Weather and Climate Extremes最新文献_第8页

Corrigendum to “Impact of urbanization on regional extreme precipitation trends observed at China national station network” [Weather and Clim. Extrem. 48 (2025) 100760] “城市化对中国国家台网观测到的区域极端降水趋势的影响”的勘误表[极端天气与气候48 (2025)100760]

IF 6.9 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-09-01 DOI: 10.1016/j.wace.2025.100779

Suonam Kealdrup Tysa , Guoyu Ren , Panfeng Zhang , Siqi Zhang

引用次数: 0

Beyond simple flash drought detection: An operational index to analyse the development speed of droughts at global scale 超越简单的突发性干旱探测：在全球范围内分析干旱发展速度的操作指数

IF 6.9 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-09-01 DOI: 10.1016/j.wace.2025.100800

Carmelo Cammalleri , Samuele Maffei , Alessandro F.G. Ceppi , Davide Bavera , Guido Fioravanti , Mercedes Peretti , Pablo C. Spennemann , Andrea Toreti

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.

由于干旱预警和管理系统面临的挑战，近年来对突发性干旱的研究兴趣日益浓厚。由于突发性干旱的主要特征是初始发展迅速，我们首先实现了一个捕捉这一特征的新指数，然后对不同的现有指数进行了测试。拟议的指数并不仅仅旨在捕捉突发性干旱，但它可以用来描述所有类型干旱的初始发展速度。使用基于专家的半定量方法对选定的一组事件进行分类，并用于评估指标。主要发现指向事件的前30年（约30天）的初始开发速率（IDR3）是一个可靠的度量。对该指数的全球分析表明：1)东南亚和亚马逊流域是平均发展速度较快的热点地区；2)平均而言，澳大利亚和美国西部以缓慢事件为特征。此外，我们的分析确定了IDR3中强烈的季节性成分，与气候和环境因素（如年平均降水量、温度、土壤湿度和植被质量）有一些明确的关系。土壤含水量和气温高，植被数量少，似乎是控制发展速度的主要变量之一。根据这些结果，IDR3似乎是一个适合干旱预测的指数，旨在预测快速发展的干旱的发生。

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引用次数: 0

Statistical modelling of extreme daily rainfall over Aotearoa New Zealand 新西兰奥特罗阿地区极端日降雨量的统计模型

IF 6.9 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-08-09 DOI: 10.1016/j.wace.2025.100799

Suzanne Rosier , Shalin Shah , Greg Bodeker , Trevor Carey-Smith , David Frame , Dáithí A. Stone

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%/

^{\circ}

C in most of the country to 8%/

^{\circ}

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.

新西兰的极端降雨，以及如何在气候变暖的过程中最好地描述这些极端降雨的预期变化，利用非常大的区域气候模式模拟集合，在五个不同的气候变化“时代”（工业化前、现在和未来三个高于工业化前1.5°C、2.0°C和3.0°C的状态）进行了研究。探讨了非平稳广义极值（GEV）模型的不同构造，以确定哪种模型能够以最小的模型复杂性提供最准确的极端降雨估计。不同的GEV模型构造不同的参数（位置、尺度和形状）的数量，这些参数被认为随着气候变化而变化，总结为与南半球平均地表温度的线性依赖。非平稳性也以不同的方式进行了探索，在五个“时代”中分别安装了一个固定的GEV。这些不同的模型应用于全国八个地点的年最大单日降雨量，这些地点被选为全国各种降雨制度的广泛代表。在具有公平但不是巨大的样本量的情况下，例如具有长期历史记录的模式，其中只有位置和规模而不是形状和参数随变暖而变化的模式具有最严格的抽样不确定性，而不会引入实质性偏差。根据这个GEV模型，在所有8个地点，每100年一次的降雨量都随着变暖而增加，幅度从该国大部分地区的5%/°C到北部地区的8%/°C不等。这种变化是由位置参数的增加引起的，尺度参数只有比例增加，这与人为增加比湿度导致的极端降雨增加相一致。

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引用次数: 0

Distinguishing environmental controls on strong vs. extreme wind gusts 区分强风和极端阵风的环境控制

IF 6.9 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-07-26 DOI: 10.1016/j.wace.2025.100788

Greeshma Surendran , Steven Sherwood , Jason Evans , Moutassem El Rafei , Andrew Dowdy , Fei Ji , Andrew Brown

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” (

\geq 18 m/s

) vs. “extreme” (

\geq 25 m/s

) 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.

阵风的统计和理论模型可能被更常见的强事件所主导，而不是罕见但具有破坏性的极端事件。我们通过将澳大利亚新南威尔士州（NSW）的六个极端阵风案例研究与统计和机器学习（随机森林）模型相结合来解决这个问题，以确定在20年数据集中区分“强”（≥18m/s）和“极端”（≥25m/s）阵风事件的环境因素。BARRA-SY高分辨率区域再分析用于增强现场观测，并为评估提供模型阵风速度诊断，以及环境预测指标。所有极端风的情况都与深层对流有关，通常被组织成线性系统。随机森林模型预测强风的准确率达到89%，其中阵风诊断和环境背景风速是最高的预测指标。在区分极端阵风和强烈阵风方面，该模型的准确率为79%，但误报率很高。统计和机器学习分析都强调对流不稳定性指标——最不稳定对流可用势能（MUCAPE）、Derecho复合参数（DCP）和k_index——是极端阵风的关键预测指标。因此，BARRA-SY阵风速度诊断告知强风阵风，但不告知极端情况，这取决于它忽略的变量。然而，不稳定性测量也不能完美地预测极端阵风，因为它们无法捕捉到在一些案例研究中看到的风暴触发条件。这些发现表明，驱动极端阵风的因素与驱动强烈但不那么极端的阵风的因素有很大不同。因此，综合考虑所有强风的统计分析或预测模型很可能无法揭示造成影响最大的最极端事件的环境因素。

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引用次数: 0

Orographic and land-sea contrast effects in convection-permitting simulations of extreme sub-daily precipitation 在允许对流的极端次日降水模拟中的地形和陆海对比效应

IF 6.9 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-07-26 DOI: 10.1016/j.wace.2025.100798

Paola Mazzoglio , Marco Lompi , Francesco Marra , Eleonora Dallan , Roberto Deidda , Pierluigi Claps , Salvatore Manfreda , Leonardo Valerio Noto , Alberto Viglione , Mario Raffa , Paola Mercogliano , Marco Marani , Enrica Caporali , Marco Borga

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.

与区域气候模式相比，允许对流的气候模式（cpm）取得了重大进展，能够在精细时空尺度上更精确地模拟极端降水。评估极端短持续时间降水CPM预估的可靠性需要了解CPM如何很好地再现观测到的极端情况，特别是在地中海地区，这种评估很少。在这项研究中，我们评估了覆盖整个意大利的高分辨率CPM （VHR-PRO_IT）模拟在再现亚日极端降水方面的准确性。为此，我们利用了I2-RED的观测数据，I2-RED是一个综合数据集，由5000多个经过质量检查的雨量计观测的年度最大时间序列组成。将1、3、6、12和24小时的年最大值的中位数作为第一步，将200年回归期的降水分位数作为第二步进行比较。我们的研究结果表明，模式性能受到距离海岸线和海拔高度的影响，突出了地形和海陆对比在解释CPM偏差方面的重要作用。此外，我们发现当考虑较长的持续时间极值时，性能会更好，而较短的持续时间受到严重低估的影响，特别是在沿海和低海拔地区。这些结果对处理气候适应和洪水风险管理的利益相关者和决策者具有重要意义。

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引用次数: 0

Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection 识别热浪的关键驱动因素：极端事件检测的新时空框架

IF 6.9 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-07-26 DOI: 10.1016/j.wace.2025.100792

J. Pérez-Aracil , C. Peláez-Rodríguez , Ronan McAdam , Antonello Squintu , Cosmin M. Marina , Eugenio Lorente-Ramos , Niklas Luther , Verónica Torralba , Enrico Scoccimarro , Leone Cavicchia , Matteo Giuliani , Eduardo Zorita , Felicitas Hansen , David Barriopedro , Ricardo García-Herrera , Pedro A. Gutiérrez , Jürg Luterbacher , Elena Xoplaki , Andrea Castelletti , S. Salcedo-Sanz

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.

热浪是产生重大社会和环境影响的极端大气事件。预测这些极端事件仍然具有挑战性，因为它们与大尺度大气和气候变量的复杂相互作用难以用传统的统计和动力学模型捕捉。这项工作提出了极端气候事件中驾驶员识别的一般方法。提出了一种时空聚类优化特征选择框架（STCO-FS），将聚类算法与集成进化算法相结合，识别关键的即时（短期）HW驱动因素。该框架分析时空数据，通过为每个变量分组相似的地理网格单元来降低维度，并在空间和时间域中开发驾驶员选择，确定预测变量与HW发生之间的最佳时间滞后。该方法已应用于意大利阿达河流域的HWs分析。该方法有效地识别了影响该地区HWs的重要变量。这项研究可能会增强我们对人力资源驱动因素和可预测性的理解。

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引用次数: 0

The role of extreme precipitation in driving the humidification of northwest China from 1961 to 2020 1961 - 2020年极端降水对西北地区加湿的驱动作用

IF 6.9 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-07-23 DOI: 10.1016/j.wace.2025.100797

Lijuan Hua , Linhao Zhong , Zhaohui Gong , Zhuguo Ma

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.

西北地区是典型的干旱半干旱区，是气候敏感和脆弱地区。在过去的几十年里，该地区经历了一个显著的加湿趋势。了解该地区降水和大气水循环的特征和趋势，对预测该湿化过程的未来演变至关重要。根据1961 ~ 2020年西北地区降水的观测资料和再分析资料，将降水事件划分为20个类区间。分析表明，总降水增加趋势的70%以上是由日降水事件的前10%引起的，极端降水频率的增加占观测到的变化的大部分，其中频率增加的贡献率约为强度增加的10倍。15 d后向水汽示踪分析表明，该地区约69%的水汽来自陆地蒸发，21%来自西北地区的局地蒸发。与轻降水事件相比，强降水事件具有更大的外部水汽输送量、更高的区域再循环率和更高的降水效率。进一步分析表明，近60 a来，西北地区与降水事件相关的水汽滞留时间为8.6 d。第95个降水百分位以上的极端事件平均水汽停留时间约为5 d，且水汽停留时间呈增加趋势，主要受印度洋跨赤道水汽输送通道的建立和增强驱动。同时，西风带陆源水汽贡献的减少，加上区域蒸发的增强，进一步提高了降水效率。这些因素导致西北地区极端降水事件日数显著增加，是该地区湿化趋势的主要驱动因素。

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引用次数: 0

Multi-task feature transfer deep learning-based tropical cyclone center estimation (MFT–TC) using geostationary satellite observations 基于多任务特征转移深度学习的地球同步卫星热带气旋中心估计

IF 6.9 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-07-16 DOI: 10.1016/j.wace.2025.100796

Juhyun Lee , Il-Ju Moon , Jungho Im , Dong-Hoon Kim , Hyeyoon Jung

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.

准确和快速的热带气旋监测对于精确预报和适当应对以减轻社会经济损害至关重要。基于地球静止卫星的观测是唯一能够在tc的整个生命周期（从形成到消散）持续监测它们的工具。然而，由于温度结构的多样性，利用静止卫星云顶观测自动提取温度信息仍然是一个挑战。为了解决这一限制，已经开发了几种基于深度学习的方法来提取TC信息。在这里，我们提出了一种基于深度学习的TC中心估计方法，该方法使用实时地球静止卫星观测。为了降低计算成本，同时捕获整个TC结构和高分辨率螺旋模式，我们提出了一种基于多任务特征转移深度学习的TC中心估计（MFT-TC）。该模型既有效地考虑了整个螺旋带，又关注TC的特定局部特征，同时保持了较高的计算效率，计算成本降低了47%)。与以往研究中广泛使用的传统的基于单一cnn的TC中心确定模型相比，本文提出的模型取得了显著的改进，技能得分提高幅度在12% - 39%之间。此外，由于有眼睛和没有眼睛的TC之间存在显著的结构差异，MFT-TC在基于训练集的两种不同方案下进行评估：方案1根据TC是否有眼睛使用单独的训练数据集（MFT-TC -div），方案2使用所有TC情况的组合（MFT-TC -whl）。评价结果表明，基于方案1的MFT-TC比基于方案2的MFT-TC提高了14.8%，表明基于TC眼在场的训练样本分离提高了TC中心估计的准确性。此外，利用可解释人工智能（XAI）方法，我们证明了MFT-TC有效地捕获了整体气旋结构和中心特定的空间特征，以准确估计TC中心。

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引用次数: 0

Decadal swing in NAO variability and summertime heat extremes in South Korea over recent decades 近几十年来NAO变率和韩国夏季极端高温的年代际变化

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-07-10 DOI: 10.1016/j.wace.2025.100795

Jung-Hee Ryu , Song-Lak Kang

South Korea experienced a lull in heatwave occurrences from the late 1990s to the early 2010s (referred to as “P1”), followed by significant heatwaves in the early 2010s (referred to as “P2”). To understand this decadal variation despite ongoing global warming, we examined the link between heatwaves in South Korea and decadal shifts in North Atlantic Oscillation (NAO) variability. Planetary-scale waves originating from Greenland in response to the NAO influence atmospheric circulation across Europe, Northeast Asia (including the Korean Peninsula), and North America, primarily on interannual scales. Specifically, positive NAO phases enhance anticyclonic circulations over the Korean Peninsula, increasing surface temperatures and heatwave frequency. During P1, the NAO exhibited a declining trend and reduced interannual variability, influenced by remote tropical Pacific forcing. Our results also suggested the potential influence of the Atlantic Ocean forcing on the rising trend of the NAO during P2, alongside a phase shift in tropical Pacific forcing. These findings highlight the role of large-scale climate variability—shaped by complex interactions among NAO trends, tropical Pacific forcing, and North Atlantic forcing, with potential contributions from anthropogenic forcing—in driving the decadal fluctuations in local heat extremes, particularly in South Korea.

从20世纪90年代末到2010年代初，韩国经历了一段热浪的间歇期（称为“P1”），随后在2010年代初出现了严重的热浪（称为“P2”）。尽管全球持续变暖，但为了理解这种年代际变化，我们研究了韩国热浪与北大西洋涛动（NAO）变率的年代际变化之间的联系。响应NAO，源自格陵兰岛的行星尺度波主要在年际尺度上影响欧洲、东北亚（包括朝鲜半岛）和北美的大气环流。具体而言，正的NAO相位增强了朝鲜半岛的反气旋环流，增加了地表温度和热浪频率。在P1期间，受遥远的热带太平洋强迫影响，NAO呈下降趋势，年际变率减小。我们的研究结果还表明大西洋强迫在P2期间对NAO上升趋势的潜在影响，以及热带太平洋强迫的相移。这些发现强调了由NAO趋势、热带太平洋强迫和北大西洋强迫之间的复杂相互作用形成的大尺度气候变化，以及人为强迫的潜在贡献，在驱动当地极端高温的年代际波动方面的作用，特别是在韩国。

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引用次数: 0

The contribution of climate drivers to compound drought and extreme temperature events increased in recent decades 近几十年来，气候驱动因素对复合干旱和极端温度事件的贡献有所增加

IF 6.1 1区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Climate Extremes

Pub Date : 2025-07-05 DOI: 10.1016/j.wace.2025.100793

Siyi Li , Bin Wang , De Li Liu , Chao Chen , Puyu Feng , Alfredo Huete , Keyu Xiang , Qiang Yu

Compound climate extremes severely impact crops more than individual events. Understanding historical changes in compound extreme events and their drivers is crucial for managing climate risks and protecting crop survival. Using a hybrid biophysical-statistical modeling approach, we investigated the connections between large-scale climate drivers of El Niño Southern Oscillation (ENSO)/Indian Ocean Dipole (IOD) and compound drought and extreme temperature (DET) across Australia's wheat belt from 1900 to 2020. DET in eastern Australia's wheat belt was more responsive to ENSO/IOD compared to the west. El Niño and positive IOD phases intensified DET and increased the probability of high-intensity DET, whereas La Niña and negative IOD reduced them. Probabilities of high-intensity DET have exhibited a temporal increase, during the strong El Niño phase and the positive IOD phase. Our findings highlight the need to assess the spatial-temporal response of compound events to climate drivers for effective early warning and mitigation.

复合极端气候对农作物的影响比个别事件更严重。了解复合极端事件的历史变化及其驱动因素对于管理气候风险和保护作物生存至关重要。采用混合生物物理-统计建模方法，研究了1900 - 2020年厄尔尼诺Niño南方涛动(ENSO)/印度洋偶极子（IOD）大尺度气候驱动因子与澳大利亚小麦带复合干旱和极端温度（DET）之间的关系。与西部相比，澳大利亚东部小麦带的DET对ENSO/IOD的响应更大。El Niño和IOD阳性期增强了DET，增加了高强度DET的概率，而La Niña和IOD阴性期则降低了DET的概率。在强El Niño期和正IOD期，高强度DET的概率在时间上有所增加。我们的研究结果强调需要评估复合事件对气候驱动因素的时空响应，以实现有效的早期预警和缓解。

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引用次数: 0