A machine learning-based approach to quantify ENSO sources of predictability
<p>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 inc...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
American Geophysical Union
2024
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| Summary: | <p>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.</p> |
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