A Machine Learning‐Based Approach to Quantify ENSO Sources of Predictability
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...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Wiley Open Access
2024
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| _version_ | 1811139827583680512 |
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| author | Colfescu, I Christensen, H Gagne, DJ |
| author_facet | Colfescu, I Christensen, H Gagne, DJ |
| author_sort | Colfescu, I |
| collection | OXFORD |
| description | 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 apredictive 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. |
| first_indexed | 2024-09-25T04:12:16Z |
| format | Journal article |
| id | oxford-uuid:7d04c30a-1813-4a48-bec1-a9339254fb14 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:12:16Z |
| publishDate | 2024 |
| publisher | Wiley Open Access |
| record_format | dspace |
| spelling | oxford-uuid:7d04c30a-1813-4a48-bec1-a9339254fb142024-07-04T20:06:46ZA Machine Learning‐Based Approach to Quantify ENSO Sources of PredictabilityJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:7d04c30a-1813-4a48-bec1-a9339254fb14EnglishJisc Publications RouterWiley Open Access2024Colfescu, IChristensen, HGagne, DJA 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 apredictive 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. |
| spellingShingle | Colfescu, I Christensen, H Gagne, DJ A Machine Learning‐Based Approach to Quantify ENSO Sources of Predictability |
| title | A Machine Learning‐Based Approach to Quantify ENSO Sources of Predictability |
| title_full | A Machine Learning‐Based Approach to Quantify ENSO Sources of Predictability |
| title_fullStr | A Machine Learning‐Based Approach to Quantify ENSO Sources of Predictability |
| title_full_unstemmed | A Machine Learning‐Based Approach to Quantify ENSO Sources of Predictability |
| title_short | A Machine Learning‐Based Approach to Quantify ENSO Sources of Predictability |
| title_sort | machine learning based approach to quantify enso sources of predictability |
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