European forests provide essential ecosystem services, with current policies focused on four key demands: carbon sequestration, timber provision, bioenergy use, and biodiversity conservation. These policies pursue multiple objectives simultaneously, creating conflicts over forest resources and services that intensify as climate change reduces forests' capacity to deliver multiple services. We synthesize here scientific evidence for these conflicting demands, their interactions and impacts on forests, revealing that current policy frameworks inadequately address fundamental trade-offs between them. Climate change impacts have begun to seriously challenge mitigation targets through negative impacts on the European forest carbon sink. Material substitution benefits face uncertainties in scale and timing as other sectors decarbonize. Bioenergy use conflicts with higher-value applications and biodiversity conservation, while existing policy frameworks inadequately enforce cascade use principles. Climate adaptation towards mixed forests faces implementation barriers including industry infrastructure optimized for softwood, fragmented ownership structures complicating coordination, and local management constraints. While innovative approaches such as Climate-Smart Forestry and landscape-scale triad zoning show potential for integrating multiple demands, they require substantial policy support and institutional capacity. Our review shows that neither technical improvements nor current policies can resolve these fundamental resource conflicts. Sustainable European forest management in the twenty-first century requires enhanced adaptation efforts alongside demand-side management to avoid overexploitation of European forests and environmental impact displacement that could undermine intended policy benefits globally.

Monitoring wind-tree interaction offers a promising sensor-based solution for assessing physiological and ecological processes in trees. While the response of trees to wind has traditionally been studied to estimate mechanical stability and damage risk, recent research highlights its potential for tracking physiological and structural changes related to drought stress, phenological changes, and other environmental conditions. However, the absence of standardised methods and widely accepted protocols limits the comparability of sensorbased data across studies. This study compares the output from different technologies being used to monitor tree motion during artificial pull-and-release tests and under natural wind conditions. Field tests were conducted on six living trees: three Scots pines growing in the Hartheim forest in the flat southern region of the Upper Rhine Valley (Germany), and three maritime pines adjacent to a sand beach on the Adriatic Sea (Italy). Signal processing included calculation of the power spectral density, damped sway frequency, and damping ratio. Results show that dynamic properties estimated from tree response to external loading are influenced by sensor sensitivity and resolution, particularly at low wind speeds, which is typical when monitoring subtle physiological processes. Therefore, selecting appropriate sensor characteristics requires a careful analysis of the minimum requirements, a fundamental step to capture reliable signals. Future research should include additional sensor types and tree architectures. This will contribute to the development of best practices for monitoring physiological responses through tree sway dynamics, considering not only instrument characteristics but also optimal mounting height of the monitoring systems. Establishing consistent methodologies will facilitate data comparison across studies and support ecologists, arborists, and researchers in long-term tree monitoring.

Offshore wind energy offers substantial potential. However, its inherent intermittency leads to the frequent occurrence of offshore wind energy droughts, which pose challenges to electricity system stability. Mitigation measures aim to reduce the number, duration, or impacts of such droughts. Among the different mitigation approaches, supply-side strategies act directly on wind power generation at the wind farm level. Nevertheless, the effectiveness of supply-side mitigation strategies remains poorly understood. This study addresses these gaps by systematically quantifying the potential of three supply-side mitigation strategies: (i) spatial diversification of wind farm locations, (ii) advances in wind turbine technology, and (iii) reductions in downtime and wake losses, to minimize the number and duration of wind energy droughts across 40 key exclusive economic zones (EEZ) worldwide. Hourly, daily, and monthly drought characteristics for both moderate and extreme offshore wind energy droughts are analyzed using wind data from the ERA5 reanalysis for the period 1993–2022. The results show that spatial diversification across multiple sub-regions is the most effective strategy for mitigating offshore wind energy droughts at the EEZ scale. In addition, the effectiveness of all mitigation strategies exhibits pronounced scale-dependent limitations, which are most evident at the monthly time scale. Overall, this study provides a robust basis for energy-policy decisions and highlights the importance of supply-side mitigation for enhancing the reliability of future electricity systems.

Wind-induced tree motion emerges from the interaction between airflow and complex biomechanical structures, yet the mechanisms linking wind loading to whole-tree response remain insufficiently constrained under field conditions. Here, local airflow measurements and distributed tree response sensing are combined to develop a physically motivated reduced-order description of wind-tree interaction across temporal and spatial scales. A physically motivated loading proxy derived from wind speed was analyzed together with wind-induced tree response using singular value decomposition and maximum covariance analysis, revealing that wind loading and tree response are dominated by a single coupled mode. Local linearization and Hill-type saturation analysis demonstrate that effective wind-tree coupling evolves systematically with increasing wind loading, transitioning from a load-sensitive regime to a saturation regime characterized by reduced incremental response. Event-based analysis shows that dynamic amplification becomes mechanically relevant only when short-term wind fluctuations act on an elevated quasi-static load state. Integrating quasi-static loading, dynamic excitation, and exposure duration, the Tree Response Index is introduced as a process-oriented descriptor of mechanically effective wind loading. Reconstruction of the tree response confirms that this dominant coupled mode represents coherent global bending with consistent scaling along the stem, indicating that whole-tree dynamics collapse onto a low-dimensional response structure. Together, the results suggest that wind impact on the sample trees emerges from distinct load regimes in which quasi-static baseline loading modulates the mechanical relevance of short-term turbulent fluctuations. The proposed framework offers a pathway toward reduced monitoring requirements and improved mechanistic risk assessment.

The expansion of wind energy is a key strategy for mitigating global climate change. To support this goal, consistent global-scale datasets of existing wind turbines are essential for planning the future deployment of wind energy. Here, we introduce GOWIRES, a comprehensive global dataset of onshore wind turbines. GOWIRES provides detailed information on 416,417 horizontal-axis wind turbines (HAWT) across 89 countries. The dataset includes geographic coordinates, key technical specifications, and site-specific environmental characteristics for each wind turbine. In addition, GOWIRES provides historical (1989-2018) and future (2030-2059) site-specific wind resource data. Wind resources are characterized by mean wind speed, mean wind power density, Weibull parameters, power law exponents, and air density. Future Weibull parameters are based on simulations from 13 statistically downscaled global climate models under the SSP2-4.5 and SSP5-8.5 scenarios. GOWIRES is a valuable resource for energy and climate research, as well as for applications in wind energy development, grid and infrastructure planning, and policy-making.

Interannual variability (IAV) of wind resources is a key source of uncertainty in wind energy planning and financial risk assessment. However, IAV is commonly quantified either from annual mean wind speed, which is not directly proportional to energy yield, or from annual energy production, which is turbine-specific and thus difficult to compare across fleets and regions. To address this limitation, capped wind power density (WPDc) is introduced as a turbine-agnostic, production-oriented metric that explicitly accounts for cut-in, rated, and cut-out wind speeds. This study assesses IAV across Germany for the period 1991–2024 using high-resolution modeled wind speed time series at 29,807 wind turbine locations. Compared to wind speed, WPDc provides a more representative measure of production-relevant energy availability. IAV is quantified using the coefficient of variation. Onshore wind speed exhibits an IAV of 3.5%, while offshore values are slightly lower at 3.2%. For WPDc, IAV increases to 8.5% onshore and 5.1% offshore, highlighting the nonlinear amplification of variability in energy-relevant metrics. A convergence analysis indicates that at least 14 years of data are required for robust IAV estimation. Overall, the WPDc framework provides a physically consistent and production-relevant basis for improved wind resource risk assessment.

Accurate solar radiation data are fundamental for solar energy research. Reliable databases are essential to quantify their spatiotemporal variability. Although long-term ground-based radiometric measurements are considered the most reliable source of global horizontal irradiance data, they are prone to inaccuracies and often lacking in many regions. This study analyzes hourly global horizontal irradiance data from 14 datasets spanning more than 50 countries to assess data availability, data quality, and statistical as well as spatiotemporal characteristics. A comprehensive stepwise quality control procedure reviews existing and new quality tests to identify suspicious data points or entire implausible time series. After quality control, 1,618 out of 2,147 stations were retained: 289 with 2-5 years, 806 with 6-15 years, and 523 with more than 15 years of reliable data in the study period 1991-2020. This highlights that measurement sites with persistent long-term, high-quality data are scarce. While analyzing long-term changes and trends remains challenging, time-frequency analysis enables estimation of the time scales contributing most to variability at each location. Depending on the site, 47.2 % to 75.2 % of total variance is explained by deterministic cycles at the semi-daily, daily, and annual scales. The contribution of different time scales primarily depends on latitude, but is also influenced by topography and local conditions. Insights gained from quality-controlled ground-based data can be combined with global reanalysis and satellite data to better characterize spatiotemporal variability in global solar radiation and improve solar energy potential estimations from local to global scale.

Wind-induced tree motion in forests reflects the interaction between atmospheric forcing and tree-specific mechanical properties, yet the extent to which stand-scale response is governed by coherent wind forcing or by individual tree characteristics remains unclear under field conditions. Here, wind-induced response of 28 neighboring trees in a planted Scots pine forest stand was analyzed using singular value decomposition and maximum covariance analysis to identify coupled wind–tree components and to partition tree response into collective and individual shares. The results reveal that tree response is dominated by a single wind-driven coupled component, onto which the response of all trees projects to a large extent. This enables a consistent decomposition into a collective response, representing the shared stand-scale dynamics, and an individual contribution capturing tree-specific deviations. Collective participation is systematically associated with alignment to the dominant wind-driven component, whereas individual contributions exhibit greater variability and do not show a consistent dependence on intrinsic dynamic properties or local structural factors such as neighborhood configuration. Partial correlation analysis indicates that neither fundamental sway frequency nor crowding index explains additional variation in wind–tree coupling beyond that associated with participation in the dominant collective response. These findings demonstrate that, in the structurally homogeneous stand, tree response is primarily governed by stand-scale wind loading, expressed by a single wind-driven coupled mode accounting for approximately 82 % of the variance in the aligned tree response field. Individual tree properties primarily modulate deviations from this dominant collective response. This provides a reduced-order, process-based perspective on wind–tree interaction in the planted Scots pine stand and highlights the central role of collective dynamics under natural wind conditions.

Abstract. Floating photovoltaic (FPV) systems influence lake heat budgets by altering radiative input, wind exposure, and air–water heat exchange. However, harmonised field observations describing these processes across contrasting climatic settings and FPV system designs remain scarce, limiting the calibration and validation of hydrodynamic and ecological impact assessments. Here, we present a high-resolution monitoring dataset from three sites: Lake Toules (deep alpine reservoir, elevated open design, Switzerland), Lake Sekdoorn (humid lowland lake, dense east–west design, Netherlands), and Lake Leimersheim (shallow temperate–continental lake, low-profile design, Germany). Water temperature anomalies between FPV-covered and open-water sites (ΔTw) can be analysed analysed during cold- and warm-period extremes. Water temperature anomalies between FPV-covered and open-water sites (ΔTw) can be analysed during cold- and warm-period extremes. The dataset captures cooler conditions beneath FPV at Toules (–0.12 °C) and Leimersheim (–0.04 °C) but warmer conditions at Sekdoorn (+0.14 °C) during cold periods. Warm-period extremes amplified these contrasts, with alternating cooling and warming at Toules (–0.06 °C mean), negligible differences at Sekdoorn, and strong shading-driven cooling at Leimersheim (–0.55 °C). While mean differences were small, short-term deviations reached –0.75 to +0.5 °C, reflecting variable meteorological forcing. The observations further encompass contrasting meteorological controls and seasonal transitions between lake mixing regimes. Seasonal analysis of the large-scale system at Lake Sekdoorn revealed regime-dependent shifts: air temperature dominated during fully mixed winter conditions, shortwave radiation during stratification onset, and wind speed during stable summer stratification. The ΔTw–air temperature relationship reversed from positive in winter (heat retention) to negative in summer (shading-driven cooling), dampening the seasonal amplitude of surface water temperatures. All monitoring systems were harmonised across sites and synchronised using a common data acquisition framework. The dataset enables applications ranging from hydrodynamic model calibration and FPV impact parameterisation to comparative assessments across climatic regions and system designs. The underlying monitoring dataset is openly available at https://doi.org/10.5281/zenodo.21156967.

Confronted with increasing urban heat stress risks, local governments need to reconcile expanding green infrastructure for urban cooling with urban densification goals. However, the impacts of incremental urban development in established neighborhoods on urban heat stress risks remain poorly understood. We demonstrate how decision support tools using Artificial Intelligence (AI) can assist complex urban land use and climate adaptation planning. Our findings are based on an inter- and transdisciplinary research project that developed and combined novel AI-supported simulation and prediction methods, namely 3D semantic models, AI-based outdoor thermal comfort models, and optimization and scenario-based AI models. Tool development was combined with transdisciplinary research to assess the real-world application potentials of AI-supported approaches in the City of Freiburg, Germany. The article demonstrates how AI-supported methods can aide and expedite urban land use and adaptation planning to support complex decision-making that needs to balance different strategic goals and interests.

The intermittency of offshore wind speed can lead to contiguous periods of low wind power production, referred to as offshore wind energy droughts. There is a lack of global studies on offshore wind energy droughts under climate change. To address this gap, we evaluate offshore wind energy droughts under both historical (1985–2014) and future (2025–2054) climate conditions. We examine the frequency and duration of drought events for 40 Exclusive Economic Zones worldwide that are expected to host the bulk of future offshore capacity. Future projections are derived from 13 statistically downscaled global climate models, each combined with the Shared Socio‐economic Pathways SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. Across all climate‐change scenarios, the majority of regions, including the coasts of China, the United Kingdom, and Germany, are projected to experience more frequent and/or longer offshore wind energy droughts. However, the spread among individual climate models and Shared Socioeconomic Pathways scenarios is substantial, indicating considerable uncertainty in the development of offshore wind energy droughts. The results imply that, in many countries, it will be necessary to address the projected increase in offshore wind energy droughts under future climatic conditions through both mitigation and adaptation measures.

This study presents an operational citywide monitoring network designed to measure meteorological and human biometeorological variables at a high spatio-temporal resolution. The network is based on an in-house developed, generic data logging and monitoring platform, with 13 stations strategically placed at the pedestrian level on public street lights within the urban canopy layer of Freiburg im Breisgau, Germany. Over the first year of deployment (August 2022 to August 2023), the stations continuously collected high-resolution data (30 s intervals) with a minimal data loss rate of 2% for half of the stations which underscores the robustness of the network. The collected data includes Black Globe temperature, used to calculate the Physiologically Equivalent Temperature (PET) and other thermal comfort indices such as Tropical Night. A case study focused on a July 2023 heatwave showed that residential mid- to low-density areas experienced 16.5 to 18.7 h of extreme heat stress, while inner-city sites recorded the highest number of tropical nights ( a ,min (1h) >= 20 degrees C), with 5 to 6 nights, compared to 3 in outer areas. The findings demonstrate significant spatial variability in thermal stress across urban microclimates, particularly during extreme weather events. To address the gap in real-time data dissemination and science communication, we developed the uniWeather outreach platform and app, providing end-users and the public with free access to real-time data, following FAIR principles. This continuous data set is invaluable for urban climate modelers, offering real-time monitoring and insights into localized thermal stress, and can inform urban heat mitigation strategies and adaptation planning for policymakers and city planners.

In recent years, frequent dry and hot summer periods in Central Europe have caused irreversible damages to many forest ecosystems. The consequences are widespread tree mortality, including forest ecosystems in the Upper Rhine Valley. We compare two management responses to a highly impacted, mature Scots Pine (Pinus sylvestris) plantation in the Upper Rhine Valley at the ICOS Site DE-Har by assessing annual and seasonal carbon fluxes in the first seven years following the management response.At the non-invasively managed site, >60% of all former Pinus sylvestris trees died since 2018 and consequently the canopy opened up considerably. Dead and fallen trees were generally not removed. The site has undergone a significant regime change in which increased sunlight under the damaged/missing tree crowns has accelerated growth of a deciduous understory (mainly Tilia cordata, Carpinus betulus, and Fagus sylvatica among others). At the clear-cut site, all Pinus sylvestris trees were fully removed in autumn 2017, and new saplings consisting of various broad-leaf trees, more suited for hot and dry weather conditions (including Acer platanoids, Corlyus colurna, Carpinus betulus), were planted in spring 2018 and 2019. Due to extreme drought, almost all of the saplings died shortly after they were planted and the area now consists of grasses, shrubs and a few deciduous trees.We use concurrent eddy covariance measurements at the non-invasively managed site since 2019 and at the clear-cut site since 2021 to quantify the effect of the two management responses on net CO2 fluxes and partitioned gross primary productivity (GPP) and ecosystem respiration (Reco). On average over the period from 2019 to 2024, the non-invasively managed site has been a small CO2 source (NEE = +75 g C m-2 year-1), compared to 20 years ago, when the mostly healthy forest was still a considerable CO2 sink. Typically, the non-invasively managed site is a CO2 source during winter and autumn and a CO2 sink in spring and summer, except for the hot and dry summer of 2022. On average over the period from 2021 to 2024, the clear-cut site has been a substantial CO2 source (NEE = +460 g C m-2 year-1), mainly because of higher values of Reco. The NEE data of the clear-cut site also show a yearly cycle, with higher values in winter and autumn and lower values in spring and summer, nevertheless the clear-cut site was a CO2 source in all seasons during the last four years. The highest annual NEE values at both sites can be found in the hot and dry year 2022. Seven years after the clear-cut, both sites are still CO2 sources and it is uncertain whether and when either of these sites will become a CO2 sink.

Onshore wind energy stands as an effective alternative to conventional energy sources, offering significant benefits in climate change mitigation. However, non-dispatchable wind energy is vulnerable to energy droughts, which can lead to soaring energy market prices and power system blackouts. Concerns are mounting that energy droughts will intensify under climate change. There is a notable lack of global-scale studies that examine onshore wind energy droughts in the context of climate change. Consequently, this study investigates the hypothesis that both the frequency and duration of onshore wind energy droughts will intensify with climate warming. Utilizing a global dataset of long-term capacity factor time series, this research uses both a historical period (1989-2014) and a future period (2025-2054) under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate change scenarios for assessing wind energy drought properties in 80 countries. A drought severity classification reveals that, in the historical period, wind energy droughts are most pronounced in tropical regions. Nations with dominant temperate and/or continental climate and substantial installed wind capacity, such as China, the USA, and Germany, are particularly affected by intensifying wind energy droughts under climate change. A significant increase in both the number and duration of wind energy droughts occurs in 21 countries under the SSP2-4.5 scenario and in 37 countries under the SSP3-7.0 scenario. These results underline the urgent need for adaptive strategies to mitigate the impacts of wind energy droughts. Developing more resilient energy infrastructures and improved turbine efficiency will be crucial to addressing the challenges posed by wind energy droughts.

Floating photovoltaic (FPV) systems are increasingly deployed on gravel pit lakes to generate renewable energy and mitigate land-use conflicts. However, their environmental impacts on hydrological and ecological processes remain insufficiently studied. This study investigates the effects of a 1.5-MWp FPV system covering 8% of a 19-ha gravel pit lake in Germany. The General Lake Model (GLM-AED2) and Delft3D-FLOW were used to simulate FPV-induced changes. Meteorological data—including irradiance, air temperature, wind speed, and relative humidity—were recorded above and below the PV modules. Water quality data—including water temperature, dissolved oxygen, pH, dissolved organic carbon, and chlorophyll-a—were collected beneath the FPV and in open water. Mussel colonisation of the FPV substructure was assessed, and its filtration impact on water quality analysed. Macrophyte distribution was assessed beneath the FPV system and along the shorelines. Results showed a modelled 88% solar irradiance and 57% wind speed reduction beneath the FPV system. Water quality impacts were minimal and primarily influenced by mussels colonising the substructure. Macrophytes occurred in littoral zones up to 5.25 m deep up to 5.25 m deep, but habitat-typical species were scarce due to gravel extraction and herbivorous fish. These findings highlight complex interactions between FPV, mussel filtration, macrophytes, and human activities, suggesting that other anthropogenic factors may outweigh FPV impacts. Model simulations indicated that FPV coverage above 45% could destabilise thermal stratification and alter primary production. This study underscores the need for empirical monitoring and modelling to optimise FPV deployment and inform regulatory frameworks for sustainable development.

This study investigates whether combining singular value decomposition with wavelet analysis can provide new insights into the spatiotemporal complementarity between wind turbine sites, surpassing previous findings. Earlier studies predominantly relied on various forms of correlation analysis to quantify complementarity. While correlation analysis offers a way to compute global metrics summarizing the relationship between entire time series, it inherently overlooks localized and time-specific patterns. The proposed approach overcomes these limitations by enabling the identification of spatially explicit and temporally resolved complementarity patterns across a large number of wind turbine sites in the study area. Because complementarity information is derived from orthogonal components obtained through singular value decomposition of a wind power density matrix, there is no need to adjust for phase shifts between sites. Moreover, the complementary contributions of these components to overall wind power density are expressed in watts per square meter, directly reflecting the magnitude of the analyzed data. This facilitates a site-specific, complementarity-optimized strategy for further wind energy expansion.

Wind is among the primary abiotic forest disturbance agents in Europe and globally. Remote sensing techniques have been efficiently used for disturbance detection and assessment but are still less exploited in vulnerability modeling and mapping. Building on a pilot study, we further explore spaceborne spectral reflectance data for deriving proxy predictors of wind disturbance occurrence, based on the Sentinel-2 imagery and the FORWIND database. Random forest regression models are built and tested on random samples from an equidistant network of virtual plots, tailored to the disturbance records of two landscape-scale case studies from the Alpine and lowland regions of Central Europe. The information content of the proxy variables is subsequently interpreted by aerial imagery on smaller subsamples. The results reveal the predictive power of the models, despite the modeling difficulties associated with orographic complexity or the meteorological field. Trend relationships are found between the proxy predictors and forest structure and spatial heterogeneity related attributes, less commonly and not uniformly addressed by previous modeling studies. The findings demonstrate the capacities of simple methods for an initial, landscape-scale assessment and identification of (less) vulnerable forest structures in an objective manner.

Research concerning the general public and influencing decision-making necessitates timely dissemination of easily accessible results and data, with a focus on directly verifiable hands-on exploration rather than authoritative assessments in order to raise awareness and engage the public. This applies, for instance, to the high spatial and temporal resolution street-level weather and thermal comfort monitoring network operated in the City of Freiburg. Germany, by the University of Freiburg, to raise awareness for the significant spatial and temporal differences in, e.g., outdoor heat stress patterns in urban areas, which are crucial for informed urban planning and climate resilience. Addressing this gap, the uniWeather™ app and platform were developed to provide end-users, stakeholder and the general public with free, easily accessible near-real-time data and interpretation. With regard to the FAIR principles, the platform is being developed to support data form other research organisations such as universities, government agencies or companies that operate environmental sensor networks to be provided free of charge. uniWeather™ aims to encourage the sharing and access to data in near real-time by providing an easy-to-integrate service for tailored visualisation and interpretation.In June 2023, the uniWeather™ app and monitoring network were announced in a press release from the University of Freiburg and in a newspaper article providing access to maps and real-time data from 42 street-level weather stations in the Freiburg region within 60 seconds of measurement. The app was readily welcomed by the public, researchers and the city of Freiburg. The project was also well received at public outreach events such as the Eucor-MobiLab Roadshow 2023 in Freiburg (26-30 June 2023) and the exhibition DATEN:RAUM:FREIBURG (4-31 August 2023) of the city of Freiburg. With more than 1.5k users in the first few weeks and continued interest in further functionalities, the platform will be continued and further developed to address the needs of the general public and different scientific communities.

Climate change leads to spatiotemporal shifts in the global distribution of wind resources. Many studies anticipate declining mean wind speed in the mid-latitudes of the Northern Hemisphere, while predicting increases in mean wind speed in the tropics. However, average wind resource conditions represent only one aspect of the meteorological wind potential. The dynamics of temporal variability and spatial wind resource complementarity under climate change remain less well understood. Thus, this study aims to investigate the impact of climate change on the temporal variability of wind resources and the potential for complementary wind resource utilization. Time series of capacity factors were calculated for a global dataset of wind farm sites using an ensemble of 18 global climate models and statistical downscaling methods. The coefficient of variation was employed to assess temporal variability, while Spearman's rank correlation coefficient was used to evaluate complementarity. The results indicate that spatial complementarity of wind resources is rare in the historical period 1989-2014. In many regions of the Northern Hemisphere the temporal variability of wind resources is increasing in the future period until 2099, while the potential for complementary wind resource utilization remains low. In China and the USA, the median of the ensemble of climate models projects a significant increase in the coefficient of variation of almost 10 % by the end of the 21st century compared to the historical period under high radiative forcing. When combined with declining average wind resources and low potential for spatial complementarity, this creates deteriorating meteorological conditions for wind energy deployment. In contrast, conditions for wind energy expansion are improving in Brazil, as average capacity factors are increasing and the coefficient of variation is declining. The findings of this study should be considered when integrating wind energy into the power system, as they are critical for energy transformation and especially for balancing electricity supply and demand.

Timely information on the effects of the increasing intensity, frequency and duration of heatwaves on cities and critical infrastructure is needed for warning, emergency management and for developing context-specific climate adaptation strategies. Aside from the challenge of deploying sensor networks within built environments, there are hardly any operational city-wide networks that continuously measure and communicate human thermal comfort indices in public spaces. To address this gap, a two-tiered weather and outdoor human thermal comfort monitoring network was developed and deployed in Freiburg in 2022. The monitoring network comprises a total of 42 automatic weather stations primarily mounted on public lamp posts at a height of 3 m, with the Tier-I network consisting of 13 customised stations, which are equipped with an in-house developed data logging unit optimised for this application, that is extend by a spatially dense but less complex Tier-II network consisting of 29 commercial weather stations. Both networks collect data on air temperature, humidity and precipitation, with the Tier-I network providing additional data on wind, radiation, pressure, lightning, solar radiation and black-globe temperature to calculate human-biometeorological thermal indices such as the Physiological Equivalent Temperature (PET). Over the course of the first year of deployment (01-Sept-2022 to 31-Aug-2023), the stations have continuously collected high-resolution data (30 and 60 sec) with only little data loss. In a case study, the intra-urban differences in thermal comfort were analysed during the hot month of July 2023, in which five official heat warnings were issued by the German Meteorological Service (DWD). The results show expected intra-urban and urban-rural contrasts and that mid-density sites experience the highest number of summer days, totalling 22, compared to 19-20 in the city centre. The highest amount of moderate heat stress and higher (PET > 29°C) was observed in FRLAND (26,3%) compared to 13-19% at rural sites. Also more tropical nights were observed at inner city sites with 5-6, compared to 3 at outer, primarily suburban sites. Remote and rural sites reported no tropical nights. Over the full annual cycle and the entire network, the number of tropical nights ranged between 0 (rural) and 29 (inner city) per year. The highest number of summer days per year was recorded in industrial and suburban areas (up to 101) compared to 84-97 days in the city centre and 62-90 days at rural sites. The average annual air temperatures reveal a distinct long-term heat island with an annual mean temperature up to 14.0°C in the city centre, and 11.6°C - 12.7°C at rural sites of same elevation.These results highlight the benefit of continued monitoring for real-time assessments, efficient identification of hot-spots for climate adaptation strategies, and model evaluation and to improve our understanding of urban heat islands and human thermal comfort patterns. In addition, an outreach platform and mobile app (uniWeatherTM) have been developed to provide end-users and the public with free access to real-time data and interpretation following FAIR principles.