Search Results (5,140)

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

As crucial weather-scale systems widely affecting the global marine-atmospheric boundary layer, cyclones exert a regulatory effect on aerosols in the marine boundary layer through interrelated physical and chemical processes, including dynamic uplift, strong wind forcing, precipitation scavenging, and cloud microphysical interactions. Following an overview of aerosol properties in the marine boundary layer and synoptic cyclone characteristics, this paper reviews the full-process regulation mechanisms and mutual feedback effects of tropical and extratropical cyclones on aerosol generation, long-range transport, and removal, integrating the latest advances in observational, numerical, and theoretical studies. Cyclone-driven aerosol generation has two key pathways: mechanical fragmentation of sea surfaces in cyclones’ strong wind cores, emitting sea salt aerosols of varying particle sizes, and cyclone-induced disturbances triggering photochemical and heterogeneous reactions that accelerate secondary aerosol formation. Cyclone movement, with strong advection and updrafts, enables cross-ocean long-distance transport and upper troposphere injection of aerosols in the marine boundary layer, altering their global distribution. Wet deposition (rainout and washout) is the dominant removal mechanism, eliminating aerosols and mediating the cyclone–aerosol–cloud feedback loop, where aerosols as cloud condensation nuclei or ice nuclei regulate cyclone intensity, precipitation, and cloud cover. Current challenges (e.g., emission quantification uncertainties, incomplete microphysical understanding, model limitations) and prospects (e.g., enhanced long-term observations, improved model parameterization) are discussed. This review provides a scientific basis for aerosol-climate effect studies under extreme weather and references for related fields. 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

Chlorophyll-a concentration is a key indicator reflecting the growth status of phytoplankton, and its accurate prediction is of great significance for assessing the degree of water eutrophication. Although existing approaches have achieved good performance, they generally pay insufficient attention to multi-scale spatial information and show limitations in characterizing the continuous spatiotemporal dynamics. To address these issues, this paper proposes a multi-scale spatiotemporal graph ODE network (MGODE) for ocean chlorophyll-a prediction. The MGODE adopts a dual-layer structure, simultaneously processing chlorophyll-a concentration data at both the region level and node level to capture multi-scale spatial features, and it enables effective interaction of cross-scale features through dynamic transmission coefficients and a gated fusion mechanism. Meanwhile, the MGODE employs a dual-ODE architecture at both the node and region levels, utilizing spatiotemporal ODE blocks to continuously and deeply capture features, thereby simulating the continuous spatiotemporal dynamic evolution of chlorophyll-a. Experiments on real-world datasets from the Bohai Sea and South China Sea show that the proposed MGODE model achieves higher prediction accuracy than several current state-of-the-art models. Compared with the best baseline, the MGODE achieves reductions of 2.78% in MAE and 1.07% in RMSE on the Bohai Sea dataset and reductions of 1.19% in MAE and 1.38% in RMSE on the South China Sea dataset. These results demonstrate the potential of the MGODE to support marine chlorophyll-a forecasting and marine ecological monitoring. Full article

Towed hydrophone arrays are widely employed for underwater target detection and direction-of-arrival (DOA) estimation. However, array shape distortion induced by ocean currents, internal waves, and platform maneuvers severely degrades beamforming performance and DOA estimation accuracy. In this paper, a novel Bayesian fusion framework is proposed to achieve robust array shape estimation. Specifically, based on the time-delay estimates derived from the phase differences of line-spectrum components in a pre-processing step, the array geometry is first reconstructed via a piecewise straight-line fitting method. Concurrently, an existing hidden Markov model (HMM)-based method is adopted to estimate the inter-segment deviation angles, in which the smoothness of the array shape is enforced through the state-transition probabilities. The proposed framework then treats these two preliminary estimates as observations from distinct sources and incorporates a smoothness prior within a maximum a posteriori (MAP) formulation that admits a non-iterative closed-form solution to enforce physical continuity constraints on the array geometry. By fusing these complementary estimates, the proposed method simultaneously preserves local sensitivity to fine-scale bends and maintains global consistency of the array shape. Both simulation and lake-trial experiments validate the effectiveness of the proposed method, reducing the array shape estimation error by more than 30% relative to representative existing methods. Moreover, by relying solely on the received acoustic data, the method lowers the dependence on auxiliary sensors and the associated system cost. Full article

The Caspian Sea’s unique environment and intense hydrocarbon extraction make it a high-risk, understudied region for oil spill modelling. This review assesses the physical, chemical, and biological processes governing oil spill transport and fate, and evaluates the principal numerical tools available for the Caspian Sea context. The weathering processes are reviewed from foundational formulations to operational implementations. Key research challenges identified include the absence of photo-oxidation from operational models, limited laboratory data for Caspian crude oil types, and simplified biodegradation parameterizations. Hydrodynamic forcing uncertainty, arising from the lack of a dedicated operational ocean model, remains the dominant source of trajectory forecast error. Seven operational oil spill modelling tools and the ROMS hydrodynamic platform are reviewed. Only OSCAR and MIKE 21 have documented applications to the Caspian Sea, representing a significant regional gap. ROMS is identified as the most suitable hydrodynamic platform for future operational forecasting. Finally, the integration of machine learning and deep learning methods, including neural network trajectory prediction and SAR detection, is discussed as a promising frontier for improving forecast accuracy in this data-sparse environment. Full article

Wake-induced magnetic fields provide a potential non-acoustic signature for underwater-vehicle sensing, but their weak amplitudes can be masked by ocean-wave magnetic backgrounds. This study evaluates their engineering observability under representative wind–wave conditions. The wake-induced field at fixed observation points was calculated from CFD-derived wake velocities of an engineering-scale fully appended SUBOFF model using discrete Biot–Savart summation. The ocean-wave background was computed using a JONSWAP spectrum and linear wave theory, and a peak-to-background-rms SNR was used as the observability indicator. Results show that speed and diving depth strongly control the target signal. At the baseline point, increasing speed from 10 to 40 kn raised $B_{wake,max}$ from 0.0406 to 1.65 nT and SNR from −1.01 to 31.2 dB under W2. Increasing diving depth from $2D$ to $4D$ reduced $B_{wake,max}$ from 0.129 to 0.0204 nT and SNR from 9.03 to −6.99 dB. Wind speed dominated the wave background: at $U_{10}=10$ m/s, $B_{wave,rms}$ reached 0.542 nT and the Case 2 SNR decreased to −12.5 dB. Sensor placement affected both signal and background; deeper underwater sensors improved observability, whereas aerial observations suffered from weak wake-signal amplitudes. Wake-field observability is therefore jointly governed by wake source strength, ocean-wave magnetic background, and observation geometry. Full article

Marine cold-air outbreaks (MCAOs) enhance air–sea exchange and are accompanied by substantial cloud-population reorganization over the North Atlantic. Previous satellite studies have documented MCAO-related cloud properties and International Satellite Cloud Climatology Project (ISCCP) weather-state distributions, but the continuous redistribution of the complete cloud population and its dependence on sampling scale remain less well quantified. We combine the merged H-series ISCCP (ISCCP-H) weather-state product with the ERA5 reanalysis over the North Atlantic sector of 35–50°N, 55–20°W during January–March and November–December of 2000–2009. Three complementary analytical strategies are used to examine scale-dependent cloud associations: intensity-binned analysis of positive-MCAO native ERA5 grid-point-days, domain-daily regressions, and ISCCP-H cell fixed-effect models. Across 1459 MCAO dates and 256,518 strict-ocean ISCCP-H cell-days, the combined frequency of shallow-cumulus-like and stratocumulus-like low-cloud weather states decreases systematically with MCAO intensity, with a domain-daily slope of −0.0562 K⁻¹. Compensating regional increases occur mainly in the midlatitude-storm and middle-to-high-cloud weather states, while the deep convective and anvil state shows no robust increase. After cell and calendar-month climatological differences are removed, the low-cloud reduction persists, together with positive associations for the midlatitude-storm and optically thick middle-top weather states. Cirrus is positively associated with MCAO intensity at the regional scale but negatively associated within fixed cells, consistent with geographical composition contributing to its regional response. MCAO intensity is therefore more consistently associated with total low-cloud weather-state occupancy than with the internal composition of the low-cloud subset. The closed weather-state framework provides an observational benchmark for satellite and climate model evaluation. Full article

Ocean sound speed profile (SSP) is a key parameter for underwater acoustic detection, remote sensing, and seafloor geodetic positioning, and its temporal prediction is essential for improving acoustic positioning accuracy. Conventional direct measurements are inefficient and spatially sparse, while statistical and acoustic inversion methods fail to capture the strong nonlinear evolution of the sound speed field. Among existing time series models, LSTM, a recurrent network for time series forecasting, lacks an explicit receptive field. In contrast, the original TCN, a temporal convolutional network with dilated convolutions, poorly captures local fine structures and relies heavily on empirical tuning. To overcome these limitations, we propose an improved TCN-based SSP prediction method and apply it to seafloor geodetic positioning. The approach first constructs a sound speed increment field via first-order time differencing to remove global trends and highlight local variations. It then employs Optuna (version 4.9.0), a Bayesian sampling-based automatic optimization framework, to automatically tune key TCN parameters within a predefined search space, reducing reliance on manual tuning. The predicted high-resolution sound speed time series is finally used for ray tracing positioning to enhance seafloor geodetic accuracy. Experiments on the GLORYS12V1 reanalysis dataset show that LSTM and the original TCN achieve root mean square error (RMSE) and mean absolute error (MAE) values of 0.414 and 0.299 m/s, as well as 0.360 and 0.258 m/s, respectively, whereas our improved TCN reduces these to 0.205 and 0.131 m/s, substantially outperforming both baselines. In simulated Global Navigation Satellite System–Acoustics (GNSS-A) seafloor positioning, the 3D positioning RMSE drops to about 0.075 m, with improved stability. The proposed method offers an effective solution for accurate SSP time series forecasting and high-precision seafloor geodesy. Full article

The long-term accumulation of polyethylene terephthalate (PET) in marine environments may drive the evolution of microbial degradation capabilities, positioning the ocean as a valuable reservoir for discovering novel PET-degrading microorganisms. In this study, we isolated 305 marine-derived microorganisms with potential PET-degrading capability from samples collected from mangrove areas of Zhanjiang and the intertidal zones of Daya Bay, Shenzhen, China, using PET powder as a major carbon source. Subsequent evaluation of degradation performance via scanning electron microscopy and Fourier-transform infrared spectroscopy analysis identified 14 isolates capable of degrading PET film. These 14 strains belonged to 14 distinct species, none of which, to the best of our knowledge, has been previously documented as PET degraders. Among them, Microbacterium aurum SCSIO 85700 exhibited the most potent PET-degrading activity, achieving a weight loss of 2.1 mg (2.1%) and a 6.5% increase in relative crystallinity over 30 days. Genome analysis revealed the genetic basis underlying PET degradation and associated metabolic pathways in strain SCSIO 85700. Notably, genome mining and structural modeling identified two candidate polyester hydrolases, MA2267 and MA2443, possessing conserved His–Asp–Ser catalytic triads and exposed substrate-binding clefts resembling those of characterized PET-degrading enzymes, suggesting their potential involvement in PET depolymerization. Collectively, this study expands the recognized diversity of marine PET-degrading microorganisms and provides microbial resources for sustainable PET bioremediation. Full article

Built cultural heritage in East Africa is often interpreted and taught through monument-based, stylistic, or externally derived frameworks that insufficiently account for the region’s overlapping environmental, cultural, historical, and socio-spatial processes. Responding to this theoretical and pedagogical gap, this article develops a regionally grounded theoretical model of East African built heritage as a layered system, with particular attention to Kenya, Tanzania, and Uganda. Methodologically, the study adopts a qualitative, interpretive approach, combining a critical literature review, analysis of selected built heritage layers, and expert perspectives from East African and European/international respondents. The findings show that East African built heritage is shaped by five interrelated layers: environmental foundations, indigenous and vernacular systems, Swahili/coastal Indian Ocean urbanism, colonial reconfiguration, and postcolonial/contemporary transformation. These layers do not form a linear historical sequence; rather, they overlap, interact, are disrupted, and are reinterpreted through material practices, spatial transformation, memory, environmental knowledge, and everyday use. The article argues that East African built heritage is defined by a layered system that is hybrid, fragile, unevenly recognized, partly intangible, and negotiated. It concludes that architectural education must move beyond object-based conservation knowledge transfer toward competency building in historical interpretation, environmental understanding, community engagement, adaptive reuse, and critical design intervention. 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

Floating Offshore Wind (FOW) enables offshore wind deployment in deeper waters not suitable for bottom-fixed turbines, unlocking new areas for renewable energy generation. Most major cost contributors to FOW have clear pathways for cost reduction however mooring systems are the exception due to the pre-existing market maturity. Solutions to lower mooring costs include Mechanical Compliance Devices (MCDs) aimed at reducing the high peak and snatch loads in mooring lines and thus driving down the capital, operations and maintenance costs. In this paper, a comparison of a physical tank testing campaign and corresponding numerical analysis, for an MCD is described and analysed. The objective of the testing campaign was to validate the component-only tank results with the modelling of an MCD, namely Dublin Offshore’s Load Reduction Device (LRD) using a multi-body dynamics (MBD) approach. The paper presents analysis of the experimental testing and numerical modelling and compares the results with the validated Load–Extension Curve (LEC). Experimental testing was carried out at 1:38.5 scale using bespoke mooring test apparatus at Lír, Ireland’s National Ocean Test Facility. The results of testing are presented for all of the MCD model scales tested and compared with the modelled LEC. The correlation between the experimental and numerical data and with the LEC, characterised by Pearson Correlation Coefficient (R) in the range of 0.952 to 0.999, demonstrates the ability to model the LRD using the MBD approach. 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