<scp>DCENT</scp> ‐I: A Globally Infilled Extension of the Dynamically Consistent <scp>ENsemble</scp> 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.