Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set
Open accessAbstract
Recent developments in observational near‐surface air temperature and sea‐surface temperature analyses are combined to produce HadCRUT4, a new data set of global and regional temperature evolution from 1850 to the present. This includes the addition of newly digitized measurement data, both over land and sea, new sea‐surface temperature bias adjustments and a more comprehensive error model for describing uncertainties in sea‐surface temperature measurements. An ensemble approach has been adopted to better describe complex temporal and spatial interdependencies of measurement and bias uncertainties and to allow these correlated uncertainties to be taken into account in studies that are based upon HadCRUT4. Climate diagnostics computed from the gridded data set broadly agree with those of other global near‐surface temperature analyses. Fitted linear trends in temperature anomalies are approximately 0.07°C/decade from 1901 to 2010 and 0.17°C/decade from 1979 to 2010 globally. Northern/southern hemispheric trends are 0.08/0.07°C/decade over 1901 to 2010 and 0.24/0.10°C/decade over 1979 to 2010. Linear trends in other prominent near‐surface temperature analyses agree well with the range of trends computed from the HadCRUT4 ensemble members.
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Like Morice et al. (2012), we produce an ensemble of the GHCNm v4 dataset as a way to quantify uncertainty that arises from correlated error structures in the data.
“Further, like Morice et al. (2012), we produce an ensemble of the GHCNm v4 dataset as a way to quantify uncertainty that arises from correlated error structures in the data.”
From The Global Historical Climatology Network Monthly Temperature Dataset, Version 4 (2018) · cites this paper as Morice et al. 2012
Our approach to building an uncertainty budget for GHCNm v4 temperatures broadly follows methods described in Brohan et al. (2006) and Morice et al. (2012) that have been applied to the CRUTEM dataset.
“Our approach to building an uncertainty budget for GHCNm v4 temperatures broadly follows methods described in Brohan et al. (2006) and Morice et al. (2012) that have been applied to the CRUTEM dataset (Jones et al. 2012).”
From The Global Historical Climatology Network Monthly Temperature Dataset, Version 4 (2018) · cites this paper as Morice et al. 2012
The method relies on a comparison of gridbox-scale trends in observational datasets for temperature and precipitation with those produced in climate model runs from CMIP6.
“The method relies on a comparison of gridbox-scale trends in observational datasets for temperature (HadCRUT4 version 4.6 54 ) and precipitation (GPCC v2018, obtainable from https://psl.noaa.gov/data/gridded/data.gpcc.html),with those produced in climate model runs from CMIP6 55 .”
From Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies (2021) · cites this paper as [55]
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“Our approach to building an uncertainty budget for GHCNm v4 temperatures broadly follows methods described in Brohan et al. (2006) and Morice et al. (2012) that have been applied to the CRUTEM dataset (Jones et al. 2012).”
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- Authors
- Morice, C. P., Kennedy, J. J., Rayner, N. A., & Jones, P. D.
- Affiliations
- Met Office (GB)
- Venue
- Journal of Geophysical Research: Atmospheres
- Publisher
- American Geophysical Union (AGU)
- Published
- 2012-04-17
- Volume
- 117
- Issue
- D8
- ISSN
- 0148-0227 (ISSN-L)
- OpenAlex
- W2031027346
- Cited by
- 1,657
- References
- 50
- Citations by year
- 2026: 21 · 2025: 38 · 2024: 59 · 2023: 96 · 2022: 111 · 2021: 201 · 2020: 205 · 2019: 154 · 2018: 170 · 2017: 153 · 2016: 109 · 2015: 130 · 2014: 104 · 2013: 84 · 2012: 20
- FWCI
- 89.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Geophysical Research Atmospheres (publishedVersion)
- UEA Digital Repository (University of East Anglia) (acceptedVersion)
- Funders
- U.S. Department of Energy (DESC0005689)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Geology and Paleoclimatology Research
- Keywords
- HadCRUT4, global temperature change, regional temperature change, surface air temperature, sea surface temperature, temperature uncertainty, error model, temperature trends, climate diagnostics
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