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The long‐known, widely documented inverse relationship between body size and environmental temperature ("temperature‐size rule") has recently led to predictions of body size decline following current climatic warming ("size shrinking effect"). For keystone pollinators such as wild bees, body shrinking in response to warming can have significant effects on pollination processes but there is still little direct evidence of the phenomenon because adequate tests require controlling for confounding factors linked to climate change (e.g., habitat change). This paper assesses the shrinking effect in a community of solitary bees from well‐preserved habitats in the core of a large nature reserve experiencing climatic warming without disturbances or habitat changes. Long‐term variation in mean body mass was evaluated using data from 1704 individual bees (137 species, 27 genera, 6 families) sampled over 1990–2023. Climate warmed at a fast rate during this period, annual mean of daily maximum temperature increasing 0.069°C/year on average during 2000–2020. Changes in bee body mass verified expectations from the size shrinking effect. The mean individual body mass of the community of solitary bees declined significantly, irrespective of whether the analysis referred to the full species sample or only to the subset of species that were sampled in both the old (1990–1997) and recent (2022–2023) periods. On average, body mass declined ~0.7%·year−1, or an estimated average cumulative reduction of ~20 mg per individual bee from 1990 to 2023. Proportional size reduction was greatest among large‐bodied species, ranging from around −0.6%·year−1 for the smallest species to −0.9%·year−1 for the largest ones. Declining rate was steeper for cavity‐nesting than ground‐nesting species. The pollination and mating systems of bee‐pollinated plants in the study region are probably undergoing significant alterations as a consequence of supra‐annual decline in bee body mass. [ABSTRACT FROM AUTHOR]

In this paper, an innovative framework is introduced for assessing the impact of climate change on coastal structures, with a primary emphasis on modeling future climate conditions. The framework encompasses several key components: the development of a new climate emulator to facilitate multivariate extreme value analysis; a hybrid statistical–numerical wave propagation strategy; and the integration of a novel compound modeling approach specific to coastal structure sites. These advancements are designed to effectively manage multiple climate scenarios and models within the context of high uncertainty. The methodology is applied and validated for a coastal structure located on the Mediterranean coast in Spain. Focusing on compound wave and sea-level events, the projections indicate an increase in the frequency and severity of extreme conditions, exacerbating coastal impacts such as wave overtopping. For instance, the present-day 500-year coastal flooding level would be associated with a return period of 50 years by mid-century under a stabilization scenario and would be further reduced to 25 years under a high-emission global warming scenario, resulting in important consequences for structure performance if no adaptation is implemented. • Develops a framework for assessing the impact of climate change on coastal structures. • It develops a new climate emulator to facilitate multivariate extreme value analysis. • It encompasses a hybrid statistical–numerical wave propagation strategy. • It integrates a novel compound modeling approach specific to coastal structures sites. • It has been applied and validated for a coastal structure in Spain. [ABSTRACT FROM AUTHOR]

Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France. [ABSTRACT FROM AUTHOR]

The Climate Change scenario projected by the IPCC for the year 2050 predicts noticeable increases in temperature. In severe summer climates, such as the Mediterranean area, this would have very negative effects on thermal comfort in the existing housing stock, given the current high percentage of dwellings which are obsolete in energy terms and house a population at serious risk of energy poverty. The main aim of this paper is to generate a predictive model in order to assess the impact of this future climate scenario on thermal comfort conditions in an entire building category. To do so, calibrated models representing linear-type social multi-family buildings, dating from the post-war period and located in southern Spain, will be simulated extensively using transient energy analyses performed by EnergyPlus. In addition, a sensitivity analysis will be performed to identify the most influential parameters on thermal discomfort. The main results predict a generalized deterioration in indoor thermal comfort conditions due to global warming, increasing the average percentage of discomfort hours during the summer by more than 35%. This characterization of the future thermal behaviour of the residential stock in southern Spain could be a trustworthy tool for decision-making in energy retrofitting projects which are so badly needed. To do so, further work is required on some limitations of this model so that different user profiles and typologies can be represented in detail and an economic assessment can be included. [ABSTRACT FROM AUTHOR]