Dynamics of Atmospheres and Oceans最新文献

{"title":"Aircraft observations of tropical cyclones core structure over the South China Sea and their impacts on track and intensity forecasts","authors":"Chengzhong Zhang ,&nbsp;Pak-wai Chan ,&nbsp;Sheng Hu ,&nbsp;Yerong Feng ,&nbsp;Shuixin Zhong ,&nbsp;Yanyan Huang","doi":"10.1016/j.dynatmoce.2025.101580","DOIUrl":"10.1016/j.dynatmoce.2025.101580","url":null,"abstract":"<div><div>In this study, characteristics of wind and temperature in the inner-core region of tropical cyclones (TCs) are analyzed using dropsonde data from three aircraft observation missions over the South China Sea (SCS) conducted by the Hong Kong Observatory between 2022 and 2023. The impact of assimilating inner-core dropsonde data on TC track and intensity forecasts is also evaluated. Results indicate that both TCs Nesat and Suola exhibit shared features: Tangential wind speeds weaken significantly above 850–500 hPa; A distinct warm core structure persists between 700 and 300 hPa; The boundary layer height varies across cases. Two parallel experiments，ASSI_drop (with inner-core dropsonde assimilation) and No_da (without assimilation), were implemented using the GRAPES (Global/Regional Assimilation and Prediction System) model with 3DVar. Data assimilation significantly reduced 72-hour mean track errors (49 % of reduction), while intensity forecasts showed neutral to weak positive improvements. The track improvement closely aligns with Feng et al. (2023)'s reduction via inner-core assimilation in northern SCS cases, while substantially exceeding the 10–13 % range documented in analogous SCS studies (Chan et al., 2018; Zhang et al., 2020). North Pacific DOTSTAR（Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region）cases demonstrated a 22 % mean improvement (Wu et al., 2007), contrasting with the SCS's 10–49 % variability range. This regional inconsistency underscores the necessity for expanded airborne dropsonde campaigns across the SCS to establish result representativeness and regional applicability.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"111 ","pages":"Article 101580"},"PeriodicalIF":1.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alteration in the large-scale circulation of south america triggered by the upper-tropospheric cyclonic vortices of Northeast Brazil","authors":"Matheus José Arruda Lyra ,&nbsp;Josefina Moraes Arraut ,&nbsp;Enio Pereira de Souza","doi":"10.1016/j.dynatmoce.2025.101582","DOIUrl":"10.1016/j.dynatmoce.2025.101582","url":null,"abstract":"<div><div>This study investigates how Upper-Tropospheric Cyclonic Vortices (UTCVs), which commonly form over Northeast Brazil (NEB), influence large-scale moisture transport across South America. UTCVs are the most important weather systems occurring in the pre-rainy season in Northeast Brazil and their understanding provides insights for improving short-term weather forecasting. Using 20 years of ERA-Interim reanalysis data (1999–2018) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) and GOES satellite imagery, we analyze circulation changes associated with UTCVs and their impacts on rainfall in NEB. Results show that while moisture typically enters South America from the tropical Atlantic and moves westward into the Amazon, approximately 36 % of UTCVs disrupted this pattern by inducing a west-to-east moisture flow from the Amazon toward NEB. These events led to enhanced convergence over the vortex region and significantly increased precipitation. We present a conceptual model summarizing these mechanisms, providing new insights into the hydrometeorological role of UTCVs in regional rainfall variability and forecasting.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"111 ","pages":"Article 101582"},"PeriodicalIF":1.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heatwave-induced thermoregulatory stress in Odisha's coastal and north-eastern districts: Examining the April 2024 event using advanced statistical and geospatial techniques","authors":"Roshan Beuria ,&nbsp;Dipak Sahu ,&nbsp;Sarat Chandra Sahu ,&nbsp;Debabrata Nandi ,&nbsp;Susmita Behera ,&nbsp;Kamal Lochan Mohanta ,&nbsp;Rakesh Ranjan Thakur","doi":"10.1016/j.dynatmoce.2025.101577","DOIUrl":"10.1016/j.dynatmoce.2025.101577","url":null,"abstract":"<div><div>This work investigates the remarkable heatwave that struck Odisha in April 2024, with special emphasis on intensity and duration in the coastal and interior regions. It is of special concern if such patterns arise in coastal districts where comfortable weather is due to the proximity of the sea. There's been an uninterrupted recording of temperatures crossing 40°C for 16 consecutive days i.e., from the 15th to the 30th of April which includes some regions usually considered less vulnerable to extreme heat. The aim of this particular research is focused on synoptic features and focuses on drivers for the specific anomaly in northeastern and coastal districts. The study utilises the historical temperature dataset ranging from 1969 to 2024, applying the heat wave trends according to the IMD classification and 90th percentile threshold. The examination of synoptic features of atmospheric circulation along with vertical thermal structures is the main scope of this research. From data collection, it is observed that there is an exacerbating increase in the frequency and intensity of heat waves alongside UHI effects, and they are linked with transitions of El Niño–La Niña. Along with this, stepping over vegetation and the increase in impervious surfaces also led to delayed sea breeze onset and higher heat retention in coastal zones. With the quantitative analysis that was supplemented by the UHI phenomenon, it was revealed that Thom's discomfort index also displayed significant discomfort to the inhabitants of the area during the heatwave, with values exceeding 28 in most locations, indicating severe discomfort levels. Unlike prior research in Odisha that primarily focused on long-term trends or socio-economic vulnerability, this study uniquely applies high-resolution WRF simulation, satellite reanalysis, and thermoregulatory indices to capture the April 2024 heatwave’s spatial variability, urban–coastal thermal interactions, and atmospheric drivers in an integrated and operationally relevant framework. These findings underscore the urgency of developing adaptive strategies such as urban planning reforms (e.g., greening and zoning), early warning systems tailored to heat thresholds, and region-specific public health interventions to protect vulnerable populations in Odisha and similar coastal environments.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"111 ","pages":"Article 101577"},"PeriodicalIF":1.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The method of single points wave spectrum generation for regional sea based on multimodal model in the Bohai sea, China","authors":"Xiandong Cao ,&nbsp;Mingli Zuo ,&nbsp;Xianbin Wang ,&nbsp;Zhifeng Wang ,&nbsp;Yuze Niu ,&nbsp;Ping Wang ,&nbsp;Yunqi Lv","doi":"10.1016/j.dynatmoce.2025.101586","DOIUrl":"10.1016/j.dynatmoce.2025.101586","url":null,"abstract":"<div><div>Wave spectra provide comprehensive information on ocean waves, including frequency and direction. However, due to the influence of topographical obstructions and variations in bathymetry, the application of deep learning in wave spectra prediction in semi-enclosed seas is limited. To address this issue, this study proposes a multimodal wave spectrum generation model for single points in regional seas. The model is developed based on an Encoder-Decoder architecture and further enhanced by incorporating the Coordinate Attention mechanism. The dataset for deep learning was simulated using the WAVEWATCH III numerical wave model and validated against buoy measurements. The input data for the deep learning model consists of multi-scale data, including regional wind fields and bathymetry, wind parameters and bathymetry at the target point. To evaluate the model's performance, we assessed the accuracy of the wave spectrum and wave spectrum integral parameters using the correlation coefficient, mean absolute error and root mean square error. Statistical analysis of wave spectrum and wave spectrum integral parameters illustrates that the generated wave spectra accurately reflect the total energy and energy distribution characteristics of the real wave spectra. The results demonstrate that the deep learning model effectively learns the nonlinear relationship between the input data and the wave spectrum in semi-enclosed seas.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"111 ","pages":"Article 101586"},"PeriodicalIF":2.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying climate change-driven variations in projected wind condition in the Gulf of Guinea","authors":"Adeola M. Dahunsi ,&nbsp;Frederic Bonou ,&nbsp;Olusegun A. Dada ,&nbsp;Ezinvi Baloïtcha","doi":"10.1016/j.dynatmoce.2025.101543","DOIUrl":"10.1016/j.dynatmoce.2025.101543","url":null,"abstract":"<div><div>Understanding wind climate dynamics in the Gulf of Guinea (GoG) is critical for addressing climate-related challenges and supporting sustainable development in the region. This study evaluates the wind climate using observational buoy data from the PIRATA network and multiple General Circulation Models (GCMs) under historical and future Representative Concentration Pathway (RCP 8.5) scenarios. An ensemble dataset, constructed as the average of GCM outputs, was validated against PIRATA buoy measurements and demonstrated better performance to individual GCMs. The study revealed distinct temporal and spatial variability in wind conditions across the dry and rainy seasons during the baseline period (1961–2014). Projections under RCP 8.5 for mid-century (2026–2060) and end-century (2066–2100) consistently indicate increasing wind speeds, with the most significant changes projected during the rainy season. These findings highlight the critical role of ensemble modelling in mitigating biases inherent in individual datasets and its contribution to a robust understanding of wind dynamics in the region. The observed trends have significant implications for coastal upwelling, maritime safety, renewable energy development, and climate resilience strategies in the GoG. This study highlights the necessity of fine-scale spatio-temporal modelling to improve predictions and guide evidence-based adaptive strategies to mitigate climate change impacts on coastal ecosystems and vulnerable communities.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"110 ","pages":"Article 101543"},"PeriodicalIF":1.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Projected changes in wind speed and wind energy resources over the Persian Gulf based on bias corrected CMIP6 models","authors":"Amirmahdi Gohari ,&nbsp;Adem Akpınar","doi":"10.1016/j.dynatmoce.2025.101539","DOIUrl":"10.1016/j.dynatmoce.2025.101539","url":null,"abstract":"<div><div>This study investigates future wind speed and wind energy changes in the Persian Gulf using a multi-model ensemble mean (MMM) derived from 20 CMIP6 models under the SSP5–8.5 scenario. ERA5 reanalysis wind speed data for the historical period (1995–2015) is compared to projections for the mid-future (2040–2059) and far-future (2080–2099). Quantile mapping based on Weibull distribution as a bias correction technique applied to the raw future data to obtain more reliable projections. Results show suitable wind conditions for power generation are expected to increase slightly, by 1.16 % in the mid-future and 0.75 % in the far-future. However, average annual wind speed and wind power density are projected to decrease by up to 2 % and 7 % respectively. The winter season is consistently shown to have the highest average wind speed, projected to increase over 5–7 % in the future. Spatial analysis identifies current and future wind energy hot spots, with a northward shift by the far-future. Assessments of variability over time highlight potential future alterations. The future change analysis reveals irregular regional shifts, indicating decreases in wind strength nearshore in the northern Gulf, while the southern part may experience increases, suggesting a promising trend for wind energy potential there.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"110 ","pages":"Article 101539"},"PeriodicalIF":1.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monsoon dynamics and future projections in the Himalaya: Insights into sea surface temperature, sea level pressure relationships, and future flood risks","authors":"Mayank Shekhar ,&nbsp;Pushpendra Pandey ,&nbsp;Shaktiman Singh ,&nbsp;Anupam Sharma","doi":"10.1016/j.dynatmoce.2025.101558","DOIUrl":"10.1016/j.dynatmoce.2025.101558","url":null,"abstract":"<div><div>The Himalaya influences the fundamental features of the Indian Summer Monsoon (ISM) circulation across South Asia. Reliable predictions of ISM dynamics require an improved understanding of the relationship with the El Niño–Southern Oscillation (ENSO)–sea surface temperature (SST) and Sea Level Pressure (SLP) relationships in the Niño region. To address this, we conducted a detailed analysis of teleconnections, correlations, regime shifts, periodicity, and return periods of monsoons over the Karakoram Himalaya (KH), Western Himalaya (WH), Central Himalaya (CH), and Eastern Himalaya (EH) using gridded precipitation, ENSO-SST, and SLP data across the Niño 4, 3.4, and 3 regions. Additionally, we examined Coupled Model Intercomparison Project phase 5 (CMIP5) projections considering evolving relationships. The findings show that the monsoons over the CH, WH, EH, and KH have stronger, moderate, weaker, and weakest spatial correlations, respectively, with the SST of Niño regions. Notably, the SST anomalies for the Niño regions 4 and 3.4 reveal distinct regime shift for the years 1977 C.E. and 1990 C.E., however, Niño regions 3 and 1 + 2, do not exhibit any such shift. The SST anomalies for the Niño 3.4 and Niño 4 from 1977 to 2019 C.E., and 1990–2019 C.E., were 0.087°C and 0.060°C, respectively, indicating systematic warming of the sea surface. This warming trend is further linked to projections from CMIP5 RCP 4.5 and RCP 8.5, which suggest that the EH region may experience significant flooding during the monsoon months by 2100 due to high and extreme precipitation.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"110 ","pages":"Article 101558"},"PeriodicalIF":1.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effects of climate change on the thermal stratification of the Gulf of Oman","authors":"Shirin Farkhani,&nbsp;Nasser Hadjizadeh Zaker","doi":"10.1016/j.dynatmoce.2025.101560","DOIUrl":"10.1016/j.dynatmoce.2025.101560","url":null,"abstract":"<div><div>Water temperature and thermal stratification have fundamental effects on marine environments, ecosystems, and water circulation patterns. Marine ecosystems are generally highly sensitive to thermal changes. Global warming can fundamentally alter oceanic temperature fields and thermal stratification(Cheng, 2019)(Cheng, 2019). Therefore, studying the effects of climate change on the thermal characteristics of the oceans and seas is vital. Using numerical modeling, and three Representative Concentration Pathway (RCP) scenarios, we studied the effects of global warming on the sea surface temperature and vertical thermal structure of the Gulf of Oman. Atmospheric data from the ERA5 and CORDEX models were used for recent past (1980–2000) and future (2080–2100) modeling, respectively. Results indicated that, in the future climate, the temperature across the upper 1000 m of the Gulf of Oman will increase. In summer, temperature increments in the surface mixed layer were estimated at + 1.9, + 2.5, and + 3.4°C for RCP 2.6, 4.5, and 8.5, respectively. Below the thermocline, the temperature increments were less than the ones in the surface mixed layer. In winter, future temperature increments in the surface mixed layer were + 1.2, + 1.6, and + 2°C for RCP 2.6, 4.5, and 8.5, respectively. The results indicated a stronger summer thermocline in the future with temperature gradients of 0.055, 0.057, and 0.06 °C/m in the RCP 2.6, 4.5, and 8.5, respectively, which could significantly reduce dissolved oxygen concentration in the lower layers. This study provides insights that can help develop adaptable strategies to manage and mitigate the harmful impacts of global warming on the Gulf of Oman.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"110 ","pages":"Article 101560"},"PeriodicalIF":1.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating weather trends and forecasting with machine learning: Insights from maximum temperature, minimum temperature, and rainfall data in India","authors":"Guhan V ,&nbsp;A. Dharma Raju ,&nbsp;Rama Krishna ,&nbsp;K. Nagaratna","doi":"10.1016/j.dynatmoce.2025.101562","DOIUrl":"10.1016/j.dynatmoce.2025.101562","url":null,"abstract":"<div><div>This research presents a comprehensive evaluation of meteorological trends using a combination of statistical and machine learning approaches, focusing on rainfall, minimum temperature (MinT), and maximum temperature (MaxT). The Mann-Kendall trend test and Sen’s slope estimator identified statistically significant upward trends in both MaxT (slope = 0.0154, p = 9.42E-06) and MinT (slope = 0.0190, p = 4.73E-07), indicating a consistent warming climate. Rainfall displayed a positive trend but was not statistically significant (p = 0.9516, slope = 4.07E-05), suggesting random variability rather than a sustained increase.Machine learning models were leveraged to enhance forecasting accuracy for these meteorological parameters. ARIMA exhibited the highest precision for MaxT and Rainfall (MAE = 3.0080, 0.1728; RMSE = 3.4967, 0.2916), while XGBoost demonstrated superior performance for MinT (MAE = 2.7726, RMSE = 3.8555). These findings highlight the critical need for climate adaptation measures, as rising temperatures could intensify heatwaves, escalate energy demands, and affect agricultural productivity.The study underscores the importance of integrating advanced forecasting techniques to support proactive climate resilience planning. By incorporating machine learning models with traditional statistical analyses, this research provides valuable insights into climate trends, aiding policymakers and researchers in formulating effective climate adaptation strategies.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"110 ","pages":"Article 101562"},"PeriodicalIF":1.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensitive area in the tropical Indian Ocean for advancing beyond the summer predictability barrier of Indian Ocean Dipole","authors":"Rong Feng ,&nbsp;Wansuo Duan","doi":"10.1016/j.dynatmoce.2025.101552","DOIUrl":"10.1016/j.dynatmoce.2025.101552","url":null,"abstract":"<div><div>Using the geophysical fluid dynamics laboratory climate model version 2p1 (GFDL CM2p1), perfect model predictability experiments have been conducted to identify the sensitive area in the tropical Indian Ocean for advancing beyond the summer predictability barrier (SPB) of positive Indian Ocean Dipole (IOD) events. In these experiments, the model is assumed to be perfect, and prediction errors are only caused by initial errors. Initially, the impact of initial error patterns on prediction uncertainties was assessed by comparing dipole pattern initial errors with three sets of spatially correlated noises. The results revealed that dipole pattern initial errors tend to result in larger prediction errors and higher error growth rates in summer, leading to a significant SPB phenomenon. Notably, the large values of these dipole pattern initial errors are concentrated in specific areas. By eliminating initial errors within these areas, the prediction errors in summer are largely reduced, underscoring the sensitivity of prediction uncertainties in summer to initial errors in these areas. Moreover, the prediction errors in summer exhibit a higher sensitivity to initial errors within the subsurface large value area compared to those within the surface large value area. Consequently, the subsurface large value area in the tropical Indian Ocean is the sensitive area for advancing beyond the SPB, aligning with the corresponding location for advancing beyond the WPB. Eliminating initial errors within this area leads to a rapid decrease in prediction uncertainties, with a more pronounced reduction in winter than in summer. Through intensive observations in this sensitive area, significant reductions in prediction errors in both summer and winter can be achieved, thereby greatly improve the forecast skill of IOD events.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"110 ","pages":"Article 101552"},"PeriodicalIF":1.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143790880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}