Weather and Climate Extremes最新文献

{"title":"Advancing global hindcast of extreme sea levels: Insights from a 65-year study","authors":"Pengcheng Wang ,&nbsp;Natacha B. Bernier","doi":"10.1016/j.wace.2025.100805","DOIUrl":"10.1016/j.wace.2025.100805","url":null,"abstract":"<div><div>Extreme sea levels (ESLs) are a leading cause of coastal hazards. Assessing risks and associated impacts requires reliable ESL statistics. These are typically derived from long but sparsely available tide-gauge records or through records obtained from long hindcasts. Here we present a 65-year global hindcast of hourly total sea levels that dynamically includes contributions from storm surges, tides, changes in water density (or baroclinicity) and their interactions. Evaluation shows good agreement between modelled and available observed sea levels, including extremes driven by extratropical and tropical cyclones. Significant improvements over other simulations result from our efforts in addressing underestimated reanalysis winds and incorporating baroclinicity, both of which have been overlooked in other global studies. The improvements can translate into reductions of return periods for given critical levels by decades. We therefore provide improved global estimates of ESL. In a first step toward developing seasonal forecast of flood risk, we also quantified ENSO-induced ESL modulations. The modulations show coherent spatial variabilities, consistent with ENSO-induced changes in the atmosphere and ocean. We also highlight the relevance of the often-overlooked neutral phase in regions where both El Niño and La Niña may suppress sea level variabilities.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"50 ","pages":"Article 100805"},"PeriodicalIF":6.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118950","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":"Increased precipitation variability at multi-timescales in China since the 1960s","authors":"Xuyang Mo ,&nbsp;Wenxia Zhang ,&nbsp;Tianjun Zhou","doi":"10.1016/j.wace.2025.100808","DOIUrl":"10.1016/j.wace.2025.100808","url":null,"abstract":"<div><div>The frequency and intensity of precipitation have changed significantly in China as previously reported. A relevant behavior is the variability of precipitation, which describes temporal fluctuations of precipitation events. Yet it remains unclear how precipitation variability has changed at different timescales over China. In this study, we show that precipitation variability has increased significantly since the 1960s, averaging 2.3 % per decade across China. The increase exists across the synoptic to intraseasonal timescales. The increase in precipitation variability is evident in all seasons with the greatest rate in winter in percentage, which is approximately three times as much as that in summer. Regionally, precipitation variability has risen significantly in northwestern, northeastern, and southeastern China, but has decreased insignificantly along the wet-dry transition belt extending from the north to southwestern China. Compared to trends in mean and extreme precipitation, the increase of precipitation variability is more widespread and with greater magnitudes. The changes in the top 10 % extreme precipitation events contribute ∼75 % of the amplification of precipitation variability nationwide. In addition to long-term trend, summer precipitation variability over eastern China is modulated by the Pacific Decadal Oscillation. This study revealed robust increases in precipitation variability over China since the 1960s across different timescales, seasons, and regions, which have far-reaching impacts on droughts, floods, and water resource management.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"50 ","pages":"Article 100808"},"PeriodicalIF":6.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118949","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 impact of aerosol forcing on the statistical attribution of heatwaves","authors":"Florian Kraulich,&nbsp;Peter Pfleiderer,&nbsp;Sebastian Sippel","doi":"10.1016/j.wace.2025.100803","DOIUrl":"10.1016/j.wace.2025.100803","url":null,"abstract":"<div><div>Heatwaves are becoming more frequent and intense due to anthropogenic climate change. Accurately attributing changes in their occurrence probability and intensity is crucial for effective climate change adaptation strategies. A common practice for calculating heatwave return periods in observations relies on extreme value statistics, where the Generalized Extreme Value distribution (GEV) shifts linearly with a covariate on global mean temperature (GMT) to capture the global forced response of climate change (‘standard method’, from now onwards). Although generally effective, this approach does not explicitly include regional aerosol trends, which strongly influence local heat extremes by reflecting solar radiation and altering cloud properties. Depending on the region, aerosol forcing trends can amplify or counteract greenhouse gas-induced warming. Here, we assess the impact of regional aerosol trends on statistical extreme event attribution of heatwaves using climate model simulations from the Community Earth System Model 2 (CESM2) large ensemble and single forcing large ensembles. To examine the impact of aerosols on extreme event trends, we introduce aerosol optical depth (AOD) as an additional covariate in the GEV model and compare this approach with the ‘standard method’. Our results show substantial biases of the ‘standard method’ in regions and periods of strong aerosol changes, particularly in industrial regions of North America, Central and Eastern Europe, and East Asia. Including AOD as a covariate significantly reduces these biases and improves return period estimates. This study highlights the importance of incorporating regional aerosol trends into statistical attribution frameworks to improve the estimation of return periods, and thus attribution statements.</div></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"50 ","pages":"Article 100803"},"PeriodicalIF":6.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059757","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":"Drought attribution of climate drivers using machine learning techniques","authors":"Milton S. Speer,&nbsp;Lance M. Leslie","doi":"10.1016/j.wace.2025.100801","DOIUrl":"10.1016/j.wace.2025.100801","url":null,"abstract":"","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"50 ","pages":"Article 100801"},"PeriodicalIF":6.9,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027666","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":"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}