Pub Date : 2025-12-01DOI: 10.1038/s41612-025-01277-5
So-Hee Kim, Seung-Ki Min, Soon-Il An, Maeng-Ki Kim, Hyo-Seok Park, Jong-Yeon Park, Doo-Sun R. Park, Hyun-Min Sung, Young-Hwa Byun, Kyung-On Boo
{"title":"Hysteresis response of Northern Hemisphere winter temperature variability under different CO₂ removal pathways","authors":"So-Hee Kim, Seung-Ki Min, Soon-Il An, Maeng-Ki Kim, Hyo-Seok Park, Jong-Yeon Park, Doo-Sun R. Park, Hyun-Min Sung, Young-Hwa Byun, Kyung-On Boo","doi":"10.1038/s41612-025-01277-5","DOIUrl":"https://doi.org/10.1038/s41612-025-01277-5","url":null,"abstract":"","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"198200 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1038/s41612-025-01273-9
Lingfeng Li, Huan Wu, Lulu Jiang, Yiwen Mei, John S. Kimball, Lorenzo Alfieri, Zhijun Huang, Ying Hu, Sirong Chen, Shaorou Dong, Yaming Hu, Wei Wu
Sub-seasonal to seasonal (S2S) precipitation forecasting has long been regarded as a “forecasting desert” due to limited skill beyond seven lead days, undermining downstream hydrological forecasts. However, the higher predictability of streamflow compared to precipitation, and its disproportionate improvement relative to precipitation forecast, have often been overlooked. This study integrates a distributed hydrological model with a probabilistic statistical model to enhance S2S flood forecast by assimilating statistical hydroclimate relationships. The ensemble approach is validated at 24 hydrological stations across Pearl River Basin with complex hydrology. Its modest forecasts show mean Nash–Sutcliffe Efficiency (NSE) scores ranging from 0.36 to 0.16 for weeks 2 to 6, and a 15% improvement in Continuous Ranked Probability Score Skill (CRPSS) compared to hydrological model alone. This study underscores the value of integrating physical and statistical models to improve S2S streamflow prediction, offering a practical pathway to enhance forecast skill in flood-prone basins.
{"title":"A hybrid framework for sub-seasonal to seasonal streamflow prediction: integrating numerical and statistical models","authors":"Lingfeng Li, Huan Wu, Lulu Jiang, Yiwen Mei, John S. Kimball, Lorenzo Alfieri, Zhijun Huang, Ying Hu, Sirong Chen, Shaorou Dong, Yaming Hu, Wei Wu","doi":"10.1038/s41612-025-01273-9","DOIUrl":"https://doi.org/10.1038/s41612-025-01273-9","url":null,"abstract":"Sub-seasonal to seasonal (S2S) precipitation forecasting has long been regarded as a “forecasting desert” due to limited skill beyond seven lead days, undermining downstream hydrological forecasts. However, the higher predictability of streamflow compared to precipitation, and its disproportionate improvement relative to precipitation forecast, have often been overlooked. This study integrates a distributed hydrological model with a probabilistic statistical model to enhance S2S flood forecast by assimilating statistical hydroclimate relationships. The ensemble approach is validated at 24 hydrological stations across Pearl River Basin with complex hydrology. Its modest forecasts show mean Nash–Sutcliffe Efficiency (NSE) scores ranging from 0.36 to 0.16 for weeks 2 to 6, and a 15% improvement in Continuous Ranked Probability Score Skill (CRPSS) compared to hydrological model alone. This study underscores the value of integrating physical and statistical models to improve S2S streamflow prediction, offering a practical pathway to enhance forecast skill in flood-prone basins.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"1 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-25DOI: 10.1038/s41612-025-01260-0
Ye-Seul Lee, Hye-Yeong Chun
Low-level turbulence (LLT), primarily driven by terrain-induced and convective processes, remains a critical hazard to aviation safety. This study establishes the applicability of machine-learning to global LLT forecasting below 10,000 ft, alongside the LLT-adapted Graphical Turbulence Guidance (GTG LLT) system. Using ~3 million pairs of turbulence diagnostics and in situ eddy dissipation rate observations, we trained and evaluated random forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine models. All three consistently outperformed GTG LLT but shared limitations in seasonal, diurnal, and altitude-dependent performance patterns. SHapley Additive exPlanations analysis was applied to interpret diagnostic contributions, offering clues on the processes influential for turbulence prediction. To refine performance, three strategies were introduced: (i) threshold adjustment, (ii) regression-adapted Synthetic Minority Over-sampling Technique to address class imbalance in rare turbulence events, and (iii) quantile regression with tree ensembles to produce predictive intervals and quantify spatially varying uncertainty critical for safety-critical aviation operations.
{"title":"Machine learning application and operational strategy for global low-level aviation turbulence forecasting","authors":"Ye-Seul Lee, Hye-Yeong Chun","doi":"10.1038/s41612-025-01260-0","DOIUrl":"https://doi.org/10.1038/s41612-025-01260-0","url":null,"abstract":"Low-level turbulence (LLT), primarily driven by terrain-induced and convective processes, remains a critical hazard to aviation safety. This study establishes the applicability of machine-learning to global LLT forecasting below 10,000 ft, alongside the LLT-adapted Graphical Turbulence Guidance (GTG LLT) system. Using ~3 million pairs of turbulence diagnostics and in situ eddy dissipation rate observations, we trained and evaluated random forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine models. All three consistently outperformed GTG LLT but shared limitations in seasonal, diurnal, and altitude-dependent performance patterns. SHapley Additive exPlanations analysis was applied to interpret diagnostic contributions, offering clues on the processes influential for turbulence prediction. To refine performance, three strategies were introduced: (i) threshold adjustment, (ii) regression-adapted Synthetic Minority Over-sampling Technique to address class imbalance in rare turbulence events, and (iii) quantile regression with tree ensembles to produce predictive intervals and quantify spatially varying uncertainty critical for safety-critical aviation operations.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"114 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Ocean-driven shifts in circulation regime frequency modulate South China rainfall","authors":"Dongdong Peng, Tianjun Zhou, Sheng Hu, Jiayu Zheng","doi":"10.1038/s41612-025-01244-0","DOIUrl":"https://doi.org/10.1038/s41612-025-01244-0","url":null,"abstract":"","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"149 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1038/s41612-025-01258-8
So-Eun Park, Soon-Il An, Hajoon Song, Gagan Mandal, Jun-Young Moon
{"title":"Inter-model diversity and Its Drivers in Southern Ocean Meridional Overturning Circulation","authors":"So-Eun Park, Soon-Il An, Hajoon Song, Gagan Mandal, Jun-Young Moon","doi":"10.1038/s41612-025-01258-8","DOIUrl":"https://doi.org/10.1038/s41612-025-01258-8","url":null,"abstract":"","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"13 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1038/s41612-025-01242-2
Edoardo Mazza, Shuyi S. Chen, Brandon W. Kerns, Andrew C. Winters
{"title":"Multiscale drivers of extreme southern California flooding: ENSO, MJO, North Pacific jet, and atmospheric rivers","authors":"Edoardo Mazza, Shuyi S. Chen, Brandon W. Kerns, Andrew C. Winters","doi":"10.1038/s41612-025-01242-2","DOIUrl":"https://doi.org/10.1038/s41612-025-01242-2","url":null,"abstract":"","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"183 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145554321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1038/s41612-025-01257-9
Marc Prange, Ming Zhao, Elena Shevliakova, Sergey Malyshev
{"title":"Elucidating the loose tie between precipitation and streamflow sensitivities to warming across the contiguous United States","authors":"Marc Prange, Ming Zhao, Elena Shevliakova, Sergey Malyshev","doi":"10.1038/s41612-025-01257-9","DOIUrl":"https://doi.org/10.1038/s41612-025-01257-9","url":null,"abstract":"","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"4 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145554301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1038/s41612-025-01239-x
Sandro W. Lubis, Chuan-Chieh Chang, Samson Hagos, Ming Zhao, Ziming Chen, Karthik Balaguru, L. Ruby Leung
Recent decades have witnessed unprecedented extreme precipitation and catastrophic flooding in some of the most populated regions in Southeast Asia during boreal winter. These extreme events are often influenced by the cross-equatorial northerly surge (CENS), characterized by a strengthening of northerly moist monsoon winds south of the equator in the western Maritime Continent. However, the potential future changes in CENS and its hydrological impacts remain underexplored. Here, using an ensemble of high-resolution climate model simulations (CMIP6 HighResMIP), we show that the regional impacts of CENS on the intensity and frequency of extreme precipitation events are projected to significantly increase in the near future (2030–2050), particularly over the adjacent coastal regions of southern Indonesia and northwestern Australia. More specifically, the risk of CENS-related extreme precipitation is projected to increase by up to 39 ± 1.2% relative to the seasonal probability, despite no apparent changes in the CENS characteristics in the near future. Such stronger impact is attributed to the enhanced moistening efficiency and moist static instability due to a more humid and warmer environment, which leads to more intense CENS convection. Our results suggest the need for effective monitoring and disaster managements to respond to the increasing severity of such events in the near future.
{"title":"Projected changes in cross-equatorial northerly surges and their hydrological impacts in the near future","authors":"Sandro W. Lubis, Chuan-Chieh Chang, Samson Hagos, Ming Zhao, Ziming Chen, Karthik Balaguru, L. Ruby Leung","doi":"10.1038/s41612-025-01239-x","DOIUrl":"https://doi.org/10.1038/s41612-025-01239-x","url":null,"abstract":"Recent decades have witnessed unprecedented extreme precipitation and catastrophic flooding in some of the most populated regions in Southeast Asia during boreal winter. These extreme events are often influenced by the cross-equatorial northerly surge (CENS), characterized by a strengthening of northerly moist monsoon winds south of the equator in the western Maritime Continent. However, the potential future changes in CENS and its hydrological impacts remain underexplored. Here, using an ensemble of high-resolution climate model simulations (CMIP6 HighResMIP), we show that the regional impacts of CENS on the intensity and frequency of extreme precipitation events are projected to significantly increase in the near future (2030–2050), particularly over the adjacent coastal regions of southern Indonesia and northwestern Australia. More specifically, the risk of CENS-related extreme precipitation is projected to increase by up to 39 ± 1.2% relative to the seasonal probability, despite no apparent changes in the CENS characteristics in the near future. Such stronger impact is attributed to the enhanced moistening efficiency and moist static instability due to a more humid and warmer environment, which leads to more intense CENS convection. Our results suggest the need for effective monitoring and disaster managements to respond to the increasing severity of such events in the near future.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"23 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145554364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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