Journal of Advances in Modeling Earth Systems最新文献

{"title":"A Comparison of Diagnostics for AMOC Heat Transport Applied to the CESM Large Ensemble","authors":"C Spencer Jones,&nbsp;Scout Jiang,&nbsp;Ryan P. Abernathey","doi":"10.1029/2023MS003978","DOIUrl":"https://doi.org/10.1029/2023MS003978","url":null,"abstract":"<p>Atlantic time-mean heat transport is northward at all latitudes and exhibits strong multidecadal variability between about 30°N and 55°N. Atlantic heat transport variability influences many aspects of the climate system, including regional surface temperatures, subpolar heat content, Arctic sea-ice concentration and tropical precipitation patterns. Atlantic heat transport and heat transport variability are commonly partitioned into two components: the heat transport by the Atlantic Meridional Overturning Circulation (AMOC) and the heat transport by the gyres. In this paper we compare four different methods for performing this partition, and we apply these methods to the Community Earth System Model Large Ensemble at 34°N, 26°N and 5°S. We discuss the strengths and weaknesses of each method. The four methods all give significantly different estimates for the proportion of the time-mean heat transport performed by AMOC. One of these methods is a new physically-motivated method based on the pathway of the northward-flowing part of AMOC. This paper presents a preliminary version of our method that works only when the AMOC follows the western boundary of the basin. All the methods agree that at 26°N, 80%–100% of heat transport variability at 2–10 years timescales is performed by AMOC, but there is more disagreement between methods in attributing multidecadal variability, with some methods showing a compensation between the AMOC and gyre heat transport variability.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 8","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS003978","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968340","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":"Training Warm-Rain Bulk Microphysics Schemes Using Super-Droplet Simulations","authors":"Sajjad Azimi,&nbsp;Anna Jaruga,&nbsp;Emily de Jong,&nbsp;Sylwester Arabas,&nbsp;Tapio Schneider","doi":"10.1029/2023MS004028","DOIUrl":"10.1029/2023MS004028","url":null,"abstract":"<p>Cloud microphysics is a critical aspect of the Earth's climate system, which involves processes at the nano- and micrometer scales of droplets and ice particles. In climate modeling, cloud microphysics is commonly represented by bulk models, which contain simplified process rates that require calibration. This study presents a framework for calibrating warm-rain bulk schemes using high-fidelity super-droplet simulations that provide a more accurate and physically based representation of cloud and precipitation processes. The calibration framework employs ensemble Kalman methods including Ensemble Kalman Inversion and Unscented Kalman Inversion to calibrate bulk microphysics schemes with probabilistic super-droplet simulations. We demonstrate the framework's effectiveness by calibrating a single-moment bulk scheme, resulting in a reduction of data-model mismatch by more than 75% compared to the model with initial parameters. Thus, this study demonstrates a powerful tool for enhancing the accuracy of bulk microphysics schemes in atmospheric models and improving climate modeling.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141847706","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":"Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data-Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM","authors":"Y. Qiang Sun,&nbsp;Hamid A. Pahlavan,&nbsp;Ashesh Chattopadhyay,&nbsp;Pedram Hassanzadeh,&nbsp;Sandro W. Lubis,&nbsp;M. Joan Alexander,&nbsp;Edwin P. Gerber,&nbsp;Aditi Sheshadri,&nbsp;Yifei Guan","doi":"10.1029/2023MS004145","DOIUrl":"10.1029/2023MS004145","url":null,"abstract":"<p>Neural networks (NNs) are increasingly used for data-driven subgrid-scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non-linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large-amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN-based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics-based gravity wave (GW) parameterizations as a test case. WACCM has complex, state-of-the-art parameterizations for orography-, convection-, and front-driven GWs. Convection- and orography-driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto-encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO₂). However, their performance is significantly improved by applying transfer learning, for example, re-training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data-driven parameterizations for various processes, including (but not limited to) GWs.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141847359","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":"Physically-Informed Super-Resolution Downscaling of Antarctic Surface Melt","authors":"Sophie de Roda Husman,&nbsp;Zhongyang Hu,&nbsp;Maurice van Tiggelen,&nbsp;Rebecca Dell,&nbsp;Jordi Bolibar,&nbsp;Stef Lhermitte,&nbsp;Bert Wouters,&nbsp;Peter Kuipers Munneke","doi":"10.1029/2023MS004212","DOIUrl":"10.1029/2023MS004212","url":null,"abstract":"<p>Because Antarctic surface melt is mostly driven by local processes, its simulation necessitates high-resolution regional climate models (RCMs). However, the current horizontal resolution of RCMs (≈25–30 km) is inadequate for capturing small-scale melt processes. To address this limitation, we present SUPREME (SUPer-REsolution-based Melt Estimation over Antarctica), a deep learning method to downscale surface melt to 5.5 km resolution using a physically-informed super-resolution model. The physical information integrated into the model originates from observations tied to surface melt, specifically remote sensing-derived albedo and elevation. These remote sensing data, in addition to a Regional Atmospheric Climate Model (RACMO) run at 27 km resolution, account for the diverse drivers of surface melt across Antarctica, facilitating effective generalization beyond the training region of the Antarctic Peninsula. A comparison of SUPREME with a dynamically downscaled RACMO run at 5.5 km over the Antarctic Peninsula shows high accuracy, with average yearly RMSE and bias of 5.5 mm w.e. yr⁻¹ and 4.5 mm w.e. yr⁻¹, respectively. Validation at five automatic weather stations reveals SUPREME's marked improvement with substantially lower average RMSE (81 mm w.e.) compared to RACMO 27 km (129 mm w.e.). Beyond the training region, SUPREME aligns more closely with remote sensing products associated with surface melt than super-resolution models lacking physical constraints. While further validation of SUPREME is needed, our study highlights the potential of super-resolution techniques with physical constraints for high-resolution surface melt monitoring in Antarctica, providing insights into the impacts of localized melting on processes affecting ice shelf integrity such as hydrofracturing.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141850916","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":"Regression Forest Approaches to Gravity Wave Parameterization for Climate Projection","authors":"David S. Connelly,&nbsp;Edwin P. Gerber","doi":"10.1029/2023MS004184","DOIUrl":"10.1029/2023MS004184","url":null,"abstract":"<p>We train random and boosted forests, two machine learning architectures based on regression trees, to emulate a physics-based parameterization of atmospheric gravity wave momentum transport. We compare the forests to a neural network benchmark, evaluating both offline errors and online performance when coupled to an atmospheric model under the present day climate and in 800 and 1,200 ppm CO₂ global warming scenarios. Offline, the boosted forest exhibits similar skill to the neural network, while the random forest scores significantly lower. Both forest models couple stably to the atmospheric model, and control climate integrations with the boosted forest exhibit lower biases than those with the neural network. Integrations with all three data-driven emulators successfully capture the Quasi-Biennial Oscillation (QBO) and sudden stratospheric warmings, key modes of stratospheric variability, with the boosted forest more accurate than the random forest in replicating their statistics across our range of carbon dioxide perturbations. The boosted forest and neural network capture the sign of the QBO period response to increased CO₂, though both struggle with the magnitude of this response under the more extreme 1,200 ppm scenario. To investigate the connection between performance in the control climate and the ability to generalize, we use techniques from interpretable machine learning to understand how the data-driven methods use physical information. We leverage this understanding to develop a retraining procedure that improves the coupled performance of the boosted forest in the control climate and under the 800 ppm CO₂ scenario.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141841171","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":"SUPHRE: A Reactive Transport Model With Unsaturated and Density-Dependent Flow","authors":"Zhaoyang Luo,&nbsp;Jun Kong,&nbsp;Chengji Shen,&nbsp;D. A. Barry","doi":"10.1029/2023MS003975","DOIUrl":"10.1029/2023MS003975","url":null,"abstract":"<p>Although unsaturated and density-dependent flow affect solute fate in groundwater, they are rarely both included in reactive transport models. Using the operator-splitting method, a new reactive transport model (SUPHRE) was developed by combining a variable-saturation and variable-density multiple-component solute transport model (SUTRA-MS) with a geochemical reaction module (PhreeqcRM). In contrast to existing reactive transport models, SUPHRE accounts for both unsaturated and density-dependent flow. Model setup for SUPHRE is convenient, as only one setup file is required in addition to the usual input files of SUTRA-MS and PhreeqcRM. By further implementing a time-variant boundary condition into SUTRA-MS, SUPHRE can simulate multi-component reactive transport in tidally influenced coastal unconfined aquifers where unsaturated and density-dependent flow prevail. Two examples were used to validate the new reactive transport model, including single-species decay and sorption within a one-dimensional soil column and a four-species decay chain in a two-dimensional aquifer. Following validation, SUPHRE was adopted to reveal unsaturated flow effects on oxygen consumption and nitrate reduction in tidally influenced coastal unconfined aquifers. Whether for simulating oxygen consumption or nitrate reduction, there were visible deviations between numerical results without and with unsaturated flow, highlighting that unsaturated flow can affect reactive solute transport and transformation.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS003975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141849207","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":"Solar Radiation Triggers the Bimodal Leaf Phenology of Central African Evergreen Broadleaved Forests","authors":"Liyang Liu,&nbsp;Philippe Ciais,&nbsp;Fabienne Maignan,&nbsp;Yuan Zhang,&nbsp;Nicolas Viovy,&nbsp;Marc Peaucelle,&nbsp;Elizabeth Kearsley,&nbsp;Koen Hufkens,&nbsp;Marijn Bauters,&nbsp;Colin A. Chapman,&nbsp;Zheng Fu,&nbsp;Shangrong Lin,&nbsp;Haibo Lu,&nbsp;Jiashun Ren,&nbsp;Xueqin Yang,&nbsp;Xianjin He,&nbsp;Xiuzhi Chen","doi":"10.1029/2023MS004014","DOIUrl":"10.1029/2023MS004014","url":null,"abstract":"<p>Central African evergreen broadleaved forests around the equator exhibit a double annual cycle for canopy phenology and carbon uptake seasonality. The underlying drivers of this behavior are poorly understood and the double seasonality is not captured by land surface models (LSM). In this study, we developed a new leaf phenology module into the ORCHIDEE LSM (hereafter ORCHIDEE-AFP), which utilizes short-wave incoming radiation (SWd) as the main driver of leaf shedding and partial rejuvenation of the canopy, to simulate the double seasonality of central African forests. The ORCHIDEE-AFP model has been evaluated by using field data from two forest sites and satellite observations of the enhanced vegetation index (EVI), which is a proxy of young leaf area index (LAI_Young) with leafage less than 6 months, as well as six products of GPP or GPP proxies. Results demonstrate that ORCHIDEE-AFP successfully reproduces observed leaf turnover (<i>R</i> = 0.45) and young leaf abundance (<i>R</i> = 0.74), and greatly improve the representation of the bimodal leaf phenology. The proportion of grid cells with a significant positive correlation between the seasonality of modeled LAI_Young and observed EVI increased from 0.2% in the standard model to 27% in the new model. For photosynthesis, the proportions of grid cells with significant positive correlations between modeled and observed seasonality range from 26% to 65% across the six GPP evaluation products. The improved performance of the ORCHIDEE-AFP model in simulating leaf phenology and photosynthesis of central African forests will allow a more accurate assessment of the impacts of climate change in tropical forests.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141849654","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":"Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions","authors":"Sandeep Chinta,&nbsp;Xiang Gao,&nbsp;Qing Zhu","doi":"10.1029/2023MS004115","DOIUrl":"https://doi.org/10.1029/2023MS004115","url":null,"abstract":"<p>Methane (CH₄) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH₄ fluxes is examined at 14 FLUXNET- CH₄ sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH₄ production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH₄ observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968022","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":"A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models","authors":"Ranjini Swaminathan,&nbsp;Tristan Quaife,&nbsp;Richard Allan","doi":"10.1029/2023MS004097","DOIUrl":"https://doi.org/10.1029/2023MS004097","url":null,"abstract":"<p>Vegetation gross primary productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering 25%–30% of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth system models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a machine learning (ML) approach to investigate two key factors responsible for differences in simulated GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable ML framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don't necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean) and where they are inconclusive (Eastern North America).</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141732536","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":"Simultaneous Inference of Sea Ice State and Surface Emissivity Model Using Machine Learning and Data Assimilation","authors":"Alan J. Geer","doi":"10.1029/2023MS004080","DOIUrl":"https://doi.org/10.1029/2023MS004080","url":null,"abstract":"<p>Satellite microwave radiance observations are strongly sensitive to sea ice, but physical descriptions of the radiative transfer of sea ice and snow are incomplete. Further, the radiative transfer is controlled by poorly-known microstructural properties that vary strongly in time and space. A consequence is that surface-sensitive microwave observations are not assimilated over sea ice areas, and sea ice retrievals use heuristic rather than physical methods. An empirical model for sea ice radiative transfer would be helpful but it cannot be trained using standard machine learning techniques because the inputs are mostly unknown. The solution is to simultaneously train the empirical model and a set of empirical inputs: an “empirical state” method, which draws on both generative machine learning and physical data assimilation methodology. A hybrid physical-empirical network describes the known and unknown physics of sea ice and atmospheric radiative transfer. The network is then trained to fit a year of radiance observations from Advanced Microwave Scanning Radiometer 2, using the atmospheric profiles, skin temperature and ocean water emissivity taken from a weather forecasting system. This process estimates maps of the daily sea ice concentration while also learning an empirical model for the sea ice emissivity. The model learns to define its own empirical input space along with daily maps of these empirical inputs. These maps represent the otherwise unknown microstructural properties of the sea ice and snow that affect the radiative transfer. This “empirical state” approach could be used to solve many other problems of earth system data assimilation.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730212","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}