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Global Ensemble of Temperatures with Quantified Uncertainties in Observations, Coverage and Spatial modeling (GETQUOCS) from 1850-2018

Latest Data Update: 2023-03-29
Status: Ongoing
Online Status: ONLINE
Publication State: Published
Publication Date: 2023-04-26
Download Stats: last 12 months
Dataset Size: 102 Files | 490GB


Instrumental global temperature records are derived from the network of in situ measurements of land and sea surface temperatures. This observational evidence is seen as being fundamental to climate science. Therefore, the accuracy of these measurements is of prime importance for the analysis of temperature variability. There are spatial gaps in the distribution of instrumental temperature measurements across the globe. This lack of spatial coverage introduces coverage error. An approximate Bayesian computation based multi-resolution lattice kriging is developed and used to quantify the coverage errors through the variance of the spatial process at multiple spatial scales. It critically accounts for the uncertainties in the parameters of this advanced spatial statistics model itself, thereby providing, for the first time, a full description of both the spatial coverage uncertainties along with the uncertainties in the modeling of these spatial gaps. These coverage errors are combined with the existing estimates of uncertainties due to observational issues at each station location. It results in an ensemble of 100,000 monthly temperatures fields over the entire globe that samples the combination of coverage, parametric and observational uncertainties from 1850 to 2018 on a 5° by 5° grid.

The 100,000 equally-plausible ensemble members are stored in a series of separate netcdf files each containing 1000 realisations.

Additionally, there is 100-realisation subsample that provides an estimate of the uncertainty in the full ensemble. This has been created using conditional Latin hypercube sampling across 25 key regions of the globe. It many cases it would be sufficient to analyse just this 100-member subsample, for example to compute a likely range in a quantity. It is recommended that full 100,000 member ensemble is only investigated in those situations where the precise shape of the uncertainty distribution is required. NetCDF files at both the annual and monthly resolution are provided for this subsample.

Citable as:  Ilyas, M.; Nychka, D.; Brierley, C.; Guillas, S. (2023): Global Ensemble of Temperatures with Quantified Uncertainties in Observations, Coverage and Spatial modeling (GETQUOCS) from 1850-2018. NERC EDS Centre for Environmental Data Analysis, date of citation.
Abbreviation: Not defined
Keywords: GETQUOCS, temperature, uncertainty, climate reconstruction, sea-surface temperature, approximate bayesian computation, historical climatology network, hypercube, homogenization, reliability


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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: When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

This spatially complete global temperature dataset is built upon HadCRUT., which is itself derived from a merger of CRUTEM. and HadSST. Data supplied for archiving at the Centre for Environmental Data Analysis (CEDA).

Data Quality:
Research data
File Format:

Process overview

This dataset was generated by the computation detailed below.

Uncertainty sampling procedure


The starting point for the creation of this dataset was HadCRUT., which is a 100-member ensemble of gridded temperature 'observations'. This was then infilled (i.e. expanded to give data everywhere) using multi-resolution lattice krigging with the uncertainty in the statistical model also sample. The R package used for the infilling and sampling was 'fields' (

Input Description


Output Description


Software Reference


  • var_id: temperature
  • units: celsius
  • long_name: Temperature_anomaly
  • units: degrees_north
  • var_id: latitude
  • long_name: latitude
  • units: degrees_east
  • var_id: longitude
  • long_name: longitude
  • var_id: time_bnds

Co-ordinate Variables

  • long_name: time
  • standard_name: time
  • var_id: time
  • units: days
Temporal Range
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Geographic Extent