The European Alps and their surroundings (hereby referred to as the Extended European Alpine Region, EEAR) are known to be a hot-spot of climate change as they are experiencing a faster warming rate than other regions in the world. However, the complex nature of the Alpine terrain makes it more difficult to understand how climatic changes are distributed over space, and in particular with elevation. In this study, we present a comprehensive analysis of how air temperature, precipitation and a broad set of extreme indices have changed over the EEAR during the period 1961–2020, based on a newly developed daily observational dataset with unprecedented spatial density. The analysis relies on robust trend estimation using the non-parametric Sen’s slope method, with statistical significance assessed via the Mann–Kendall test. In addition, elevation-dependent climate change is investigated through a twofold approach that accounts for both linear and non-linear patterns. The analysis of the trends of air temperature and precipitation highlights the enhanced warming in the Alpine region, which amounts to about +2^∘C on average during the 1961–2020 period. In terms of temperature extremes, the same period is characterized by a significant increase in warm spells duration index (WSDI), +10.1 days, and in both minimum and maximum temperature indices, respectively +48 warm nights (TN90p) and +49 warm days (TX90p). While mean precipitation does not show a significant change in time, the frequency of extreme rainfall events (R95p index) significantly increased by about +13 days since 1961. Moreover, an enhanced warming with elevation is observed for mean and minimum temperature from February to May, while increasing precipitation trends with elevation are found, mainly in summer.
Wildfires, insect outbreaks, and storms cause large pulses of tree mortality. Climate change amplifies these forest disturbances, yet their future magnitude and extent remain uncertain. Here, we simulated future forest disturbance regimes at 100-meter resolution across Europe using a deep learning-based simulation framework. Our results show that forest disturbances will continue to increase throughout the 21st century, with disturbed areas more than doubling relative to the recent past under an unabated continuation of climate change. Wildfires are the main agent driving future disturbance change. Changing disturbances result in an increase in young forests, substantially altering Europe's forest demography. Because of their profound implications for forest carbon storage and the habitat value of forest ecosystems, disturbances should be a priority of forest policy and management.
This paper introduces WrenNet, an efficient neural network enabling real-time multi-species bird audio classification on low-power microcontrollers for scalable biodiversity monitoring. We propose a semi-learnable spectral feature extractor that adapts to avian vocalizations, outperforming standard mel-scale and fully-learnable alternatives. On an expert-curated 70-species dataset, WrenNet achieves up to 90.8% accuracy on acoustically distinctive species and 70.1% on the full task. When deployed on an AudioMoth device (≤1MB RAM), it consumes only 77mJ per inference. Moreover, the proposed model is over 16x more energy-efficient compared to Birdnet when running on a Raspberry Pi 3B+. This work demonstrates the first practical framework for continuous, multi-species acoustic monitoring on low-power edge devices.
Recent advances in natural language processing (NLP) and large language models (LLMs) have enabled the systematic use of large-scale textual data from news, social media, and reports to create datasets with socio-economic impacts of climate hazards such as floods, droughts, storms, and multi-hazard events. As the field of text-as-data for impact assessment expands, so does its methodological complexity. Yet research remains fragmented, with no clear guidelines for defining what constitutes an impact, handling temporal and spatial biases, and selecting appropriate modeling and post-processing strategies. This lack of coherence limits transparency and comparability across studies. Here, we address this gap by synthesising common practices, describing key challenges specific to the use of text-as-data methods for analyzing socio-economic impact data, and proposing recommendations to address them. By providing guidance on best practices, we aim to support the construction of robust text-derived socio-economic impact datasets that can more accurately inform disaster risk management and attribution studies.
Shrubs are expanding across the cold ecosystems of our planet with potentially profound consequences for their biodiversity and functioning. However, evidence is still strongly biased towards the Arctic tundra, while a large-scale assessment of shrub expansion in alpine areas above the elevational treeline is missing so far. Here we quantified shrub cover changes over the past two decades in 576 permanent plots of 1 m2 spread across the alpine vegetation belt of Europe's major mountain chains. Total shrub cover clearly increased in the plots with an average rate of about 2.6% per m2 per decade (95% CI = 1.9%-3.4%), and this expansion was more pronounced for evergreen (2.0% per m2 per decade, CI = 1.3%-2.7%) than for deciduous species (1.7% per m2 per decade, CI = 0.9%-2.4%). The magnitude of individual species' cover shifts was positively associated with their plant height, but negatively with their leaf nitrogen content and light affinity. In sum, we show that shrub expansion is a widespread phenomenon also in the alpine zone of European mountains, with potentially far-reaching consequences for alpine plant dynamics, soil microclimates, snow patterns, carbon cycling, food chains and livelihoods.
