Pub Date : 2026-02-06DOI: 10.1038/s41612-025-01294-4
Fei Luo, Frank Selten, Dim Coumou
Soil moisture strongly modulates heat waves and droughts by altering land-atmosphere feedbacks, yet its influence on large-scale circulation remains inadequately quantified. Using large-ensemble simulations with the state-of-the-art climate model EC-Earth 3, we demonstrate that interactive soil moisture has a substantial impact on Northern Hemisphere summer circulation climatology. Two experiments were conducted: a fully interactive simulation and one with prescribed soil moisture states. The results reveal pronounced circulation shifts. Relative to the prescribed case, the interactive experiment drives a poleward displacement of the subtropical jets. It strengthens the polar front jet and enhances land-atmosphere coupling, amplifying wave amplitudes over land by ~24%. Interactive soil moisture raises mean summer surface temperatures by up to +1.5 K and extremes by +3.0 K. These findings demonstrate that soil moisture fluctuations can modify mean atmospheric circulation, with important implications for future summer climate projections.
{"title":"The role of soil moisture on summer atmospheric circulation climatology in the Northern Hemisphere","authors":"Fei Luo, Frank Selten, Dim Coumou","doi":"10.1038/s41612-025-01294-4","DOIUrl":"https://doi.org/10.1038/s41612-025-01294-4","url":null,"abstract":"Soil moisture strongly modulates heat waves and droughts by altering land-atmosphere feedbacks, yet its influence on large-scale circulation remains inadequately quantified. Using large-ensemble simulations with the state-of-the-art climate model EC-Earth 3, we demonstrate that interactive soil moisture has a substantial impact on Northern Hemisphere summer circulation climatology. Two experiments were conducted: a fully interactive simulation and one with prescribed soil moisture states. The results reveal pronounced circulation shifts. Relative to the prescribed case, the interactive experiment drives a poleward displacement of the subtropical jets. It strengthens the polar front jet and enhances land-atmosphere coupling, amplifying wave amplitudes over land by ~24%. Interactive soil moisture raises mean summer surface temperatures by up to +1.5 K and extremes by +3.0 K. These findings demonstrate that soil moisture fluctuations can modify mean atmospheric circulation, with important implications for future summer climate projections.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"240 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135576","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 : 2026-02-04DOI: 10.1038/s41612-026-01341-8
Ruotong Xiao, Liang Wu, Zhiqiang Gong, Zhiping Wen, Tao Feng, Xi Cao, Shangfeng Chen
{"title":"The relationship between the origin of tropical cyclones and their maximum attained intensity","authors":"Ruotong Xiao, Liang Wu, Zhiqiang Gong, Zhiping Wen, Tao Feng, Xi Cao, Shangfeng Chen","doi":"10.1038/s41612-026-01341-8","DOIUrl":"https://doi.org/10.1038/s41612-026-01341-8","url":null,"abstract":"","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"1 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115636","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 : 2026-02-04DOI: 10.1038/s41612-026-01347-2
Yanqin Li, Bolan Gan, Ruichen Zhu, Xianyao Chen, Yingzhe Cui, Hong Wang, Lixin Wu
{"title":"Significance of Atlantic sea surface temperature anomalies to Arctic sea ice variability revealed by deep learning","authors":"Yanqin Li, Bolan Gan, Ruichen Zhu, Xianyao Chen, Yingzhe Cui, Hong Wang, Lixin Wu","doi":"10.1038/s41612-026-01347-2","DOIUrl":"https://doi.org/10.1038/s41612-026-01347-2","url":null,"abstract":"","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"89 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115993","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 : 2026-02-04DOI: 10.1038/s41612-026-01321-y
Albin Wells, David R. Rounce, Mark Fahnestock
Glaciers in Alaska contribute greatly to sea-level rise and are losing mass at a faster rate than any other region. Yet, our understanding of ongoing changes and ability to model them are hindered by a lack of observations, particularly at high spatiotemporal resolution. Here, we leverage Sentinel-1 synthetic aperture radar (SAR) data to produce temporally-varying glacier melt extents and snowlines from mid-2016 to 2024 for 99% of glaciers in Alaska greater than 2 km 2 . The melt extents are strongly correlated with temperatures, revealing that each 1°C increase in summer temperature causes up to 3 additional weeks of glacier melt. The high spatiotemporal resolution also captures subseasonal changes such as the 2019 heat wave, which caused subregional snowlines to retreat up to 105 m higher and exposed up to 28% more of the underlying glacier compared to typical years. Our snowlines agree well with optical datasets (r 2 up to 0.94), thus providing unprecedented reliable data unencumbered by clouds or lighting conditions. Moving forward, our automated, open-source workflow can easily be applied to other regions. These data also present unique opportunities to calibrate and validate large-scale glacier evolution models, a critical step for improving projections of glacier changes and their impacts.
{"title":"Seasonal progression of melt and snowlines in Alaska from SAR reveals impacts of warming","authors":"Albin Wells, David R. Rounce, Mark Fahnestock","doi":"10.1038/s41612-026-01321-y","DOIUrl":"https://doi.org/10.1038/s41612-026-01321-y","url":null,"abstract":"Glaciers in Alaska contribute greatly to sea-level rise and are losing mass at a faster rate than any other region. Yet, our understanding of ongoing changes and ability to model them are hindered by a lack of observations, particularly at high spatiotemporal resolution. Here, we leverage Sentinel-1 synthetic aperture radar (SAR) data to produce temporally-varying glacier melt extents and snowlines from mid-2016 to 2024 for 99% of glaciers in Alaska greater than 2 km <jats:sup>2</jats:sup> . The melt extents are strongly correlated with temperatures, revealing that each 1°C increase in summer temperature causes up to 3 additional weeks of glacier melt. The high spatiotemporal resolution also captures subseasonal changes such as the 2019 heat wave, which caused subregional snowlines to retreat up to 105 m higher and exposed up to 28% more of the underlying glacier compared to typical years. Our snowlines agree well with optical datasets (r <jats:sup>2</jats:sup> up to 0.94), thus providing unprecedented reliable data unencumbered by clouds or lighting conditions. Moving forward, our automated, open-source workflow can easily be applied to other regions. These data also present unique opportunities to calibrate and validate large-scale glacier evolution models, a critical step for improving projections of glacier changes and their impacts.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"280 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115642","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 : 2026-02-03DOI: 10.1038/s41612-026-01340-9
Dong Wan Kim, Young-Oh Kwon, Claude Frankignoul, Clara Deser, Gokhan Danabasoglu, Adam Herrington, Sunyong Kim
The complex nature of extratropical air-sea coupling has hampered a detailed physical understanding of how the atmosphere responds to sea surface temperature (SST) anomalies over the Kuroshio-Oyashio Extension (KOE) region. Departing from the conventional approach of examining the seasonal-mean response, this study focuses on how atmospheric latent heating structures in early winter are modulated by synoptic weather patterns, and how those weather patterns selectively respond to KOE SST anomalies. The results are based on high-resolution atmospheric model experiments (1/8 degree over the North Pacific, tapering to 1 degree over the rest of the globe). While three dominant synoptic weather patterns that enhance latent heating over the KOE region are identified, only one of them, corresponding to anticyclonic baroclinic wave, systematically responds to the imposed SST anomalies. Warm SST anomalies induce stronger updrafts, which enhance atmospheric latent heating and ultimately strengthen and anchor the anomalous anticyclone over the North Pacific. Because this anticyclonic baroclinic system occurs more frequently than other types of weather patterns and has the greatest sensitivity to KOE SST anomalies, it dominates the seasonal-mean atmospheric response. The results demonstrate that a synoptic view is needed for an improved understanding of the mechanisms governing the seasonal-mean atmospheric circulation response to KOE SST forcing.
{"title":"A synoptic view of the atmospheric circulation response to SST anomalies in the Kuroshio-Oyashio Extension Region: the importance of latent heating structure","authors":"Dong Wan Kim, Young-Oh Kwon, Claude Frankignoul, Clara Deser, Gokhan Danabasoglu, Adam Herrington, Sunyong Kim","doi":"10.1038/s41612-026-01340-9","DOIUrl":"https://doi.org/10.1038/s41612-026-01340-9","url":null,"abstract":"The complex nature of extratropical air-sea coupling has hampered a detailed physical understanding of how the atmosphere responds to sea surface temperature (SST) anomalies over the Kuroshio-Oyashio Extension (KOE) region. Departing from the conventional approach of examining the seasonal-mean response, this study focuses on how atmospheric latent heating structures in early winter are modulated by synoptic weather patterns, and how those weather patterns selectively respond to KOE SST anomalies. The results are based on high-resolution atmospheric model experiments (1/8 degree over the North Pacific, tapering to 1 degree over the rest of the globe). While three dominant synoptic weather patterns that enhance latent heating over the KOE region are identified, only one of them, corresponding to anticyclonic baroclinic wave, systematically responds to the imposed SST anomalies. Warm SST anomalies induce stronger updrafts, which enhance atmospheric latent heating and ultimately strengthen and anchor the anomalous anticyclone over the North Pacific. Because this anticyclonic baroclinic system occurs more frequently than other types of weather patterns and has the greatest sensitivity to KOE SST anomalies, it dominates the seasonal-mean atmospheric response. The results demonstrate that a synoptic view is needed for an improved understanding of the mechanisms governing the seasonal-mean atmospheric circulation response to KOE SST forcing.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"9 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102135","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 : 2026-02-03DOI: 10.1038/s41612-026-01343-6
Estela A. Monteiro, Giang Tran, Matthew J. Gidden, Nadine Mengis
Aerosols have played an important role in defining the climate over the historical period, due to their net cooling effect in the atmosphere. However, as their emissions are expected to decrease in upcoming decades, they will be associated with reduced cooling, i.e. future warming, of the planet. Despite their importance and high uncertainty associated with their radiative forcing, aerosols inclusion in simple climate models, impact models and carbon-based climate assessment metrics requires simplifications and assumptions. Typically, interactions between physical and biogeochemical processes are disregarded by such. By varying the spatial implementation of aerosols in an intermediate complexity model we explore the variability in Earth system responses under an ambitious mitigation scenario due to aerosols-radiation interactions. When aerosols are implemented disregarding their spatial distribution, surface air temperature is higher by almost 0.1 °C when compared to a regionally heterogeneous implementation, corresponding to an uncertainty of ca. 200 GtCO2 of remaining carbon budgets. The main processes driving these responses are the land surface temperature and its effect on soil respiration, as well as changed ocean heat fluxes due to differences in incoming shortwave radiation at the surface. The spatial distribution of aerosols triggers important climate-carbon feedbacks, which should be specifically considered when assessing climate evolution and simulated Earth system responses. Even if aerosol-cloud interactions aren’t explored, the results already indicate that aerosols should be deliberately accounted for in simple models and assessment tools, as their triggered feedbacks will be instrumental in defining pathways for temperature stabilisation and evaluating, for example, remaining carbon budgets.
{"title":"Carbon-climate feedback responses to spatial aerosol model implementation variations","authors":"Estela A. Monteiro, Giang Tran, Matthew J. Gidden, Nadine Mengis","doi":"10.1038/s41612-026-01343-6","DOIUrl":"https://doi.org/10.1038/s41612-026-01343-6","url":null,"abstract":"Aerosols have played an important role in defining the climate over the historical period, due to their net cooling effect in the atmosphere. However, as their emissions are expected to decrease in upcoming decades, they will be associated with reduced cooling, i.e. future warming, of the planet. Despite their importance and high uncertainty associated with their radiative forcing, aerosols inclusion in simple climate models, impact models and carbon-based climate assessment metrics requires simplifications and assumptions. Typically, interactions between physical and biogeochemical processes are disregarded by such. By varying the spatial implementation of aerosols in an intermediate complexity model we explore the variability in Earth system responses under an ambitious mitigation scenario due to aerosols-radiation interactions. When aerosols are implemented disregarding their spatial distribution, surface air temperature is higher by almost 0.1 °C when compared to a regionally heterogeneous implementation, corresponding to an uncertainty of ca. 200 GtCO2 of remaining carbon budgets. The main processes driving these responses are the land surface temperature and its effect on soil respiration, as well as changed ocean heat fluxes due to differences in incoming shortwave radiation at the surface. The spatial distribution of aerosols triggers important climate-carbon feedbacks, which should be specifically considered when assessing climate evolution and simulated Earth system responses. Even if aerosol-cloud interactions aren’t explored, the results already indicate that aerosols should be deliberately accounted for in simple models and assessment tools, as their triggered feedbacks will be instrumental in defining pathways for temperature stabilisation and evaluating, for example, remaining carbon budgets.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"90 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102134","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 : 2026-02-03DOI: 10.1038/s41612-026-01345-4
Mingyang Li, Wei Jia, Yan Yang, Hai Cheng, Jingyao Zhao, Shaoneng He, Guangxin Liu, Haowen Fan, Ting-Yong Li, Lidan Lei, Xiaofeng Ren, Na Zhang, Yinhuan Zhang, Jingfeng Lin, R. Lawrence Edwards
Despite numerous proxy-based reconstructions of climate since the Last Glacial Maximum, spatial hydroclimate variability in the Chinese monsoon region remains enigmatic. Here, we examine four stalagmites from northern China that collectively provide a continuous, highly resolved multi-proxy record over the past 25.5 ka. δ18O records capture large-scale variations in Asian summer monsoon (ASM) circulation, whereas trace element ratios and δ13C reflect regional precipitation variability; both follow precessional rhythms. Chinese stalagmite δ18O exhibits a zonal tripolar pattern, reflecting moisture sources and transport pathways. During Termination I, a meridional tripolar spatial precipitation pattern emerged, driven by El Niño–Southern Oscillation (ENSO) and mid-latitude westerlies dynamics. While precipitation peaked during the Middle Holocene, ASM circulation was strongest in the Early Holocene, a dipole hydrological pattern linked to residual Northern Hemisphere ice volume. A similar tripolar pattern re-emerged during the 4.2 ka event, suggesting a dominant role of ENSO in shaping this anomaly.
{"title":"Spatial patterns of Asian summer monsoon precipitation in the Chinese monsoon region since the LGM","authors":"Mingyang Li, Wei Jia, Yan Yang, Hai Cheng, Jingyao Zhao, Shaoneng He, Guangxin Liu, Haowen Fan, Ting-Yong Li, Lidan Lei, Xiaofeng Ren, Na Zhang, Yinhuan Zhang, Jingfeng Lin, R. Lawrence Edwards","doi":"10.1038/s41612-026-01345-4","DOIUrl":"https://doi.org/10.1038/s41612-026-01345-4","url":null,"abstract":"Despite numerous proxy-based reconstructions of climate since the Last Glacial Maximum, spatial hydroclimate variability in the Chinese monsoon region remains enigmatic. Here, we examine four stalagmites from northern China that collectively provide a continuous, highly resolved multi-proxy record over the past 25.5 ka. δ18O records capture large-scale variations in Asian summer monsoon (ASM) circulation, whereas trace element ratios and δ13C reflect regional precipitation variability; both follow precessional rhythms. Chinese stalagmite δ18O exhibits a zonal tripolar pattern, reflecting moisture sources and transport pathways. During Termination I, a meridional tripolar spatial precipitation pattern emerged, driven by El Niño–Southern Oscillation (ENSO) and mid-latitude westerlies dynamics. While precipitation peaked during the Middle Holocene, ASM circulation was strongest in the Early Holocene, a dipole hydrological pattern linked to residual Northern Hemisphere ice volume. A similar tripolar pattern re-emerged during the 4.2 ka event, suggesting a dominant role of ENSO in shaping this anomaly.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"5 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102136","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 : 2026-02-02DOI: 10.1038/s41612-025-01311-6
Dongjin Cho, Yoo-Geun Ham, Suyeon Jeong, Seon-Yu Kang
Extreme heatwaves are intensifying under climate change, yet their prediction remains limited by inadequate representation of land–atmosphere (L–A) interactions. Most deep learning–based weather models rely solely on atmospheric variables, overlooking the influence of land surface conditions on heat extremes. Here, we present an L–A coupled prediction framework for Northern Hemisphere summer that incorporates multi-layer soil moisture (SM) and temperature into atmospheric forecasting. To better capture delayed land surface feedbacks, the model is trained with a multi-step loss. This approach improved the representation of L–A interactions across 1–7 day lead times. Using multi-step loss, the L–A coupled model achieved a 5.9–11.2% improvement in heatwave forecast accuracy relative to the atmosphere-only model, as measured by root mean squared error, whereas single-step loss achieved only 0.4–2.4% improvement. Skill gain was strongest at short leads (~ 3 day) when both SM and circulation predictability were high, and sustained through 7 days by L–A coupling driven by SM predictability. Case studies of recent heatwaves further demonstrated its ability to capture land surface drying and associated temperature extremes. These findings underscore the importance of incorporating L–A coupling with multi-step optimization for advancing data-driven heatwave prediction.
{"title":"A deep learning-based land-atmosphere coupled model for heatwave prediction","authors":"Dongjin Cho, Yoo-Geun Ham, Suyeon Jeong, Seon-Yu Kang","doi":"10.1038/s41612-025-01311-6","DOIUrl":"https://doi.org/10.1038/s41612-025-01311-6","url":null,"abstract":"Extreme heatwaves are intensifying under climate change, yet their prediction remains limited by inadequate representation of land–atmosphere (L–A) interactions. Most deep learning–based weather models rely solely on atmospheric variables, overlooking the influence of land surface conditions on heat extremes. Here, we present an L–A coupled prediction framework for Northern Hemisphere summer that incorporates multi-layer soil moisture (SM) and temperature into atmospheric forecasting. To better capture delayed land surface feedbacks, the model is trained with a multi-step loss. This approach improved the representation of L–A interactions across 1–7 day lead times. Using multi-step loss, the L–A coupled model achieved a 5.9–11.2% improvement in heatwave forecast accuracy relative to the atmosphere-only model, as measured by root mean squared error, whereas single-step loss achieved only 0.4–2.4% improvement. Skill gain was strongest at short leads (~ 3 day) when both SM and circulation predictability were high, and sustained through 7 days by L–A coupling driven by SM predictability. Case studies of recent heatwaves further demonstrated its ability to capture land surface drying and associated temperature extremes. These findings underscore the importance of incorporating L–A coupling with multi-step optimization for advancing data-driven heatwave prediction.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"44 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102138","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 : 2026-02-02DOI: 10.1038/s41612-026-01339-2
Jian Ma, Jing Feng, Hui Su, Matthew Collins, Jing Su, In-Sik Kang, Masahiro Watanabe, Jianping Li, Yinding Zhang
Clouds significantly influence Earth’s radiative balance with complex changes in response to surface warming. The key drivers of the changes are the sea surface temperature (SST) pattern effect that reshapes cloud distributions, and the beta feedback that scales low-level fraction change to climatological amounts. Cloud radiative feedback remains the largest source of uncertainty in future climate projections, but current constraints are insufficient. Here, we demonstrate that the percentage change in tropical cloud fraction, driven by spatial patterns in SST increase, is linked to cloud height variations. We introduce a proportional warmer-get-higher paradigm and develop a pattern-based analytical framework, identifying three key factors governing cloud feedback: percentage cloud sensitivity to SST, climatological cloud cover, and SST warming patterns relative to the tropical mean. By leveraging recent observations to constrain these factors in two stages, we establish a process-oriented emergent constraint on projected cloud feedback in the 21st century. The first stage substitutes simulated cloud sensitivity and mean cloud cover to correct biases and reduce the spread by half. Then, the second stage attempts to further constrain the SST pattern effect, which explains 79% of the remaining spread in an attribution procedure. This percentage framework yields total, low, middle, and high cloud feedback of 0.49 ± 0.27, 0.33 ± 0.21, 0.09 ± 0.09, and 0.07 ± 0.06 W m-2 K-1 (90% confidence), respectively. It reduces intermodel uncertainty by 59% for cloud feedback and 33% for surface warming, resulting in a climate sensitivity of 4.08 ± 0.97 K.
云显著影响地球的辐射平衡，对地表变暖作出复杂的反应。这些变化的关键驱动因素是重塑云分布的海表温度（SST）模式效应，以及将低层部分变化缩放为气候量的β反馈。云辐射反馈仍然是未来气候预估中最大的不确定性来源，但目前的限制是不够的。在这里，我们证明了由海温增加的空间格局驱动的热带云分数的百分比变化与云高度变化有关。我们引入了一个比例变暖越高的范式，并开发了一个基于模式的分析框架，确定了控制云反馈的三个关键因素：云对海温的百分比敏感性、气候云量和相对于热带平均值的海温变暖模式。通过利用最近的观测结果将这些因素分为两个阶段，我们建立了一个面向过程的21世纪预计云反馈的紧急约束。第一阶段用模拟的云敏感性和平均云量代替，以纠正偏差并将传播减少一半。然后，第二阶段试图进一步约束海温模式效应，这解释了归因过程中剩余传播的79%。该百分比框架产生的总、低、中、高云反馈分别为0.49±0.27、0.33±0.21、0.09±0.09和0.07±0.06 W m-2 K-1（90%置信度）。它将云反馈的模式间不确定性降低了59%，将地表变暖的模式间不确定性降低了33%，导致气候敏感性为4.08±0.97 K。
{"title":"An analytical framework reduces cloud feedback uncertainty by linking percentage cloud change to surface ocean warming patterns","authors":"Jian Ma, Jing Feng, Hui Su, Matthew Collins, Jing Su, In-Sik Kang, Masahiro Watanabe, Jianping Li, Yinding Zhang","doi":"10.1038/s41612-026-01339-2","DOIUrl":"https://doi.org/10.1038/s41612-026-01339-2","url":null,"abstract":"Clouds significantly influence Earth’s radiative balance with complex changes in response to surface warming. The key drivers of the changes are the sea surface temperature (SST) pattern effect that reshapes cloud distributions, and the beta feedback that scales low-level fraction change to climatological amounts. Cloud radiative feedback remains the largest source of uncertainty in future climate projections, but current constraints are insufficient. Here, we demonstrate that the percentage change in tropical cloud fraction, driven by spatial patterns in SST increase, is linked to cloud height variations. We introduce a proportional warmer-get-higher paradigm and develop a pattern-based analytical framework, identifying three key factors governing cloud feedback: percentage cloud sensitivity to SST, climatological cloud cover, and SST warming patterns relative to the tropical mean. By leveraging recent observations to constrain these factors in two stages, we establish a process-oriented emergent constraint on projected cloud feedback in the 21st century. The first stage substitutes simulated cloud sensitivity and mean cloud cover to correct biases and reduce the spread by half. Then, the second stage attempts to further constrain the SST pattern effect, which explains 79% of the remaining spread in an attribution procedure. This percentage framework yields total, low, middle, and high cloud feedback of 0.49 ± 0.27, 0.33 ± 0.21, 0.09 ± 0.09, and 0.07 ± 0.06 W m-2 K-1 (90% confidence), respectively. It reduces intermodel uncertainty by 59% for cloud feedback and 33% for surface warming, resulting in a climate sensitivity of 4.08 ± 0.97 K.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"39 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102137","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 : 2026-01-30DOI: 10.1038/s41612-026-01334-7
Pouria Behnoudfar, Charlotte Moser, Marc Bocquet, Sibo Cheng, Nan Chen
Computer models are indispensable tools for understanding the Earth system. While high-resolution operational models have achieved many successes, they exhibit persistent biases, particularly in simulating extreme events and statistical distributions. In contrast, coarse-grained idealized models isolate fundamental processes and can be precisely calibrated to excel in characterizing specific dynamical and statistical features. However, different models remain siloed by disciplinary boundaries. By leveraging the complementary strengths of models of varying complexity, we develop an explainable AI framework for Earth system emulators. It bridges the model hierarchy through a reconfigured latent data assimilation technique, uniquely suited to exploit the sparse output from the idealized models. The resulting bridging model inherits the high resolution and comprehensive variables of operational models while achieving global accuracy enhancements through targeted improvements from idealized models. Crucially, the mechanism of AI provides a clear rationale for these advancements, moving beyond black-box correction to physically insightful understanding in a computationally efficient framework that enables effective physics-assisted digital twins and uncertainty quantification. We demonstrate its power by significantly correcting biases in CMIP6 simulations of El Niño spatiotemporal patterns, leveraging statistically accurate idealized models. This work also highlights the importance of pushing idealized model development and advancing communication between modeling communities.
{"title":"Bridging idealized and operational models: an explainable AI framework for Earth system emulators","authors":"Pouria Behnoudfar, Charlotte Moser, Marc Bocquet, Sibo Cheng, Nan Chen","doi":"10.1038/s41612-026-01334-7","DOIUrl":"https://doi.org/10.1038/s41612-026-01334-7","url":null,"abstract":"Computer models are indispensable tools for understanding the Earth system. While high-resolution operational models have achieved many successes, they exhibit persistent biases, particularly in simulating extreme events and statistical distributions. In contrast, coarse-grained idealized models isolate fundamental processes and can be precisely calibrated to excel in characterizing specific dynamical and statistical features. However, different models remain siloed by disciplinary boundaries. By leveraging the complementary strengths of models of varying complexity, we develop an explainable AI framework for Earth system emulators. It bridges the model hierarchy through a reconfigured latent data assimilation technique, uniquely suited to exploit the sparse output from the idealized models. The resulting bridging model inherits the high resolution and comprehensive variables of operational models while achieving global accuracy enhancements through targeted improvements from idealized models. Crucially, the mechanism of AI provides a clear rationale for these advancements, moving beyond black-box correction to physically insightful understanding in a computationally efficient framework that enables effective physics-assisted digital twins and uncertainty quantification. We demonstrate its power by significantly correcting biases in CMIP6 simulations of El Niño spatiotemporal patterns, leveraging statistically accurate idealized models. This work also highlights the importance of pushing idealized model development and advancing communication between modeling communities.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"104 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089762","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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