Journal of Advances in Modeling Earth Systems最新文献

{"title":"E3SM-GCAM: A Synchronously Coupled Human Component in the E3SM Earth System Model Enables Novel Human-Earth Feedback Research","authors":"Alan V. Di Vittorio,&nbsp;Eva Sinha,&nbsp;Dalei Hao,&nbsp;Balwinder Singh,&nbsp;Katherine V. Calvin,&nbsp;Tim Shippert,&nbsp;Pralit Patel,&nbsp;Ben Bond-Lamberty","doi":"10.1029/2024MS004806","DOIUrl":"https://doi.org/10.1029/2024MS004806","url":null,"abstract":"<p>Modeling human-environment feedbacks is critical for assessing the effectiveness of climate change mitigation and adaptation strategies under a changing climate. The Energy Exascale Earth System Model (E3SM) now includes a human component, with the Global Change Analysis Model (GCAM) at its core, that is synchronously coupled with the land and atmosphere components through the E3SM coupling software. Terrestrial productivity is passed from E3SM to GCAM to make climate-responsive land use and CO₂ emission projections for the next 5-year period, which are interpolated and passed to E3SM annually. Key variables affected by the incorporation of these feedbacks include land use/cover change, crop prices, terrestrial carbon, local surface temperature, and climate extremes. Regional differences are more pronounced than global differences because the effects are driven primarily by differences in land use. This novel system enables a new type of scenario development and provides a powerful modeling framework that facilitates the addition of other feedbacks between these models. This system has the potential to explore how human responses to climate change impacts in a variety of sectors, including heating/cooling energy demand, water management, and energy production, may alter emissions trajectories and Earth system changes.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Key Gaps in Models' Physical Representation of Climate Intervention and Its Impacts","authors":"Sebastian D. Eastham,&nbsp;Amy H. Butler,&nbsp;Sarah J. Doherty,&nbsp;Blaž Gasparini,&nbsp;Simone Tilmes,&nbsp;Ewa M. Bednarz,&nbsp;Ulrike Burkhardt,&nbsp;Gabriel Chiodo,&nbsp;Daniel J. Cziczo,&nbsp;Michael S. Diamond,&nbsp;David W. Keith,&nbsp;Thomas Leisner,&nbsp;Douglas G. MacMartin,&nbsp;Johannes Quaas,&nbsp;Philip J. Rasch,&nbsp;Odran Sourdeval,&nbsp;Isabelle Steinke,&nbsp;Chelsea Thompson,&nbsp;Daniele Visioni,&nbsp;Robert Wood,&nbsp;Lili Xia,&nbsp;Pengfei Yu","doi":"10.1029/2024MS004872","DOIUrl":"https://doi.org/10.1029/2024MS004872","url":null,"abstract":"<p>Solar radiation modification (SRM) is increasingly discussed as a potential method to ameliorate some negative effects of climate change. However, unquantified uncertainties in physical and environmental impacts of SRM impede informed debate and decision making. Some uncertainties are due to lack of understanding of processes determining atmospheric effects of SRM and/or a lag in development of their representation in models, meaning even high-quality model intercomparisons will not necessarily reveal or address them. Although climate models at multiple scales are advancing in complexity, there are specific areas of uncertainty where additional model development (often requiring new observations) could significantly advance understanding of SRM's effects, and improve our ability to assess and weigh potential risks against those of choosing to not use SRM. We convene expert panels in the areas of atmospheric science most critical to understanding the three most widely discussed forms of SRM. Each identifies three key modeling gaps relevant to either stratospheric aerosols, cirrus, or low-altitude marine clouds. Within each area, key challenges remain in capturing impacts due to complex interactions in aerosol physics, atmospheric chemistry/dynamics, and aerosol-cloud interactions. Across all three, in addition to arguing for more observations, the panels argue that model development work to either leverage different capabilities of existing models, bridge scales across which relevant processes operate, or address known modeling gaps could advance understanding. By focusing on these knowledge gaps we believe the modeling community could advance understanding of SRM's physical risks and potential benefits, allowing better-informed decision-making about whether and how to use SRM.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Moisture Mode Oscillations in Steady-State Weak Temperature Gradient Simulations","authors":"Miguel Bernardez,&nbsp;Larissa Back","doi":"10.1029/2024MS004391","DOIUrl":"https://doi.org/10.1029/2024MS004391","url":null,"abstract":"<p>Weak Temperature Gradient modeling using a small cloud-resolving model admits multiple equilibria depending upon the initial model conditions. There were thought to be two equilibrium states, a moist precipitating state and a dry non-precipitating state. In this paper, we describe a periodic equilibrium which has oscillatory behavior from static boundary conditions. We show that the periodic oscillation has the characteristics of a moisture mode in the vertical dimension, instead of in the horizontal dimension. Further, we show that the oscillation occurs due to a balance between vertical advection and radiation, which can be described using a simple two vertical mode model. The first mode is related to the column relative humidity anomaly and a first baroclinic mode, while the second mode is related to a moisture dipole centered around 600 hPa and a second baroclinic mode. The first mode is associated with the generation of a moisture dipole, while the second mode is associated with the generation of a column moisture anomaly.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Assessing Cloud Feedbacks Over the Atlantic With Bias-Corrected Downscaling","authors":"Shuchang Liu,&nbsp;Christian Zeman,&nbsp;Christoph Schär","doi":"10.1029/2024MS004661","DOIUrl":"https://doi.org/10.1029/2024MS004661","url":null,"abstract":"<p>Clouds exert a significant impact on global temperatures and climate change. Cloud-radiative feedback (CRF) is one of the major sources of climate change uncertainty. Understanding CRF is therefore crucial for accurate climate projections. Biases like the double-ITCZ problem in Global Climate Models (GCMs) hamper precise climate projections. Here, we explore a bias-corrected downscaling method to constrain the cloud feedback uncertainties in the tropical and sub-tropical Atlantic region. We use regional climate model (RCM) simulations with convection permitting resolution, driven by debiased driving fields from three different global climate models (GCMs). Bias-corrected downscaling significantly reduces biases in ITCZ intensity and position, eliminating the double-ITCZ bias across all six experiments (three GCMs for historical and future periods). We explore the new methodology's potential to investigate the CRF in comparison to that of the driving GCMs. Results indicate that additional GCMs and RCMs are necessary for a more comprehensive uncertainty estimation and more conclusive results, while our simulations suggest a potentially narrower range of CRF over the tropical and subtropical Atlantic, primarily due to an improved representation of stratocumulus clouds. Our study highlights the potential of bias-corrected downscaling in constraining the uncertainty of simulations and estimates of cloud feedback and equilibrium climate sensitivity. The results advocate for further simulations with additional RCMs and domains for a more comprehensive analysis.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144292448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Assessing CO2 Fluxes for European Peatlands in ORCHIDEE-PEAT With Multiple Plant Functional Types","authors":"Liyang Liu,&nbsp;Chunjing Qiu,&nbsp;Yi Xi,&nbsp;Elodie Salmon,&nbsp;Aram Kalhori,&nbsp;Rebekka R. E. Artz,&nbsp;Christophe Guimbaud,&nbsp;Matthias Peichl,&nbsp;Joshua L. Ratcliffe,&nbsp;Koffi Dodji Noumonvi,&nbsp;Efrén López-Blanco,&nbsp;Jiří Dušek,&nbsp;Tiina Markkanen,&nbsp;Torsten Sachs,&nbsp;Mika Aurela,&nbsp;Thu-Hang Nguyen,&nbsp;Annalea Lohila,&nbsp;Ivan Mammarella,&nbsp;Philippe Ciais","doi":"10.1029/2025MS004940","DOIUrl":"https://doi.org/10.1029/2025MS004940","url":null,"abstract":"<p>Peatlands are significant carbon reservoirs vulnerable to climate change and land use change such as drainage for cultivation or forestry. We modified the ORCHIDEE-PEAT global land surface model, which has a detailed description of peat processes, by incorporating three new peatland-specific plant functional types (PFTs), namely deciduous broadleaf shrub, moss and lichen, as well as evergreen needleleaf tree in addition to previously peatland graminoid PFT to simulate peatland vegetation dynamic and soil CO₂ fluxes. Model parameters controlling photosynthesis, autotrophic respiration, and carbon decomposition have been optimized using eddy-covariance observations from 14 European peatlands and a Bayesian optimization approach. Optimization was conducted for each individual site (single-site calibration) or all sites simultaneously (multi-site calibration). Single-site calibration performed better, particularly for gross primary production (GPP), with root mean square deviation (RMSD) reduced by 53%. While multi-site calibration showed limited improvement (e.g., RMSD of GPP reduced by 22%) due to the model's inability to account for spatial parameter variations under different climatic contexts (trait-climate correlations). Site-optimized parameters, such as <b>Q</b>_<b>10</b>, the temperature sensitivity of heterotrophic respiration, revealed strong empirical relationships with environmental factors, such as air temperature. For instance, <b>Q</b>_<b>10</b> decreased significantly at warmer sites, consistent with independent field data. To improve the model by using the lessons from single-site optimization, we incorporated two key trait-climate relationships for <b>Q</b>_<b>10</b> and <b><i>V</i></b>_<b>cmax</b> (maximum carboxylation rate) into a new version of the ORCHIDEE-PEAT models. Using this description of spatial variability of parameters holds significant promise for improving the accuracy of carbon cycle simulations in peatlands.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS004940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Constraining Black Carbon Aging in Global Models to Reflect Timescales for Internal Mixing","authors":"Laura Fierce,&nbsp;Yinrui Li,&nbsp;Yan Feng,&nbsp;Nicole Riemer,&nbsp;Nick A. J. Schutgens,&nbsp;Allison C. Aiken,&nbsp;Manvendra K. Dubey,&nbsp;Po-Lun Ma,&nbsp;Donald Wuebbles","doi":"10.1029/2024MS004471","DOIUrl":"https://doi.org/10.1029/2024MS004471","url":null,"abstract":"<p>The radiative effects of black carbon depend critically on its atmospheric lifetime, which is controlled by the rate at which freshly emitted combustion particles become internally mixed with other aerosol components. Global aerosol models strive to represent this process, but the timescale for aerosol mixing is not easily constrained using observations. In this study, we apply a timescale parameterization derived from particle-resolved simulations to quantify, in a global aerosol model, the timescale for internal mixing. We show that, while highly variable, the average timescale for internal mixing is approximately 3 hr, which is much shorter than the 24-hr aging timescale traditionally applied in bulk aerosol models. We then use the mixing timescale to constrain the aging criterion in the Modal Aerosol Module. Our analysis reveals that, to best reflect timescales for internal mixing, modal models should assume that particles transition from the hydrophobic (fresh) to the hydrophilic (aged) class once they accumulate a coating thickness equal to four monolayers of sulfuric acid, as opposed to the model's current aging criterion of eight monolayers. We show that, in remote regions like the Arctic and Antarctic, predictions of black carbon loading and its seasonal variation are particularly sensitive to the model representation of aging. By constraining aging in global models to reflect mixing timescales simulated by the particle-resolved model, we eliminate one of the free parameters governing black carbon's long-range transport and spatiotemporal distribution.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004471","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Gaussian Framework and Optimal Projection of Weather Fields for Prediction of Extreme Events","authors":"Valeria Mascolo,&nbsp;Alessandro Lovo,&nbsp;Corentin Herbert,&nbsp;Freddy Bouchet","doi":"10.1029/2024MS004487","DOIUrl":"https://doi.org/10.1029/2024MS004487","url":null,"abstract":"<p>Extreme events are the major weather-related hazard for humanity. It is then of crucial importance to have a good understanding of their statistics and to be able to forecast them. However, lack of sufficient data makes their study particularly challenging. In this work, we provide a simple framework for studying extreme events that tackles the lack of data issue by using the entire available data set, rather than focusing on the extremes of the data set. To do so, we make the assumption that the set of predictors and the observable used to define the extreme event follow a jointly Gaussian distribution. This naturally gives the notion of an optimal projection of the predictors for forecasting the event. We take as a case study extreme heatwaves over France, and we test our method on an 8,000-year-long intermediate complexity climate model time series and on the ERA5 reanalysis data set. For a-posteriori statistics, we observe and motivate the fact that composite maps of very extreme events look similar to less extreme ones. For prediction, we show that our method is competitive with off-the-shelf neural networks on the long data set and outperforms them on reanalysis. The optimal projection pattern, which makes our forecast intrinsically interpretable, highlights the importance of soil moisture deficit and quasi-stationary Rossby waves as precursors to extreme heatwaves.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004487","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Vertically Recurrent Neural Networks for Sub-Grid Parameterization","authors":"P. Ukkonen,&nbsp;M. Chantry","doi":"10.1029/2024MS004833","DOIUrl":"https://doi.org/10.1029/2024MS004833","url":null,"abstract":"<p>Machine learning has the potential to improve the physical realism and/or computational efficiency of parameterizations. A typical approach has been to feed concatenated vertical profiles to a dense neural network. However, feed-forward networks lack the connections to propagate information sequentially through the vertical column. Here we examine if predictions can be improved by instead traversing the column with recurrent neural networks (RNNs) such as Long Short-Term Memory (LSTMs). This method encodes physical priors (locality) and uses parameters more efficiently. Firstly, we test RNN-based radiation emulators in the Integrated Forecasting System. We achieve near-perfect offline accuracy, and the forecast skill of a suite of global weather simulations using the emulator are for the most part statistically indistinguishable from reference runs. But can radiation emulators provide both high accuracy and a speed-up? We find optimized, state-of-the-art radiation code on CPU generally faster than RNN-based emulators on GPU, although the latter can be more energy efficient. To test the method more broadly, and explore recent challenges in parameterization, we also adapt it to data sets from other studies. RNNs outperform reference feed-forward networks in emulating gravity waves, and when combined with horizontal convolutions, for non-local unified parameterization. In emulation of moist physics with memory, the RNNs have similar offline accuracy as ResNets, the previous state-of-the-art. However, the RNNs are more efficient, and more stable in autoregressive semi-prognostic tests. Multi-step autoregressive training improves performance in these tests and enables a latent representation of convective memory. Recently proposed linearly recurrent models achieve similar performance to LSTMs.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004833","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Stable Simulation of the Community Atmosphere Model Using Machine-Learning Physical Parameterization Trained With Experience Replay","authors":"Jianda Chen,&nbsp;Minghua Zhang,&nbsp;Tao Zhang,&nbsp;Wuyin Lin,&nbsp;Wei Xue","doi":"10.1029/2024MS004722","DOIUrl":"https://doi.org/10.1029/2024MS004722","url":null,"abstract":"<p>In recent years, machine learning (ML) models have been used to improve physical parameterizations of general circulation models (GCMs). A significant challenge of integrating ML models into GCMs is the online instability when they are coupled for long-term simulation. We present a new strategy that demonstrates robust online stability when the physical parameterization package of an atmospheric GCM is replaced by a deep ML model. The method uses experience replay with a multistep training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. The training method reduces the gap between the offline and online environments of the ML model. The method is used to train the ML model as the physical parameterization of the Community Atmosphere Model (CAM5) with training data from the Multi-scale Modeling Framework high resolution simulations. Three 6-year online simulations of the CAM5 are carried out by using the ML physics package. The simulated spatial distributions of precipitation, surface temperature and zonally averaged atmospheric fields demonstrate overall better accuracy than that of the standard CAM5 and benchmark model even without the use of additional physical constraints or tuning. This work is the first to demonstrate a solution to address the online instability problem in climate modeling with ML physics by using experience replay.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004722","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Internal Ocean-Atmosphere Variability in Kilometer-Scale Radiative-Convective Equilibrium","authors":"Adam B. Sokol,&nbsp;Vlad A. Munteanu,&nbsp;Peter N. Blossey,&nbsp;Dennis L. Hartmann","doi":"10.1029/2024MS004567","DOIUrl":"https://doi.org/10.1029/2024MS004567","url":null,"abstract":"<p>We describe internal, low-frequency variability in a 21-year simulation with a cloud-resolving model. The model domain is the length of the equatorial Pacific and includes a slab ocean, which permits coherent cycles of sea surface temperature (SST), atmospheric convection, and the convectively coupled circulation. The warming phase of the cycle is associated with near-uniform SST, less organized convection, and sparse low cloud cover, while the cooling phase exhibits strong SST gradients, highly organized convection, and enhanced low cloudiness. Both phases are quasi-stable but, on long timescales, are ultimately susceptible to instabilities resulting in rapid phase transitions. The internal cycle is leveraged to understand the factors controlling the strength and structure of the tropical overturning circulation and the stratification of the tropical troposphere. The overturning circulation is strongly modulated by convective organization, with SST playing a lesser role. When convection is highly organized, the circulation is weaker and more bottom-heavy. Alternatively, tropospheric stratification depends on both convective organization and SST, depending on the vertical level. SST-driven variability dominates aloft while organization-driven variability dominates at lower levels. A similar pattern is found in ERA5 reanalysis of the equatorial Pacific. The relationship between convective organization and stratification is explicated using a simple entraining plume model. The results highlight the importance of convective organization for tropical variability and lay a foundation for future work using coupled, idealized models that explicitly resolve convection.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}