The Qinghai-Tibet Plateau, known for its high altitude, cold climate, and fragile ecosystem, presents unique challenges and opportunities for the implementation of an intelligent sponge urban system. The heat island effect, a phenomenon where urban areas experience higher temperatures compared to surrounding rural areas, can be particularly problematic in such a sensitive environment. Predicting and mitigating heat island intensity is crucial for improving urban livability and environmental sustainability. To develop a procedure for predicting heat island intensity in an intelligent sponge urban system, ensuring accurate and real-time predictions through a series of steps. Collect parameter information of the underlying surface using meteorological observation data from the sponge city, field observation data, and investigation data of the sponge city. Gather comprehensive data on the physical and environmental characteristics of the urban surface. Establish a set of digital labels with feature data derived from the collected information. Add the labeled data to the training sample set for the prediction model of sponge city surface heat island intensity. A crucial input for establishing a real-time prediction model. Train the prediction model function for sponge city surface heat island intensity using the data. This Experiment contains 2 datasets, corrected surface air temperature data and training data. The corrected surface air temperature data has been processed using meteorological observation data and thermal infrared remote sensing data. The data covers a high-altitude area of the Qinghai-Tibet Plateau, with a spatial resolution of 30 meters. The original temperature data were obtained from multiple sources, including thermal infrared remote sensing data from Landsat 8(L8) and Landsat 9 (L9) Collection 2 (C2) Level 2 (L2) products, as well as ground station measurements from National Tibetan Plateau/Third Pole Environment Data Center. The regression algorithms in supervised learning was trained to correct for biases and inaccuracies in updating the spatialized data of near-surface air temperature. This dataset is suitable for climate research, environmental monitoring, and other applications requiring relatively accurate surface air temperature data.

CHELSA_v1.0 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. Version 1.0 is a first release. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. CHELSA_v1 is based on a quasi-mechanistical statistical downscaling of the ERA interim global circulation model (http://www.ecmwf.int/en/research/climate-reanalysis/era-interim) with a GPCC (https://www.dwd.de/EN/ourservices/gpcc/gpcc.html) and GHCN (https://www.ncdc.noaa.gov/ghcnm/) bias correction. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Monthly coverage 1979 - 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). Downscaled ERA-interim model. Allows calculation of derived parameters based on monthly values such as length of dry periods etc.

CHELSA_v1.1 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns as well as derived bioclimatic and interannual parameters for the time period 1979-2013. CHELSA_v1.1 is based on a quasi-mechanistical statistical downscaling of the ERA interim global circulation model (http://www.ecmwf.int/en/research/climate-reanalysis/era-interim) with a GPCC (https://www.dwd.de/EN/ourservices/gpcc/gpcc.html) and GHCN (https://www.ncdc.noaa.gov/ghcnm/) bias correction.

CHELSA_v1.1 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns as well as derived bioclimatic and interannual parameters for the time period 1979-2013. CHELSA_v1.1 is based on a quasi-mechanistical statistical downscaling of the ERA interim global circulation model (http://www.ecmwf.int/en/research/climate-reanalysis/era-interim) with a GPCC (https://www.dwd.de/EN/ourservices/gpcc/gpcc.html) and GHCN (https://www.ncdc.noaa.gov/ghcnm/) bias correction.

CHELSA_v1.0 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. Version 1.0 is a first release. It includes monthly and annual mean temperature and precipitation patterns for the time period 1979-2013. CHELSA_v1 is based on a quasi-mechanistical statistical downscaling of the ERA interim global circulation model (http://www.ecmwf.int/en/research/climate-reanalysis/era-interim) with a GPCC (https://www.dwd.de/EN/ourservices/gpcc/gpcc.html) and GHCN (https://www.ncdc.noaa.gov/ghcnm/) bias correction. Specifications: High resolution (30 arcsec, ~1 km) Precipitation & Temperature Monthly coverage 1979 - 2013 Incorporation of topoclimate (e.g. orographic rainfall & wind fields). Downscaled ERA-interim model. Allows calculation of derived parameters based on monthly values such as length of dry periods etc.

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. Version 1.1 of DCENT_MLE is now available, which includes updated source data ending in December 2024.

HadCRU_MLE_v1.2 is a dataset of monthly gridded surface temperatures for the Earth during the instrumental period (since 1850). The name ‘HadCRU_MLE_v1.2’ 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_v1.2 include land surface air temperature anomalies of non-infilled HadCRUT5, 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_v1.2 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-SST2) 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 increased the estimate of global mean surface temperature change during the instrumental period. HadCRU_MLE_v1.2 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. To support publication of the referenced research article in the Quarterly Journal of the Royal Meteorological Society, HadCRU_MLE_v1.2 was created to respond to suggestions by peer reviewers, including extended coverage until the end of 2023 and additional sensitivity tests. HadCRU_MLE_v1.2 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 1961-1990 temperature climatology for HadCRU_MLE_v1.2 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_v1.2 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. Version 1.3 of HadCRU_MLE is now available, which includes updated source data ending in December 2024.

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. Version 1.4 of HadCRU_MLE is now available, which includes updated source data ending in December 2025.

HadCRU_MLE_v1.4 is a dataset of monthly gridded surface temperatures for the Earth during the instrumental period (since 1850). The name ‘HadCRU_MLE_v1.4’ 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_v1.4 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_v1.4 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 G02202v6) 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_v1.4 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_v1.4 includes mean surface temperature anomalies for each month from 1850 to 2025 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_v1.4 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_v1.4 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. HadCRU_MLE_v1.4 is an annual update to extend HadCRU_MLE until the end of 2025. The median estimate of the change in global mean surface temperature change from 1850-1900 to 2025 is 1.52 °C, with a 95% confidence interval of [1.42,1.62] °C. Future versions of HadCRU_MLE may become available to extend the temporal coverage beyond 2025.

DCENT_MLE_v1.2 is a dataset of monthly gridded surface temperatures for the Earth during the instrumental period (since 1850). The name ‘DCENT_MLE_v1.2’ 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_v1.2 include land surface air temperatures of Chan, Gebbie and Huybers (2024), non-infilled DCLSAT, and GHCNv4; sea surface temperatures of DCSST; sea ice coverage of HadISST2; land mask data of OSTIAv2; surface elevation data of GMTED2010; and climate model output of CCSM4 for a pre-industrial control simulation. DCENT_MLE_v1.2 was generated using information from the DCENT project, the Met Office Hadley Centre, 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-SST3) and the National Snow and Ice Data Center (modified G10010v2 appended with G02202v6) are also available. DCENT_MLE_v1.2 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_v1.2 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_v1.2 includes mean surface temperature anomalies for each month from 1850 to 2025 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_v1.2 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. DCENT_MLE_v1.2 is an annual update to extend DCENT_MLE until the end of 2025. The median estimate of the change in global mean surface temperature change from 1850-1900 to 2025 is 1.61 °C, with a 95% confidence interval of [1.47,1.75] °C. Future versions of DCENT_MLE may become available to extend the temporal coverage beyond 2025.