Exploring dominant processes for multi-month predictability of western Pacific precipitation using deep learning
Abstract Over the past half-century, there has been an increasing trend in the magnitude and duration of the Madden-Julian Oscillation (MJO) attributable to the significant warming trend in the Western Pacific (WP). The MJO, bridging weather and climate, influences global and regional climate throug...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-09-01
|
| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-023-00478-0 |
| _version_ | 1797453424827039744 |
|---|---|
| author | Young-Min Yang Jeong-Hwan Kim Jae-Heung Park Yoo-Geun Ham Soon-Il An June-Yi Lee Bin Wang |
| author_facet | Young-Min Yang Jeong-Hwan Kim Jae-Heung Park Yoo-Geun Ham Soon-Il An June-Yi Lee Bin Wang |
| author_sort | Young-Min Yang |
| collection | DOAJ |
| description | Abstract Over the past half-century, there has been an increasing trend in the magnitude and duration of the Madden-Julian Oscillation (MJO) attributable to the significant warming trend in the Western Pacific (WP). The MJO, bridging weather and climate, influences global and regional climate through atmospheric teleconnections, and climate models can predict it for up to 4–5 weeks. In this study, we use deep learning (DL) methods to investigate the predictability of the MJO-related western Pacific precipitation on a multi-month time scale (5–9 weeks). We examine numerous potential predictors across the tropics, selected based on major MJO theories and mechanisms, to identify key factors for long-term MJO prediction. Our results show that DL-based useful potential predictability of the WP precipitation can be extended up to 6–7 weeks, with a correlation coefficient skill ranging from 0.60 to 0.65. Observational and heat map analysis suggest that cooling anomalies in the central Pacific play a crucial role in enhancing westerly anomalies over the Indian Ocean and warming in the WP, thereby strengthening the Walker circulation in the equatorial Pacific. In addition, the predictability of WP precipitation is higher in La Nina years than in El Nino or normal years, suggesting that mean cooling in the central Pacific may contribute to increased predictability of the MJO-related WP precipitation on the multi-month time scale. Additional model experiments using observed sea surface temperature (SST) anomalies over the central Pacific confirmed that these anomalies contribute to enhanced MJO-related convective anomalies over the WP. The study highlights that DL is a valuable tool not only for improving MJO-related WP prediction but also for efficiently exploring potential mechanisms linked to long-term predictability. |
| first_indexed | 2024-03-09T15:22:37Z |
| format | Article |
| id | doaj.art-a57625c7bef442ae90e82c6e5d55d1f8 |
| institution | Directory Open Access Journal |
| issn | 2397-3722 |
| language | English |
| last_indexed | 2024-03-09T15:22:37Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Climate and Atmospheric Science |
| spelling | doaj.art-a57625c7bef442ae90e82c6e5d55d1f82023-11-26T12:45:14ZengNature Portfolionpj Climate and Atmospheric Science2397-37222023-09-01611710.1038/s41612-023-00478-0Exploring dominant processes for multi-month predictability of western Pacific precipitation using deep learningYoung-Min Yang0Jeong-Hwan Kim1Jae-Heung Park2Yoo-Geun Ham3Soon-Il An4June-Yi Lee5Bin Wang6Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and TechnologyDepartment of Oceanography, Chonnam National UniversityDivision of Environmental Science and Engineering. Pohang University of Science and TechnologyDepartment of Oceanography, Chonnam National UniversityDepartment of Atmospheric Sciences and Irreversible Climate Change Research Center, Yonsei UniversityResearch Center for Climate Sciences, Pusan National UniversityKey Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and TechnologyAbstract Over the past half-century, there has been an increasing trend in the magnitude and duration of the Madden-Julian Oscillation (MJO) attributable to the significant warming trend in the Western Pacific (WP). The MJO, bridging weather and climate, influences global and regional climate through atmospheric teleconnections, and climate models can predict it for up to 4–5 weeks. In this study, we use deep learning (DL) methods to investigate the predictability of the MJO-related western Pacific precipitation on a multi-month time scale (5–9 weeks). We examine numerous potential predictors across the tropics, selected based on major MJO theories and mechanisms, to identify key factors for long-term MJO prediction. Our results show that DL-based useful potential predictability of the WP precipitation can be extended up to 6–7 weeks, with a correlation coefficient skill ranging from 0.60 to 0.65. Observational and heat map analysis suggest that cooling anomalies in the central Pacific play a crucial role in enhancing westerly anomalies over the Indian Ocean and warming in the WP, thereby strengthening the Walker circulation in the equatorial Pacific. In addition, the predictability of WP precipitation is higher in La Nina years than in El Nino or normal years, suggesting that mean cooling in the central Pacific may contribute to increased predictability of the MJO-related WP precipitation on the multi-month time scale. Additional model experiments using observed sea surface temperature (SST) anomalies over the central Pacific confirmed that these anomalies contribute to enhanced MJO-related convective anomalies over the WP. The study highlights that DL is a valuable tool not only for improving MJO-related WP prediction but also for efficiently exploring potential mechanisms linked to long-term predictability.https://doi.org/10.1038/s41612-023-00478-0 |
| spellingShingle | Young-Min Yang Jeong-Hwan Kim Jae-Heung Park Yoo-Geun Ham Soon-Il An June-Yi Lee Bin Wang Exploring dominant processes for multi-month predictability of western Pacific precipitation using deep learning npj Climate and Atmospheric Science |
| title | Exploring dominant processes for multi-month predictability of western Pacific precipitation using deep learning |
| title_full | Exploring dominant processes for multi-month predictability of western Pacific precipitation using deep learning |
| title_fullStr | Exploring dominant processes for multi-month predictability of western Pacific precipitation using deep learning |
| title_full_unstemmed | Exploring dominant processes for multi-month predictability of western Pacific precipitation using deep learning |
| title_short | Exploring dominant processes for multi-month predictability of western Pacific precipitation using deep learning |
| title_sort | exploring dominant processes for multi month predictability of western pacific precipitation using deep learning |
| url | https://doi.org/10.1038/s41612-023-00478-0 |
| work_keys_str_mv | AT youngminyang exploringdominantprocessesformultimonthpredictabilityofwesternpacificprecipitationusingdeeplearning AT jeonghwankim exploringdominantprocessesformultimonthpredictabilityofwesternpacificprecipitationusingdeeplearning AT jaeheungpark exploringdominantprocessesformultimonthpredictabilityofwesternpacificprecipitationusingdeeplearning AT yoogeunham exploringdominantprocessesformultimonthpredictabilityofwesternpacificprecipitationusingdeeplearning AT soonilan exploringdominantprocessesformultimonthpredictabilityofwesternpacificprecipitationusingdeeplearning AT juneyilee exploringdominantprocessesformultimonthpredictabilityofwesternpacificprecipitationusingdeeplearning AT binwang exploringdominantprocessesformultimonthpredictabilityofwesternpacificprecipitationusingdeeplearning |