Extreme Heat: Difference between revisions

From CRL Wiki
Jump to navigation Jump to search
← Older edit
VisualWikitext
 
(29 intermediate revisions by the same user not shown)
Line 1: Line 1:
= Extreme heat =
'''Extreme heat,''' or called "hot extremes" is a period of unusually high temperatures that exceed historical norms for a specific location. In most of the United States, it is defined as high heat and humidity with temperatures above 90 degrees lasting for at least two to three days. <ref>[https://www.ready.gov/heat#:~:text=There%20is%20hot%2C%20and%20then,which%20can%20lead%20to%20death. https://www.ready.gov/heat#:~:text=There%20is%20hot%2C%20and%20then,which%20can%20lead%20to%20death.]</ref><ref>https://community.fema.gov/ProtectiveActions/s/article/Extreme-Heat</ref>In extreme heat your body works extra hard to maintain a normal temperature, which can lead to death. Extreme heat is responsible for the highest number of annual deaths among all weather-related hazards.
 
[[File:Extreme heat2.png|thumb|500px|Extreme Heat (Source: The Conversation<ref>Retrieved from https://theconversation.com/extreme-heat-is-a-threat-to-lives-in-africa-but-its-not-being-monitored-149921 on Oct. 24, 2024</ref>)]]
== Overview ==
 
* What is extreme heat?


=== Changes in extreme heat with global warming ===
=== Changes in extreme heat with global warming ===
The frequency and intensity of hot extremes (including heatwaves) have increased since 1950 both globally and at regional scale, with more than 80% of AR6 regions showing similar changes. This trend is expected to persist as global warming continues. The IPCC AR6 report summarizes the projected changes in extreme heat with ongoing global warming, stating: "The frequency and intensity of hot extremes will continue to increase at global and continental scales and in nearly all inhabited regions with increasing global warming levels. This will be the case even if global warming is stabilized at 1.5°C. Relative to present-day conditions, changes in the intensity of extremes would be at least double at 2°C, and quadruple at 3°C of global warming, compared to changes at 1.5°C of global warming. The number of hot days and hot nights and the length, frequency, and/or intensity of warm spells or heatwaves will increase over most land areas. In most regions, future changes in the intensity of temperature extremes will ''very likely'' be proportional to changes in global warming, and up to two to three times larger. The highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions and in the South American Monsoon region, at about 1.5 times to twice the rate of global warming (''high confidence''). The highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming (''high confidence''). The frequency of hot temperature extreme events will ''very likely'' increase nonlinearly with increasing global warming, with larger percentage increases for rarer events."
The frequency and intensity of hot extremes (including heatwaves) have increased since 1950 both globally and at regional scale, with more than 80% of AR6 regions showing similar changes. This trend is expected to persist as global warming continues. The IPCC AR6 report summarizes the projected changes in extreme heat with ongoing global warming, stating: "The frequency and intensity of hot extremes will continue to increase at global and continental scales and in nearly all inhabited regions with increasing global warming levels. This will be the case even if global warming is stabilized at 1.5°C. Relative to present-day conditions, changes in the intensity of extremes would be at least double at 2°C, and quadruple at 3°C of global warming, compared to changes at 1.5°C of global warming. The number of hot days and hot nights and the length, frequency, and/or intensity of warm spells or heatwaves will increase over most land areas. In most regions, future changes in the intensity of temperature extremes will ''very likely'' be proportional to changes in global warming, and up to two to three times larger. The highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions and in the South American Monsoon region, at about 1.5 times to twice the rate of global warming (''high confidence''). The highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming (''high confidence''). The frequency of hot temperature extreme events will ''very likely'' increase nonlinearly with increasing global warming, with larger percentage increases for rarer events."


=== How it makes impacts: WG2 16.2.3.5 and WG1 12.3.1 ===
=== Impacts of extreme heat ===
Extreme heat events, such as heat waves can cause large economic loss via reducing employee's productivity<ref>Chavaillaz, Y., Roy, P., Partanen, AI. ''et al.'' Exposure to excessive heat and impacts on labour productivity linked to cumulative CO2 emissions. ''Sci Rep'' 9, 13711 (2019). <nowiki>https://doi.org/10.1038/s41598-019-50047-w</nowiki></ref>, increasing hospital visits, reducing crop yields, stressing livestocks, and straining infrastructure. For example, the European Environment Agency (EEA) estimates that, between 1980 and 2000, heat waves in 32 European countries cost up to $70 billion euros<ref name=":0">https://phys.org/news/2022-06-deadly-heatwaves-threaten-economies.html</ref>. The total estimated damages attributed to heatwaves of 2003, 2010, 2015, and 2018 amounted to 0.3–0.5% of European GDP<ref>García-León, D., Casanueva, A., Standardi, G. ''et al.'' Current and projected regional economic impacts of heatwaves in Europe. ''Nat Commun'' 12, 5807 (2021). <nowiki>https://doi.org/10.1038/s41467-021-26050-z</nowiki></ref><ref name=":1">https://www.weforum.org/agenda/2022/07/heat-waves-economy-climate-crisis/</ref>.
Extreme heat events, such as heat waves can cause large economic loss via reducing employee's productivity<ref>Chavaillaz, Y., Roy, P., Partanen, AI. ''et al.'' Exposure to excessive heat and impacts on labour productivity linked to cumulative CO2 emissions. ''Sci Rep'' 9, 13711 (2019). <nowiki>https://doi.org/10.1038/s41598-019-50047-w</nowiki></ref>, increasing hospital visits, reducing crop yields, stressing livestocks, and straining infrastructure. For example, the European Environment Agency (EEA) estimates that, between 1980 and 2000, heat waves in 32 European countries cost up to $70 billion euros<ref name=":0">https://phys.org/news/2022-06-deadly-heatwaves-threaten-economies.html</ref>. The total estimated damages attributed to heatwaves of 2003, 2010, 2015, and 2018 amounted to 0.3–0.5% of European GDP<ref>García-León, D., Casanueva, A., Standardi, G. ''et al.'' Current and projected regional economic impacts of heatwaves in Europe. ''Nat Commun'' 12, 5807 (2021). <nowiki>https://doi.org/10.1038/s41467-021-26050-z</nowiki></ref><ref name=":1">https://www.weforum.org/agenda/2022/07/heat-waves-economy-climate-crisis/</ref>.


Line 30: Line 27:
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.
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.


== Data analysis ==
== Data ==


=== Metrics/indicators WG1 12.3.1 Indicators to measure it (based on impacts): WG1 Ch12.3.1 ===
=== Indices of extreme heat ===
Since SREX (IPCC, 2012) and AR5 (IPCC, 2014), many regional-scale studies have examined trends in temperature extremes using different metrics that are based on daily temperatures, such as the Commission for Climatology/World Climate Research Program/Commission for Oceanography and Marine Meteorology joint Expert Team on Climate Change Detection and Indices (ETCCDI) indices (Dunn et al., 2020).<div style="margin-left: 90px;">
Impacts and risk assessments utilize a large variety of indices and approaches tailored to evaluate the impacts of extreme heat. Table 1 below listed some of the indices used.<div style="margin-left: 90px;">
{| class="wikitable" style="width:70em"
{| class="wikitable" style="width:70em"
|+
|+Table 1 Heat-related indices
!Indicator
!Indicator
!Description
!Description
Line 45: Line 42:
|-
|-
|ERA5-HEAT
|ERA5-HEAT
|A complete historical reconstruction for a set of indices representing human thermal stress and discomfort in outdoor conditions. This dataset represents the current state-of-the-art for bioclimatology data record production.
|A complete historical reconstruction for a set of indices representing human thermal stress and discomfort in outdoor conditions. 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. This dataset represents the current state-of-the-art for bioclimatology data record production.
|[https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-utci-historical?tab=overview Access]
|[https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-utci-historical?tab=overview Access]
|-
|-
Line 56: Line 53:
|[https://ephtracking.cdc.gov/Applications/HeatRisk/ View]; [https://www.wpc.ncep.noaa.gov/heatrisk/data.html Access]
|[https://ephtracking.cdc.gov/Applications/HeatRisk/ View]; [https://www.wpc.ncep.noaa.gov/heatrisk/data.html Access]
|-
|-
|Heat and Health Index<ref>https://ephtracking.cdc.gov/Applications/heatTracker/</ref>
|The Heat and Health Index is the first national tool to incorporate spatially granular heat-related illness and community characteristics data to measure extreme heat vulnerability and help communities prepare for warming temperatures in a changing climate. For more details, please refer to its [https://www.atsdr.cdc.gov/placeandhealth/hhi/docs/technical_documentation/ technical documentation].
|[https://ephtracking.cdc.gov/Applications/heatTracker/ View and Access]
|-
|Temperature Condition Index (TCI)<ref>https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/VH-Syst_10ap30.php</ref>
|Using AVHRR thermal bands, TCI is used to determine stress on vegetation caused by temperatures and excessive wetness. Conditions are estimated relative to the maximum and minimum temperatures and modified to reflect different vegetation responses to temperature.
|[https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_ftp.php Access]
|-
|Heat extreme indices used in IPCC AR6 (reproduced from Table AVI.1)
|
|
|
* Monthly maximum value of daily maximum temperature
|
* Monthly minimum value of daily maximum temperature
* Monthly minimum value of daily minimum temperature
* Monthly maximum value of daily minimum temperature
* Percentage of days when daily maximum temperature is greater than the 90th percentile
* '''Percentage of days when daily maximum temperature is less than the 10th percentile'''
* Percentage of days when daily minimum temperature is greater than the 90th percentile
* '''Percentage of days when daily minimum temperature is less than the 10th percentile'''
* '''Number of icing days: annual count of days when TX (daily maximum temperature) <0°C'''
* '''Number of frost days: annual count of days when TN (daily minimum temperature) <0°C'''
* Warm spell duration index: annual count of days with at least six consecutive days when TX >90th percentile
* Cold spell duration index: annual count of days with at least six consecutive days when TN <10th percentile
* Number of summer days: annual count of days when TX (daily maximum temperature) >25°C
* Number of tropical nights: annual count of days when TN (daily minimum temperature) >20°C
* Daily temperature range: monthly mean difference between TX and TN
* Growing season length: annual (1 Jan to 31 Dec in Northern Hemisphere (NH), 1 July to 30 June in Southern Hemisphere (SH)) count between first span of at least six days with daily mean temperature TG >5°C and first span after July 1 (Jan 1 in SH) of six days with TG <5°C
* One-in-20 year return value of monthly maximum value of daily maximum temperature
* One-in-20 year return value of monthly minimum value of daily maximum temperature
* One-in-20 year return value of monthly minimum value of daily minimum temperature
* One-in-20 year return value of monthly maximum value of daily minimum temperature
|Some of these indices are included in the [https://interactive-atlas.ipcc.ch/regional-information#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 Interactive Atlas] of IPCC. All of them can be calculated using temperature data listed below; methods of calculation are provided in [https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVI.pdf Annex VI] of IPCC AR6.
|-
|-
|
|HadEX3<ref>https://www.metoffice.gov.uk/hadobs/hadex3/</ref>
|
|Land-based surface climate extremes indices covering 1901 to 2018 on a 1.25° x 1.875° grid. It is produced through the coordination of the joint WMO CCl/WCRP/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) and the WMO Expert Team on Sector-specific indices (ET-SCI). It currently comprises of over 80 indices of temperature and precipitation, including the indices used in IPCC AR6 listed above.
|
|[https://www.metoffice.gov.uk/hadobs/hadex3/download.html Access]
|}
</div>
 
=== Temperature datasets ===
{| class="wikitable" style="width:70em"
|+Table 2 Datasets of temperature
!Indicator
!Description
!Temporal Position
!Data Access
|-
|-
|
|Berkeley Earth Surface Temperature<ref>[https://climatedataguide.ucar.edu/climate-data/global-surface-temperatures-best-berkeley-earth-surface-temperatures Cowtan, Kevin & National Center for Atmospheric Research Staff (Eds). Last modified 2023-08-08 "The Climate Data Guide: Global surface temperatures: BEST: Berkeley Earth Surface Temperatures.” Retrieved from https://climatedataguide.ucar.edu/climate-data/global-surface-temperatures-best-berkeley-earth-surface-temperatures on 2024-08-22.]</ref><ref>https://berkeleyearth.org/data/</ref>
|
|Berkeley Earth provides high-resolution land and ocean time series data and gridded temperature data. It incorporates more temperature observations than other available products, and often has better coverage. Global datasets begin in 1850, with some land-only areas reported back to 1750. The newest generation of the dataset is augmented by machine learning techniques to improve the spatial resolution.
|
|Historical
|[https://berkeleyearth.org/data/ Access]
|-
|-
|
|GISTEMP<ref>GISTEMP Team, 2024: . NASA Goddard Institute for Space Studies. Dataset accessed 2024-08-23 at <nowiki>https://data.giss.nasa.gov/gistemp/</nowiki>.</ref>
|
|The GISS Surface Temperature Analysis version 4 (GISTEMP v4) is an estimate of global surface temperature change. Graphs and tables are updated around the middle of every month using current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas).
|
|Historical
|[https://data.giss.nasa.gov/gistemp/ Access]
|-
|-
|
|HadCRUT5<ref>https://www.metoffice.gov.uk/hadobs/hadcrut5/</ref>
|
|HadCRUT is a global temperature dataset that combines the CRUTEM land surface temperature data with the HadSST sea surface temperature data. It does not involve interpolation, which results in significant coverage gaps across certain regions. It is the longest global temperature dataset, extending back to 1850.
|
|Historical
|[https://www.metoffice.gov.uk/hadobs/hadcrut5/data/HadCRUT.5.0.2.0/download.html Access]
|-
|-
|
|CRUTEM5<ref>https://www.metoffice.gov.uk/hadobs/crutem5/</ref>
|
|CRUTEM is a gridded dataset of observed near-surface air temperature anomalies over land, dating back to 1850.
|
|Historical
|[https://www.metoffice.gov.uk/hadobs/crutem5/data/CRUTEM.5.0.2.0/download.html Access]
|-
|-
|
|NOAAGlobalTemp v6.0<ref>https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp</ref>
|
|This global temperature dataset integrates long-term sea surface (water) temperature and land surface (air) temperature records. It is used to support climate monitoring activities, including the Monthly Global Climate Assessment, and serves as input data for various climate models.
|
|Historical
|[https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp Access]
|-
|-
|
|ERA5<ref>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</ref>
|
|ERA5 is a reanalysis dataset that integrates extensive historical observations from diverse sources into global, gridded estimates using advanced modeling and data assimilation systems. It delivers hourly estimates for a wide range of atmospheric, land, and oceanic climate variables at a spatial resolution of 31 km.
|
|Historical
|[https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview Access]; [https://registry.opendata.aws/nsf-ncar-era5/ AWS access]
|-
|-
|
|JRA-55<ref>https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55</ref>
|
|JRA-55 is also a reanalysis datasets that spans 1958 to present. It offers several versions at different resolution, with the finest as 0.562 degree.  
|
|Historical
|}
|[https://rda.ucar.edu/datasets/d628000/ Access]
</div>
 
== 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.<ref>[https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-utci-historical?tab=overview ERA5-HEAT Dataset Overview], ECMWF.</ref> 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:''
[[File:76f6c4c9-7946-4691-9046-5f7e09b7293d.png|thumb|center|Universal Thermal Climate Index (UTCI) Global Map]]
 
''Heatwaves in the USA:''
[[File:E5iL- fXEAMz7vF.png|thumb|center|Recent temperature increase in the USA summers.<ref>[https://twitter.com/RARohde/status/1412032319993651201 Heatwaves Tweet], Robert Rohde via Twitter.</ref>]]
 
''Historical Heatwave Data:''
[[File:Heat-waves figure1 2022.png|thumb |center|Historical heatwaves.<ref>[https://www.epa.gov/climate-indicators/climate-change-indicators-heat-waves Climate Change Indicators: Heat Waves], EPA.</ref>]]
 
''Heatwave Characteristics:''
[[File:Heat-waves figure2 2022.png|thumb|center|Heatwave characteristics.<ref>[https://www.epa.gov/climate-indicators/climate-change-indicators-heat-waves Climate Change Indicators: Heat Waves], EPA.</ref>]]
 
=== Sample Dataset ===
 
A sample of the ERA5-HEAT dataset is presented below, showcasing the format and type of data available.<ref>[https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-utci-historical?tab=overview ERA5-HEAT Sample Data], ECMWF.</ref>
 
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: [https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-utci-historical?tab=overview ERA5-HEAT Dataset]
{| class="wikitable"
|+heat extreme indices
!
!
!
!
|-
|-
|
|GHCNv4<ref>Menne, Matthew J.; Gleason, Byron E.; Lawrimore, Jay; Rennie, Jared; and Williams, Claude N. (2017): Global Historical Climatology Network - Monthly Temperature [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V5XW4GTH [2024-08-23].</ref>
|
|The Global Historical Climatology Network (GHCN) is the core global land surface air temperature dataset used for climate monitoring and assessment activities. It contains temperature of over 25,000 stations across the globe.
|
|Historical
|
|[https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00950 Access];
[https://registry.opendata.aws/noaa-ghcn/ AWS link]
|-
|-
|
|PRISM<ref>https://prism.oregonstate.edu/</ref>
|
|PRISM datasets provide estimates of seven primary climate elements (precipitation, minimum temperature, maximum temperature, mean dew point, minimum vapor pressure deficit, maximum vapor pressure deficit, and total global shortwave solar radiation on a horizontal surface) over the US using climate observations from a wide range of monitoring networks. It is available at both 800 m and 4 km.
|
|Historical
|
|[https://prism.oregonstate.edu/ Access]
|-
|-
|
|'''GHCNd<ref>https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily</ref>'''
|
|The Global Historical Climatology Network daily (GHCNd) is an integrated database of daily climate summaries from land surface stations across the globe. GHCNd is made up of daily climate records from numerous sources that have been integrated and subjected to a common suite of quality assurance reviews.
|
GHCNd contains records from more than 100,000 stations in 180 countries and territories. NCEI provides numerous daily variables, including maximum and minimum temperature, total daily precipitation, snowfall, and snow depth. About half the stations only report precipitation. Both record length and period of record vary by station and cover intervals ranging from less than a year to more than 175 years.
|
|Historical
|}
|[https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/ Access]
{| class="wikitable"
|+temperature or heat extreme data
!
!
!
!
|-
|-
|
|EEA data<ref>https://www.eea.europa.eu/en/datahub/datahubitem-view/1e006660-816d-49da-aaa8-d44cb305efee?activeAccordion=1070108</ref>
|
|Projected number of heatwaves (2068-2100; RCP 8.5) over Europe
|
|Future
|
|[https://www.eea.europa.eu/en/datahub/datahubitem-view/1e006660-816d-49da-aaa8-d44cb305efee?activeAccordion=1070108 Access]
|-
|-
|
|NEX-GDDP-CMIP6<ref>https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6</ref>
|
|Global downscaled climate simulations generated from statistically downscaling CMIP6 General Circulation Model runs. It focuses on two "Tier 1" greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). Daily resolution.
|
|Future
|
|[https://ds.nccs.nasa.gov/thredds/catalog/AMES/NEX/GDDP-CMIP6/catalog.html Access]; [https://registry.opendata.aws/nex-gddp-cmip6/ AWS link]
|-
|-
|
|nClimGrid<ref>https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332</ref>
|
|The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. On
|
|Historical
|
|[https://registry.opendata.aws/noaa-nclimgrid/ AWS link]
|}
|}
=== Regional indices to measure heat ===
[[File:Regional climate impact driver indices.png|thumb|IPCC regional climate impact driver indices]]
== 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. <nowiki>https://doi.org/10.5194/essd-12-3469-2020</nowiki>.
* GISTEMP v4: GISTEMP Team, 2022: GISS Surface Temperature Analysis (GISTEMP), Version 4. NASA Goddard Institute for Space Studies, <nowiki>https://data.giss.nasa.gov/gistemp/</nowiki>.
* 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. <nowiki>https://doi</nowiki>. org/10.1029/2019JD032361. HadCRUT.5.0.2.0 data were obtained from <nowiki>http://www</nowiki>. metoffice.gov.uk/hadobs/hadcrut5 on 17 January 2024 and are © British Crown Copyright, Met Office 2024, provided under an Open Government Licence, <nowiki>http://www</nowiki>. 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. <nowiki>https://doi</nowiki>. 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. <nowiki>https://doi.org/10.1029/2020GL090873</nowiki>.
* 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. <nowiki>https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds</nowiki>. 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. <nowiki>https://doi.org/10.2151/jmsj.2015–001</nowiki>
* 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. <nowiki>https://doi.org/10.1038/s41561-020-0582-5</nowiki>.
* 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. <nowiki>https://doi.org/10.5194/essd-12-3469-202</nowiki>.
* 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. <nowiki>https://doi.org/10.1029/2019JD032352</nowiki>. CRUTEM.5.0.2.0 data were obtained from <nowiki>http://www.metoffice.gov.uk/hadobs/</nowiki> crutem5 on 17 January 2024 and are © British Crown Copyright, Met Office 2024, provided under an Open Government Licence, <nowiki>http://www.nationalarchives.gov.uk/doc/</nowiki> 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. <nowiki>https://doi</nowiki>. org/10.1029/2018JD029867. HadSST.4.0.1.0 data were obtained from <nowiki>http://www.metoffice</nowiki>. gov.uk/hadobs/hadsst4 on 17 January 2024 and are © British Crown Copyright, Met Office 2024, provided under an Open Government Licence, <nowiki>http://www.nationalarchives.gov.uk/</nowiki> 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
Extreme heat by NRDC https://www.nrdc.org/resources/climate-change-and-health-extreme-heat#/map https://www.nrdc.org/sites/default/files/extreme_heat_chart.pdf
Historical temperature data for all cooperative weather stations for all years were downloaded from the Global Historical Climatology Network (GHCN), formerly the National Climatic Data Center.2 Geographic detail for stations was also downloaded from GHCN, which defines cooperative stations as “U.S. stations operated by local observers which generally report max/min temperatures and precipitation. National Weather Service (NWS) data are also included in this dataset. The data receive extensive automated + manual quality control.”
Heatwaves by EPA: https://www.epa.gov/climate-indicators/climate-change-indicators-heat-waves https://www.epa.gov/climate-indicators/climate-change-indicators-high-and-low-temperatures
projection of heat extremes as well as impacts: https://www.c2es.org/content/heat-waves-and-climate-change/
heatwaves by WMO: https://wmo.int/content/climate-change-and-heatwaves
heatwaves by US global change research: https://www.globalchange.gov/indicators/heat-waves
https://www.heat.gov/
HadEX3: Gridded Temperature and Precipitation Climate Extremes Indices (CLIMDEX data) https://climatedataguide.ucar.edu/climate-data/hadex3-gridded-temperature-and-precipitation-climate-extremes-indices-climdex-data
* '''Berkeley Earth Surface Temperature Dataset:'''
** Provides global land and ocean temperature data, focusing on long-term temperature trends and extremes.
** Berkeley Earth Dataset
* '''PRISM Climate Data:'''
** High-resolution spatial climate datasets for the United States, including temperature, precipitation, and drought indices.
*
=== General Climate Extremes: ===
# '''World Meteorological Organization (WMO) Global Data:'''
#* Offers a variety of datasets on climate extremes, including temperature and precipitation records.
#* WMO Data
# '''NASA GISS Surface Temperature Analysis (GISTEMP):'''
#* Provides global temperature anomaly data, including extremes, from 1880 to the present.
#* NASA GISS Data
# '''Climate Data Store (CDS) by Copernicus:'''
#* Offers access to various climate datasets, including heatwave indicators and temperature extremes.
#* Climate Data Store


== References ==
== References ==
<references />
<references />

Latest revision as of 20:44, 24 October 2024

Extreme heat, or called "hot extremes" is a period of unusually high temperatures that exceed historical norms for a specific location. In most of the United States, it is defined as high heat and humidity with temperatures above 90 degrees lasting for at least two to three days. [1][2]In extreme heat your body works extra hard to maintain a normal temperature, which can lead to death. Extreme heat is responsible for the highest number of annual deaths among all weather-related hazards.

Extreme Heat (Source: The Conversation[3])

Changes in extreme heat with global warming

The frequency and intensity of hot extremes (including heatwaves) have increased since 1950 both globally and at regional scale, with more than 80% of AR6 regions showing similar changes. This trend is expected to persist as global warming continues. The IPCC AR6 report summarizes the projected changes in extreme heat with ongoing global warming, stating: "The frequency and intensity of hot extremes will continue to increase at global and continental scales and in nearly all inhabited regions with increasing global warming levels. This will be the case even if global warming is stabilized at 1.5°C. Relative to present-day conditions, changes in the intensity of extremes would be at least double at 2°C, and quadruple at 3°C of global warming, compared to changes at 1.5°C of global warming. The number of hot days and hot nights and the length, frequency, and/or intensity of warm spells or heatwaves will increase over most land areas. In most regions, future changes in the intensity of temperature extremes will very likely be proportional to changes in global warming, and up to two to three times larger. The highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions and in the South American Monsoon region, at about 1.5 times to twice the rate of global warming (high confidence). The highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming (high confidence). The frequency of hot temperature extreme events will very likely increase nonlinearly with increasing global warming, with larger percentage increases for rarer events."

Impacts of extreme heat

Extreme heat events, such as heat waves can cause large economic loss via reducing employee's productivity[4], increasing hospital visits, reducing crop yields, stressing livestocks, and straining infrastructure. For example, the European Environment Agency (EEA) estimates that, between 1980 and 2000, heat waves in 32 European countries cost up to $70 billion euros[5]. The total estimated damages attributed to heatwaves of 2003, 2010, 2015, and 2018 amounted to 0.3–0.5% of European GDP[6][7].

Health

  • Heat is an important environmental and occupational health hazard. Heat stress is the leading cause of weather-related deaths and can exacerbate underlying illnesses including cardiovascular disease, diabetes, mental health, asthma, and can increase the risk of accidents and transmission of some infectious diseases. Heatstroke is a medical emergency with a high-case fatality rate[8]. Between 1998 and 2017, more than 166,000 people died as a result of heat waves[7]. In Europe, heatwaves accounted for about 90 percent of weather-related mortality between 1980 and 2022, the European Environment Agency (EEA) has reported[5].

Agriculture and Livelihood

  • Extreme heat stress, often accompanied by water stress, can lead to significantly reduced crop yields or even total crop failure. High temperatures can also degrade the nutrient content of crops, diminishing the overall nutritional quality of food products. Prolonged extreme heat and drought can reduce soil fertility and, in severe cases, cause soil erosion and desertification, making the land less productive or even unsuitable for agriculture.
  • For livestock, extreme heat impacts include heat stress, water scarcity, and increased vulnerability to diseases and infections. These factors can lead to reduced feed intake, lower milk production in dairy cows, decreased weight gain, reduced reproductive performance, and in severe cases, death.

Infrastructure

  • Direct quote from IPCC AR6[9]: "Extreme heat events raise temperatures in buildings and cities already warmed by the urban heat island effect and can induce disruptions in critical infrastructure networks. Heat affects transportation infrastructure by warping roads and airport runways or buckling railways, and high temperatures reduce air density leading to aircraft take-off weight restrictions. Heat extremes increase peak cooling demand and challenge transmission and transformer capacity and may cause transmission lines to sag or fail. Thermal and nuclear electricity plants may be challenged when using warmer river waters for cooling or when mixing waste waters back into waterways without causing ecosystem impacts. Extreme temperature can also reduce photovoltaic panel efficiency"[10].

Ecosystem and Biodiversity

  • Direct quote from IPCC AR6[11]: "Heat extremes factor in mortality, morbidity and the range of some thermally sensitive ecosystem species. Combined heat and drought stress can reduce forest and grassland primary productivity and even cause tree mortality at higher extremes"[10].

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.

Data

Indices of extreme heat

Impacts and risk assessments utilize a large variety of indices and approaches tailored to evaluate the impacts of extreme heat. Table 1 below listed some of the indices used.

Table 1 Heat-related indices
Indicator Description Data Access
Heat index A combination of temperature and humidity to measure the conditions of human body's comfort. Calculation
ERA5-HEAT A complete historical reconstruction for a set of indices representing human thermal stress and discomfort in outdoor conditions. 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. This dataset represents the current state-of-the-art for bioclimatology data record production. Access
Wet Bulb Globe Temperature A useful index that measures heat stress in direct sunlight, taking many factors such as humidity, solar radiation, and wind speed into account.
Heat Risk[12] A color-numeric-based index that provides a forecast of the potential level of risk for heat-related impacts to occur over a 24-hour period. It utilizes both the high and low temperatures for a location and compares them to historical values at that location to classify those temperatures that are in the top 5% and above levels identified by the CDC heat-health data as excessive for that climate. View; Access
Heat and Health Index[13] The Heat and Health Index is the first national tool to incorporate spatially granular heat-related illness and community characteristics data to measure extreme heat vulnerability and help communities prepare for warming temperatures in a changing climate. For more details, please refer to its technical documentation. View and Access
Temperature Condition Index (TCI)[14] Using AVHRR thermal bands, TCI is used to determine stress on vegetation caused by temperatures and excessive wetness. Conditions are estimated relative to the maximum and minimum temperatures and modified to reflect different vegetation responses to temperature. Access
Heat extreme indices used in IPCC AR6 (reproduced from Table AVI.1)
  • Monthly maximum value of daily maximum temperature
  • Monthly minimum value of daily maximum temperature
  • Monthly minimum value of daily minimum temperature
  • Monthly maximum value of daily minimum temperature
  • Percentage of days when daily maximum temperature is greater than the 90th percentile
  • Percentage of days when daily maximum temperature is less than the 10th percentile
  • Percentage of days when daily minimum temperature is greater than the 90th percentile
  • Percentage of days when daily minimum temperature is less than the 10th percentile
  • Number of icing days: annual count of days when TX (daily maximum temperature) <0°C
  • Number of frost days: annual count of days when TN (daily minimum temperature) <0°C
  • Warm spell duration index: annual count of days with at least six consecutive days when TX >90th percentile
  • Cold spell duration index: annual count of days with at least six consecutive days when TN <10th percentile
  • Number of summer days: annual count of days when TX (daily maximum temperature) >25°C
  • Number of tropical nights: annual count of days when TN (daily minimum temperature) >20°C
  • Daily temperature range: monthly mean difference between TX and TN
  • Growing season length: annual (1 Jan to 31 Dec in Northern Hemisphere (NH), 1 July to 30 June in Southern Hemisphere (SH)) count between first span of at least six days with daily mean temperature TG >5°C and first span after July 1 (Jan 1 in SH) of six days with TG <5°C
  • One-in-20 year return value of monthly maximum value of daily maximum temperature
  • One-in-20 year return value of monthly minimum value of daily maximum temperature
  • One-in-20 year return value of monthly minimum value of daily minimum temperature
  • One-in-20 year return value of monthly maximum value of daily minimum temperature
 Some of these indices are included in the Interactive Atlas of IPCC. All of them can be calculated using temperature data listed below; methods of calculation are provided in Annex VI of IPCC AR6.
HadEX3[15] Land-based surface climate extremes indices covering 1901 to 2018 on a 1.25° x 1.875° grid. It is produced through the coordination of the joint WMO CCl/WCRP/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) and the WMO Expert Team on Sector-specific indices (ET-SCI). It currently comprises of over 80 indices of temperature and precipitation, including the indices used in IPCC AR6 listed above. Access

Temperature datasets

Table 2 Datasets of temperature
Indicator Description Temporal Position Data Access
Berkeley Earth Surface Temperature[16][17] Berkeley Earth provides high-resolution land and ocean time series data and gridded temperature data. It incorporates more temperature observations than other available products, and often has better coverage. Global datasets begin in 1850, with some land-only areas reported back to 1750. The newest generation of the dataset is augmented by machine learning techniques to improve the spatial resolution. Historical Access
GISTEMP[18] The GISS Surface Temperature Analysis version 4 (GISTEMP v4) is an estimate of global surface temperature change. Graphs and tables are updated around the middle of every month using current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas). Historical Access
HadCRUT5[19] HadCRUT is a global temperature dataset that combines the CRUTEM land surface temperature data with the HadSST sea surface temperature data. It does not involve interpolation, which results in significant coverage gaps across certain regions. It is the longest global temperature dataset, extending back to 1850. Historical Access
CRUTEM5[20] CRUTEM is a gridded dataset of observed near-surface air temperature anomalies over land, dating back to 1850. Historical Access
NOAAGlobalTemp v6.0[21] This global temperature dataset integrates long-term sea surface (water) temperature and land surface (air) temperature records. It is used to support climate monitoring activities, including the Monthly Global Climate Assessment, and serves as input data for various climate models. Historical Access
ERA5[22] ERA5 is a reanalysis dataset that integrates extensive historical observations from diverse sources into global, gridded estimates using advanced modeling and data assimilation systems. It delivers hourly estimates for a wide range of atmospheric, land, and oceanic climate variables at a spatial resolution of 31 km. Historical Access; AWS access
JRA-55[23] JRA-55 is also a reanalysis datasets that spans 1958 to present. It offers several versions at different resolution, with the finest as 0.562 degree. Historical Access
GHCNv4[24] The Global Historical Climatology Network (GHCN) is the core global land surface air temperature dataset used for climate monitoring and assessment activities. It contains temperature of over 25,000 stations across the globe. Historical Access;

AWS link

PRISM[25] PRISM datasets provide estimates of seven primary climate elements (precipitation, minimum temperature, maximum temperature, mean dew point, minimum vapor pressure deficit, maximum vapor pressure deficit, and total global shortwave solar radiation on a horizontal surface) over the US using climate observations from a wide range of monitoring networks. It is available at both 800 m and 4 km. Historical Access
GHCNd[26] The Global Historical Climatology Network daily (GHCNd) is an integrated database of daily climate summaries from land surface stations across the globe. GHCNd is made up of daily climate records from numerous sources that have been integrated and subjected to a common suite of quality assurance reviews.

GHCNd contains records from more than 100,000 stations in 180 countries and territories. NCEI provides numerous daily variables, including maximum and minimum temperature, total daily precipitation, snowfall, and snow depth. About half the stations only report precipitation. Both record length and period of record vary by station and cover intervals ranging from less than a year to more than 175 years.

Historical Access
EEA data[27] Projected number of heatwaves (2068-2100; RCP 8.5) over Europe Future Access
NEX-GDDP-CMIP6[28] Global downscaled climate simulations generated from statistically downscaling CMIP6 General Circulation Model runs. It focuses on two "Tier 1" greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). Daily resolution. Future Access; AWS link
nClimGrid[29] The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. On Historical AWS link

References

  1. ↑ https://www.ready.gov/heat#:~:text=There%20is%20hot%2C%20and%20then,which%20can%20lead%20to%20death.
  2. ↑ https://community.fema.gov/ProtectiveActions/s/article/Extreme-Heat
  3. ↑ Retrieved from https://theconversation.com/extreme-heat-is-a-threat-to-lives-in-africa-but-its-not-being-monitored-149921 on Oct. 24, 2024
  4. ↑ Chavaillaz, Y., Roy, P., Partanen, AI. et al. Exposure to excessive heat and impacts on labour productivity linked to cumulative CO2 emissions. Sci Rep 9, 13711 (2019). https://doi.org/10.1038/s41598-019-50047-w
  5. ↑ 5.0 5.1 https://phys.org/news/2022-06-deadly-heatwaves-threaten-economies.html
  6. ↑ García-León, D., Casanueva, A., Standardi, G. et al. Current and projected regional economic impacts of heatwaves in Europe. Nat Commun 12, 5807 (2021). https://doi.org/10.1038/s41467-021-26050-z
  7. ↑ 7.0 7.1 https://www.weforum.org/agenda/2022/07/heat-waves-economy-climate-crisis/
  8. ↑ https://www.who.int/news-room/fact-sheets/detail/climate-change-heat-and-health
  9. ↑ IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 35-115, doi: 10.59327/IPCC/AR6-9789291691647.
  10. ↑ 10.0 10.1 https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12/
  11. ↑ IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 35-115, doi: 10.59327/IPCC/AR6-9789291691647.
  12. ↑ https://www.wpc.ncep.noaa.gov/heatrisk/
  13. ↑ https://ephtracking.cdc.gov/Applications/heatTracker/
  14. ↑ https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/VH-Syst_10ap30.php
  15. ↑ https://www.metoffice.gov.uk/hadobs/hadex3/
  16. ↑ Cowtan, Kevin & National Center for Atmospheric Research Staff (Eds). Last modified 2023-08-08 "The Climate Data Guide: Global surface temperatures: BEST: Berkeley Earth Surface Temperatures.” Retrieved from https://climatedataguide.ucar.edu/climate-data/global-surface-temperatures-best-berkeley-earth-surface-temperatures on 2024-08-22.
  17. ↑ https://berkeleyearth.org/data/
  18. ↑ GISTEMP Team, 2024: . NASA Goddard Institute for Space Studies. Dataset accessed 2024-08-23 at https://data.giss.nasa.gov/gistemp/.
  19. ↑ https://www.metoffice.gov.uk/hadobs/hadcrut5/
  20. ↑ https://www.metoffice.gov.uk/hadobs/crutem5/
  21. ↑ https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp
  22. ↑ https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5
  23. ↑ https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55
  24. ↑ Menne, Matthew J.; Gleason, Byron E.; Lawrimore, Jay; Rennie, Jared; and Williams, Claude N. (2017): Global Historical Climatology Network - Monthly Temperature [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V5XW4GTH [2024-08-23].
  25. ↑ https://prism.oregonstate.edu/
  26. ↑ https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily
  27. ↑ https://www.eea.europa.eu/en/datahub/datahubitem-view/1e006660-816d-49da-aaa8-d44cb305efee?activeAccordion=1070108
  28. ↑ https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6
  29. ↑ https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332
Retrieved from "http://climateriskwiki.uw.edu/index.php?title=Extreme_Heat&oldid=1709"