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How Does a Typical Lightning Flash Estimated Thunder Day Look Like Over the Indian Region in the Period 2019–2023? 2019-2023年期间印度地区典型的闪电估计雷日是什么样的?
IF 2.8 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2025-10-22 DOI: 10.1002/joc.70165
Pavan R. Vaidya, Rakesh Ghosh, Manoj A. Domkawale, Sunil D. Pawar, V. Gopalakrishnan

Thunderstorms and lightning are the deadliest natural disasters in the Indian subcontinent. A better understanding of the regional and seasonal features of thunderstorm occurrences can provide valuable information on mitigating hazards in the region. The study demonstrated an improved method of understanding thunderstorm occurrences and thunder day (TD), an essential climate variable over the Indian region, using the total lightning data sets from the last 5 years. Total lightning data sets are used to find the Lightning flash rate density (FRD) in 0.05° grid resolution. Total lightning data are used to calculate TD, with the condition that a grid is considered to have a TD if it records at least two lightning flashes ensuring that TD corresponds to grids covering an area of approximately 15 km radius. A simultaneous comparison of lightning FRD and TD distribution is done in annual, interannual, seasonal, and monthly time scales spatially and statistically. The results reveal that the maximum TD observes 130–150 days in a year over a few places, including North-west Himalayan Jammu (Rajori), Southern peninsular Kerala (Kottayam), and north Odisha (Mayurbhanj). The north-western Himalayan foothills show the maximum TD but not the maximum FRD. The maximum number of TD in the study surpassed all the previous studies made by the station data sets where maximum TD remains in the range of 120–130 days per year. The minimum occurrences of the TD (< 15 days) show over Ladakh, a few parts of Arunachal, and the Kutch Gujarat region. Eastern part of the Indian region observes the most intense and frequent thunderstorms in terms of the number of lightning flash discharges per thunder day.

雷暴和闪电是印度次大陆最致命的自然灾害。更好地了解雷暴发生的区域和季节特征,可以为减轻该地区的灾害提供有价值的信息。该研究展示了一种改进的方法来理解雷暴发生和雷日(TD),这是印度地区一个重要的气候变量,使用了过去5年的总闪电数据集。使用总闪电数据集计算0.05°网格分辨率下的闪电闪速密度(FRD)。总闪电数据用于计算TD,如果一个网格记录了至少两次闪电,并确保TD对应的网格覆盖半径约为15公里,则认为该网格具有TD。在空间和统计上同时比较了年、年际、季节和月时间尺度上闪电FRD和TD的分布。结果表明,在喜马拉雅查谟西北部(Rajori),喀拉拉邦南部半岛(Kottayam)和奥里萨邦北部(Mayurbhanj)等几个地方,一年的最大TD为130-150天。喜马拉雅山麓西北部的TD最大,而FRD不最大。该研究的最大TD数超过了以往所有台站数据集的研究,最大TD保持在每年120-130天之间。就每个雷日的闪电次数而言,印度东部地区观察到最强烈和频繁的雷暴。
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引用次数: 0
Spatio-Temporal Change of Climate Regions in Türkiye 新疆气候区域的时空变化
IF 2.8 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2025-10-21 DOI: 10.1002/joc.70166
Mehmet Hakan Pektaş, Hakan Aksu

This study explores the climate zones' shift in Türkiye over time as a result of climate change, focusing on the period from 1954 to 2023. The analysis is based on high-resolution ERA5-Land reanalysis data and includes the record-breaking temperature extremes of the past decade. Climate zones were identified using the Ward linkage method, a hierarchical clustering algorithm approach, applied across 10-year moving windows within 30-year periods. This approach revealed eight distinct homogeneous climate regions and allowed for tracking their changes in location and extent over time. To examine these clusters, the De Martonne aridity index was used to assess temporal and spatial variability. The results indicate a notable contraction of very humid and extremely humid regions, alongside the emergence of a new arid zone in Southeastern Anatolia. The proportion of semi-arid areas increased significantly, from 9.03% in 1954–1983 to 14.62% in 1994–2023, highlighting a clear trend toward aridification. The changes in the zones were not uniform across the country. The Southeastern Anatolia, Mediterranean, Aegean, Marmara, and Central Anatolia geographical regions exhibit the most pronounced shifts, indicating region-specific impacts of climate change. The findings demonstrate the growing influence of climate change on Türkiye's hydroclimatic regime, indicating a clear tendency toward aridification and supporting broader global projections of a northward expansion of the subtropical climate zone.

本研究以1954年至2023年为研究对象,探讨了气候变化对基耶岛气候带的影响。该分析基于高分辨率ERA5-Land再分析数据,包括过去十年创纪录的极端温度。使用Ward联动方法确定气候带,这是一种分层聚类算法方法,应用于30年周期内的10年移动窗口。这种方法揭示了8个不同的同质气候区域,并允许追踪它们的位置和范围随时间的变化。为了检验这些集群,我们使用De marton干旱指数来评估时间和空间变化。结果表明,在安纳托利亚东南部出现了一个新的干旱区的同时,非常潮湿和极端潮湿地区的显著收缩。半干旱区占比从1954-1983年的9.03%显著增加到1994-2023年的14.62%,干旱化趋势明显。这些区域的变化在全国范围内并不一致。安纳托利亚东南部、地中海、爱琴海、马尔马拉和安纳托利亚中部地理区域表现出最明显的变化,表明气候变化对区域的具体影响。研究结果表明,气候变化对 rkiye水文气候状况的影响越来越大,表明有明显的干旱化趋势,并支持了更广泛的全球预测,即亚热带气候带向北扩展。
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引用次数: 0
Analysis of the Unprecedented Reduction in Antarctic Sea Ice During the Years 2022–2023 对2022-2023年南极海冰空前减少的分析
IF 2.8 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2025-10-19 DOI: 10.1002/joc.70147
Helena Barbieri de Azevedo, Fernanda Casagrande, Sofia Allende

This study analyses the unprecedented decline in Antarctic sea ice extent during 2022 and 2023. The results reveal that the minimum records in Antarctic sea ice, observed in both summer (February) and winter (September), display inhomogeneous spatial patterns and may be linked to intricate air-sea interactions, including internal climate variability and changes in wind patterns. Events such as the positive southern annular mode, intensification of the Amundsen Sea Low and La Niña played a critical role during September 2022, driving atmospheric and oceanic conditions that enhanced reduction and limited ice formation, particularly in the Weddell and Amundsen Seas. Conversely, in September 2023, characterised by a negative southern annular mode, a weakened Amundsen Sea Low and El Niño conditions, the Ross Sea exhibits pronounced negative sea ice concentration anomalies. The results suggest that internal climate variability can contribute to changes in Antarctic sea ice, primarily through wind pattern variations associated with changes in the Amundsen Sea Low. However, the recent decline of the Antarctic sea ice remains a subject of debate and lacks conclusive answers.

这项研究分析了2022年和2023年南极海冰范围前所未有的下降。结果表明,在夏季(2月)和冬季(9月)观测到的南极海冰最小记录显示出不均匀的空间格局,可能与复杂的海气相互作用有关,包括内部气候变率和风型的变化。南部正环模、阿蒙森海低压和La Niña的增强等事件在2022年9月发挥了关键作用,推动了大气和海洋条件,促进了冰的减少和限制了冰的形成,特别是在威德尔海和阿蒙森海。相反,在2023年9月,以负南环模、减弱的阿蒙森海低压和El Niño条件为特征,罗斯海表现出明显的负海冰浓度异常。结果表明,内部气候变率可以促进南极海冰的变化,主要是通过与阿蒙森海低压变化相关的风型变化。然而,最近南极海冰的减少仍然是一个争论的话题,缺乏确凿的答案。
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引用次数: 0
Northerly and Southerly Wind Events in the Eastern Bay of Campeche, Mexico 墨西哥坎佩切东湾的北风和南风事件
IF 2.8 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2025-10-19 DOI: 10.1002/joc.70134
Geimond Jarumi Antemate-Velasco, Gabriela Athié, Mark Marín-Hernández, Ruth Cerezo-Mota, Jorge A. Kurczyn, Alejandro Granados-Barba

Cold surges entering the Gulf of Mexico (GoM) generate northerly winds (Norte events), preceded by warm southerly winds (Surada events). These events were characterised in the Eastern Bay of Campeche (EBC) using ERA5 reanalysis data for 2002–2024. Both Norte and Surada events exhibited distinct intensity and predominant direction patterns in the EBC, compared with the broader GoM. Therefore, the separate identification of both events specific to the EBC was proposed. A minimum wind magnitude threshold (E) was determined for the EBC, above which events were considered sufficiently intense to be classified as Norte (2.4 ms−1) or Surada (1.3 ms−1) events in this region. It was shown that Norte events lasting < 24 h were associated with more intense Surada events; overall, 70.1% of Surada events exceeded the intensity of the corresponding Norte events, with a mean difference of 1.8 m s−1. Empirical Orthogonal Functions analysis for the southern GoM revealed that, in ~30% of the cases (Modes 2 and 3), cold fronts moved with a significant zonal component in the southern GoM. This pattern was associated with intense Surada events and weak or even absent Norte events in the EBC. Mode 1 accounted for more than 63% of the variability and corresponded to Surada events followed by Norte events with similar behaviour in the entire region. This study demonstrates a distinct wind behaviour associated with cold surges on the eastern and western sides of the Bay of Campeche in the southern GoM. Norte and Surada events can negatively impact local marine and fisheries sectors, as wind intensity forecasts are often generalised for the entire GoM and can be unreliable in the EBC. These events are critical for coastal circulation and influence ecosystem dynamics, fisheries, and key oceanographic processes such as coastal-trapped waves, upwellings, and wave fields.

进入墨西哥湾(GoM)的寒潮产生北风(Norte事件),之前是温暖的南风(Surada事件)。利用2002-2024年ERA5再分析数据,在坎佩切东湾(EBC)对这些事件进行了表征。与墨西哥湾相比,北纬和苏拉达事件在东太平洋地区表现出明显的强度和主导的方向模式。因此,建议对EBC特有的两个事件进行单独识别。确定了EBC的最小风级阈值(E),高于该阈值的事件被认为足够强烈,可归类为该地区的Norte (2.4 ms−1)或Surada (1.3 ms−1)事件。结果表明,持续24 h的Norte事件与更强烈的Surada事件相关;总体而言,70.1%的Surada事件的强度超过了相应的Norte事件,平均差异为1.8 m s−1。对墨西哥湾南部的经验正交函数分析表明,在约30%的情况下(模态2和模态3),冷锋在墨西哥湾南部以显著的纬向分量移动。这种模式与EBC中强烈的Surada事件和弱的甚至不存在的Norte事件有关。模态1占变率的63%以上,与整个地区的Surada事件相对应,随后是Norte事件,具有相似的行为。这项研究表明,墨西哥湾南部坎佩切湾的东西两侧有一种与寒潮相关的独特风行为。Norte和Surada事件可能会对当地海洋和渔业部门产生负面影响,因为风力强度预测通常是针对整个墨西哥湾的,在EBC中可能不可靠。这些事件对海岸环流至关重要,并影响生态系统动力学、渔业和关键的海洋学过程,如海岸困波、上升流和波场。
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引用次数: 0
Evaluation of the Linkage Between the Variations in Jet Stream Shape and Surface Air Temperature From CMIP6 CMIP6对急流形态变化与地面气温关系的评价
IF 2.8 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2025-10-19 DOI: 10.1002/joc.70164
Wangchu Zhou, Shan Zhao, Anqi Liu, Ning Wang, Danqing Huang

Jet streams are key components of mid- to high-latitude atmospheric circulation, strongly influencing global weather and climate through heat and momentum transport. Based on our previous work on jet stream shape variability and its climatic impacts, this study assesses the ability of CMIP6 models to simulate the relationship between jet stream shapes and surface air temperature (SAT), using both AMIP and historical runs. Based on weighted Taylor scores, interannual variability skill (IVS) scores and jet stream indices, AMIP simulations generally outperform historical runs in representing both jet streams and SAT. Most models capture SAT anomalies over East Asia and North America, underscoring a robust link between regional temperature extremes and distinct jet patterns. They also reproduce sea level pressure anomalies resembling the Arctic Oscillation (AO), suggesting that AO-like circulation is a key driver of temperature extremes across jet groups. In addition, horizontal temperature advection anomalies closely match SAT anomalies, highlighting their role as a primary term in the thermodynamic equation shaping temperature distributions in critical regions.

急流是中高纬度大气环流的重要组成部分,通过热量和动量输送对全球天气和气候产生强烈影响。基于我们之前关于急流形状变率及其气候影响的工作,本研究评估了CMIP6模式利用AMIP和历史运行模拟急流形状与地面气温(SAT)之间关系的能力。基于加权Taylor分数、年际变率技能(IVS)分数和急流指数,AMIP模拟在表现急流和SAT方面总体上优于历史运行。大多数模式捕捉东亚和北美的SAT异常,强调了区域极端温度与不同的急流模式之间的强大联系。他们还重现了类似北极涛动(AO)的海平面压力异常,表明类似AO的环流是急流群温度极端的关键驱动因素。此外,水平温度平流异常与SAT异常密切匹配,突出了它们在形成临界区域温度分布的热力学方程中的主要作用。
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引用次数: 0
Impact of the Atlantic Zonal Mode on Ocean Surface Waves in the Tropical Indo-West Pacific During Boreal Summer 大西洋纬向模态对热带印度洋-西太平洋夏季海面波的影响
IF 2.8 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2025-10-17 DOI: 10.1002/joc.70156
Vijay Pottapinjara, Meenakshi Sreejith, P. G. Remya, Sudheer Joseph, T. M. Balakrishnan Nair

Ocean surface waves, which are gravity waves primarily driven by surface winds, significantly influence air-sea interactions, coastal ecology, and marine operations, among others. Tropical climate variability is shaped by ocean–atmosphere coupled modes such as the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), which also influence ocean surface waves through changes in surface winds. This study examines the impact of the Atlantic Zonal Mode (AZM), a less-studied climate driver, on the interannual variability of ocean surface waves in the tropical Indo-West Pacific. A warm AZM induces surface wind anomalies that oppose the climatological winds. As a result, it reduces the wind sea by 0.2–0.3 m in the South China Sea (SCS) and by 0.1–0.2 m each in the Philippine Sea (PS) and the Bay of Bengal (BoB), and causes Southern Ocean swells to dominate by 0.1–0.2 m in the Arabian Sea. It is substantiated by consistent changes in the mean wave period and energy flux into waves. A warm AZM can favour marine operations in the SCS, PS, and BoB. On the contrary, a cold AZM is not observed to cause any statistically significant change in wave activity during the analysis period, highlighting an asymmetric impact of the AZM, the reasons for which require further investigation using wave model sensitivity experiments. Given the future projections of a weakening Atlantic cold tongue under global warming, the effects of warm AZM events may intensify, potentially leading to further reductions in tropical Indo-West Pacific wave activity. These findings highlight the importance of incorporating AZM impacts in wave climate assessments.

海洋表面波主要是由地面风驱动的重力波,对海气相互作用、沿海生态和海洋作业等产生重大影响。热带气候变率是由El Niño-Southern涛动(ENSO)和印度洋偶极子(IOD)等海洋-大气耦合模式塑造的,它们也通过地面风的变化影响海洋表面波。本研究考察了大西洋纬向模态(AZM)对热带印度洋-西太平洋海洋表面波年际变化的影响,AZM是一个研究较少的气候驱动因素。温暖的AZM引起与气候风相反的地面风异常。其结果是南海(SCS)、菲律宾海(PS)和孟加拉湾(BoB)的风海分别减少0.2 ~ 0.3 m和0.1 ~ 0.2 m,导致南大洋在阿拉伯海占主导地位,减少0.1 ~ 0.2 m。平均波动周期和成波能量通量的一致变化证实了这一点。温暖的AZM有利于南海、南海和南海的海上作业。相反,在分析期间,未观察到冷AZM引起任何统计上显著的波活动变化,这突出了AZM的不对称影响,其原因需要使用波模型灵敏度实验进一步研究。考虑到未来在全球变暖下大西洋冷舌减弱的预测,温暖的AZM事件的影响可能会加剧,可能导致热带印度洋-西太平洋波活动进一步减少。这些发现突出了在波浪气候评估中纳入AZM影响的重要性。
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引用次数: 0
A Machine Learning Approach for Improving the Accuracy of Gridded Precipitation With Uncertainty Quantification 一种提高不确定量化网格降水精度的机器学习方法
IF 2.8 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2025-10-17 DOI: 10.1002/joc.70161
Vinh Ngoc Tran, Manh-Hung Le, Tam V. Nguyen, Trong Dieu Hien Le, Hung T. T. Nguyen, Hong Xuan Do, Binh Quang Nguyen, Doan Van Binh, Tu Hoang Le, Hung Thanh Pham, Hoang Tran, Thanh Duc Dang, John D. Bolten, Tan Phan-Van, Thanh Ngo-Duc, Venkataraman Lakshmi

The proliferation of gridded precipitation datasets produced through diverse methods has led to user confusion due to discrepancies in values for identical locations and times, underscoring the inherent uncertainty in current precipitation data. However, quantifying this uncertainty remains challenging since most datasets are deterministic and offer no easy mechanism for such quantification. This study proposes a novel machine learning (ML) approach that involves merging multiple satellite-based gridded precipitation datasets and rain gauge observations. The ultimate goal is to create an improved high-resolution (0.1°) daily precipitation product for Vietnam (2001–2010) that includes uncertainty quantification. By combining eXtreme gradient boosting (XGB) with quantile regression, we generate both deterministic estimates along with the associated uncertainty intervals. The resulting dataset, VNpu (Vietnam Precipitation with Uncertainty), outperforms individual input products, benchmark interpolation methods and an existing gauge-based product (VnGP), particularly for heavy and extreme rainfall events. The VNpu provides spatiotemporally varying uncertainty ranges, with larger uncertainties in sparse gauge coverage areas. The analysis of ML interpretability reveals the complementary nature of multiple precipitation inputs (IMERG and MERRA2) and auxiliary topographic information. This study not only highlights the necessity of gridded precipitation products with uncertainty estimates but also demonstrates the value of ML approaches in developing improved precipitation products for data-scarce regions.

通过不同方法生成的网格化降水数据集的激增导致用户困惑,因为相同地点和时间的值存在差异,强调了当前降水数据固有的不确定性。然而,量化这种不确定性仍然具有挑战性,因为大多数数据集是确定性的,并且没有提供这种量化的简单机制。本研究提出了一种新的机器学习(ML)方法,该方法涉及合并多个基于卫星的网格降水数据集和雨量计观测数据。最终目标是为越南(2001-2010)创建一个改进的高分辨率(0.1°)日降水产品,其中包括不确定性量化。通过将极端梯度增强(XGB)与分位数回归相结合,我们生成了确定性估计以及相关的不确定性区间。结果数据集VNpu(越南降水不确定性)优于单个输入产品,基准插值方法和现有的基于测量的产品(VnGP),特别是对于强降雨和极端降雨事件。VNpu提供了时空变化的不确定性范围,在稀疏测量覆盖区域不确定性较大。ML可解释性分析揭示了多个降水输入(IMERG和MERRA2)和辅助地形信息的互补性。这项研究不仅强调了不确定性估算的网格化降水产品的必要性,而且还证明了ML方法在为数据稀缺地区开发改进降水产品方面的价值。
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引用次数: 0
Comprehensive Evaluation of Precipitation Reanalysis Products and CMIP6 Models Using Statistical and Machine Learning Techniques With Nature-Inspired Optimization 基于统计和机器学习技术的降水再分析产品和CMIP6模型的综合评价
IF 2.8 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2025-10-17 DOI: 10.1002/joc.70159
Sourav Choudhary, Santosh Murlidhar Pingale, Deepak Khare, Ruchir Patidar, Radha Krishan

A comprehensive strategy that incorporates trend analysis, machine learning (ML), and climate model review is needed to improve water resource forecasts and evaluate hydroclimatic variability. The present study effectively combined various forms of categorical and continuous performance metrics for the CMIP6 and the reanalysis datasets in the Upper Godavari Sub-basin area (UGSB) (India). MERRA2 reanalysis datasets demonstrated the highest accuracy for precipitation forecasting, achieving a POD of 0.82 and CSI of 0.71, while JRA-55 closely followed with a CSI of 0.69. CMIP6 models exhibited overestimation tendencies, with a mean FAR of 0.34, highlighting their limitations in capturing precipitation extremes. Thereafter, to understand the long-term variability of the best reanalysis product, trend analysis was also performed using the Mann-Kendall test, Pettitt's test, Van Neumann ratio (VNR), and Innovative Trend Analysis (ITA). This analysis revealed properly the spatial variability of the precipitation, showing increasing (1.5–2.3 mm/year) and decreasing rates for various stations inside the UGSB. Thereafter, the temporal frequency and the intensity were captured by the Continuous Wavelet Transform (CWT) analysis, which further identified shifts in hydroclimatic variability towards higher frequencies after 2000. Thereafter, the prediction accuracy of prediction datasets of various ML models, which included Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and XGBoost models, were optimised by The Harris Hawks Optimization (HHO) algorithm, and the best optimised model, RF-HHO, showed reducing RMSE to 4.92 at Ambajogai, 4.81 at Bodhegaon, and 5.21 at Ranjni. The study highlights the importance of combining reanalysis products, trend analysis, and optimised ML models to improve future precipitation predictions and support effective water resource management.

需要一个综合趋势分析、机器学习(ML)和气候模型审查的综合战略来改进水资源预测和评估水文气候变率。本研究有效地将CMIP6的各种形式的分类和连续性能指标与印度上戈达瓦里亚盆地地区(UGSB)的再分析数据集结合起来。MERRA2再分析数据集的降水预报精度最高,POD为0.82,CSI为0.71,JRA-55紧随其后,CSI为0.69。CMIP6模式呈现高估趋势,平均FAR为0.34,凸显了其在捕获极端降水方面的局限性。之后,为了了解最佳再分析产品的长期变动性,还使用Mann-Kendall检验、Pettitt检验、Van Neumann比率(VNR)和创新趋势分析(ITA)进行趋势分析。该分析较好地揭示了降水的空间变异性,显示出UGSB内各站点降水速率增加(1.5 ~ 2.3 mm/年)和减少的趋势。在此基础上,利用连续小波变换(CWT)分析了2000年以后水文气候变化的时间频率和强度,进一步确定了其向高频方向的变化。随后,采用Harris Hawks Optimization (HHO)算法对随机森林(RF)、多层感知器(MLP)、长短期记忆(LSTM)和XGBoost模型等多种机器学习模型的预测数据集进行了预测精度优化,优化后的RF-HHO模型在Ambajogai、Bodhegaon和Ranjni的RMSE分别降至4.92、4.81和5.21。该研究强调了将再分析产品、趋势分析和优化ML模型相结合的重要性,以改进未来降水预测并支持有效的水资源管理。
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引用次数: 0
The Role of Synoptic-Scale Weather Systems on the Ohio River Valley, USA, Snowfall Variations and Trends 天气尺度天气系统在美国俄亥俄河流域的作用、降雪变化和趋势
IF 2.8 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2025-10-17 DOI: 10.1002/joc.70148
Harmony L. Guercio, Zachary J. Suriano

Over the last 75 years, the Ohio River Valley has experienced significant changes in snowfall frequency, timing and magnitude that resulted in meaningful societal impacts. Through the application of a synoptic climatology, this study furthers the understanding of the synoptic-scale atmospheric forcings responsible for snowfall and explains how their variations influenced observed snowfall trends from 1948 to 2021. Three dominant atmospheric environments were identified as being responsible for snowfall in this region: (1) types resulting in northwesterly flow conductive to lake-effect snowfall primarily affecting the northeast region, (2) types with mid-latitude cyclones generating wrap-around or frontal snowfall in the central and southern regions and (3) types with high-pressure centers over or adjacent to the region producing lighter snowfall due likely to smaller-scale processes. Analysis revealed significant temporal trends in the seasonal frequencies of specific snowfall types that, in combination with changes to daily snowfall magnitudes over time, result in significant trends to seasonal snowfall totals per type and align with previously reported domain-wide snowfall trends. Further, many of the snowfall-producing weather types' inter-annual frequencies were significantly correlated to the phase of teleconnection indices. When the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) were negatively phased, more than half of the weather types were significantly more frequent due to the larger-scale atmosphere providing suitable conditions for the development of the snowfall-producing types. These findings underscore the importance of understanding the integration of weather systems across scales and how changes manifest across a cascade of physical forcing mechanisms.

在过去的75年里,俄亥俄河谷经历了降雪频率、时间和规模的重大变化,导致了有意义的社会影响。通过应用天气气候学,本研究进一步了解了导致降雪的天气尺度大气强迫,并解释了它们的变化如何影响1948 - 2021年观测到的降雪趋势。确定了三种主要的大气环境对该地区的降雪负责:(1)导致西北气流导致主要影响东北地区的湖效应降雪的类型;(2)中纬度气旋在中部和南部地区产生环绕或锋面降雪的类型;(3)高压中心在该地区上方或附近由于可能发生较小规模的过程而产生较小降雪的类型。分析揭示了特定降雪类型的季节频率的显著时间趋势,结合日降雪量随时间的变化,导致每种类型的季节性降雪总量的显著趋势,并与先前报告的全域降雪趋势一致。此外,许多产雪天气类型的年际频率与遥相关指数的相位显著相关。当北极涛动(AO)和北大西洋涛动(NAO)为负相位时,半数以上的天气类型明显增加,这是由于大尺度大气为产雪类型的发展提供了适宜的条件。这些发现强调了理解跨尺度天气系统的整合以及变化如何在一系列物理强迫机制中显现的重要性。
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引用次数: 0
Rapid 21st Century Warming in the Southern Subtropical Indian Ocean Driven by Altered Inter-Basin Connections 21世纪南亚热带印度洋快速变暖:盆地间连接改变的驱动
IF 2.8 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Pub Date : 2025-10-17 DOI: 10.1002/joc.70157
C. K. Sajidh, Abhisek Chatterjee, Raghu Murtugudde, Michael J. McPhaden, S. S. C. Shenoi, P. N. Vinayachandran

The Indian Ocean has been warming rapidly over the last few decades. However, this warming has not been uniform, with the southern subtropical Indian Ocean (STIO, 15°S–35°S) cooling until the late 20th century and then warming abruptly. We show that the Indonesian Throughflow (ITF) into the Indian Ocean exhibits strong decadal variability with a weaker negative trend over the recent decades, and hence cannot explain the continued heat gain by the upper ocean of the STIO at a rate of 0.41 (± 0.02) × 1022 J/decade. An increased ITF during the hiatus in global surface warming initiated the STIO warming, resulting in a weaker Mascarene high and its decoupling from the Southern Ocean atmospheric variability. This enhanced Tasman inflow into the Indian Ocean and weakened Agulhas outflow offsetting the recently weakened ITF, caused a positive feedback that continued to warm the upper ocean of the STIO.

在过去的几十年里,印度洋一直在迅速变暖。然而,这种变暖并不是均匀的,南亚热带印度洋(STIO, 15°S - 35°S)直到20世纪末才变冷,然后突然变暖。研究表明,进入印度洋的印尼通流(ITF)在近几十年表现出较强的年代际变率和较弱的负趋势,因此不能解释印度洋上层海洋以0.41(±0.02)× 1022 J/ 10年的速率持续增加热量。在全球表面变暖中断期间,ITF的增加引发了STIO变暖,导致Mascarene高变弱并与南大洋大气变率脱钩。流入印度洋的塔斯曼暖流增强,流入印度洋的阿古拉斯暖流减弱,抵消了最近减弱的ITF,形成了一个正反馈,继续使热带副热带环流上层海洋变暖。
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International Journal of Climatology
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