ENSO‐Related Precursor Pathways of Interannual Thermal Anomalies Identified Using a Transformer‐Based Deep Learning Model in the Tropical Pacific
Abstract Recent studies have demonstrated great values of deep‐learning (DL) methods for improving El Niño‐Southern Oscillation (ENSO) predictions. However, the black‐box nature of DL makes it challenging to physically interpret mechanisms responsible for successful ENSO predictions. Here, we demons...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-06-01
|
| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023GL107347 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850134804093206528 |
|---|---|
| author | Lu Zhou Rong‐Hua Zhang |
| author_facet | Lu Zhou Rong‐Hua Zhang |
| author_sort | Lu Zhou |
| collection | DOAJ |
| description | Abstract Recent studies have demonstrated great values of deep‐learning (DL) methods for improving El Niño‐Southern Oscillation (ENSO) predictions. However, the black‐box nature of DL makes it challenging to physically interpret mechanisms responsible for successful ENSO predictions. Here, we demonstrate an interpretable method by performing perturbation experiments to predictors and quantifying input‐output relationships in predictions by using a transformer‐based model; ENSO‐related thermal precursors serving as initial conditions during multi‐month time intervals (TIs) are identified in the equatorial‐northern Pacific, acting to precondition input predictors to provide for long‐lead ENSO predictability. Results reveal the existence of upper‐ocean temperature anomaly pathways and consistent phase propagations of thermal precursors around the tropical Pacific. It is illustrated that three‐dimensional thermal fields and their basinwide evolution during long TIs act to enhance long‐lead prediction skills of ENSO. These physically explainable results indicate that neural networks can adequately represent predictable precursors in the input predictors for successful ENSO predictions. |
| format | Article |
| id | doaj-art-017ba7839d034150bb488f5751d13aed |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-017ba7839d034150bb488f5751d13aed2025-08-20T02:31:38ZengWileyGeophysical Research Letters0094-82761944-80072024-06-015112n/an/a10.1029/2023GL107347ENSO‐Related Precursor Pathways of Interannual Thermal Anomalies Identified Using a Transformer‐Based Deep Learning Model in the Tropical PacificLu Zhou0Rong‐Hua Zhang1Key Laboratory of Ocean Circulation and Waves Institute of Oceanology Chinese Academy of Sciences Qingdao ChinaUniversity of Chinese Academy of Sciences Beijing ChinaAbstract Recent studies have demonstrated great values of deep‐learning (DL) methods for improving El Niño‐Southern Oscillation (ENSO) predictions. However, the black‐box nature of DL makes it challenging to physically interpret mechanisms responsible for successful ENSO predictions. Here, we demonstrate an interpretable method by performing perturbation experiments to predictors and quantifying input‐output relationships in predictions by using a transformer‐based model; ENSO‐related thermal precursors serving as initial conditions during multi‐month time intervals (TIs) are identified in the equatorial‐northern Pacific, acting to precondition input predictors to provide for long‐lead ENSO predictability. Results reveal the existence of upper‐ocean temperature anomaly pathways and consistent phase propagations of thermal precursors around the tropical Pacific. It is illustrated that three‐dimensional thermal fields and their basinwide evolution during long TIs act to enhance long‐lead prediction skills of ENSO. These physically explainable results indicate that neural networks can adequately represent predictable precursors in the input predictors for successful ENSO predictions.https://doi.org/10.1029/2023GL107347ENSO predictionsthermal precursorsmultivariate three‐dimensional (3D) predictionstransformer‐based modelexplainable artificial intelligence (XAI) |
| spellingShingle | Lu Zhou Rong‐Hua Zhang ENSO‐Related Precursor Pathways of Interannual Thermal Anomalies Identified Using a Transformer‐Based Deep Learning Model in the Tropical Pacific Geophysical Research Letters ENSO predictions thermal precursors multivariate three‐dimensional (3D) predictions transformer‐based model explainable artificial intelligence (XAI) |
| title | ENSO‐Related Precursor Pathways of Interannual Thermal Anomalies Identified Using a Transformer‐Based Deep Learning Model in the Tropical Pacific |
| title_full | ENSO‐Related Precursor Pathways of Interannual Thermal Anomalies Identified Using a Transformer‐Based Deep Learning Model in the Tropical Pacific |
| title_fullStr | ENSO‐Related Precursor Pathways of Interannual Thermal Anomalies Identified Using a Transformer‐Based Deep Learning Model in the Tropical Pacific |
| title_full_unstemmed | ENSO‐Related Precursor Pathways of Interannual Thermal Anomalies Identified Using a Transformer‐Based Deep Learning Model in the Tropical Pacific |
| title_short | ENSO‐Related Precursor Pathways of Interannual Thermal Anomalies Identified Using a Transformer‐Based Deep Learning Model in the Tropical Pacific |
| title_sort | enso related precursor pathways of interannual thermal anomalies identified using a transformer based deep learning model in the tropical pacific |
| topic | ENSO predictions thermal precursors multivariate three‐dimensional (3D) predictions transformer‐based model explainable artificial intelligence (XAI) |
| url | https://doi.org/10.1029/2023GL107347 |
| work_keys_str_mv | AT luzhou ensorelatedprecursorpathwaysofinterannualthermalanomaliesidentifiedusingatransformerbaseddeeplearningmodelinthetropicalpacific AT ronghuazhang ensorelatedprecursorpathwaysofinterannualthermalanomaliesidentifiedusingatransformerbaseddeeplearningmodelinthetropicalpacific |