1- 1Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Atmospheric Physics, Bremerhaven, Germany
- 2Osnabrück University, Osnabrück, Germany
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
How to cite: Scholle, S. and Pithan, F.: Bias-Correcting Arctic ERA5 Surface Air Temperatures using Deep Learning , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18557, https://doi.org/10.5194/egusphere-egu26-18557, 2026.
