Maximum Likelihood Estimates of Temperatures using Data from the Dynamically Consistent Ensemble of Temperature (Version 1.0)

doi:10.26050/WDCC/DCENT_MLE_v1_0

Calvert, Bruce

ExperimentDOI
amplificationbias-correctionclimate changeEl Niñoobservational datasea iceTemperature
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.

DCENT_MLE_v1.0 uses the approach of HadCRU_MLE_v1.2 (https://doi.org/10.26050/WDCC/HadCRU_MLE_v1.2), which is described in “Improving global temperature datasets to better account for non-uniform warming” (https://doi.org/10.1002/qj.4791), but uses different source data. Additional details about DCENT_MLE_v1.0 are available in the DCENT_MLE_v1.0 information document. The primary motivation to develop HadCRU_MLE_v1.0 was to better account for spatially nonuniform warming across the planet 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.

DCENT_MLE_v1.0 includes mean surface temperature anomalies for each month from 1850 to 2023 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 1982-2014 temperature climatology is available, which was produced by blending an extension of the DCLSAT temperature climatology for land and sea ice regions with the DCSST temperature climatology for open sea regions. Other information of DCENT_MLE_v1.0 is available, including model parameters, the estimated amplification function, the internal variability pattern, the land area fractions, measurement and sampling uncertainties of land surface air temperature anomalies, and the impacts of sea ice concentrations and the El Niño Southern Oscillation on surface temperature anomalies.

Future versions of DCENT_MLE may become available to extend the temporal coverage beyond 2023.
Project
DCENT_MLE (Maximum Likelihood Estimates of Temperatures using Data from the Dynamically Consistent Ensemble of Temperature)
Contact
Mr. Bruce T. T. Calvert (
 brucetcalvert@nullgmail.com
 0000-0002-1124-9632)
Spatial Coverage
Longitude -180 to 180 Latitude -90 to 90
Temporal Coverage
1850-01-01 to 2023-12-31 (gregorian)
Use constraints
Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/)
Data Catalog
World Data Center for Climate
Size
18.18 GiB (19517970707 Byte)
Format
NetCDF, ascii
Status
completely archived
Creation Date
Future Review Date
2034-09-21
Find data Export dataset acronyms
Cite as
Calvert, Bruce (2024). Maximum Likelihood Estimates of Temperatures using Data from the Dynamically Consistent Ensemble of Temperature (Version 1.0). World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/DCENT_MLE_v1_0

BibTeX RIS
Description
The quality of DCENT_MLE_v1.0 has been approved by Bruce T. T. Calvert on September 22, 2024. DCENT_MLE_v1.0 is a further derived product (classifying as a level 4 data processing level) derived from various source datasets, including 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. Sensitivity tests of DCENT_MLE_v1.0 use alternate sea ice source datasets (COBE-SST2, G10010v2, and G02202v4). 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.
Result Date
2024-09-22
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. In general, other information of DCENT_MLE_v1.0 is also spatially and temporally complete. The one exception is that the available estimates of measurement and sampling uncertainties for land surface air temperature anomalies are not spatially and temporally complete since the observational coverages of source datasets of land surface air temperature anomalies are not spatially and temporally complete.
Description
Summary:
Findable: 6 of 7 level;
Accessible: 2 of 3 level;
Interoperable: 3 of 4 level;
Reusable: 5 of 10 level
Method
F-UJI online v3.2.0 automated
Method Description
Checks performed by WDCC. Metrics documentation: https://doi.org/10.5281/zenodo.4081213 Metric Version: metrics_v0.5
Result Date
2024-10-11
Result Date
2024-10-11
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 spatial-temporal coverage description (metadata) is consistent to the data, time steps are correct and the time coordinate is continuous: 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
Method Url
https://www.wdc-climate.de/docs/WDCC_User_Guide_Data_v1.pdf
Result Date
2024-10-11
Contact typePersonORCIDOrganization
Contact
Mr. Bruce T. T. Calvert
0000-0002-1124-9632
-
Author
Mr. Bruce T. T. Calvert
0000-0002-1124-9632
-
Investigator
Mr. Bruce T. T. Calvert
0000-0002-1124-9632
-

Cites

[1] DOI Calvert, Bruce T. T. (2024). Improving global temperature datasets to better account for non‐uniform warming. doi:10.1002/qj.4791
[2] DOI Brohan, P.; Kennedy, J. J.; Harris, I.; Tett, S. F. B.; Jones, P. D. (2006). Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. doi:10.1029/2005JD006548
[3] DOI Chan, Duo; Gebbie, Geoffrey; Huybers, Peter. (2023). Global and Regional Discrepancies between Early-Twentieth-Century Coastal Air and Sea Surface Temperature Detected by a Coupled Energy-Balance Analysis. doi:10.1175/JCLI-D-22-0569.1

Is derived from

[1] DOI Danielson, Jeffrey J.; Gesch, Dean B. (2011). Global multi-resolution terrain elevation data 2010 (GMTED2010). doi:10.3133/ofr20111073
[2] DOI Gent, Peter R.; Danabasoglu, Gokhan; Donner, Leo J.; Holland, Marika M.; Hunke, Elizabeth C.; Jayne, Steve R.; Lawrence, David M.; Neale, Richard B.; Rasch, Philip J.; Vertenstein, Mariana; Worley, Patrick H.; Yang, Zong-Liang; Zhang, Minghua. (2011). The Community Climate System Model Version 4. doi:10.1175/2011jcli4083.1
[3] DOI Good, Simon; Fiedler, Emma; Mao, Chongyuan; Martin, Matthew J.; Maycock, Adam; Reid, Rebecca; Roberts-Jones, Jonah; Searle, Toby; Waters, Jennifer; While, James; Worsfold, Mark. (2020). The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. doi:10.3390/rs12040720
[4] DOI Hirahara, Shoji; Ishii, Masayoshi; Fukuda, Yoshikazu. (2014). Centennial-Scale Sea Surface Temperature Analysis and Its Uncertainty. doi:10.1175/jcli-d-12-00837.1
[5] DOI Kennedy, J. J.; Rayner, N. A.; Atkinson, C. P.; Killick, R. E. (2019). An Ensemble Data Set of Sea Surface Temperature Change From 1850: The Met Office Hadley Centre HadSST.4.0.0.0 Data Set. doi:10.1029/2018jd029867
[6] DOI Osborn, T. J.; Jones, P. D.; Lister, D. H.; Morice, C. P.; Simpson, I. R.; Winn, J. P.; Hogan, E.; Harris, I. C. (2021). Land Surface Air Temperature Variations Across the Globe Updated to 2019: The CRUTEM5 Data Set. doi:10.1029/2019jd032352
[7] DOI Titchner, Holly A.; Rayner, Nick A. (2014). The Met Office Hadley Centre sea ice and sea surface temperature data set, version 2: 1. Sea ice concentrations. doi:10.1002/2013jd020316
[8] DOI Meier, W. N.; Fetterer, F.; Windnagel, A. K.; Stewart, S. (2021). NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4. doi:10.7265/efmz-2t65
[9] DOI Walsh, J. E.; Chapman, W. L.; Fetterer, F.; Stewart, S. (2019). Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward, Version 2. doi:10.7265/jj4s-tq79
[10] DOI Chan, Duo; Gebbie, Geoffrey; Huybers, Peter. (2024). An Improved Ensemble of Land Surface Air Temperatures Since 1880 Using Revised Pair-Wise Homogenization Algorithms Accounting for Autocorrelation. doi:10.1175/JCLI-D-23-0338.1
[11] DOI Chan, Duo; Gebbie, Geoffrey; Huybers, Peter; Kent, Elizabeth C. (2024). A Dynamically Consistent ENsemble of Temperature at the Earth surface since 1850 from the DCENT dataset. doi:10.1038/s41597-024-03742-x
[12] DOI Menne, Matthew, J.; Williams, Claude N.; Gleason, Byron E.; Rennie, J. Jared; Lawrimore, Jay H. (2018). The Global Historical Climatology Network Monthly Temperature Dataset, Version 4. doi:10.1175/JCLI-D-18-0094.1

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Maximum Likelihood Estimates of Temperatures using Data from the Dynamically Consistent Ensemble of Temperature

[Entry acronym: DCENT_MLE_v1_0] [Entry id: 5281178]