Shaozhen Liu, James W. Kirchner, Louise J. Slater, Marius G. Floriancic, Ilja van Meerveld, Wouter R. Berghuijs
Land cover affects the runoff response of catchments. However, such land-cover effects remain difficult to decipher because experimental studies are site-specific, while large-sample analyses are often confounded by climate gradients that obscure the role of land cover. Site-to-site comparisons that ignore differences in antecedent wetness may overestimate runoff responses in forested catchments because they are typically found in humid climates. Here we quantify runoff responses to unit precipitation inputs and examine how they vary across 252 U.S. catchments with different land covers and forest fractions. For comparable antecedent wetness conditions (as quantified by antecedent streamflow), peak runoff responses decline as forest cover increases, with peak responses in forested catchments being 16%–63% lower than in catchments dominated by cropland or grassland. By accounting for climate-driven differences among catchments, our approach isolates the influences of land cover on reducing peak flows, which are often masked by climate in large-sample analyses.
{"title":"Forest Impacts on Peak Runoff Revealed by Accounting for the Effects of Climate","authors":"Shaozhen Liu, James W. Kirchner, Louise J. Slater, Marius G. Floriancic, Ilja van Meerveld, Wouter R. Berghuijs","doi":"10.1029/2025gl121139","DOIUrl":"https://doi.org/10.1029/2025gl121139","url":null,"abstract":"Land cover affects the runoff response of catchments. However, such land-cover effects remain difficult to decipher because experimental studies are site-specific, while large-sample analyses are often confounded by climate gradients that obscure the role of land cover. Site-to-site comparisons that ignore differences in antecedent wetness may overestimate runoff responses in forested catchments because they are typically found in humid climates. Here we quantify runoff responses to unit precipitation inputs and examine how they vary across 252 U.S. catchments with different land covers and forest fractions. For comparable antecedent wetness conditions (as quantified by antecedent streamflow), peak runoff responses decline as forest cover increases, with peak responses in forested catchments being 16%–63% lower than in catchments dominated by cropland or grassland. By accounting for climate-driven differences among catchments, our approach isolates the influences of land cover on reducing peak flows, which are often masked by climate in large-sample analyses.","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"10 5-6 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147462133","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}
Jianlong Chen, Peizhen Zhang, Xin Qiao, Zicheng Huang, Lejun Lu
Quantifying fault frictional properties is fundamental to understanding slip behavior and seismic hazard. We analyze 2 years of Sentinel-1 SAR data following the 2023 Turkey earthquake doublet using Independent Component Analysis-enhanced Small Baseline Subset-InSAR, to resolve postseismic deformation and invert for afterslip on the East Anatolian and Çardak faults. Within a rate-and-state framework, we estimate the friction parameter <span data-altimg="/cms/asset/fd8e17c4-d7e8-4832-8d71-7a1b328d5200/grl72251-math-0001.png"></span><mjx-container ctxtmenu_counter="85" ctxtmenu_oldtabindex="1" jax="CHTML" role="application" sre-explorer- style="font-size: 103%; position: relative;" tabindex="0"><mjx-math aria-hidden="true" location="graphic/grl72251-math-0001.png"><mjx-semantics><mjx-mrow data-semantic-children="4" data-semantic-content="0,5" data-semantic- data-semantic-role="leftright" data-semantic-speech="left parenthesis a minus b right parenthesis" data-semantic-type="fenced"><mjx-mo data-semantic- data-semantic-operator="fenced" data-semantic-parent="6" data-semantic-role="open" data-semantic-type="fence" style="margin-left: 0.056em; margin-right: 0.056em;"><mjx-c></mjx-c></mjx-mo><mjx-mrow data-semantic-children="1,3" data-semantic-content="2" data-semantic- data-semantic-parent="6" data-semantic-role="subtraction" data-semantic-type="infixop"><mjx-mi data-semantic-annotation="clearspeak:simple" data-semantic-font="italic" data-semantic- data-semantic-parent="4" data-semantic-role="latinletter" data-semantic-type="identifier"><mjx-c></mjx-c></mjx-mi><mjx-mo data-semantic- data-semantic-operator="infixop,−" data-semantic-parent="4" data-semantic-role="subtraction" data-semantic-type="operator" rspace="4" space="4"><mjx-c></mjx-c></mjx-mo><mjx-mi data-semantic-annotation="clearspeak:simple" data-semantic-font="italic" data-semantic- data-semantic-parent="4" data-semantic-role="latinletter" data-semantic-type="identifier"><mjx-c></mjx-c></mjx-mi></mjx-mrow><mjx-mo data-semantic- data-semantic-operator="fenced" data-semantic-parent="6" data-semantic-role="close" data-semantic-type="fence" style="margin-left: 0.056em; margin-right: 0.056em;"><mjx-c></mjx-c></mjx-mo></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display="inline" unselectable="on"><math altimg="urn:x-wiley:00948276:media:grl72251:grl72251-math-0001" display="inline" location="graphic/grl72251-math-0001.png" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow data-semantic-="" data-semantic-children="4" data-semantic-content="0,5" data-semantic-role="leftright" data-semantic-speech="left parenthesis a minus b right parenthesis" data-semantic-type="fenced"><mo data-semantic-="" data-semantic-operator="fenced" data-semantic-parent="6" data-semantic-role="open" data-semantic-type="fence" stretchy="false">(</mo><mrow data-semantic-="" data-semantic-children="1,3" data-semantic-content="2" data-semantic-parent="6" data-semantic-role="subtraction" data-semantic-type="in
Tropical low-cloud feedback is the largest source of uncertainty in climate sensitivity, yet multi-century records of surface shortwave radiation are scarce. We calibrate Porites coral δ13C against satellite photosynthetically available radiation (PAR) and reconstruct monthly PAR for the northern South China Sea during the Medieval Climate Anomaly (1129–1264 CE) and the Little Ice Age (1631–1771 CE). After correcting for the Suess effect and propagating errors via Monte Carlo resampling techniques, annual PAR during the Little-Ice-Age is ∼22% lower and seasonality slightly weaker. The dimming aligns with regional proxies for cooler, wetter conditions and is best explained by brighter low clouds, likely boosted by volcanic aerosol–cloud interactions. CMIP6/PMIP4 past1000 simulations, however, yield <0.2% change over the same interval, indicating that current models understate volcanic microphysics and tropical low-cloud sensitivity. The coral PAR record thus provides a quantitative pre-industrial target for evaluating tropical cloud processes and reducing uncertainty in equilibrium climate sensitivity.
{"title":"Coral δ13C Reveals Little Ice Age Dimming of Tropical Surface Shortwave Radiation Not Captured by Climate Models","authors":"Guangchao Deng, Huimin Guo, Xuefei Chen, Jian-xin Zhao, Gangjian Wei, Wenfeng Deng","doi":"10.1029/2026gl121885","DOIUrl":"https://doi.org/10.1029/2026gl121885","url":null,"abstract":"Tropical low-cloud feedback is the largest source of uncertainty in climate sensitivity, yet multi-century records of surface shortwave radiation are scarce. We calibrate <i>Porites</i> coral δ<sup>13</sup>C against satellite photosynthetically available radiation (PAR) and reconstruct monthly PAR for the northern South China Sea during the Medieval Climate Anomaly (1129–1264 CE) and the Little Ice Age (1631–1771 CE). After correcting for the Suess effect and propagating errors via Monte Carlo resampling techniques, annual PAR during the Little-Ice-Age is ∼22% lower and seasonality slightly weaker. The dimming aligns with regional proxies for cooler, wetter conditions and is best explained by brighter low clouds, likely boosted by volcanic aerosol–cloud interactions. CMIP6/PMIP4 past1000 simulations, however, yield <0.2% change over the same interval, indicating that current models understate volcanic microphysics and tropical low-cloud sensitivity. The coral PAR record thus provides a quantitative pre-industrial target for evaluating tropical cloud processes and reducing uncertainty in equilibrium climate sensitivity.","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"44 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147462136","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}
Chao Zhang, Anmin Duan, Thomas J. Browning, Yuxin Xie, Eric P. Achterberg
The El Niño–Southern Oscillation (ENSO) and Tibetan Plateau, as key drivers of Earth's climate system, exert bidirectional controls that complicate causal attribution. Here, we integrate satellite-derived Tibetan Plateau snow cover (TPSC) with causal inference to establish TPSC-ENSO conversion factor. Using this factor, we estimate that TPSC anomalies contribute 24.8% (6.4%–44.1%) of September ENSO variability. Notably, TPSC-modulated biogeochemical processes are as influential as equatorial zonal wind mechanisms, constituting an additional ENSO driver. Reduced TPSC intensifies the Tibetan Plateau heat source, driving ascendant easterly anomalies. This accelerates the tropical easterly jet, transporting more Saharan dust to the tropical Pacific. Dust-iron fertilization stimulates phytoplankton accumulation across iron-limited central-eastern Equatorial Pacific, reducing solar irradiance penetration depth, lowering upper ocean heat content by 21% (7%–29%), and promoting La Niña development. Conversely, high TPSC favors El Niño development. These findings quantify TPSC's impact on ENSO variability, unveiling a biogeochemical pathway linking dust-iron fertilization to ocean energetics.
{"title":"Influence of Tibetan Plateau Snow Cover on ENSO Variability via the Dust-Iron Fertilization","authors":"Chao Zhang, Anmin Duan, Thomas J. Browning, Yuxin Xie, Eric P. Achterberg","doi":"10.1029/2025gl120206","DOIUrl":"https://doi.org/10.1029/2025gl120206","url":null,"abstract":"The El Niño–Southern Oscillation (ENSO) and Tibetan Plateau, as key drivers of Earth's climate system, exert bidirectional controls that complicate causal attribution. Here, we integrate satellite-derived Tibetan Plateau snow cover (TPSC) with causal inference to establish TPSC-ENSO conversion factor. Using this factor, we estimate that TPSC anomalies contribute 24.8% (6.4%–44.1%) of September ENSO variability. Notably, TPSC-modulated biogeochemical processes are as influential as equatorial zonal wind mechanisms, constituting an additional ENSO driver. Reduced TPSC intensifies the Tibetan Plateau heat source, driving ascendant easterly anomalies. This accelerates the tropical easterly jet, transporting more Saharan dust to the tropical Pacific. Dust-iron fertilization stimulates phytoplankton accumulation across iron-limited central-eastern Equatorial Pacific, reducing solar irradiance penetration depth, lowering upper ocean heat content by 21% (7%–29%), and promoting La Niña development. Conversely, high TPSC favors El Niño development. These findings quantify TPSC's impact on ENSO variability, unveiling a biogeochemical pathway linking dust-iron fertilization to ocean energetics.","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"12 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447896","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}
M. Pollock, R. Diamond, H. Heorton, L. C. Sime, D. Schroeder, C. Brierley
With ongoing anthropogenic warming, the Arctic is increasingly dominated by thin, first-year sea ice. Understanding the ice–ocean–atmosphere interactions in warmer climates is therefore essential. We analyze the Arctic sea-ice energy budget in nine CMIP6-PMIP4 lig127k simulations of the Last Interglacial warm Arctic. All models show reduced Last Interglacial summer sea ice, but with substantial inter-model spread. We demonstrate that this arises from differences in surface energy anomalies, which are highly correlated with sea ice area anomalies ( of 74%). Ice–albedo feedbacks dominate this response: reduced ice cover exposes more open ocean, enhances shortwave absorption, and warms the upper ocean. This heat is released in autumn, delaying sea-ice regrowth. Although modern warming is driven by longwave forcing, our results highlight that shortwave absorption from reduced albedo is a key driver of summer sea-ice loss, underscoring the need for accurate representation of surface heat-balance processes in future Arctic projections.
{"title":"An Arctic Sea Ice Energy Budget for the Last Interglacial","authors":"M. Pollock, R. Diamond, H. Heorton, L. C. Sime, D. Schroeder, C. Brierley","doi":"10.1029/2025gl120781","DOIUrl":"https://doi.org/10.1029/2025gl120781","url":null,"abstract":"With ongoing anthropogenic warming, the Arctic is increasingly dominated by thin, first-year sea ice. Understanding the ice–ocean–atmosphere interactions in warmer climates is therefore essential. We analyze the Arctic sea-ice energy budget in nine CMIP6-PMIP4 <i>lig127k</i> simulations of the Last Interglacial warm Arctic. All models show reduced Last Interglacial summer sea ice, but with substantial inter-model spread. We demonstrate that this arises from differences in surface energy anomalies, which are highly correlated with sea ice area anomalies (<span data-altimg=\"/cms/asset/e0d06622-eccf-4ad0-9ccd-847995bb6baf/grl72248-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"148\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/grl72248-math-0001.png\"><mjx-semantics><mjx-mrow><mjx-msup data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"normal r squared\" data-semantic-type=\"superscript\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: 0.363em;\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00948276:media:grl72248:grl72248-math-0001\" display=\"inline\" location=\"graphic/grl72248-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><msup data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"latinletter\" data-semantic-speech=\"normal r squared\" data-semantic-type=\"superscript\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\" mathvariant=\"normal\">r</mi><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\">2</mn></msup></mrow>${mathrm{r}}^{2}$</annotation></semantics></math></mjx-assistive-mml></mjx-container> of 74%). Ice–albedo feedbacks dominate this response: reduced ice cover exposes more open ocean, enhances shortwave absorption, and warms the upper ocean. This heat is released in autumn, delaying sea-ice regrowth. Although modern warming is driven by longwave forcing, our results highlight that shortwave absorption from reduced albedo is a key driver of summer sea-ice loss, underscoring the need for accurate representation of surface heat-balance processes in future Arctic projections.","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"62 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447897","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}
D. Bhattacharyya, J. T. Clarke, P. Stephenson, T. Koskinen, J.-Y. Chaufray, L. Moore, H. Melin
Uranus' thermospheric temperature decreased from ∼800K in 1986 to ∼450K in 2022 as determined from observations of H3+ and H2 infrared emissions. Spitzer 2007 lower atmosphere observations do not emulate this cooling trend. Here we show that the atomic H Lyman ⍺ emission from the disk of Uranus observed by HST from 2011 to 2022 are not consistent with radiative transfer models based on a constant atmospheric structure retrieved from the Voyager 2 flyby of 1986. Instead, the optical depth of the H column matching the Uranus Lyman ⍺ disk brightness decreased after 1998, consistent with the long-term cooling trend. This decrease is irrespective of auroral activity. While the origin of the cooling is poorly understood, it indicates that the density and extent of the Uranssian exosphere changes on a time scale of years impacting the atmospheric structure, the magnetospheric proton source, and exospheric drag on the inner rings.
{"title":"Steady Collapse of Uranus' Exosphere After 1998 to the Present Decade","authors":"D. Bhattacharyya, J. T. Clarke, P. Stephenson, T. Koskinen, J.-Y. Chaufray, L. Moore, H. Melin","doi":"10.1029/2025gl120292","DOIUrl":"https://doi.org/10.1029/2025gl120292","url":null,"abstract":"Uranus' thermospheric temperature decreased from ∼800K in 1986 to ∼450K in 2022 as determined from observations of H<sub>3</sub><sup>+</sup> and H<sub>2</sub> infrared emissions. Spitzer 2007 lower atmosphere observations do not emulate this cooling trend. Here we show that the atomic H Lyman ⍺ emission from the disk of Uranus observed by HST from 2011 to 2022 are not consistent with radiative transfer models based on a constant atmospheric structure retrieved from the Voyager 2 flyby of 1986. Instead, the optical depth of the H column matching the Uranus Lyman ⍺ disk brightness decreased after 1998, consistent with the long-term cooling trend. This decrease is irrespective of auroral activity. While the origin of the cooling is poorly understood, it indicates that the density and extent of the Uranssian exosphere changes on a time scale of years impacting the atmospheric structure, the magnetospheric proton source, and exospheric drag on the inner rings.","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"62 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447907","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}
Chuanhua Zhu, Jiaqiao Gan, Zuqi Lan, Chisheng Wang
The efficacy of rapid seismic response is fundamentally constrained by the sequential, multi-step nature of conventional InSAR processing, where error propagation and reliance on auxiliary data hinder automation. Here, we present a holistic framework using Physics-Aware Generative Adversarial Networks (GANs) to directly retrieve absolute coseismic displacement fields from single, noisy interferograms. By synthesizing the distinct spectral signatures of tectonic deformation against stratified and turbulent atmosphere, orbital ramps, and topographic residuals, our model achieves end-to-end signal extraction. This approach effectively bypasses adaptive filtering, external error corrections, and the fragile phase unwrapping step. Validation against 18 real-world earthquakes confirms the robust removal of segmentation artifacts. Crucially, comparison with GPS data from the 2016 Amatrice earthquake demonstrates high physical fidelity (>69 within 1σ) without post-processing. This self-contained paradigm eliminates manual intervention, establishing a new standard for instantaneous, automated, post-event situational awareness.
{"title":"Holistic Retrieval of Absolute Coseismic Displacement Fields From Single Interferograms via Physics-Aware GANs","authors":"Chuanhua Zhu, Jiaqiao Gan, Zuqi Lan, Chisheng Wang","doi":"10.1029/2025gl121419","DOIUrl":"https://doi.org/10.1029/2025gl121419","url":null,"abstract":"The efficacy of rapid seismic response is fundamentally constrained by the sequential, multi-step nature of conventional InSAR processing, where error propagation and reliance on auxiliary data hinder automation. Here, we present a holistic framework using Physics-Aware Generative Adversarial Networks (GANs) to directly retrieve absolute coseismic displacement fields from single, noisy interferograms. By synthesizing the distinct spectral signatures of tectonic deformation against stratified and turbulent atmosphere, orbital ramps, and topographic residuals, our model achieves end-to-end signal extraction. This approach effectively bypasses adaptive filtering, external error corrections, and the fragile phase unwrapping step. Validation against 18 real-world earthquakes confirms the robust removal of segmentation artifacts. Crucially, comparison with GPS data from the 2016 Amatrice earthquake demonstrates high physical fidelity (>69 within 1σ) without post-processing. This self-contained paradigm eliminates manual intervention, establishing a new standard for instantaneous, automated, post-event situational awareness.","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"26 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440199","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}
{"title":"Thank You to Our 2025 Peer Reviewers","authors":"Kristopher Karnauskas, Anantha Aiyyer, Suzana Camargo, Fabio Capitanio, Peter Chi, Sloan Coats, Sylvia Dee, Christine Dow, Sarah Feakins, Robinson Fulweiler, Valier Galy, Neil Ganju, Alessandra Giannini, Yu Gu, Jianping Guo, Christian Huber, Valeriy Ivanov, Gregory Johnson, Monika Korte, Sujay Kumar, Soléne Lejosne, Kevin Lewis, Huixin Liu, Gudrun Magnusdottir, Mathieu Morlighem, Yuichi Otsuka, Paola Passalacqua, Christina Patricola-DiRosario, Germán Prieto, Bo Qiu, Lynn Russell, Kanako Seki, Hui Su, Daoyuan Sun, Hari Viswanathan, Guiling Wang, Kaicun Wang, Angelicque White, Quentin Williams, Lei Zhang, Zhaoru Zhang","doi":"10.1029/2026gl122706","DOIUrl":"https://doi.org/10.1029/2026gl122706","url":null,"abstract":"<p>On behalf of the journal, AGU, and the scientific community, the editors of Geophysical Research Letters would like to sincerely thank those who reviewed manuscripts in 2025. The hours reading and commenting on manuscripts not only improve the manuscripts but also increase the scientific rigor of future research in the field. We greatly appreciate the assistance of the reviewers in advancing open science, which is a key objective of AGU's data policy. We particularly appreciate the timely reviews in light of the demands imposed by the rapid review process at Geophysical Research Letters. We received 5,930 submissions in 2025, and 6,036 reviewers contributed to their evaluation by providing 10,970 reviews in total. We deeply appreciate their contributions. Individuals in <i>italics</i> provided three or more reviews for GRL during the year.</p>\u0000<p>A. Jayakumar</p>\u0000<p>A. Karp</p>\u0000<p>A. Petrukovich</p>\u0000<p>Aakash Sane</p>\u0000<p>Aaron Ashley</p>\u0000<p><i>Aaron Breneman</i></p>\u0000<p><i>Aaron Donohoe</i></p>\u0000<p>Aaron Hendry</p>\u0000<p>Aaron Hill</p>\u0000<p>Aaron Johnson</p>\u0000<p>Aaron Levine</p>\u0000<p>Aaron Match</p>\u0000<p>Aaron Mohammed</p>\u0000<p>Aaron Naeger</p>\u0000<p>Aaron Wech</p>\u0000<p>Abby Hutson</p>\u0000<p>Abdallah Zaki</p>\u0000<p>Abdelhaq Hamza</p>\u0000<p>Abdulamid Fakoya</p>\u0000<p>Abhijit Das</p>\u0000<p>Abhik Santra</p>\u0000<p>Abhinav Gupta</p>\u0000<p>Abhishek Rajhans</p>\u0000<p>Abhishek Savita</p>\u0000<p><i>Abigail Azari</i></p>\u0000<p>Abigail Langston</p>\u0000<p>Abigail Lute</p>\u0000<p>Abigail Swann</p>\u0000<p>Abigail Whittington</p>\u0000<p><i>Abigail Williams</i></p>\u0000<p>Abraham Emond</p>\u0000<p>Abram Jacobson</p>\u0000<p>Acacia Pepler</p>\u0000<p>Adam Blaker</p>\u0000<p><i>Adam Burnett</i></p>\u0000<p>Adam Clark</p>\u0000<p>Adam Forte</p>\u0000<p>Adam Masters</p>\u0000<p><i>Adam Povey</i></p>\u0000<p>Adam Scaife</p>\u0000<p>Adam Schreiner-McGraw</p>\u0000<p>Adam Smith</p>\u0000<p>Adam Sobel</p>\u0000<p>Adam Sokol</p>\u0000<p>Addison Rice</p>\u0000<p><i>Adele Igel</i></p>\u0000<p>Adeline Montlaur</p>\u0000<p>Ademe Mekonnen</p>\u0000<p>Adeyemi Adebiyi</p>\u0000<p>Adina Pusok</p>\u0000<p>Aditya Gusman</p>\u0000<p>Aditya Khuller</p>\u0000<p><i>Adrià Barbeta</i></p>\u0000<p>Adrian Burd</p>\u0000<p>Adrián Flores Orozco</p>\u0000<p>Adrian Grocott</p>\u0000<p><i>Adrian Harpold</i></p>\u0000<p>Adrian Martin</p>\u0000<p>Adrian Matthews</p>\u0000<p>Adrian Muxworthy</p>\u0000<p>Adrian Sheppard</p>\u0000<p>Adrian Tasistro-Hart</p>\u0000<p>Adrian Tompkins</p>\u0000<p>Adriano Gualandi</p>\u0000<p>Adrianus Reniers</p>\u0000<p>Adrienne Marshall</p>\u0000<p>Advik Eswaran</p>\u0000<p>Agnès Borbon</p>\u0000<p>Agnes Kontny</p>\u0000<p>Agnes Lim</p>\u0000<p>Agnese Marcato</p>\u0000<p>Agniv Sengupta</p>\u0000<p>Agus Santoso</p>\u0000<p>Agust Gudmundsson</p>\u0000<p>Ahmad Ghassemi</p>\u0000<p>Ahmad Lalti</p>\u0000<p>Ahmed Elkouk</p>\u0000<p>Aibing Li</p>\u0000<p>Aidan Blaser</p>\u0000<p>Aiguo Dai</p>\u0000<p>Aihui Wang</p>\u0000<p>Aiko Voigt</p>\u0000<p>Aimée Slangen</p>\u0000<p>Aimin Du</p>\u0000<p>Aislinn Fox</p>\u0000<p>Aitaro Kato</p>\u0000<p><i>Aixue Hu</i></p>\u0000<p>Ajay Kumar</p>\u0000<p><i>Ajay Raghavendra</i></p>\u0000<p>Akash Koppa</p>\u0000<p><i>Akira Kuwano-Yoshida</i></p>\u0000<p>Akira Oka</p>\u0000<p>Akira Yamazaki</p>\u0000<p>Akiyoshi Wada</p>\u0000<p>Akshay Deoras</p>\u0000<p><i>Akshaya Nikumbh</i></p>\u0000<p>Ala Aldahan</p>\u0000<p>Alain Pietroniro</p>\u0000<p>","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"54 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147462135","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}
Weibo Li, Qinglu Deng, Limin Zhang, Pengju An, Xingwei Ren, Xuexue Su, Ruiqiang Bai, Jiren Xie, Li Zeng, Kun Fang
Inverse grading, where coarse particles overlay finer materials, is common on talus slopes, yet its progressive formation under realistic conditions is rarely quantified. We integrate multi-year field observations with controlled freeze-thaw experiments to elucidate the processes driving particle migration that result in inverse grading and slope creep. Data from a talus slope in Northeast China show spatially varied downslope movements (2.0–19 mm/a), seasonal uplift during freezing, and net subsidence upon thawing. Laboratory models reveal systematic sorting: small particles move downward and downslope through expanded pores, while large particles shift and rotate with minimal descent. This vertical mobility contrast (small vs. large displaced by factors of 11–15) results in inverse grading over time. Depth-dependent displacement shows differential deformation, with surface layers moving more than deeper layers. Our findings demonstrate that seasonal particle-scale variations consistently drive talus restructuring, linking granular dynamics to landscape deformation and enhancing risk assessments in cold regions.
{"title":"Inverse Grading Emerges From Particle-Scale Migration Under Seasonal Freeze-Thaw Forcing: Evidence From Multi-Year Monitoring and Physical Modeling","authors":"Weibo Li, Qinglu Deng, Limin Zhang, Pengju An, Xingwei Ren, Xuexue Su, Ruiqiang Bai, Jiren Xie, Li Zeng, Kun Fang","doi":"10.1029/2025gl119590","DOIUrl":"https://doi.org/10.1029/2025gl119590","url":null,"abstract":"Inverse grading, where coarse particles overlay finer materials, is common on talus slopes, yet its progressive formation under realistic conditions is rarely quantified. We integrate multi-year field observations with controlled freeze-thaw experiments to elucidate the processes driving particle migration that result in inverse grading and slope creep. Data from a talus slope in Northeast China show spatially varied downslope movements (2.0–19 mm/a), seasonal uplift during freezing, and net subsidence upon thawing. Laboratory models reveal systematic sorting: small particles move downward and downslope through expanded pores, while large particles shift and rotate with minimal descent. This vertical mobility contrast (small vs. large displaced by factors of 11–15) results in inverse grading over time. Depth-dependent displacement shows differential deformation, with surface layers moving more than deeper layers. Our findings demonstrate that seasonal particle-scale variations consistently drive talus restructuring, linking granular dynamics to landscape deformation and enhancing risk assessments in cold regions.","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"19 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440201","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}
Yohei Sawada, Masashi Minamide, Yuyue Yan, Kazumune Hashimoto, Le Duc
Although the chaotic nature of the atmosphere may enable efficient control of tropical cyclones (TCs) via small-scale perturbations, few studies have proposed data-driven optimization methods to identify such perturbations. Here, we apply the recently proposed Ensemble Kalman Control (EnKC) to a TC simulation. We show that EnKC finds small-scale perturbations that mitigate TC. An EnKC-estimated reduction in surface water vapor, located approximately 250 km from the TC center, suppresses convective activity and latent heat release in the eye wall, leading to a reduction of TC intensity. To advance the discovery of feasible TC mitigation strategies, we discuss the potential of this data-driven method for leveraging chaos, as well as its remaining challenges.
{"title":"Data-Driven Exploration of Tropical Cyclone's Controllability","authors":"Yohei Sawada, Masashi Minamide, Yuyue Yan, Kazumune Hashimoto, Le Duc","doi":"10.1029/2025gl120393","DOIUrl":"https://doi.org/10.1029/2025gl120393","url":null,"abstract":"Although the chaotic nature of the atmosphere may enable efficient control of tropical cyclones (TCs) via small-scale perturbations, few studies have proposed data-driven optimization methods to identify such perturbations. Here, we apply the recently proposed Ensemble Kalman Control (EnKC) to a TC simulation. We show that EnKC finds small-scale perturbations that mitigate TC. An EnKC-estimated reduction in surface water vapor, located approximately 250 km from the TC center, suppresses convective activity and latent heat release in the eye wall, leading to a reduction of TC intensity. To advance the discovery of feasible TC mitigation strategies, we discuss the potential of this data-driven method for leveraging chaos, as well as its remaining challenges.","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"189 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447895","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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