Alaska glaciers are among the regions experiencing the most severe mass loss globally. Studying the long-term mass balance in this region is of critical importance for understanding glacial responses to climate change. This dataset reconstructs monthly-scale mass balance for nine glaciers in Alaska over the past six decades, conducts comparative analyses of mass balance variations, and reveals the similarities, differences, and underlying mechanisms of these changes. Using a newly developed glacier mass balance reconstruction model, the monthly mass balance of nine glaciers in Alaska from 1961 to 2023 has been reconstructed, clarifying the spatial heterogeneity of mass balance changes in Alaska glaciers. Sensitivity experiments were further conducted to uncover the driving mechanisms behind the spatial variations in mass balance across Alaska glaciers.
The observed glacier mass balance data were sourced from the World Glacier Monitoring Service (WGMS, https://wgms.ch/). Meteorological forcing data (temperature and precipitation) were retrieved from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis dataset (ERA5 monthly averaged data on single levels from 1940 to present, https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-monthly-means?tab=overview).
Developed a multi-parameter automatic calibration model for glacier mass balance: The original glacier model (Open Global Glacier Model, OGGM) was improved and developed to create the Glacier Mass Balance Multi-Parameter Calibration Model (OGGM_MPAC: Multi-Parameter Automatic Calibration for OGGM). The multi-parameters automatically calibrated by OGGM_MPAC include: precipitation correction factor (Pf), glacier temperature index (μ), bias correction factor (ε), glacier melt onset threshold (Tmelt), temperature lapse rate with altitude (Tlap), and rain-snow separation threshold (Tsolid). In the original model, only the first three parameters were calibrated, while the latter three were assigned uniform default values. This setting fails to adequately reflect the climatic differences in the distribution environments among various glaciers. Therefore, the new model incorporates calibration for these three additional parameters. The model employs a stepwise iterative method for automatic parameter calibration and utilizes "leave-one-out cross-validation."
The Root Mean Square Error (RMSE) was employed to evaluate the discrepancy between the simulated and observed glacier mass balance. Furthermore, the simulation results were validated using mass balance data for these nine glaciers derived from altimetry measurements from 2000 to 2020.The validation results indicate that on an annual scale (annual mass balance), the RMSE between simulated and observed values is 0.68 m w.e/a. On a seasonal scale, the RMSE is 0.68 m w.e/a for the winter mass balance and 1.07 m w.e/a for the summer mass balance. Consequently, this dataset serves as a reliable indicator for assessing the response of glaciers in this region to climate change.
| # | number | name | type |
| 1 | 42201163 | Modeling and predicting studies of the rapid glacier change in Alaska | National Natural Science Foundation of China |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ARCTIC-CHANGE.DB7132.2026
10.12072/ncdc.arctic-change.db7132.2026
