Search Results (5,462)

Arctic regions are warming rapidly, elevating extinction risks and accelerating ecosystem change, yet widely used bioclimatic datasets rarely represent polar-specific ecological constraints. Here we present BioClimPolar_2300 v1.0, a raster bioclimatic dataset designed for terrestrial Arctic biodiversity research under climate change. The dataset includes 33 gridded bioclimatic layers at a 10 km spatial resolution, covering seven discrete temporal intervals from 2010 to 2300 AD. In addition to conventional variables used globally, BioClimPolar_2300 incorporates three polar-relevant constraint domains: (1) polar day–night phenomena (PDNs), including degree-day metrics during polar night and polar day; (2) temperature-defined seasonal cycles (TSCs), including seasonal temperature, precipitation, aridity, and season length; (3) hot/cold stresses (HCSs), capturing indices of extreme summer heat and winter cold. Precipitation during snow-melting days (P_melting) is also included due to its relevance for species depending on subnivean habitats. Climate fields were extracted from CMIP6 models and statistically downscaled to 10 km using a change-factor approach under a polar projection. Monthly fields were linearly interpolated to derive daily grids, enabling the computation of variables that require daily inputs. Validation against observations from 30 Arctic weather stations indicates performance suitable for biodiversity applications, and two exemplar range shift case studies (one animal and one plant) illustrate biological relevance and provide practical guidance for data extraction and use. BioClimPolar_2300 fills a key gap in Arctic bioclimatic resources and supports more realistic biodiversity assessments and conservation planning through 2300. Full article

The Southwest Asia Anticyclone (SWAA) plays a pivotal role in governing the regional precipitation regime. This study analyzes the structure and spatiotemporal variability of the SWAA core at the 850, 700, and 500 hPa levels, and its relationship with precipitation across Southwest Asia. Monthly precipitation and geopotential height (HGT) data were obtained from ERA5 reanalysis with a 0.25° spatial resolution over 1940–2023. The results showed that in September the SWAA core migrates from northwestern and western Saudi Arabia, shifting southward during colder periods and retreating landward in warmer periods. At 850 hPa, the core is absent during June–August, while at 700 hPa it is positioned over the southeastern Caspian Sea. The SWAA has intensified in recent decades, and its directional shifts exert a marked influence on precipitation variability: northeastward, eastward, southeastward, and southward displacements enhance rainfall, whereas northward, northwestward, and westward movements suppress it. Overall, the intensity and positioning of the SWAA are strongly linked to precipitation patterns in Southwest Asia. These findings contribute to refining precipitation and climate projections and offer practical implications for water resource management and agricultural planning in the region. Full article

Global climate change is intensifying extreme weather events such as floods and heatwaves, posing serious threats to lake ecosystems. The Huayanghe Lakes experienced a catastrophic flood in 2020 and a prolonged heatwave in 2022, providing an opportunity to compare zooplankton responses to contrasting extreme climate events. Based on summer water quality and zooplankton data collected from the Huayanghe Lakes during 2020–2023, this study used 2021 and 2023 as reference years to examine the summer zooplankton community state during the post-event period following extreme climate events. In 2020, 43 species belonging to 14 families and 25 genera were recorded, dominated by rotifers such as Polyarthra euryptera and Trichocerca spp., with a mean density of 239.26 ind./L. In contrast, 34 species from 12 families and 21 genera were identified in 2022, with dominant taxa including Diurella rousseoeti, Trichocerca cylindrica and Thermocyclops hyalinus, resulting in a lower mean density of 149.17 ind./L. Zooplankton density and species richness were higher during flood conditions but declined under prolonged heatwave conditions. Mantel correlation analysis identified water transparency as the primary environmental factor shaping zooplankton communities. Overall, zooplankton responded more strongly to flooding than to sustained heatwaves, indicating that different extreme climate events amplify the regulatory roles of distinct environmental drivers. Full article

Progressive climate change and building morphology influence the specific microclimate of built-up areas. This has a fundamental role in research on energy use and thermal comfort inside buildings. Most studies using data for dynamic energy simulation are based on information collected at meteorological stations in rural areas. This can lead to erroneous predictions. The main goal of the study was to combine two simulation tools—ENVI-met for microclimate predictions around historical building layouts, and DesignBuilder for assessing indoor comfort. Illustrating the impact of input data on simulation results was conducted using three types of weather data: (1) from a field campaign, (2) from a suburban station, and (3) from the typical meteorological year. The obtained results confirm that the highest precision was achieved in analyses where information obtained at a real scale in the city centre was used as boundary conditions (field measurements: MAPE = 0.6 °C, RMSE = 0.7 °C). The next step was to estimate the thermal sensations inside the living room of the existing residential building. Thermal comfort was determined using the operative temperature as an indicator. Incorporating realistic urban weather inputs enhanced the reliability of indoor comfort modelling and provided a more accurate basis for planning thermal resilience in historic residential buildings. Full article

Urban populations are exposed to elevated local temperatures compared to surrounding rural areas due to the urban heat island (UHI) effect, which increases health risks and energy demand. The literature highlights that accurately quantifying UHIs at broader territorial scales remains challenging because of limited microscale climate data availability and, at the same time, the difficulty of increasing the spatial coverage of the outcomes. Within the PRIN2022-PNRR CRiStAll (Climate Resilient Strategies by Archetype-based Urban Energy Modeling) project, this work addresses these limitations by coupling Urban Building Energy Modeling with archetype-based representation of urban form and high-resolution climatic data. Urban archetypes are defined as representative microscale configurations derived from combinations of urban canyon geometries and building typologies, accounting for different climatic zones, use categories, and construction periods. The proposed methodology was applied to the city of Turin (Italy), where representative urban blocks were identified and modeled to evaluate key urban context metrics under short-, medium-, and long-term climate scenarios. The UHI effect was assessed using Urban Weather Generator, while energy simulations were performed with CitySim. The urban archetype approach enables both fine spatial resolution and extensive spatial coverage, supporting urban-scale mapping. Full article

Soil erosion is largely driven by climate change and land use dynamics. The objective of this study is to assess the dynamic variation in erosion under the combined effects of precipitation and land use change in the Tigrigra watershed, located in the mountainous region of the Middle Atlas. The RUSLE (Revised Universal Soil Loss Equation) model is used in the methodological approach to estimate soil loss based on various parameters such as precipitation, soil, topography, land cover, and conservation practices. Geographic Information Systems (GIS) and remote sensing tools are essential for applying this method. In addition, the CA-Markov model (cellular automata), which models and predicts land use changes over time, is used to project future land cover scenarios that influence soil erosion dynamics. The research focuses on four previous periods (1991–2000, 2001–2010, 2011–2015, and 2016–2023), as well as a future period (2024–2050), considering two climate scenarios, RCP 2.6 and RCP 4.5. Precipitation data from local weather stations and the CMIP5 climate model were used to calculate the R factor (precipitation erosivity). Land cover analysis was performed using Landsat satellite images (30 m resolution) integrated into the CA-Markov model to calculate the C factor (land cover management). The results show that erosion has gradually decreased over both past and future periods, mainly due to variations in precipitation and vegetation cover. It should be noted that the period from 1991–2000 to 2016–2023 shows higher erosion compared to the future periods, with a maximum value of 17.83 t/ha/year recorded between 1991 and 2000. For the future period 2024–2050, a continuous decrease in erosion is observed under both scenarios, with an average value of 15.30 t/ha/year for the RCP2.6 scenario and 15.86 t/ha/year for the RCP4.5 scenario, with erosion remaining slightly higher under RCP4.5. Overall, erosion decreases across both historical (1991–2023) and projected (2024–2050) periods due to reduced rainfall erosivity. The northern part of the basin is particularly prone to erosion due to the low vegetation cover. The results indicate that areas susceptible to erosion require conservation measures to reduce soil loss. Implementing sustainable agricultural practices is crucial for maintaining long-term soil health and preventing degradation. However, some limitations of the study, such as the lack of data on conservation practices and daily precipitation, might affect the overall robustness of the findings. Full article

As the most frequent dust event in the Tarim Basin (TB), persistent floating dust significantly impacts the regional weather and climate. Long-term analysis (2015–2024) showed that the occurrence of persistent floating dust is significantly associated with the presence of the nocturnal low-level jet (NLLJ). To investigate this potential linkage, the Weather Research and Forecasting model with Chemistry (WRF-Chem) was used to simulate the persistent floating dust event accompanied by the NLLJ in the TB from 29 to 31 July 2006. Results indicated that a typical NLLJ occurred during the event, with an easterly jet core (>12 m/s) near 850-hPa facilitating the westward dust transport and accumulation within the TB, as well as strong convergence and vertical uplift on its front side elevating the dust layer height (DLH). Quantification showed that the NLLJ enhanced dust column concentrations (mean maximum > 100 mg/m²) and DLH (mean maximum > 300 m) over the central and western TB, and the cumulative maximum increase in dust emissions exceeded 200 mg/m², in the NLLJ region. Furthermore, nocturnal dust radiative forcing intensified the NLLJ by up to 1 m/s, thereby establishing a positive feedback mechanism. These results reveal the crucial role of the NLLJ in persistent floating dust events and enrich our understanding of such events in the TB. Full article

With the increasing frequency extreme weather events associated with climate change, real-time monitoring of rainfall intensity is critical for water resource management, disaster warning, and other applications. Traditional methods, such as ground-based rain gauges, radar, and satellites, face challenges like high costs, low resolution, and monitoring gaps. This study proposes a novel real-time rainfall intensity monitoring method based on deep learning and audio signal processing, using acoustic features from rainfall to predict intensity. Conducted in the semi-arid region of Northwest China, the study employed a custom-designed sound collection device to capture acoustic signals from raindrop-surface interactions. The method, combining multi-feature extraction and regression modeling, accurately predicted rainfall intensity. Experimental results revealed a strong linear relationship between sound pressure and rainfall intensity (r = 0.916, R² = 0.838), with clear nonlinear enhancement of acoustic energy during heavy rainfall. Compared to traditional methods like CML and radio link techniques, the acoustic approach offers advantages in cost, high-density deployment, and adaptability to complex terrain. Despite some limitations, including regional and seasonal biases, the study lays the foundation for future improvements, such as expanding sample coverage, optimizing sensor design, and incorporating multi-source data. This method holds significant potential for applications in urban drainage, agricultural irrigation, and disaster early warning. Full article

Validation of ICON model configurations optimized over a limited domain is essential before accepting new semi-empirical parameters that influence the behavior of subgrid-scale schemes. Because such parameters can modify the dynamics of a numerical weather prediction (NWP) model in highly nonlinear ways, we analyze one season of forecasts (December 2022, January and February 2023) generated with the NWP ICON-LAM through the lens of machine learning–based diagnostics as a complement to traditional evaluation metrics. The goal is to extract physically interpretable information on the model behavior induced by the optimized parameters. This work represents the first part of a wider study exploring machine learning tools for model validation, focusing on two specific approaches: Empirical Orthogonal Functions (EOFs), which are widely used in meteorology and climate science, and autoencoders, which are increasingly adopted for their nonlinear feature extraction capability. In this first part, EOF analysis is used as the primary tool to decompose weather fields from observed reanalysis and forecast datasets. Hourly 2-m temperature forecasts for winter 2022–2023 from multiple regional ICON configurations are compared against downscaled ERA5 data and in situ observations from ground station. EOF analyses revealed that the optimized configurations demonstrate a high skill in predicting surface temperature. From the signal error decomposition, the fourth EOF mode is effective particularly during night-time hours, and contributes to enhancing the performance of ICON. Analyses based on autoencoders will be presented in a companion paper (Part II). Full article

Baroclinic wave resonance, particularly Rossby waves, has attracted great interest in ocean and atmospheric physics since the 1970s. Research on Rossby wave resonance covers a wide variety of phenomena that can be unified when focusing on quasi-stationary Rossby waves traveling at the interface of two stratified fluids. This assumes a clear differentiation of the pycnocline, where the density varies strongly vertically. In the atmosphere, such stationary Rossby waves are observable at the tropopause, at the interface between the polar jet and the ascending air column at the meeting of the polar and Ferrel cell circulation, or between the subtropical jet and the descending air column at the meeting of the Ferrel and Hadley cell circulation. The movement of these air columns varies according to the declination of the sun. In oceans, quasi-stationary Rossby waves are observable in the tropics, at mid-latitudes, and around the subtropical gyres (i.e., the gyral Rossby waves GRWs) due to the buoyant properties of warm waters originating from tropical oceans, transported to high latitudes by western boundary currents. The thermocline oscillation results from solar irradiance variations induced by the sun’s declination, as well as solar and orbital cycles. It is governed by the forced, linear, inviscid shallow water equations on the β-plane (or β-cone for GRWs), namely the momentum, continuity, and potential vorticity equations. The coupling of multi-frequency wave systems occurs in exchange zones. The quasi-stationary Rossby waves and the associated zonal/polar and meridional/radial geostrophic currents modify the geostrophy of the basin. Here, it is shown that the ubiquity of resonant forcing in (sub)harmonic modes of Rossby waves in stratified media results from two properties: (1) the natural period of Rossby wave systems tunes to the forcing period, (2) the restoring forces between the different multi-frequency Rossby waves assimilated to inertial Caldirola–Kanai (CK) oscillators are all the stronger when the imbalance between the Coriolis force and the horizontal pressure gradients in the exchange zones is significant. According to the CK equations, this resonance mode ensures the sustainability of the wave systems despite the variability of the forcing periods. The resonant forcing of quasi-stationary Rossby waves is at the origin of climate variations, as well-known as El Niño, glacial–interglacial cycles or extreme events generated by cold drops or, conversely, heat waves. This approach attempts to provide some new avenues for addressing climate and weather issues. Full article

Climate change-induced extreme weather events increasingly threaten public health, with a particularly acute impact on the mental well-being of urban populations. This study evaluates regional disparities in mental health outcomes associated with climate-induced extreme weather in urban environments, where social and infrastructural vulnerabilities exacerbate environmental stressors. We synthesized data from cohort and cross-sectional studies using both traditional frequentist and Bayesian meta-analytic frameworks to assess the mental health sequelae of extreme weather events (e.g., heatwaves, floods, droughts, and storms). The traditional meta-analysis indicated a significant increase in the odds of adverse mental health outcomes (OR = 1.32, 95% CI: 1.07–1.57). However, this global estimate was characterized by extreme heterogeneity (I² = 95.8%), indicating that the risk is not uniform but highly context-dependent. Subgroup analyses revealed that this risk is concentrated in specific regions; the strongest associations were observed in Africa (OR = 2.23) and Europe (OR = 2.26). Conversely, the Bayesian analysis yielded a conservative estimate, suggesting a slight reduction in odds (mean OR = 0.92, 95% CrI: 0.87–0.98). This divergence is driven by the Bayesian model’s shrinkage of high-magnitude outliers toward the high-precision data observed in resilient, high-income settings (e.g., USA). Given the extreme heterogeneity observed (I² = 95.8%), we caution against interpreting either pooled estimate as a universal effect size. Instead, the regional subgroup findings—particularly the consistently elevated risks in Africa and Europe—offer more stable and policy-relevant conclusions. These findings emphasize urgent, context-specific interventions in urban areas facing compounded climate social risks. Full article

Public health impacts of non-optimal temperatures and air pollution have received insufficient attention in Southeast Europe, one of the most air-polluted regions in Europe, simultaneously pressured by climate change. This study employed a multimodal approach to characterize the microclimate and air quality and conduct a health impact assessment in the three biggest cities in Bulgaria. Simulation of atmospheric thermo-hydrodynamics and assessment of urban microclimate relied on the Weather Research and Forecasting model. Concentrations of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) were calculated with a land-use regression model. Ischemic heart disease (IHD) hospital admissions were linked to daily measurements at background air quality stations. The results showed declining trends in PM_2.5 but persistent levels of NO₂, especially in Sofia and Plovdiv. Distributed lag nonlinear models revealed that, in Sofia and Plovdiv, PM_2.5 was associated with IHD hospitalizations, with a fifth of cases in Sofia attributable to PM_2.5. For NO₂, an increased risk was observed only in Sofia. In Sofia, the risk of IHD was increased at cold temperatures, while both high and low temperatures were associated with IHD in Plovdiv and Varna. Short-term effects were observed in response to heat, while the effects of cold weather took up to several weeks to become apparent. These findings highlight the complexity of exposure–health interactions and emphasize the need for integrated policies addressing traffic emissions, urban design, and disease burden. Full article

Enhanced silicate rock weathering (ESRW) has been proposed as a promising carbon dioxide removal strategy, yet its carbon sequestration pathways, durability, and ecosystem dependence remain incompletely understood. Here, we synthesize evidence from field experiments, observational studies, and modeling to compare ESRW-induced carbon dynamics across forest and cropland ecosystems using a unified SOC–SIC dual-pool framework. Across both systems, ESRW operates through shared geochemical processes, including proton consumption during silicate dissolution and base cation release, which promote atmospheric CO₂ uptake. However, carbon fate diverges markedly among ecosystems. Forest systems, characterized by high biomass production, deep rooting, and strong hydrological connectivity, primarily favor biologically mediated pathways, enhancing net primary productivity and mineral-associated organic carbon (MAOC) formation, while facilitating downstream export of dissolved inorganic carbon (DIC). In contrast, intensively managed croplands more readily accumulate measurable soil inorganic carbon (SIC) and soil DIC over short to medium timescales, particularly under evapotranspiration-dominated or calcium-rich conditions, although SOC responses are often moderate and variable. Importantly, only a subset of ESRW-driven pathways—such as MAOC formation and secondary carbonate precipitation—represent durable carbon storage on decadal to centennial timescales. By explicitly distinguishing carbon storage from carbon transport, this synthesis clarifies the conditions under which ESRW can contribute to climate change mitigation and highlights the need for ecosystem-specific deployment and monitoring strategies. Full article

Against the backdrop of global warming and an increase in extreme weather events, the freshwater aquaculture industry, which is highly dependent on environmental conditions, faces severe challenges. As the world’s largest producer of freshwater aquaculture, the stability of China’s production is crucial for ensuring national food security and rural livelihoods. This study utilizes provincial panel data from China (2007–2023) and employs the HP filter separately for each province to construct a “climate-induced output” indicator. A panel data model is then established to examine the impact and transmission mechanisms of extreme climate risks on freshwater aquaculture output. The findings reveal the following: (1) Climate risks exert a significant negative impact on freshwater aquaculture production, with extreme low temperatures, droughts, and extreme rainfall having particularly pronounced effects. (2) Natural disasters play a partial mediating role between extreme climate and output, accounting for approximately 26.35% of the total effect. (3) From an overall perspective, both increased labor productivity and greater operational scale can significantly mitigate the negative impacts of climate risks. This study provides empirical evidence to inform policies on optimizing regional aquaculture layouts, enhancing climate resilience, and formulating adaptive strategies. Full article

Seed germination performance and storability are fundamental components of seed quality and critical for successful crop establishment. However, information on the impact of different crop production systems on the quality and storability of seed material is still limited. Therefore, the aim of this study was to compare the effects of different crop production systems (ecological, integrated, conventional, and monoculture) on seed germination and predisposition for storage. The research was carried out on four varieties of winter wheat. Seed material was produced within a two-year period, during which different weather conditions occurred. Four germination-related traits were assessed: germination capacity NS (%), total germination (TG%), time to reach 50% germination (t50) and the area under the germination curve (AUC). The results demonstrated that the cultivar, the cultivation system and the year of study had a significant impact on germination characteristics. The ecological system ensured the highest germination rate in fresh seeds. However, in the CD test, the conventional system demonstrated the highest levels of stress resistance and stability, suggesting the best storage potential. The significant system × variety interaction demonstrates the importance of accurate matching of the genotype to the growing conditions to ensure optimal seed quality. Furthermore, the data demonstrated a strong influence of climatic conditions in the year of production, which is crucial for seed vigor. Full article