Bias-Correcting Arctic ERA5 Surface Air Temperatures using Deep Learning 

Bias-Correcting Arctic ERA5 Surface Air Temperatures using Deep Learning Fine-tuning AtmoRep, a climate dynamics foundational model for improved Arctic 2m temperature predictions Due to the Arctic's harsh environment, comprehensive observational networks remain incomplete, leading to a reliance on biased reanalysis datasets such as ERA5. [1] This study investigates the potential of fine-tuning AtmoRep, a pre-trained transformer model for global atmospheric dynamics, to improve bias correction of Arctic 2-meter temperature (t2m) predictions. [2] Our methodology involves fine-tuning AtmoRep using ERA5 fields as input and bias-corrected Arctic t2m synthetic data, from a parallel project, as a target. [3] The project goal is to leverage AtmoReps global climate representations to further push the bias-corrected synthetic Arctic t2m data, given ERA5 as input (evaluated against observational data).Preliminary results demonstrate stable validation performance of AtmoRep over the Arctic, achieving a t2m RMSE of 0.27 K during fine-tuning. Model robustness was further evaluated under severely masked target fields (up to 90% masking), and comparing BERT-style reconstruction with a forecasting-based training strategy. This study represents a novel application of foundation pretrained climate models for bias correction in sparsely observed Arctic regions, highlighting the potential of machine learning approaches to advance atmospheric science. Tian, T., Yang, S., Høyer, J. L., Nielsen-Englyst, P., & Singha, S. (2024). Cooler Arctic surface temperatures simulated by climate models are closer to satellite-based data than the ERA5 reanalysis. Communications Earth & Environment, 5(1). https://doi.org/10.1038/s43247-024-01276-z Lessig, C., Luise, I., Gong, B., Langguth, M., Stadtler, S., & Schultz, M. (2023b, August 25). AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning. arXiv.org. https://arxiv.org/abs/2308.13280 Hossain, A., Keil, P., Grover, H., et al. Machine Learning Eliminates Reanalysis Warm Bias and Reveals Weaker Winter Surface Cooling over Arctic Sea Ice. ESS Open Archive . December 24, 2025.  https://doi.org/10.22541/essoar.176659533.30384251/v1