Sea surface temperature (SST) data sets are an essential resource for monitoring and understanding climate variability and climate change. By surface area, SSTs are the dominant (~71%) input into merged global land-ocean surface temperature data products. Historically, SST measurments have been made from ships. Ship data have been compiled into databases like ICOADS, which in turn form the main input into long-term climate data sets like HadSST3 and ERSSTv3b. Moored and drifting buoys are another primary source of in-situ SST data, especially in remote regions like the Southern Ocean, where ARGO floats offer much improved coverage. Over the tropical Pacific, the dense TAO-TRITON array provides key measurements for monitoring the emergence and evolution of El Niño events. In-situ data are also the primary reference for calibrating satellite-based SST estimates. Satellite-based estimates utilize measurements from infrared (IR) and/or microwave wavelengths. Microwave observations are less sensitive to clouds than IR measurements, but are more sensitive to scattering by rain, and have lower spatial resolution. For climate research, the longest satellite-based data set is NOAA's OI SSTv2, extending from 1981 to present, with AVHRR IR measurements as the primary source data. The Group for High-Resolution SST (GHRSST) is an umbrella organization coordinating the development of multi-spectral SST data products for both the operational and climate communities. Currently, one of the longest global GHRSST products is the Multi-Scale Ultra-High Resolution (MUR) SST analysis, a 0.011 degree lat-lon gridded data set developed by NASA-JPL, covering 2002-present.
Considerations for utilizing SST data sets in climate research and model evaluation will be discussed in the Expert Guidance section. Common considerations include (1) The spatial resolution - e.g., are features like the Gulf Stream and its fronts are eddies resolved? ; (2) The quantity being measured - e.g., is it a "skin" temperature of a very thin surface layer, or a bulk temperature of the upper meter or more?; (3) The homogeneity of the temporal record - e.g., have issues like satellite drift, sampling frequency and density, and changing observational practices been dealt with in the construction of the data product?; (3) Spatial Interpolation- e.g. have the data been binned, or statistically interpolated in some manner, and what effect does this have on the spatial and temporal variance of climate signals?
The figures below illustrate climatologies, standard deviations, and trends in several of the SST data sets commonly used for climate research. Further information on these data sets is contained in the table below.
SSTs on 20 Oct 2012 in the North Atlantic (just before the passage of Hurricane Sandy), comparing MUR L4 SST (NASA-JPL) with the AVHRR-only version of OISSTv2 (NOAA-NCDC). Credit: NCAR, David Schneider
Climatology of annual SST in the tropical Pacific in 8 data sets (D Shea, NCAR)
Climatology of annual SST in the North Atlantic (D Shea, NCAR)
Standard Deviation of monthly SST anomalies in the tropical Pacific (D Shea, NCAR)
Standard Deviation of monthly SST anomalies in the north Atlantic (D Shea, NCAR)
Trend of annual SSTs in the tropical Pacific (D Shea, NCAR)
Trend of annual SST in the north Atlantic (D Shea, NCAR)