Seasonal forecast of sea surface temperatures via Neural ODEs

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Abstract
Global meteorological agencies provide seasonal forecasts based on indicators such as El Niño–Southern Oscillation and Indian Ocean variability. The NOAA operates forecasts up to 9 months, while the ECMWF offers extended predictions up to 13 months. However, these physics-based forecasting systems suffer from the chaotic nature of the atmosphere, leading to a rapid decline in forecast accuracy as the lead times increase. This study proposes a novel seasonal forecasting framework utilizing neural ordinary differential equations (Neural ODEs). Through comparative experiments with the North American Multi-Model Ensemble model, we demonstrate that Neural ODEs significantly reduce computational costs and mitigate cumulative errors, thereby enhancing long-term forecasting capability. Furthermore, we employ multifractal detrended fluctuation analysis to quantify the memory characteristics, revealing the intrinsic limits of predictability in the climate system.
Funding Information
  • Ministry of Education (RS-2023-00281017)

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