Reviews of Geophysics最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 2","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148123333","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 2","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148091866","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 2","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148091867","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":"Processes Driving Drift-Ice Evolution and Its Interaction With the Oceanic and Atmospheric Boundary Layers","authors":"Jari Haapala,&nbsp;Morven Muilwijk,&nbsp;Ioanna Merkouriadi,&nbsp;Pedro Duarte,&nbsp;Ilker Fer,&nbsp;Mats A. Granskog,&nbsp;Virginie Guemas,&nbsp;Tore Hattermann,&nbsp;Robinson Hordoir,&nbsp;Doroteaciro Iovino,&nbsp;Polona Itkin,&nbsp;Roberta Pirazzini,&nbsp;Felix Pithan,&nbsp;Igor Polyakov,&nbsp;Annette Rinke,&nbsp;Letizia Tedesco,&nbsp;Petteri Uotila,&nbsp;Martin Vancoppenolle,&nbsp;Marcello Vichi,&nbsp;Timo Vihma","doi":"10.1029/2024RG000874","DOIUrl":"https://doi.org/10.1029/2024RG000874","url":null,"abstract":"<p>Sea ice is a crucial component of polar climate systems and is undergoing substantial changes in both hemispheres due to evolving climatic conditions. Arctic sea ice is transitioning from perennial to seasonal cover, and the Southern Ocean sea ice is exhibiting recent minima and enhanced seasonality. As global warming continues, the role of sea ice in polar climate systems is expected to transform further. However, many theoretical frameworks and parameterizations in current sea-ice models are based on observations from an earlier era dominated by thicker multi-year ice. Here, we synthesize the physical processes governing the dynamics and thermodynamics of drift ice—the mobile pack ice—and its coupling with the atmospheric and oceanic boundary layers. Our goal is to provide a coherent theoretical framework of the sea-ice evolution equations and to summarize parameterizations of sub-grid processes used across models of varying complexity. These include representations of momentum and scalar fluxes, ice-thickness distribution and redistribution, snow and melt-pond processes, wave–ice interactions, and physical–biogeochemical feedbacks. We also examine how sea ice impacts ocean stratification and mixing, as well as the atmospheric boundary layer and clouds. Finally, we highlight recent observational findings and outline priorities for improving the representation of drift-ice processes, particularly in light of the changing climate and ice state.</p>","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 1","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024RG000874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147666161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 1","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148121001","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 1","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148092870","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 1","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148116997","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":"Bayesian Inference for Subsurface Geophysical Inverse Problems","authors":"Mingliang Liu,&nbsp;Dario Grana,&nbsp;Klaus Mosegaard,&nbsp;Mrinal K. Sen,&nbsp;Minghui Xu,&nbsp;Tapan Mukerji","doi":"10.1029/2025RG000884","DOIUrl":"10.1029/2025RG000884","url":null,"abstract":"<p>In subsurface studies, <i>geophysical inverse modeling</i> aims to infer key Earth physical properties, such as deep geological structures, lithology, and fluid distribution from indirect observations, particularly geophysical data, while rigorously quantifying uncertainty. These inverse problems are typically high-dimensional and computationally demanding, requiring efficient probabilistic inference methods. Bayesian inversion provides a coherent statistical framework that integrates prior geological knowledge with observed data, enabling systematic uncertainty quantification in subsurface characterization. Gradient-free Bayesian sampling methods have long been used to characterize complex posterior distributions and remain foundational in geophysical inversion. Recently, in scenarios where gradient information can be efficiently obtained, gradient-informed Bayesian inference methods have emerged as effective alternatives. By leveraging the local geometry of the posterior, these methods enable more efficient exploration of high-dimensional parameter spaces. Concurrently, deep learning has further enhanced Bayesian inversion by facilitating implicit geological priors, surrogate forward modeling, and automatic differentiation for efficient gradient computation. This review provides a comprehensive synthesis of both gradient-free and gradient-informed Bayesian inference techniques, with an emphasis on the latter, and examines their applications in seismic, electromagnetic, gravity, and multiphysics inverse problems. Building on these developments, we introduce <i>Differentiable Bayesian Inversion</i> as a potential unifying conceptual framework that integrates deep-learning-based geological prior parameterization, physics-based or surrogate forward modeling, and probabilistic reasoning within a modular, differentiable architecture. We conclude by outlining open challenges and future research directions toward developing robust, interpretable, and uncertainty-aware inversion frameworks for increasingly complex geoscientific applications.</p>","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 1","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492700","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 1","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148088308","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":"Synergistic Integration of Flood Inundation Modeling Methods: A Review of Computational, Data-Driven, Observational and Experimental, and Conceptual Models","authors":"Behzad Nazari,&nbsp;Ebrahim Ahmadisharaf,&nbsp;Paul D. Bates,&nbsp;Hamid Moradkhani,&nbsp;Venkatesh Merwade,&nbsp;Thomas Wahl,&nbsp;Yu Zhang,&nbsp;Ning Lin,&nbsp;Ali Abdolali,&nbsp;Brett F. Sanders,&nbsp;Dai Yamazaki","doi":"10.1029/2025RG000898","DOIUrl":"10.1029/2025RG000898","url":null,"abstract":"<p>Flood inundation models are foundational to a variety of engineering design, risk mitigation, and real-time decision making and response. The models have evolved, driven primarily by advances in data and computational resources. Despite these advances, modeling methods have increasingly diverged into separate development paths. Rather than experiencing parallel growth, where emerging approaches complement and enhance well-established approaches, newer methods such as geomorphic and machine learning algorithms have, in some cases, supplanted or stalled the continued advancement of robust, time-tested methodologies. This trend toward replacement rather than synergistic integration may limit opportunities to leverage the respective strengths of both established and innovative approaches. We define “siloing” as the isolation that occurs when development efforts evolve vertically and concentrate within narrow methodological boundaries, potentially overlooking opportunities for integration across different modeling paradigms. This phenomenon can arise when methods are selected based on convenience, lack of familiarity with parallel tracks, or popular trends. The negative consequences can lead to application of certain methods well beyond their intended scope and hinder progress by underutilization of complementary strengths across different approaches to overcome challenges. This paper first discusses four categories of state-of-the-art flood inundation modeling methods—computational, data-driven, observational and experimental, and conceptual—alongside their major strengths and limitations, followed by instances of methodological siloing challenges. We then propose a vision for future research emphasizing synergistic integration across all modeling trajectories rather than isolated development. We hope to spur dialog among modelers with the short-term goal of convergent research and long-term of integrated practice.</p>","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 1","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380635","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}