DOI: 10.26050/wdcc/hadcru_mle_v1.3
title: Maximum Likelihood Estimates of Temperatures using Data from the Hadley Centre and the Climate Research Unit (Version 1.3)

description: Project: Maximum Likelihood Estimates of Temperatures using Data from the Hadley Centre and the Climate Research Unit - The primary goal of HadCRU_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 HadCRU_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. HadCRU_MLE uses source datasets primarily from the Met Office Hadley Centre and the Climate Research Unit of the University of East Anglia. This project has not received funding from any source.
Summary: HadCRU_MLE_v1.3 is a dataset of monthly gridded surface temperatures for the Earth during the instrumental period (since 1850). The name ‘HadCRU_MLE_v1.3’ reflects the dataset’s use of maximum likelihood estimation and observational data primarily from the Met Office Hadley Centre and the Climate Research Unit of the University of East Anglia. Source datasets used to create HadCRU_MLE_v1.3 include land surface air temperature anomalies of non-infilled HadCRUT5, exposure bias adjustments of Wallis et al. (2024), sea surface temperature anomalies of HadSST4, sea ice coverage of HadISST2, the surface temperature climatology of Jones et al. (1999), the sea surface temperature climatology of HadSST3, land mask data of OSTIAv2, surface elevation data of GMTED2010, and climate model output of CCSM4 for a pre-industrial control scenario. HadCRU_MLE_v1.3 was generated using information from the Met Office Hadley Centre, the Climate Research Unit of the University of East Anglia, 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-SST3) and the National Snow and Ice Data Center (modified G10010v2 appended with G02202v4) are also available.

 The primary motivation to develop HadCRU_MLE_v1.0 was to better account for spatially nonuniform warming across the planet. HadCRU_MLE_v1.0 better accounts for nonuniform warming by fitting an amplification function to observations to better account for spatially nonuniform warming trends, and by using differences in temperature climatologies and temperature anomalies between open sea and sea ice regions to better account for the impacts of changes in sea ice concentrations. These improvements, as described in “Improving global temperature datasets to better account for non-uniform warming” (https://doi.org/10.1002/qj.4791), increased the estimate of global mean surface temperature change during the instrumental period. HadCRU_MLE_v1.3 has additional improvements compared to HadCRUT5 Analysis, including correcting for a small underestimation of LSAT warming between 1961 and 1990, taking advantage of temporal correlations of observations, taking advantage of correlations between land and open sea observations, and better treatment of the El Niño Southern Oscillation.

 HadCRU_MLE_v1.3 includes mean surface temperature anomalies for each month from 1850 to 2024 and for each 5° latitude by 5° longitude grid cell. The maximum likelihood estimation approach allows for the estimated field of surface temperature anomalies to be temporally and spatially complete for the entire instrumental period and for the entire surface of the Earth. A 5° by 5° gridded 1961-1990 temperature climatology for HadCRU_MLE_v1.3 is available, although caution is advised when interpreting this temperature climatology since the source datasets used for temperature climatologies do not correspond perfectly with the source datasets used for temperature anomalies. Other information of HadCRU_MLE_v1.3 is available, including model parameters, the estimated amplification function, the internal variability pattern, the land area fractions, and the impacts of sea ice concentrations and the El Niño Southern Oscillation on surface temperature anomalies.

Future versions of HadCRU_MLE may become available to extend the temporal coverage beyond 2024.