A Multi-Year Forecast Dataset of Arctic Sea Ice Concentration Based on Spectral Analysis and Modeling

1. Data Description The data include both codes, forecasted and observed Arctic SIC. The forecasted data, contains the predicted SIC from January, 1, 2024 to December, 31, 2035. The observed data is SIC in 2024 is provided by the University of Bremen (https://seaice.uni-bremen.de/sea-ice-concentration/amsre-amsr2/.), which can be used for validation. 2. SIC data Observed Data: Temporal Coverage: January 1, 2024, to December 31, 2024. Data Formats: NetCDF format. Naming Convention: Files are named according to the date in the format YYYYMMDD.tif. Forecasted Data: Temporal Coverage: January 1, 2024, to December 31, 2035. Data Formats: GeoTIFF format. Projection System: Northern Polar Stereographic. Spatial Resolution: 6.25 km. 3. code Data Formats: py format. Function: SIC_plot.py: Visualizes sea ice concentration using NetCDF data, applying a polar stereographic projection, custom colormap, and land/coastline features for better geographic context. SIM_plot.py: Compares forecasted and observed sea ice margin using binary segmentation, erosion, and contour extraction, overlaying results on a polar stereographic map with land masking. SIC_forecast.py: Implements long-term sea ice concentration forecasting by combining Least Squares (LS) trend modeling with an AutoRegressive (AR) model for residual correction. LS fits a quadratic trend with periodic components, while AR captures temporal dependencies in residuals, improving forecast accuracy. The method extrapolates future SIC values by predicting both trend and residual components.