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

hdl:21.14106/7fb71c3441c2168baa5d857619074bfd1b3c2d9c

Calvert, Bruce

Dataset
Summary
Time series of DCENT_MLE_v1.0 are also available as text files. These time series include estimates of global mean surface temperature anomalies, and estimates of the impacts of sea ice concentrations and an internal variability pattern on the global mean surface temperature anomaly. Estimated temperature anomalies include these impacts. The internal variability pattern corresponds well to the El Niño Southern Oscillation. 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.

Estimates are available for each month and year from 1850 to 2023. The maximum likelihood estimation approach allows for the estimated global mean surface temperature anomalies to be temporally complete for the entire time period. Other estimates of this dataset are also temporally complete.
Project
DCENT_MLE (Maximum Likelihood Estimates of Temperatures using Data from the Dynamically Consistent Ensemble of Temperature)
Additional Information
Code of Maximum Likelihood Estimates of Temperatures using Data from the Dynamically Consistent Ensemble of Temperature (Version 1.0)
Information Document of Maximum Likelihood Estimates of Temperatures using Data from the Dynamically Consistent Ensemble of Temperature (Version 1.0)
Text File describing Updated Code of Maximum Likelihood Estimates of Temperatures using Data from the Hadley Centre and the Climate Research Unit (Version 1.2)
Spatial Coverage
Longitude -180 to 180 Latitude -90 to 90
Temporal Coverage
1850-01-01 to 2023-12-31
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
Access constraints
registered users
Size
484.00 KiB (495615 Byte)
Format
ascii
Status
completely archived
Creation Date
Future Review Date
2034-09-21
Download Permission
Please login to check permission and download options
Cite as
[ Derived from parent entry - See data hierarchy tab ]
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 this dataset has been approved by Bruce T. T. Calvert on September 22, 2024. This dataset 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.
Description
Due to the maximum likelihood estimation approach used, the estimated global mean surface temperature anomalies are temporally complete for the entire time period. Other estimates of this dataset also temporally complete. As a result, there are no missing values in the dataset.

Parent

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

Parent project(s)

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

[Entry acronym: DCENT_MLE_v1_0_tim] [Entry id: 5281188]