Search Results (2,092)

The Arctic is warming approximately four times faster than the global mean, accelerating retreat of marine-terminating glaciers. Changes in glacier extent are linked to environmental factors, and their accurate quantification depends on the measurement methods used. This study compares five terminus change quantification methods applied to Austre Torellbreen, analyses terminus position changes of four outlet glaciers of the Amundsenisen Glacial System—Paierlbreen, Austre Torellbreen, Vestre Torellbreen, and Recherchebreen—in SW Svalbard, over 1975–2022, and assesses environmental controls on glacier retreat. Curvilinear box and GTT emerge as the most broadly applicable methods. Multi-centreline, Rectangle box, and Curvilinear box methods form the most internally consistent group, while GTT diverges moderately from this group. The Centreline method deviates most strongly from all others and is unsuitable for short-term analysis. A ~15° change in fjord orientation caused the Rectangle box to underestimate cumulative recession by ~330 m relative to the Curvilinear box, confirming that rectilinear approaches are limited to glaciers with low fjord sinuosity. Fjord depth and surge phase are likely key modulators of the environmental signal: deep-water, marine-terminating fronts show the strongest associations with sea surface temperature and runoff, whereas shallow fjords and restricted near-terminus water circulation weaken the oceanic imprint. Land-terminating sections of glaciers retreat approximately 3.4 times more slowly than marine counterparts and show no significant annual correlations with environmental variables. The terminus record constrains the timing and magnitude of surge-related frontal advance at Paierlbreen (1993–1995, ~280 m), Vestre Torellbreen (2008–2013, ~170 m), and Recherchebreen (2018–2020, ~660 m). Full article

This study presents an integrated statistical framework for identifying representative large-scale climate teleconnection indices associated with total burned area and for supporting the selection of climate predictors in wildfire-related statistical models. A large set of seasonally resolved climate indices, including the North Atlantic Oscillation (NAO—two versions), the Arctic Oscillation (AO), the Atlantic Multidecadal Oscillation (AMO), the Mediterranean Oscillation (MO—two versions), the East Atlantic–West Russia pattern (EAWR), the Tropical North Atlantic (TNA), and the Atlantic Meridional Mode (AMM), was examined. Because many of these indices describe related atmospheric and oceanic processes, dimensionality reduction and predictor selection were required to limit multicollinearity. Principal component analysis (PCA) was first used to identify groups of interrelated climate indices, followed by partial correlation analysis to distinguish redundant predictors from those retaining independent information with respect to total burned area. Finally, LASSO regression was applied to evaluate the relative explanatory contribution of candidate indices and to perform automatic variable selection. The PCA solution identified ten rotated components explaining 82.31% of the total variance. The results indicate that several seasonal NAO and MO indices contain highly overlapping information, whereas selected indices, particularly MOI2 spring and MOI2 summer, retain comparatively stronger independent associations with total burned area. The integrated PCA–partial correlation–LASSO framework provides a systematic approach for reducing redundant climate predictors and identifying large-scale climate signals that may be informative for understanding variability in total burned area and for supporting statistical analyses of wildfire–climate relationships. Full article

The Indian Ocean Dipole (IOD) and the El Niño-Southern Oscillation (ENSO) are two major modes of interannual climate variability that interact across the Indo-Pacific region. Although the autumn IOD is known to influence ENSO with a lag of about one year, the decadal stability of this relationship remains poorly understood. Here we investigate the decadal variability of the lagged IOD–ENSO relationship using observations and 30 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Observational analyses reveal pronounced non-stationarity in the lagged IOD-ENSO linkage, characterized by a stronger (weaker) relationship during the negative (positive) phase of the Atlantic Multidecadal Oscillation (AMO). CMIP6 models exhibit a wide spread in their ability to reproduce this behavior, with only a subset capturing the observed decadal modulation. The inter-model differences are consistent with variations in the simulated amplitude of AMO variability. Models with more realistic AMO variability tend to better reproduce the observed decadal variability of the lagged IOD-ENSO linkage. These results suggest that AMO variability may be one contributing factor to the modulation of the lagged IOD–ENSO relationship, with potential implications for ENSO prediction. Full article

The Luzon Strait is a critical generation site for global internal tides. Their generation and propagation are significantly modulated by background currents, including the Kuroshio Current and mesoscale eddies. This study investigates nonlinear effects of these background currents on low-mode (modes 1–3) internal tides using a high-resolution numerical simulation. We apply the Taylor–Goldstein equation considering the Earth’s rotation and background currents to perform modal decomposition, and utilize a nonlinear internal tidal energy equation to quantify three crucial energy pathways: inter-modal energy conversion, nonlinear energy exchange with background currents, and nonlinear advection effects. Results demonstrate that while stationary mode-1 internal tides dominate in the generation region of the Luzon Strait, non-stationary energy increases significantly in the western and eastern propagation regions, driven largely by seasonal variability of the Kuroshio Current. Inter-modal energy conversion follows a cascade from lower to higher modes, with conversion efficiency increasing with mode number. Nonlinear exchanges between background currents and internal tides are one order of magnitude smaller than inter-modal conversions but exhibit a bidirectional transfer, where advection redistributes internal tidal energy within the eddy structures. This study provides a quantitative framework for understanding multiscale energy pathways of internal tides under complex ocean dynamics. Full article

El Niño–Southern Oscillation (ENSO) modulates tropical South American rainfall, but its Colombian expression is filtered by terrain, rainfall regime, moisture pathways, and atmospheric state. We quantify ENSO-related sensitivity of standardized precipitation anomalies in the Colombian Andes and Orinoquia using Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2.0; 1981–February 2026), station records from Colombia’s Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), ERA5 atmospheric fields, and 1981–2010 climatologies. CHIRPS reproduced station-derived standardized anomalies ($r=0.94$ in the Andes; $r=0.91$ in Orinoquia), supporting regional anomaly analysis while retaining cautious comparison framing. Lagged associations with the Oceanic Niño Index (ONI) were evaluated for lags 0–6 months using effective sample size, block-bootstrap confidence intervals, and maximum-lag tests. ENSO sensitivity was stronger and more coherent in the Andes: annual lag-1 ONI–precipitation correlation was $-0.374$, with marked December–February and June–August responses. El Niño minus La Niña composites of column water vapor, 850-hPa moisture-flux convergence, 500-hPa vertical velocity, and Convective Available Potential Energy (CAPE) revealed seasonally heterogeneous moisture and convergence responses, but coherent positive $\omega$ anomalies over the Andes in DJF and JJA, consistent with reduced ascent. CAPE was significantly higher in MAM–SON, whereas the positive DJF difference was not statistically significant, showing that thermodynamic instability alone did not determine rainfall. Orinoquia did not exhibit a comparably consistent four-variable atmospheric signature. An elevation-stratified analysis showed a modest lowland-to-upland strengthening that plateaued above approximately 1000 m. A strictly antecedent ONI-lag model retained modest fixed-split skill in the Andes ($R^2=0.138$) but negligible skill in Orinoquia ($R^2=0.003$). The results support regional diagnosis, not causal or operational claims. Full article

Accurate sea surface temperature (SST) forecasting underpins operational oceanography and climate surveillance, yet it remains constrained by the inherent multiscale spatiotemporal variability. Existing deep learning methods often struggle to identify the physical hierarchies of SST fields, typically treating them as homogeneous inputs and thus failing to decouple large-scale coherent structures from transient features effectively. To bridge this gap, this study introduces MS-SSTNet, a scale-aware framework designed for spatiotemporal SST forecasting that leverages iterative multiscale decomposition. By iteratively distilling SST fields into hierarchical spatial modes and their principal components (PCs), the architecture facilitates a rigorous scale-decoupling representation of ocean dynamics. A dual-window temporal module is then integrated to characterize the coupling between long-term persistent trends and short-term stochastic fluctuations. Evaluations using satellite-derived SST products over the South China Sea (SCS) demonstrate that MS-SSTNet achieves robust 10th day forecast skill, yielding an overall spatiotemporal average MAE of 0.3031 °C, an average RMSE of 0.4062 °C, and an average ACC of 0.7973 across the entire 1–10 day forecast horizon. Ablation studies further underscore the indispensability of multiscale decomposition and dual-window integration in enhancing forecast fidelity across diverse spatiotemporal scales. Full article

This article examines whether gender is associated with differences in trading style and performance in a simulated stock-market environment. It uses data from a four-hour CAC 40 trading simulation involving 133 second-year Management students, each managing a virtual EUR 100,000 portfolio under transaction costs, no short selling, and continuous ranking incentives. The analysis controls for age, prior market exposure, and the five OCEAN personality traits. The empirical strategy relies on a series of ordinary least squares regressions that distinguish trading style from performance. Trading style is measured through average transaction size, invested capital during the simulation, portfolio variability, a composite capital-engagement index, and a turnover ratio, while performance is the portfolio return. The results show that gender is not significantly associated with return. By contrast, gender is associated with several dimensions of trading style. Male participants take larger positions, retain a lower share of cash, display more variable portfolios, and a more intensive trading style. Beyond the widely reported finding that men tend to trade more frequently, the study documents gender-related differences across several dimensions of trading style. These associations remain statistically significant after controlling for prior market exposure and OCEAN personality traits, but they are not accompanied by a statistically significant difference in return. The contribution lies in documenting multidimensional differences in trading style among novice investors operating under identical conditions, rather than in replicating the finding that greater male trading activity is associated with lower performance. Full article

Maritime navigation efficiency is commonly assessed using isolated route, speed, energy, or traffic indicators that do not fully represent voyage context. This study proposes a context-aware framework based on GPS–AIS data fusion, planned-route geofencing, metocean information, and encounter-based validation. The Navigation Efficiency Resilience Index (NERI) combines target achievement, trajectory-derived response activity, and disturbance intensity into a bounded, time-resolved diagnostic index. The framework was evaluated using a Singapore–Montevideo container-ship voyage with 30 s position data, surrounding-vessel AIS, corridor-specific cross-track limits, and collocated metocean variables. The voyage-level mean NERI was 0.679, and its 10th percentile was 0.519. Lower values occurred mainly in constrained waters, approach areas, and the metocean-intensive Cape transition, whereas the Indian Ocean and South Atlantic legs achieved higher mean values of 0.704 and 0.736, respectively. For the analysed datasets, the regular own-ship position record produced more stable trajectory-derived indicators than the less regularly sampled own-ship AIS series, without implying an inherent accuracy advantage. The full NERI formulation achieved an AUROC of 0.83 and an AUPRC of 0.41 for CPA/TCPA conflict-window classification. NERI therefore provides a decomposable, plan-relative analytical layer for retrospective voyage monitoring and diagnostics, but it is not a direct safety or collision-risk measure. Full article

Coastal power plants draw seawater from the open ocean through their cooling-water circulation systems. The cooling water falls over an overflow weir inside the siphon well, entraining large quantities of air, and generates a foam pollution plume upon discharge to the sea. By combining physical model experiments with numerical simulation, this study investigates the key factors governing foam stability and the aeration behavior of the water downstream of the siphon-well overflow weir. The principal conclusions are as follows: among the three single-factor variables tested in controlled laboratory conditions—temperature, salinity, and shellfish-flesh suspension concentration—the biological substance proxy showed the strongest effect on foam stability; when the shellfish-flesh suspension concentration reaches 20% (mass/volume basis, independently prepared), the foam volume and half-life increase by factors of 1.4 and 3.36, respectively, relative to the 4% baseline condition. When the dimensionless aeration depth z/z₉₀ < 0.75, the air-concentration profile rises relatively slowly with depth, whereas it increases more rapidly as the free surface is approached. Within the investigated viscosity range of 1.0–8.3 mPa·s (1.0 mPa·s for the pure-water control and 1.5–8.3 mPa·s for the measured viscosities of the 4–20% shellfish-flesh suspensions), the cross-sectional mean air concentration shows an overall decreasing trend as the liquid-phase viscosity increases, and the total bubble number density decreases correspondingly. The findings provide a laboratory-based indication of the mechanisms that must be addressed in the development of physical foam-suppression technologies; confirmation against field discharge water is required. Full article

Knowledge about Callinectes danae and Callinectes ornatus is thorough, although scarce in different ecological regions like Macaé, which are influenced by the Cabo Frio upwelling phenomenon in the southwestern Atlantic Ocean. This study analyzes spatiotemporal variation in the abundance of two swimming crab species in a region influenced by upwelling. Samplings took place from July 2013 to June 2014, on a monthly basis, with the aid of a shrimp fishing boat, in four different sampling sites. In total, 92 C. danae specimens were captured; C. ornatus was the most abundant species over the year; 1.436 individuals were captured. Both species were the most abundant in the shallowest sites and warmest seasons. Visual trends in temperature, salinity and sediment size (Phi) were observed for both species. Phi was the only significant variable associated with the sampled swimming crabs. The results indicated similar distribution between C. danae and C. ornatus, and a clear prevalence of C. ornatus, possibly due to C. danae’s estuarine dependence on its life cycle and to C. ornatus’s generalist behavior. The abundance of both species in Macaé is modulated by a combination of spatiotemporal and environmental variation in this upwelling region. Full article

The mechanistic understanding of biogeochemical dynamics in the Gulf of Guinea (GoG) has historically been hindered by persistent cloud cover and reliance on static geographic boundaries. In this study, we analysed a 20-year (2003–2022) satellite-derived chlorophyll-a (Chl-a) dataset to overcome these observational limitations through a three-part spatial and machine-learning framework. First, the Data Interpolating Empirical Orthogonal Functions (DINEOF) algorithm reconstructed a gap-free climatology, demonstrating robustness under extreme simulated cloud cover (R² = 0.884). Second, a Fuzzy C-Means (FCM) clustering algorithm objectively partitioned the basin into three dynamic, physically driven bioregions: an oligotrophic gyre, river plumes, and an upwelling mega-cluster. Third, we applied an explainable Random Forest framework, supported by SHapley Additive exPlanations (SHAP), to identify the main physical and biogeochemical predictors associated with coastal Chl-a variability using hindcast nutrients and a strict chronological split (training: 2003–2018; test: 2019–2022). The models produced conservative but meaningful independent test-period performance across coastal zones, with R²_log values from 0.437 to 0.595. Rather than revealing a new ecological paradox, the framework provides a basin-specific interpretation of a globally documented pattern: offshore oligotrophication alongside localized coastal enrichment. The open ocean and transition/upwelling sectors show negative Chl-a tendencies consistent with sea surface warming, enhanced stratification, and reduced upward nutrient supply. Conversely, coastal ecosystems are structured by local hydrological and wind-driven forcings that modulate the regional climate signal. In the Congo plume, Chl-a variability is primarily structured by haline plume dynamics and secondary nutrient constraints, whereas the Niger plume reflects coupled mixed-layer and terrigenous nutrient controls. These findings establish a spatially objective typology of the GoG, providing a regional reference framework for future high-resolution missions, monitoring, and coupled physical–biogeochemical modelling. Full article

This study develops a rigorous, leakage-free forecasting framework for monthly Robusta coffee prices using historical observations from January 1975 to December 2025. A comprehensive set of explanatory variables is constructed from lagged coffee prices, moving averages, logarithmic returns, rolling volatility, and exogenous variables such as the Oceanic Niño Index (ONI), the U.S. Dollar Index, and Brent crude oil prices. To ensure methodological fairness, all predictors are generated exclusively from information available at the forecast origin, and all competing models are evaluated under a unified expanding-window walk-forward validation framework. Seven forecasting models are compared: Naïve, Exponential Smoothing (ETS), ARIMA, ARIMAX, Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Forecasting performance is evaluated using R², RMSE, MAE, and MAPE, while Taylor diagrams and the Diebold–Mariano test are employed to assess model agreement and differences in predictive accuracy. The results show that XGBoost achieves the highest forecasting accuracy (R² = 0.956, RMSE = 0.264), followed closely by the Naïve (R² = 0.954, RMSE = 0.271) and ARIMA (R² = 0.954, RMSE = 0.270) benchmarks, whereas ARIMAX and ETS provide comparable performance and the deep learning models (LSTM and GRU) produce substantially larger prediction errors. Feature importance analysis further indicates that the first lag of coffee price is the dominant predictor, accounting for approximately 94% of the predictive gain in XGBoost. Overall, the findings demonstrate that rigorous leakage-free validation is essential for reliable forecasting research and that, for monthly Robusta coffee prices, increased model complexity does not necessarily yield superior predictive performance. Full article

HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms. Full article

Rift Valley fever (RVF) is a climate-sensitive mosquito-borne zoonosis long regarded as a rural, pastoral disease, yet accelerating tropical urbanization and intensifying climate variability may be reshaping its epidemiology at the urban–peri-urban interface. This narrative review examines how global climate change and urban-specific climatic conditions jointly shape RVF virus (RVFV) vector habitats, transmission, and burden in tropical and subtropical cities, synthesizing 51 of 412 English-language records identified by a structured, non-systematic search of PubMed, Scopus, Web of Science, and Google Scholar (2009–2026) and selected for relevance to urban and peri-urban RVF. This research draws on human, livestock, and vector evidence from Sub-Saharan Africa, the Arabian Peninsula, and Indian Ocean islands across epidemic and inter-epidemic periods. The synthesis indicates that impervious surfaces, poor drainage, and open water storage can recreate the water-retaining function of rural dambos, sustaining a year-round larval habitat, and that Culex quinquefasciatus dominance together with peri-urban cattle may form an amplification bridge to humans. Direct evidence remains scarce, anchored by a single peri-urban serosurvey and limited urban slaughterhouse entomology. Critical gaps include urban primary-vector ecology, infection-rate data, urban-heat effects, city-specific exposure studies, and coupled climate–urban burden models. We conclude that RVF is a plausible emerging urban threat warranting proactive inter-epidemic surveillance and integration of RVF into urban planning and One Health systems. Full article

This study presents a multivariate spatio-temporal clustering framework to characterise joint wind–wave regimes across European seas using the fifth-generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5; 1979–2014). Seasonal and annual statistics of significant wave height, mean wave period, wind speed, and wave/wind direction were computed at 0.5° resolution. Principal component analysis was used to reduce dimensionality, retaining 30 components that captured 99% of the variance. K-means clustering was then used to identify nine coherent dynamical regimes with persistent spatio-temporal signatures. These regimes were grouped into open-ocean, transitional, and enclosed/semi-enclosed categories based on internal variability, directional spread, and geographic exposure. Open-Atlantic regimes are found to be energy-rich, exhibiting clear December–February maxima in significant wave height ($H_s$), mean wave period ($T_{02}$), and 10 m wind speed ($W_{s10}$); enclosed and semi-enclosed basins show lower amplitudes and reduced variability, while transitional shelves and the southern Mediterranean display intermediate conditions, characterised by moderate $T_{02}$ levels and seasonal rotation of wave and wind directions, reflecting a mixed influence of locally generated seas and remotely forced swell. Dispersion analysis highlights a clear Atlantic–Mediterranean partition, with transitional shelves forming a dynamical bridge between open-ocean and enclosed basins. Teleconnection analysis shows that the North Atlantic Oscillation and Arctic Oscillation dominate Atlantic regimes, while the Scandinavia, East Atlantic, and Polar/Eurasia patterns modulate variability and directional persistence in transitional and enclosed seas. The classification defines a climatological framework of European wind–wave conditions and establishes a practical basis for renewable energy assessment, engineering design, and long-term change analysis, with methods transferable to other basins. Full article