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.
Assessing the inherent uncertainties in satellite data products is a challenging task. Different technical approaches have been developed in the Earth Observation (EO) communities to address the validation problem which results in a large variety of methods as well as terminology. This paper reviews state‐of‐the‐art methods of satellite validation and documents their similarities and differences. First, the overall validation objectives and terminologies are specified, followed by a generic mathematical formulation of the validation problem. Metrics currently used as well as more advanced EO validation approaches are introduced thereafter. An outlook on the applicability and requirements of current EO validation approaches and targets is given.
Sea surface temperature (SST) datasets have been generated from satellite observations for the period 1991-2010, intended for use in climate science applications. Attributes of the datasets specifically relevant to climate applications are: first, independence from in situ observations; second, effort to ensure homogeneity and stability through the time-series; third, context-specific uncertainty estimates attached to each SST value; and, fourth, provision of estimates of both skin SST (the fundamental measurement, relevant to air-sea fluxes) and SST at standard depth and local time (partly model mediated, enabling comparison with historical in situ datasets). These attributes in part reflect requirements solicited from climate data users prior to and during the project. Datasets consisting of SSTs on satellite swaths are derived from the Along-Track Scanning Radiometers (ATSRs) and Advanced Very High Resolution Radiometers (AVHRRs). These are then used as sole SST inputs to a daily, spatially complete, analysis SST product, with a latitude-longitude resolution of 0.05°C and good discrimination of ocean surface thermal features. A product user guide is available, linking to reports describing the datasets' algorithmic basis, validation results, format, uncertainty information and experimental use in trial climate applications. Future versions of the datasets will span at least 1982-2015, better addressing the need in many climate applications for stable records of global SST that are at least 30 years in length.
The relationship between satellite land surface temperature (LST) and ground‐based observations of 2 m air temperature (T2m) is characterized in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along‐Track Scanning Radiometer series, which has been produced within the European Space Agency GlobTemperature project (http://www.globtemperature.info/). Global LST‐T2m differences are analyzed with respect to location, land cover, vegetation fraction, and elevation, all of which are found to be important influencing factors. LSTnight (~10 P.M. local solar time, clear‐sky only) is found to be closely coupled with minimum T2m (Tmin, all‐sky) and the two temperatures generally consistent to within ±5°C (global median LSTnight‐Tmin = 1.8°C, interquartile range = 3.8°C). The LSTday (~10 A.M. local solar time, clear‐sky only)‐maximum T2m (Tmax, all‐sky) variability is higher (global median LSTday‐Tmax = −0.1°C, interquartile range = 8.1°C) because LST is strongly influenced by insolation and surface regime. Correlations for both temperature pairs are typically >0.9 outside of the tropics. The monthly global and regional anomaly time series of LST and T2m—which are completely independent data sets—compare remarkably well. The correlation between the data sets is 0.9 for the globe with 90% of the CDR anomalies falling within the T2m 95% confidence limits. The results presented in this study present a justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T2m data acquired at meteorological stations.
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