Search Results (7,734)

Future climate change poses unprecedented challenges to agricultural production worldwide. Therefore, designing region-specific rotation patterns is crucial for achieving synergies among multiple objectives, including agricultural productivity and ecological conservation. Based on a long-term field experiment in the Northern Agro-pastoral Ecotone of China, we calibrated and validated the Agricultural Production Systems Simulator (APSIM) and simulated rotation patterns involving four representative crops under eight climate scenarios, including warming, extreme precipitation, and combined temperature–precipitation changes. Analysis combined with carbon footprint assessment was employed to quantitatively evaluate the productivity, ecological benefits, and economic returns of different rotation patterns. The results showed that warming generally reduced crop productivity and economic returns, weakened soil carbon sequestration, and increased net carbon emissions across rotation patterns. Increasing intensity of extreme precipitation further constrained the capacity of rotation patterns to enhance yields, improve incomes, and reduce carbon emissions. Under scenarios of warming and extreme precipitation, the faba bean–oat rotation pattern was found to be the most effective for increasing crop yields, while the faba bean–potato rotation is beneficial for enhancing the incomes from local agriculture. The potato–faba bean rotation pattern was most effective for environmental sustainability due to low net carbon emissions. The findings provide a scientific basis for developing diversified planting strategies with synergistic multi-objectives in the Northern Agro-pastoral Ecotone of China, contributing to food security and sustainable agricultural development under a changing climate focused on changes in temperature and precipitation. Nevertheless, the potential effects of rising atmospheric CO₂ concentrations may be incorporated in future studies. Full article

Aridity represents a fundamental climatic constraint governing water resources, ecosystem functioning, and agricultural systems in transitional climate zones. This study examines the spatial organization and temporal variability of aridity and thermal continentality in North Macedonia using observational records from 13 meteorological stations distributed across contrasting altitudinal and physiographic settings. The analysis is based on homogenized monthly and annual air temperature and precipitation series covering the period 1991–2020. Aridity and continentality were quantified using the Johansson Continentality Index (JCI), the De Martonne Aridity Index (I_DM), and the Pinna Combinative Index (I_P). Temporal consistency and trend behavior were evaluated using Pettitt’s nonparametric change-point test, linear regression, the Mann–Kendall test, and Sen’s slope estimator. Links between aridity variability and large-scale atmospheric circulation were examined using correlations with the North Atlantic Oscillation (NAO) and the Southern Oscillation Index (SOI). The results show a spatially consistent and statistically significant increase in mean annual air temperature, with a common change point around 2006, while precipitation displays strong spatial variability and limited temporal coherence. Aridity patterns display a strong altitudinal control, with extremely humid to very humid conditions prevailing in mountainous western regions and semi-humid to semi-dry conditions dominating lowland and southeastern areas, particularly during summer. Trend analyses do not reveal statistically significant long-term changes in aridity or continentality over the study period, although low-elevation stations exhibit weak drying tendencies. A moderate positive association between I_DM and I_P (r = 0.66) confirms internal consistency among aridity indices, while summer aridity shows a statistically significant relationship with the NAO. These results provide a robust climatic reference for North Macedonia, establishing a first climatological baseline of aridity conditions based on multiple indices applied to homogenized observations, and contributing to regional assessments of hydroclimatic variability relevant to climate adaptation planning. Full article

We investigate the multi-layer atmospheric impacts of Typhoon Hagibis (2019), which formed on 6 October, tracked across 12–35${}^{\circ }$ N and 135–155${}^{\circ }$ E, and made landfall on 12 October over the Izu Peninsula, central Honshu, Japan. We present a multi-layer study that involves the troposphere, stratosphere and upper ionosphere to examine the thermodynamic and electromagnetic coupling between these layers due to such extreme weather conditions. Using ERA5 reanalysis, we identify pronounced stratospheric temperature perturbations, elevated atmospheric gravity wave (AGW) potential energy, substantial spatiotemporal variability in the zonal (U) and meridional (V) wind components, relative humidity, and specific rainwater content throughout the cyclone’s evolution. Quantitatively, AGW potential energy increased from background levels of <5 J kg${}^{-1}$ to >40 J kg${}^{-1}$ near the cyclone core, while tropospheric wind anomalies reached $\pm 30$–40 m s${}^{-1}$, accompanied by relative humidity values exceeding 90% and specific rainwater content up to $1.5\times {10}^{-3}$ kg kg${}^{-1}$, indicative of vigorous moist convection and strong vertical energy transport. The ionospheric response, derived from GPS-based Total Electron Content (TEC) at 10 Japanese IGS stations, reveals vertical TEC (VTEC) perturbations whose amplitudes and temporal evolution vary systematically with GPS-station-to-typhoon-eye distance, including clear enhancements and reductions around the closest-approach day. These signatures indicate a measurable ionospheric response to cyclone-driven atmospheric forcing under geomagnetically quiet conditions, confirming that Hagibis produced vertically coupled disturbances linking stratospheric AGW activity with ionospheric electron density variability. Full article

This paper proposes a valorization approach for solid lavender residue, a by-product of the essential oil industry. The biomass residue was carbonized at atmospheric pressure and two temperatures (450 °C and 650 °C), followed by solvothermal modification with zinc ions (Zn²⁺, 3 and 5 mmol). The effects of temperature and Zn²⁺ incorporation on the elemental composition and morphology of the resulting biochar were examined using X-ray Fluorescence (XRF), Fourier Transform Infrared (FTIR) spectroscopy, and Scanning Electron Microscopy/Energy-Dispersive X-ray Spectroscopy (SEM/EDS) analyses. The applied Zn²⁺ modification was effective at both concentrations for the biochar obtained at both carbonization temperatures. However, a more uniform metal ion distribution was observed at 3 mmol, while at 5 mmol, a partial particle agglomeration occurred. Progressive degradation of the O–H, C=O, and C–O groups with increasing temperature and the presence of Zn–O-related interactions was observed. The results demonstrated consistent and reproducible trends, suggesting that controlled carbonization combined with Zn²⁺ incorporation can convert lavender residues into modified carbonaceous materials. Full article

Green roofs in Southern Europe are interest-growing nature-based solutions, capable of improving urban sustainability by positively impacting the water cycle, biodiversity, pollution, and, in some cases, energy consumption and carbon sequestration. Native plants adapted to Mediterranean climates exhibit drought-resistant traits, making them highly suitable for the challenging microclimate of green roofs. This microclimate features intense solar radiation, strong winds, and higher temperatures, in comparison to ground level, leading to increased atmospheric evaporative demand, driven by the interplay of radiation, wind, temperature, and humidity. Consequently, native plants from ecosystems resembling this microclimate are likely better suited for green roofs than local ground-level species. The current review synthesizes current knowledge on the use of native plants in Southern European green roofs, focusing on water management challenges given the region’s climate and scarce water resources. Out of roughly 12,500 native plant species in the Mediterranean basin, only about 124 have been examined in the past 20 years for green roof applications, with just 16% appearing in multiple scientific studies, highlighting a significant knowledge gap. The data indicate that ca. 85% of these species are perennials, valued for their low maintenance needs, a key consideration for green roof sustainability. Some of the studied species retain adequate aesthetic value when cultivated on green roofs with limited water availability. These species are mainly associated with four habitat types—rocky, coastal, dry, or well-drained environments—with a few linked to humid or adaptable conditions. This study aims to document the selection of drought-adapted native plant species best suited for green roof implementation in Southern Europe, contributing to enhancing sustainable urban design in the region, considering water management best practices and water use efficiency. Full article

The conversion of methane (CH₄) to value-added fuels (e.g., alcohol) is a promising technology for clean energy. However, conventional thermal methods of converting CH₄ to fuels require high temperatures (700–1100 °C) and have low conversion efficiency and selectivity. Therefore, it is highly desirable to develop novel cost-effective technologies that can convert CH₄ to fuels and chemicals at low temperature and atmospheric pressure with improved conversion efficiency, selectivity, and durability of products. The low-temperature or non-thermal plasma-assisted catalytic conversion of CH₄ is gaining increasing interest because the plasma species (e.g., electrons) have sufficient energies for producing higher hydrocarbons, alcohols, and oxygenates with higher yields and selectivity while reducing coke formation under mild conditions. The key challenges of this green technology are as follows: increasing conversion efficiency of CH₄, design of hybrid plasma reactors with proper catalysts and optimized conditions, addition of efficient oxidants (e.g., O₂ or CO₂) and diluents, etc., at low temperature and atmospheric pressure. In this regard, the present review aims to provide a comprehensive account of the current development of plasma-assisted catalytic conversion of methane, with focus on conversion efficiency of CH₄, selectivity and stability of products, and catalyst durability with the variation in plasmas, electrode design, and reactor configurations. Further, the review presents the current and future challenges. Full article

Topography plays a crucial role in shaping local urban microclimates and can drive the formation of cold-air pools in valley bottoms. This study examines the Eiras Valley (Coimbra, Portugal), a rapidly growing peri-urban area, to identify the conditions under which cold-air pools form and to characterize their spatial and vertical dynamics. Field measurements were carried out using Tinytag Plus 2 data loggers at the surface (≈1.5 m above ground) and mounted on an unmanned aerial vehicle (UAV) for vertical profiles, complemented by high-resolution thermal mapping through Empirical Bayesian Kriging. The results show that a nocturnal cold-air pool develops within the valley under clear, anticyclonic winter conditions, persisting into the early morning hours and dissipating after sunrise due to solar heating. In contrast, under overcast or summer conditions, no cold-air pooling was observed. The temperature inversion capping the cold-air pool was found at approximately 275 m altitude, inhibiting vertical mixing and trapping pollutants near the ground. These findings underscore the importance of topoclimatology in urban and regional planning, with implications for thermal comfort, air quality, and public health. The study contributes to urban climate research by highlighting how local topography and seasonal atmospheric stability govern cold-air pool formation in valley environments, supporting the development of mitigation strategies aligned with urban sustainability goals. Full article

To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. Despite its importance, there has been limited research on vegetation changes in the upper sections of the Irtysh River. In our study, we combined various datasets, including NDVI, temperature, precipitation, soil moisture, elevation, and land cover. We conducted several analyses, such as Theil–Sen median trend analysis, Mann–Kendall trend and mutation tests, partial correlation analysis, the geographical detector model, and wavelet analysis, to reveal the region’s pronounced warming and moistening trend in recent years, the response relationship between NDVI and the climate, and the primary drivers influencing NDVI variations. We also delved into the spatiotemporal evolution of NDVI and identified key factors driving these changes by analyzing atmospheric circulation patterns. Our main findings are as follows: (1) Between 1901 and 2022, the area’s temperature rose by 0.018 °C/a, with a noticeable increase in the rate of warming around 1990; precipitation increased by 0.292 mm/a. From 1950 to 2022, soil moisture exhibited a steady increase of 0.0002 m³ m⁻³/a. Spatial trend distributions indicated that increasing trends in temperature and precipitation were evident across the entire region, while trends in soil moisture showed significant spatial variation. (2) During 1982 to 2022, the vegetation greening trend was 0.002/10a, indicating a gradual improvement in vegetation growth in the study area. The spatial distribution of monthly average NDVI values revealed that the main growing season of vegetation spanned April to November, with peak NDVI values occurring in June–August. Combined with serial partial correlation and spatial partial correlation analysis, temperatures during April to May effectively promoted the germination and growth of vegetation, while soil moisture accumulation from June to August (or January to August) effectively met the water demand of vegetation during its growth process, with a significant promoting effect. Geographical detector results demonstrate that temperature exhibits the strongest explanatory power for NDVI variation, whereas land cover has the weakest. The synergistic promotional effect of multiple climatic factors is highly pronounced. (3) Wavelet analysis revealed that the periodic characteristics of NDVI and climate variables over a 2–15-year timescale may have been associated with the impacts of atmospheric circulation. Taking NDVI and climatic factors from June to August as an example, before 2000, temperature was the dominant influencing factor, followed by precipitation and soil moisture; after 2000, precipitation and soil moisture became the primary drivers. The North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were the primary atmospheric circulation patterns influencing vegetation variability in the region. Their effects were reflected in the inverse relationship observed between NAO/AO indices and NDVI, with typical phases of high and low NDVI closely corresponding to shifts in NAO and AO activity. This study helps us to understand how plants have been changing in the upper parts of the Irtysh River. These insights are critical for guiding efforts to develop the area in a way that is sustainable and beneficial for the environment. Full article

Based on a previously developed empirical model of global solar irradiance (EMGSI) at the Dome C station under all-sky conditions, and on good simulations of global solar radiation and its losses in the atmosphere caused by absorption and scattering components, as well as albedos at the top of the atmosphere (TOA) and the surface (TOAsur) during 2006–2016, similar estimations for the above parameters during 2018–2021 and 2006–2021 were computed by further application of this empirical model, and reliable calculations were also obtained, as in 2006–2016. The long-term variations in the above variables were thoroughly investigated during 2006–2021. For annual averages over 2006–2021, the calculated and observed global solar radiation decreased, and the absorption and scattering losses increased, well associated with increases in absorption and scattering atmospheric substances. Air temperature increased by 0.99 °C, showing regional climate warming. The mechanisms of air temperature increase were fully studied, and the basic mechanism reported previously was further confirmed. Additionally, the mechanisms of air temperature change vary with gases, liquids, and particles (GLPs) and with sites. Therefore, a proposal is recommended that, to reduce climate warming, all forms of direct emissions of GLPs and the secondary formation of new GLPs in the atmosphere produced by these directly emitted GLPs via chemical and photochemical reactions (CPRs) should be controlled. The estimated and satellite-derived albedos during 2006–2021 decreased at the TOAsur. An integrated understanding of solar radiation transfer in the atmosphere and of energy balance at the TOAsur is necessary. Full article

Greenhouse gases (GHG), accumulated in the atmosphere, are the main cause of climate change. In 2017, the increase in average temperature was about 1 °C (between 0.8 °C–1.2 °C) above pre-industrial levels. Global warming refers to the increase in air surface, sea surface, and soil surface temperature and according to IPCC (Intergovernmental Panel Climate Change), since the industrial revolution, C emissions are due to land use changes like deforestation, biomass burning, conversion of natural lands, drainage of wetlands, soil cultivation, and tillage. As the world population has increased, world food production has risen too with a subsequent increase in GHG emissions and agricultural production, which is worsened by climate change. Negative consequences are well known such as the loss in water availability and in soil fertility, and pest infestations which are climate change’s effects on agriculture activity. Climate change’s main aftermath is the frequency of extreme weather events influencing crop yields. As climate change exacerbates degradation processes, land management can mitigate its impact and aid adaptation strategies for climate change. About 21–37% of GHGs have been caused by the agriculture activity, so the application of Nature-based Solutions (NbS) like sustainable agriculture could be a way to reduce GHGs worldwide. The aim of this article is to review how NbS may mitigate GHG emissions from soil, with solutions defined as an integrated approach to tackle climate change and to sustainably restore and manage ecosystems, delivering multiple benefits. NbS is a low-cost tool working within and with nature, which holds many benefits for people and the environment. Full article

First continuous measurements of atmospheric CH₄ were carried out for one year (June 2023–May 2024) at Liberti Observatory of CNR-IIA, in a semi-rural site near Rome. Seasonal and diurnal variations were analyzed. CH₄ monthly mean concentrations showed maximum and minimum values in winter and summer, respectively, which agree with the other European trends. Minimum CH₄ values during summer could likely be due to a combination of favorable atmospheric mixing properties and increased atmospheric CH₄ oxidation. The correlation analysis showed that temperature, global radiation, and wind speed revealed significant negative correlations with this greenhouse gas, indicating the influence of local sources. However, poor correlations during different seasonal periods also suggested the role of air mass transport sources. The CH₄ concentrations exhibited clear diurnal cycles with daytime low and night-time high values, mainly driven by atmospheric stability conditions and photochemistry. A cluster analysis of air mass trajectories showed that CH₄ concentrations were influenced all year by anthropogenic emissions. Elevated concentrations arrived from NE Europe, except in winter when the influence of NW European and local contributions became more significant. Furthermore, level-3 XCH₄ data from the satellite TROPOMI showed a methane columnar concentration increase from 2018 to 2024 in agreement with the global annual increase from the NOAA network during the same period. Full article

Mixed-phase clouds, in which liquid droplets and ice crystals coexist at temperatures between $-38{}^{\circ }$C and $0{}^{\circ }$C, play a critical role in Earth’s radiation budget. Here, we assess the ability of climate and storm-resolving models to represent mixed-phase cloud properties and their hemispheric contrasts as inferred from satellite observations. We compare observations from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) with one global climate model, the Community Atmosphere Model version 6, Oslo configuration (CAM6-Oslo), and three storm-resolving models: the ICOsahedral Non-hydrostatic model (ICON), the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), and the Goddard Earth Observing System model (GEOS). Our results show that all models reproduce the geographic distribution of mixed-phase clouds but differ significantly in detail. CAM6-Oslo yields the closest agreement in hemispheric contrasts of supercooled liquid fraction and its relationship with the liquid effective radius. Our results highlight the role of aerosol–cloud interactions and microphysics schemes in determining model performance and demonstrate that storm-resolving models still do not overcome the challenge of representing mixed-phase clouds at global scales. Full article

Data scarcity limits robust assessment of urban overheating and its implications for building energy use, especially in complex-terrain cities such as those in mountain environments. In this context, Land Surface Temperature (LST) from thermal remote sensing can be used to map urban hotspots at high spatial resolution. Nevertheless, it does not provide the full set of hourly atmospheric variables required to run building energy simulations aimed at quantifying their impact and defining mitigation measures. Given these premises, this study proposes a methodology combining satellite-derived LST with ground meteorological measurements to assess Urban Heat Island (UHI) patterns and quantify how measured weather data selection affects urban building energy modeling (UBEM) outcomes. After selecting as a case study Bolzano, an Alpine city in Northern Italy, ECOSTRESS LST (2019–2025, May–August) was first processed and quality-screened to (1) compute ΔLST (urban–rural) and (2) identify diurnal and spatial overheating patterns across the building stock. Second, four measured weather datasets—one rural station and three urban stations located in the city core, in the industrial district, and in the urban edge—were used as boundary conditions in an EnergyPlus-based UBEM parametric campaign for 253 residential buildings, covering multiple envelope insulation levels and window-to-wall ratios. Results show strong diurnal asymmetry in surface overheating, with the largest contrasts in the afternoon and prominent industrial hotspots. Ground measurements confirm persistent intra-urban microclimatic differences, and the choice of measured weather dataset causes systematic shifts in simulated cooling demand and thermal comfort. The study highlights the need for weather data selection strategies based on microclimatic context rather than simple proximity, improving representativeness in UBEM applications for Alpine and other heterogeneous urban environments. Full article

Climate warming impacts arctic and subarctic lands, subjecting it to a generalized rise in soil temperature and causing changes in the surface cover. Land cover is a key control parameter for soil hydrothermal states, and its study by satellite imagery is necessary for monitoring boreal surface changes over time at large scales. Understanding the links between land cover and environmental conditions is also crucial to anticipate the impacts of atmospheric changes on continental surfaces. Sentinel-1 and Sentinel-2 data combined with a field campaign in July 2024 were used to produce a 10 m spatial resolution land cover map in the Abisko region, northern Sweden, covering 2180 km² and including three watersheds with an overall accuracy exceeding 94%. In parallel, temperature and precipitation fields were statistically downscaled at 100 m spatial resolution using topography, ordinary kriging based on weather stations and reanalysis. The relationships between surface areas and average summer temperature–precipitation clusters reveal that the vegetation distribution closely reflects the recent atmospheric conditions with the treeline following the 10.2 °C July–August isotherm in the considered area. This study provides a spatial basis for investigating the complex atmosphere–surface interactions and for assessing the sensitivity of boreal landscapes to ongoing climate warming. Full article

During tropical cyclones (TCs), intense exchanges of momentum, heat, and moisture occur across the air–sea interface. The present study was conducted to investigate the role of surface roughness parameterizations under such conditions. To this end, a series of sensitivity experiments was conducted with a focus on Tropical Cyclone Biparjoy, which originated from the Indian Ocean in 2023. The experiments evaluate the impact of different schemes for momentum, thermal, and moisture roughness length on TC track, intensity, significant wave height, and air–sea heat fluxes. The results indicate that the momentum roughness length scheme is critical for accurately forecasting TC track and intensity and for simulating significant wave height; furthermore, Drennan’s parameterization yielded slightly better results in this case, with the smallest track error (72.0 km MAE) among the momentum schemes. Under the premise that Drennan’s parameterization scheme has high accuracy in momentum roughness, sensitivity experiments on thermal and moisture roughness parameterization were conducted. The Drennan–Fairall2014 combination achieved the lowest errors in TC central pressure (4.25 hPa RMSE) and the maximum sustained wind speed (5.31 m/s RMSE). Thermal and moisture roughness mainly affects the efficiency of turbulent heat transfer between the ocean and the atmosphere and thus has a limited impact on the cooling of sea surface temperature, with SST RMSE differences among schemes within 0.3 °C. This effect is mainly confined to the uppermost ocean layer and does not significantly change the thermal structure of the upper layers. Full article