npj Climate and Atmospheric Science最新文献_第3页

Amplified fossil dependence and environmental burden during temperature extremes amid the low-carbon energy transition 低碳能源转型过程中极端温度加剧对化石燃料的依赖和环境负担

IF 9 1区地球科学

npj Climate and Atmospheric Science Pub Date : 2026-10-07 DOI: 10.1038/s41612-026-01552-z

Rui Zhong,Yue Qin,Shiyu Li,Liangdian Huang,Gang Yan,Yixuan Zheng,Steven J. Davis,Minghao Qiu,Chuan Zhang,Xin Liu,Huibin Mo,Mengyao Xu,Peidong He,Xinlei Chen,Tong Zhu,Chaopeng Hong

引用次数: 0

Explainable Deep Learning Reveals Future Changes in East Asian Extreme Winter Predictability 可解释深度学习揭示东亚极端冬季可预测性的未来变化

IF 9 1区地球科学

npj Climate and Atmospheric Science Pub Date : 2026-10-05 DOI: 10.1038/s41612-026-01564-9

Seo-Young Jo, Jeong-Hwan Kim, Daehyun Kang, Jae-Heung Park, Seung-Ki Min, Minju Kim, Mi-Kyung Sung

{"title":"Explainable Deep Learning Reveals Future Changes in East Asian Extreme Winter Predictability","authors":"Seo-Young Jo, Jeong-Hwan Kim, Daehyun Kang, Jae-Heung Park, Seung-Ki Min, Minju Kim, Mi-Kyung Sung","doi":"10.1038/s41612-026-01564-9","DOIUrl":"https://doi.org/10.1038/s41612-026-01564-9","url":null,"abstract":"Deep learning-based approaches have emerged as powerful tools for seasonal prediction, yet their skill remains limited for extreme anomalies, and warming climates may alter predictability sources. Here we present a convolutional neural network (CNN)-based framework to assess how seasonal predictability changes in a future climate, focusing on East Asian extreme winters. Our CNN, trained on CESM2-LENS simulations for historical and SSP3-7.0 scenarios and optimized with Amplitude Focal Loss, reliably captures seasonal mean and extreme temperatures. Overall prediction skill increases in the future climate, with warm winter extremes becoming more predictable while cold extremes show little change. Using Integrated Gradients, an explainable AI method, we show this future asymmetry is linked to enhanced sensitivity of East Asian winter temperatures to tropical Pacific SST under background warming, where warm-phase SST more effectively drives extratropical circulation changes. Our framework provides a useful tool for exploring how seasonal predictability mechanisms evolve under climate change.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"58 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087868","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}

引用次数: 0

Twenty years of Kilimanjaro observations across 5000 m reveal tropical elevation-dependent warming under clear-sky conditions 乞力马扎罗山长达20年的5000米观测揭示了晴空条件下热带海拔依赖型变暖

IF 9 1区地球科学

npj Climate and Atmospheric Science Pub Date : 2026-10-05 DOI: 10.1038/s41612-026-01560-z

Yaping Mo, Nick Pepin, Harold Lovell, Yongming Xu

{"title":"Twenty years of Kilimanjaro observations across 5000 m reveal tropical elevation-dependent warming under clear-sky conditions","authors":"Yaping Mo, Nick Pepin, Harold Lovell, Yongming Xu","doi":"10.1038/s41612-026-01560-z","DOIUrl":"https://doi.org/10.1038/s41612-026-01560-z","url":null,"abstract":"Mountain regions are experiencing rapid climate change. However, temperature trends along elevation gradients remain poorly understood in the tropics, where long-term high-elevation air temperature (Tair) observations are sparse. Here, we examine elevation-dependent warming (EDW) on Mount Kilimanjaro by combining a unique long-term in situ Tair transect with Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST), surface and topographic datasets. Using machine learning, we map clear-sky Tair at four MODIS overpasses at 1 km resolution for 2004–2024. The models showed strong predictive performance, with RMSE values of 1.76 °C and 1.36 °C, and R2 values of 0.94 and 0.95 for daytime and nighttime models, respectively. Modelled Tair reveals strong diurnal contrasts: daytime Tair shows strong spatial variability affected by radiation and surface properties, while nighttime Tair varies more gradually with elevation. Trend analysis (2004–2019) identifies EDW under clear-sky conditions. Daytime trends become positive near the summit (+0.05 to +0.10 °C/decade), while strongest cooling occurs at 3000–3500 m (−0.13 to −0.14 °C/decade), coinciding with vegetation and land-cover change. Nighttime weak warming dominates most elevations (+0.04 to +0.15 °C/decade), while the summit remains near-neutral. These contrasts indicate that EDW on Kilimanjaro is shaped not only by elevation-dependent climate responses, but also by vegetation and land-cover changes that modify local surface-atmosphere coupling.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"6 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087832","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}

引用次数: 0

Environment-conditioned deep learning for severe convection nowcasting 强对流临近预报的环境条件深度学习

IF 9 1区地球科学

npj Climate and Atmospheric Science Pub Date : 2026-10-02 DOI: 10.1038/s41612-026-01558-7

Lei Chen, Zijian Zhu, Xiaoran Zhuang, Tianyuan Qi, Yuxuan Feng, Xiaohui Zhong, Hao Li

{"title":"Environment-conditioned deep learning for severe convection nowcasting","authors":"Lei Chen, Zijian Zhu, Xiaoran Zhuang, Tianyuan Qi, Yuxuan Feng, Xiaohui Zhong, Hao Li","doi":"10.1038/s41612-026-01558-7","DOIUrl":"https://doi.org/10.1038/s41612-026-01558-7","url":null,"abstract":"Severe convection produces localized hazards that often require warnings before radar echoes fully reveal storm development. Convective initiation and the maintenance of intense convection remain challenging for radar-only nowcasting because pre-convective signals may be absent from recent radar observations, and strong echoes often decay rapidly in forecasts. Here we present FuXi-Nowcast, an environment-conditioned deep learning system that combines high-resolution observations with three-dimensional atmospheric forecasts to predict composite reflectivity, precipitation, wind gusts, and surface variables up to 12 h ahead. In April–July 2024 evaluations over East China, FuXi-Nowcast outperforms operational numerical weather prediction, persistence, and extrapolation baselines for reflectivity and precipitation. Case studies, diagnostics, and ablation experiments suggest that atmospheric moisture information and explicit preservation of strong convective signals contribute to forecasts of convective initiation and maintenance. These results show that environmental conditioning can mitigate important failure modes of radar-only nowcasting for high-impact convective weather.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"10 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087835","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}

引用次数: 0

A physics-guided integrated global to regional method for high-resolution weather forecasting 高分辨率天气预报的物理导向综合全球到区域方法

IF 9 1区地球科学

npj Climate and Atmospheric Science Pub Date : 2026-10-01 DOI: 10.1038/s41612-026-01555-w

Jing Zhang, Yutong Dai, Yu Wang, Fang He, Pengbo Xu, Junping Yin

{"title":"A physics-guided integrated global to regional method for high-resolution weather forecasting","authors":"Jing Zhang, Yutong Dai, Yu Wang, Fang He, Pengbo Xu, Junping Yin","doi":"10.1038/s41612-026-01555-w","DOIUrl":"https://doi.org/10.1038/s41612-026-01555-w","url":null,"abstract":"High-resolution regional forecasts support disaster mitigation and sectoral decisions, yet numerical regional models remain costly. Here we introduce AERO-ODE, a physics-guided framework that combines global dynamical information, parameterization increments, and regional terrain constraints. From global initial conditions, AERO-ODE generates 72 h regional forecasts at hourly intervals and 3 km resolution in approximately 21 s, covering pressure-level Z, T, S, U and V and near-surface MSLP, U10, V10 and T2m, without separate global surface lateral-boundary inputs. At 48 h, the median reductions in RMSE across the 29 outputs are 29–32% and 28–33% compared with WRF-ARW and YingLong-WRF, respectively; all reductions relative to WRF-ARW are significant in both regions (p < 0.05). Median anomaly correlation is 0.04–0.05 higher than WRF-ARW, while extreme-event CSI and ETS improve for most variables. AERO-ODE generally shows lower errors than interpolated NeuralGCM, IFS and PanGu-Weather fields. These results demonstrate rapid, high-resolution regional forecasting from global dynamical information and regional constraints.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"298 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087836","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}

引用次数: 0

A tri‑basin matching index improves Indian Summer Monsoon Rainfall predictability 三流域匹配指数提高了印度夏季风降雨的可预测性

IF 9 1区地球科学

npj Climate and Atmospheric Science Pub Date : 2026-09-29 DOI: 10.1038/s41612-026-01553-y

Shi-Yun Yu, Rong-Hua Zhang, Lei Fan

{"title":"A tri‑basin matching index improves Indian Summer Monsoon Rainfall predictability","authors":"Shi-Yun Yu, Rong-Hua Zhang, Lei Fan","doi":"10.1038/s41612-026-01553-y","DOIUrl":"https://doi.org/10.1038/s41612-026-01553-y","url":null,"abstract":"Interannual variability of Indian Summer Monsoon Rainfall (ISMR) is vital for South Asian water resources and agriculture, but its prediction is challenged by non-stationary ocean–monsoon interactions. This study proposes a Matching Index (MI), defined as the pointwise product of normalized ISMR and sea surface temperature anomalies, combined with Empirical Orthogonal Function analysis (MI–EOF) to identify state-dependent ocean–monsoon coupling regimes. MI–EOF reveals three dominant regimes: ENSO intensity, distinguishing strong and weak ENSO states modulated by the Indian Ocean Dipole and tropical South Atlantic; ENSO lifecycle, highlighting phase-dependent effects of Indian Ocean Basin warming on ISMR; and ENSO spatial patterns, separating central- and eastern-Pacific ENSO influences on monsoon variability. Based on these physically interpretable regimes, a scenario-dependent reconstruction framework is developed to adaptively select predictors. The framework improves ISMR reconstruction correlation from 0.73 using conventional multivariate linear regression to 0.83, with stable performance throughout the record. Results demonstrate that ISMR variability is fundamentally state dependent and cannot be described by stationary linear relationships. The MI framework provides a physically based approach for diagnosing evolving air–sea coupling and improving monsoon prediction under climate change.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"36 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087866","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}

引用次数: 0

An Arctic climatology of particle number size distributions and cloud condensation nuclei during warm and moist intrusions 暖湿侵入期间微粒数大小分布和云凝结核的北极气候学

IF 9 1区地球科学

npj Climate and Atmospheric Science Pub Date : 2026-09-29 DOI: 10.1038/s41612-026-01461-1

Berkay Dönmez, Jakob Boyd Pernov, Romanos Foskinis, Radiance Calmer, Paraskevi Georgakaki, Hélène Angot, Eija Asmi, John Backman, Tak Chan, Radovan Krejci, Peter Tunved, Andreas Massling, Henrik Skov, Alfred Wiedensohler, Athanasios Nenes, Julia Schmale

{"title":"An Arctic climatology of particle number size distributions and cloud condensation nuclei during warm and moist intrusions","authors":"Berkay Dönmez, Jakob Boyd Pernov, Romanos Foskinis, Radiance Calmer, Paraskevi Georgakaki, Hélène Angot, Eija Asmi, John Backman, Tak Chan, Radovan Krejci, Peter Tunved, Andreas Massling, Henrik Skov, Alfred Wiedensohler, Athanasios Nenes, Julia Schmale","doi":"10.1038/s41612-026-01461-1","DOIUrl":"https://doi.org/10.1038/s41612-026-01461-1","url":null,"abstract":"Recent studies indicate that warm and moist intrusions are significant sources of aerosols in the Arctic. Yet their contribution to different aerosol particle size modes, cloud condensation nuclei (CCN), and droplet number concentrations (Nd) remains largely unconstrained. Here, we use long-term aerosol observations from five Arctic observatories and show that intrusions are a major driver of CCN perturbations, particularly in summer, significantly increasing accumulation mode particle, CCN and Nd number concentrations at all sites. In winter and spring, intrusions decrease concentrations at sites near 0° longitude (Zeppelin, Villum, Alert) but increase them at sites near 180° (Tiksi, Utqiaġvik/Barrow). These contrasting patterns reflect the interplay between emission sources and wet scavenging along air mass trajectories. Our findings highlight that intrusions have a strong impact on Arctic aerosol and cloud properties and are a key component of the Arctic climate system.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"11 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087869","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}

引用次数: 0

Artificial intelligence for atmospheric chemistry: from observation to process understanding 大气化学的人工智能：从观察到过程理解

IF 9 1区地球科学

npj Climate and Atmospheric Science Pub Date : 2026-09-28 DOI: 10.1038/s41612-026-01559-6

Nan Wang,Yunjiang Zhang,Jing Wei,Yuying Wang,Yele Sun

引用次数: 0

Observed weakening of the Kuroshio driven by the East Asian winter monsoon under ongoing climate warming 观测到在气候持续变暖的情况下，东亚冬季风驱动的黑潮减弱

IF 9 1区地球科学

npj Climate and Atmospheric Science Pub Date : 2026-09-25 DOI: 10.1038/s41612-026-01543-0

Yu-Xiang Qiao,Shusaku Sugimoto,Bo Qiu,Shuya Wang,Shoichiro Kido,Hirohiko Nakamura

{"title":"Observed weakening of the Kuroshio driven by the East Asian winter monsoon under ongoing climate warming","authors":"Yu-Xiang Qiao,Shusaku Sugimoto,Bo Qiu,Shuya Wang,Shoichiro Kido,Hirohiko Nakamura","doi":"10.1038/s41612-026-01543-0","DOIUrl":"https://doi.org/10.1038/s41612-026-01543-0","url":null,"abstract":"Abstract Western boundary currents are generally thought to respond to global warming through basin-scale wind forcing and ocean stratification, yet the role of local winds remains unclear. Here, we investigate changes in the Kuroshio under ongoing climate warming using satellite altimetry and high-resolution reanalysis data. We find that the Kuroshio surface velocity weakened significantly in autumn–winter during 1993–2022, by 10 ± 4% of its climatological mean, whereas no coherent trend is detected in spring–summer. A trend in local wind stress opposing the mean Kuroshio flow—possibly linked to the response of the East Asian winter monsoon to a tropical La Niña–like trend—can account for more than half of the upper-layer Kuroshio weakening, primarily through nonlinear Ekman pumping. Remote wind forcing also weakens the Kuroshio but cannot explain the pronounced seasonal dependence of the trend. These results suggest that the Kuroshio responds to global warming through a local wind-driven process in addition to basin-scale gyre adjustment.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"108 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049815","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}

引用次数: 0

Deep learning in reduced-order space accelerates high-resolution multi-model climate downscaling 深度学习在低阶空间加速高分辨率多模型气候降尺度

IF 9 1区地球科学

npj Climate and Atmospheric Science Pub Date : 2026-09-24 DOI: 10.1038/s41612-026-01545-y

Donghyuk Kim,Hajoon Song,Yujin Kim,Yeji Choi,Junghee Yun

引用次数: 0