Advances in Atmospheric Sciences最新文献

{"title":"Refining the Factors Affecting N2O Emissions from Upland Soils with and without Nitrogen Fertilizer Application at a Global Scale","authors":"Wenqian Jiang, Siqi Li, Yong Li, Meihui Wang, Bo Wang, Ji Liu, Jianlin Shen, Xunhua Zheng","doi":"10.1007/s00376-024-3234-7","DOIUrl":"https://doi.org/10.1007/s00376-024-3234-7","url":null,"abstract":"<p>Nitrous oxide (N₂O) is a long-lived greenhouse gas that mainly originates from agricultural soils. More and more studies have explored the sources, influencing factors and effective mitigation measures of N₂O in recent decades. However, the hierarchy of factors influencing N₂O emissions from agricultural soils at the global scale remains unclear. In this study, we carry out correlation and structural equation modeling analysis on a global N₂O emission dataset to explore the hierarchy of influencing factors affecting N₂O emissions from the nitrogen (N) and non-N fertilized upland farming systems, in terms of climatic factors, soil properties, and agricultural practices. Our results show that the average N₂O emission intensity in the N fertilized soils (17.83 g N ha⁻¹ d⁻¹) was significantly greater than that in the non-N fertilized soils (5.34 g N ha⁻¹ d⁻¹) (<i>p</i>&lt; 0.001). Climate factors and agricultural practices are the most important influencing factors on N₂O emission in non-N and N fertilized upland soils, respectively. For different climatic zones, without fertilizer, the primary influence factors on soil N₂O emissions are soil physical properties in subtropical monsoon zone, whereas climatic factors are key in the temperate zones. With fertilizer, the primary influence factors for subtropical monsoon and temperate continental zones are soil physical properties, while agricultural measures are the main factors in the temperate monsoon zone. Deploying enhanced agricultural practices, such as reduced N fertilizer rate combined with the addition of nitrification and urease inhibitors can potentially mitigate N₂O emissions by more than 60% in upland farming systems.</p>","PeriodicalId":7249,"journal":{"name":"Advances in Atmospheric Sciences","volume":"205 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic Impacts of Indian Ocean SST and Indo-China Peninsula Soil Moisture on the 2020 Record-breaking Mei-yu","authors":"Yinshuo Dong, Haishan Chen, Xuan Dong, Wenjian Hua, Wenjun Zhang","doi":"10.1007/s00376-024-3204-0","DOIUrl":"https://doi.org/10.1007/s00376-024-3204-0","url":null,"abstract":"<p>The Yangtze River basin (YRB) experienced a record-breaking mei-yu season in June–July 2020. This unique long-lasting extreme event and its origin have attracted considerable attention. Previous studies have suggested that the Indian Ocean (IO) SST forcing and soil moisture anomaly over the Indochina Peninsula (ICP) were responsible for this unexpected event. However, the relative contributions of IO SST and ICP soil moisture to the 2020 mei-yu rainfall event, especially their linkage with atmospheric circulation changes, remain unclear. By using observations and numerical simulations, this study examines the synergistic impacts of IO SST and ICP soil moisture on the extreme mei-yu in 2020. Results show that the prolonged dry soil moisture led to a warmer surface over the ICP in May under strong IO SST backgrounds. The intensification of the warm condition further magnified the land thermal effects, which in turn facilitated the westward extension of the western North Pacific subtropical high (WNPSH) in June–July. The intensified WNPSH amplified the water vapor convergence and ascending motion over the YRB, thereby contributing to the 2020 mei-yu. In contrast, the land thermal anomalies diminish during normal IO SST backgrounds due to the limited persistence of soil moisture. The roles of IO SST and ICP soil moisture are verified and quantified using the Community Earth System Model. Their synergistic impacts yield a notable 32% increase in YRB precipitation. Our findings provide evidence for the combined influences of IO SST forcing and ICP soil moisture variability on the occurrence of the 2020 super mei-yu.</p>","PeriodicalId":7249,"journal":{"name":"Advances in Atmospheric Sciences","volume":"208 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal Variability and Environmental Controls of Temperature Sensitivity of Ecosystem Respiration across the Tibetan Plateau","authors":"Danrui Sheng, Xianhong Meng, Shaoying Wang, Zhaoguo Li, Lunyu Shang, Hao Chen, Lin Zhao, Mingshan Deng, Hanlin Niu, Pengfei Xu, Xiaohu Wen","doi":"10.1007/s00376-024-3167-1","DOIUrl":"https://doi.org/10.1007/s00376-024-3167-1","url":null,"abstract":"<p>Warming-induced carbon loss via ecosystem respiration (<i>R</i>_<i>e</i>) is probably intensifying in the alpine grassland ecosystem of the Tibetan Plateau owing to more accelerated warming and the higher temperature sensitivity of <i>R</i>_<i>e</i> (<i>Q</i>₁₀). However-little is known about the patterns and controlling factors of <i>Q</i>₁₀ on the plateau, impeding the comprehension of the intensity of terrestrial carbon–climate feedbacks for these sensitive and vulnerable ecosystems. Here, we synthesized and analyzed multiyear observations from 14 sites to systematically compare the spatiotemporal variations of <i>Q</i>₁₀ values in diverse climate zones and ecosystems, and further explore the relationships between <i>Q</i>₁₀ and environmental factors. Moreover-structural equation modeling was utilized to identify the direct and indirect factors predicting <i>Q</i>₁₀ values during the annual-growing, and non-growing seasons. The results indicated that the estimated <i>Q</i>₁₀ values were strongly dependent on temperature- generally, with the average <i>Q</i>₁₀ during different time periods increasing with air temperature and soil temperature at different measurement depths (5 cm, 10 cm, 20 cm). The <i>Q</i>₁₀ values differentiated among ecosystems and climatic zones, with warming-induced <i>Q</i>₁₀ declines being stronger in colder regions than elsewhere based on spatial patterns. NDVI was the most cardinal factor in predicting annual <i>Q</i>₁₀ values, significantly and positively correlated with <i>Q</i>₁₀. Soil temperature (<i>T</i>_<i>s</i>) was identified as the other powerful predictor for <i>Q</i>₁₀, and the negative <i>Q</i>₁₀–<i>T</i>_<i>s</i> relationship demonstrates a larger terrestrial carbon loss potentiality in colder than in warmer regions in response to global warming. Note that the interpretations of the effect of soil moisture on <i>Q</i>₁₀ were complicated, reflected in a significant positive relationship between <i>Q</i>₁₀ and soil moisture during the growing season and a strong quadratic correlation between the two during the annual and non-growing season. These findings are conducive to improving our understanding of alpine grassland ecosystem carbon–climate feedbacks under warming climates.</p>","PeriodicalId":7249,"journal":{"name":"Advances in Atmospheric Sciences","volume":"35 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ResoNet: Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks","authors":"Pumeng Lyu, Tao Tang, Fenghua Ling, Jing-Jia Luo, Niklas Boers, Wanli Ouyang, Lei Bai","doi":"10.1007/s00376-024-3316-6","DOIUrl":"https://doi.org/10.1007/s00376-024-3316-6","url":null,"abstract":"<p>Recent studies have shown that deep learning (DL) models can skillfully forecast El Niño–Southern Oscillation (ENSO) events more than 1.5 years in advance. However, concerns regarding the reliability of predictions made by DL methods persist, including potential overfitting issues and lack of interpretability. Here, we propose ResoNet, a DL model that combines CNN (convolutional neural network) and transformer architectures. This hybrid architecture enables our model to adequately capture local sea surface temperature anomalies as well as long-range inter-basin interactions across oceans. We show that ResoNet can robustly predict ENSO at lead times of 19 months, thus outperforming existing approaches in terms of the forecast horizon. According to an explainability method applied to ResoNet predictions of El Niño and La Niña from 1- to 18-month leads, we find that it predicts the Niño-3.4 index based on multiple physically reasonable mechanisms, such as the recharge oscillator concept, seasonal footprint mechanism, and Indian Ocean capacitor effect. Moreover, we demonstrate for the first time that the asymmetry between El Niño and La Niña development can be captured by ResoNet. Our results could help to alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.</p>","PeriodicalId":7249,"journal":{"name":"Advances in Atmospheric Sciences","volume":"17 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula: Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation","authors":"Jiwon Hwang, Dong-Hyun Cha, Donghyuck Yoon, Tae-Young Goo, Sueng-Pil Jung","doi":"10.1007/s00376-024-3232-9","DOIUrl":"https://doi.org/10.1007/s00376-024-3232-9","url":null,"abstract":"<p>This study evaluated the simulation performance of mesoscale convective system (MCS)-induced precipitation, focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula. The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) analysis data, as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model. Particularly, temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data. Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis, which exhibited a cold and dry bias in the lower levels below 850 hPa. The model, in terms of the precipitation simulations, particularly for high-intensity precipitation over the Yellow Sea, demonstrated higher accuracy when applying ECMWF analysis data as the initial condition. This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features (i.e., warm and humid lower-level atmosphere) over the Yellow Sea. In conclusion, this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data. Furthermore, it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.</p>","PeriodicalId":7249,"journal":{"name":"Advances in Atmospheric Sciences","volume":"102 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the Low Predictability of the 2015/16 El Niño Event Based on a Deep Learning Model","authors":"Tingyu Wang, Ping Huang, Xianke Yang","doi":"10.1007/s00376-024-3238-3","DOIUrl":"https://doi.org/10.1007/s00376-024-3238-3","url":null,"abstract":"<p>The 2015/16 El Niño event ranks among the top three of the last 100 years in terms of intensity, but most dynamical models had a relatively low prediction skill for this event before the summer months. Therefore, the attribution of this particular event can help us to understand the cause of super El Niño–Southern Oscillation events and how to forecast them skillfully. The present study applies attribute methods based on a deep learning model to study the key factors related to the formation of this event. A deep learning model is trained using historical simulations from 21 CMIP6 models to predict the Niño-3.4 index. The integrated gradient method is then used to identify the key signals in the North Pacific that determine the evolution of the Niño-3.4 index. These crucial signals are then masked in the initial conditions to verify their roles in the prediction. In addition to confirming the key signals inducing the super El Niño event revealed in previous attribution studies, we identify the combined contribution of the tropical North Atlantic and the South Pacific oceans to the evolution and intensity of this event, emphasizing the crucial role of the interactions among them and the North Pacific. This approach is also applied to other El Niño events, revealing several new precursor signals. This study suggests that the deep learning method is useful in attributing the key factors inducing extreme tropical climate events.</p>","PeriodicalId":7249,"journal":{"name":"Advances in Atmospheric Sciences","volume":"2010 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141190083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of Future Changes in Heavy Precipitation and Extreme Drought on the Economy over South China and Indochina","authors":"Bin Tang, Wenting Hu, Anmin Duan, Yimin Liu, Wen Bao, Yue Xin, Xianyi Yang","doi":"10.1007/s00376-023-3158-7","DOIUrl":"https://doi.org/10.1007/s00376-023-3158-7","url":null,"abstract":"<p>Heavy precipitation and extreme drought have caused severe economic losses over South China and Indochina (INCSC) in recent decades. Given the areas with large gross domestic product (GDP) in the INCSC region are distributed along the coastline and greatly affected by global warming, understanding the possible economic impacts induced by future changes in the maximum consecutive 5-day precipitation (RX5day) and the maximum consecutive dry days (CDD) is critical for adaptation planning in this region. Based on the latest data released by phase 6 of the Coupled Model Intercomparison Project (CMIP6), future projections of precipitation extremes with bias correction and their impacts on GDP over the INCSC region under the fossil-fueled development Shared Socioeconomic Pathway (SSP5-8.5) are investigated. Results indicate that RX5day will intensify robustly throughout the INCSC region, while CDD will lengthen in most regions under global warming. The changes in climate consistently dominate the effect on GDP over the INCSC region, rather than the change of GDP. If only considering the effect of climate change on GDP, the changes in precipitation extremes bring a larger impact on the economy in the future to the provinces of Hunan, Jiangxi, Fujian, Guangdong, and Hainan in South China, as well as the Malay Peninsula and southern Cambodia in Indochina. Thus, timely regional adaptation strategies are urgent for these regions. Moreover, from the sub-regional average viewpoint, over two thirds of CMIP6 models agree that maintaining a lower global warming level will reduce the economic impacts from heavy precipitation over the INCSC region.</p>","PeriodicalId":7249,"journal":{"name":"Advances in Atmospheric Sciences","volume":"34 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141062061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation and Projection of Population Exposure to Temperature Extremes over the Beijing–Tianjin–Hebei Region Using a High-Resolution Regional Climate Model RegCM4 Ensemble","authors":"Peihua Qin, Zhenghui Xie, Rui Han, Buchun Liu","doi":"10.1007/s00376-023-3123-5","DOIUrl":"https://doi.org/10.1007/s00376-023-3123-5","url":null,"abstract":"<p>Temperature extremes over rapidly urbanizing regions with high population densities have been scrutinized due to their severe impacts on human safety and economics. First of all, the performance of the regional climate model RegCM4 with a hydrostatic or non-hydrostatic dynamic core in simulating seasonal temperature and temperature extremes was evaluated over the historical period of 1991–99 at a 12-km spatial resolution over China and a 3-km resolution over the Beijing–Tianjin–Hebei (JJJ) region, a typical urban agglomeration of China. Simulations of spatial distributions of temperature extremes over the JJJ region using RegCM4 with hydrostatic and non-hydrostatic cores showed high spatial correlations of more than 0.8 with the observations. Under a warming climate, temperature extremes of annual maximum daily temperature (TXx) and summer days (SU) in China and the JJJ region showed obvious increases by the end of the 21st century while there was a general reduction in frost days (FD). The ensemble of RegCM4 with different land surface components was used to examine population exposure to temperature extremes over the JJJ region. Population exposure to temperature extremes was found to decrease in 2091–99 relative to 1991–99 over the majority of the JJJ region due to the joint impacts of increases in temperature extremes over the JJJ and population decreases over the JJJ region, except for downtown areas. Furthermore, changes in population exposure to temperature extremes were mainly dominated by future population changes. Finally, we quantified changes in exposure to temperature extremes with temperature increase over the JJJ region. This study helps to provide relevant policies to respond future climate risks over the JJJ region.</p>","PeriodicalId":7249,"journal":{"name":"Advances in Atmospheric Sciences","volume":"14 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141062144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model","authors":"Wenbo Xue, Hui Yu, Shengming Tang, Wei Huang","doi":"10.1007/s00376-023-3087-5","DOIUrl":"https://doi.org/10.1007/s00376-023-3087-5","url":null,"abstract":"<p>Numerical weather prediction (NWP) models have always presented large forecasting errors of surface wind speeds over regions with complex terrain. In this study, surface wind forecasts from an operational NWP model, the SMS-WARR (Shanghai Meteorological Service-WRF ADAS Rapid Refresh System), are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features, with the intent of providing clues to better apply the NWP model to complex terrain regions. The terrain features are described by three parameters: the standard deviation of the model grid-scale orography, terrain height error of the model, and slope angle. The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography. The minimum ME (the mean value of bias) is 1.2 m s⁻¹ when the standard deviation is between 60 and 70 m. A positive correlation exists between bias and terrain height error, with the ME increasing by 10%–30% for every 200 m increase in terrain height error. The ME decreases by 65.6% when slope angle increases from (0.5°–1.5°) to larger than 3.5° for uphill winds but increases by 35.4% when the absolute value of slope angle increases from (0.5°–1.5°) to (2.5°–3.5°) for downhill winds. Several sensitivity experiments are carried out with a model output statistical (MOS) calibration model for surface wind speeds and ME (RMSE) has been reduced by 90% (30%) by introducing terrain parameters, demonstrating the value of this study.</p>","PeriodicalId":7249,"journal":{"name":"Advances in Atmospheric Sciences","volume":"132 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141062178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Track-Pattern-Based Characteristics of Extratropical Transitioning Tropical Cyclones in the Western North Pacific","authors":"Hong Huang, Dan Wu, Yuan Wang, Zhen Wang, Yu Liu","doi":"10.1007/s00376-023-2330-4","DOIUrl":"https://doi.org/10.1007/s00376-023-2330-4","url":null,"abstract":"<p>Based on the Regional Specialized Meteorological Center (RSMC) Tokyo-Typhoon Center best-track data and the NCEP-NCAR reanalysis dataset, extratropical transitioning (ET) tropical cyclones (ETCs) over the western North Pacific (WNP) during 1951–2021 are classified into six clusters using the fuzzy c-means clustering method (FCM) according to their track patterns. The characteristics of the six hard-clustered ETCs with the highest membership coefficient are shown. Most tropical cyclones (TCs) that were assigned to clusters C2, C5, and C6 made landfall over eastern Asian countries, which severely threatened these regions. Among landfalling TCs, 93.2% completed their ET after landfall, whereas 39.8% of ETCs completed their transition within one day. The frequency of ETCs over the WNP has decreased in the past four decades, wherein cluster C5 demonstrated a significant decrease on both interannual and interdecadal timescales with the expansion and intensification of the western Pacific subtropical high (WPSH). This large-scale circulation pattern is favorable for C2 and causes it to become the dominant track pattern, owning to it containing the largest number of intensifying ETCs among the six clusters, a number that has increased insignificantly over the past four decades. The surface roughness variation and three-dimensional background circulation led to C5 containing the maximum number of landfalling TCs and a minimum number of intensifying ETCs. Our results will facilitate a better understanding of the spatiotemporal distributions of ET events and associated environment background fields, which will benefit the effective monitoring of these events over the WNP.</p>","PeriodicalId":7249,"journal":{"name":"Advances in Atmospheric Sciences","volume":"46 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}