Pub Date : 2025-12-12DOI: 10.1038/s41612-025-01282-8
Xin He, Chunsong Lu, Guang J. Zhang, Junjun Li, Lei Zhu, Hengqi Wang, Te Li, Xiaohao Guo, Sinan Gao, Yuhao Lin, Kai Yang, Wenhui Liu
Entrainment rate parameterization is important for convection schemes but uncertain in climate models. A new deep convective entrainment rate (λ) parameterization (HL parameterization) is developed from aircraft observations and implemented into the convection scheme (Song and Zhang, 2018, https://doi.org/10.1002/2017MS001191) in the Community Integrated Earth System Model version 1.1.0, replacing the previously used parameterization (Gregory parameterization). Compared with the Gregory parameterization, the HL parameterization simulates overall larger λ values and improves convective and large-scale precipitation simulations in the 30°S-30°N region, agreeing better with observations. The mechanism is that the HL parameterization suppresses deep convective cloud development macrophysically and microphysically compared with the Gregory parameterization. Indirectly, compared with the Gregory parameterization, the HL parameterization increases large-scale precipitation and reduces shallow convective precipitation, lowering total precipitation closer to observations. The HL parameterization enhances the model’s ability to simulate precipitation, providing a valuable reference for improving the deep convection scheme in climate models.
携射率参数化对对流方案很重要，但在气候模式中不确定。基于飞机观测发展了一种新的深层对流携流速率（λ）参数化（HL参数化），并将其应用于社区综合地球系统模型1.1.0版本的对流方案中（Song and Zhang, 2018, https://doi.org/10.1002/2017MS001191），取代了之前使用的参数化（Gregory参数化）。与Gregory参数化相比，HL参数化模拟的λ值总体上更大，并改善了30°S-30°N区域对流和大尺度降水的模拟，与观测结果吻合得更好。与Gregory参数化相比，HL参数化抑制了深层对流云的宏观物理和微观物理发展。间接地，与Gregory参数化相比，HL参数化增加了大尺度降水，减少了浅层对流降水，使总降水更接近观测值。HL参数化提高了模式对降水的模拟能力，为气候模式中对流方案的改进提供了有价值的参考。
{"title":"Improving precipitation simulations in CIESM through a new entrainment rate parameterization","authors":"Xin He, Chunsong Lu, Guang J. Zhang, Junjun Li, Lei Zhu, Hengqi Wang, Te Li, Xiaohao Guo, Sinan Gao, Yuhao Lin, Kai Yang, Wenhui Liu","doi":"10.1038/s41612-025-01282-8","DOIUrl":"https://doi.org/10.1038/s41612-025-01282-8","url":null,"abstract":"Entrainment rate parameterization is important for convection schemes but uncertain in climate models. A new deep convective entrainment rate (λ) parameterization (HL parameterization) is developed from aircraft observations and implemented into the convection scheme (Song and Zhang, 2018, https://doi.org/10.1002/2017MS001191) in the Community Integrated Earth System Model version 1.1.0, replacing the previously used parameterization (Gregory parameterization). Compared with the Gregory parameterization, the HL parameterization simulates overall larger λ values and improves convective and large-scale precipitation simulations in the 30°S-30°N region, agreeing better with observations. The mechanism is that the HL parameterization suppresses deep convective cloud development macrophysically and microphysically compared with the Gregory parameterization. Indirectly, compared with the Gregory parameterization, the HL parameterization increases large-scale precipitation and reduces shallow convective precipitation, lowering total precipitation closer to observations. The HL parameterization enhances the model’s ability to simulate precipitation, providing a valuable reference for improving the deep convection scheme in climate models.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"145 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145746790","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 : 2025-12-11DOI: 10.1038/s41612-025-01264-w
Changgui Lin, Kun Yang, Deliang Chen, Siyu Yue, Xu Zhou, Yonghui Lei, Jinmei Pan, Xi Cao, Yongkang Xue, Jiancheng Shi
The remote influences of springtime Third Pole (TP) snow cover (TPSC) on the Indian Summer Monsoon (ISM) and the East Asian Summer Monsoon (EASM) have been extensively studied. However, a clear mechanism explaining the cross-season links remains not well established. Before we confirm any remote effects, it is essential to first verify local influences. Here, we bear out the enduring local impact of the springtime TPSC according to a numerical experiment together with an observational investigation. By examining the evolution of underlying heat sources, we propose a self-sustaining mechanism elucidating the enduring local impact: considering the case of the springtime TPSC deficit, the excessive precipitation that initially responds to the enhanced surface heat and water fluxes releases extra atmospheric latent heat, which in turn drives an anomalous circulation favoring the next-coming precipitation. This finding adds credit to the cross-season influences of the springtime TPSC remotely on the ISM and the EASM. Furthermore, our work implicates that the TP may get more summer precipitation in a warmer future since there will be an inevitable decrease in springtime TPSC.
{"title":"Enduring local impact of springtime snow cover over the Third Pole","authors":"Changgui Lin, Kun Yang, Deliang Chen, Siyu Yue, Xu Zhou, Yonghui Lei, Jinmei Pan, Xi Cao, Yongkang Xue, Jiancheng Shi","doi":"10.1038/s41612-025-01264-w","DOIUrl":"https://doi.org/10.1038/s41612-025-01264-w","url":null,"abstract":"The remote influences of springtime Third Pole (TP) snow cover (TPSC) on the Indian Summer Monsoon (ISM) and the East Asian Summer Monsoon (EASM) have been extensively studied. However, a clear mechanism explaining the cross-season links remains not well established. Before we confirm any remote effects, it is essential to first verify local influences. Here, we bear out the enduring local impact of the springtime TPSC according to a numerical experiment together with an observational investigation. By examining the evolution of underlying heat sources, we propose a self-sustaining mechanism elucidating the enduring local impact: considering the case of the springtime TPSC deficit, the excessive precipitation that initially responds to the enhanced surface heat and water fluxes releases extra atmospheric latent heat, which in turn drives an anomalous circulation favoring the next-coming precipitation. This finding adds credit to the cross-season influences of the springtime TPSC remotely on the ISM and the EASM. Furthermore, our work implicates that the TP may get more summer precipitation in a warmer future since there will be an inevitable decrease in springtime TPSC.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"20 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718533","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 : 2025-12-04DOI: 10.1038/s41612-025-01262-y
Michael Sigmond, Lantao Sun
Previous studies using an emergent constraint have suggested that climate models underestimate the winter jet stream response to sea ice loss, casting doubt on the quality of mid-latitude climate projections. However, the robustness of this emergent constraint has been questioned. Here, we propose a more robust emergent constraint based on lower stratospheric winds. Using coordinated sea ice loss experiments with bespoke versions of two state-of-the-art climate models along with a multi-model archive, we identify a strong relationship between these winds and the jet stream response. The new emergent constraint reduces the uncertainty in the response by 62% and indicates that the real-world response closely matches the multi-model mean—suggesting no systematic underestimation, in contrast to earlier studies. Our results underscore the importance of reducing lower stratospheric wind biases and increase confidence in climate model projections of a future poleward shift of the jet stream in response to global warming.
{"title":"Jet stream response to future Arctic sea ice loss not underestimated by climate models","authors":"Michael Sigmond, Lantao Sun","doi":"10.1038/s41612-025-01262-y","DOIUrl":"https://doi.org/10.1038/s41612-025-01262-y","url":null,"abstract":"Previous studies using an emergent constraint have suggested that climate models underestimate the winter jet stream response to sea ice loss, casting doubt on the quality of mid-latitude climate projections. However, the robustness of this emergent constraint has been questioned. Here, we propose a more robust emergent constraint based on lower stratospheric winds. Using coordinated sea ice loss experiments with bespoke versions of two state-of-the-art climate models along with a multi-model archive, we identify a strong relationship between these winds and the jet stream response. The new emergent constraint reduces the uncertainty in the response by 62% and indicates that the real-world response closely matches the multi-model mean—suggesting no systematic underestimation, in contrast to earlier studies. Our results underscore the importance of reducing lower stratospheric wind biases and increase confidence in climate model projections of a future poleward shift of the jet stream in response to global warming.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"11 Suppl 3 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664437","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 : 2025-12-01DOI: 10.1038/s41612-025-01231-5
Idris Hayward, Nicholas A. Martin, Valerio Ferracci, Mohsen Kazemimanesh, Simon Jude, Christopher Walton, Zaheer Ahmad Nasir, Prashant Kumar
Low-cost air quality sensors have shown great promise as a complement to high-cost reference and equivalent methods. Though not currently as accurate, their low barrier of entry and smaller form factor allow them to be deployed in greater numbers, thus enabling air quality measurements to be made at a far higher spatial and temporal resolution than previously possible. However, their measurements require corrections as they suffer from both short-term biases (e.g., changes in environmental conditions such as temperature and humidity), and long-term measurement drift due to degradation. Many studies have focused on calibration and re-calibration of sensors, but fewer focus on correcting pre-calibrated sensor measurements. Correcting measurements is a likely scenario for people buying off-the-shelf devices, as they will not have access to the raw data that underpins the measurements, such as sensor voltages. Previous studies focused on a small range of correction techniques, without accounting for the variances that can occur between devices or locations. This work aimed to perform a comprehensive assessment of different correction techniques applied to air quality sensor systems. More than 470,000 unique measurement corrections were tested across two sites to determine best practices for correction campaigns going forward, resulting in a far more robust study than previous works. It highlights the large variances in results that occurred between sites, particularly for NO 2 , with results often more impacted by device type and location than the regression technique used. Simpler linear models were also found to perform just as well as, and sometimes better than, more complex non-parametric techniques. This study highlights that, though a strong focus is often put on comparing different regression methods, the choice of technique has less impact than the configuration of the device or the conditions of the co-location site. Therefore, future studies should focus less on small-scale comparisons of regression techniques and more on how to improve the transferability and applicability of results from a co-location campaign to another.
{"title":"Comprehensive comparison of correction techniques for low-cost air quality sensors: the impact of device type and deployment environment","authors":"Idris Hayward, Nicholas A. Martin, Valerio Ferracci, Mohsen Kazemimanesh, Simon Jude, Christopher Walton, Zaheer Ahmad Nasir, Prashant Kumar","doi":"10.1038/s41612-025-01231-5","DOIUrl":"https://doi.org/10.1038/s41612-025-01231-5","url":null,"abstract":"Low-cost air quality sensors have shown great promise as a complement to high-cost reference and equivalent methods. Though not currently as accurate, their low barrier of entry and smaller form factor allow them to be deployed in greater numbers, thus enabling air quality measurements to be made at a far higher spatial and temporal resolution than previously possible. However, their measurements require corrections as they suffer from both short-term biases (e.g., changes in environmental conditions such as temperature and humidity), and long-term measurement drift due to degradation. Many studies have focused on calibration and re-calibration of sensors, but fewer focus on correcting pre-calibrated sensor measurements. Correcting measurements is a likely scenario for people buying off-the-shelf devices, as they will not have access to the raw data that underpins the measurements, such as sensor voltages. Previous studies focused on a small range of correction techniques, without accounting for the variances that can occur between devices or locations. This work aimed to perform a comprehensive assessment of different correction techniques applied to air quality sensor systems. More than 470,000 unique measurement corrections were tested across two sites to determine best practices for correction campaigns going forward, resulting in a far more robust study than previous works. It highlights the large variances in results that occurred between sites, particularly for NO <jats:sub>2</jats:sub> , with results often more impacted by device type and location than the regression technique used. Simpler linear models were also found to perform just as well as, and sometimes better than, more complex non-parametric techniques. This study highlights that, though a strong focus is often put on comparing different regression methods, the choice of technique has less impact than the configuration of the device or the conditions of the co-location site. Therefore, future studies should focus less on small-scale comparisons of regression techniques and more on how to improve the transferability and applicability of results from a co-location campaign to another.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"32 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645136","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 : 2025-12-01DOI: 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}
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