GloSATref.1.0.0.0: An observational record of global gridded near surface air temperature change over land and ocean from 1781

The GloSAT reference analysis (GloSATref) is a global gridded data set of air temperature change since 1781. GloSATref combines temperature series from land based meteorological stations with marine air temperature observation from ships. The use of marine air temperature (MAT) data differs from the typical use of sea-surface temperature (SST) data in global near surface temperature data sets, with the use of all-day MAT allowing the data set to extended further into the past than records based on SST.

Data are provided as air temperature anomalies relative to 1961-1990 average conditions on a 5-degree latitude by 5-degree longitude grid. Time series of average temperature changes and their uncertainties are available for the globe and for a selection of regions. The gridded data set is produced using methods developed for the HadCRUT5 ensemble global temperature data set. Data is provided as a 200-member ensemble spatially infilled “analysis” data set. A “noninfilled” version of the data set is also provided.

GloSATref uses the HadCRUT5 data processing system to produce the gridded data set, time series and uncertainty estimates.

Sources of additional information:
The following papers are provided in the related documents section with further information about GloSATref.1.0.0.0 and its underpinning data.

Gridded dataset description:
Morice, C. P., et al. (2025). An observational record of global gridded near surface air temperature change over land and ocean from 1781, Earth Syst. Sci. Data Discuss. https://doi.org/10.5194/essd-2024-500.

Land station data processing:
Taylor, M. et al. (2025, in review). GloSAT LATsdb: a global compilation of land air temperature station records with updated climatological normals from local expectation kriging. Submitted to Geoscience Data Journal.
Wallis, E. J., et al. (2024). Quantifying exposure biases in early instrumental land surface air temperature observations. International Journal of Climatology, 44(5), 1611–1635. https://doi.org/10.1002/joc.8401

Marine air temperature processing:
Cropper, T. E., et al. (2023). Quantifying Daytime Heating Biases in Marine Air Temperature Observations from Ships. J. Atmos. Oceanic Technol., 40, 427–438, https://doi.org/10.1175/JTECH-D-22-0080.1.