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Indicator Specification

European sea surface temperature

Indicator Specification
  Indicator codes: CSI 046 , CLIM 013
Published 30 Jun 2021 Last modified 30 Jun 2021
10 min read
This indicator monitors trends in average SST anomalies in Europe’s 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. 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). 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. 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 (Gilbert et al., 2014; Baker-Austin et al., 2016; Collins et al., 2019).

Assessment versions

Published (reviewed and quality assured)
 

Rationale

Justification for indicator selection

No rationale has been identified for this indicator

Scientific references

  • No rationale references available

Indicator definition

This indicator monitors trends in average SST anomalies in Europe’s 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.

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).

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.

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 (Gilbert et al., 2014; Baker-Austin et al., 2016; Collins et al., 2019).

Units

  • Temperature (°C).
 

Policy context and targets

Context description

In February 2021, the European Commission adopted a new EU strategy for adaptation to climate change (EC, 2021b). 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 evaluation of the 2013 EU adaptation strategy (EC, 2018b), accompanied by a Commission staff working document (EC, 2018a). An open public consultation was conducted in preparation for the new strategy between May and August 2020.

Targets

No targets have been specified.

Related policy documents

No related policy documents have been specified

Key policy question

Sea surface temperature, aggregated assessment level

 

Methodology

Methodology for indicator calculation

This indicator primarily uses information from the HadISST1 (Rayner et al., 2003), HadSST4 (Kennedy et al., 2019), Extended Reconstruction Sea Surface Temperature version 5 (ERSSTv5) (Huang et al., 2017), European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) Analysis (Merchant et al., 2019) and Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) (Stark et al., 2007) data sets.

Each data set was averaged on to a common 5 °-latitude-by-5 °-longitude monthly grid. These averaged data sets were used to calculate the regional area averages. Regional area averages were calculated by a weighted average of grid cell values, where the weights were equal to the area of ocean in that grid cell (determined using the SST CCI Analysis land mask). The OSTIA real-time updates include some lakes not considered in the SST CCI Analysis data set or other data sets. These lakes were masked out of the OSTIA data set.

There is a small, geographically varying difference between the OSTIA and SST CCI Analysis data sets. The OSTIA data set represents the ‘foundation’ SST and the SST CCI Analysis data set represents SST at a depth of 0.2 m; at least part of the variability is due to these differences in definitions.

A monthly time series was calculated for each of the seas and regions. A trailing 120-month (i.e. decadal) mean was calculated from the monthly series. Consequently, the first available decadal mean for a series is 120 months after the start date of that series.

Uncertainty in the long-term data sets was assessed as the range of the three data sets — HadISST1 (Rayner et al., 2003), ERSSTv5 (Huang et al., 2017) and HadSST4 (Kennedy et al., 2019) — including the estimated uncertainty range from HadSST4. This therefore covers uncertainties arising from measurement, sampling, bias adjustment and spatial infilling, as well as structural uncertainty. The HadSST4 uncertainty range was calculated as described in the paper by Kennedy et al. (2019). Correlated errors were assumed to have been correlated within a year and uncorrelated between years.


References

Baker-Austin, C., et al., 2016, ‘Heatwave-associated vibriosis, Sweden and Finland, 2014’,Emerging Infectious Diseases22(7), pp. 1216-1220 (DOI: 10.32032/eid2207.151996).

Collins, M., et al., 2019, ‘Extremes, abrupt changes and managing risks’, in:IPCC special report on the ocean and cryosphere in a changing climate, Cambridge University Press, Cambridge, UK.

Copernicus Marine Service, 2021, ‘Ocean products’, Copernicus Marine Service (https://resources.marine.copernicus.eu/?option=com_csw&task=results) accessed 21 March 2021.

EC, 2021a, ‘A European Green Deal’, European Commission (https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en) accessed 8 March 2021.

EC, 2021b, ‘EU adaptation strategy’, European Commission (https://ec.europa.eu/clima/policies/adaptation/what_en#tab-0-0) accessed 8 March 2021.

Gilbert, P. M., et al., 2014, ‘Vulnerability of coastal ecosystems to changes in harmful algal bloom distribution in response to climate change: projections based on model analysis’,Global Change Biology20(12), pp. 3845-3858 (DOI: 10.1111/gcb.12662).

Good, S. A., et al., 2019, ‘ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis Climate Data Record, version 2.1’, Centre for Environmental Data Analysis, 22 August 2019 (https://catalogue.ceda.ac.uk/uuid/62c0f97b1eac4e0197a674870afe1ee6) accessed 21 March 2021.

Helcom, 2013,Climate change in the Baltic Sea Area — Helcom thematic assessment in 2013, Baltic Sea Environment Proceedings No 137, Helsinki Commission — Baltic Marine Environment Protection Commission, Helsinki (http://www.helcom.fi/Lists/Publications/BSEP137.pdf) accessed 27 October 2013.

Huang, B., et al., 2017, ‘Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, validations, and intercomparisons’,Journal of Climate30(20), pp. 8179-8205.

IPCC, 2019,IPCC special report on the ocean and cryosphere in a changing climate, Cambridge University Press, Cambridge, UK.

Kennedy, J. J., et al., 2019, ‘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: Atmospheres124(14), pp. 7719-7763.

Merchant, C. J., et al., 2019, ‘Satellite-based time-series of sea-surface temperature since 1981 for climate applications’,Scientific Data6, 223 (DOI: 10.1038/s41597-019-0236-x).

Met Office Hadley Centre, 2008, ‘Met Office Hadley Centre observations datasets — HadISST1 data: download’, Met Office Hadley Centre (https://hadleyserver.metoffice.gov.uk/hadisst/data/download.html) accessed 21 March 2021.

Met Office Hadley Centre, 2021, ‘Met Office Hadley Centre observations datasets — HadSST.4.0.0.0 data: download’, Met Office Hadley Centre (https://www.metoffice.gov.uk/hadobs/hadsst4/data/download.html) accessed 21 March 2021.

NOAA, 2021, ‘NOAA Extended Reconstructed Sea Surface Temperature (ERSST), version 5’, National Centers for Environmental Information, National Oceanic and Atmospheric Administration (https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00927) accessed 21 March 2021.

Oliver, E. C. J., et al., 2018, ‘Longer and more frequent marine heatwaves over the past century’,Nature Communications9, 1324 (DOI: 10.1038/s41467-018-03732-9).

Rayner, N. A., et al., 2003, ‘Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century’,Journal of Geophysical Research108(D14), 4407 (DOI: 10.1029/2002JD002670).

Stark, J. D., et al., 2007, ‘OSTIA: An operational, high resolution, real time, global sea surface temperature analysis system’, conference paper presented at: Oceans 2007 — Europe, Aberdeen, United Kingdom, June 2007.

Methodology for gap filling

Not applicable.

Methodology references

 

Uncertainties

Methodology uncertainty

Not applicable.

Data sets uncertainty

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.

Rationale uncertainty

No uncertainty has been specified

Further work

Short term work

Work specified here requires to be completed within 1 year from now.

Long term work

Work specified here will require more than 1 year (from now) to be completed.

General metadata

Responsibility and ownership

EEA Contact Info

Hans-Martin Füssel

Ownership

European Environment Agency (EEA)

Identification

Indicator code
CSI 046
CLIM 013
Specification
Version id: 5

Frequency of updates

Updates are scheduled once per year

Classification

DPSIR: Impact
Typology: Descriptive indicator (Type A - What is happening to the environment and to humans?)
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