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: | , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
American Geophysical Union
2024
|
| _version_ | 1811139927372464128 |
|---|---|
| author | Colfescu, I Christensen, H Gagne, DJ |
| author_facet | Colfescu, I Christensen, H Gagne, DJ |
| author_sort | Colfescu, I |
| collection | OXFORD |
| description | <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> |
| first_indexed | 2024-09-25T04:13:52Z |
| format | Journal article |
| id | oxford-uuid:65eb4593-4b7b-4040-8279-d75443e91772 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:13:52Z |
| publishDate | 2024 |
| publisher | American Geophysical Union |
| record_format | dspace |
| spelling | oxford-uuid:65eb4593-4b7b-4040-8279-d75443e917722024-07-10T11:55:49ZA machine learning-based approach to quantify ENSO sources of predictabilityJournal articlehttp://purl.org/coar/resource_type/c_545buuid:65eb4593-4b7b-4040-8279-d75443e91772EnglishSymplectic ElementsAmerican Geophysical Union2024Colfescu, IChristensen, HGagne, DJ<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> |
| 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 |
| work_keys_str_mv | AT colfescui amachinelearningbasedapproachtoquantifyensosourcesofpredictability AT christensenh amachinelearningbasedapproachtoquantifyensosourcesofpredictability AT gagnedj amachinelearningbasedapproachtoquantifyensosourcesofpredictability AT colfescui machinelearningbasedapproachtoquantifyensosourcesofpredictability AT christensenh machinelearningbasedapproachtoquantifyensosourcesofpredictability AT gagnedj machinelearningbasedapproachtoquantifyensosourcesofpredictability |