Extreme Heat

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Heatwaves: Thermal Comfort Indices from ERA5 Reanalysis Dataset

Overview

Heatwaves are a significant climate risk characterized by prolonged periods of excessively hot weather, which can be detrimental to human health and comfort. The ERA5-HEAT dataset provides essential insights into thermal comfort indices that quantify human thermal stress during such events. This dataset is instrumental for research and planning in climatology, urban development, and public health initiatives.

ERA5-HEAT: Human Thermal Comfort Analysis

Analysis

The ERA5-HEAT dataset comprises an extensive historical reconstruction of Mean Radiant Temperature (MRT) and the Universal Thermal Climate Index (UTCI), which are pivotal in assessing human thermal stress and discomfort in outdoor environments.[1] Derived from the ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF), it merges model data with observations to offer a consistent global climate profile from January 1940 to the present.

Visualizations

Visual representations of the data illustrate the geographic distribution of thermal comfort levels across the globe and historical changes over time.

Global UTCI Map:

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Universal Thermal Climate Index (UTCI) Global Map

Heatwaves in the USA:

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Recent temperature increase in the USA summers.[2]

Historical Heatwave Data:

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Historical heatwaves.[3]

Heatwave Characteristics:

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Heatwave characteristics.[4]

NetCDF format

This dataset uses NetCDF format:

NetCDF (Network Common Data Form) is a widely used data format for storing and distributing multi-dimensional scientific data, such as temperature, humidity, wind speed, and direction across various points in time and space. Developed and maintained by the University Corporation for Atmospheric Research (UCAR), it is particularly popular in the fields of meteorology, oceanography, and atmospheric science.

Sample Dataset

A sample of the ERA5-HEAT dataset is presented below, showcasing the format and type of data available.[5]

The sample header of the dataset, displayed in a tabular format, includes the following columns:

time: The specific time point for the data (in this sample, all rows correspond to '2023-07-01'). lon: Longitude values, indicating the geographical longitude where the measurement was taken. lat: Latitude values, indicating the geographical latitude where the measurement was taken. utci: Values of the Universal Thermal Climate Index (UTCI) at the respective time and geographical coordinates. This tabular representation shows how the UTCI values are mapped against specific time points and geographic locations (latitude and longitude). The UTCI values are indicative of human thermal comfort at these locations and times.

Full dataset access: ERA5-HEAT Dataset


Datasets:

The following six datasets were used, including four traditional datasets:

  • Berkeley Earth: Rohde, R.A.; Hausfather, Z. The Berkeley Earth Land/Ocean Temperature Record. Earth System Science Data 2020, 12 (4), 3469–3479. https://doi.org/10.5194/essd-12-3469-2020.
  • GISTEMP v4: GISTEMP Team, 2022: GISS Surface Temperature Analysis (GISTEMP), Version 4. NASA Goddard Institute for Space Studies, https://data.giss.nasa.gov/gistemp/.
  • HadCRUT.5.0.2.0: Morice, C.P.; Kennedy, J.J.; Rayner, N.A. et al. An Updated Assessment of Near-surface Temperature Change from 1850: The HadCRUT5 Data Set. Journal of Geophysical Research: Atmospheres 2021, 126 (3), e2019JD032361. https://doi. org/10.1029/2019JD032361. HadCRUT.5.0.2.0 data were obtained from http://www. metoffice.gov.uk/hadobs/hadcrut5 on 17 January 2024 and are © British Crown Copyright, Met Office 2024, provided under an Open Government Licence, http://www. nationalarchives.gov.uk/doc/open-government-licence/version/3/.
  • Lenssen, N.J.L.; Schmidt, G.A.; Hansen, J.E. et al. Improvements in the GISTEMP Uncertainty Model. Journal of Geophysical Research: Atmospheres 2019, 124 (12), 6307–6326. https://doi. org/10.1029/2018JD029522.
  • NOAAGlobalTemp-Interim v5.1: Vose, R.S.; Huang, B.; Yin, X. et al. Implementing Full Spatial Coverage in NOAA’s Global Temperature Analysis. Geophysical Research Letters 2021, 48 (4), e2020GL090873. https://doi.org/10.1029/2020GL090873.
  • And two reanalyses:
  • ERA5: Hersbach, H.; Bell, B.; Berrisford, P. et al. ERA5 Monthly Averaged Data on Single Levels from 1940 to Present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2023. https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds. f17050d7?tab=overview.
  • JRA-55: Kobayashi, S.; Ota, Y.; Harada, Y. et al. The JRA-55 Reanalysis: General Specifications and Basic Characteristics. Journal of the Meteorological Society of Japan. Ser. II 2015, 93 (1), 5–48. https://doi.org/10.2151/jmsj.2015–001
  • IPCC used an additional dataset. Combining the six datasets used in the present publication with Kadow et al., 2020 reduces the estimated global mean for 2023 by 0.01 °C and increases the uncertainty range by a similar amount:
    • Kadow, C.; Hall, D.M.; Ulbrich, U. Artificial Intelligence Reconstructs Missing Climate Information. Nature Geoscience 2020, 13, 408–413. https://doi.org/10.1038/s41561-020-0582-5.
  • A new reanalysis produced by JRA-3Q is now also available. JRA-55 was used in the present publication for consistency with the provisional statement released in December 2023. For comparison, the values for 2023 are shown relative to the four baselines for JRA-3Q and JRA-55 in Table 2 (see below). Note that the replacement of JRA-55 with JRA-3Q in the mean of the six datasets has a negligible effect on the global mean temperature for 2023.


LAND TEMPERATURES AND SEA-SURFACE TEMPERATURES

The land temperature assessment is based on three data sets:

  • Berkeley Earth: Rohde, R.A.; Hausfather, Z. The Berkeley Earth Land/Ocean Temperature Record. Earth System Science Data 2020, 12 (4), 3469–3479. https://doi.org/10.5194/essd-12-3469-202.
  • CRUTEM.5.0.2.0: Osborn, T.J.; Jones, P.D.; Lister, D.H. et al. Land Surface Air Temperature Variations Across the Globe Updated to 2019: The CRUTEM5 Data Set. Journal of Geophysical Research 2021, 126 (2), e2019JD032352. https://doi.org/10.1029/2019JD032352. CRUTEM.5.0.2.0 data were obtained from http://www.metoffice.gov.uk/hadobs/ crutem5 on 17 January 2024 and are © British Crown Copyright, Met Office 2024, provided under an Open Government Licence, http://www.nationalarchives.gov.uk/doc/ open-government-licence/version/3/.
  • GHCNv4: Menne, M.J.; Gleason, B.E.; Lawrimore, J. et al. Global Historical Climatology Network – Monthly Temperature [Global mean]. NOAA National Centers for Environmental Information, 2017. doi:10.7289/V5XW4GTH.
  • The sea-surface temperature (SST) assessment is based on two datasets:
    • HadSST.4.0.1.0: Kennedy, J.J.; Rayner, N.A.; Atkinson, C.P. et al. An Ensemble Data Set of Sea Surface Temperature Change from 1850: The Met Office Hadley Centre HadSST.4.0.0.0 Data Set. Journal of Geophysical Research: Atmospheres 2019, 124 (14), 7719–7763. https://doi. org/10.1029/2018JD029867. HadSST.4.0.1.0 data were obtained from http://www.metoffice. gov.uk/hadobs/hadsst4 on 17 January 2024 and are © British Crown Copyright, Met Office 2024, provided under an Open Government Licence, http://www.nationalarchives.gov.uk/ doc/open-government-licence/version/3/.
    • ERSSTv5: Huang, B.; Thorne, P.W.; Banzon, V.F. et al. NOAA Extended Reconstructed Sea-Surface Temperature (ERSST), Version 5. [Global mean]. NOAA National Centers for Environmental Information, 2017. doi:10.7289/V5T72FNM

References

  1. ↑ ERA5-HEAT Dataset Overview, ECMWF.
  2. ↑ Heatwaves Tweet, Robert Rohde via Twitter.
  3. ↑ Climate Change Indicators: Heat Waves, EPA.
  4. ↑ Climate Change Indicators: Heat Waves, EPA.
  5. ↑ ERA5-HEAT Sample Data, ECMWF.
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