Atmospheric nitrogen deposition is an important external nitrogen source to the ocean that can fuel export production, yet its origin and contribution remain uncertain in the nitrogen-limited North Pacific Subtropical Gyre (NPSG). We present aerosol nitrate and reduced nitrogen (RN) concentrations and nitrogen isotopic composition (δ15N), along with air-mass back trajectories, across the NPSG in summer and winter. High δ15N values (–0.4‰ to 3‰) of aerosol nitrate and RN suggest that natural sources dominate in both seasons, contributing only modestly to the local external nitrogen supply. A synthesis of historical observations reveals pronounced zonal gradients in aerosol nitrogen concentrations and δ15N between the NPSG and transition zone, indicating enhanced anthropogenic influence in the latter, where nitrogen limitation is weaker. We estimate that lateral ocean transport from the transition zone increases external nitrogen inputs to the NPSG by 18%, highlighting an indirect pathway linking human emissions to oligotrophic ocean productivity.
{"title":"Atmospheric deposition and lateral ocean transport enhance nitrogen supply to the North Pacific Subtropical Gyre","authors":"Lunbi Wu, Dongchen Dai, Wentao Ma, Jin-Yu Terence Yang, Ziyang Zhang, Li Luo, Xin Liu, Hongyan Bao, Shuh-Ji Kao, Minhan Dai","doi":"10.1038/s41612-026-01388-7","DOIUrl":"https://doi.org/10.1038/s41612-026-01388-7","url":null,"abstract":"Atmospheric nitrogen deposition is an important external nitrogen source to the ocean that can fuel export production, yet its origin and contribution remain uncertain in the nitrogen-limited North Pacific Subtropical Gyre (NPSG). We present aerosol nitrate and reduced nitrogen (RN) concentrations and nitrogen isotopic composition (δ15N), along with air-mass back trajectories, across the NPSG in summer and winter. High δ15N values (–0.4‰ to 3‰) of aerosol nitrate and RN suggest that natural sources dominate in both seasons, contributing only modestly to the local external nitrogen supply. A synthesis of historical observations reveals pronounced zonal gradients in aerosol nitrogen concentrations and δ15N between the NPSG and transition zone, indicating enhanced anthropogenic influence in the latter, where nitrogen limitation is weaker. We estimate that lateral ocean transport from the transition zone increases external nitrogen inputs to the NPSG by 18%, highlighting an indirect pathway linking human emissions to oligotrophic ocean productivity.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"81 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506150","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 : 2026-03-25DOI: 10.1038/s41612-026-01387-8
Seyoung Yang, Yejin Kim, Minki Choo, Hyunyoung Choi, Jungho Im
Methane (CH4) is a dominant driver of near-term warming, yet global emission monitoring remains constrained by slow processing and large uncertainties. Hyperspectral spectrometers enable sensitive detection of CH4 plumes, but the relative advantages of enhancement-based (ENH) and radiance-based (RAD) approaches have not been systematically evaluated. Here we introduce a dual-path deep-learning framework that systematically compares both approaches using globally distributed, expert-validated CH4 plume datasets from EMIT and Tanager-1. The ENH models exhibit higher segmentation accuracy across plume scales, whereas the RAD models, operating directly on 49 shortwave-infrared channels, avoid computationally expensive preprocessing (e.g., matched filtering) and enable rapid screening. Both pathways markedly reduce labor-intensive workflows and latency relative to traditional processing while maintaining competitive performance by utilizing deep learning. Explainable AI analyses demonstrate that the models learn spatial-spectral features consistent with CH4 absorption structure and plume morphology, providing evidence of scientific validity. Cross-sensor evaluation demonstrates architectural robustness across EMIT and Tanager-1, establishing a physics-grounded framework adaptable across hyperspectral sensors.
{"title":"Beyond localized methane plume detection: a dual-path deep learning framework for sensor-agnostic global hyperspectral methane plume monitoring","authors":"Seyoung Yang, Yejin Kim, Minki Choo, Hyunyoung Choi, Jungho Im","doi":"10.1038/s41612-026-01387-8","DOIUrl":"https://doi.org/10.1038/s41612-026-01387-8","url":null,"abstract":"Methane (CH4) is a dominant driver of near-term warming, yet global emission monitoring remains constrained by slow processing and large uncertainties. Hyperspectral spectrometers enable sensitive detection of CH4 plumes, but the relative advantages of enhancement-based (ENH) and radiance-based (RAD) approaches have not been systematically evaluated. Here we introduce a dual-path deep-learning framework that systematically compares both approaches using globally distributed, expert-validated CH4 plume datasets from EMIT and Tanager-1. The ENH models exhibit higher segmentation accuracy across plume scales, whereas the RAD models, operating directly on 49 shortwave-infrared channels, avoid computationally expensive preprocessing (e.g., matched filtering) and enable rapid screening. Both pathways markedly reduce labor-intensive workflows and latency relative to traditional processing while maintaining competitive performance by utilizing deep learning. Explainable AI analyses demonstrate that the models learn spatial-spectral features consistent with CH4 absorption structure and plume morphology, providing evidence of scientific validity. Cross-sensor evaluation demonstrates architectural robustness across EMIT and Tanager-1, establishing a physics-grounded framework adaptable across hyperspectral sensors.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"16 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506152","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 : 2026-03-24DOI: 10.1038/s41612-026-01386-9
Ke-Xin Li, Fei Zheng
Inaccurate autumn-initialized prediction of global mean surface temperature (GMST) limits its practical value for climate-sensitive sectors, largely due to an incomplete understanding of key physical drivers. To improve this, we identified a previously underrepresented El Niño–Southern Oscillation (ENSO)-driven pantropical coupling mechanism that links ENSO evolution to tropical ocean–atmosphere interactions and a coherent autumn–winter global temperature response, and show that lack of this process contributes to increasing errors. Based on this mechanism, we developed a dynamic-statistical framework that incorporates skillful ENSO realistic forecasts into GMST prediction. The new framework extends reliable GMST prediction lead-time from two to four months and reduces hindcast errors by an average of 41% in 64% of years during 1980–2024, with particularly large improvements during ENSO-active periods, especially El Niño years (85%). These gains strengthen seasonal climate early-warning and have broad applications as tropical ocean variability and impacts may intensify under climate change.
{"title":"Improving seasonal prediction of global mean surface temperature by incorporating dynamic ENSO realistic forecasts","authors":"Ke-Xin Li, Fei Zheng","doi":"10.1038/s41612-026-01386-9","DOIUrl":"https://doi.org/10.1038/s41612-026-01386-9","url":null,"abstract":"Inaccurate autumn-initialized prediction of global mean surface temperature (GMST) limits its practical value for climate-sensitive sectors, largely due to an incomplete understanding of key physical drivers. To improve this, we identified a previously underrepresented El Niño–Southern Oscillation (ENSO)-driven pantropical coupling mechanism that links ENSO evolution to tropical ocean–atmosphere interactions and a coherent autumn–winter global temperature response, and show that lack of this process contributes to increasing errors. Based on this mechanism, we developed a dynamic-statistical framework that incorporates skillful ENSO realistic forecasts into GMST prediction. The new framework extends reliable GMST prediction lead-time from two to four months and reduces hindcast errors by an average of 41% in 64% of years during 1980–2024, with particularly large improvements during ENSO-active periods, especially El Niño years (85%). These gains strengthen seasonal climate early-warning and have broad applications as tropical ocean variability and impacts may intensify under climate change.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"38 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496697","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 : 2026-03-24DOI: 10.1038/s41612-026-01384-x
Ho-Young Ku, Hayeon Noh, Muyin Wang, James Overland, Seong-Joong Kim, Baek-Min Kim
Ural blocking (UB) and the associated Warm Arctic–Cold Eurasia (WACE) pattern are typically linked to the negative Arctic Oscillation (AO). However, robust UB events are surprisingly observed even during the positive AO phase, a condition generally expected to suppress blocking due to enhanced zonal flow. This study investigates how positive AO magnitude modulates UB persistence. We find that under strong positive AO conditions (AO > +1), UB events persist significantly longer (6.1 days) than weak positive AO (4.7 days). This enhanced persistence results from organized North Atlantic storm tracks that facilitate intense heat and moisture into the Barents–Kara Sea. The resulting Arctic warming and sea ice loss trigger a thermodynamic feedback loop that weakens the meridional potential vorticity (PV) gradient, effectively anchoring the UB system. Our findings reveal that strong positive AO paradoxically promotes persistent blocking through storm-PV coupling, offering critical insights for improving sub-seasonal predictions of Eurasian winter extremes.
{"title":"Enhanced persistence of Ural blocking under strong positive AO: the role of North Atlantic storm tracks and potential vorticity dynamics","authors":"Ho-Young Ku, Hayeon Noh, Muyin Wang, James Overland, Seong-Joong Kim, Baek-Min Kim","doi":"10.1038/s41612-026-01384-x","DOIUrl":"https://doi.org/10.1038/s41612-026-01384-x","url":null,"abstract":"Ural blocking (UB) and the associated Warm Arctic–Cold Eurasia (WACE) pattern are typically linked to the negative Arctic Oscillation (AO). However, robust UB events are surprisingly observed even during the positive AO phase, a condition generally expected to suppress blocking due to enhanced zonal flow. This study investigates how positive AO magnitude modulates UB persistence. We find that under strong positive AO conditions (AO > +1), UB events persist significantly longer (6.1 days) than weak positive AO (4.7 days). This enhanced persistence results from organized North Atlantic storm tracks that facilitate intense heat and moisture into the Barents–Kara Sea. The resulting Arctic warming and sea ice loss trigger a thermodynamic feedback loop that weakens the meridional potential vorticity (PV) gradient, effectively anchoring the UB system. Our findings reveal that strong positive AO paradoxically promotes persistent blocking through storm-PV coupling, offering critical insights for improving sub-seasonal predictions of Eurasian winter extremes.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"92 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506153","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 : 2026-03-23DOI: 10.1038/s41612-026-01385-w
Do-Yeon Kim, Heung-Il Suk
SpatIotemporal Gaussian Mixture correlAtion transformer (SIGMAformer) is a spatiotemporal forecasting architecture that integrates a Gaussian mixture pattern extractor (GMPE) with a dynamic spatiotemporal correlation (DSTC) mechanism. The DSTC module leverages GMPE to automatically compute spatiotemporal pattern-specific weights from the data. These weights are first used to calculate temporal correlations within each station and then integrated with global pattern weights to evaluate spatial correlations across stations. This nonlinear, dynamically adaptive modeling approach emphasizes critical spatiotemporal patterns while suppressing less relevant ones. Experiments on global weather datasets reveal that SIGMAformer consistently outperforms state-of-the-art forecasting models and significantly improves wind speed prediction. Removing DSTC increased the mean squared error values by up to 7.18% and 7.22% for wind speed and temperature predictions, respectively. These findings underscore SIGMAformer’s capacity to capture essential spatiotemporal patterns and establish a scalable methodology for intelligent sensor-network fusion in environmental forecasting.
{"title":"SIGMAformer: a spatiotemporal Gaussian mixture correlation transformer for global weather forecasting","authors":"Do-Yeon Kim, Heung-Il Suk","doi":"10.1038/s41612-026-01385-w","DOIUrl":"https://doi.org/10.1038/s41612-026-01385-w","url":null,"abstract":"SpatIotemporal Gaussian Mixture correlAtion transformer (SIGMAformer) is a spatiotemporal forecasting architecture that integrates a Gaussian mixture pattern extractor (GMPE) with a dynamic spatiotemporal correlation (DSTC) mechanism. The DSTC module leverages GMPE to automatically compute spatiotemporal pattern-specific weights from the data. These weights are first used to calculate temporal correlations within each station and then integrated with global pattern weights to evaluate spatial correlations across stations. This nonlinear, dynamically adaptive modeling approach emphasizes critical spatiotemporal patterns while suppressing less relevant ones. Experiments on global weather datasets reveal that SIGMAformer consistently outperforms state-of-the-art forecasting models and significantly improves wind speed prediction. Removing DSTC increased the mean squared error values by up to 7.18% and 7.22% for wind speed and temperature predictions, respectively. These findings underscore SIGMAformer’s capacity to capture essential spatiotemporal patterns and establish a scalable methodology for intelligent sensor-network fusion in environmental forecasting.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"24 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496701","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 : 2026-03-20DOI: 10.1038/s41612-026-01373-0
Zhiqiang Lyu, Feng Shi, Yuanyuan Yang, Aiguo Dai, Kevin Tyle, Duncan A. Christie, Mariano Morales, M. Eugenia Ferrero, Mathias Vuille
Reliable projections of the South American Summer Monsoon (SASM) are critical for managing regional hydroclimatic risks, yet remain highly uncertain due to internal climate variability. Here, we reconstruct a robust historical SASM index ensemble from 1850 CE onward by integrating high-resolution paleoclimate proxies (tree rings and ice cores), historical documents, and instrumental observations. We further analyze future changes using large ensembles from the CESM2 and CanESM5 climate models. Our results demonstrate that multidecadal variability in the SASM is primarily driven by the Interdecadal Pacific Oscillation (IPO) and the associated changes in the Pacific Walker Circulation (PWC), whereas the influence of the tropical Atlantic sea surface temperature (SST) gradient is comparatively minor. By constraining these key Pacific modes, we reduce the uncertainty in projected SASM intensity by approximately 30%, highlighting their dominant role in shaping near-term monsoon trajectories. This study underscores the importance of improved simulation and representation of Pacific variability for advancing hydroclimate projections and informing climate adaptation strategies in tropical South America.
{"title":"Pacific and Atlantic teleconnections reduce uncertainty in multidecadal projections of the South American Summer Monsoon","authors":"Zhiqiang Lyu, Feng Shi, Yuanyuan Yang, Aiguo Dai, Kevin Tyle, Duncan A. Christie, Mariano Morales, M. Eugenia Ferrero, Mathias Vuille","doi":"10.1038/s41612-026-01373-0","DOIUrl":"https://doi.org/10.1038/s41612-026-01373-0","url":null,"abstract":"Reliable projections of the South American Summer Monsoon (SASM) are critical for managing regional hydroclimatic risks, yet remain highly uncertain due to internal climate variability. Here, we reconstruct a robust historical SASM index ensemble from 1850 CE onward by integrating high-resolution paleoclimate proxies (tree rings and ice cores), historical documents, and instrumental observations. We further analyze future changes using large ensembles from the CESM2 and CanESM5 climate models. Our results demonstrate that multidecadal variability in the SASM is primarily driven by the Interdecadal Pacific Oscillation (IPO) and the associated changes in the Pacific Walker Circulation (PWC), whereas the influence of the tropical Atlantic sea surface temperature (SST) gradient is comparatively minor. By constraining these key Pacific modes, we reduce the uncertainty in projected SASM intensity by approximately 30%, highlighting their dominant role in shaping near-term monsoon trajectories. This study underscores the importance of improved simulation and representation of Pacific variability for advancing hydroclimate projections and informing climate adaptation strategies in tropical South America.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"60 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496699","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 : 2026-03-20DOI: 10.1038/s41612-026-01378-9
Xiansheng Liu, Minghan Wang, Taicheng An, Xun Zhang, Tao Wang, Rosa Lara, Marta Monge, Marvin Dufresne, Ana Maria Yañez-Serrano, Roger Seco, Marie Gohy, Paul Petit, Audrey Chevalier, Marie-Pierre Vagnot, Yann Fortier, Alexia Baudic, Véronique Ghersi, Grégory Gille, Ludovic Lanzi, Valérie Gros, Jean-Eudes Petit, Leïla Simon, Heidi Hellén, Stefan Reimann, Zoé Le Bras, Michelle Jessy Müller, David Beddows, Siqi Hou, Zongbo Shi, Roy M. Harrison, William Bloss, James Dernie, Stéphane Sauvage, Alastair Lewis, Jim Hopkins, Xiaoli Duan, Philip K. Hopke, Andrés Alastuey, Xavier Querol, Thérèse Salameh
Urban volatile organic compounds (VOCs) are key precursors of tropospheric ozone and secondary organic aerosols (SOA), yet their long-term dynamics and health implications remain unclear across Europe. Here, we synthesize two decades of VOC observations (2002–2023) from 21 urban monitoring sites in six countries to assess emission trends, oxidation potentials, and human exposure risks. Consistent declines in total hydrocarbons were observed at most sites, reflecting the effectiveness of emission control policies. Aromatic hydrocarbons such as toluene, xylene, and benzene were the dominant contributors to ozone and SOA formation. Physiologically based toxicokinetic (PBTK) modeling suggests that key VOCs preferentially accumulate in the kidney and liver. The integration of atmospheric monitoring with toxicokinetic modeling provides a multi-scale understanding of how urban VOCs influence both air quality and internal human exposure, offering new insight into effective pollution control strategies.
{"title":"Analysis of the abundance and impacts of volatile organic compounds across Europe","authors":"Xiansheng Liu, Minghan Wang, Taicheng An, Xun Zhang, Tao Wang, Rosa Lara, Marta Monge, Marvin Dufresne, Ana Maria Yañez-Serrano, Roger Seco, Marie Gohy, Paul Petit, Audrey Chevalier, Marie-Pierre Vagnot, Yann Fortier, Alexia Baudic, Véronique Ghersi, Grégory Gille, Ludovic Lanzi, Valérie Gros, Jean-Eudes Petit, Leïla Simon, Heidi Hellén, Stefan Reimann, Zoé Le Bras, Michelle Jessy Müller, David Beddows, Siqi Hou, Zongbo Shi, Roy M. Harrison, William Bloss, James Dernie, Stéphane Sauvage, Alastair Lewis, Jim Hopkins, Xiaoli Duan, Philip K. Hopke, Andrés Alastuey, Xavier Querol, Thérèse Salameh","doi":"10.1038/s41612-026-01378-9","DOIUrl":"https://doi.org/10.1038/s41612-026-01378-9","url":null,"abstract":"Urban volatile organic compounds (VOCs) are key precursors of tropospheric ozone and secondary organic aerosols (SOA), yet their long-term dynamics and health implications remain unclear across Europe. Here, we synthesize two decades of VOC observations (2002–2023) from 21 urban monitoring sites in six countries to assess emission trends, oxidation potentials, and human exposure risks. Consistent declines in total hydrocarbons were observed at most sites, reflecting the effectiveness of emission control policies. Aromatic hydrocarbons such as toluene, xylene, and benzene were the dominant contributors to ozone and SOA formation. Physiologically based toxicokinetic (PBTK) modeling suggests that key VOCs preferentially accumulate in the kidney and liver. The integration of atmospheric monitoring with toxicokinetic modeling provides a multi-scale understanding of how urban VOCs influence both air quality and internal human exposure, offering new insight into effective pollution control strategies.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"29 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496707","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}
Numerical weather prediction is the cornerstone of modern weather forecasting, yet its operational implementation demands vast computational resources. While artificial intelligence (AI)-based forecasting models offer a computationally efficient alternative, these purely data-driven approaches often sacrifice physical consistency. Here, we bridge physics-based and AI-based models through a novel, efficient hybrid framework that integrates a low-resolution atmospheric dynamical core with a neural operator in the multigrid architecture. This framework achieves performance comparable to that of state-of-the-art medium-range global weather forecasting models, while incurring much lower training costs, and simultaneously enhances the physical consistency that black-box models often lack. Furthermore, our framework provides substantial flexibility in the choice of dynamical cores, since the training process of the neural network does not require gradient propagation through the dynamical core, which ensures scalability to a wide range of operational forecasting systems.
{"title":"A hybrid framework for global weather forecasting via low-resolution dynamical core and multigrid neural operator","authors":"Yifan Hu, Fukang Yin, Weimin Zhang, Kaijun Ren, Junqiang Song, Kefeng Deng","doi":"10.1038/s41612-026-01374-z","DOIUrl":"https://doi.org/10.1038/s41612-026-01374-z","url":null,"abstract":"Numerical weather prediction is the cornerstone of modern weather forecasting, yet its operational implementation demands vast computational resources. While artificial intelligence (AI)-based forecasting models offer a computationally efficient alternative, these purely data-driven approaches often sacrifice physical consistency. Here, we bridge physics-based and AI-based models through a novel, efficient hybrid framework that integrates a low-resolution atmospheric dynamical core with a neural operator in the multigrid architecture. This framework achieves performance comparable to that of state-of-the-art medium-range global weather forecasting models, while incurring much lower training costs, and simultaneously enhances the physical consistency that black-box models often lack. Furthermore, our framework provides substantial flexibility in the choice of dynamical cores, since the training process of the neural network does not require gradient propagation through the dynamical core, which ensures scalability to a wide range of operational forecasting systems.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"58 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496704","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 : 2026-03-20DOI: 10.1038/s41612-026-01382-z
Susmit Subhransu Satpathy, Christian L. E. Franzke, Naiming Yuan, Nicola Maher, Wonsun Park, Sun-Seon Lee
Changes in the large-scale atmospheric circulation that regulate Earth’s climate can slow its rotation by increasing the Length of Day (LOD). Using large-ensemble simulations from three global climate models under the SSP3-7.0 scenario, we find that global warming-driven changes in Atmospheric Angular Momentum (AAM) propagate into measurable variations in LOD. These arise from modifications in both the mass and motion components of AAM. As the climate warms, expansion of the Hadley cell, intensification of subtropical jets, and weakening of tropical trade winds enhance the motion of the AAM, while a strengthened westward pressure gradient force associated with mountain torque and weakened surface friction torque indicate a reduced efficiency of momentum exchange with the solid Earth. Together, these processes accelerate the atmosphere and slow Earth’s rotation. By the late 21st century, AAM-driven LOD increases reach 10–18% of the lunar tidal friction trend, highlighting anthropogenic climate change’s role in Earth’s rotational dynamics.
{"title":"Anthropogenic warming-driven atmospheric circulation shifts and angular momentum increase: influence on the Earth’s rotation","authors":"Susmit Subhransu Satpathy, Christian L. E. Franzke, Naiming Yuan, Nicola Maher, Wonsun Park, Sun-Seon Lee","doi":"10.1038/s41612-026-01382-z","DOIUrl":"https://doi.org/10.1038/s41612-026-01382-z","url":null,"abstract":"Changes in the large-scale atmospheric circulation that regulate Earth’s climate can slow its rotation by increasing the Length of Day (LOD). Using large-ensemble simulations from three global climate models under the SSP3-7.0 scenario, we find that global warming-driven changes in Atmospheric Angular Momentum (AAM) propagate into measurable variations in LOD. These arise from modifications in both the mass and motion components of AAM. As the climate warms, expansion of the Hadley cell, intensification of subtropical jets, and weakening of tropical trade winds enhance the motion of the AAM, while a strengthened westward pressure gradient force associated with mountain torque and weakened surface friction torque indicate a reduced efficiency of momentum exchange with the solid Earth. Together, these processes accelerate the atmosphere and slow Earth’s rotation. By the late 21st century, AAM-driven LOD increases reach 10–18% of the lunar tidal friction trend, highlighting anthropogenic climate change’s role in Earth’s rotational dynamics.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"14 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496698","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 : 2026-03-18DOI: 10.1038/s41612-026-01369-w
Tuukka Kokkola, Timothy A. Sipkens, Andreas Paul, Deeksha Shukla, Mika Ihalainen, Anusmita Das, Jason Scott, Johannes Passig, Aleksandrs Kalamašņikovs, Uwe Etzien, Zheng Fang, Santtu Mikkonen, Anni Hartikainen, Viljami Luostari, Arya Mukherjee, Hendryk Czech, Martin Sklorz, Bert Buchholz, Thorsten Streibel, Thorsten Hohaus, Yinon Rudich, Johan Øvrevik, Ralf Zimmermann, Joel C. Corbin, Olli Sippula
Particulate matter (PM) from marine traffic interacts with solar radiation and clouds, ultimately influencing Earth’s radiative balance. Ships operated with conventional fossil fuel oils emit light-absorbing carbonaceous PM that offsets aerosol-driven cooling and can even exert a net positive radiative forcing, i.e. warming effect. Radiative properties of PM are possibly further altered by atmospheric aging processes, the effects of which are not fully understood. We present black carbon (BC) emission factors (EF) and optical properties of fresh and photochemically aged particle emissions from a marine engine, operated using low-sulfur heavy fuel oil (LS-HFO) and marine gas oil (MGO), complying with recent maritime sulfur regulations by the International Maritime Organization (IMO). The fresh particle emissions comprised mostly BC, with average BC EFs of 144 and 43.2 mg/kWh for LS-HFO and MGO, respectively. Light absorption was mostly attributed to BC in particles from both fuels, with absorption Ångström exponent (AAE, 370 to 880 nm) values 0.9–1.0 (interquartile range), and 870 nm single scattering albedo (SSA) values 0.15–0.24 during the full cycles. Fresh LS-HFO emissions exhibited lower SSA values than those of high-sulfur fuels reported in literature, primarily associated with reduced sulfate emissions. Photochemical aging led to an absorption enhancement (Eabs) of 1.2–1.5 and an increase in SSA relative to fresh emissions, although SSA remained below 0.5, and the estimated direct radiative forcing effect stayed positive. Our results show that sulfur-compliant marine fuels can emit highly absorbing particles with an atmospheric warming potential, which is mostly maintained even after photochemical aging.
{"title":"Highly light-absorbing particle emissions from low-sulfur marine fuels","authors":"Tuukka Kokkola, Timothy A. Sipkens, Andreas Paul, Deeksha Shukla, Mika Ihalainen, Anusmita Das, Jason Scott, Johannes Passig, Aleksandrs Kalamašņikovs, Uwe Etzien, Zheng Fang, Santtu Mikkonen, Anni Hartikainen, Viljami Luostari, Arya Mukherjee, Hendryk Czech, Martin Sklorz, Bert Buchholz, Thorsten Streibel, Thorsten Hohaus, Yinon Rudich, Johan Øvrevik, Ralf Zimmermann, Joel C. Corbin, Olli Sippula","doi":"10.1038/s41612-026-01369-w","DOIUrl":"https://doi.org/10.1038/s41612-026-01369-w","url":null,"abstract":"Particulate matter (PM) from marine traffic interacts with solar radiation and clouds, ultimately influencing Earth’s radiative balance. Ships operated with conventional fossil fuel oils emit light-absorbing carbonaceous PM that offsets aerosol-driven cooling and can even exert a net positive radiative forcing, i.e. warming effect. Radiative properties of PM are possibly further altered by atmospheric aging processes, the effects of which are not fully understood. We present black carbon (BC) emission factors (EF) and optical properties of fresh and photochemically aged particle emissions from a marine engine, operated using low-sulfur heavy fuel oil (LS-HFO) and marine gas oil (MGO), complying with recent maritime sulfur regulations by the International Maritime Organization (IMO). The fresh particle emissions comprised mostly BC, with average BC EFs of 144 and 43.2 mg/kWh for LS-HFO and MGO, respectively. Light absorption was mostly attributed to BC in particles from both fuels, with absorption Ångström exponent (AAE, 370 to 880 nm) values 0.9–1.0 (interquartile range), and 870 nm single scattering albedo (SSA) values 0.15–0.24 during the full cycles. Fresh LS-HFO emissions exhibited lower SSA values than those of high-sulfur fuels reported in literature, primarily associated with reduced sulfate emissions. Photochemical aging led to an absorption enhancement (Eabs) of 1.2–1.5 and an increase in SSA relative to fresh emissions, although SSA remained below 0.5, and the estimated direct radiative forcing effect stayed positive. Our results show that sulfur-compliant marine fuels can emit highly absorbing particles with an atmospheric warming potential, which is mostly maintained even after photochemical aging.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"1 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496700","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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