Search Results (2,382)

Monitoring thermal effluent is critical for assessing aquatic ecosystem health, yet traditional satellite remote sensing and in situ point measurements often fail to capture fine-scale thermal dynamics in narrow streams and complex coastal areas due to spatiotemporal resolution limitations. This study establishes a high-precision surface water temperature mapping protocol using a low-cost Unmanned Aerial Vehicle (UAV) equipped with an uncooled thermal infrared sensor (FLIR Vue Pro R) to overcome these observational gaps. We investigated two distinct hydrological environments—an inland stream and a coastal sea—to provide initial evidence for the applicability of an in situ-based linear regression calibration model across contrasting aquatic settings. The initial uncalibrated radiometric temperatures exhibited significant bias errors reaching up to 9.2 °C in the stream and 9.4 °C in the coastal area, primarily driven by atmospheric attenuation and environmental factors. However, the proposed calibration method dramatically reduced these discrepancies, achieving Root Mean Square Errors (RMSE) of 0.43 °C and 0.42 °C, respectively, with high determination coefficients (R² > 0.87). The derived high-resolution thermal maps successfully visualized the detailed diffusion patterns of thermal plumes, revealing a steep temperature gradient of approximately 13 °C in the stream discharge zone and a distinct 5 °C elevation in the coastal effluent area relative to the ambient water. These findings demonstrate that UAV-based thermal remote sensing, when coupled with a rigorous radiometric calibration strategy, can serve as a cost-effective and reliable tool for environmental monitoring, bridging the critical scale gap between local point measurements and regional satellite observations. Full article

In recent years, typhoon activity over the South China Sea (SCS) has intensified, and interactions between typhoons and mesoscale eddies profoundly regulate the regional oceanic environment and air–sea energy exchange. To systematically investigate the position- and polarity-dependent eddy responses to typhoon forcing, we developed a typhoon–eddy spatial matching algorithm and analyzed the global mesoscale eddy dataset (2006–2020) combined with China Meteorological Administration (CMA) best-track typhoon records. Composite and correlation analyses were employed to examine variations in the eddy surface available potential energy (SAPE) and sea-surface temperature (SST) within a 7-day window before and after typhoon passage, with the typhoon power dissipation index (PDI) used to quantify storm intensity. Composite results reveal distinct dual-asymmetric responses: (1) Energetically, eddies on the left side of typhoon tracks exhibit overall weakening, with anticyclonic eddies (ACEs) showing more pronounced energy decay; in contrast, right-side eddies undergo significant intensification, and cyclonic eddies (CEs) display stronger enhancement than ACEs. (2) Thermally, all eddy types experience net cooling after typhoon passage, with right-side eddies showing stronger SST reductions than left-side ones, and CEs exhibiting more intense cooling than ACEs. Time-scale correlation analyses further demonstrate that the eddy energy change rate (EECR) of left-side CEs, right-side CEs, and right-side ACEs is positively correlated with PDI, whereas left-side ACEs show no significant correlation. For the SST change rate (SSTCR), all types of eddy events exhibit significant negative correlations with PDI, with weaker correlations for CEs and stronger correlations for ACEs. This study demonstrates that the track-relative position of tropical cyclones and the polarity of pre-existing mesoscale eddies exert a systematic control on the observed eddy responses to tropical cyclone forcing in the SCS. These results provide observational constraints on the asymmetric oceanic responses induced by tropical cyclones and offer insights into the interpretation of typhoon–ocean interaction diagnostics in marginal seas. Full article

Porphyra suborbiculata exhibits strong heat tolerance and has considerable commercial potential under rising sea temperatures; however, its bioactive components remain insufficiently explored. In this study, a heat-tolerant new strain of P. suborbiculata (PS-M4), cultivated by the College of Fisheries, was used as the experimental material. Polysaccharides were extracted using an ultrasound-assisted composite enzymatic method, and extraction conditions were optimized through single-factor experiments and response surface methodology, yielding a maximum extraction yield of 12.45 ± 0.09%. Crude polysaccharides were further purified using a purification apparatus, yielding two fractions, designated PSP-I and PSP-II. Preliminary structural characterization showed that PSP-I possessed a weight-average molecular weight (Mw) of 26.149 kDa, a number-average molecular weight (Mn) of 11.267 kDa, and a polydispersity index of 2.321. Monosaccharide composition analysis indicated that PSP-I was predominantly composed of galactose. Fourier transform infrared spectroscopy (FT-IR) revealed typical polysaccharide functional groups, and scanning electron microscopy (SEM) analysis revealed a porous lamellar morphology. In vitro cell-based assays demonstrated that PSP-I significantly alleviated ultraviolet B (UVB)-induced damage in HaCaT cells by reducing intracellular reactive oxygen species (ROS) levels, enhancing antioxidant enzyme activities, inhibiting apoptosis, and downregulating the expression of matrix metalloproteinases (MMPs). These results suggest that PSP-I has potential as a functional ingredient for mitigating UVB-induced skin damage. 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

Ocean temperature and salinity structures are crucial in understanding ocean circulation and heat–salt transport processes. However, the high cost and limited spatiotemporal coverage of in situ observations make it difficult to reconstruct high-resolution three-dimensional temperature–salinity (T-S) fields. To address these limitations and the strong spatiotemporal heterogeneity of T-S structures in the South China Sea (SCS), the Smart Synthetic Ocean Profile (SSOP) model is proposed, which is a two-stage machine learning-based inversion framework for reconstructing subsurface T-S profiles from satellite surface data. The framework integrates localized training, adaptive model selection, and an error correction strategy. Using climate-state grids with a consistent spatiotemporal resolution as a baseline, multiple candidate regression models are independently trained for each grid point–depth layer–month combination, and the optimal model is selected through performance validation to generate initial T-S profiles. An error correction module is then introduced to refine temperature profile deviations, improving profile consistency and overall accuracy. Experiments using three independent observational periods from the SCS show that SSOP reliably reconstructs vertical T-S structures, particularly in the upper ocean and thermocline. Comparisons with in situ observations indicate that SSOP achieves improved accuracy relative to the Modular Ocean Data Assimilation System and climatology. Full article

In this study, we examined the interannual distribution patterns of the giant jellyfish Nemopilema nomurai and their relationship with marine environmental factors using field survey data collected from the East China Sea from 2020 to 2024. Acoustic surveys and jellyfish-specific trawl sampling were conducted to analyze their vertical distribution characteristics in relation to water temperature, salinity, seawater density, and chlorophyll-a concentrations. The results revealed that N. nomurai were mainly distributed in the mid-to-lower water layers at depths of 40–60 m, particularly in areas with strong stratification between the surface and bottom waters. Temperatures and salinity at these depths were relatively stable, and jellyfish were concentrated within high-density layers (1024.6–1025.0 kg·m⁻³). Correlation analysis revealed significant positive relationships between jellyfish occurrence frequency and salinity and seawater density, whereas no significant relationship was observed with water temperature. The chlorophyll-a concentrations varied between the years but did not directly correspond to the primary habitat depths of jellyfish, although synchronous variations in jellyfish abundance occurred in some years. These results indicate that the vertical distribution of N. nomurai is primarily controlled by physical oceanographic factors rather than by chlorophyll-a concentrations, reflecting an ecological adaptation for efficient energy use. Full article

Coral reefs around Hainan Island in the northern South China Sea represent a marginal reef system exposed to interacting climatic and anthropogenic stresses. This study used an optimized MaxEnt model, remote-sensing-derived coral reef occurrence data, key environmental variables, and CMIP6 climate projections to assess habitat suitability, identify key environmental thresholds associated with suitability change, and examine areas with potential refugial significance. The optimized model showed high predictive performance (mean AUC = 0.947). Bathymetry was the dominant predictor of habitat suitability, while sea surface temperature (SST) and dissolved oxygen (DO) concentration were also important predictors. Predicted suitability declined markedly when water depth exceeded 8.9 m or when multiannual mean SST exceeded 26.8 °C. Under current climate conditions, suitable habitat was limited in extent and showed strong spatial heterogeneity. Future projections indicated severe habitat contraction under SSP2-4.5 and SSP5-8.5, whereas under SSP1-1.9 suitable habitat contracted sharply by the 2050s but partially re-emerged by the 2090s. Under SSP1-1.9, parts of eastern Hainan, especially the coastal waters of southern Wenchang, Qionghai, and Wanning, may retain refugial potential. These results help clarify future spatial patterns of habitat persistence and decline, providing a scientific reference for regional conservation prioritization and adaptive management. 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

With the rapid exploitation of offshore wind energy in the Beibu Gulf (BG), understanding local low-level wind variability is essential for wind farm operations. This study examines the interannual relationships between the BG low-level winds in June and tropical sea surface temperature (SST) during 1993–2021 using multiple datasets. The meridional and zonal winds show negligible correlation on interannual time scales. Further analysis indicates that the meridional wind over the BG is significantly linked to the tropical Indian Ocean (TIO) and tropical Atlantic (TA) SST. The TIO warming is able to intensify the Western Pacific Subtropical High via eastward-propagating Kelvin waves, inducing southerly wind anomalies over the BG. In contrast, the TA warming modulates the Walker circulation and triggers westward-propagating Rossby wave trains, forming an anomalous Philippine anticyclone and associated southerly winds. The anomalous southerly winds associated with TIO (TA) warming are contributed by changes in both rotational and divergent wind components (primarily divergent wind component). Conversely, the zonal wind over the BG is significantly correlated with the tropical Pacific SST. The equatorial eastern Pacific warming excites westward-propagating Rossby waves, generating an anomalous anticyclone and resulting in westerly anomalies over the BG. Air–sea coupling links warm SST in the northwestern Pacific to a local anticyclonic circulation, forming easterly anomalies in the BG. Notably, the tropical SST associated zonal wind anomalies are primarily driven by rotational wind component. This study clarifies how tropical SST anomalies influence low-level winds over the Beibu Gulf and distinguishes the roles of rotational and divergent wind components, providing new insights into the predictability of local wind variability. Full article

Rapid changes in Arctic summer sea ice exert substantial influences on the polar climate system, maritime navigation, and resource exploitation, while subseasonal-to-seasonal (S2S) prediction of sea ice state remains highly uncertain. Using daily observations and reanalysis data of sea ice concentration (SIC) and thickness (SIT) from 1979 to 2023, together with concurrent atmospheric and oceanic fields, this study develops a multivariate linear Markov model to perform S2S predictions of Arctic summer sea ice. Sensitivity experiments with different variable combinations, weighting strategies, and modal truncation schemes are conducted, and predictive skill is systematically evaluated against persistence and climatological baselines. Results indicate that the model exhibits stable forecast skill without pronounced error accumulation at extended lead times. SIC predictability is primarily governed by its intrinsic spatiotemporal persistence and is significantly modulated by oceanic thermodynamic forcing, particularly sea surface temperature and surface net energy flux, highlighting a pronounced oceanic memory effect. In contrast, local atmospheric dynamic variables provide limited incremental skill. For SIT, predictability is dominated by its own historical state, with SIC contributing marginal short-term improvement and air–sea coupling exerting weak influence. Overall, the proposed framework effectively extracts dominant predictable signals with clear physical interpretability, providing a computationally efficient statistical approach for S2S prediction of Arctic summer sea ice. Full article

Coral reefs in the South China Sea (SCS) are critical for regional marine biodiversity and ecosystem services but face escalating threats from climate change and anthropogenic stressors. However, a holistic evaluation of habitat suitability spanning the distinct environmental gradients from low-latitude deep-water atolls to high-latitude marginal reefs remains limited. This study utilized high-resolution remote sensing data and the MaxEnt (Maximum Entropy) model combined with Principal Component Analysis (PCA) to systematically map potential habitat suitability and elucidate the multi-scale environmental drivers shaping the realized niche of SCS corals. The results revealed significant spatial heterogeneity characterized by a distinct “High South, Low North” latitudinal gradient, with Unsuitable areas dominating 85.5% of the study region, followed by Marginally Suitable habitats at 5.0%, while the northern Nansha Islands were identified as the core distribution area with the highest suitability and continuity. Minimum Phosphate (Min. Phos.) concentration and Sea Surface Temperature (SST) were identified as the core environmental factors determining the spatial distribution of coral reefs in the South China Sea. The optimal environmental ranges were identified as: SST between 28.52 °C and 29.41 °C, water depth shallower than 34 m, extremely low phosphate (0–0.005 mmol/m³), and low cumulative thermal stress (DHW < 0.83 °C-weeks). Crucially, PCA further quantified two potential climate refugia: low-latitude thermal refugia in the southern Nansha Islands, characterized by high environmental stability, and high-latitude marginal refugia in the Beibu Gulf, which offer physical buffering against warming, while necessitating targeted efforts to mitigate the risks of habitat degradation and eutrophication driven by intensifying anthropogenic activities These findings challenge the traditional conservation view relying solely on high-latitude migration, advocating for a climate-resilient spatial planning strategy that prioritizes strict protection of southern biodiversity source banks while enhancing the connectivity of northern marginal stepping stones. Full article

Italy and the surrounding seas are recognised as one of the European hotspots for tornadoes and waterspouts. In recent years, the town of Rosignano Solvay (on the Northern Tyrrhenian coast) experienced repeated waterspouts affecting the same areas, raising local concern about the possible influence of heated wastewater discharged into the sea by a nearby industrial site. We reconstruct the mesoscale meteorological conditions of four intense waterspouts near Rosignano Solvay using a limited-area weather model at a high-to-very-high resolution (inner domain grid spacing of 500 m; sensitivity tests at 100 m). At the reported event times, the intensity of key mesoscale precursors (low-level wind shear, 1 km storm-relative helicity, maximum updraft intensity, and lifting condensation level) is consistent with the values typically associated with EF1 (or stronger) tornadoes and waterspouts. The model systematically predicts the peak of instability indices 2–3 h earlier than the reported event times. For one case study, we conduct two sea surface temperature sensitivity experiments to assess the potential atmospheric impact of heated wastewater discharge (temperature increases of +1.5 K and +5 K over a 10 km² area). The resulting changes in instability indices are marginal, with differences of at most 3% relative to the control run. A simple mass-balance estimate for the modified sea patch suggests that, given the reported discharge rates, a plausible impact of the warm water released from the industrial site could lead to an increase in the local sea surface temperature of approximately +0.7 °C over two months. We conclude that synoptic and mesoscale conditions primarily govern waterspout initiation in this region, while the direct effect of the small warm coastal plume from the industrial discharge appears to be minor. Full article

Escalating climate volatility, particularly the El Niño/Southern Oscillation (ENSO), poses severe operational and financial risks to corporate sustainability in the energy sector. However, quantitative evidence regarding how macro-level climate shocks transmit to micro-level operational performance remains scarce. Integrating dynamic capability and social network theories, this study analyzes a panel of 103 Chinese listed energy firms (2005–2022) using System GMM, mediation, and moderation models. The results indicate that ENSO intensity significantly impairs performance; specifically, a 1 °C rise in sea surface temperature anomalies decreases firms’ return on assets (ROAs) by 0.142%. We identify supply chain resilience as a critical strategic mechanism for climate adaptation, where response capacity acts as the dominant mediating channel, while recovery capacity functions as an independent compensatory mechanism. Conversely, supply network complexity—across horizontal, vertical, and spatial dimensions—amplifies the negative impact of climate disruptions by hindering resource mobility. Heterogeneity analysis reveals that state-owned enterprises exhibit stronger institutional resilience, and firms in southern regions partially offset impacts through hydropower advantages. This study bridges climate science with operations management, offering strategic guidance for managers to configure resilient, sustainable supply chains capable of withstanding environmental turbulence. Full article

Sea surface temperature (SST) serves as a fundamental driver of ocean–atmosphere interactions and global climate variability, exhibiting strong nonstationarity, multiscale dynamics, and cross-variable coupling. However, current deep learning models often fail to capture these complex characteristics, limiting their ability to support accurate and physically consistent long-term SST prediction. To address these issues, we propose PAMSTnet, a unified deep learning framework for physics-informed adaptive multiscale spatiotemporal prediction. PAMSTnet leverages three-dimensional empirical wavelet transform (3DEWT) to learn interpretable multiscale spatiotemporal dynamics from raw observations, and applies multivariate spatiotemporal empirical orthogonal function (MSTEOF) to identify dominant cross-variable coupled modes. These physically meaningful representations are integrated into a deep ConvLSTM predictive network (DCPN) to support coordinated multiscale dynamical learning. Furthermore, PAMSTnet introduces physics-informed consistency learning (PICL) to enforce thermodynamic and dynamic constraints, enhancing physical consistency and interpretability. Extensive experiments demonstrate that PAMSTnet achieves superior performance against state-of-the-art baselines in long-term global SST prediction, reducing RMSE by 8.1% and improving ACC by 2.8% compared with the best-performing baseline, particularly under extreme climate events. Interpretation insights further highlight PAMSTnet’s adaptive representation of variable contributions and regional physical drivers. These findings position PAMSTnet as a promising paradigm for developing intelligent ocean prediction systems with enhanced physical consistency and interpretability. Full article

This study investigates the spatiotemporal variability of the near-surface air temperature lapse rate (NSATLR) in Calabria, a region representative of typical Mediterranean environmental and climatic conditions. Through the integration of observational datasets and model simulations, a global sensitivity analysis using the Sobol method, and Bayesian linear regression modelling across annual, seasonal, and monthly scales, the primary drivers of near-surface air temperature (NSAT) variability were identified. Results demonstrate that altitude is the dominant factor influencing temperature distribution, with minimal contributions from other geographical parameters such as latitude, longitude, and proximity to the sea. The Bayesian models yielded robust performance for mean and maximum temperatures, while minimum temperature proved more challenging to predict. Lapse rate analyses confirmed a consistent inverse relationship between temperature and elevation, with the steepest gradients observed for Tmin. In particular, a significant long-term decline in lapse rates over the past 70 years, especially during winter and autumn, points to accelerated warming at higher elevations, primarily driven by rising Tmin values. This trend suggests a gradual homogenization of temperature across altitudes, with important implications for ecosystem dynamics, snowpack stability, and climate-sensitive sectors such as agriculture and urban planning. Full article