Satellite-based time-series of sea-surface temperature since 1981 for climate applications

Abstract

A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

Use of Uncertainty Inflation in OSTIA to Account for Correlated Errors in Satellite-Retrieved Sea Surface Temperature Data

“…Similarly, for the same regions of the globe, typical observation error variances were estimated from the SST CCI observations. The length scale of the synoptic scale error correlations, which occur due to limitations in representing atmospheric effects in the SST retrievals [5], was given in the files as 100 km. The advice given regarding the distribution of the correlated errors was that they are assumed to be Gaussian (C. Merchant, pers.…”

“…where d is the (Euclidean) distance between gridpoints, Var is the variance of the error component, and l is the associated correlation length scale. For specific regions of the globe, B and R were (fully) determined from Equation (5). The areas examined are regions of the Pacific Ocean and allow testing of the impact of differences in sizes of the error variances and the distance over which background errors are correlated [8].…”

“…OSTIA also has a climate configuration. This is used within the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) [4,5] and the Copernicus Climate Change Service (C3S; climate.copernicus.eu) to generate climate datasets. These use input satellite data that have been adjusted to represent the temperature at a consistent depth and local time in order to generate analyses that approximate the daily average temperature at 20 cm depth [5].…”

“…However, the ESA SST CCI project also provides information on observation error correlations. They supply observational uncertainties that have been decomposed depending on the spatial scales over which the errors they represent are correlated [5]. Uncertainty due to errors that are spatially uncorrelated, correlated on synoptic scales (given as approximately 100 km) and correlated on large scales are provided as standard deviations.…”

Coastal Tidal Effects on Industrial Thermal Plumes in Satellite Imagery

“…The simulations are run for a prior state obtained from numerical weather prediction (NWP) atmospheric profiles. The prior SST is daily gridded global sea surface temperature (level 4 SST) data from the European Space Agency Climate Change Initiative (ESA CCI) programme [27]. For each state vector there exists an "ideal, synthetic measurement" vector, and OE requires simulation of measurements as part of the retrieval process.…”