Search Results (2,389)

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters. Full article

The significant interannual variability in Madden–Julian Oscillation (MJO) intensity remains incompletely understood. Empirical orthogonal function (EOF) analysis reveals that the first three leading EOF modes of the annual mean MJO intensity are significantly correlated with the Quasi-Biennial Oscillation (QBO), Eastern Pacific El Niño-Southern Oscillation (ENSO), and Central Pacific ENSO. Focusing on the distinct EOFs related to three key tropical interannual variabilities, we conduct an investigation into the potential governing processes through which the changes in background mean states impact MJO intensity based on the MJO moisture mode theory. Observations suggest that the accumulation of moist static energy (MSE) during MJO moistening phases and its dissipation during drying phases play a crucial role in regulating MJO amplitude. At the interannual timescale, regions characterized by positive EOF values display positive (negative) MSE tendency anomalies during MJO moistening (drying) phases, leading to amplified MSE accumulation (dissipation) throughout the MJO lifecycle and subsequently facilitating an increase in MJO amplitude. Conversely, regions with negative EOF values exhibit opposing trends. Further analysis reveals that these MSE tendency anomalies are mainly associated with the zonal advection term, which is influenced by interannual changes in the background mean MSE and low-level winds. The spatial pattern of the background mean MSE is strongly linked to sea surface temperature (SST) anomalies, with low-level background winds aligning well with the horizontal gradients of SST anomalies. Full article

The Galápagos Islands, located in the eastern equatorial Pacific approximately 1000 km west of mainland Ecuador, are highly sensitive to the El Niño–Southern Oscillation. However, the mechanisms linking sea surface temperature (SST) variability to daily rainfall extremes remain poorly understood. Focusing on Santa Cruz Island, one of the main islands of the archipelago, we analyzed the response of daily rainfall to four El Niño events (1991–1992, 1997–1998, 2015–2016 and 2023–2024) and their relationship with SST spatial patterns. Our approach followed three steps: (1) Daily rainfall observations were classified using percentile thresholds; (2) SST spatial clusters were identified using Local Indicators of Spatial Association (LISA), which explicitly incorporates spatial autocorrelation to distinguish warm and cold SST spatial clusters; and (3) SST cluster metrics (mean temperature, spatial extent, and persistence) were extracted and related to rainfall intensification. Results show that El Niño can increase daily extreme rainfall (>P95) in frequency and in totals, with the strongest and most persistent signal during 1997–1998; in contrast, the 2015–2016 event, despite being classified as very strong by the Oceanic Niño Index (ONI), exhibited a limited and short-lived >P95 rainfall response in Santa Cruz. The link between SST clusters and extreme rainfall strengthened during El Niño (r from ~0.40 to 0.70). Correspondingly, SST clusters underwent significant spatial reorganization in their extent and persistence. Contrasts were most evident in the central–southern domain, where 1997–1998 showed strong warm incursion and persistent ≥28 °C coverage, while 2015–2016 remained more spatially constrained and less coherent. The area where clusters reached mean SST ≥ 28 °C became widespread in 1997–1998 (98.55%), whereas it remained more localized in 1991–1992 (30.28%), 2015–2016 (27.02%), and 2023–2024 (26.55%) and was absent in neutral years (0%). Persistent warm-cluster coverage increased from neutral conditions (38.53%) in 1991–1992 (47.49%), 1997–1998 (53.42%), and 2023–2024 (42.97%), but was lower in 2015–2016 (34.53%). Overall, these results provide a process-oriented link between SST cluster organization and event-to-event differences in Galápagos rainfall extremes, highlighting the value of local SST metrics beyond basin-scale ENSO indices. Full article

Tropical cyclones (TCs) are among the most dangerous types of weather, originate over warm tropical oceans and can seriously harm people, infrastructure, ecosystems, and country economies. The Arabian Sea is an important area for the development of TCs, but not much research has been done on how cyclones behave there over time and in different seasons. This study looks at TC activity over the Arabian Sea from 1982 to 2021, focusing on TC tracks, energy metrics, including the accumulated cyclone energy (ACE) and power dissipation index (PDI), and TC duration. The results show a big change in TC tracks over time and between seasons. In the pre-monsoon, northwest and northeast tracks are the dominant tracks, whereas in the post-monsoon, northwest and westward tracks are the most common. There has been a big increase in the ACE, the PDI, and the lifespan of TCs, especially in the post-monsoon season, over the second half of the study. The study also looks at how large-scale synoptic characteristics, like sea surface temperature (SST), vertical wind shear (VWS), upper-level winds, sea level pressure (SLP), and relative humidity (RH) affect the behavior of TCs. The results show that higher SSTs and lower VWSs have made TCs stronger and last longer. Also, upper-level winds, SLP, and RH are significant for changing the paths of TCs. This study provides a comprehensive, seasonally resolved look at how TC activity has changed over the past four decades in the Arabian Sea. It also gives us new insights into how environmental factors have affected TC behavior over time. Full article

Understanding the reproductive physiology of marine fish is critical for sustainable fisheries management, particularly under environmental variability. This study evaluated seasonal changes in body parameters (condition factor, Kn, and gonadosomatic index, GSI, as proxies for body condition and reproductive status, respectively) and biochemical composition (P, proteins; G, glucose; L, lipids; fatty acids; and bioenergetic ratios L/P, LG, all as proxy of integrated biochemical condition) of female gonads in Strangomera bentincki, a key pelagic species in the Humboldt Current System (HCS) off south-central Chile. Moreover, environmental factors (sea surface temperature and chlorophyll-a) were also analyzed to explore their influence on the FA profile of gonads. Female body parameters showed significant seasonal variations, with high values of Kn and GSI in autumn and spring, respectively. The biochemical composition also revealed significant seasonal variation in protein and glucose content, with the highest protein levels in winter and elevated glucose in autumn. While total lipid and energy content remained relatively stable across seasons, the L/P and L/G ratios presented seasonal variations. Similarly, the fatty acid composition showed pronounced seasonal differences, particularly with increased polyunsaturated fatty acids (e.g., DHA) in winter. The SST was the environmental factor with the greatest influence on the seasonal variations in the gonadal FA profile. Altogether, these findings suggest a partial capital breeding strategy in S. bentincki, where reproductive investment depends on both accumulated reserves and environmental conditions during reproduction. This study underscores the importance of incorporating reproductive biochemical indicators into ecosystem-based fisheries management models to improve assessments of stock health and reproductive potential. Full article

We analyze the evolution of atmospheric and surface physical properties over the region of the Earth observed by the Meteosat Second Generation (MSG) satellites during the period 2005–2024. Long-term changes are detected in the observed radiances, with a decrease in the solar domain (−1.3%) and an increase in the thermal infrared domain (+0.4%), consistent with trends reported by independent broadband radiometers such as CERES. The outgoing solar radiance (OSR) exhibits a marked decline, which we associate with a reduction in low-level cloud cover within the nominal Meteosat field of view (MFoV) centered at 0° longitude. Changes in atmospheric CO₂ concentration also contribute to the observed radiative imbalance at the top of the atmosphere (TOA). Instrument calibration stability and inter-satellite homogenization across the MSG series are explicitly addressed, enabling the detection of robust interdecadal signals. By subdividing the MFoV into 60 regional sectors, we characterize spatial variations in cloud amount at low and high atmospheric levels and relate these changes to regional TOA radiative imbalances and concurrent variations in Atlantic sea surface temperature (SSTs). The spectral information provided by SEVIRI allows a more detailed attribution of radiative changes than broadband observations alone from other instruments. In particular, radiances measured in the atmospheric split-window region near 11 µm are shown to be sensitive to variations in low-tropospheric humidity, which exhibits a widespread decadal-scale increase. The results indicate a close coupling between cloud-cover changes, radiative fluxes, and SST evolution on the recent interdecadal time scale. The observed decrease in low-level total cloud cover is independently in line with ECMWF ERA5 reanalysis data. These findings highlight the value of long, stable geostationary observations for investigating atmosphere–ocean interactions and their role in regional climate variability. Full article

Accurate regional ocean forecasting requires models that are both computationally efficient and capable of representing predictive uncertainty. This work investigates ensemble learning strategies for sea surface temperature (SST) forecasting using Graph Neural Networks (GNNs), with a focus on how input perturbation design affects forecast skill and uncertainty representation. We adapt a GNN architecture to the Canary Islands region in the North Atlantic and implement a homogeneous ensemble approach inspired by bagging, where diversity is introduced during inference by perturbing initial ocean states rather than retraining multiple models. Several noise-based ensemble generation strategies are evaluated, including Gaussian noise, Perlin noise, and fractal Perlin noise, with systematic variation of noise intensity and spatial structure. Ensemble forecasts are assessed over a 15-day horizon using deterministic metrics (RMSE and bias) and probabilistic metrics, including the Continuous Ranked Probability Score (CRPS) and the Spread–skill ratio. The results show that, while deterministic skill remains comparable to the single-model forecast, the type and structure of input perturbations influence uncertainty representation, particularly at longer lead times. Ensembles generated with spatially coherent perturbations, such as low-resolution Perlin noise, achieve improved calibration and lower CRPS compared to purely random Gaussian perturbations. These findings highlight the role of noise structure and scale in ensemble GNN design, indicating that specifically structured input perturbations can improve ensemble diversity and calibration without additional training cost. These results provide a methodological contribution toward the study of ensemble-based GNN approaches for regional ocean forecasting. Full article

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