Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability
- Frances V. Davenport
, - Frances V Davenport,
- Elizabeth A Barnes,
- Emily M Gordon
Frances V Davenport
Department of Atmospheric Science, Colorado State University, Department of Civil and Environmental Engineering, Colorado State University
Elizabeth A Barnes
Department of Atmospheric Science, Colorado State University
Emily M Gordon
Department of Earth System Science, Stanford University, Department of Atmospheric Science, Colorado State University
Abstract
• Neural networks can learn predictable signals of internal sea surface temperature variability at 1-3, 1-5, and 3-7 year lead times • Neural networks trained on climate model output can skillfully predict sea surface temperature variability in reconstructed observations • Neural network skill in predicting observed sea surface temperature variability depends on the climate model used for training06 Mar 2024Submitted to ESS Open Archive 15 Mar 2024Published in ESS Open Archive
