Reviews of Geophysics最新文献

{"title":"Eruption Source Parameters in Volcanic Plume Modeling: Advances, Challenges, and Future Directions","authors":"A. Costa,&nbsp;A. Folch,&nbsp;Y. J. Suzuki,&nbsp;L. Mingari,&nbsp;G. Macedonio","doi":"10.1029/2025RG000897","DOIUrl":"10.1029/2025RG000897","url":null,"abstract":"<p>Accurately predicting the atmospheric dispersion of volcanic ash and gases is crucial for both scientific understanding and hazard mitigation. Estimating Eruption Source Parameters (ESP), such as mass eruption rate, plume height, duration, and particle size distribution and properties, remains challenging due to the complex nature of volcanic processes and measurement uncertainties. This review examines recent advancements in assessing ESPs from the perspective of modeling, evaluating current limitations and potential future improvements. We overview how current models quantify crucial ESPs, either directly from observations or indirectly from proxies or modeling strategies, and address the inherent uncertainties. We identify critical areas needing further research and emphasize the importance of developing more robust and widely applicable methodologies. Finally, we propose innovative strategies to enhance ESPs estimations, ultimately improving volcanic hazard assessments and our understanding of eruption dynamics and their atmospheric interactions.</p>","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 2","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025RG000897","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147664426","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":"Arc Heat Flow and Magmatic Heat Budgets","authors":"S. E. Ingebritsen,&nbsp;Kazuki Sawayama,&nbsp;Yuri Taran,&nbsp;Giovanni Chiodini,&nbsp;Tsuneomi Kagiyama,&nbsp;Hiroshi Shinohara,&nbsp;Julie V. Rowland,&nbsp;Benjamin A. Black","doi":"10.1029/2025RG000922","DOIUrl":"10.1029/2025RG000922","url":null,"abstract":"<p>We evaluate hydrothermal heat loss from 11 volcanic-arc segments (∼6,000 km of arc length, ∼10% of the global total), motivated by the observation that much magmatic heat ultimately crosses the land surface as heated aqueous fluid. Heat loss takes place by volcanic eruption, geothermal heat conduction to the surface, fumarolic (vapor) discharge, thermal springs, and discharge of groundwater that has been heated by only a few degrees. Heat loss by extrusion of volcanic products ranges from 0.1 to ∼4 MW/km. For some arc segments, the hydrothermal heat loss is dominated by “slightly thermal” springs (maximum ∼10 MW/km arc length) or thermal springs (maximum ∼21 MW/km), but more commonly by hydrothermal steam vents and volcanic fumaroles (maximum ∼7 MW/km). Total hydrothermal heat-loss rates range from &lt;2.5 MW/km (Southwest Japan, Cascade Range, Northeast Japan, Kurils) to ≥8 MW/km (Ryukyu, Apennines, Taupo Volcanic Zone). We use these hydrothermal heat losses to estimate rates of magma supply, and combine these estimates of magma supply with existing estimates of eruption rates to obtain intrusion:extrusion ratios. Potential causal influences include state-of-stress and the abundance of silicic magma in the midcrust. Likely causes of along-arc variations range from the near-surface (0–5 km) hydraulic architecture (Cascade Range) to the nature of the subducting plate (Ryukyu vs. the rest of southwest Japan). Inferred intrusion: extrusion ratios are generally between 0.5 and 9. Whole-arc comparisons between heat-flow-based intrusion rates and those based on volatile fluxes and petrologic models are complicated by the wide range of along-arc behavior and the fact that we sometimes rely on volatile fluxes (e.g., SO₂) to help calculate hydrothermal heat losses, so that the data sets are not fully independent. However, reasonable agreement can be demonstrated in some examples of arc subsections.</p>","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 2","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025RG000922","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147649381","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":"Expressing Gratitude to Reviewers: A Message From the Editors of Reviews of Geophysics for 2025","authors":"Qingyun Duan,&nbsp;Valerio Acocella,&nbsp;Ann Marie Carlton,&nbsp;Minhan Dai,&nbsp;Paolo D’Odorico,&nbsp;Joshua Feinberg,&nbsp;Fabio Florindo,&nbsp;Andrew Gettelman,&nbsp;Ruth Harris,&nbsp;Yangting Lin,&nbsp;Gesine Mollenhauer,&nbsp;Alan Robock,&nbsp;Armin Sorooshian,&nbsp;Claudine Stirling,&nbsp;Yusuke Yokoyama","doi":"10.1029/2026RG000940","DOIUrl":"10.1029/2026RG000940","url":null,"abstract":"<p>On behalf of the authors and readers of Reviews of Geophysics (RoG), the American Geophysical Union, and the broader scientific community, the editors wish to wholeheartedly thank those who reviewed manuscripts for RoG in 2025.</p>","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 2","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2026RG000940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147617450","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":"Terrestrial Analogs to Titan for Geophysical Research","authors":"Conor A. Nixon,&nbsp;Samuel Birch,&nbsp;Audrey Chatain,&nbsp;Charles Cockell,&nbsp;Kendra K. Farnsworth,&nbsp;Peter M. Higgins,&nbsp;Stéphane Le Mouélic,&nbsp;Rosaly M. C. Lopes,&nbsp;Michael J. Malaska,&nbsp;Mohit Melwani Daswani,&nbsp;Kelly E. Miller,&nbsp;Catherine D. Neish,&nbsp;Olaf G. Podlaha,&nbsp;Jani Radebaugh,&nbsp;Lauren R. Schurmeier,&nbsp;Ashley Schoenfeld,&nbsp;Krista M. Soderlund,&nbsp;Anezina Solomonidou,&nbsp;Christophe Sotin,&nbsp;Nicholas A. Teanby,&nbsp;Tetsuya Tokano,&nbsp;Steven D. Vance","doi":"10.1029/2025RG000909","DOIUrl":"10.1029/2025RG000909","url":null,"abstract":"<p>Saturn's moon Titan exhibits remarkable parallels to the Earth in many geophysical and geological processes not found elsewhere in the solar system at the present day. These include a nitrogen atmosphere with a condensible gas—methane—replacing the Earth's water, leading to an active meteorology with rainfall and surface manifestations including rivers, lakes and seas, and the dissolution of karstic terrain. Other phenomena such as craters, dunes, and tectonic features are found elsewhere—for example on Mars and Venus—but their continuing alteration by pluvial, fluvial and lacustrine processes can be studied only on Earth and Titan. Meanwhile Titan also hosts an interior liquid water ocean with similarities to the Earth as well as to ocean worlds such as Europa and Enceladus. Our focus in this review paper is twofold: to describe the geophysical and geological parallels between Earth and Titan, and to evaluate the yet-underexploited possibilities for field analog research to gain new knowledge about these processes. To date, Titan's much colder temperature and different atmospheric and crustal materials have led to a skepticism that useful analogs can be found on Earth. Our conclusion, however, is that a much larger range of useful analog field work is possible and this work will substantially enhance our knowledge of both worlds. Such investigation will supplement the existing sparse data for Titan returned by space missions, will greatly enhance our understanding of such data sets, and will help to provide science impetus and goals for future missions.</p>","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 2","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025RG000909","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147599671","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":"Mesoscale and Submesoscale Variability in the Indian Ocean","authors":"Lei Zhou,&nbsp;Dongxiao Wang,&nbsp;Xuhua Cheng,&nbsp;Lixin Qu,&nbsp;Yun Qiu,&nbsp;Fuan Xiao,&nbsp;Lin Wang,&nbsp;Qiaoyan Wu,&nbsp;Xiaoyi Yang,&nbsp;Zhigang Lai,&nbsp;Danian Liu,&nbsp;Hui Zhao,&nbsp;Lin Liu,&nbsp;Ke Huang,&nbsp;Jianhuang Qin,&nbsp;Baosheng Li,&nbsp;Guangli Zhang,&nbsp;Chunhua Qiu,&nbsp;Haibo Tang,&nbsp;Huaming Huang","doi":"10.1029/2025RG000915","DOIUrl":"10.1029/2025RG000915","url":null,"abstract":"<p>Material transport and air-sea coupling dynamics associated with monsoon-related mesoscale and submesoscale processes in the Indian Ocean significantly modulate biogeochemical cycles, the large-scale energy balance, and both regional and global climate change. A thorough understanding of mesoscale and submesoscale variability in the Indian Ocean is therefore crucial for elucidating the physical mechanisms governing complex interactions among the ocean, ecosystems, and climate. However, substantial challenges remain in accurate observations, diagnoses, and simulations of such variability in the Indian Ocean, which hinders our ability to quantify their impacts on large-scale processes. This synthetic paper presents an interdisciplinary review on key characteristics of the unique mesoscale and submesoscale processes in the Indian Ocean. We first synthesize current understanding of the generation of bidirectional energy cascades, feedback mechanisms between mesoscale/submesoscale dynamics and the atmosphere, and physical-biogeochemical interactions mediated by horizontal and vertical transport induced by mesoscale/submesoscale motions. We then highlight outstanding knowledge gaps and uncertainties related to small-scale physical properties, and provide recommendations for observational, modeling, and theoretical strategies to advance future research on air-sea interactions and climate change. This review aims to complement existing global ocean process syntheses. Future directions are outlined with an emphasis on short-term (e.g., dedicated observational campaigns), long-term (e.g., improved modeling, climate integration) goals, and the emerging applications of machine learning and artificial intelligence in small-scale parameterization and data assimilation.</p>","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":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025RG000915","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147535845","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":"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":"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":"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}

{"title":"Monitoring Flood Inundation Dynamics From Space","authors":"C. Campo,&nbsp;P. Tamagnone,&nbsp;S. Choy,&nbsp;T. D. Tran,&nbsp;G. J.-P. Schumann,&nbsp;Y. Kuleshov","doi":"10.1029/2025RG000885","DOIUrl":"10.1029/2025RG000885","url":null,"abstract":"<p>With the increasing intensity and frequency of flood events worldwide, the need for accurate and timely inundation mapping has never been more critical. Large-scale flood extent estimations are vital for coordinating effective disaster response, facilitating recovery, and building future resilience. Traditional ground-based and aerial monitoring methods are often impractical during major floods, limited by cost, safety, and their inability to capture the full scope of an event. Satellite-based remote sensing provides the necessary large-scale perspective with a unique vantage point to monitor extreme inundation events. This review assesses the potential of public satellite sensors to capture flood events using a novel analysis of the Dartmouth Flood Observatory (DFO) global flood database. Our analysis quantifies the major performance gaps between these sensors, demonstrating that no single instrument is sufficient for complete and continuous flood monitoring. Passive microwave radiometers are capable of capturing &gt;95% of flood events, albeit at a coarse spatial resolution that may be unsuitable for detailed mapping or local risk assessment. In contrast, popular multispectral sensors such as Landsat and Sentinel-2 capture no more than 30% of flood events. The number of sensors capable of capturing flood events doubled between 2015 and 2020, signaling immense potential for multi-sensor integration. We examine how combining observations from multiple sensors can improve temporal coverage of flood events, however noting that temporal sampling along does not guarantee successful flood detection and how the rapid, dynamic nature of floods compounds the challenges inherent to satellite-based monitoring.</p>","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 1","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025RG000885","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147330236","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":"Systematic Benchmarking of Climate Models: Methodologies, Applications, and New Directions","authors":"Birgit Hassler,&nbsp;Forrest M. Hoffman,&nbsp;Rebecca Beadling,&nbsp;Ed Blockley,&nbsp;Bo Huang,&nbsp;Jiwoo Lee,&nbsp;Valerio Lembo,&nbsp;Jared Lewis,&nbsp;Jianhua Lu,&nbsp;Luke Madaus,&nbsp;Elizaveta Malinina,&nbsp;Brian Medeiros,&nbsp;Wilfried Pokam,&nbsp;Enrico Scoccimarro,&nbsp;Ranjini Swaminathan","doi":"10.1029/2025RG000891","DOIUrl":"10.1029/2025RG000891","url":null,"abstract":"<p>As climate models become increasingly complex, there is a growing need to comprehensively and systematically assess model performance with respect to observations. Given the increasing number and diversity of climate model simulations in use, the community has moved beyond simple model intercomparison and toward developing methods capable of benchmarking a large number of simulations against a suite of climate metrics. Here, we present a detailed review of evaluation and benchmarking methods and approaches developed in the last decade, focusing primarily on scientific implications for Coupled Model Intercomparison Project (CMIP) simulations and CMIP6 results that contributed to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Based on this review, we explain the resulting contemporary philosophy of model benchmarking, and provide clear distinctions and definitions of the terms model verification, process validation, evaluation, and benchmarking. While significant progress has been made in model development based on systematic evaluation and benchmarking efforts, some climate system biases still remain. The development of open-source community software packages has played a fundamental role in identifying areas of significant model improvement and bias reduction. We review the key features of several software packages that have been commonly used over the past decade to evaluate and benchmark global and regional climate models. Additionally, we discuss best practices for the selection of evaluation and benchmarking metrics and for interpreting the obtained results, the importance of selecting suitable sources of reference data and accurate uncertainty quantification.</p>","PeriodicalId":21177,"journal":{"name":"Reviews of Geophysics","volume":"64 1","pages":""},"PeriodicalIF":37.3,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025RG000891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146153370","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}