Predicting Atlantic Multidecadal Variability
This work addresses climate prediction for societal utility in North America and Europe, but it is incremental as it applies existing machine learning methods to a known climate modeling problem.
The paper tackled the problem of predicting Atlantic Multidecadal Variability (AMV) to improve regional climate forecasts, and found that multiple machine learning models outperformed the traditional persistence forecast baseline using data from a state-of-the-art climate model with 3,440 years of data.
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.