Probabilistic bias adjustment of seasonal forecasts using generative machine learning: A case study of Arctic sea ice predictions
arXiv:2605.29172v1 Announce Type: new Abstract: Seasonal climate predictions support planning and risk management by offering early information of the most likely-to-occur climate conditions in the coming months, and associated uncertainties. Ensemble forecasts enable this by simulating many plausible outcomes, allowing predictions to be expressed as usable probabilities. Large ensembles and high-resolution forecasts strengthen this guidance by better sampling uncertainty and capturing finer-scale processes but come with significant computational cost. Moreover, forecast ensembles drift and ex
The increasing availability of large climate datasets and advances in generative AI models are converging, making these applications viable now.
Improving the accuracy and reliability of climate forecasts, especially for critical regions like the Arctic, directly enhances planning and risk management for various sectors.
Machine learning, specifically generative models, can now more effectively address biases in seasonal climate forecasts, leading to more actionable predictions.
- · Climate scientists
- · Logistics and shipping
- · Energy sector
- · Government agencies
- · Traditional statistical bias correction methods
More accurate Arctic sea ice predictions will improve navigational safety and resource extraction planning in the region.
Enhanced climate foresight could lead to better infrastructure resilience planning and reduced economic losses from climate-related events.
The successful application in climate science could accelerate the adoption of generative AI for bias adjustment in other complex forecasting domains.
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Read at arXiv cs.LG