Trained model coefficients, validation and testing data, and predictions, for the modeled driving of low-frequency regional Arctic sea ice concentration variability by large-scale climate modes, 1920-2022 ...

• Main Author: Wyburn-Powell, Christopher
• Format: Dataset
• Language: English
• Published: NSF Arctic Data Center 2024
• Subjects: Sea Ice Concentration; Coupled Climate Models; Machine Learning Models; Sea ice
• Online Access: https://dx.doi.org/10.18739/a2ms3k35m; https://arcticdata.io/catalog/view/doi:10.18739/A2MS3K35M

This dataset contains data describing the relationship between global climate modes of variability and regional Arctic sea ice concentration anomalies lagged by 2-20 years. Data originates from global climate models from the Coupled Climate Model Intercomparison Project - Phase 6 (CMIP6), which are freely available from the Earth System Grid Federation (ESGF). This data from CMIP6 focuses on the historical simulation period of 1920-2014, but also leverages pre-industrial control simulations. Climate mode of variability data is obtained using the Climate Variability Diagnostics Package datasets (doi:10.1002/2014EO490002). Observational Arctic sea ice concentration data are obtained from the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST1), doi:10.1029/2002JD002670. Firstly, this dataset firstly provides coefficients for the linear model relating standardized climate modes of variability with regional Arctic sea ice concentration (SIC) anomalies for 42 individual large ensembles and ...