Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties
Abstract Uncertainties in land surface processes notably limit subseasonal heat wave (HW) onset predictions. A better representation of the uncertainties in land surface processes using ensemble prediction methods may be an important way to improve HW onset predictions. However, generating ensemble...
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Nature Portfolio
2025-01-01
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| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-024-00876-y |
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| author | Qiyu Zhang Mu Mu Guodong Sun |
| author_facet | Qiyu Zhang Mu Mu Guodong Sun |
| author_sort | Qiyu Zhang |
| collection | DOAJ |
| description | Abstract Uncertainties in land surface processes notably limit subseasonal heat wave (HW) onset predictions. A better representation of the uncertainties in land surface processes using ensemble prediction methods may be an important way to improve HW onset predictions. However, generating ensemble members that adequately represent land surface process uncertainties, particularly those related to land surface parameters, remains challenging. In this study, a conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach was employed to generate ensemble members for representing the uncertainties in land surface processes resulting from parameters. Via six strong and long-lasting HW events over the middle and lower reaches of the Yangtze River (MLYR), HW onset ensemble forecast experiments were conducted with the Weather Research and Forecasting (WRF) model. The performance of the CNOP-P approach and the traditional random parameter perturbation ensemble prediction method was evaluated. The results demonstrate that the deterministic and probabilistic skills of HW onset predictions show greater excellence using the CNOP-P approach, leading to much better predictions of extreme air temperatures than those using the traditional method. This occurred because the ensemble members generated by the CNOP-P method better represented the uncertainties in important land physical processes determining HW onsets over the MLYR, notably vegetation process uncertainties, whereas the ensemble members generated by the random parameter perturbation method could not. This finding suggests that the CNOP-P method is suitable for producing ensemble members that more appropriately represent model uncertainties through more reasonable parameter error characterization. |
| format | Article |
| id | doaj-art-8a2a81c6ba5b4037b97c718c2caa951f |
| institution | Kabale University |
| issn | 2397-3722 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | npj Climate and Atmospheric Science |
| spelling | doaj-art-8a2a81c6ba5b4037b97c718c2caa951f2025-01-19T12:16:10ZengNature Portfolionpj Climate and Atmospheric Science2397-37222025-01-018111510.1038/s41612-024-00876-ySkillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertaintiesQiyu Zhang0Mu Mu1Guodong Sun2Key Laboratory of Core Tech on Numerical Model-AI Integrated Forecast for Hazardous Precipitation, Chongqing Institute of Meteorological SciencesDepartment of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan UniversityState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of SciencesAbstract Uncertainties in land surface processes notably limit subseasonal heat wave (HW) onset predictions. A better representation of the uncertainties in land surface processes using ensemble prediction methods may be an important way to improve HW onset predictions. However, generating ensemble members that adequately represent land surface process uncertainties, particularly those related to land surface parameters, remains challenging. In this study, a conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach was employed to generate ensemble members for representing the uncertainties in land surface processes resulting from parameters. Via six strong and long-lasting HW events over the middle and lower reaches of the Yangtze River (MLYR), HW onset ensemble forecast experiments were conducted with the Weather Research and Forecasting (WRF) model. The performance of the CNOP-P approach and the traditional random parameter perturbation ensemble prediction method was evaluated. The results demonstrate that the deterministic and probabilistic skills of HW onset predictions show greater excellence using the CNOP-P approach, leading to much better predictions of extreme air temperatures than those using the traditional method. This occurred because the ensemble members generated by the CNOP-P method better represented the uncertainties in important land physical processes determining HW onsets over the MLYR, notably vegetation process uncertainties, whereas the ensemble members generated by the random parameter perturbation method could not. This finding suggests that the CNOP-P method is suitable for producing ensemble members that more appropriately represent model uncertainties through more reasonable parameter error characterization.https://doi.org/10.1038/s41612-024-00876-y |
| spellingShingle | Qiyu Zhang Mu Mu Guodong Sun Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties npj Climate and Atmospheric Science |
| title | Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties |
| title_full | Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties |
| title_fullStr | Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties |
| title_full_unstemmed | Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties |
| title_short | Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties |
| title_sort | skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties |
| url | https://doi.org/10.1038/s41612-024-00876-y |
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