Enhancing Glacier-Climate Modeling: Integrating High-Resolution Climate Models for Improved Equilibrium Line Altitude Projections in the Alpine Glaciers

Glacier-climate models are crucial for understanding and predicting climate change impacts on snow and glacier-fed regions. However, their accuracy depends heavily on the quality of climate input data. While general circulation models (GCMs) provide broad-scale insights, their coarse spatial resolution limits their ability to capture fine-scale climatic variability, especially in complex mountainous regions. We integrate high-resolution Regional Climate Models (RCMs) and Convection-Permitting Models (CPMs) into a glacier-climate coupled model namely Open Global Glacier Model (OGGM) to improve equilibrium line altitude (ELA) projections for the glaciers of Aosta valley. We calibrate OGGM using the snow line altitude (SLA) dataset. SLA at the end of the ablation season is an indicator of climate change and a proxy to equilibrium line altitude (ELA). We compute SLA by incorporating satellite imagery of Landsat data for calibration period through image segmentation. K-Means Clustering is utilizing to divide the image into three classes: snow, ice and barren. By computing snow cover ratio across various elevations, the SLA is computed. The clustered classes are validated with manual segmentation while the SLA time series is by the dataset provided by the World Glacier Monitoring Service (WGMS). Historical ELA is constructed based on the Historical Instrumental climatological Time series of the greater Alpine region (HISTALP) for the calibration period. The calibration period gives Pearson’s correlation coefficient of 0.69. We force the high resolution RCMS and CPMs for both the historical period (1985-2005) and future period (2006-2100). The reason is to validate the model for both periods and to analyze whether the provided models perform well in the historical period or not. The RCMs and the CPMs offer advantages over the GCMs by resolving finer-scale atmospheric processes, such as orographic precipitation and temperature gradients, crucial for accurate glacier modeling. Our results indicate that the RCM and the CPM  reduce biases in the ELA predictions, aligning more closely with observational data compared to GCM-driven simulations. These advancements highlight the transformative potential of high-resolution climate models in glacier research, offering more reliable projections of glacier mass loss, water resource availability, and climate-driven hazards in alpine regions.