Polar regions are data sparse regions. Research ships operating in polar regions often record sea-ice conditions during their transects through ice infested waters. Such observations of the sea-ice conditions are often the only information that can be provided in addition to satellite-based estimates of the sea-ice conditions, such as sea-ice concentration or sea-ice thickness. Such observations have been carried out and gathered using two protocols. For the Antarctic, this is the so-called ASPeCt protocol [Worby and Allison, 1999; Worby and Dirita, 1999; Worby et al., 2008]. For the Arctic, this is the so-called ASSIST/IceWatch protocol [Hutchings et al., 2018]. The latter builds on the ASPeCt protocol, incorporating surface melt conditions being more ubiquitous in the Arctic. Ship-based observations of the sea-ice conditions are conducted manually, visually, i.e. by eye, regularly every hour taking into account an area around the ship of about one kilometer radius. Note that this area distorts to an elliptically shaped area as a function of observers' experience, ships' cruising speed and ice and visibility conditions. Each observation comprises the total sea-ice concentration, and the concentration, level ice thickness, level ice snow depth, fraction and height of ridges, ice type, snow type, and floe size for the up to three thickest ice types. For the Arctic, melt-pond fraction and stage-of-melt are also part of the observables. In addition to the ships' position often auxiliary parameters such as visibility, wind speed and direction, or air and water temperature are recorded. For development and evaluation of satellite-based sea-ice products, such ship-based observations are of great value. Because of this, within the ESA-CCI sea-ice ECV project (ESA-SICCI), phase 2, a standardized data set of such ship-based observations was generated for both polar regions. It comprises data from June 2002 through December 2015. This time period is motivated by the purpose to evaluate sea-ice concentration data retrieved from AMSR-E and AMSR2 brightness temperature measurements which, at the time the project was initiated, were planned to be retrieved until the end of 2015. The data set incorporates observational data from various collections, e.g. a part of the original ASPeCt collection [Worby et al., 2008], which ended in May 2005. More information about all data sources is given below. All data have been manually standardized to the same format (i.e., number of decimals, unit), using the same value to describe missing data, using the same temporal ordering, and filling gaps with the respective missing-data value. Double data entries have been removed. The data set is split into two ascii text files, one for the Arctic, one for the Antarctic. It has been successfully used to evaluate sea-ice concentration and thickness products of the ESA-SICCI phase 2 project.

Polar regions are data sparse regions. Research ships operating in polar regions often record sea-ice conditions during their transects through ice infested waters. Such observations of the sea-ice conditions are often the only information that can be provided in addition to satellite-based estimates of the sea-ice conditions, such as sea-ice concentration or sea-ice thickness. Such observations have been carried out and gathered using two protocols. For the Antarctic, this is the so-called ASPeCt protocol [Worby and Allison, 1999; Worby and Dirita, 1999; Worby et al., 2008]. For the Arctic, this is the so-called ASSIST/IceWatch protocol [Hutchings et al., 2018]. The latter builds on the ASPeCt protocol, incorporating surface melt conditions being more ubiquitous in the Arctic during summer. Ship-based observations of the sea-ice conditions are conducted manually, visually, i.e. by eye, regularly every hour taking into account an area around the ship of about one kilometer radius. Note that this area distorts to an elliptically shaped area as a function of observers' experience, ships' cruising speed and ice and visibility conditions. Each observation comprises the total sea-ice concentration, and the concentration, level ice thickness, level ice snow depth, fraction and height of ridges, ice type, snow type, and floe size for the up to three thickest ice types. For the Arctic, melt-pond fraction and stage-of-melt are also part of the observables. In addition to the ships' position often auxiliary parameters such as visibility, wind speed and direction, or air and water temperature are recorded. For development and evaluation of satellite-based sea-ice products, such ship-based observations are of great value. Because of this, within the ESA-CCI sea-ice ECV project (ESA-SICCI), phase 2, a standardized data set of such ship-based observations was generated for both polar regions. It comprised data from June 2002 through December 2015. This time period was motivated by the purpose to evaluate sea-ice concentration data retrieved from AMSR-E and AMSR2 brightness temperature measurements which, at the time the project was initiated, were planned to be retrieved until the end of 2015. In this version 2 of this data set the temporal coverage has been extended until the end of 2019. The data set incorporates observational data from various collections, e.g. a part of the original ASPeCt collection [Worby et al., 2008], which ended in May 2005. More information about all data sources is given in the global attributes of the netCDF files and in two separate reference lists. All data have been manually standardized to the same format (i.e., number of decimals, unit), using the same value to describe missing data, using the same temporal ordering, and filling gaps with the respective missing-data value. Double data entries have been removed. Dubious / obviously wrong entries have been set to missing values. The data set is available as two separate netCDF files, one for the Arctic, one for the Antarctic. It is additionally available as two separate ascii-text files under https://icdc.cen.uni-hamburg.de/en/seaiceparameter-shipobs.html , where the netCDF files are available as well. The data set has been successfully used to evaluate sea-ice concentration and thickness products of the ESA-SICCI phase 2 project.

Polar regions are data sparse regions. Research ships operating in polar regions often record sea-ice conditions during their transects through ice infested waters. Such observations of the sea-ice conditions are often the only information that can be provided in addition to satellite-based estimates of the sea-ice conditions, such as sea-ice concentration or sea-ice thickness. Such observations have been carried out and gathered using two protocols. For the Antarctic, this is the so-called ASPeCt protocol [Worby and Allison, 1999; Worby and Dirita, 1999; Worby et al., 2008]. For the Arctic, this is the so-called ASSIST/IceWatch protocol [Hutchings et al., 2018]. The latter builds on the ASPeCt protocol, incorporating surface melt conditions being more ubiquitous in the Arctic during summer. Ship-based observations of the sea-ice conditions are conducted manually, visually, i.e. by eye, regularly every hour taking into account an area around the ship of about one kilometer radius. Note that this area distorts to an elliptically shaped area as a function of observers' experience, ships' cruising speed and ice and visibility conditions. Each observation comprises the total sea-ice concentration, and the concentration, level ice thickness, level ice snow depth, fraction and height of ridges, ice type, snow type, and floe size for the up to three thickest ice types. For the Arctic, melt-pond fraction and stage-of-melt are also part of the observables. In addition to the ships' position often auxiliary parameters such as visibility, wind speed and direction, or air and water temperature are recorded. For development and evaluation of satellite-based sea-ice products, such ship-based observations are of great value. Because of this, within the ESA-CCI sea-ice ECV project (ESA-SICCI), phase 2, a standardized data set of such ship-based observations was generated for both polar regions. It comprised data from June 2002 through December 2015. This time period was motivated by the purpose to evaluate sea-ice concentration data retrieved from AMSR-E and AMSR2 brightness temperature measurements which, at the time the project was initiated, were planned to be retrieved until the end of 2015. In this version 2 of this data set the temporal coverage has been extended until the end of 2019. The data set incorporates observational data from various collections, e.g. a part of the original ASPeCt collection [Worby et al., 2008], which ended in May 2005. More information about all data sources is given in the global attributes of the netCDF files and in two separate reference lists. All data have been manually standardized to the same format (i.e., number of decimals, unit), using the same value to describe missing data, using the same temporal ordering, and filling gaps with the respective missing-data value. Double data entries have been removed. Dubious / obviously wrong entries have been set to missing values. The data set is available as two separate netCDF files, one for the Arctic, one for the Antarctic. It is additionally available as two separate ascii-text files under https://icdc.cen.uni-hamburg.de/en/seaiceparameter-shipobs.html , where the netCDF files are available as well. The data set has been successfully used to evaluate sea-ice concentration and thickness products of the ESA-SICCI phase 2 project.

DCENT_MLE_v1.1 is a dataset of monthly gridded surface temperatures for the Earth during the instrumental period (since 1850). The name ‘DCENT_MLE_v1.1’ 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_v1.1 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_v1.1 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-SST3) and the National Snow and Ice Data Center (modified G10010v2 appended with G02202v4) are also available. DCENT_MLE_v1.1 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_v1.1 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_v1.1 is an annual update to DCENT_MLE_v1. The median estimate of the change in global mean surface temperature change from 1850-1900 to 2024 is 1.71 °C, with a 95% confidence interval of [1.57,1.85] °C. DCENT_MLE_v1.1 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 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_v1.1 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 2024. Version 1.2 of DCENT_MLE is now available, which includes updated source data ending in December 2025.

The field experiment DAMOCLES 2007 (Hamburg Arctic Ocean Buoy Drift Experiment DAMOCLES 2007-2008) consisted of the deployment and tracking of an array of 16 drifting autonomous buoys in the Central Arctic Ocean. The buoys were deployed in a quadratic array with 400 kilometres side length in the Siberian sector of the Central Arctic Ocean in April 2007. While drifting towards Fram Strait the buoys delivered at approximately 1-hourly time intervalls position, sea level pressure and temperature for several months with the last buoy transmitting until January 2008. The aim of the experiment was to study the Atmosphere-Ice-Ocean interaction, especially the impact of cyclones on the formation and transport of sea ice. DAMOCLES 2007 and DAMOCLES 2008 are a contribution to European integrated project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies) which is funded by the European Union. DAMOCLES is a contribution to IPY 2007-2008 (International Polar Year).

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_v1.3 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_v1.2 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.

The field experiment DAMOCLES 2008 (Hamburg Arctic Ocean Buoy Drift Experiment DAMOCLES 2008-2009) consisted of the deployment and tracking of 9 drifting autonomous ice buoys in the Arctic Ocean. Seven buoys were deployed in the Canadian sector of the Arctic Ocean in late April 2008. Two more buoys were deployed in the Beaufort Sea and in the Laptev Sea in September and October 2008. The platforms report position, atmospheric pressure, temperature and humidity, wind speed and ice temperature at 3-hourly time steps. The last two buoys additionally report wind direction. The aim of the experiment was to study the Atmosphere-Ice-Ocean interaction, especially the impact of cyclones on the formation and transport of sea ice. DAMOCLES 2008 and its predecessor DAMOCLES 2007 are a contribution to European integrated project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies) which is funded by the European Union. DAMOCLES is a contribution to IPY 2007-2008 (International Polar Year).

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.