Transformers

Background Accurate forecasting of Arctic sea ice is a critical challenge in climate science, with implications for shipping routes, ecosystem monitoring, and climate modelling. Recent work has shown that deep learning models can produce skillful sea ice forecasts and downscaled sea ice concentration (SIC) maps, often outperforming classical numerical methods at certain lead times and spatial scales. A central open question in this space is what constitutes the strongest deterministic baseline before introducing probabilistic or generative approaches. ...

Project availability The project is already fully booked for Spring 2026. It will run again in Fall 2026. Background Global climate datasets (reanalyses or CMIP simulations) are commonly available as monthly means and daily aggregates (mean, max., and min.) for key atmospheric variables. In this study, we compiled such temporal low-resolution data from the ERA5 [1] reanalysis, using high-resolution hourly data from the CERRA [2] regional reanalysis as ground truth. ...