Search Results (215)

This study presents a comprehensive assessment of longwave radiation variability in the Arctic based on unique measurements collected at the North Pole drifting station SP-28 in 1987. The primary objective is to compare these historical observations with modern datasets from the Surface Heat Budget of the Arctic Ocean (SHEBA, 1997–1998) and the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019–2020) to evaluate long-term changes in the Arctic radiation regime. Continuous longwave radiation measurements were obtained using high-precision spectral pyrgeometers to identify Arctic haze. The results show that in 1987, Arctic haze layers enhanced the downward longwave flux by 15–20 W·m⁻² and increased atmospheric emissivity. In contrast, MOSAiC observations reveal emissivity values that closely match aerosol-free model calculations, indicating a substantial decline in Arctic haze and the diminishment of radiatively significant aerosol layers. This shift is in alignment with the long-term reduction of global anthropogenic sulfur dioxide emissions across the Northern Hemisphere. Full article

Nitrogen is a key determinant of both yield and quality in cereal crops; however, its efficiency is strongly influenced by environmental conditions and genotype. This study evaluated the impact of different sowing densities and nitrogen fertilization regimes on grain quality indices in four triticale (×Triticosecale Wittmack) varieties—Negoiu, Utrifun, Zvelt, and Tulnic—using a split-plot arrangement of the 4 × 3 × 3 type, under the climatic conditions of northwestern Romania. The experiment, conducted over two contrasting growing seasons (2021–2023), employed a split-plot design testing three sowing densities (450, 550, and 650 seeds/m²) and three fertilization levels: basic soil nitrogen fertilization, soil + foliar N-P-K application, and soil + foliar + biostimulant. The results indicated that climatic variability had a predominant effect on grain quality, followed by the genetic characteristics of the varieties and their response to water stress. In the drought-affected 2021–2022 season, the Zvelt variety recorded the highest protein content (14.2%), significantly outperforming the control (13.3%). Supplementary foliar fertilization and the use of biostimulants under drought conditions did not improve quality; in some cases, they led to significant decreases in protein content (from 14.36% to 13.69%) and thousand-kernel weight (TKW). Under optimal precipitation conditions in the 2022–2023 season, supplementary fertilization significantly improved hectoliter weight and TKW (reaching 46.7 g compared to 44.2 g in the soil-only treatments). Higher sowing densities (650 seeds/m²) generally led to decreases in hectoliter weight and TKW in favorable years. These results suggest that nitrogen fertilization can improve triticale quality. In this study, high yields, both quantitatively and qualitatively, appear to be mainly influenced by varieties and climatic conditions, especially water availability during critical growth stages. Full article

Land productivity assessments used in SDG 15.3.1 commonly rely on NDVI trends, which may be strongly influenced by precipitation variability and can therefore misrepresent actual land condition change, particularly in dryland environments where vegetation productivity responds rapidly to rainfall fluctuations. To address this issue, this study presents a land degradation assessment (2000–2022) using a fully reproducible Google Earth Engine workflow integrating high-resolution 30 m Landsat time-series NDVI, precipitation, land cover, and soil organic carbon datasets. The core methodological contribution is a precipitation-conditioned hybrid productivity framework that dynamically selects among NDVI trends, Rain-Use Efficiency (RUE), and Residual Trends (RESTREND) according to local rainfall dynamics. By adapting productivity metrics to precipitation conditions, the framework reduces precipitation-driven misinterpretation of vegetation trends, operationalizes a more climate-aware implementation of the land productivity (LP) sub-indicator within SDG 15.3.1, and enables systematic comparison of productivity metrics under contrasting rainfall regimes. Results for the 2015–2022 monitoring period, which included multiple drought years, indicate that $18\%$ of land showed declining productivity, $75\%$ remained stable, and $6\%$ showed improvement. Decline was spatially concentrated in arid and semi-arid regions, whereas irrigated and managed landscapes exhibited localized improvements. The hybrid indicator provides an additional option for LP assessment that explicitly accounts for precipitation variability, supporting more climate-sensitive interpretation of productivity trends. This transferable, reproducible methodology strengthens national capacity for SDG 15.3.1 reporting and offers a scalable framework for land degradation assessments in other drought-prone regions. Full article

Pinyon–juniper (PJ) woodlands, one of the most extensive mature and old-growth woodland types in the Western United States, provide critical ecological, cultural, and economic benefits but face increasing threats from climate change, altered disturbance regimes, invasive species, and pests. We developed the PJ Woodland Climate Adaptation Management Menu, a decision support tool designed to guide adaptive, climate-informed management of PJ ecosystems, particularly within the Colorado Plateau ecoregion. The menu was created through an iterative, collaborative process involving literature review, integration of strategies from existing adaptation frameworks, and extensive input from scientists, land managers, and community partners during workshops and focus groups. The menu links specific, evidence-based approaches to each of six broad strategies, including soliciting community input, mitigating disturbance, enhancing and maintaining biodiversity, conserving ecotones, timing actions for optimal outcomes, and accepting climate-driven changes when appropriate. It is intended for use with the Adaptation Workbook to help managers connect local goals and climate vulnerabilities to tailored management tactics. Hypothetical scenarios demonstrate the menu’s application to contrasting PJ woodland conditions, from die-off events to old-growth maintenance. Lessons learned during development underscore the value of early stakeholder engagement, cross-sector collaboration, and balancing diverse ecological objectives. This menu offers a flexible, transferable framework to strengthen climate resilience in PJ woodlands and serves as a model that could improve adaptation planning in other dryland forest ecosystems. Full article

Based on monthly meteorological observations from 57 stations in Jiangsu Province during 1961–2022, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated to characterize regional dry–wet variability. Empirical Orthogonal Function (EOF) analysis was applied to extract the dominant spatially coherent dry–wet modes, and cross-wavelet analysis was further employed to examine, in the time–frequency domain, the mode-specific responses to multiscale climate drivers, including the El Niño–Southern Oscillation (ENSO), Sunspot Number (SSN), Arctic Oscillation (AO), and Pacific Decadal Oscillation (PDO). The results show that dry–wet variability in Jiangsu Province is primarily organized by a regionally coherent mode (EOF1, explaining 56.3% of the total variance) and a north–south dipole mode (EOF2, explaining 17.8%), with the zero-value line of EOF2 closely aligned with the Huaihe River–Subei Irrigation Canal climatic transition zone. The temporal coefficient of EOF1 (PC1) exhibits a significant regime shift around 2013, followed by a pronounced wetting trend across the entire region. This change may reflect recent hydroclimatic adjustments in the study area, although the present study does not attempt a formal attribution of the respective thermal and precipitation contributions. In contrast, the temporal coefficient of EOF2 (PC2) undergoes an abrupt change around 1980, indicating a transition of the spatial dry–wet pattern from “southern drought–northern flood” to “southern flood–northern drought,” broadly consistent with an interdecadal climatic transition. Cross-wavelet analysis further reveals that PC1 is closely associated with ENSO at interannual timescales, with a lag of approximately 4–6 months, while its long-term variability shows time–frequency coherence with SSN. PC2 also exhibits time–frequency coherence with SSN at longer timescales, with an apparent phase transition around the 1980s; however, this low-frequency signal should be interpreted cautiously because the underlying physical mechanism remains uncertain. Overall, this study shows that dry–wet variability in Jiangsu Province is organized by two leading spatial modes with distinct temporal evolution and scale-dependent climate linkages. These findings provide new evidence for understanding hydroclimatic variability in monsoon transition zones and offer a basis for spatially differentiated drought–flood risk assessment. Full article

This study presents a fire risk prediction framework applied to two conservation units within the Atlantic Forest biome (AFb): Serra da Gandarela National Park (PNSG), Minas Gerais, and Campos de Palmas Wildlife Refuge (RVSCP), Paraná. Daily climate data (2001–2023), remote sensing vegetation indices Normalized Difference Vegetation Index (NDVI) and Normalized Multi Band Drought Index (NMDI), fire foci, and estimates of soil volumetric moisture were integrated to analyze the climatic and environmental drivers of fire occurrence and to develop predictive models. Sea Surface Temperature (SST) anomalies in the Niño 3.4 region revealed the influence of El Niño–Southern Oscillation (ENSO) variability on local hydrometeorological dynamics. Vegetation indices and soil moisture data reinforced this relationship, with NMDI values below 0.4 and sharp declines in volumetric moisture indicating water stress during the dry season. Kernel density maps identified clusters of fire foci during this period, confirming the strong seasonality of fire occurrence. Based on climatic predictors and environmental indicators, fire risk indices were developed for each conservation unit and validated using independent data. Model performance showed moderate explanatory capacity, with coefficients of determination ranging from 0.53 to 0.68 and high agreement between estimated and observed values. Validation stratified by ENSO phases (Neutral, El Niño, and La Niña) demonstrated stable performance across contrasting climatic regimes, indicating temporal resilience of the modeling framework. Overall, the integration of climate data, spectral indices, and soil moisture information improves the ability to anticipate fire risk in Atlantic Forest conservation units, providing a useful tool to support prevention, monitoring, and decision-making in protected areas. Full article

Temperature extremes represent a major constraint for the cultivation of yellow passion fruit (Passiflora edulis Sims f. flavicarpa), a tropical crop increasingly exposed to heat waves and chilling events under climate change. Glycine betaine (GB) is a widely studied osmoprotectant in plants, yet its influence on metabolic responses of passion fruit under contrasting temperature stresses remains poorly characterized. This study investigated the effects of exogenous GB on primary metabolite profiles of passion fruit seedlings subjected to heat (25, 35, and 45 °C) and cold (25, 15, and 5 °C) conditions. Seedlings were treated with GB (100 mM) or left untreated, and leaf metabolites were quantified using GC–MS-based metabolomics. Heat exposure was associated with pronounced changes in amino acids, organic acids, sugars, polyamines, and γ-aminobutyric acid (GABA), while GB-treated plants showed altered levels of proline, GABA, polyamines, and selected tricarboxylic acid intermediates. Under cold conditions, several amino acids and organic acids decreased, whereas soluble sugars accumulated, particularly in GB-treated plants. Principal component analysis revealed distinct metabolic configurations under heat and cold treatments and indicated that GB modified metabolite profiles in a stress-dependent manner rather than restoring control-like states. These findings describe how GB is associated with shifts in central carbon and nitrogen metabolism under contrasting temperature regimes, providing a metabolomic perspective on stress-related metabolic adjustments in passion fruit. Full article

Surface water extent (SWE) is a key indicator of the regional water balance in dryland environments. However, the hydrological processes regulating SWE responses remain poorly constrained. Focusing on the Mu Us Sandy Land (MUSL), this study integrates multi-source remote sensing and hydrological datasets to investigate the long-term evolution of SWE and, critically, to distinguish the hydrological linkages between SWE dynamics and water storage variability in endorheic and exorheic regions during 1987–2024. An improved water extraction method was implemented on the Google Earth Engine platform, and SWE dynamics were interpreted within a water-balance framework supported by attribution analysis using machine learning. The results show that total SWE exhibited a significant increasing trend (7.95 km² yr⁻¹, p < 0.05) during 1987–2024, primarily driven by permanent SWE, while fundamentally different hydrological regimes governed SWE evolution. In the endorheic basin, SWE exhibited strong co-variation with subsurface water storage, with soil moisture and groundwater storage changes occurring concurrently with SWE changes. In contrast, no synchronous increase in SWE with groundwater storage was observed in the exorheic region. Instead, SWE expansion was mainly associated with accelerated groundwater storage depletion and reservoir construction. These contrasting patterns indicated that SWE dynamics in the endorheic basin were primarily controlled by subsurface water storage, whereas in exorheic regions they were largely driven by human-induced water redistribution rather than increases in total water storage. These findings highlight the importance of integrated surface–subsurface water management for sustaining long-term water security under climate change and increasing human water regulation. Full article

Intensity–Duration–Frequency (IDF) curves are the cornerstone of hydraulic infrastructure design, yet standard methodologies often fail to account for the complex dependence structure of rainfall characteristics and the non-stationary effects of climate change. This study develops a robust Regional Copula Framework for the Kastoria Lake basin, Greece, utilizing sub-hourly rainfall records from four meteorological stations (2007–2024). We employ a forensic data quality control process to pool 277 independent storm events. Unlike traditional approaches, our analysis demonstrates that the Generalized Extreme Value (GEV) distribution (ξ = 0.348) significantly outperforms the standard Lognormal distribution in modeling heavy-tailed rainfall intensities. The dependence between storm duration and intensity was found to be consistently negative (τ = −0.35), a structure best captured by the Rotated Gumbel (90°) copula, which physically reflects the region’s convective storm dynamics. Trend analysis revealed a statistically significant decrease in peak intensity (τ = −0.14) coupled with an increase in storm duration (τ = 0.22), a hydro-climatic shift that contrasts with increasing intensity trends reported in the wider Balkan region. These findings suggest a regime transition from flash-flood dominance to volume-critical events, necessitating updated design criteria that integrate both multivariate dependence and local climatic non-stationarity. Full article

Compound Drought and Heatwave (CDH) events increasingly threaten terrestrial carbon uptake, yet the spatiotemporal heterogeneity of Gross Primary Productivity (GPP) responses in urban agglomerations remains unclear. This study analyzed CDH impacts in China’s three major urban agglomerations, namely the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions, using ERA5 and satellite GPP data (GOSIF and FluxSat) for representative CDH years (2007 for BTH; 2022 for YRD and PRD). CDH conditions exhibited a coherent hot–dry coupling, with temperature anomalies of 0.46–1.26 K and soil moisture deficits of −0.042 to −0.169 m³ m⁻³, accompanied by enhanced atmospheric dryness. Pronounced spatial heterogeneity in GPP responses aligned with regional climatic regimes and ecosystem types. The water-limited BTH region exhibited significant GPP deficits, with anomalies of −1.13 Standard Deviations (STD) and −0.96 STD for GPP_FluxSat and GPP_GOSIF, respectively. Conversely, the energy-limited regions showed positive anomalies: the YRD recorded +0.32 and +1.79 STD, while the PRD reached +1.86 and +1.06 STD for GPP_FluxSat and GPP_GOSIF, respectively. Mechanistically, the north–south contrast suggests a transition from water-limited vulnerability to energy-limited resilience, with vegetation traits and management (e.g., potential irrigation buffering in croplands and deeper water access in forests) modulating sensitivity to atmospheric dryness. These findings provide quantitative benchmarks for improving regional carbon-cycle assessments and adaptation planning under increasing compound extremes. Full article

Annual area burned correlates with temperature and fuel aridity, yet extreme wildfire outcomes arise from a small fraction of fires and rapid-growth days. This asymmetry indicates that thermodynamic favorability sets background susceptibility but does not determine when extreme growth occurs. This Perspective proposes a cross-scale framework that distinguishes susceptibility from regime-conditioned event-scale realization. At seasonal and regional scales, temperature and humidity influence fuel dryness, ignition likelihood, and fire-season length, explaining substantial interannual variability in area burned. These variables vary smoothly in space and retain signal under aggregation. By contrast, extreme fire growth occurs during short-lived synoptic configurations that organize winds, pressure gradients, and stability into discrete opportunity windows that permit sustained spread. The strongest winds governing rapid spread are intermittent, terrain-structured, and often unresolved in coarse datasets or aggregated indices. Within these windows, terrain interactions, organized flow, and fire–atmosphere feedbacks can amplify growth until circulation patterns shift. Extreme wildfire behavior therefore operates as a gated joint-probability process requiring the coincidence of susceptibility (S), dynamical weather opportunity (W), and ignition (I). Separating susceptibility from realization reconciles strong climate–fire correlations with the dynamical control of event-scale extremes. Full article

The study is based on Land Surface Temperature (LST) and Air Temperature data and Nonparametric Data Envelopment Analysis (DEA) technique to evaluate heat efficiency and detect anomalies in the thermal regime in the Eastern Black Sea Region, particularly in Hopa and Artvin, during the period 2000–2024. The regulating role of the Black Sea has resulted in Hopa having the warmest and most stable temperature patterns, with daytime temperatures 1.8 to 3.7 °C higher than Artvin. Previous DEA analysis of daytime temperatures has shown that the 2018–2020 period had the highest daily temperatures, while the 2001–2010 decade was characterized by the highest nighttime temperatures. A future heat map based on Monte Carlo simulation using six climate change scenarios indicates that in the most optimistic case, assuming a temperature increase of +0.8 °C, efficiency scores could increase as high as 0.995. On the other hand, if global warming leads to a sudden temperature increase above +7.2 °C, there is a 21.7% climate efficiency loss. Sensitivity analysis showed that technological innovation and good governance are the main positive factors affecting climate efficiency. Random Forest (RF) and SHapley Additive Explanations (SHAP) analyses were applied to determine the impact of climate factors on DEA scores and also indicated areas requiring risk assessment. The findings highlight the importance of considering location-specific climate adaptation strategies. Based on the observed thermal contrasts between coastal and inland environments, potential adaptation considerations may include urban heat management and agricultural water stress in coastal areas such as Hopa, and cold-climate resilience and energy-efficient infrastructure in inland locations such as Artvin. Full article

Climate change is reshaping climatic regimes worldwide, with direct consequences for species distributions and ecosystem services, including those provided by wild edible fungi. In Mexico, these fungi represent a resource of ecological, cultural, and economic importance, yet their vulnerability to future climate scenarios remains poorly understood. This study evaluated projected changes in the potential distributions of ten frequently consumed edible fungal species in central Mexico under current and future climate scenarios (2061–2080 and 2081–2100). Ecological niche models were performed using Maxent with 19 bioclimatic variables, spatial block cross-validation, and model tuning based on the AICc and partial ROC curves. Additionally, associations between species suitability and land use and vegetation variables were assessed through multivariate analyses. The most influential predictors were the mean temperature of the warmest quarter (71.929%), temperature seasonality (47.589%), and annual precipitation (41.962%). Current models identify high environmental suitability primarily within the TMVB, Sierra Madre Occidental, and southern mountainous regions such as Chiapas. Future projections revealed heterogeneous, species-specific responses. Suitability gains were projected for Cantharellus cibarius (21–50%), Infundibulicybe gibba (20–34%), Lactarius deliciosus (13–48%), and Lyophyllum decastes (8–141%), whereas Helvella crispa (1–99%), Agaricus campestris (2–88%), and Russula brevipes (74–100%) showed marked contractions under high-emission scenarios. These contrasting patterns suggest that climate change may restructure the spatial availability of edible fungi in Mexico, potentially affecting forest sustainability and the biocultural practices of communities that depend on these resources. Integrating species-specific climatic sensitivity into conservation and sustainable management strategies will be essential under future climate conditions. Full article

This study develops and validates an integrated framework that combines UAV multispectral imagery and canopy structural metrics with gridded climatic variables to predict soybean (Glycine max (L.) Merrill) foliar potassium (K) status and grain yield. Field experiments were conducted over three consecutive growing seasons (2022–2023, 2023–2024, and 2024–2025) under different potassium fertilisation strategies and environmental conditions. Machine learning models, particularly the random forest algorithm, were applied to multisource datasets, including UAV-derived canopy structural traits (height and canopy area), spectral indices (NDVI), meteorological variables, and fertilisation information. The foliar K prediction models achieved high accuracy (R² up to 0.85), while the yield prediction models achieved R² values between 0.71 and 0.81. The inclusion of the potassium rate and fertilisation strategy further improved model performance, highlighting the strong influence of potassium supply and fertilisation management on plant physiological responses. Interestingly, compared with those required to stabilise grain yield, foliar potassium saturation occurred at substantially higher K₂O rates, indicating the occurrence of luxury potassium uptake. The association of UAV-derived canopy metrics with this pattern suggests that remote sensing may help detect subtle nutritional dynamics that are not directly reflected in yield responses. Model interpretability using SHAP analysis identified relationships within the analysed dataset that were consistent with physiological expectations, with positive contributions associated with canopy vigour and negative contributions associated with thermal stress. In addition, probabilistic SHAP analysis provided a decision-oriented perspective by quantifying yield probabilities under contrasting potassium management regimes and climate scenarios. Overall, within the experimental conditions studied, the proposed framework enabled a rapid assessment of crop nutritional status, yield prediction, and the evaluation of fertilisation strategies. The integration of UAV data, climatic variables, and machine learning provides an interpretable basis for potassium management and soybean yield forecasting within the experimental conditions studied, while broader transferability requires external validation. Full article

Ocean warming driven by climate change is altering regional circulation patterns and the balance of hydrodynamic forcings in semi-enclosed seas. Understanding how these changes affect ocean circulation and stratification is critical, as they directly influence marine productivity and ecosystem functioning in highly sensitive regions such as the Gulf of California. This study examines the hydrodynamic response of the Gulf of California under three climate change scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5) projected from 2015 to 2100 using the CNRM-CM6-1-HR global climate model. We evaluate changes in sea surface temperature, surface circulation, and the relative contributions of dominant dynamic forcing mechanisms at annual and interannual scales. Results reveal a basin-wide warming trend accompanied by an increased frequency of extreme heat events. Surface current velocities weaken throughout the Gulf, exhibiting a consistent negative trend, with the strongest decline occurring under SSP5–8.5 in the central basin ($-5.1\times {10}^{-4}$ m s⁻¹ year⁻¹). Wind speed also shows a general decreasing tendency, contributing to reduced circulation intensity and enhanced stratification. The analysis of dimensionless numbers indicates moderate but consistent changes in the relative balance among inertial, baroclinic, and wind-driven processes. Although their proportions vary slightly across scenarios, the dominant forcing hierarchy remains largely preserved, suggesting a gradual modulation in forcing intensity rather than a fundamental reorganization of the hydrodynamic regime. These findings highlight spatial contrasts in climate sensitivity within the Gulf of California and underscore the importance of regional-scale assessments for anticipating future changes in circulation dynamics and marine ecosystem responses. Full article