Weather and Climate Extremes最新文献

{"title":"Changes in atmospheric circulation amplify extreme snowfall fueled by Arctic sea ice loss over high-latitude land","authors":"Yubo Liu ,&nbsp;Qiuhong Tang ,&nbsp;L. Ruby Leung ,&nbsp;Deliang Chen ,&nbsp;Jennifer A. Francis ,&nbsp;Chi Zhang ,&nbsp;Hans W. Chen ,&nbsp;Steven C. Sherwood","doi":"10.1016/j.wace.2025.100802","DOIUrl":"10.1016/j.wace.2025.100802","url":null,"abstract":"<div><div>Arctic sea-ice retreat has been linked to increased winter precipitation and heavy snowfall over land, likely due to a combination of enhanced evaporation from ice-free Arctic marginal seas (AMS) and changes in atmospheric circulation. However, their relative roles and contributions remain uncertain. Here, we show that a greater proportion of AMS evaporative moisture reached high-latitude land during the cold seasons from 1980–1989 to 2012–2021. Atmospheric circulation changes added an additional 13 % increase in the AMS moisture contribution, accounting for 11 % of the total increase in AMS-sourced land precipitation. Notably, 46 % of the increase in AMS-sourced extreme snowfall is attributed to circulation-driven landward moisture transport, representing an 84 % increase beyond the effect of enhanced AMS evaporation alone. Further analysis indicates that both the rise in Arctic moisture and the atmospheric circulation shifts are primarily driven by anthropogenic forcing. These findings highlight how atmospheric circulation changes amplify extreme snowfall fueled by AMS evaporation, underscoring the synergistic effects of Arctic sea ice loss and circulation change on high-latitude winter precipitation.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"50 ","pages":"Article 100802"},"PeriodicalIF":6.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discernability of the vertical vortex structure of pre-existing disturbances and their implication for tropical cyclone formation","authors":"Hung Ming Cheung ,&nbsp;Jung-Eun Chu","doi":"10.1016/j.wace.2025.100804","DOIUrl":"10.1016/j.wace.2025.100804","url":null,"abstract":"<div><div>The formation of a tropical cyclone (TC) is often rooted in a pre-existing disturbance, yet our understanding of their structural differences and evolution into TCs remains limited. To bridge the knowledge gap, we examine tropical disturbances and depressions in the western North Pacific during the period 2004–2021 from a best-track dataset. Here we show four discernible structures of pre-existing disturbances in terms of their vertical and radial extents: broad vortex dominated by lower-tropospheric vorticity (Cluster 1), narrow vortex with its vorticity maximum in the lower troposphere (Cluster 2), broad and deep vortex spanning most of the troposphere (Cluster 3), and narrow vortex dominated by upper-tropospheric vorticity (Cluster 4), by applying unsupervised machine learning techniques. Out of the 2014 samples analyzed, almost 80 % exhibit vorticity maximum in the lower troposphere, while the others peak aloft. While these different structures have varying implications for stratiform and convective precipitations, there is no clear preference for specific vortex structures in pre-existing disturbances for TC genesis in the next 6 h. On the other hand, the time it takes for TC genesis or the intensification rate is more closely related to the upper-level extent of relative vorticity rather than the local maximum magnitude or radial size of the vortices. Despite the uncertainty concerning the data during the earlier lifetime, the study introduces a systematic approach to categorizing the vortex structures of pre-existing disturbances which provides new insights into their role in TC formation.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"50 ","pages":"Article 100804"},"PeriodicalIF":6.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Impact of urbanization on regional extreme precipitation trends observed at China national station network” [Weather and Clim. Extrem. 48 (2025) 100760]","authors":"Suonam Kealdrup Tysa ,&nbsp;Guoyu Ren ,&nbsp;Panfeng Zhang ,&nbsp;Siqi Zhang","doi":"10.1016/j.wace.2025.100779","DOIUrl":"10.1016/j.wace.2025.100779","url":null,"abstract":"","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"49 ","pages":"Article 100779"},"PeriodicalIF":6.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144305061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond simple flash drought detection: An operational index to analyse the development speed of droughts at global scale","authors":"Carmelo Cammalleri ,&nbsp;Samuele Maffei ,&nbsp;Alessandro F.G. Ceppi ,&nbsp;Davide Bavera ,&nbsp;Guido Fioravanti ,&nbsp;Mercedes Peretti ,&nbsp;Pablo C. Spennemann ,&nbsp;Andrea Toreti","doi":"10.1016/j.wace.2025.100800","DOIUrl":"10.1016/j.wace.2025.100800","url":null,"abstract":"<div><div>Research interest on flash droughts has recently risen due to the challenges posed on drought early warning and management systems. Since the main characteristic of flash drought is a rapid initial development, we first implemented a novel index capturing this feature, and then tested it against different existing ones. The proposed index does not aim at capturing only flash droughts, but it can be used to characterize the initial development speed of all types of droughts. A selected set of events were classified with an expert-based semi-quantitative approach and used to evaluate the indices. The main finding points to the Initial Development Rate in the first 3 dekads (about 30 days) of the event (IDR₃) as a robust metric. A global analysis of the index highlights: 1) south-eastern Asia and the Amazon basin as hotspots with faster mean development rates; 2) Australia and the western US as areas characterized by slow events, on average. Additionally, our analysis identifies a strong seasonal component in the IDR₃, with some clear relationships with climatic and environmental factors such as annual average precipitation, temperature, soil moisture, and vegetation mass. High soil moisture content and air temperature, and low vegetation amount, seem to be among the main variables controlling the speed of development. Following these results, the IDR₃ seems to be a suitable index for drought forecasts aiming at anticipating the occurrence of rapid developing droughts.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"49 ","pages":"Article 100800"},"PeriodicalIF":6.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical modelling of extreme daily rainfall over Aotearoa New Zealand","authors":"Suzanne Rosier ,&nbsp;Shalin Shah ,&nbsp;Greg Bodeker ,&nbsp;Trevor Carey-Smith ,&nbsp;David Frame ,&nbsp;Dáithí A. Stone","doi":"10.1016/j.wace.2025.100799","DOIUrl":"10.1016/j.wace.2025.100799","url":null,"abstract":"<div><div>Extreme rainfall in New Zealand, and how best to characterise expected changes in those extremes as the climate warms, is investigated using very large ensembles of regional climate model simulations at five different ‘epochs’ of climate change (pre-industrial, present-day, and three future states at 1.5 °C, 2.0 °C, and 3.0 °C above pre-industrial). Different constructs of non-stationary Generalised Extreme Value (GEV) models are explored to determine which provides the most accurate estimates of extreme rainfall for the minimum model complexity. The different GEV model constructs vary the number of parameters (location, scale and shape) that are assumed to vary as climate changes, summarised as a linear dependence on Southern Hemisphere mean land surface temperature. Non-stationarity is also explored a different way, with a stationary GEV fitted separately within each of the five ’epochs’. These different models are applied to annual maximum one-day rainfall at eight locations around the country, chosen to be broadly representative of the various rainfall regimes countrywide. In situations with fair but not enormous sample sizes, such as with long historical records, the model in which only the location and scale, but not the shape, parameters vary with warming has the tightest sampling uncertainty without introducing substantial bias. According to this GEV model, 1-in-100-year rainfall increases with warming at all eight locations, ranging from about 5%/<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>C in most of the country to 8%/<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>C in the north. The change arises from an increase in the location parameter, with only a proportional increase in the scale parameter, consistent with extreme rainfall increases dictated by anthropogenic increases in specific humidity.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"49 ","pages":"Article 100799"},"PeriodicalIF":6.9,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distinguishing environmental controls on strong vs. extreme wind gusts","authors":"Greeshma Surendran ,&nbsp;Steven Sherwood ,&nbsp;Jason Evans ,&nbsp;Moutassem El Rafei ,&nbsp;Andrew Dowdy ,&nbsp;Fei Ji ,&nbsp;Andrew Brown","doi":"10.1016/j.wace.2025.100788","DOIUrl":"10.1016/j.wace.2025.100788","url":null,"abstract":"<div><div>Statistical and theoretical models of wind gusts may be dominated by more common strong events, rather than rare but damaging extreme ones. We address this by combining case studies of six extreme gust cases in New South Wales (NSW), Australia, with statistical and machine-learning (random forest) models to identify environmental factors distinguishing “strong” (<span><math><mrow><mo>≥</mo><mn>18</mn><mspace></mspace><mi>m/s</mi></mrow></math></span>) vs. “extreme” (<span><math><mrow><mo>≥</mo><mn>25</mn><mspace></mspace><mi>m/s</mi></mrow></math></span>) gust events in a 20-year dataset. The BARRA-SY high-resolution regional reanalysis is used to augment in-situ observations and provide a model gust speed diagnostic for evaluation, as well as environmental prediction metrics. All the extreme wind cases were linked to deep convection, often organized into linear systems. A random forest model achieved 89% accuracy for predicting strong winds generally, with the gust diagnostic and environmental background wind speeds as the top predictors. For distinguishing extreme from strong gusts, the model’s accuracy was 79%, but with a high false alarm rate. Both statistical and machine-learning analyses highlight convective instability metrics — Most Unstable Convective Available Potential Energy (MUCAPE), Derecho Composite Parameter (DCP), and k_index - as key predictors of extreme gusts. The BARRA-SY gust speed diagnostic thus informs about strong wind gusts, but not extremes, which depend on variables it ignores. Instability measures, however, are also imperfect predictors of extreme gusts because they fail to capture storm trigger conditions, seen in some of the case studies. These findings demonstrate that the factors driving extreme wind gusts differ substantially from those driving strong but less extreme gusts. Therefore, statistical analyses or predictive models that consider all strong gusts collectively will likely fail to uncover the environmental factors responsible for the most extreme events with greatest impact.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"49 ","pages":"Article 100788"},"PeriodicalIF":6.9,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Orographic and land-sea contrast effects in convection-permitting simulations of extreme sub-daily precipitation","authors":"Paola Mazzoglio ,&nbsp;Marco Lompi ,&nbsp;Francesco Marra ,&nbsp;Eleonora Dallan ,&nbsp;Roberto Deidda ,&nbsp;Pierluigi Claps ,&nbsp;Salvatore Manfreda ,&nbsp;Leonardo Valerio Noto ,&nbsp;Alberto Viglione ,&nbsp;Mario Raffa ,&nbsp;Paola Mercogliano ,&nbsp;Marco Marani ,&nbsp;Enrica Caporali ,&nbsp;Marco Borga","doi":"10.1016/j.wace.2025.100798","DOIUrl":"10.1016/j.wace.2025.100798","url":null,"abstract":"<div><div>Convection-permitting climate models (CPMs) represent a significant advancement compared to regional climate models, enabling more accurate simulations of extreme precipitation at fine spatial and temporal scales. Assessing the reliability of CPM projections for extreme short-duration precipitation requires understanding how well CPMs reproduce observed extremes—especially in Mediterranean regions, where such evaluations are rare. In this study, we assess the accuracy of simulations from a high-resolution CPM covering the entire Italy (VHR-PRO_IT), in reproducing sub-daily precipitation extremes. For this, we exploit observations from I²-RED, a comprehensive dataset of more than 5 000 quality-checked annual maximum time series from rain gauge observations. The comparison is performed by considering the median values of the annual maxima at 1, 3, 6, 12 and 24-h as a first step and rainfall quantiles up to 200-year return period as a second step. Our results show that model performance is influenced by both the distance from the coastline and elevation, highlighting an important role of orography and land-sea contrast in explaining CPM biases. Moreover, we find better performances when longer duration extremes are considered, while shorter durations are affected by strong underestimations, especially in coastal and low-elevation areas. These results hold significant implications for stakeholders and policymakers dealing with climate adaptation and flood risk management.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"49 ","pages":"Article 100798"},"PeriodicalIF":6.9,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection","authors":"J. Pérez-Aracil ,&nbsp;C. Peláez-Rodríguez ,&nbsp;Ronan McAdam ,&nbsp;Antonello Squintu ,&nbsp;Cosmin M. Marina ,&nbsp;Eugenio Lorente-Ramos ,&nbsp;Niklas Luther ,&nbsp;Verónica Torralba ,&nbsp;Enrico Scoccimarro ,&nbsp;Leone Cavicchia ,&nbsp;Matteo Giuliani ,&nbsp;Eduardo Zorita ,&nbsp;Felicitas Hansen ,&nbsp;David Barriopedro ,&nbsp;Ricardo García-Herrera ,&nbsp;Pedro A. Gutiérrez ,&nbsp;Jürg Luterbacher ,&nbsp;Elena Xoplaki ,&nbsp;Andrea Castelletti ,&nbsp;S. Salcedo-Sanz","doi":"10.1016/j.wace.2025.100792","DOIUrl":"10.1016/j.wace.2025.100792","url":null,"abstract":"<div><div>Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework named Spatio-Temporal Cluster-Optimized Feature Selection (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical grid cells for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"49 ","pages":"Article 100792"},"PeriodicalIF":6.9,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 role of extreme precipitation in driving the humidification of northwest China from 1961 to 2020","authors":"Lijuan Hua ,&nbsp;Linhao Zhong ,&nbsp;Zhaohui Gong ,&nbsp;Zhuguo Ma","doi":"10.1016/j.wace.2025.100797","DOIUrl":"10.1016/j.wace.2025.100797","url":null,"abstract":"<div><div>Northwest China is a typical arid and semi-arid region, and a critical climate-sensitive and vulnerable area. Over the past few decades, the region has experienced a significant humidification trend. Understanding the characteristics and trends of precipitation and atmospheric water cycles in this region is crucial for predicting the future evolution of this humidification process. Based on observational and reanalysis data, this study categorizes precipitation events in Northwest China from 1961 to 2020 into 20 class intervals. The analysis reveals that over 70 % of the total increasing trend in precipitation is due to the upper 10 % of daily precipitation events, and the rise in extreme precipitation frequency accounting for most of the observed changes, in which the contribution rate of the increase in frequency is approximately 10 times that of the increase in intensity. A 15-day backward moisture tracing analysis indicates that approximately 69 % of the moisture in the region originates from terrestrial evaporation, and 21 % contributed by local evaporation within Northwest China. Compared to light precipitation events, strong precipitation events are associated with more substantial external moisture transport, higher regional recycling ratios, and greater precipitation efficiency. Further analysis shows that over the past 60 years, the residence time of moisture associated with the precipitation events in Northwest China is 8.6 days. The extreme events above the 95th precipitation percentile have a mean moisture residence time of about 5 days, with an increasing trend that is mainly driven by the establishment and enhancement of the cross-equatorial moisture transport channel from the Indian Ocean. Concurrently, the decline in moisture contributions from terrestrial sources in the westerly belt, combined with strengthened regional evaporation, has further improved precipitation efficiency. These factors have led to a significant increase in the number of days with extreme precipitation events in Northwest China, serving as a primary driver of the humidification trend of this region.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"49 ","pages":"Article 100797"},"PeriodicalIF":6.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-task feature transfer deep learning-based tropical cyclone center estimation (MFT–TC) using geostationary satellite observations","authors":"Juhyun Lee ,&nbsp;Il-Ju Moon ,&nbsp;Jungho Im ,&nbsp;Dong-Hoon Kim ,&nbsp;Hyeyoon Jung","doi":"10.1016/j.wace.2025.100796","DOIUrl":"10.1016/j.wace.2025.100796","url":null,"abstract":"<div><div>Accurate and rapid tropical cyclone (TC) monitoring is crucial for precise forecasting and appropriate response to mitigate socio-economic damages. Geostationary satellite-based observations are the only tools that allow continuous monitoring of TCs throughout their entire lifetime, from formation to dissipation. However, owing to the diversity of TC structures, the automatic extraction of TC information using geostationary satellite-based cloud-top observations is still challenging. To address this limitation, several deep-learning-based approaches for extracting TC information have been developed. Here, we propose a novel deep learning-based TC center estimation approach using real-time geostationary satellite observations. To reduce computational costs while capturing both the entire TC structure and high-resolution spiral patterns, we propose a multi-task feature transfer deep learning-based TC center estimation (MFT–TC). This model effectively considers both the entire spiral band and focuses on specific local characteristics of TC while maintaining high computing efficiency, reducing computing costs by 47 %). Compared to the conventional single-CNN-based TC center determination model, which has been widely used in previous studies, the proposed model achieved significant improvements, with skill score increases ranging from 12 % to 39 %. Additionally, since there are significant structural differences between TCs with and without an eye, MFT–TC was evaluated under two different schemes based on the training sets: scheme 1, which uses separate training datasets depending on whether the TC has an eye (MFT–TC-div) and scheme 2, which uses all TC cases combined (MFT–TC-whl). Evaluation results showed scheme 1-based MFT–TC achieved a 14.8 % improvement over scheme 2-based MFT–TC, suggesting that separating training samples based on TC eye presence enhances the accuracy of TC center estimation. Furthermore, using the explainable artificial intelligence (XAI) approach, we demonstrated that MFT–TC efficiently captures both overall cyclonic structures and center-specific spatial characteristics to estimate the TC center accurately.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"49 ","pages":"Article 100796"},"PeriodicalIF":6.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}