Dataset
GloSATref.1.0.0.0: An observational record of global gridded near surface air temperature change over land and ocean from 1781
Abstract
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.
Details
| Previous Info: |
No news update for this record
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| Previously used record identifiers: |
No related previous identifiers.
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| Access rules: |
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence(s): http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record. |
| Data lineage: |
Marine air temperature data are sourced from the GloSATMAT data set, also developed as part of the GloSAT project and a successor to the CLASSnmat data set. GloSATMAT adds new estimates of diurnal heating biases, enabling the use of daytime observations and allowing the extension of the dataset further into the past compared to nighttime-only marine air temperature data. Data were produced by the project team before uploading to CEDA for archival. |
| Data Quality: |
Data are quality controlled, assessed for biases and provided with uncertainty information. See documentation for details.
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| File Format: |
NetCDF
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Related Documents
Process overview
| Title | HadCRUT5 data processing deployed on Met Office computers |
| Abstract | HadCRUT5 has been processed on Met Office computing facilities, using HadSST4 and CRUTEM5 inputs. CRUTEM5 station data are gridded onto a regular latitude-longitude grid using the HadCRUT4 ensemble method. Resulting land air temperature anomaly grids are merged with sea-surface temperature anomalies from the HadSST4 dataset though weighted averaging based on grid cell areal land fractions to produce the non-infilled HadCRUT5 grids. HadCRUT5 analysis grids are computed using a Gaussian process based statistical method to provide improved estimates of surface temperature anomaly fields, extending the data coverage into regions for which the available observations are informative. Analysis uncertainties are presented through an ensemble method. The Gaussian process based method is applied separately for land and marine temperature grids. Global grids are produced using a land-sea area fraction based weighting, in which sea ice regions (as defined by HadISST2 sea-ice concentrations) are treated as land. Regional time series are derived as grid box area weighted averages, with accompanying uncertainties. Global time series are defined as the average of northern and southern hemisphere series. Remaining uncertainty from unrepresented regions is estimated from sub-sampling experiments using the ERA5 reanalysis. |
| Input Description | None |
| Output Description | None |
| Software Reference | None |
No variables found.
Temporal Range
1781-01-01T00:00:00
2021-12-31T00:00:00
Geographic Extent
90.0000° |
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-180.0000° |
180.0000° |
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-90.0000° |