Journal of Advances in Modeling Earth Systems最新文献

{"title":"Effects of Horizontal Resolution on Long-Range Equatorward Radiation of Near-Inertial Internal Waves in Ocean General Circulation Models","authors":"Bingrong Sun,&nbsp;Zhao Jing,&nbsp;Man Yuan,&nbsp;Haiyuan Yang,&nbsp;Lixin Wu","doi":"10.1029/2024MS004216","DOIUrl":"https://doi.org/10.1029/2024MS004216","url":null,"abstract":"<p>Wind-generated near-inertial internal waves (NIWs) are characterized by dominant long-range equatorward radiation due to the gradient of the planetary vorticity, known as the <i>β</i>-refraction effect. In this study, we analyze the effects of horizontal model resolution on the long-range equatorward radiation of NIWs. In a high-resolution Community Earth System Model (CESM-HR) with a 0.1° oceanic resolution, about 25% (15%) of NIW energy flux injected downward the surface boundary layer base poleward of 30°N (30°S) radiates into the lower-latitude region. This ratio decreases to about 15% (8%) in a low-resolution CESM (CESM-LR) with a 1° oceanic resolution. The higher long-range equatorward radiation efficiency in the CESM-HR than the CESM-LR is directly attributed to the faster equatorward group velocity of the NIWs of the first three vertical modes, which reflects the better representation of equatorward propagation and beta-refraction of smaller scale NIWs in the CESM-HR. The enhancement of equatorward wavenumber induced by the <i>β</i>-refraction is inhibited in the CESM-LR, which underrepresent the long-range equatorward radiation of NIWs. These results underscore the necessity of high-resolution ocean models in accurately simulating the spatial variabilities of NIWs and their induced turbulent diapycnal mixing in the global ocean.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142152167","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":"An Emulator of Stratocumulus Cloud Response to Two Cloud-Controlling Factors Accounting for Internal Variability","authors":"Rachel W. N. Sansom,&nbsp;Ken S. Carslaw,&nbsp;Jill S. Johnson,&nbsp;Lindsay Lee","doi":"10.1029/2023MS004179","DOIUrl":"https://doi.org/10.1029/2023MS004179","url":null,"abstract":"<p>Large uncertainties persist in modeling shallow, low clouds because of many interacting nonlinear processes and multiple cloud-controlling environmental factors. In addition, sharp changes in behavior occur when environmental thresholds are met. Model studies that follow a traditional approach of exploring the effects of factors “one-at-a-time” are unable to capture interactions between factors. We simulate a stratocumulus cloud based on the Second Dynamics and Chemistry of Marine Stratocumulus field study using a large-eddy simulation model coupled with a two-moment cloud microphysics scheme. The simulations are used to train a Gaussian process emulator, which we then use to visualize the relationships between two cloud-controlling factors and domain-averaged cloud properties. Only 29 model simulations were required to train the emulators, which then predicted cloud properties at thousands of new combinations of the two factors. Emulator response surfaces of cloud liquid water path and cloud fraction show two behavioral regimes, one of thin and patchy yet steady stratocumulus and one of thick, growing stratocumulus with cloud fraction near 1. Internal variability (initial-condition uncertainty) creates unrealistic “bumpy” response surfaces. However, we show that the variability causing the bumpiness can be characterized in an emulator “nugget term” that is adjusted to match the distribution of a small number of initial-condition ensemble simulations at various points on the surface, thereby allowing a smoother, deterministic response surface to be constructed. Accounting for variability allows the transition between regimes, and the joint interactions of parameters, to be visualized in a more deterministic way that has not been done before.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100498","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":"High-Dimensional Covariance Estimation From a Small Number of Samples","authors":"David Vishny,&nbsp;Matthias Morzfeld,&nbsp;Kyle Gwirtz,&nbsp;Eviatar Bach,&nbsp;Oliver R. A. Dunbar,&nbsp;Daniel Hodyss","doi":"10.1029/2024MS004417","DOIUrl":"https://doi.org/10.1029/2024MS004417","url":null,"abstract":"<p>We synthesize knowledge from numerical weather prediction, inverse theory, and statistics to address the problem of estimating a high-dimensional covariance matrix from a small number of samples. This problem is fundamental in statistics, machine learning/artificial intelligence, and in modern Earth science. We create several new adaptive methods for high-dimensional covariance estimation, but one method, which we call Noise-Informed Covariance Estimation (NICE), stands out because it has three important properties: (a) NICE is conceptually simple and computationally efficient; (b) NICE guarantees symmetric positive semi-definite covariance estimates; and (c) NICE is largely tuning-free. We illustrate the use of NICE on a large set of Earth science–inspired numerical examples, including cycling data assimilation, inversion of geophysical field data, and training of feed-forward neural networks with time-averaged data from a chaotic dynamical system. Our theory, heuristics and numerical tests suggest that NICE may indeed be a viable option for high-dimensional covariance estimation in many Earth science problems.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100497","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":"Improved Precipitation Diurnal Cycle in GFDL Climate Models With Non-Equilibrium Convection","authors":"Bosong Zhang,&nbsp;Leo J. Donner,&nbsp;Ming Zhao,&nbsp;Zhihong Tan","doi":"10.1029/2024MS004315","DOIUrl":"https://doi.org/10.1029/2024MS004315","url":null,"abstract":"<p>Most global climate models with convective parameterization have trouble in simulating the observed diurnal cycle of convection. Maximum precipitation usually happens too early during summertime, especially over land. Observational analyses indicate that deep convection over land cannot keep pace with rapid variations in convective available potential energy, which is largely controlled by boundary-layer forcing. In this study, a new convective closure in which shallow and deep convection interact strongly, out of equilibrium, is implemented in atmosphere-only and ocean-atmosphere coupled models. The diurnal cycles of convection in both simulations are significantly improved with small changes to their mean states. The new closure shifts maximum precipitation over land later by about three hours. Compared to satellite observations, the diurnal phase biases are reduced by half. Shallow convection to some extent equilibrates rapid changes in the boundary layer at subdiurnal time scales. Relaxed quasi-equilibrium for convective available potential energy holds in significant measure as a result. Future model improvement will focus on the remaining biases in the diurnal cycle, which may be further reduced by including stochastic entrainment and cold pools.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100070","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":"Impact of Instantaneous Parameter Sensitivity on Ensemble-Based Parameter Estimation: Simulation With an Intermediate Coupled Model","authors":"Lige Cao,&nbsp;Guijun Han,&nbsp;Wei Li,&nbsp;Haowen Wu,&nbsp;Xiaobo Wu,&nbsp;Gongfu Zhou,&nbsp;Qingyu Zheng","doi":"10.1029/2024MS004253","DOIUrl":"https://doi.org/10.1029/2024MS004253","url":null,"abstract":"<p>On ensemble-based coupled data assimilation, cross-component parameter estimation (CPE), has not been as extensively developed and applied as weakly coupled state and parameter estimation along with cross-component state estimation. This discrepancy is partially attributed to the lack of emphasis on the instantaneous response of coupled model states with respect to parameters across different components. We define so-called response as the instantaneous parameter sensitivity (IPS). Under the framework of sequential assimilation, the prior information heavily relies on the IPS of coupled states with different time scales. Based on the IPS analysis for an intermediate coupled model, a series of twin experiments of state and parameter estimation are conducted, in which an IPS-inspired adaptive inflation scheme for parameter ensemble is introduced. Results show that the success of a parameter estimation strategy is closely tied to the significant IPS of the observed state to the parameter targeted for optimization, as it maintains a high signal-to-noise ratio in the error covariance between parameter and prior state, thereby enhancing parameter estimation. An interesting finding in the context of IPS-based CPE is: an atmospheric parameter can be successfully estimated by assimilating observations from slow-varying oceanic component, but not vice versa. In comparison with cross-component state estimation, successful CPE significantly enhances the estimation accuracy of coupled states by mitigating model bias.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004253","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100067","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":"Development of an Aerosol Assimilation System Using a Global Non-Hydrostatic Model, a 2-Dimensional Variational Method, and Multiple Satellite-Based Aerosol Products","authors":"D. Goto,&nbsp;T. Nishizawa,&nbsp;J. Uchida,&nbsp;K. Yumimoto,&nbsp;Y. Jin,&nbsp;A. Higurashi,&nbsp;A. Shimizu,&nbsp;S. Sugata,&nbsp;H. Yashiro,&nbsp;M. Hayasaki,&nbsp;T. Dai,&nbsp;Y. Cheng,&nbsp;H. Tanimoto","doi":"10.1029/2023MS004046","DOIUrl":"https://doi.org/10.1029/2023MS004046","url":null,"abstract":"<p>The computational balance between the model grid resolution and the complexity of the data assimilation technique is essential for accurate aerosol forecasting and obtaining aerosol reanalysis data sets. This study aimed to develop a high-resolution aerosol assimilation system. A 2-dimensional variational method (2DVar) was implemented in a non-hydrostatic icosahedral atmospheric model (NICAM). This new model (NICAM/2DVar), with a global grid size of 56 km, assimilated the observed aerosol optical depth (AOD) that is estimated by combining multiple products of geostationary and polar-orbital satellites. The model results were evaluated against ground-based AOD observations on a global scale. They exhibited higher correlations, lower uncertainties, and lower biases than those obtained without the 2DVar. The model also reproduced the observed surface aerosols (PM_2.5) mass concentrations, especially in Kyushu, Japan. This occurred because the satellite-estimated AODs over ocean close to air pollution sources were obtained for many occasions. The correlation coefficient values against the PM_2.5 observations increased from 0.44 to 0.65 compared to the results without the 2DVar. The impact of the 2DVar on the forecast results was investigated, and the forecast values for 2–3 days were improved. Because satellite-retrieved AODs are often lacking over land owing to retrieval difficulties, the use of ground-based AODs in assimilations is essential for precise processing the of aerosol reanalysis data sets. The computational cost with the use of the 2DVar was only 0.6% more than that without its use. Thus, aerosol assimilation using the NICAM/2DVar can be realistically extended to finer grid sizes.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100061","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":"Using Machine Learning to Predict Cloud Turbulent Entrainment-Mixing Processes","authors":"Sinan Gao,&nbsp;Chunsong Lu,&nbsp;Jiashan Zhu,&nbsp;Yabin Li,&nbsp;Yangang Liu,&nbsp;Binqi Zhao,&nbsp;Sheng Hu,&nbsp;Xiantong Liu,&nbsp;Jingjing Lv","doi":"10.1029/2024MS004225","DOIUrl":"https://doi.org/10.1029/2024MS004225","url":null,"abstract":"<p>Different turbulent entrainment-mixing mechanisms between clouds and environment are essential to cloud-related processes; however, accurate representation of entrainment-mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment-mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the “black box” scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment-mixing. Specifically, number concentration after entrainment-mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment-mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment-mixing is negatively correlated with liquid water content affected by entrainment-mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment-mixing in weather/climate models.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050506","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":"Reimagining Earth in the Earth System","authors":"Gordon B. Bonan,&nbsp;Oliver Lucier,&nbsp;Deborah R. Coen,&nbsp;Adrianna C. Foster,&nbsp;Jacquelyn K. Shuman,&nbsp;Marysa M. Laguë,&nbsp;Abigail L. S. Swann,&nbsp;Danica L. Lombardozzi,&nbsp;William R. Wieder,&nbsp;Kyla M. Dahlin,&nbsp;Adrian V. Rocha,&nbsp;Michael D. SanClements","doi":"10.1029/2023MS004017","DOIUrl":"https://doi.org/10.1029/2023MS004017","url":null,"abstract":"<p>Terrestrial, aquatic, and marine ecosystems regulate climate at local to global scales through exchanges of energy and matter with the atmosphere and assist with climate change mitigation through nature-based climate solutions. Climate science is no longer a study of the physics of the atmosphere and oceans, but also the ecology of the biosphere. This is the promise of Earth system science: to transcend academic disciplines to enable study of the interacting physics, chemistry, and biology of the planet. However, long-standing tension in protecting, restoring, and managing forest ecosystems to purposely improve climate evidences the difficulties of interdisciplinary science. For four centuries, forest management for climate betterment was argued, legislated, and ultimately dismissed, when nineteenth century atmospheric scientists narrowly defined climate science to the exclusion of ecology. Today's Earth system science, with its roots in global models of climate, unfolds in similar ways to the past. With Earth system models, geoscientists are again defining the ecology of the Earth system. Here we reframe Earth system science so that the biosphere and its ecology are equally integrated with the fluid Earth to enable Earth system prediction for planetary stewardship. Central to this is the need to overcome an intellectual heritage to the models that elevates geoscience and marginalizes ecology and local land knowledge. The call for kilometer-scale atmospheric and ocean models, without concomitant scientific and computational investment in the land and biosphere, perpetuates the geophysical view of Earth and will not fully provide the comprehensive actionable information needed for a changing climate.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142045158","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":"Interpretable Multiscale Machine Learning-Based Parameterizations of Convection for ICON","authors":"Helge Heuer,&nbsp;Mierk Schwabe,&nbsp;Pierre Gentine,&nbsp;Marco A. Giorgetta,&nbsp;Veronika Eyring","doi":"10.1029/2024MS004398","DOIUrl":"https://doi.org/10.1029/2024MS004398","url":null,"abstract":"<p>Machine learning (ML)-based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid-scale processes or to accelerate computations. ML-based parameterizations within hybrid ESMs have successfully learned subgrid-scale processes from short high-resolution simulations. However, most studies used a particular ML method to parameterize the subgrid tendencies or fluxes originating from the compound effect of various small-scale processes (e.g., radiation, convection, gravity waves) in mostly idealized settings or from superparameterizations. Here, we use a filtering technique to explicitly separate convection from these processes in simulations with the Icosahedral Non-hydrostatic modeling framework (ICON) in a realistic setting and benchmark various ML algorithms against each other offline. We discover that an unablated U-Net, while showing the best offline performance, learns reverse causal relations between convective precipitation and subgrid fluxes. While we were able to connect the learned relations of the U-Net to physical processes this was not possible for the non-deep learning-based Gradient Boosted Trees. The ML algorithms are then coupled online to the host ICON model. Our best online performing model, an ablated U-Net excluding precipitating tracer species, indicates higher agreement for simulated precipitation extremes and mean with the high-resolution simulation compared to the traditional scheme. However, a smoothing bias is introduced both in water vapor path and mean precipitation. Online, the ablated U-Net significantly improves stability compared to the non-ablated U-Net and runs stable for the full simulation period of 180 days. Our results hint to the potential to significantly reduce systematic errors with hybrid ESMs.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142045296","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":"Improved Atmosphere-Ocean Coupled Simulation by Parameterizing Sub-Diurnal Scale Air-Sea Interactions","authors":"K. Wang,&nbsp;S. Zhang,&nbsp;Y. Jin,&nbsp;C. Zhu,&nbsp;Z. Song,&nbsp;Y. Gao,&nbsp;G. Yang","doi":"10.1029/2023MS003903","DOIUrl":"https://doi.org/10.1029/2023MS003903","url":null,"abstract":"<p>The atmosphere-ocean is a highly coupled system with significant diurnal and hourly variations. However, current coupled models usually lack sub-diurnal scale processes at the air-sea interface due to the finite vertical resolution for ocean discretization. Previous modeling studies showed that sub-diurnal scale air-sea interaction processes are important for ocean mixing. Here, by designing an integrated sub-diurnal parameterization (ISDP) scheme which combines different temperature profiling functions, we stress sub-diurnal air-sea interactions to better represent the local ocean mixing. This scheme has been implemented into two coupled models which contributed to the Climate Model Intercomparison Project (CMIP), referenced by the Intergovernmental Panel on Climate Change—Community Earth System Model and Coupled Model version 2. The results show that the ISDP scheme improves model simulations with better climatology and more realistic spectra, especially in the tropics and North Pacific Ocean. With the scheme, the tropical cold tongue bias is significantly relaxed by reducing the overestimation of ocean upper mixing, and the cold bias of North Pacific Ocean is reduced due to the improvement on currents and net heat fluxes. Our scheme may help better the simulation and prediction skills of coupled models when their horizontal resolution becomes fine but vertical resolution remains relatively coarse as it describes high-frequency air-sea interactions more realistically.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS003903","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021752","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}