Lead Image
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Lead Image Caption
Metadata
DPSIR
State
Typology
Performance indicator (Type B - Does it matter?)
UN SDGs
SDG13: Climate action
Topics
Climate change adaptation
Temporal coverage
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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},
{
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"value": "1900"
},
{
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"value": "1901"
},
{
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},
{
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{
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{
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},
{
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},
{
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},
{
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},
{
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{
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{
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},
{
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},
{
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"value": "1913"
},
{
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"value": "1914"
},
{
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"value": "1915"
},
{
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"value": "1916"
},
{
"label": "1917",
"value": "1917"
},
{
"label": "1918",
"value": "1918"
},
{
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"value": "1919"
},
{
"label": "1920",
"value": "1920"
},
{
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"value": "1921"
},
{
"label": "1922",
"value": "1922"
},
{
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"value": "1923"
},
{
"label": "1924",
"value": "1924"
},
{
"label": "1925",
"value": "1925"
},
{
"label": "1926",
"value": "1926"
},
{
"label": "1927",
"value": "1927"
},
{
"label": "1928",
"value": "1928"
},
{
"label": "1929",
"value": "1929"
},
{
"label": "1930",
"value": "1930"
},
{
"label": "1931",
"value": "1931"
},
{
"label": "1932",
"value": "1932"
},
{
"label": "1933",
"value": "1933"
},
{
"label": "1934",
"value": "1934"
},
{
"label": "1935",
"value": "1935"
},
{
"label": "1936",
"value": "1936"
},
{
"label": "1937",
"value": "1937"
},
{
"label": "1938",
"value": "1938"
},
{
"label": "1939",
"value": "1939"
},
{
"label": "1940",
"value": "1940"
},
{
"label": "1941",
"value": "1941"
},
{
"label": "1942",
"value": "1942"
},
{
"label": "1943",
"value": "1943"
},
{
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"value": "1944"
},
{
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"value": "1945"
},
{
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"value": "1946"
},
{
"label": "1947",
"value": "1947"
},
{
"label": "1948",
"value": "1948"
},
{
"label": "1949",
"value": "1949"
},
{
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"value": "1950"
},
{
"label": "1951",
"value": "1951"
},
{
"label": "1952",
"value": "1952"
},
{
"label": "1953",
"value": "1953"
},
{
"label": "1954",
"value": "1954"
},
{
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"value": "1955"
},
{
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"value": "1956"
},
{
"label": "1957",
"value": "1957"
},
{
"label": "1958",
"value": "1958"
},
{
"label": "1959",
"value": "1959"
},
{
"label": "1960",
"value": "1960"
},
{
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"value": "1961"
},
{
"label": "1962",
"value": "1962"
},
{
"label": "1963",
"value": "1963"
},
{
"label": "1964",
"value": "1964"
},
{
"label": "1965",
"value": "1965"
},
{
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"value": "1966"
},
{
"label": "1967",
"value": "1967"
},
{
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"value": "1968"
},
{
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"value": "1969"
},
{
"label": "1970",
"value": "1970"
},
{
"label": "1971",
"value": "1971"
},
{
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"value": "1972"
},
{
"label": "1973",
"value": "1973"
},
{
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"value": "1974"
},
{
"label": "1975",
"value": "1975"
},
{
"label": "1976",
"value": "1976"
},
{
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"value": "1977"
},
{
"label": "1978",
"value": "1978"
},
{
"label": "1979",
"value": "1979"
},
{
"label": "1980",
"value": "1980"
},
{
"label": "1981",
"value": "1981"
},
{
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"value": "1982"
},
{
"label": "1983",
"value": "1983"
},
{
"label": "1984",
"value": "1984"
},
{
"label": "1985",
"value": "1985"
},
{
"label": "1986",
"value": "1986"
},
{
"label": "1987",
"value": "1987"
},
{
"label": "1988",
"value": "1988"
},
{
"label": "1989",
"value": "1989"
},
{
"label": "1990",
"value": "1990"
},
{
"label": "1991",
"value": "1991"
},
{
"label": "1992",
"value": "1992"
},
{
"label": "1993",
"value": "1993"
},
{
"label": "1994",
"value": "1994"
},
{
"label": "1995",
"value": "1995"
},
{
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"value": "1996"
},
{
"label": "1997",
"value": "1997"
},
{
"label": "1998",
"value": "1998"
},
{
"label": "1999",
"value": "1999"
},
{
"label": "2000",
"value": "2000"
},
{
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"value": "2001"
},
{
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"value": "2002"
},
{
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"value": "2003"
},
{
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"value": "2004"
},
{
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"value": "2005"
},
{
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"value": "2006"
},
{
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"value": "2007"
},
{
"label": "2008",
"value": "2008"
},
{
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"value": "2009"
},
{
"label": "2010",
"value": "2010"
},
{
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"value": "2011"
},
{
"label": "2012",
"value": "2012"
},
{
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"value": "2013"
},
{
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"value": "2014"
},
{
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"value": "2015"
},
{
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"value": "2016"
},
{
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"value": "2017"
},
{
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"value": "2018"
},
{
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"value": "2019"
},
{
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"value": "2020"
},
{
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"value": "2021"
},
{
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},
{
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},
{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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Geographic coverage
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Workflow
Content responsible Head of Group
Blaz Kurnik
Layout
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Supporting information
Methodology
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"children": [
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"text": "Methodology for indicator calculation"
}
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"type": "h4"
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"children": [
{
"text": "The following global meteorological datasets have been used to compute the time series of global mean temperature and European land temperature:"
}
],
"type": "p"
},
{
"children": [
{
"text": "\u00b7 "
},
{
"children": [
{
"text": "HadCRUT5"
}
],
"data": {
"footnote": "<?xml version=\"1.0\"?>\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; \">\n <div class=\"csl-entry\">Morice, C. P., Kennedy, J. J., Rayner, N. A., Winn, J. P., Hogan, E., Killick, R. E., Dunn, R. J. H., Osborn, T. J., Jones, P. D. and Simpson, I. R., 2021, 'An Updated Assessment of Near-Surface Temperature Change From 1850: The HadCRUT5 Data Set', <i>Journal of Geophysical Research: Atmospheres</i> 126(3), pp. e2019JD032361 (https://onlinelibrary.wiley.com/doi/abs/10.1029/2019JD032361) accessed December 14, 2021.</div>\n</div>\n",
"footnoteTitle": "Morice, C. P., 2021, An Updated Assessment of Near-Surface Te, Journal of Geophysical Research: Atmospheres",
"uid": "pdOJ7",
"zoteroId": "JTPS7SDH"
},
"type": "zotero"
},
{
"text": ": This dataset is a collaborative product of the Met Office Hadley Centre and the Climatic Research Unit (CRU) of the University of East Anglia. HadCRUT5 is a combination of sea-surface temperature (SST) measurements over the ocean from ships and buoys and near-surface air temperature measurements from weather stations over the land surface."
}
],
"type": "p"
},
{
"children": [
{
"text": "\u00b7 "
},
{
"children": [
{
"text": "NOAA Global Temp v5"
}
],
"data": {
"footnote": "<?xml version=\"1.0\"?>\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; \">\n <div class=\"csl-entry\">Karl, T. R., Arguez, A., Huang, B., Lawrimore, J. H., McMahon, J. R., Menne, M. J., Peterson, T. C., Vose, R. S. and Zhang, H.-M., 2015, 'Possible artifacts of data biases in the recent global surface warming hiatus', <i>Science</i> 348(6242), pp. 1469–1472 (http://www.sciencemag.org/content/348/6242/1469.abstract).</div>\n</div>\n",
"footnoteTitle": "Karl, Thomas R., June 26, 2015, Possible artifacts of data biases in the, Science",
"uid": "xfmET",
"zoteroId": "RHA26UEN"
},
"type": "zotero"
},
{
"text": ""
},
{
"children": [
{
"text": " "
}
],
"data": {
"footnote": "<?xml version=\"1.0\"?>\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; \">\n <div class=\"csl-entry\">Zhang, H.-M., Lawrimore, J., Huang, B., Menne, M., Yin, X., S�nchez-Lugo, A., Gleason, B., Vose, R., Arndt, D., Rennie, J. and Williams, C., 2019, 'Updated Temperature Data Give a Sharper View of Climate Trends', <i>Eos</i> 100 (https://eos.org/science-updates/updated-temperature-data-give-a-sharper-view-of-climate-trends) accessed September 7, 2020.</div>\n</div>\n",
"footnoteTitle": "Zhang, Huai-Min, 2019-07-19, Updated Temperature Data Give a Sharper , Eos",
"uid": "4ETaK",
"zoteroId": "M4FG8MVZ"
},
"type": "zotero"
},
{
"text": ": This dataset is a product of the National Centre for Environmental Information of the U.S. National Oceanic and Atmospheric Administration (NOAA)."
}
],
"type": "p"
},
{
"children": [
{
"text": "\u00b7 "
},
{
"children": [
{
"text": "GISTEMP v4"
}
],
"data": {
"footnote": "<?xml version=\"1.0\"?>\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; \">\n <div class=\"csl-entry\">Lenssen, N. J. L., Schmidt, G. A., Hansen, J. E., Menne, M. J., Persin, A., Ruedy, R. and Zyss, D., 2019, 'Improvements in the GISTEMP Uncertainty Model', <i>Journal of Geophysical Research: Atmospheres</i> 124(12), pp. 6307–6326 (https://onlinelibrary.wiley.com/doi/abs/10.1029/2018JD029522) accessed September 7, 2020.</div>\n</div>\n",
"footnoteTitle": "Lenssen, Nathan J. L., 2019-06-27, Improvements in the GISTEMP Uncertainty , Journal of Geophysical Research: Atmospheres",
"uid": "W0Xf1",
"zoteroId": "3ZJW8S72"
},
"type": "zotero"
},
{
"text": ": This dataset is a product of the NASA Goddard Institute for Space Studies (GISS)."
}
],
"type": "p"
},
{
"children": [
{
"text": "The temperature anomalies from the original datasets were adjusted here to the \u2018pre-industrial\u2019 period between 1850 and 1899."
}
],
"type": "p"
},
{
"children": [
{
"text": "\u00b7 "
},
{
"children": [
{
"text": "Berkeley Earth"
}
],
"data": {
"url": "https://berkeleyearth.org/data/"
},
"type": "link"
},
{
"text": ": temperature dataset produced by Berkeley Earth; an independent U.S. non-profit organization focused on environmental data science."
}
],
"type": "p"
},
{
"children": [
{
"text": "\u00b7 ERA5 Dataset produced by ECMWF and available in the Climate Data Store. ERA5\nis the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. Currently data is available from 1950. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset."
}
],
"type": "p"
},
{
"children": [
{
"text": "\u00b7 "
},
{
"children": [
{
"text": "JRA-55: The Japan Meteorological Agency (JMA)"
}
],
"data": {
"footnote": "<?xml version=\"1.0\"?>\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; \">\n <div class=\"csl-entry\">Shinya Kobayashi, Chiaki Kobayashi, Yayoi Harada, Masami Moriya, Ayataka Ebita, Hirokatsu Onoda, Yukinari Ota, Kiyotoshi Takahashi, Hirotaka Kamahori, Hirokazu Endo, Kengo Miyaoka and Kazutoshi Onogi, 2015, 'The JRA-55 Reanalysis: General Specifications and Basic Characteristics', <i>Meteorological Society of Japan</i>.</div>\n</div>\n",
"footnoteTitle": "Shinya Kobayashi, 2015, The JRA-55 Reanalysis: General Specifica, Meteorological Society of Japan",
"uid": "48AX4"
},
"type": "zotero"
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"text": " conducted the second Japanese global atmospheric reanalysis, called the Japanese 55-year Reanalysis or JRA-55. It covers the period from 1958, when regular radiosonde observations began on a global basis."
}
],
"type": "p"
},
{
"children": [
{
"text": " "
}
],
"type": "p"
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{
"children": [
{
"text": "These datasets were provided by the "
},
{
"children": [
{
"text": "Copernicus Climate Change Service"
}
],
"data": {
"url": "https://climate.copernicus.eu/climate-indicators/about-data#Temperatureindicator"
},
"type": "link"
},
{
"text": " (C3S), following the developed methodology to relate recent (1991-2020) global temperature to 1850-1900, a period taken to represent the pre-industrial level. New and updated global temperature datasets have been recently published, which has resulted in a new estimate for the latest 30-year reference period 1991-2020, defined by the World Meteorological Organization (WMO). The new approach for monitoring global temperature change since the 1850-1900 period is being used in the WMO statements on 'The state of the global climate' from the\u00a0"
},
{
"children": [
{
"text": "Preliminary Statement for 2021"
}
],
"data": {
"url": "https://library.wmo.int/doc_num.php?explnum_id=10859"
},
"type": "link"
},
{
"text": "\u00a0onwards (see details there, under 'Datasets and methods \u2013 Global temperature data'). This approach results in a best estimate of 0.68\u00b0C with an uncertainty range (0.54 to 0.78\u00b0C) to relate the standard WMO reference period 1981-2010 to 1850-1900. Extending this approach to the WMO reference period 1991-2010 gives a best estimate of 0.88\u00b0C with an uncertainty range (0.72-0.99\u00b0C), which sums the 0.68\u00baC of additional warming from pre-industrial to the 1981-2010 period, the 0.19\u00baC difference from 1981-2010 to 1991-2020, according to ERA5, and an adjustment of 0.01\u00baC for consistency with new estimates documented by the "
},
{
"children": [
{
"text": "Sixth IPCC Assessment Report"
}
],
"data": {
"url": "https://www.ipcc.ch/report/ar6/wg1/"
},
"type": "link"
},
{
"text": ". In the datasets here provided by C3S this method has been used, by\u00a0calculating\u00a0the\u00a0anomalies\u00a0of each dataset\u00a0relative to\u00a0its own\u00a0average\u00a0for 1981-2010 and adding the offset of 0.88\u00b0C, to then relate it to 1850-1900."
}
],
"type": "p"
},
{
"children": [
{
"text": " "
}
],
"type": "p"
},
{
"children": [
{
"text": "IPCC estimates are not available for the all-land, European and Arctic regions for which C3S provides temperature indicators. For these cases, the changes from 1850-1900 are based on estimates of the differences between 1991-2020 and 1850-1900 averages derived from the Berkeley Earth, GISTEMPv4, HadCRUT5 and NOAAGlobalTempv5 datasets.\u00a0"
}
],
"type": "p"
},
{
"children": [
{
"text": " "
}
],
"type": "p"
},
{
"children": [
{
"text": "Spatially explicit temperature trends in Europe are derived from ERA5-Land reanalysis"
},
{
"children": [
{
"text": " "
}
],
"data": {
"footnote": "<?xml version=\"1.0\"?>\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; \">\n <div class=\"csl-entry\">Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C. and Thépaut, J.-N., 2021, 'ERA5-Land: a state-of-the-art global reanalysis dataset for land applications', <i>Earth System Science Data</i> 13(9), pp. 4349–4383 (https://essd.copernicus.org/articles/13/4349/2021/) accessed May 18, 2025.</div>\n</div>\n",
"footnoteTitle": "Mu\u00f1oz-Sabater, Joaqu\u00edn, 2021-09-07, ERA5-Land: a state-of-the-art global rea, Earth System Science Data",
"uid": "PRpg9",
"zoteroId": "A9YJNQS2"
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"type": "zotero"
},
{
"text": ". ERA5-Land provides high resolution information of surface variables over global land areas. It is produced by the European Centre for Medium-Range Weather Foreacasts (ECMWF). The dataset covers the period from 1950 on. Trends are calculated using the annual mean and then applying an ordinary least squares regression using the model y=a+b*x+e, where e is assumed to be a first order autoregressive process. The significance is computed from a two-sided Student\u2019s test for the null hypothesis of no trend. The code used to extract the trend and significance results is the same as in the "
},
{
"children": [
{
"text": "IPCC Atlas"
}
],
"data": {
"url": " https://github.com/SantanderMetGroup/ATLAS"
},
"type": "link"
},
{
"text": ". The underlying temperature data is a model result coming from a replay of the land component of the ERA5 climate reanalysis. ERA5-Land comes with a series of improvements, including higher spatial resolution, making it more accurate for all types of land applications.\u00a0Trend analyses\u00a0should be treated with caution. "
}
],
"type": "p"
},
{
"children": [
{
"text": " "
}
],
"type": "p"
},
{
"children": [
{
"text": "The projected changes in European near-surface air temperature (\u00b0C) are based on the multi-model ensemble average of GCM simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) initiative. CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP)."
}
],
"type": "p"
},
{
"children": [
{
"text": "Further information on all these datasets is available from the cited publications."
}
],
"type": "p"
},
{
"type": "p",
"children": [
{
"text": ""
}
]
},
{
"type": "p",
"children": [
{
"text": "For more past and future changes the "
},
{
"type": "link",
"data": {
"url": "https://climate-adapt.eea.europa.eu/en/knowledge/european-climate-data-explorer"
},
"children": [
{
"text": "European Climate Data Explorer "
}
]
},
{
"text": "(ECDE) provides interactive access to a growing selection of climate indices reflecting the priorities of the European Environment Agency (EEA). The underlying data is from the Climate Data store (CDS) of the Copernicus climate change service (C3S). Access the indices below according to the related themes and sectors."
}
]
}
]
Data sources and providers
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"@id": "90f6e9b5-a3c2-4da4-95f5-05119e139246",
"title": "HadCRUT5 ",
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"link": "https://www.metoffice.gov.uk/hadobs/hadcrut5/"
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{
"@id": "9af008d5-d0c1-4648-b3fb-1777574ca5fb",
"title": "NOAAGlobalTempv6 (version 6.0.0)",
"organisation": "NOAA's National Centers for Environmental",
"link": "https://www.ncei.noaa.gov/data/noaa-global-surface-temperature/v6/access/gridded/"
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{
"@id": "17bf4251-71d9-4ac6-bd63-fc7cc6fcaf96",
"title": "GISTEMPv4",
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"link": "https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv"
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"@id": "8b87ce54-79fa-4750-b165-232c1f63e2b6",
"title": "ERA5 monthly averaged data on single levels from 1940 to present",
"organisation": "The Copernicus Climate Change Service (C3S), European Centre for Medium-Range Weather Forecasts (ECMWF)",
"link": "https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=form"
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"@id": "bcfaa38b-2742-405b-810e-94943e0ce78a",
"title": "Japanese Reanalysis for Three Quarters of a Century",
"organisation": "Japan Meteorological Agency (JMA), The Copernicus Climate Change Service (C3S)",
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"title": "Berkeley Earth",
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"title": "Monthly mean temperature, ERA5-Land from Jan. 1960 to Dec. 2024",
"link": "https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means?tab=download",
"organisation": "European Centre of Medium-Range Weather Forecasts (ECMWF)"
},
{
"@id": "2ab8d928-3bd1-44a6-b201-3855708e2755",
"title": "CMIP6 - Mean temperature (T) Change deg C - Long Term (2081-2100) SSP1-2.6 (rel. to 1981-2010) - Annual (32 models)",
"link": "https://interactive-atlas.ipcc.ch/regional-information#eyJ0eXBlIjoiQVRMQVMiLCJjb21tb25zIjp7ImxhdCI6Mzc1ODEsImxuZyI6NDAwNjkyLCJ6b29tIjo0LCJwcm9qIjoiRVBTRzo1NDAzMCIsIm1vZGUiOiJjb21wbGV0ZV9hdGxhcyJ9LCJwcmltYXJ5Ijp7InNjZW5hcmlvIjoic3NwMTI2IiwicGVyaW9kIjoiZmFyIiwic2Vhc29uIjoieWVhciIsImRhdGFzZXQiOiJDTUlQNiIsInZhcmlhYmxlIjoidGFzIiwidmFsdWVUeXBlIjoiQU5PTUFMWSIsImhhdGNoaW5nIjoiU0lNUExFIiwicmVnaW9uU2V0IjoiYXI2IiwiYmFzZWxpbmUiOiJXTU8yIiwicmVnaW9uc1NlbGVjdGVkIjpbXX0sInBsb3QiOnsiYWN0aXZlVGFiIjoicGx1bWUiLCJtYXNrIjoibm9uZSIsInNjYXR0ZXJZTWFnIjpudWxsLCJzY2F0dGVyWVZhciI6bnVsbCwic2hvd2luZyI6ZmFsc2V9fQ==",
"organisation": "Intergovernmental Panel for Climate Change (IPCC)"
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{
"@id": "4cbc584d-5405-429f-8556-f0134ba7e988",
"title": "CMIP6 - Mean temperature (T) Change deg C - Long Term (2081-2100) SSP5-8.5 (rel. to 1981-2010) - Annual (34 models)",
"link": "https://interactive-atlas.ipcc.ch/regional-information#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",
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Definition
[
{
"children": [
{
"text": "This indicator shows observed and projected changes in annual average near-surface temperature globally and for Europe. Europe is defined here as the land area in the range 34\u00b0 to 72\u00b0 northern latitude and -25\u00b0 to 45\u00b0 eastern longitude."
}
],
"type": "p"
},
{
"children": [
{
"text": ""
}
],
"type": "p"
},
{
"children": [
{
"text": "Temperature anomalies are presented relative to a \u2018pre-industrial\u2019 period between 1850 and 1899 (the beginning of instrumental temperature records). During this period, greenhouse gases from the industrial revolution are considered to have had a relatively small influence on the global climate compared with natural influences."
}
],
"type": "p"
}
]
Unit of measure
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{
"children": [
{
"text": "The units used in this indicator are degrees Celsius (\u00b0C) and degrees Celsius per decade (\u00b0C/decade)."
}
],
"type": "p"
},
{
"children": [
{
"text": ""
}
],
"type": "p"
},
{
"children": [
{
"text": "Temperature anomalies are presented relative to a \u2018pre-industrial\u2019 period between 1850 and 1899\u00a0(the beginning of instrumental temperature records)."
}
],
"type": "p"
}
]
Policy / environmental relevance
[
{
"children": [
{
"text": "Near-surface air temperature gives one of the clearest signals of global and regional climate change. Anthropogenic influence, mainly through emissions of greenhouse gases, is responsible for most of the observed increase in global mean temperature (GMT) in recent decades. For these reasons, GMT has been chosen as the indicator to monitor the 'ultimate objective' of the United Nations Framework Convention on Climate Change (UNFCCC). "
}
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"text": "The Paris Agreement adopted in December 2015 defines the long-term goal to 'hold the increase in the global average temperature to well below 2\u00b0C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5\u00b0C above pre-industrial levels, since this would significantly "
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"text": ". The need to limit the increase in GMT in accordance with the goals of the UNFCCC is also recognised in the Sendai Framework for Disaster Risk Reduction 2015-2030 and in "
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"text": "Goal\u00a013 of the 2030 Agenda for Sustainable development"
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"footnote": "<?xml version=\"1.0\"?>\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; \">\n <div class=\"csl-entry\">UN, 2015, Resolution adopted by the General Assembly on 25 September 2015: transforming our world: the 2030 agenda for sustainable development, A/RES/70/1</div>\n</div>\n",
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"text": "Rising mean temperatures are also increasing the frequency and severity of heatwaves globally and in Europe."
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Frequency of dissemination
1
Accuracy and uncertainties
[
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"text": "No uncertainties have been specified"
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Settings
Short name
global-and-european-temperatures
Versioning enabled
yes
Contents
Above chart: Global annual averages of near-surface temperature of land and ocean expressed as the anomaly relative to the pre-industrial period 1850-1900 according to the datasets used by the Copernicus Climate Change Service (C3S): ERA5 (C3S/ECMWF), JRA-55 (JMA), GISTEMPv4 (NASA), HadCRUT5 (Met Office Hadley Centre), NOAAGlobalTempv6 (NOAA) and Berkeley Earth. Below chart: European annual averages of near-surface temperature expressed as the anomaly relative to the pre-industrial period 1850-1900 according to the datasets used by the Copernicus Climate Change Service (C3S): ERA5 (C3S/ECMWF), JRA-55 (JMA), GISTEMPv4 (NASA), HadCRUT5 (Met Office Hadley Centre), NOAAGlobalTempv6 (NOAA) and Berkeley Earth.
Left panel: Temperature trend from reanalysis dataset ERA5-Land produced by European Centre of Medium-Range Weather Forecasts (ECMWF). Right panel: Projected temperature change between the WMO reference period 1981-2010 and the end of the 21 st century (period 2081-2100) under the scenario SSP1-2.6, and projected temperature change between the WMO reference period 1981-2010 and the end of the 21 st century (period 2081-2100) under the scenario SSP5-8.5.
