Natural Selection after Severe Winter Favors Larger and Duller Bluebirds.

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.

Drought-induced decline of productivity in the dominant grassland species Lolium perenne L. depends on soil type and prevailing climatic conditions

People experiencing poverty and inequality will be affected first and worst by the impacts of climate change to infrastructure and human settlements, including those caused by increasingly frequent and intense extreme weather events and natural disasters. They have the least capacity to cope, to adapt, to move and to recover. Community service organisations (CSOs) play a critical role in supporting individuals, families and communities experiencing poverty and inequality to build resilience and respond to adverse changes in circumstances. As such, the services they provide comprise a critical component of social infrastructure in human settlements. However, very little is understood about CSOs own vulnerability to – or their role in managing and mitigating risks to their clients and the community from – climate change impacts to physical infrastructure. The Extreme Weather, Climate Change and the Community Sector – Risks and Adaptations project examined the relationship between physical and social infrastructure (in the form of CSO service provision). Specifically, the ways in which the climate-driven failure of CSO service delivery worsens risks to the individuals and communities they serve and, on the other hand, how preparedness may reduce vulnerability to climate change and extreme weather impacts to human settlements and infrastructure. The research comprised a comprehensive and critical scoping, examination and review of existing research findings and an audit, examination and judgment-based evaluation of the current vulnerabilities and capacities of CSOs under projected climate change scenarios. It employed three key methods of consultation and data collection. A literature review examined research conducted to date in Australia and comparative countries internationally on the vulnerability and climate change adaptation needs of CSOs. A program of 10 Community Sector Professional Climate Workshops consulted over 150 CSO representatives to develop a qualitative record of extreme event and climate change risks and corresponding adaptation strategies specific to CSOs. A national survey of CSOs, which resulted in the participation of approximately 500 organisations, produced a quantitative data set about the nature of CSO vulnerability to climate change and extreme weather impacts to infrastructure, whether and how CSOs are approaching the adaptation task and key barriers to adaptation. While the methods employed and the absence of empirical data sets quantifying CSO vulnerability to climate change impacts create limitations to the evidence-base produced, findings from the research suggest that CSOs are highly vulnerable and not well prepared to respond to climate change and extreme weather impacts to physical infrastructure and that this underlying organisational vulnerability worsens the vulnerability of people experiencing poverty and inequality to climate change. However, the project results indicate that if well adapted, CSOs have the willingness, specialist skills, assets and capacity to make a major contribution to the resilience and adaptive capacity of their clients and the community more broadly (sections of which will be plunged into adversity by extreme events). Despite this willingness, the evidence presented shows that few CSOs have undertaken significant action to prepare for climate change and worsening extreme weather events. Key barriers to adaptation identified through the research are inadequate financial resources, lack of institutionalised knowledge and skills for adaptation and the belief that climate change adaptation is beyond the scope of CSOs core business. On the other hand, key indicators of organisational resilience to climate change and extreme weather impacts include: level of knowledge about extreme weather risks, past experience of an extreme weather event and organisational size. Given its size, scope and the critical role the Australian community sector plays in building client and community resilience and in assisting communities to respond to and recover from the devastating impacts of extreme weather events and natural disasters, the research identifies serious gaps in both the policy frameworks and the research base required to ensure the sector’s resilience and adaptive capacity – gaps which appear to have already had serious consequences. To address these gaps, a series of recommendations has been prepared to enable the development and implementation of a comprehensive, sector-specific adaptation and preparedness program, which includes mechanisms to institutionalise knowledge and skills, streamlined tools appropriate to the needs and capacity of a diverse range of organisations and a benchmarking system to allow progress towards resilience and preparedness to be monitored. Future research priorities for adaptation in this sector have also been identified.

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.

Climate change affects organisms worldwide with profound ecological and evolutionary consequences, often increasing population extinction risk. Climatic factors can increase the strength, variability, or direction of natural selection on phenotypic traits, potentially driving adaptive evolution. Phenotypic plasticity in relation to temperature can allow organisms to maintain fitness in response to increasing temperatures, thereby "buying time" for subsequent genetic adaptation and promoting evolutionary rescue. Although many studies have shown that organisms respond plastically to increasing temperatures, it is unclear if such thermal plasticity is adaptive. Moreover, we know little about how natural and sexual selection operate on thermal reaction norms, reflecting such plasticity. Here, we investigate how natural and sexual selection shape phenotypic plasticity in two congeneric and phenotypically similar sympatric insect species. We show that the thermal optima for longevity and mating success differ, suggesting temperature-dependent trade-offs between survival and reproduction in both sexes. Males in these species have similar thermal reaction norm slopes but have diverged in baseline body temperature (intercepts), being higher for the more northern species. Natural selection favored reduced thermal reaction norm slopes at high ambient temperatures, suggesting that the current level of thermal plasticity is maladaptive in the context of anthropogenic climate change and that selection now promotes thermal canalization and robustness. Our results show that ectothermic animals also at high latitudes can suffer from overheating and challenge the common view of phenotypic plasticity as being beneficial in harsh and novel environments.

ABSTRACTExtreme weather and climate-related events affect human health by causing death, injury, and illness, as well as having large socioeconomic impacts. Climate change has caused changes in extreme event frequency, intensity, and geographic distribution, and will continue to be a driver for change in the future. Some of these events include heat waves, droughts, wildfires, dust storms, flooding rains, coastal flooding, storm surges, and hurricanes. The pathways connecting extreme events to health outcomes and economic losses can be diverse and complex. The difficulty in predicting these relationships comes from the local societal and environmental factors that affect disease burden. More information is needed about the impacts of climate change on public health and economies to effectively plan for and adapt to climate change. This paper describes some of the ways extreme events are changing and provides examples of the potential impacts on human health and infrastructure. It also identifies key research gaps to be addressed to improve the resilience of public health to extreme events in the future.Implications: Extreme weather and climate events affect human health by causing death, injury, and illness, as well as having large socioeconomic impacts. Climate change has caused changes in extreme event frequency, intensity, and geographic distribution, and will continue to be a driver for change in the future. Some of these events include heat waves, droughts, wildfires, flooding rains, coastal flooding, surges, and hurricanes. The pathways connecting extreme events to health outcomes and economic losses can be diverse and complex. The difficulty in predicting these relationships comes from the local societal and environmental factors that affect disease burden.

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

Climate change is significantly modifying the characteristics of extreme precipitation events, with relevant implications for society and water-related risks. While most studies focus on changes in the magnitude or frequency of this hazard, this work investigates shifts in the seasonality of extreme precipitation across the Sicily Island over a period spanning almost 100 years. We use circular statistics to characterize the seasonality of annual maximum precipitation at canonical durations. By applying two different circular-linear regression tests (i.e., a Generalized Linear Model developed for circular outcomes within the Bayesian framework and a circular-adapted version of the Theil-Sen slope estimator), we also analyze changes in the extreme events timing over time. Our findings reveal a general anticipation in the timing of extreme events, particularly for short durations, reaching up to ~ 20 days over the study period (1928–2018) and aligning with previous studies reporting increasing severity of short-lived extremes in the region. By integrating circular statistics with spatial clustering methods, we also identify seasonal clusters that reflect the spatial coherence of extreme precipitation timing. In this case as well, especially at shorter durations, a particularly distinct cluster of stations emerges in the northeastern part of the island, an area known to be highly affected by intense convective events and prone to flash floods. This study has a twofold significance. First, it challenges the conventional paradigm in the analysis of seasonality of extreme precipitation by introducing a novel, robust methodology that fully respects the circular nature of the data and can be extended to other geographical contexts or to different variables. Second, it highlights how changes in the timing of extremes are more pronounced for short-duration events, an aspect that could have substantial consequences for flash flooding, urban drainage systems, and agricultural planning, particularly in regions already vulnerable to climate change impacts. Graphical abstract Climate change is altering not only the intensity of extreme precipitation but also its seasonal timing. Using almost one century of rain-gauge observations over Sicily, this study investigates long-term changes in the seasonality of annual maximum precipitation at canonical durations (1, 3, 6, 12 and 24 h) through a fully circular statistical framework. Event occurrence dates are treated as angular variables and analyzed using circular descriptors and two complementary circular–linear regression tests to detect temporal shifts in the timing of extremes. To account for spatial coherence, circular information is further integrated within a spatially constrained clustering approach, allowing the identification of regions sharing similar seasonal behavior of extreme precipitation. The results reveal a widespread anticipation of extreme events, with the most pronounced shifts emerging for short-duration rainfall (1 and 3 h), in agreement with recent evidence of increasing short-lived precipitation severity in the region. Spatial clustering highlights coherent seasonal patterns across Sicily, particularly at sub-daily timescales, suggesting that changes in timing are not spatially random but organized at regional scales. Overall, the study demonstrates that changes in the seasonality of extremes represent an additional and relevant dimension of climate change impacts. By introducing a robust and transferable methodology that explicitly respects the circular nature of timing data and integrates spatial structure, this work provides new insights into how and where extreme precipitation is occurring earlier in the year, with important implications for flash-flood hazard, urban drainage design and climate-adaptation planning in Mediterranean environments.

Geographic clines in life‐history traits are often recognized as adaptations to the associated transitional environments. As life‐history traits evolve in response to anthropogenic processes, these geographic clines can change over time. The geographic and temporal trends of reproductive traits in Japanese chum salmon Oncorhynchus keta were analysed. Data were collected from 23 rivers located between 36° and 45° north latitude and 136° and 146° east longitude from 1994 to 2010. We confirmed the geographic clines of reproductive traits: relative gonad weight increased in more northeasterly locations, and females had fewer, but larger, eggs in more northeasterly locations after standardization by body size. The geographic clines changed over the years. The northeastward geographic trend of increasing gonad weight became more pronounced over time. Temporal trends towards smaller but more numerous eggs were evident, especially in northeasterly locations. Under natural and sexual selection, gonadal investment should be constrained by the energetic demands of the cost of migration, particularly in southwesterly locations (which are farthest from the feeding grounds), and by breeding competition during natural reproduction. In addition, females should have fewer but larger eggs owing to a constraint on growth opportunities for their offspring in more northeasterly regions of Japan, which are colder and have less available food. However, global warming may mitigate this constraint on growth opportunities in northeastern Japan by increasing river water temperatures. Moreover, we consider that relaxation of the effects of natural and sexual selection on intense breeding competition and early growth conditions has occurred through domestication selection by hatchery programmes. These may have caused temporal shifts in geographic clines. We should consider several co‐occurring anthropogenic impacts on natural and sexual selection when evaluating the life‐history traits of organisms. For the sustainable use of biological resources, maintaining geographically adapted life‐history traits during adaptation to climate change is essential. Therefore, the conservation of wild salmon populations formed by natural selection is preferable to the stocking of hatchery‐reared fry.

Extreme weather events lead to many adverse societal, economic, and environmental consequences. Anthropogenic climate change has been identified as a factor that, in many cases, increases the frequency and intensity of these weather extremes. In the last two decades, the methods of Extreme Event Attribution (EEA) have been used to quantify the extent to which climate change affected the nature of specific recent extreme weather events. More recently, these methods are being combined with socioeconomic impact data to quantify extreme weather’s impacts attributable to climate change in what we term Extreme Event Impacts Attribution (EEIA). EEIA is a quickly developing field that considers which kinds of questions about the impacts of climate change on extreme weather events we should ask, what methods are best suited to answer them, how to interpret the results these methods provide, and what purpose these results can serve. In this survey, we discuss the basic structure and methods of EEIA, review the results of the existing EEIA studies, and discuss the implications and outlook for this strand of research including its relevance for quantification of climate change costs, the Loss and Damage Fund, climate litigation, or adaptation planning.

Mid-latitude extreme weather events are responsible for a large part of climate-related damage. Yet large uncertainties remain in climate model projections of heat waves, droughts, and heavy rain/snow events on regional scales, limiting our ability to effectively use these projections for climate adaptation and mitigation. These uncertainties can be attributed to both the lack of spatial resolution in the models, and to the lack of a dynamical understanding of these extremes. The objectives of this proposal were to first to develop empirical and theoretical understanding of how fine-scale features of the atmospheric circulation that are responsible for extreme events (such as blocking, atmospheric rivers, Rossby wave breaking) are related to more easily quantifiable larger scale circulation patterns, and on the basis of this develop specific metrics that can be applied to a range of atmospheric and coupled atmosphere-ocean models to diagnose their prediction of extremes. These metrics were used to understand what model resolution is necessary to describe the probability of extreme events realistically, and will be applied to CMIP5 models and high resolution time-slice projections of future climate to better understand the potential for the shifts and intensification of extremes over North America in a changing climate. The approach was to relate the fine-scale features to the large scales in current climate simulations, seasonal re-forecasts, and climate change projections in a very wide range of models, including the atmospheric and coupled models of ECMWF over a range of horizontal resolutions (125 to 10 km), aqua-planet configuration of the Model for Prediction Across Scales and High Order Method Modeling Environments (resolutions ranging from 240 km – 7.5 km) with various physics suites, and selected CMIP5 model simulations. The large scale circulation will be quantified both on the basis of the well-tested preferred circulation regime approach, and very recently developed measures, the finite amplitude Wave Activity and its spectrum. The fine scale structures related to extremes were diagnosed following the latest approaches in the literature. The goal is to use the large scale measures as indicators of the probability of occurrence of the finer scale structures, and hence extreme events. These indicators will then be applied to the CMIP5 models and time-slice projections of a future climate. The expected outcomes of this research were: (i) a unified methodology and set of metrics that will enable modelers to assess the probability of extreme events in various current and future climate simulations, (ii) an estimate of the model resolution needed to capture extreme events in a realistic way, and (iii) a better understanding of the extreme events over North America in a changing climate.

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.

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

<p>During 22-24 June 2009, Austria witnessed a rampant rainfall spell that spread across populated areas of the country. High-intensity rainfall caused 3000+ landslides in Feldbach, and property damages worth €10,000,000 in Styria itself. Numerous synoptic-scale studies indicated the presence of a cut-off low over the Adriatic and excessive moisture convergence behind the extreme event. In a warmer climate change scenario, such an extreme precipitation event may become more intense due to higher water holding capacity of air with increased temperatures, but this reasoning may not be so straightforward considering the complex physics of precipitation.</p><p>Precipitation, as a natural atmospheric phenomenon, is dependent upon the dynamic and thermodynamic characteristics of the atmosphere. While it is safe to say that the thermodynamic characteristics of the atmosphere are relatively easier to simulate with confidence using available global models, the same cannot be said about the dynamics. This can be blamed on the chaotic non-linear behaviour of the atmosphere and problem in resolving sub-grid scale processes that reduce the model accuracy for longer spatial scales.</p><p>CCLM regional model is used to study this extreme precipitation event. Our setup uses IFS data to calculate initial and boundary conditions for the simulations of the ‘present’ case where our attempt is to recreate the event over the same location as the original event. Further we use CMIP5 global climate models (at the RCP8.5) scenario. In particular, these will be applied in the ‘surrogate climate change’ method. Here, the climate change signals are calculated by computing the difference between the thermodynamic fields of the CMIP5 simulations for the future and the past. These climate change signals are applied to the original fields to obtain the ‘changed’ fields which are used to calculate new initial and boundary conditions resembling a climate-change future. A similar approach is to be applied for the ‘past’ case simulations.</p><p>The idea behind this experimental setup is to establish a ‘storyline’ for the event as it would have occurred in the past, present and the future. The storyline approach provides an alternative to the traditional probabilistic approach for assessing risk enhancement and can serve to study responses of different mechanisms to climate change. The storyline approach also helps in decision-making as event-oriented risk management is easy for people to perceive and respond to. An associated landslide modelling study, which uses the precipitation output of our simulations as input, looks into the probable increased risks of landslides in the region and will directly aid the lives of those living in Southeast Austria.</p>

Climate change is predicted to severely impact interactions between prey, predators and habitats. In Southern Europe, within the Mediterranean climate, herbaceous vegetation achieves its maximum growth in middle spring followed by a three-month dry summer, limiting prey availability for insectivorous birds. Lesser kestrels (Falco naumanni) breed in a time-window that matches the nestling-rearing period with the peak abundance of grasshoppers and forecasted climate change may impact reproductive success through changes in prey availability and abundance. We used Normalised Difference Vegetation Index (NDVI) as a surrogate of habitat quality and prey availability to investigate the impacts of forecasted climate change and extreme climatic events on lesser kestrel breeding performance. First, using 14 years of data from 15 colonies in Southwestern Iberia, we linked fledging success and climatic variables with NDVI, and secondly, based on these relationships and according to climatic scenarios for 2050 and 2070, forecasted NDVI and fledging success. Finally, we evaluated how fledging success was influenced by drought events since 2004. Despite predicting a decrease in vegetation greenness in lesser kestrel foraging areas during spring, we found no impacts of predicted gradual rise in temperature and decline in precipitation on their fledging success. Notwithstanding, we found a decrease of 12% in offspring survival associated with drought events, suggesting that a higher frequency of droughts might, in the future, jeopardize the recent recovery of the European population. Here, we show that extreme events, such as droughts, can have more significant impacts on species than gradual climatic changes, especially in regions like the Mediterranean Basin, a biodiversity and climate change hotspot.