Changes in extreme events and the potential impacts on human health

In this study, spatial and temporal patterns of changes in extreme events of temperature and precipitation at 143 weather stations in ten Asia‐Pacific Network (APN) countries and their associations with changes in climate means are examined for the 1955–2007 period. Averaged over the APN region, annual frequency of cool nights (days) has decreased by 6.4 days/decade (3.3 days/decade), whereas the frequency of warm nights (days) has increased by 5.4 days/decade (3.9 days/decade). The change rates in the annual frequency of warm nights (days) over the last 20 years (1988–2007) have exceeded those over the full 1955–2007 period by a factor of 1.8 (3.4). Seasonally, the frequencies of summer warm nights and days are changing more rapidly per unit change in mean temperatures than the corresponding frequencies for cool nights and days. However, normalization of the extreme and mean series shows that the rate of changes in extreme temperature events are generally less than that of mean temperatures, except for winter cold nights which are changing as rapidly as the winter mean minimum temperature. These results indicate that there have been seasonally and diurnally asymmetric changes in extreme temperature events relative to recent increases in temperature means in the APN region.There are no systematic, regional trends over the study period in total precipitation, or in the frequency and duration of extreme precipitation events. Statistically significant trends in extreme precipitation events are observed at fewer than 30% of all weather stations, with no spatially coherent pattern of change, whereas statistically significant changes in extreme temperature events have occurred at more than 70% of all weather stations, forming strongly coherent spatial patterns. Copyright © 2009 Royal Meteorological Society

Numerous weather and climate extremes impact human society, the societal infrastructure, and the natural environment. The main purposes of this study were to review the historical record of weather extremes in the United States, identify the frequency and intensity of severe storms in the historical record, and examine the extent of economic damage resulting from those extreme events which occurred over the most recent decades. Potential changes in climate are forecast to result in possible frequency and/or intensity changes in extreme events, increases in precipitation, decreases in extreme low temperatures, increases in extreme high temperature, and changes in ecological systems such as climate-induced phonological shifts and possible biological extinctions. However, the impacts of tropical storms, hurricanes, and rainfalls on society and the nature systems need to be further investigated due to the difficulties of evaluations on variations of storms’ activities (intensity and frequency). Climatologists, biologists, and social scientists need to work together to bridge the gaps among the disciplines. Future research may consider focusing on future trends and changes in frequency of extreme events based on the outcomes of the most integrated climate models to evaluate the relationships between the severe weather extremes and the continued greenhouse gas scenarios of the coming decades.

Summary We review the predicted changes in extreme events following climate change in flowing waters in arctic and subarctic regions. These regions are characterised by tundra or taiga ecosystems in either erosional or depositional glacial landforms or presently glacierised areas of the Northern Hemisphere. The ecological and geomorphic effects of extreme meteorological and hydrological events, such as episodes of strongly increased precipitation, temperatures and flows, can be exacerbated by altered base conditions. For example, winter temperature variations between frost and thaw will become more frequent at many places because mean temperature during the winter is closer to 0 °C, potentially leading to changes in the production of ice and intensified disturbance of riparian and aquatic habitats during extreme floods. Additionally, thawing of permafrost and glaciers can lead to increased bank erosion because of thaw slump and glacial outburst floods. We discuss the abiotic and biotic effects of these and other extreme events, including heavy precipitation, floods, drought and extreme air or water temperatures, and summarise our findings in a model that aims to stimulate further research in this field.

The weather conditions of the summer of 2022 were very unusual, particularly in Eastern Asia, Europe, and North America. The devasting impact of climate change has come to our attention, with much hotter and drier conditions, and with more frequent and intense flooding events. Some extreme events have reached a dangerous level, increasingly threatening human lives. The interconnected risks caused by these extreme disaster events are triggering a chain effect, forcing us to respond to these crises through changes in our living environment, which affect the atmosphere, the biosphere, the economy including the availability of energy, our cities, and our global society. Moreover, we have to confront the abnormal consequences of untypical, rapid changes of extreme events and fast switches between extreme states, such as from severe drought to devastating flooding. Recognizing this new situation, it is crucial to improve the adaptation capacity of our societies in order to reduce the risks associated with climate change, and to develop smarter strategies for climate governance. High-quality development must be science-based, balanced, safe, sustainable, and climate-resilient, supported by the collaborative governance of climate mitigation and adaptation. This article provides some recommendations and suggestions for resilience building and collaborative governance with respect to climate adaptation in response to a new planetary state that is characterized by more frequent and severe extreme weather events.

Extreme weather events and their resulting impacts threaten all levels of society. Climate change can amplify the frequency and intensity of associated hazards. One of the key-challenges for decision-makers in civil protection is adapting to the changing landscape of weather-induced impacts driven by climate change. It is therefore essential to assess and estimate the changing conditions for extreme weather events under climate change.This study investigates the changing landscape of regional extreme weather events in the Alpine region by utilizing weather circulation type classification through its relationship with weather-induced potential extreme events. Therein, large-scale weather types take the role of relevant precursors for regional extreme events. The local-scale potential impact events that are associated with prevailing weather types are derived by using percentile-based methods on high-resolution, gridded precipitation data. ERA5 mean sea level pressure and the classification scheme GWT ('Gross-Wetter-Typen') are used to derive 18 classes of cyclonic and anti-cyclonic weather types, representing a prevailing circulation on a daily basis for the whole Alpine region. Subsequently, unsupervised hierarchical clustering (Agglomerative clustering) is used to evaluate overlaps between cluster families in order to derive a subset of 'high-impact' weather types. The relationship between those weather types and local-scale extreme events is further characterized by analyzing percentile-based indicators from station data and high-resolution observational data. Finally, we extend our analysis by applying found relationships to state-of-the-art climate models from the Coupled Model Intercomparison Project 6 (CMIP6) to investigate the changing landscape for extreme weather events under different climate change scenarios.Our findings indicate that a subset of weather types, related to specific cyclonic circulation patterns, is mostly responsible as precursor for extreme precipitation events. Those patterns furthermore show increases in frequency under the scenario SSP3-7.0, that are consistent across the climate model ensemble. Associated changes of precipitation totals suggest increases in intensity, however these intensity changes are not as conclusive due to large inter-model spread.

Abstract. The economic and human consequences of extreme precipitation and the related flooding of urban areas have increased rapidly over the past decades. Some of the key factors that affect the risks to urban areas include climate change, the densification of assets within cities and the general expansion of urban areas. In this paper, we examine and compare quantitatively the impact of climate change and recent urban development patterns on the exposure of four European cities to pluvial flooding. In particular, we investigate the degree to which pluvial floods of varying severity and in different geographical locations are influenced to the same extent by changes in urban land cover and climate change. We have selected the European cities of Odense, Vienna, Strasbourg and Nice for analyses to represent different climatic conditions, trends in urban development and topographical characteristics. We develop and apply a combined remote-sensing and flood-modelling approach to simulate the extent of pluvial flooding for a range of extreme precipitation events for historical (1984) and present-day (2014) urban land cover and for two climate-change scenarios (i.e. representative concentration pathways, RCP 4.5 and RCP 8.5). Changes in urban land cover are estimated using Landsat satellite imagery for the period 1984–2014. We combine the remote-sensing analyses with regionally downscaled estimates of precipitation extremes of current and expected future climate to enable 2-D overland flow simulations and flood-hazard assessments. The individual and combined impacts of urban development and climate change are quantified by examining the variations in flooding between the different simulations along with the corresponding uncertainties. In addition, two different assumptions are examined with regards to the development of the capacity of the urban drainage system in response to urban development and climate change. In the stationary approach, the capacity resembles present-day design, while it is updated in the evolutionary approach to correspond to changes in imperviousness and precipitation intensities due to urban development and climate change respectively. For all four cities, we find an increase in flood exposure corresponding to an observed absolute growth in impervious surfaces of 7–12 % during the past 30 years of urban development. Similarly, we find that climate change increases exposure to pluvial flooding under both the RCP 4.5 and RCP 8.5 scenarios. The relative importance of urban development and climate change on flood exposure varies considerably between the cities. For Odense, the impact of urban development is comparable to that of climate change under an RCP 8.5 scenario (2081–2100), while for Vienna and Strasbourg it is comparable to the impacts of an RCP 4.5 scenario. For Nice, climate change dominates urban development as the primary driver of changes in exposure to flooding. The variation between geographical locations is caused by differences in soil infiltration properties, historical trends in urban development and the projected regional impacts of climate change on extreme precipitation. Developing the capacity of the urban drainage system in relation to urban development is found to be an effective adaptation measure as it fully compensates for the increase in run-off caused by additional sealed surfaces. On the other hand, updating the drainage system according to changes in precipitation intensities caused by climate change only marginally reduces flooding for the most extreme events.

The frequency of extreme rainfall events in Southern Brazil is impacted by El Niño—Southern Oscillation (ENSO) episodes, especially in austral spring. There are two areas in which this impact is more significant: one is on the coast, where extreme events are more frequent during El Niño (EN) and the other one extends inland, where extreme events increase during EN and decrease during La Niña (LN). Atmospheric circulation patterns associated with severe rainfall in those areas are similar (opposite) to anomalous patterns characteristic of EN (LN) episodes, indicating why increase (decrease) of extreme events in EN (LN) episodes is favoured. The most recurrent precipitation patterns during extreme rainfall events in each of these areas are disclosed by Principal Component Analysis (PCA) and evidence the separation between extreme events in these areas: a severe precipitation event generally does not occur simultaneously in the coast and inland, although they may occur inland and in the coastal region in sequence. Although EN predominantly enhances extreme rainfall, there are EN years in which fewer severe events occur than the average of neutral years, and also the enhancement of extreme rainfall is not uniform for different EN episodes, because the interdecadal non‐ENSO variability also modulates significantly the frequency of extreme events in Southern Brazil. The inland region, which is more affected, shows increase (decrease) of extreme rainfall in association with the negative (positive) phase of the Atlantic Multidecadal Variability, with the negative (positive) phase of the Pacific Multidecadal Variability and with the positive (negative) phase of the Pacific Interdecadal Variability. Copyright © 2008 Royal Meteorological Society

<p>Changes in extreme events, such as the recent devastating tropical cyclones (TC), are a visible way in which climate change can directly impact society and coastal communities. This work presents the results of a model hierarchy within the Community Earth System Model (CESM), that spans idealized radiative convective equilibrium to realistic decadal projections of future climate change configurations, to explore how TC rainfall characteristics change with surface warming. The Community Atmosphere Model (CAM) component of CESM is forced with prescribed sea-surface temperatures (SSTs) and greenhouse gas concentrations for idealized and realistic representations of past, present, and future climates using global and variable-resolution setups with high-resolution horizontal grid spacing equal to 28 km. An analysis framework that allows for the extraction of TC-related rainfall throughout the full storm lifecycle is utilized. This analysis includes the evaluation of conventional (AMIP-style) decadal simulations typical of climate models, short 7-day ensemble hindcasts of recent devastating events, and reduced complexity simulations of idealized states of the climate system. Through this hierarchical modeling approach the impact of climate change on the characteristics (rainfall, structure, intensity, etc.) of TCs can be quantified This work is part of a growing effort in the scientific community to quantify the impact of climate change on recent and future extreme weather events.</p>

As one of the most important extreme weather event types, extreme precipitation events have significant impacts on human and natural environment. This study assesses the projected long term trends in frequency of occurrence of extreme precipitation events represented by heavy precipitation days, very heavy precipitation days, very wet days and extreme wet days over Ontario, based on results of 21 CMIP3 GCM runs. To achieve this goal, first, all model data are linearly interpolated onto 682 grid points (0.45° × 0.45°) in Ontario; Next, biases in model daily precipitation amount are corrected with a local intensity scaling method to make the total wet days and total wet day precipitation from each of the GCMs are consistent with that from the climate forecast system reanalysis data, and then the four indices are estimated for each of the 21 GCM runs for 1968–2000, 2046–2065 and 2081–2100. After that, with the assumption that the rate parameter of the Poisson process for the occurrence of extreme precipitation events may vary with time as climate changes, the Poisson regression model which expresses the log rate as a linear function of time is used to detect the trend in frequency of extreme events in the GCMs simulations; Finally, the trends and their uncertainty are estimated. The result shows that in the twenty-first century annual heavy precipitation days, very heavy precipitation days and very wet days and extreme wet days are likely to significantly increase over major parts of Ontario and particularly heavy precipitation days, very wet days are very likely to significantly increase in some sub-regions in eastern Ontario. However, trends of seasonal indices are not significant.

Extreme compound events, defined as the “combination of multiple drivers and/or hazards that contributes to societal or environmental risk”, present agrowing concern for the scientists and the civil society (Zscheischler et al. 2020). Climate models provide physical simulations of the climate until 2100, which permits to better understand the evolution of the extreme events under climate change. This study proposes a novel modeling of bivariate extreme events using bivariate Generalized Pareto Distributions (biGPD), with Extended GPD for the univariate part (EGPD). This novel semi-parametric modeling is applied to an extreme event: the flooding of the Seine and the Loire watersheds in June 2016. This event is a spatially compound event between the accumulated precipitations over the two watersheds. The accumulation of rain over several days is approximated by the Antecedent Precipitation Index (API), and high values of API are considered to lead to flooding. This approach is compared to a more classic copula modeling over simulations in Jacquemin et al. (2025, submitted). As climate simulations often have statistical biases, they must be corrected using bias correction algorithms. This is also the case for their simulations of compound events. This study compares several multivariate bias correction algorithms (CDF-t, dOTC and R2D2) on this event. dOTC seems to perform better than R2D2 for extreme values. The proposed methodology illustrates how compound events can be analyzed, and their evolution in frequency projected. As a perspective, this method can be applied to more diverse compound events, and it could be generalized to events in higher dimensions.Bibliography:Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M.D., et al.: A typology of compound weather and climate events, Nature reviews earth & environment, 1, 333–347, 2020.Jacquemin, G., Vrac, M., Allard, D., and Freulon, X.: Estimating the return period of climate compound events using a non parametric bivariate Generalized Pareto representation. Submitted

"The Sky Is Falling," Government Is Not Responding, What Can Scientists Do?

Climate change in Italy indicated by agrometeorological indices over 122 years

The increasing intensity and frequency of extreme weather events resulting from climate change have led to grid outages and other negative consequences. To ensure the resilience of buildings which serve as primary shelters for occupants, resilient strategies are being developed to improve their ability to withstand these extreme events (e.g., building upgrades and renewable energy generators and storage). However, a crucial step towards creating a resilient built environment is accurately estimating building performance during such conditions using historical extreme climate change-induced weather events. To conduct Building Performance Simulation (BPS) in extreme conditions, such as weather events induced by climate change, it is essential to utilize Actual Meteorological Year (AMY) weather files instead of Typical Meteorological Year (TMY) files. AMY files capture the precise climatic conditions during extreme weather events, enabling accurate simulation of such scenarios. These weather files provide valuable data that can be used to assess the vulnerabilities and resilience of buildings to extreme weather events. By analyzing past events and their impacts using BPS tools, we can gain insights into the specific weaknesses and areas that require improvement. This approach applies to both existing buildings needing climate change-resilient retrofits and new building designs that must be compatible with future climatic conditions. Moreover, the intensification and frequency increase of these extreme weather events makes developing adaptation and resilient-building measures imperative. This involves understanding the potential losses that households may experience due to the intensification of extreme events and developing farsighted coping strategies and climate-proof resilient-building initiatives. However, addressing the knowledge gap caused by the absence of an AMY weather file dataset of extreme events is essential. This will allow for accurate BPS during past extreme climate change-induced weather events. To fill this gap, this article introduces a comprehensive .epw format weather file dataset focusing on historical extreme weather events in Canada. This collection encompasses a diverse array of past extreme climate change occurrences in various locations, with potential for future expansion to include additional locations and countries. This dataset enables energy simulations for different types of buildings and considers a diverse range of historical weather conditions, allowing for better estimation of thermal performance.

Abstract. A high-resolution daily gridded precipitation dataset was built from raw data of 12 858 observatories covering a period from 1950 to 2012 in peninsular Spain and 1971 to 2012 in Balearic and Canary islands. The original data were quality-controlled and gaps were filled on each day and location independently. Using the serially complete dataset, a grid with a 5 × 5 km spatial resolution was constructed by estimating daily precipitation amounts and their corresponding uncertainty at each grid node. Daily precipitation estimations were compared to original observations to assess the quality of the gridded dataset. Four daily precipitation indices were computed to characterise the spatial distribution of daily precipitation and nine extreme precipitation indices were used to describe the frequency and intensity of extreme precipitation events. The Mediterranean coast and the Central Range showed the highest frequency and intensity of extreme events, while the number of wet days and dry and wet spells followed a north-west to south-east gradient in peninsular Spain, from high to low values in the number of wet days and wet spells and reverse in dry spells. The use of the total available data in Spain, the independent estimation of precipitation for each day and the high spatial resolution of the grid allowed for a precise spatial and temporal assessment of daily precipitation that is difficult to achieve when using other methods, pre-selected long-term stations or global gridded datasets. SPREAD dataset is publicly available at https://doi.org/10.20350/digitalCSIC/7393.

Plant-pollinator interactions are essential for the functioning of terrestrial ecosystems, but are increasingly affected by global change. The risks to such mutualistic interactions from increasing temperature and more frequent extreme climatic events such as drought or advanced snow melt are assumed to depend on network specialization, species richness, local climate and associated parameters such as the amplitude of extreme events. Even though elevational gradients provide valuable model systems for climate change and are accompanied by changes in species richness, responses of plant-pollinator networks to climatic extreme events under different environmental and biotic conditions are currently unknown. Here, we show that elevational climatic gradients, species richness and experimentally simulated extreme events interactively change the structure of mutualistic networks in alpine grasslands. We found that the degree of specialization in plant-pollinator networks (H2') decreased with elevation. Nonetheless, network specialization increased after advanced snow melt at high elevations, whereas changes in network specialization after drought were most pronounced at sites with low species richness. Thus, changes in network specialization after extreme climatic events depended on climatic context and were buffered by high species richness. In our experiment, only generalized plant-pollinator networks changed in their degree of specialization after climatic extreme events. This indicates that contrary to our assumptions, network generalization may not always foster stability of mutualistic interaction networks.