Transferability of ML emulators for polar RCM surface melt under different forcings: Limitations and recommendations

Elke Schlager , Sebastian Scher , Ruth Helen Mottram, Peter Langen

Machine learning emulators provide a computationally efficient alternative to physically based regional climate and surface mass balance models, but their applicability depends on their ability to generalize beyond training conditions. In this study, we assess the transferability of a melt emulator trained on output from the regional climate model HIRHAM5 forced by ERA-Interim, CESM2, and EC-Earth under historical and future (SSP5-8.5) climates. Transferability is evaluated across forcing datasets and climate states without retraining.
The emulator generalizes reasonably well within a given climate regime, producing biases below the inter-model spread, but exhibits limited transferability between historical and future climate conditions.
Predictor distributions analyses, perturbation experiments, and ablation studies indicate that these errors arise from shifts in predictor distributions together with an insufficient representation of the surface state. Although perturbation experiments demonstrate physically plausible relationships between surface energy fluxes and melt generation, physical consistency alone does not ensure robust extrapolation to novel climate conditions. Increasing training data diversity does not overcome these transferability limitations, highlighting the importance of training on data that are representative of the target application. Future emulator development should focus on incorporating information across multiple temporal scales and systematically evaluate transfer performance to improve generalizability.