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Sea-surface temperature (SST) 

Observations

Sea-surface temperature is taken from various providers, who process the observational data in different ways.  Each provider uses data from several different observational sources:

  • satellites measure sea-surface temperature in a layer a few microns thick in the uppermost mm of the ocean,
  • drifting buoys measure sea-surface temperature at a depth of about 0.2-1.5m,
  • ships sample sea water down to about 10m while the vessel is underway,
  • bathymetric measure sea temperature down to 2000m, including from instrumented mammals.    

Diurnal variations are seen,. but not at at depths below a few metres.

Sometimes satellite-derived sea-surface temperatures are shown over lakes or over model land areas near the coast.  These are set to the FLake model mixed layer temperature.  This gives a much smoother field than Cy48 and earlier, helping to give much more realistic values around coastlines.   

Assimilation

Real time assimilation of sea-surface temperature is made using ORTS6, constrained by analyses received daily from the Met Office (OSTIA, 5 km resolution).  Sea-surface temperature is taken from a forecast made by coupling the NEMO ocean model to the IFS.  In this case, the sea-surface temperature is the average temperature of the uppermost metre of the ocean and does exhibit diurnal variations.

Forecast

Throughout the forecast period, NEMO (and the SI3 subprogram within it which deals with sea-ice evolution), predicts changes in the sea-surface temperature.  Output is used interactively by all IFS atmospheric models.

Sea-ice

The SI3 sub-program of NEMO deals with the dynamic and thermodynamic evolution of sea-ice cover.

Throughout the forecast period, the extent of sea-ice and the variation of the ice shelf changes with time.  It responds to:

  • sea temperatures.
  • air temperatures.
  • ocean currents.
  • wind and wind waves, particularly if the wind is persistent in direction.  

The variations in ice distribution and albedo have important effects upon the energy and moisture balance at the atmosphere/surface boundary.  Melt ponds that exist or form on extensive sea-ice can reduce albedo locally.  

When comparing forecasts, consider the differing impacts of analysed and predicted sea-surface temperature and sea-ice cover.  This should be considered even when based the same data time.  

Ice cover can be extensive in fjords and inlets but may be too small to be shown on ice charts.  Local sea-ice cover may influence forecast parameters nearby (e.g. 2m temperature, fog, etc.).  The Baltic Sea and Bay of Bothnia can be particularly affected.

The impact of waves on the sea ice is not yet at completely represented in ECMWF models.  There is particular uncertainty when the wave height at the sea ice edge is large.

Sea ice cover

Sea ice cover evolves according to the sea-ice model SI3.  In general the SI3 sea ice model tends:

  • to freeze and melt sea-ice more rapidly than in LIM2 (sea-ice model used in Cy49 and earlier).  This helps reduce errors in the extent of perennial (extensive, long-lasting or multi-year, pack) sea-ice and marginal (high variability, changing, transitional, broken) sea-ice. 
  • In the northern hemisphere:
    • there is generally a broad reduction in errors in sea-ice concentration.
    • there is some increase in errors in the transition between marginal ice and perennial ice.  This is particularly in the region extending from northeast Greenland towards northern Russia.
    • generally there is thinner ice on average in the Arctic compared to satellite derived sea-ice thickness products, particularly to the north of Greenland.
  • In the southern hemisphere:
    • results are more mixed for the maximum ice extent
    • increased errors in the Davis Sea.
    • Large reductions in error in the autumn Weddell Sea.  This area is sensitive to snow loading on ice.

The averages hide a lot of features and the bias in ice thickness in SI3 is not uniform.

Incorrect ice analysis and/or forecast near coasts can give anomalous 2m temperatures at nearby land locations.  Too much ice tends to make land temperatures too cold, too little tends to keep land temperatures nearer sea temperatures.


Fig2A.1.4.8-1: Root mean square error (RMSE) comparing sea-ice concentration errors relative to satellite measurements.  Estimates from NEMO relative to NEMO3.4 in (a) September–November, and (b) March–May.  Negative (blue) values show an improvement when using NEMO.  The scale is in percent/100.  The averages hide a lot of features and the bias in ice distribution in SI3 and LIM2 is not uniform.


Fig2A.1.4.8-2: Root mean square error (RMSE) relative to satellite estimates, comparing sea-ice thickness errors in estimates from NEMO4 relative to NEMO3.4 in (a) September–November, and (b) March–May.  Negative (blue) values show an improvement when using NEMO.  The scale is in m.  The averages hide a lot of features and the bias in ice thickness in SI3 and LIM2 is not uniform


Sea ice extent 

The climate location of the ice edge is based on nearly 30 years.  However, the area of winter ice cover has been seen to be reduced over recent years.  Maximum area of antarctic sea ice in the southern winters in 2023 and 2024 (not shown) are also lower than the available long term satellite records.  The climate of ice cover, particularly during recent years, probably generally covers too great an area.   This can have an impact on forecast temperatures and the derivation of Extreme Forecast Index in polar regions.  Users should inspect the observed sea ice extent and compare with that of the the model climate.


Fig2A.1.4.8-3: Sea-ice concentration in early January 2025 compared with the Median ice edge over the period 1981-2010 (orange line).  Observed arctic sea ice extent is taken from Sea Ice Today from the National Snow and Ice Center (NSIDC) at University of Colorado Boulder.   

Fig2A.1.4.8-4: Observed arctic sea-ice extent has become less than the general extent during previous years.  Over recent years the extent of ice has been less than the median winter extent.

Ice charts

The extent of ice cover is shown with different thresholds on Opencharts and ecCharts..

Ice cover on Opencharts

Fig2A.1.4.8-5: Example of sea temperatures and sea ice fraction shown by Opencharts.  Sea ice fraction (cyan hues, %) is shown where ice coverage is greater than 50% but most of the marginal ice zone (15%-50%) is not plotted. This gives the impression that there is no sea ice outside this area.  However, patchy ice and bergs are likely in the surrounding waters.Ice in smaller discontinuous patches or as bergs can occur elsewhere mainly in purple coloured areas but particularly where sea temperatures are sub-zero.  Current sea temperatures and ice cover chart on Opencharts.

The climatological location of the sea ice edge is shown for reference as an overlaid contour (magenta) which shows where sea ice fraction is on average >50%, and so can be compared with the edge of the turquoise sea-ice areas; stippling highlights climatological ice cover above 50%.  The climate location of the ice edge is based on an earlier period and may show too great an area of climate ice cover.


Sea temperatures anomaly on Opencharts.

Fig2A.1.4.8-6: Example of sea-temperature anomaly Openchart.  Temperature anomalies are shown only where the current sea ice fraction is <50%.  Sea ice fraction (cyan hues, %) is shown where ice coverage is greater than 50% but most of the marginal ice zone (15%-50%) is not plotted.  In areas of large sea ice depletion relative to climatology (i.e. inside the magenta contour), the sea surface temperature anomaly may be unreliable (or zero, white) due to a lack of past sea surface temperature data.  Current sea temperatures anomaly on Opencharts.

The climatological location of the sea ice edge is shown for reference as an overlaid contour (magenta) which shows where sea ice fraction is on average >50%, and so can be compared with the edge of the turquoise sea ice areas; stippling highlights climatological ice cover above 50%.  The climate location of the ice edge is based on an earlier period and may show too great an area of climate ice cover.

Ice cover on ecCharts

Fig2A.1.4.8-7: Example of ecChart display of sea-ice cover as fraction of a grid box. - only one range of coverage (0.2 to 1.0 fraction of grid box).  The extent of ice is larger than Opencharts (which only shows ice cover to 50%) and includes broken ice cover but without any detail.


Fig2A.1.4.8-8: Example of ecChart display of ensemble mean of sea-ice cover as fraction of a grid box. - range of coverage (0.1 to 1.0 fraction of grid box).  The extent of ice is larger with more detail including probability of the broken ice coverage.


Fig2A.1.4.8-9: Example of ecChart display of sea-ice cover as fraction of a grid box and ensemble mean for sea ice cover as fraction of a grid box displayed together.  The green fringe shows approximate areas between 0.1 and 0.2 sea ice coverage (fraction of grid box). 

Example of sea ice forecast sequence

Fig2A.1.4.8-10: Sequence of sea-ice and sea-surface temperatures from the ENS CTRL run data time 00 UTC 27 April 2017.   T+0hr (00UTC 27 April 17), T+120hr (00UTC 02 May 17),  T+240hr (00UTC 07 May 17), and  T+360hr (00UTC 12 May 17).  On such plots the climatological average sea ice cover is shown in pink (contour and stippling, for >50%),can just be seen in the northern Gulf of Bothnia and in the White Sea.   Dark purple areas (SST between 0C and -2C) are prone to ice formation if not already in existence.   Areas of sea ice are shown as turquoise. 

Note:  

  • Movement of ice (turquoise) in the northern Gulf of Bothnia  due to the winds.
  • Steady rise of sea-surface temperatures in the Black Sea, and especially in the shallow waters of both the Sea of Azov and the northern Caspian Sea.   In the White Sea (east of Finland, top of plot) sea-ice cover is less than the climatological average for this time of year.  Using these plots, the user can assess where sea-ice cover is above/below average.



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