Redefining the North Atlantic Oscillation index generation using autoencoder neural network

• Published in: Machine Learning: Science and Technology
• Main Author: Ibebuchi, Chibuike Chiedozie
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: IOP Publishing 2024
• Subjects: North Atlantic; North Atlantic oscillation
• Online Access: http://dx.doi.org/10.1088/2632-2153/ad1c32; https://iopscience.iop.org/article/10.1088/2632-2153/ad1c32; https://iopscience.iop.org/article/10.1088/2632-2153/ad1c32/pdf

Abstract Understanding the spatial patterns of the North Atlantic Oscillation (NAO) is vital for climate science. For this reason, empirical orthogonal function (EOF) analysis is commonly applied to sea-level pressure (SLP) anomaly data in the North Atlantic region. This study evaluated the traditional EOF-based definition of the NAO index against the autoencoder (AE) neural network-based definition, using the Hurrell NAO Index (Station-Based) as a reference. Specifically, EOF and AE were applied to monthly SLP anomaly data from ERA5 (1950–2022) to derive spatial modes of variability in the North Atlantic region. Both methods produced spatial patterns consistent with the traditional NAO definition, with dipole centers of action between the Icelandic Low and the Azores High. During boreal winter (December to March), when the NAO is most active, the AE-based method achieved a correlation of 0.96 with the reference NAO index, outperforming the EOF-based method’s correlation of 0.90. The all-season Adjusted R-squared values were 50% for the AE-based index and 34% for the EOF-based index. Notably, the AE-based index revealed several other non-linear patterns of the NAO, with more than one encoded pattern correlating at least 0.90 with the reference NAO index during boreal winter. These results not only demonstrate the AE’s superiority over traditional EOF in representing the station-based index but also uncover previously unexplored complexities in the NAO that are close to the reference temporal pattern. This suggests that AE offers a promising approach for defining climate modes of variability, potentially capturing intricacies that traditional linear methods like EOF might miss.