Pub Date : 2026-01-16DOI: 10.1038/s41612-025-01316-1
Ajin Cho, Hajoon Song, Il-Ju Moon, Hyodae Seo, Rui Sun, Matthew R. Mazloff, Aneesh C. Subramanian, Bruce D. Cornuelle, Arthur J. Miller
Current–wind interaction modulates air–sea momentum and turbulent heat fluxes, which are critical in the energy cycle of tropical cyclones (TCs). However, the effects of the surface currents on air–sea exchange under TCs have remained unclear. Here, using an atmosphere–ocean coupled model, we investigate the role of current–wind interaction in determining TC intensity. Surface currents generally align with surface winds. Accounting for the current–wind interaction, the alignment reduces both the air–sea turbulent heat flux and momentum flux (average 1.0% and 2.5%), which serve as the energy source and sink of TCs, respectively. The reduction in the energy source (sink) decreases (increases) the TC growth −1.9% (+1.3%) on average and up to −13.7% (+11.1%). For simulations extending beyond the seasonal scale, the accumulated impacts of current–wind interaction alter TC genesis, affecting surface wind speed and sea surface temperature during the TC season. These findings reveal an important feedback mechanism associated with TCs driven by the current–wind interaction.
{"title":"Modulation of tropical cyclone intensity by current–wind interaction","authors":"Ajin Cho, Hajoon Song, Il-Ju Moon, Hyodae Seo, Rui Sun, Matthew R. Mazloff, Aneesh C. Subramanian, Bruce D. Cornuelle, Arthur J. Miller","doi":"10.1038/s41612-025-01316-1","DOIUrl":"https://doi.org/10.1038/s41612-025-01316-1","url":null,"abstract":"Current–wind interaction modulates air–sea momentum and turbulent heat fluxes, which are critical in the energy cycle of tropical cyclones (TCs). However, the effects of the surface currents on air–sea exchange under TCs have remained unclear. Here, using an atmosphere–ocean coupled model, we investigate the role of current–wind interaction in determining TC intensity. Surface currents generally align with surface winds. Accounting for the current–wind interaction, the alignment reduces both the air–sea turbulent heat flux and momentum flux (average 1.0% and 2.5%), which serve as the energy source and sink of TCs, respectively. The reduction in the energy source (sink) decreases (increases) the TC growth −1.9% (+1.3%) on average and up to −13.7% (+11.1%). For simulations extending beyond the seasonal scale, the accumulated impacts of current–wind interaction alter TC genesis, affecting surface wind speed and sea surface temperature during the TC season. These findings reveal an important feedback mechanism associated with TCs driven by the current–wind interaction.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"180 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968805","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 : 2026-01-15DOI: 10.1038/s41612-025-01318-z
Min Sothearith, Daeryong Park, Kuk-Hyun Ahn
Extreme precipitation (EP) is a major climate risk, yet its projections remain uncertain due to the combined influence of thermodynamic (TH) and dynamic (DY) processes. Using multi-model simulations under three emission scenarios, we separate TH and DY contributions to the annual maximum 1-day precipitation (Rx1Day) and quantify their uncertainties. TH consistently intensifies extremes with warming, while DY strongly modulates their magnitude and direction. DY processes dominate Rx1Day uncertainty, with internal variability within DY emerging as the leading contributor. Signal-to-noise ratio (SNR) analysis shows that the forced signal emerges more clearly for TH than DY, where chaotic variability fundamentally limits predictability. The strongest intensification occurs in equatorial regions, raising equity concerns for vulnerable populations. These results demonstrate that DY internal variability is the primary driver of EP uncertainty, highlighting limits to long-term predictability and the importance of properly representing natural dynamical fluctuations in future projections.
{"title":"Dynamic internal variability dominates uncertainty in modeling future extreme precipitation","authors":"Min Sothearith, Daeryong Park, Kuk-Hyun Ahn","doi":"10.1038/s41612-025-01318-z","DOIUrl":"https://doi.org/10.1038/s41612-025-01318-z","url":null,"abstract":"Extreme precipitation (EP) is a major climate risk, yet its projections remain uncertain due to the combined influence of thermodynamic (TH) and dynamic (DY) processes. Using multi-model simulations under three emission scenarios, we separate TH and DY contributions to the annual maximum 1-day precipitation (Rx1Day) and quantify their uncertainties. TH consistently intensifies extremes with warming, while DY strongly modulates their magnitude and direction. DY processes dominate Rx1Day uncertainty, with internal variability within DY emerging as the leading contributor. Signal-to-noise ratio (SNR) analysis shows that the forced signal emerges more clearly for TH than DY, where chaotic variability fundamentally limits predictability. The strongest intensification occurs in equatorial regions, raising equity concerns for vulnerable populations. These results demonstrate that DY internal variability is the primary driver of EP uncertainty, highlighting limits to long-term predictability and the importance of properly representing natural dynamical fluctuations in future projections.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"49 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968806","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 : 2026-01-15DOI: 10.1038/s41612-026-01320-z
Lluís Palma, Alejandro Peraza, David Civantos-Prieto, Amanda Duarte, Stefano Materia, Ángel G. Muñoz, Jesús Peña-Izquierdo, Laia Romero, Albert Soret, Markus G. Donat
Most operational climate services providers base their seasonal predictions on initialised general circulation models (GCMs) or empirical statistical techniques. GCMs are widely used but require substantial computational resources, limiting their capacity. In contrast, statistical methods often lack robustness due to the short historical records available. Recent works propose machine learning methods trained on climate model output, leveraging larger sample sizes. Yet, many of these studies focus on prediction tasks that may be restricted in spatial or temporal extent, thereby creating a gap with existing operational predictions. Others fail to disentangle the sources of skill in the context of climate change, where strong trends provide spurious estimates. This study combines variational inference with transformers to predict global and regional seasonal anomalies of temperature and rainfall. The model is trained on output from CMIP6 and tested using ERA5 reanalysis data. Temperature predictions demonstrate skill beyond the climatology and climate-change trend and even outperform the numerical state-of-the-art system SEAS5 in some ocean and land areas. Precipitation forecasts show more limited skill, with fewer regions outperforming climatology and fewer surpassing SEAS5. Furthermore, the consistency found in both teleconnections and skill spatial patterns against SEAS5 suggests that both systems build on similar sources of predictability.
{"title":"Data-driven seasonal climate predictions via variational inference and transformers","authors":"Lluís Palma, Alejandro Peraza, David Civantos-Prieto, Amanda Duarte, Stefano Materia, Ángel G. Muñoz, Jesús Peña-Izquierdo, Laia Romero, Albert Soret, Markus G. Donat","doi":"10.1038/s41612-026-01320-z","DOIUrl":"https://doi.org/10.1038/s41612-026-01320-z","url":null,"abstract":"Most operational climate services providers base their seasonal predictions on initialised general circulation models (GCMs) or empirical statistical techniques. GCMs are widely used but require substantial computational resources, limiting their capacity. In contrast, statistical methods often lack robustness due to the short historical records available. Recent works propose machine learning methods trained on climate model output, leveraging larger sample sizes. Yet, many of these studies focus on prediction tasks that may be restricted in spatial or temporal extent, thereby creating a gap with existing operational predictions. Others fail to disentangle the sources of skill in the context of climate change, where strong trends provide spurious estimates. This study combines variational inference with transformers to predict global and regional seasonal anomalies of temperature and rainfall. The model is trained on output from CMIP6 and tested using ERA5 reanalysis data. Temperature predictions demonstrate skill beyond the climatology and climate-change trend and even outperform the numerical state-of-the-art system SEAS5 in some ocean and land areas. Precipitation forecasts show more limited skill, with fewer regions outperforming climatology and fewer surpassing SEAS5. Furthermore, the consistency found in both teleconnections and skill spatial patterns against SEAS5 suggests that both systems build on similar sources of predictability.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"1 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968807","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 : 2026-01-14DOI: 10.1038/s41612-025-01280-w
Fei Ji, Moutassem El Rafei, Giovanni Di Virgilio, Jason P. Evans, Jatin Kala, Stephen White, Julia Andrys, Dipayan Choudhury, Eugene Tam, Yue Li, Rishav Goyal, Carlos Vieira Rocha, Matthew L. Riley
Regional climate simulations provide essential high-resolution information for climate services. This study evaluates future changes in mean climate and 10 extremes using three generations of the NARCliM (NSW and Australian Regional Climate Modelling) project, which downscale CMIP3, CMIP5, and CMIP6 models. Projections show statistically significant increases in maximum and minimum temperatures across all NARCliM generations, with consistent spatial patterns. The magnitude of warming is primarily influenced by driving GCMs and emissions scenarios. In contrast, precipitation projections exhibit greater variability between generations, reflecting model and scenario differences and underscoring the challenge of projecting future precipitation. Extreme heat indices are projected to increase across Australia, with consistent spatial patterns and stronger changes under higher emissions, indicating more frequent and severe extreme heat events. Precipitation extremes display more variability across regions, model generations, and scenarios, although certain trends are robust. The intensity of very extreme rainfall (above the 99th percentile) is projected to increase, as is the maximum length of dry spells. Conversely, the maximum length of wet spells and the number of heavy rain days are expected to decrease. NARCliM2.0 specifically suggests shorter wet periods and fewer heavy rain days, but more intense extreme rainfall. These findings demonstrate the relative robustness of temperature and its extremes compared to precipitation and emphasize the value of broader GCM ensembles in future downscaling efforts to improve confidence in regional projections.
{"title":"Three generations of NARCliM: future projections of mean and extreme climate over the CORDEX Australasia domain","authors":"Fei Ji, Moutassem El Rafei, Giovanni Di Virgilio, Jason P. Evans, Jatin Kala, Stephen White, Julia Andrys, Dipayan Choudhury, Eugene Tam, Yue Li, Rishav Goyal, Carlos Vieira Rocha, Matthew L. Riley","doi":"10.1038/s41612-025-01280-w","DOIUrl":"https://doi.org/10.1038/s41612-025-01280-w","url":null,"abstract":"Regional climate simulations provide essential high-resolution information for climate services. This study evaluates future changes in mean climate and 10 extremes using three generations of the NARCliM (NSW and Australian Regional Climate Modelling) project, which downscale CMIP3, CMIP5, and CMIP6 models. Projections show statistically significant increases in maximum and minimum temperatures across all NARCliM generations, with consistent spatial patterns. The magnitude of warming is primarily influenced by driving GCMs and emissions scenarios. In contrast, precipitation projections exhibit greater variability between generations, reflecting model and scenario differences and underscoring the challenge of projecting future precipitation. Extreme heat indices are projected to increase across Australia, with consistent spatial patterns and stronger changes under higher emissions, indicating more frequent and severe extreme heat events. Precipitation extremes display more variability across regions, model generations, and scenarios, although certain trends are robust. The intensity of very extreme rainfall (above the 99th percentile) is projected to increase, as is the maximum length of dry spells. Conversely, the maximum length of wet spells and the number of heavy rain days are expected to decrease. NARCliM2.0 specifically suggests shorter wet periods and fewer heavy rain days, but more intense extreme rainfall. These findings demonstrate the relative robustness of temperature and its extremes compared to precipitation and emphasize the value of broader GCM ensembles in future downscaling efforts to improve confidence in regional projections.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"24 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968809","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 : 2026-01-14DOI: 10.1038/s41612-025-01281-9
Dimitrios Bousiotis, Dylan S. Sanghera, Jenny Carrington, Glyn Hodgkiss, Farzaneh Jajarmi, Khalid Z. Rajab, Francis D. Pope
Indoor air quality (IAQ) is increasingly recognised as one of the most important aspects for public health, workplace safety and productivity. While indoor and outdoor factors both influence indoor pollutant levels, human presence and activity are key drivers of the emission of specific pollutants, including particulate matter (PM), total volatile organic compounds (TVOCs) and carbon dioxide (CO2). This study investigates the relationship between occupancy, physical activity measured by kinetic energy (KE), and air pollution concentrations in a real-world office setting, by combining data from air quality and radar motion sensors. Two exemplar office spaces were investigated, comprising an open-office area and a meeting room. PM, in the PM1 and PM2.5 size fractions, were found to be highly correlated with the outdoor conditions, whereas PM10 correlates more closely with indoor occupancy (up to r = 0.65). Even higher correlations, up to r = 0.74, were found between human activity, quantified as KE, and the PM10 concentrations. The TVOCs and CO2 showed even stronger correlations with KE (up to r = 0.83), suggesting that these metrics can be used as excellent proxies for estimating certain types of indoor air pollution. Notably, the impact of additional occupants varies depending on room characteristics and usage, underscoring the need for contextualised models of IAQ. By quantifying both outdoor infiltration and indoor emissions, this study offers a framework for disentangling pollutant sources and guiding interventions to optimise IAQ. These insights support evidence-based strategies to create healthier and more productive office environments.
{"title":"Parameterising the effect of human occupancy and kinetic energy on indoor air pollution","authors":"Dimitrios Bousiotis, Dylan S. Sanghera, Jenny Carrington, Glyn Hodgkiss, Farzaneh Jajarmi, Khalid Z. Rajab, Francis D. Pope","doi":"10.1038/s41612-025-01281-9","DOIUrl":"https://doi.org/10.1038/s41612-025-01281-9","url":null,"abstract":"Indoor air quality (IAQ) is increasingly recognised as one of the most important aspects for public health, workplace safety and productivity. While indoor and outdoor factors both influence indoor pollutant levels, human presence and activity are key drivers of the emission of specific pollutants, including particulate matter (PM), total volatile organic compounds (TVOCs) and carbon dioxide (CO2). This study investigates the relationship between occupancy, physical activity measured by kinetic energy (KE), and air pollution concentrations in a real-world office setting, by combining data from air quality and radar motion sensors. Two exemplar office spaces were investigated, comprising an open-office area and a meeting room. PM, in the PM1 and PM2.5 size fractions, were found to be highly correlated with the outdoor conditions, whereas PM10 correlates more closely with indoor occupancy (up to r = 0.65). Even higher correlations, up to r = 0.74, were found between human activity, quantified as KE, and the PM10 concentrations. The TVOCs and CO2 showed even stronger correlations with KE (up to r = 0.83), suggesting that these metrics can be used as excellent proxies for estimating certain types of indoor air pollution. Notably, the impact of additional occupants varies depending on room characteristics and usage, underscoring the need for contextualised models of IAQ. By quantifying both outdoor infiltration and indoor emissions, this study offers a framework for disentangling pollutant sources and guiding interventions to optimise IAQ. These insights support evidence-based strategies to create healthier and more productive office environments.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"30 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968810","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 : 2026-01-13DOI: 10.1038/s41612-025-01319-y
Mingyu Park, Nathaniel C. Johnson
Western North America (WNA) is a regional hotspot for summer heat extremes. However, our understanding of the atmospheric processes driving WNA heatwaves remains largely based on a few case studies. In this study, we investigate the general characteristics of atmospheric pathways associated with WNA heatwaves using a 30-member high-resolution coupled model simulation. Synthesizing the WNA heatwave events across the large ensemble, we reinforce the view that WNA heatwaves are systematically driven by: (1) a Rossby wave train originating from the western North Pacific, (2) poleward moisture transport toward the Gulf of Alaska, occasionally via atmospheric rivers, and (3) downstream ridge amplification over WNA. Although these features also appear in the late twenty-first-century projections, notable changes include farther poleward moisture transport and broader ridge development in the future. Under the anomaly-based heatwave definition used in this study, which removes the influence of mean temperature change, the frequency of WNA heatwaves is projected to decrease. Our findings suggest that mechanisms identified in case studies, including upstream Rossby wave packets and subsequent moist processes, are broadly applicable to understanding WNA heatwaves over recent decades and their projected changes.
{"title":"Projected changes in atmospheric pathways of Western North American heatwaves simulated from high-resolution coupled model simulations","authors":"Mingyu Park, Nathaniel C. Johnson","doi":"10.1038/s41612-025-01319-y","DOIUrl":"https://doi.org/10.1038/s41612-025-01319-y","url":null,"abstract":"Western North America (WNA) is a regional hotspot for summer heat extremes. However, our understanding of the atmospheric processes driving WNA heatwaves remains largely based on a few case studies. In this study, we investigate the general characteristics of atmospheric pathways associated with WNA heatwaves using a 30-member high-resolution coupled model simulation. Synthesizing the WNA heatwave events across the large ensemble, we reinforce the view that WNA heatwaves are systematically driven by: (1) a Rossby wave train originating from the western North Pacific, (2) poleward moisture transport toward the Gulf of Alaska, occasionally via atmospheric rivers, and (3) downstream ridge amplification over WNA. Although these features also appear in the late twenty-first-century projections, notable changes include farther poleward moisture transport and broader ridge development in the future. Under the anomaly-based heatwave definition used in this study, which removes the influence of mean temperature change, the frequency of WNA heatwaves is projected to decrease. Our findings suggest that mechanisms identified in case studies, including upstream Rossby wave packets and subsequent moist processes, are broadly applicable to understanding WNA heatwaves over recent decades and their projected changes.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"51 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956346","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 : 2026-01-13DOI: 10.1038/s41612-025-01308-1
Shuai Li, Jie Yang, Fred Kucharski, ZhiQiang Gong, Ziyu Huang, Guolin Feng
The Northwest Pacific Subtropical High (NWPSH) presents a notable and even counterintuitive phenomenon: it shows a strong positive correlation between June and August (correlation coefficient: 0.55, significant at the 99.5% confidence level) during 1979–2005, while this correlation weakens sharply to -0.05 in 2006–2024. However, the relationships between June and July, and between July and August remain consistently weak throughout the entire period. Positive sea surface temperature (SST) anomalies in the tropical North Indian Ocean (TNIO) contribute to a persistent intensification of the NWPSH from June to August before 2005. Meanwhile, the Boreal Summer Intraseasonal Oscillation (BSISO) exhibits a stronger 60-day periodicity, which can cause opposite (similar) variations of NWPSH in adjacent month (cross-month). Under the combined effects of TNIO SST positive anomalies and BSISO, the NWPSH mainly exhibits the cross-month correlation feature, while the relationships between adjacent months are very weak. After 2005, the BSISO exhibits a marked shortening of its periodicity, and the key SST regions associated with the NWPSH in June, July, and August are also inconsistent, which induce the weakening of the cross-month correlation feature of the NWPSH. The shortening of the BSISO’s periodicity is attributed to warming over the Maritime Continent and the tropical western Indian Ocean, which intensify both the zonal Walker and meridional Hadley circulations. These changes enhance downward motions over the tropical eastern Indian Ocean and the northwestern Pacific, thereby suppressing the initiation of BSISO’ convection and accelerating its decay.
{"title":"The interdecadal variations of cross-month correlation feature of the NWPSH","authors":"Shuai Li, Jie Yang, Fred Kucharski, ZhiQiang Gong, Ziyu Huang, Guolin Feng","doi":"10.1038/s41612-025-01308-1","DOIUrl":"https://doi.org/10.1038/s41612-025-01308-1","url":null,"abstract":"The Northwest Pacific Subtropical High (NWPSH) presents a notable and even counterintuitive phenomenon: it shows a strong positive correlation between June and August (correlation coefficient: 0.55, significant at the 99.5% confidence level) during 1979–2005, while this correlation weakens sharply to -0.05 in 2006–2024. However, the relationships between June and July, and between July and August remain consistently weak throughout the entire period. Positive sea surface temperature (SST) anomalies in the tropical North Indian Ocean (TNIO) contribute to a persistent intensification of the NWPSH from June to August before 2005. Meanwhile, the Boreal Summer Intraseasonal Oscillation (BSISO) exhibits a stronger 60-day periodicity, which can cause opposite (similar) variations of NWPSH in adjacent month (cross-month). Under the combined effects of TNIO SST positive anomalies and BSISO, the NWPSH mainly exhibits the cross-month correlation feature, while the relationships between adjacent months are very weak. After 2005, the BSISO exhibits a marked shortening of its periodicity, and the key SST regions associated with the NWPSH in June, July, and August are also inconsistent, which induce the weakening of the cross-month correlation feature of the NWPSH. The shortening of the BSISO’s periodicity is attributed to warming over the Maritime Continent and the tropical western Indian Ocean, which intensify both the zonal Walker and meridional Hadley circulations. These changes enhance downward motions over the tropical eastern Indian Ocean and the northwestern Pacific, thereby suppressing the initiation of BSISO’ convection and accelerating its decay.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"57 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956347","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 : 2026-01-10DOI: 10.1038/s41612-025-01309-0
Juan Cao, Shaofei Kong, Haoyu Dong, Yao Hu, Xuan Xiang, Weisi Jiang, Yingying Yan, Jian Wu, Junjun Deng, Pingqing Fu
{"title":"Identification and validation of marker compounds for fine particle emitted from sub-type biomass burning","authors":"Juan Cao, Shaofei Kong, Haoyu Dong, Yao Hu, Xuan Xiang, Weisi Jiang, Yingying Yan, Jian Wu, Junjun Deng, Pingqing Fu","doi":"10.1038/s41612-025-01309-0","DOIUrl":"https://doi.org/10.1038/s41612-025-01309-0","url":null,"abstract":"","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"3 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938293","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 : 2026-01-10DOI: 10.1038/s41612-025-01315-2
Sheng Wu, Emanuele Di Lorenzo, Yingying Zhao, Matthew Newman, Zhengyu Liu, Antonietta Capotondi, Daoxun Sun, Samantha Stevenson, Yonggang Liu
{"title":"Decomposition of pacific decadal oscillation sheds light on its dominant modes and future response using linear inverse model","authors":"Sheng Wu, Emanuele Di Lorenzo, Yingying Zhao, Matthew Newman, Zhengyu Liu, Antonietta Capotondi, Daoxun Sun, Samantha Stevenson, Yonggang Liu","doi":"10.1038/s41612-025-01315-2","DOIUrl":"https://doi.org/10.1038/s41612-025-01315-2","url":null,"abstract":"","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"1 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938296","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 : 2026-01-09DOI: 10.1038/s41612-025-01317-0
A. Montoro-Mendoza, C. Calvo-Sancho, J. J. González-Alemán, J. Díaz-Fernández, P. Bolgiani, M. L. Martín
Anthropogenic climate change is intensifying tropical cyclones, and some studies suggest that they are now impacting regions farther from the equator, though uncertainties remain. This study examines the North Atlantic (NATL) basin’s autumn climatology, focusing on environments conducive to tropical transitions (TTs), as most cyclones affecting Europe that originate from TTs occur during this season. Ten CMIP6 climate models under the historical, SSP2-4.5 and SSP5-8.5 scenarios are used, covering the 1981–2100 period, with the ERA5 reanalysis employed as a reference to support the results. The study introduces the Tropical Transition Favorability Index (TTFI), which is a novel metric that integrates key parameters to quantify environmental favorability for TTs in the NATL. Findings indicate a progressive tropicalization of the NATL basin under both SSP2-4.5 and SSP5-8.5, with a more pronounced effect under the latter, driven by increased sea surface temperatures and humidity, while dynamic constraints weaken. Although in some cases the magnitude of projected future changes is comparable to existing CMIP6 models biases with respect to ERA5, the results suggest a higher likelihood of TTs, increasing the risk from these destructive systems.
{"title":"Strengthening of favorable environments for North Atlantic tropical cyclogenesis in midlatitudes in a warmer climate","authors":"A. Montoro-Mendoza, C. Calvo-Sancho, J. J. González-Alemán, J. Díaz-Fernández, P. Bolgiani, M. L. Martín","doi":"10.1038/s41612-025-01317-0","DOIUrl":"https://doi.org/10.1038/s41612-025-01317-0","url":null,"abstract":"Anthropogenic climate change is intensifying tropical cyclones, and some studies suggest that they are now impacting regions farther from the equator, though uncertainties remain. This study examines the North Atlantic (NATL) basin’s autumn climatology, focusing on environments conducive to tropical transitions (TTs), as most cyclones affecting Europe that originate from TTs occur during this season. Ten CMIP6 climate models under the historical, SSP2-4.5 and SSP5-8.5 scenarios are used, covering the 1981–2100 period, with the ERA5 reanalysis employed as a reference to support the results. The study introduces the Tropical Transition Favorability Index (TTFI), which is a novel metric that integrates key parameters to quantify environmental favorability for TTs in the NATL. Findings indicate a progressive tropicalization of the NATL basin under both SSP2-4.5 and SSP5-8.5, with a more pronounced effect under the latter, driven by increased sea surface temperatures and humidity, while dynamic constraints weaken. Although in some cases the magnitude of projected future changes is comparable to existing CMIP6 models biases with respect to ERA5, the results suggest a higher likelihood of TTs, increasing the risk from these destructive systems.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"3 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938201","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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