Predicting Atlantic Multidecadal Variability

TL;DR

This paper evaluates machine learning models to improve the prediction of Atlantic Multidecadal Variability using climate model data, outperforming traditional methods and enabling forecasts up to 25 years ahead.

Contribution

It introduces a machine learning approach to predict AMV from climate data, demonstrating improved accuracy over baseline methods.

Findings

All models outperform persistence baseline.

Models can predict AMV up to 25 years in advance.

Enhanced prediction aids in climate risk assessment.

Abstract

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.