A machine learning-based approach to quantify ENSO sources of predictability

Journal article : Letter

A machine learning method is used to identify sources of long-term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near-surface zonal wind (U10)). Tropical SST represents the primary source of predictability skill. While U10 does not increase the skill when associated with SST, our analysis suggests U10 alone has a predictive skill comparable to that of SST between 11 and 21 months in advance, from late fall up to late spring. The long-lead signal originates from coupled wind-SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond 1 year ahead and identify the key role of U10 over the IO.

Authors: Colfescu, I; Christensen, H; Gagne, DJ

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Geophysical Union
• Journal: Geophysical Research Letters
• Volume: 51
• Issue: 13
• Article number: e2023GL105194
• Publication date: 2024-07-04
• Acceptance date: 2024-04-13
• DOI: 10.1029/2023GL105194
• EISSN: 1944-8007
• ISSN: 0094-8276
• Language: English
• Keywords: predictive skill; climate models; machine learning; climate; prediction; ENSO
• Subtype: Letter