Reconstructing the Atlantic Overturning Circulation Using Linear Machine Learning Techniques. Issue 5 (20th October 2022)

• Record Type: Journal Article
• Authors: DelSole, Timothy; Nedza, Douglas
• Is Part Of: Atmosphere-ocean. Volume 60:Issue 5(2022)
• Page Start: 541
• Page End: 553
• Publication Date: 2022-10-20
• Subjects: machine learning -- AMOC reconstruction -- decadal -- CMIP; Ocean-atmosphere interaction -- Canada -- Periodicals; Ocean-atmosphere interaction -- Periodicals; Oceanography -- Canada -- Periodicals; Oceanography -- Periodicals; Meteorology -- Canada -- Periodicals; Meteorology -- Periodicals; 551.5246
• DOI: 10.1080/07055900.2021.1947181
• Languages: English
• ISSNs: 0705-5900

Abstract

ABSTRACT: This paper examines the potential of reconstructing the Atlantic Meridional Overturning Circulation (AMOC) using surface data and linear machine learning algorithms. The algorithms are trained on pre-industrial control simulations with the aim of finding an algorithm that can reconstruct the AMOC robustly across multiple climate models. Predictors include a combination of surface temperature and surface salinity, as well as a combination of simultaneous and lagged values relative to the AMOC. For most climate models, the correlation skill of the AMOC reconstructions is greater than 0.7. This reconstruction model involves thousands of predictors and is therefore difficult to interpret. To improve interpretability, machine learning algorithms were applied to Laplacian eigenvectors, which are an orthogonal set of spatial patterns that can be ordered from largest to smallest spatial scale. The skill of the new algorithms is comparable to that based on gridded data, but the new algorithms have the advantage that dimension reduction can be more meaningfully interpreted. The most important predictors were simultaneous and lagged time series of area-averaged surface temperature, and a pattern that measures the east–west salinity difference over the basin surface lagged in time. These three predictors could recover a substantial fraction of the total skill from machine learning algorithms for most climate models.