Sea surface temperature (SST) is a fundamental physical variable for understanding, quantifying and predicting complex interactions between the ocean and the atmosphere. Such processes determine how heat from the sun is redistributed across the global oceans, directly impacting large- and small-scale weather and climate patterns. The provision of daily maps of global SST for operational systems, climate modeling and the broader scientific community is now a mature and sustained service coordinated by the Group for High Resolution Sea Surface Temperature (GHRSST) and the CEOS SST Virtual Constellation (CEOS SST-VC). Data streams are shared, indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework, which is implemented internationally in a distributed manner. Products rely on a combination of low-Earth orbit infrared and microwave satellite imagery, geostationary orbit infrared satellite imagery, and in situ data from moored and drifting buoys, Argo floats, and a suite of independent, fully characterized and traceable in situ measurements for product validation (Fiducial Reference Measurements, FRM). Research and development continues to tackle problems such as instrument calibration, algorithm development, diurnal variability, derivation of high-quality skin and depth temperatures, and areas of specific interest such as the high latitudes and coastal areas. In this white paper, we review progress versus the challenges we set out 10 years ago in a previous paper, highlight remaining and new research and development challenges for the next 10 years (such as the need for sustained continuity of passive microwave SST using a 6.9 GHz channel), and conclude with needs to achieve an integrated global high-resolution SST observing system, with focus on satellite observations exploited in conjunction with in situ SSTs. The paper directly relates to the theme of Data Information Systems and also contributes to Ocean Observing Governance and Ocean Technology and Networks within the OceanObs2019 objectives. Applications of SST contribute to all the seven societal benefits, covering Discovery; Ecosystem Health & Biodiversity; Climate Variability & Change; Water, Food, & Energy Security; Pollution & Human Health; Hazards and Maritime Safety; and the Blue Economy.

A 30-yr climate data record (CDR) of sea surface temperature (SST) has been produced with daily gap-free analysis fields for the North Sea and the Baltic Sea region from 1982 to 2012 by combining the Pathfinder AVHRR satellite data record with the Along-Track Scanning Radiometer (ATSR) Reprocessing for Climate (ARC) dataset and with in situ observations. A dynamical bias correction scheme adjusts the Pathfinder observations toward the ARC and in situ observations. Largest Pathfinder–ARC differences are found in the summer months, when the Pathfinder observations are up to 0.4°C colder than the ARC observations on average. Validation against independent in situ observations shows a very stable performance of the data record, with a mean difference of −0.06°C compared to moored buoys and a 0.46°C standard deviation of the differences. The mean annual biases of the SST CDR are small for all years, with a negligible temporal trend when compared against drifting and moored buoys. Analysis of the SST CDR reveals that the monthly anomalies for the North Sea, the Danish straits, and the central Baltic Sea regions show a high degree of correlation for interannual and decadal time scales, whereas the monthly variability differs from one region to another. The linear trends of the 1982–2012 SST anomaly time series range from 0.037°C yr−1 for the North Sea to 0.041°C yr−1 for the Baltic Sea.

The offshore wind climatology in the Northern European seas is analysed from ten years of Envisat synthetic aperture radar (SAR) images using a total of 9256 scenes, ten years of QuikSCAT and two years of ASCAT gridded ocean surface vector wind products and high-quality wind observations from four meteorological masts in the North Sea. The traditional method for assessment of the wind resource for wind energy application is through analysis of wind speed and wind direction observed during one or more years at a meteorological mast equipped with well-calibrated anemometers at several levels. The cost of such measurements is very high and therefore they are only sparsely available. An alternative method is the application of satellite remote sensing. Comparison of wind resource statistics from satellite products is presented and discussed including the uncertainty on the wind resource. The diurnal wind variability is found to be negligible at some location but up to 0.5 m s− 1 at two sites. Synergetic use of observations from multiple satellites in different orbits provides wind observations at six times in the diurnal cycle and increases the number of observations. At Horns Rev M2, FINO1 and Greater Gabbard satellite and in situ collocated samples show differences in mean wind speed of − 2%, − 1% and 3%, respectively. At Egmond aan Zee the difference is 10%. It is most likely due to scatterometer data sampled further offshore than at the meteorological mast. Comparing energy density with all samples at Horns Rev M2 shows overestimation 7–19% and at FINO1 underestimation 2–5% but no clear conclusion can be drawn as the comparison data are not collocated. At eight new offshore wind farm areas in Denmark, the variability in mean energy density observed by SAR ranges from 347 W m− 2 in Sejerøbugten to 514 W m− 2 at Horns Rev 3. The spatial variability in the near-shore areas is much higher than at areas located further offshore