Maximum Likelihood Estimates of Temperatures using Data from the Hadley Centre and the Climate Research Unit (Version 1.0)
doi:10.26050/WDCC/HadCRU_MLE_v1
Calvert, Bruce T. T.
ExperimentDOI
Summary
HadCRU_MLE_v1.0 is a dataset of monthly gridded surface temperatures for the Earth during the instrumental period (since 1850). The name ‘HadCRU_MLE_v1.0’ 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_v1.0 include land surface air temperature anomalies of HadCRUT4, 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 OSTIA, surface elevation data of GMTED2010, and climate model output of CCSM4 for a pre-industrial control scenario. HadCRU_MLE_v1.0 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. The primary motivation to develop HadCRU_MLE_v1.0 was to correct for two biases that may exist in global instrumental temperature datasets. The first bias is an amplification bias caused by not adequately accounting for the tendency of different regions of the planet to warm at different rates. The second bias is a sea ice bias caused by not adequately accounting for changes in sea ice coverage during the instrumental period. Corrections to these biases increased the estimate of global mean surface temperature change during the instrumental period. The new dataset has improvements compared to the Cowtan and Way version 2 dataset, including an improved statistical foundation for estimating model parameters, taking advantage of temporal correlations of observations, taking advantage of correlations between land and sea observations, and accounting for more sources of uncertainty. To properly correct for amplification bias, HadCRU_MLE_v1.0 incorporates the behaviour of the El Niño Southern Oscillation. HadCRU_MLE_v1.0 includes mean surface temperature anomalies for each month from 1850 to 2018 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_v1.0 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_v1.0 is available, including the estimated local amplification factors, the magnitude of the corrections for sea ice bias, and the impact of the El Niño Southern Oscillation on surface temperature anomalies. Version 1.1 of HadCRU_MLE is now available, which includes updated source data ending in December 2020.
Calvert, Bruce T. T. (2021). Maximum Likelihood Estimates of Temperatures using Data from the Hadley Centre and the Climate Research Unit (Version 1.0). World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/HadCRU_MLE_v1
The quality of HadCRU_MLE_v1 has been approved by Bruce T. T. Calvert on November 30, 2020. HadCRU_MLE_v1 is a further derived product (classifying as...
Description
The quality of HadCRU_MLE_v1 has been approved by Bruce T. T. Calvert on November 30, 2020. HadCRU_MLE_v1 is a further derived product (classifying as a level 4 data processing level) derived from various source datasets, including land surface air temperature anomalies of HadCRUT4, sea surface temperature anomalies of HadSST4, sea ice coverage of HadISST2, land mask data of OSTIA, the surface temperature climatology of Jones et al. (1999), the sea surface temperature climatology of HadSST3, surface elevation data of GMTED2010, and climate model output of CCSM4 for a pre-industrial control scenario. These source datasets either have had extensive quality control or are themselves further derived products based on source datasets that have had extensive quality control.
Completeness report
Due to the maximum likelihood estimation approach used, the estimated field of surface temperature anomalies is temporally and spatially complete for ...
Description
Due to the maximum likelihood estimation approach used, the estimated field of surface temperature anomalies is temporally and spatially complete for the entire instrumental period and for the entire surface of the Earth. Other information of HadCRU_MLE_v1 is also spatially and temporally complete. As a result, there are no missing values in the dataset.
FAIR
F-UJI result: total 66 %
Description
Summary: Findable: 6 of 7 level; Accessible: 2 of 3 level; Interoperable: 3 of 4 level; Reusable: 5 of 10 level
SQA - Scientific Quality Assurance 'approved by author'
Result Date
2021-02-12
Technical Quality Assurance (TQA)
TQA - Technical Quality Assurance 'approved by WDCC'
Description
1. Number of data sets is correct and > 0: passed; 2. Size of every data set is > 0: passed; 3. The data sets and corresponding metadata are accessible: passed; 4. The data sizes are controlled and correct: passed; 5. The temporal coverage description (metadata) is consistent to the data: passed; 6. The format is correct: passed; 7. Variable description and data are consistent: passed
Method
WDCC-TQA checklist
Method Description
Checks performed by WDCC. The list of TQA metrics are documented in the 'WDCC User Guide for Data Publication' Chapter 8.1.1
[2] DOICalvert, Bruce. (2024). Maximum Likelihood Estimates of Temperatures using Data from the Hadley Centre and the Climate Research Unit (Version 1.2). doi:10.26050/WDCC/HadCRU_MLE_v1.2
Is cited by
[1] DOICalvert, Bruce T. T. (2024). Improving global temperature datasets to better account for non‐uniform warming. doi:10.1002/qj.4791