Predicting Atlantic Multidecadal Variability

• Main Authors: Liu, Glenn, Wang, Peidong, Beveridge, Matthew, Kwon, Young-Oh, Drori, Iddo
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2021
• Subjects: Machine Learning cs.LG; Computer Vision and Pattern Recognition cs.CV; Atmospheric and Oceanic Physics physics.ao-ph; FOS Computer and information sciences; FOS Physical sciences; North Atlantic
• Online Access: https://dx.doi.org/10.48550/arxiv.2111.00124; https://arxiv.org/abs/2111.00124

Atlantic Multidecadal Variability (AMV) describes variations of North Atlantic sea surface temperature with a typical cycle of between 60 and 70 years. AMV strongly impacts local climate over North America and Europe, therefore prediction of AMV, especially the extreme values, is of great societal utility for understanding and responding to regional climate change. This work tests multiple machine learning models to improve the state of AMV prediction from maps of sea surface temperature, salinity, and sea level pressure in the North Atlantic region. We use data from the Community Earth System Model 1 Large Ensemble Project, a state-of-the-art climate model with 3,440 years of data. Our results demonstrate that all of the models we use outperform the traditional persistence forecast baseline. Predicting the AMV is important for identifying future extreme temperatures and precipitation, as well as hurricane activity, in Europe and North America up to 25 years in advance. : 7 pages, 3 figures