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Predicting Atlantic Multidecadal Variability
Glenn Liu · Peidong Wang · Matthew Beveridge · Young-Oh Kwon · Iddo Drori

Tue Dec 14 09:40 AM -- 09:49 AM (PST) @
in Tackling Climate Change with Machine Learning »
Event URL: https://www.climatechange.ai/papers/neurips2021/35 »

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.

Author Information

Glenn Liu (Massachusetts Institute of Technology/Woods Hole Oceanographic Institution)
Peidong Wang (MIT)
Matthew Beveridge (Massachusetts Institute of Technology)
Young-Oh Kwon (Woods Hole Oceanographic Institution)
Iddo Drori (MIT)

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