934034, -0.27240175, -0.1811699 , -0.3751917 ,
       -0.43210554, -0.45517403, -0.25939453, -0.23247351, -0.24851352,
       -0.2775711 , -0.22983077, -0.22023346, -0.2538945 , -0.48913643,
       -0.41327626, -0.3905886 , -0.2777611 , -0.34331083, -0.2701262 ,
       -0.2839199 , -0.34365183, -0.3231848 , -0.12641443, -0.37112936,
       -0.2359808 , -0.36379242, -0.06907614, -0.20542464, -0.13779244,
       -0.04034564,  0.06257632, -0.15661713,  0.04536498, -0.12573433,
       -0.14191881, -0.09556889, -0.00960468,  0.07597412, -0.00842201,
        0.1378755 ,  0.09291187, -0.09948993, -0.03128164,  0.00182989,
        0.19177724,  0.01640899,  0.18606322,  0.2912497 ,  0.10348047,
        0.11754002,  0.25927433,  0.31287512,  0.3265392 ,  0.24369767,
        0.38980773,  0.3379023 ,  0.38573822,  0.26357874,  0.31855157,
        0.4427373 ,  0.34472087,  0.38057834,  0.3780964 ,  0.42638323,
        0.5900977 ,  0.727808  ,  0.64048296,  0.5329775 ,  0.67719775,
        0.7383068 ,  0.5341347 ,  0.592656  ], dtype=float32)
Coordinates:
  * year     (year) int64 1880 1881 1882 1883 1884 ... 2018 2019 2020 2021 2022

xarray.DataArray

'anom'

• year: 143

• -0.3944 -0.4197 -0.4037 -0.4853 ... 0.6772 0.7383 0.5341 0.5927

  array([-0.3943866 , -0.41972142, -0.4036848 , -0.48530814, -0.57469773,
         -0.5889563 , -0.57268155, -0.5909827 , -0.44562352, -0.38252443,
         -0.63903975, -0.52612305, -0.570924  , -0.59950167, -0.5950165 ,
         -0.5180988 , -0.41718104, -0.44207883, -0.56131285, -0.4545371 ,
         -0.4238661 , -0.46986705, -0.60135543, -0.66082263, -0.72585166,
         -0.5568018 , -0.4936252 , -0.6894805 , -0.72841936, -0.75785506,
         -0.7034879 , -0.75667304, -0.6826453 , -0.65288305, -0.4602562 ,
         -0.43005922, -0.6172296 , -0.71297073, -0.59762526, -0.6004663 ,
         -0.59456426, -0.47546577, -0.5666871 , -0.56307   , -0.5301119 ,
         -0.47914082, -0.38606668, -0.4893744 , -0.4795832 , -0.6426879 ,
         -0.4171341 , -0.38618568, -0.42179072, -0.59395295, -0.39917687,
         -0.45938993, -0.40029082, -0.29080656, -0.2633328 , -0.27344295,
         -0.16464116, -0.11016758, -0.20841345, -0.13299076,  0.01565617,
         -0.13738923, -0.37518796, -0.30405325, -0.35113192, -0.35852575,
         -0.4490882 , -0.32934034, -0.27240175, -0.1811699 , -0.3751917 ,
         -0.43210554, -0.45517403, -0.25939453, -0.23247351, -0.24851352,
         -0.2775711 , -0.22983077, -0.22023346, -0.2538945 , -0.48913643,
         -0.41327626, -0.3905886 , -0.2777611 , -0.34331083, -0.2701262 ,
         -0.2839199 , -0.34365183, -0.3231848 , -0.12641443, -0.37112936,
         -0.2359808 , -0.36379242, -0.06907614, -0.20542464, -0.13779244,
         -0.04034564,  0.06257632, -0.15661713,  0.04536498, -0.12573433,
         -0.14191881, -0.09556889, -0.00960468,  0.07597412, -0.00842201,
          0.1378755 ,  0.09291187, -0.09948993, -0.03128164,  0.00182989,
          0.19177724,  0.01640899,  0.18606322,  0.2912497 ,  0.10348047,
          0.11754002,  0.25927433,  0.31287512,  0.3265392 ,  0.24369767,
          0.38980773,  0.3379023 ,  0.38573822,  0.26357874,  0.31855157,
          0.4427373 ,  0.34472087,  0.38057834,  0.3780964 ,  0.42638323,
          0.5900977 ,  0.727808  ,  0.64048296,  0.5329775 ,  0.67719775,
          0.7383068 ,  0.5341347 ,  0.592656  ], dtype=float32)

• Coordinates: (1)
  • year
    (year)
    int64
    1880 1881 1882 ... 2020 2021 2022

    array([1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891,
           1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903,
           1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915,
           1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927,
           1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939,
           1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951,
           1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963,
           1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975,
           1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,
           1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999,
           2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
           2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022])

• Indexes: (1)
  • year
    PandasIndex

    PandasIndex(Index([1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889,
           ...
           2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022],
          dtype='int64', name='year', length=143))

• Attributes: (0)

Sonification

Now that we have a one-dimensional time series, we can perform our sonification.

We first need to specify our SoundMap, which will be used to turn each data point into a musical note.

from scisonify.core.soundmaps import DiscreteNoteBins

Here we choose a 3-octave F flat minor scale to be used as our notes

smap = DiscreteNoteBins.from_key("Fb:min", octave_range=(3, 6))

Next, we import scisonify.xarray, which will allow us to perform our sonification directly on an Xarray DataArray

import scisonify.xarray

anom.sonify(note_length=0.1, wave='sine', smap=smap)

In addition to listening to our data, we can plot each point as a note to visualize the audio.

anom.sonify.plot(x=anom.year,
                 xlabel="Year",
                 marker='o',
                 linewidth=0,
                 ms=3.0,
                 figsize=(20, 10),
                 title="Yearly Global Temperature Anomaly as Notes")

We can compare to this the actual plot of our Temperature anomalies

anom.plot(figsize=(20, 10),
          ms=3,
          lw=0.5,
          marker='o')

[<matplotlib.lines.Line2D at 0x7ffa0ae86290>]