Skilful global seasonal predictions from a machine learning weather model trained on reanalysis data
Chris Kent; Adam A. Scaife; Nick Dunstone; Doug Smith; Steven C. Hardiman; Tom Dunstan; Oliver Watt‐Meyer · 2025 · npj Climate and Atmospheric Science
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract Machine learning weather models trained on observed atmospheric conditions can outperform conventional physics-based models at short- to medium-range (1–14 day) forecast timescales. Here we take the machine learning model ACE2, trained to predict 6-hourly steps in atmospheric evolution and which can remain stable over long forecast periods, and assess it from a seasonal forecasting perspective (1–3 month lead time). Applying persisted sea surface temperature (SST) and sea-ice anomalies centred on 1 st November each year, we initialise a lagged ensemble of seasonal predictions covering
Abstract by Chris Kent; Adam A. Scaife; Nick Dunstone; Doug Smith; Steven C. Hardiman; Tom Dunstan; Oliver Watt‐Meyer, npj Climate and Atmospheric Science (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41612-025-01198-3