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Abstract Accurate historical records of Earth’s surface temperatures are central to climate research and policy development. Widely-used estimates based on instrumental measurements from land and sea are, however, not fully consistent at either global or regional scales. To address these challenges, we develop the Dynamically Consistent ENsemble of Temperature (DCENT), a 200-member ensemble of monthly surface temperature anomalies relative to the 1982–2014 climatology. Each DCENT member starts from 1850 and has a 5° × 5° resolution. DCENT leverages several updated or recently-developed approaches of data homogenization and bias adjustments: an optimized pairwise homogenization algorithm for identifying breakpoints in land surface air temperature records, a physics-informed inter-comparison method to adjust systematic offsets in sea-surface temperatures recorded by ships, and a coupled energy balance model to homogenize continental and marine records. Each approach was published individually, and this paper describes a combined approach and its application in developing a gridded analysis. A notable difference of DCENT relative to existing temperature estimates is a cooler baseline for 1850–1900 that implies greater historical warming.
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This content will become publicly available on April 1, 2027
DCENT ‐I: A Globally Infilled Extension of the Dynamically Consistent ENsemble of Temperature Dataset
ABSTRACT A spatially infilled Dynamically Consistent Ensemble of surface Temperature (DCENT‐I) has been created by infilling land‐air and sea‐surface temperatures from DCENT using ordinary kriging with anisotropic and heterogeneous kernels. By incorporating air temperature anomalies over sea‐ice areas, DCENT‐I provides spatially complete monthly temperature fields at 5° resolution from 1850 to the present (currently the end of 2024) as a 200‐member ensemble. Uncertainty estimates that account for the need to infill for missing observations are made using a Multivariate Gaussian Process, and these are consistent with estimates derived from masking climate model simulations. The use of anisotropic and heterogeneous kernels leads to a reconstruction of El Niño variability whose spatial pattern and temporal variance are generally consistent throughout the record. As compared with taking the unfilled average, infilling increases the global mean surface temperature (GMST) warming estimate over 2005–2024 relative to a 1850–1900 baseline from 1.09 [0.96, 1.18] (95% range across members) to 1.17 [1.05, 1.26]°C, largely because of infilling in rapidly warming high‐latitude regions. Compared with HadCRUT5, GISTEMP v4, NOAA Global Temp v6, and Berkeley Earth, DCENT‐I shows a steadier and slightly faster GMST warming trend, reflecting the bias‐adjustments inherited from DCENT.
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- PAR ID:
- 10690170
- Publisher / Repository:
- Royal Meteorological Society
- Date Published:
- Journal Name:
- Geoscience Data Journal
- Volume:
- 13
- Issue:
- 2
- ISSN:
- 2049-6060
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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