Project: Maximum Likelihood Estimates of Temperatures using Data from the Dynamically Consistent Ensemble of Temperature - The primary goal of DCENT_MLE is to combine instrumental observations with physically realistic statistical models to produce maximum likelihood estimates of surface temperature anomalies and other physical quantities of the Earth. Additional goals of DCENT_MLE include correcting for biases in estimates, producing the most efficient estimates given the available data, and better quantifying uncertainties. The maximum likelihood estimation approach allows for estimated fields to be temporally and spatially complete for the entire instrumental period (since 1850) and for the entire surface of the Earth. DCENT_MLE uses source datasets primarily from the Dynamically Consistent Ensemble of Temperature project. This project has not received funding from any source. Summary: DCENT_MLE_v1.0 is a dataset of monthly gridded surface temperatures for the Earth during the instrumental period (since 1850). The name ‘DCENT_MLE_v1.0’ reflects the dataset’s use of maximum likelihood estimation and observational data primarily from the Dynamically Consistent Ensemble of Temperature (DCENT) (Chan, Gebbie, Huybers and Kent, 2024). Source datasets used to create DCENT_MLE_v1.0 include land surface air temperatures of Chan, Gebbie and Huybers (2024), non-infilled DCLSAT, GHCNv4, and CRUTEM5; sea surface temperatures of DCSST; sea ice coverage of HadISST2; measurement and sampling uncertainties of CRUTEM5 and HadSST4; land mask data of OSTIAv2; surface elevation data of GMTED2010; and climate model output of CCSM4 for a pre-industrial control simulation. DCENT_MLE_v1.0 was generated using information from the DCENT project, the Met Office Hadley Centre, the Climate Research Unit of the University of East Anglia, the U.S. National Oceanic and Atmospheric Administration, the E.U. Copernicus Marine Service, the U.S. Geological Survey, and the University Corporation of Atmospheric Research. Results of sensitivity tests using alternate sea ice source datasets from the Japanese Meteorological Agency (COBE-SST2) and the National Snow and Ice Data Center (modified G10010v2 appended with G02202v4) are also available.
