HadSST3 provides monthly SST anomalies on a 5°x5° grid for 1850-present. The anomalies are derived from a 30-year climatology spanning 1961-90.Coverage is global but there is no interpolation; Thus, missing data occur in the final product. The primary input data are from ICOADS release 2.5. Bias adjustments to the ICOADS SSTs account for changes in measurement methods (e.g. engine room intake, bucket measurements, or buoy data). Uncertainties in these adjustments have complex spatial and temporal dependencies. The uncertainties are represented in an ensemble of 100 realizations of the data set. This allows the spatial and temporal characteristics of the uncertainties and their effects on climate signals to be explored. In addition to the ensemble, a median estimate is provided. Additional uncertainty arises from limited spatial coverage, differing numbers of samples in a grid box, errors in individual measurements and biases in measurement platforms. Unadjusted raw data are provided but their use for climate studies is discouraged. HadSST3 is the SST component of the HadCRUT4 global combined land-ocean surface temperature data set.
The following was contributed by Dr. John Kennedy (UK Met Office, Hadley Centre), October, 2014:
HadSST3 (the Hadley Centre Sea-Surface Temperature data set version 3) is a gridded data set of sea-surface temperature anomalies from January 1850 to the present. The monthly grids have a resolution of 5 degree latitude by 5 degree longitude. It is based on quality-controlled in situ measurements of sea-surface temperature from the International Comprehensive Ocean Atmosphere Data Set (ICOADS) release 2.5 and is updated using observations from the Global Telecommunication System. In situ measurements are those made at the surface by ships, drifting buoys and moored buoys. Ship measurements are made using a variety of methods. Adjustments have been applied to the data to account for artificial variability caused by changes in instrumentation. Uncertainties associated with bias adjustments, measurement and sampling error have been estimated. Grid-cell average SST anomalies are only estimated in those grid cells that contain observations, and consequently the data set is not globally complete; there are numerous data voids.
What are the key strengths of this data set?
What are the key limitations of this data set?
What are the typical research applications of this data set?
What are the most common mistakes that users encounter when processing or interpreting these data?
Some users have assumed that the ensemble contains information about all the uncertainties in the data set. This is not the case. The ensemble does not contain information about the ‘high-frequency’ measurement and sampling error uncertainties which are provided as additional fields.
What are some comparable data sets, if any?
How is uncertainty characterized in these data?
Were corrections made to account for changes in observing systems or practices, sampling density, satellite drift, or similar issues?
Yes. Historically, sea-surface temperature measurements have been measured by ships, drifting buoys and moored buoys. Ship measurements have been made using: buckets of various design to sample the surface water; water taken in below the surface to cool the ship’s engines; or using a dedicated hull-mounted sensor. Adjustments have been applied to minimise the effects of such changes on the composite record. However, there are uncertainties in these adjustments. The uncertainties are presented as an ensemble of 100 interchangeable data sets which characterise the uncertainty in the adjustments.
How do I best compare these data with model output?
It depends on what you are doing. There are two principle things to bear in mind. First, there are gaps in the data and the gaps are highly heterogeneous in time. Early in the record and during the two World Wars, the coverage is very sparse. Some analyses have accounted for this by masking the model output to have a similar coverage to that of the observations. Second, there are uncertainties in the data. The data set has been presented as an ensemble of 100 data sets that characterise the uncertainties in the bias adjustments and, together with separate fields of uncertainties associated with other measurement and sampling errors, these provide information about the estimated total uncertainty in the gridded fields. The idea behind providing an ensemble was that it would be relatively easy to assess the sensitivity of an analysis to observational uncertainty by re-running the analysis on some, or all, of the observational ensemble.
We welcome feedback from users on this general approach of using ensembles of observational data sets.
Are there spurious (non-climatic) features in the temporal record?
Probably. Although every effort has been made to minimise the effects of known changes in measurement methods, information concerning how measurements were made is limited. The period between 1935 and 1970 is particularly problematic as there were large changes in the way that measurements were made (with relatively poor documentation) and discontinuities in the data sources used to form the ICOADS data base.
Kennedy, John & National Center for Atmospheric Research Staff (Eds). Last modified 29 Oct 2015. "The Climate Data Guide: SST data: HadSST3." Retrieved from https://climatedataguide.ucar.edu/climate-data/sst-data-hadsst3.