Lead Image
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Lead Image Caption
Metadata
DPSIR
Impact
Typology
Descriptive indicator (Type A - What is happening to the environment and to humans?)
UN SDGs
SDG13: Climate action
Topics
Climate change adaptation , Seas and coasts , Extreme weather
Temporal coverage
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"value": "1859"
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"value": "1860"
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"value": "1861"
},
{
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},
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},
{
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"value": "1866"
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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": "1888"
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{
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"value": "1889"
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{
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"value": "1890"
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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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},
{
"label": "1930",
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},
{
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},
{
"label": "1932",
"value": "1932"
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{
"label": "1933",
"value": "1933"
},
{
"label": "1934",
"value": "1934"
},
{
"label": "1935",
"value": "1935"
},
{
"label": "1936",
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},
{
"label": "1937",
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{
"label": "1938",
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{
"label": "1939",
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},
{
"label": "1940",
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{
"label": "1941",
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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": "1957"
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{
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"value": "1958"
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{
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"value": "1959"
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{
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"value": "1960"
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{
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"value": "1961"
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{
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"value": "1962"
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{
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"value": "1963"
},
{
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"value": "1964"
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{
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"value": "1965"
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{
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{
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{
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{
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"value": "1969"
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{
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"value": "1970"
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{
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{
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"value": "1972"
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{
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"value": "1973"
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{
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"value": "1974"
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{
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"value": "1975"
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{
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"value": "1976"
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{
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"value": "1977"
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{
"label": "1978",
"value": "1978"
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{
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"value": "1979"
},
{
"label": "1980",
"value": "1980"
},
{
"label": "1981",
"value": "1981"
},
{
"label": "1982",
"value": "1982"
},
{
"label": "1983",
"value": "1983"
},
{
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"value": "1984"
},
{
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"value": "1985"
},
{
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"value": "1986"
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{
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"value": "1987"
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{
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"value": "1988"
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{
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"value": "1989"
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{
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"value": "1990"
},
{
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"value": "1991"
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{
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"value": "1992"
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{
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"value": "1993"
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{
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"value": "1994"
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{
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"value": "1995"
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{
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"value": "1996"
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{
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"value": "1997"
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{
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"value": "1998"
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{
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"value": "1999"
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{
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"value": "2000"
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{
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"value": "2002"
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"value": "2004"
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{
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{
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{
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"value": "2007"
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{
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"value": "2008"
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{
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"value": "2009"
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{
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"value": "2010"
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"value": "2011"
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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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"plaintext": "This increase has had considerable ecological impacts , including promoting harmful algal blooms, escalating risks to human health, ecosystems and aquaculture . For example, recent marine heatwaves have led to unprecedented levels of vibriosis infections along the Baltic Sea and North Sea coasts . Marine heatwaves can also affect climate on land, with those in the Mediterranean Sea possibly attributing to amplifying heatwaves and heavy precipitation events over central Europe and triggering intense extratropical cyclones .",
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"text": ". For example, recent marine heatwaves have led to unprecedented levels of vibriosis infections along the "
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"text": ". Marine heatwaves can also affect climate on land, with those in the Mediterranean Sea possibly attributing to amplifying heatwaves and heavy precipitation events over central Europe and triggering intense "
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"plaintext": "All five European seas have warmed considerably since 1870, particularly since the late 1970s. During 1991 and 2024, SST increased by between around 0.3\u00b0C per decade in the North Sea and around 0.5\u00b0C per decade in the Black Sea. Over the past century (1925-2016), the increase in SST has been accompanied by an increase in the frequency and intensity of marine heatwaves, both globally and in European seas, with an approximate doubling from 1982 to 2016 .",
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"text": " considerably since 1870, particularly since the late 1970s. During 1991 and 2024, SST increased by between around 0.3\u00b0C per decade in the North Sea and around 0.5\u00b0C per decade in the Black Sea. Over the past century (1925-2016), the increase in SST has been accompanied by an increase in the frequency and intensity of marine heatwaves, both globally and in European seas, with an approximate doubling from 1982 to 2016"
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Supporting information
Methodology
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"text": "This methodology is extracted from Copernicus Climate Change Service ("
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"text": "This indicator primarily uses information from the HadISST1"
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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\">Rayner, N. A., Parker, D. E. and Horton, E. B., 2003, 'Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century', <i>Journal of Geophysical Research</i> 108(D14), 4407.</div>\n</div>\n",
"footnoteTitle": "Rayner, N. A., 2003, Global analyses of sea surface temperatu, Journal of Geophysical Research",
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"text": ", HadSST4"
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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\">Kennedy, J. J., Rayner, N. A. and Atkinson, C. P., 2019, 'An ensemble data set of sea‐surface temperature change from 1850: the Met Office Hadley Centre HadSST.4.0.0.0 data set', <i>Journal of Geophysical Research: Atmospheres</i> 124(14), pp. 7719–7763.</div>\n</div>\n",
"footnoteTitle": "Kennedy, J. J., 2019, An ensemble data set of sea\u2010surface temp, Journal of Geophysical Research: Atmospheres",
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"text": ", Extended Reconstruction Sea Surface Temperature version 5 (ERSSTv5)"
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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\">Huang, B., Thorne, P. W. and Banzon, B. F., 2017, 'Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, validations, and intercomparisons', <i>Journal of Climate</i> 30(20), pp. 8179–8205.</div>\n</div>\n",
"footnoteTitle": "Huang, B., 2017, Extended Reconstructed Sea Surface Tempe, Journal of Climate",
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"text": " and the DMI v1 dataset, which is a new dataset that has been produced for C3S by DMI."
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"text": "Anomalies are calculated relative to a 1991\u20132020 average. For the in situ datasets, anomalies are calculated on a monthly basis by subtracting the 1991\u20132020 mean anomaly (relative to the original baseline used by the dataset) for each month. Daily anomalies were aggregated to monthly anomalies, and the monthly anomalies were aggregated to annual anomalies giving each month an equal weight. "
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"text": "Area-averaged anomalies were calculated using an area-weighted average of non-missing grid cells within the chosen region. A grid cell was assumed to be within a region if its center was within the region. Ocean area in the "
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"text": " products was estimated based on the high-resolution DMI satellite product, assigning 100% ocean area to grid cells populated in the satellite product and 100% land area to grid cells that are missing in that product. A 10-year rolling mean centered on right edge of the window is applied to the annual times series."
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"text": "Figure 1 presents the post processed data as a reange between the maximum and minimum value among the ensemble of datasets at each time step. This range is not an uncertainty estimate but rather the spread among the considerable datasets."
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"text": "Due to lack of observations during some periods of 19"
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"text": ", Black Sea is only represented from the 1950s onward."
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"text": "Each dataset post processed data is presented in Figure 1 for the all the regional seas defined as:\u00a0\u00a0"
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"text": "Europe: 35\u00b0-70\u00b0N, 25\u00b0W-40\u00b0E;"
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"text": "Baltic Sea: 52.5\u00b0-67.5\u00b0N, 8.5\u00b0-30.5\u00b0E;"
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"text": "Black Sea: 39.5\u00b0-48.5\u00b0N, 27.5\u00b0W-42.5\u00b0E;"
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"text": "Mediterranean: 30.5\u00b0-46.5\u00b0N, 6.5\u00b0W-38.5\u00b0E;"
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"text": "North Sea: 50.5\u00b0-60.5\u00b0N, 5.5\u00b0W-9.5\u00b0E;"
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"text": "North Atlantic: 29.5\u00b0-79.5\u00b0N, 40.5\u00b0W-0.5\u00b0E."
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"text": "\nMethodology for SST projections"
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"text": "SST projections are based on CMIP6 models data (Eyring et al., 2016). Three scenarios are used"
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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\">Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J. et al., 2017, 'The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview', <i>Global Environmental Change</i> 42, pp. 153–168 (https://linkinghub.elsevier.com/retrieve/pii/S0959378016300681) accessed December 6, 2024.</div>\n</div>\n",
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"text": ": SSP1-2.6 (32 models), SSP2-4.5 (32 models) and SSP5-8.5 (22 models). For each of the European basins (Mediterranean Sea, Baltic Sea, Black Sea, and North Sea), European seas as whole and the global ocean, these CMIP6 models are spatially averaged in their native grid. Annual anomalies with respect to the period 1991-2020 are applied for every model (those with more than one member have them previously averaged together). The time mean of the period 2071-2100 from each model is pooled in boxplots showing the percentiles 5, 25, 50, 75 and 95.\u00a0Same regional areas are used for CMIP6 projections.\u00a0"
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Data sources and providers
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Definition
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"text": "This indicator monitors trends in average SST anomalies in Europe\u2019s regional seas and in the global ocean. Care must be taken when comparing the results reported here with previous versions of the indicator, as differences can arise from the choice of underlying data sets."
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"text": "SST is an important physical characteristic of the oceans. It varies naturally with latitude, being warmest at the equator and coldest in the Arctic and Antarctic regions. As the oceans absorb more heat, SST will increase (and heat will be redistributed to deeper water layers). Increases in the mean SST are also accompanied by increases in the frequency and intensity of marine heatwaves (that is, when the daily SST exceeds a locally and seasonally defined threshold)."
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"text": "Increases in SST can lead to an increase in atmospheric water vapour over the oceans, influencing entire weather systems. The North Atlantic Ocean plays a key role in the regulation of climate over the European continent by transporting heat northwards and redistributing energy from the atmosphere to the deep parts of the ocean. The Gulf Stream and its extensions, the North Atlantic Current and Drift, partly determine weather patterns over the European continent, including precipitation and wind regimes. One of the most visible physical ramifications of increased temperature in the oceans is a reduction in the area of sea ice coverage in the Arctic polar region."
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"text": "Temperature is a determining factor for the metabolism of species, and thus for their distribution and phenology, such as the timing of seasonal migrations, spawning events and peak abundances (e.g. plankton bloom events). There is an accumulating body of evidence suggesting that many marine species and habitats, such as cetaceans in the North Atlantic Ocean, are highly sensitive to changes in SST. Increased temperature may also increase stratification of the water column. Such changes can significantly reduce vertical nutrient fluxes in the water column, thereby negatively influencing primary production and phytoplankton community structure. Further changes in SST could have widespread effects on marine species and cause the reconfiguration of marine ecosystems"
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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\">Baker-Austin, C., Trinanes, J. A., Salmenlinna, S., Löfdahl, M., Siitonen, A., Taylor, N. G. H. and Martinez-Urtaza, J., 2016, 'Heat Wave–Associated Vibriosis, Sweden and Finland, 2014', <i>Emerging Infectious Diseases</i> 22(7), pp. 1216–1220 (http://wwwnc.cdc.gov/eid/article/22/7/15-1996_article.htm) accessed July 22, 2020.</div>\n</div>\n",
"footnoteTitle": "Baker-Austin, Craig, 07/2016, Heat Wave\u2013Associated Vibriosis, Sweden a, Emerging Infectious Diseases",
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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\">Collins, M., Sutherland, M. and Bouwer, L., 2019, 'Extremes, abrupt changes and managing risk', in: Pörtner, H.-O., Roberts, D. C., and Masson-Delmotte, V. (eds), <i>IPCC special report on the ocean and cryosphere in a changing climate</i>, Cambridge University Press, Cambridge, UK.</div>\n</div>\n",
"footnoteTitle": "Collins, M., 2019, Extremes, abrupt changes and managing ri, Cambridge, UK",
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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\">Smale, D. A., Wernberg, T., Oliver, E. C. J., Thomsen, M., Harvey, B. P., Straub, S. C., Burrows, M. T., Alexander, L. V., Benthuysen, J. A., Donat, M. G., Feng, M., Hobday, A. J., Holbrook, N. J., Perkins-Kirkpatrick, S. E., Scannell, H. A., Sen Gupta, A., Payne, B. L. and Moore, P. J., 2019, 'Marine heatwaves threaten global biodiversity and the provision of ecosystem services', <i>Nature Climate Change</i> 9(4), pp. 306–312 (https://www.nature.com/articles/s41558-019-0412-1) accessed January 12, 2022.</div>\n</div>\n",
"footnoteTitle": "Smale, Dan A., 2019-04, Marine heatwaves threaten global biodive, Nature Climate Change",
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Unit of measure
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"text": "Temperature (\u00b0C)."
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Policy / environmental relevance
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"text": "In February 2021, the European Commission adopted a new "
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{
"children": [
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"text": "EU strategy for adaptation to climate change"
}
],
"type": "link",
"data": {
"url": "https://ec.europa.eu/clima/policies/adaptation/what_en#tab-0-0"
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},
{
"text": ". The new strategy sets out how the European Union can adapt to the unavoidable impacts of climate change and become climate resilient by 2050. It has four principle objectives: to make adaptation smarter, swifter and more systemic, and to step up international action on adaptation to climate change. The strategy builds on the 2018\u00a0evaluation of the 2013 EU adaptation strategy accompanied by a Commission staff working document"
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{
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"text": " "
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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\">EC, 2018, Commission Staff Working Document — Adaptation preparedness scoreboard country fiches accompanying the document ‘Report from the Commission to the European Parliament and the Council on the implementation of the EU Strategy on adaptation to climate change’, SWD(2018) 460 final.</div>\n</div>\n",
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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\">EC, 2018, Commission Staff Working Document — Evaluation of the EU Strategy on adaptation to climate change accompanying the document ‘Report from the Commission to the European Parliament and the Council on the implementation of the EU strategy on adaptation to climate change’, SWD(2018) 461 final., SWD(2018) 461 final</div>\n</div>\n",
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{
"text": ". An open public consultation was conducted in preparation for the new strategy between May and August 2020."
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{
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"text": ""
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},
{
"children": [
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"text": "Targets"
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{
"children": [
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"text": "No targets have been specified."
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],
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]
Frequency of dissemination
1
Accuracy and uncertainties
[
{
"children": [
{
"text": "Data sets uncertainty"
}
],
"type": "h4"
},
{
"children": [
{
"text": "Systematic observations of SST began around 1850. More recently, manual measurements have been complemented by satellite-based observations that have a high degree of temporal resolution and wide geographical coverage, and by measurements from drifting buoys and Argo floats that automatically measure temperature and salinity below the ocean surface."
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{
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"text": "Rationale uncertainty"
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"type": "h4"
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"text": "No uncertainty has been specified."
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Settings
Short name
european-sea-surface-temperature
Versioning enabled
yes
Contents
European basins, Europe and Global 2071-2100 sea surface temperature warmings (ºC) of all the CMIP6 models ensemble presented in box plots (compared to 1991-2010 climatologies). Horizontal colored bars show the 25 th , mean and 75 th percentile. The results are considered under three different scenarios: SSP1-2.6, SSP2-4.5 and SSP5-8.5.
Time series (1859-2024) of decadal average observed sea surface temperature anomalies (°C), with respect to the period 1991- 2020, for each of the European basins, for the European seas as a whole, and for the global ocean. Data sources: HadSST4.1.0.0 (1850-2024), ERSST v5 (1854-2024), COBE2 (1850-2024), DMI SST v1 (1982-2024) and ERA5 (1979-2022). Figure 1 presents the post processed data as a range between the maximum and minimum value among the ensemble of datasets at each time step. This range is not an uncertainty estimate but rather the spread among the considerable datasets.
