Search Results (418)

Sea surface cooling (SSC) induced by typhoons has a significant impact on typhoon intensity and regional air–sea interaction. This study develops a machine learning model based on a multilayer perceptron (MLP) to predict SSC during typhoon passage over the western North Pacific. The model uses pre-typhoon ocean background conditions and ocean states at the typhoon peak moment as inputs, including wind field, sea level anomaly (SLA), mixed layer depth (MLD), and 100 m water temperature. Trained on historical typhoon data and multi-source ocean observations from 2002 to 2018, the model directly predicts SSC during typhoon events from 2019 to 2020. Results show that the model achieves a mean absolute error (MAE) of 0.379 °C, a root mean square error (RMSE) of 0.488 °C, and a bias of 0.087 °C. The model reproduces the typical rightward bias in SSC spatial distribution. Under normal ocean conditions, such as open deep-water areas with moderate stratification and no strong eddy interference, the model performs well, with errors below 0.1 °C at some points. Although some biases exist under complex ocean environments and abrupt changes in typhoon dynamics, the model still captures the overall cooling trend. This study demonstrates the feasibility of machine learning for typhoon–ocean interaction forecasting. The proposed framework can provide technical support for typhoon intensity forecasting, marine disaster warning, and aquaculture risk prevention. Full article

Haptophytes are ubiquitous single-celled eukaryotic plankton in coastal and open oceans that play a key role in marine biogeochemical cycling. Understanding the size structure and community composition of active haptophytes is crucial for elucidating their diversity and ecological functions. This study investigated the diversity and community structure of pico- (0.2–3 μm) and nano-sized (3–20 μm) haptophytes in the surface waters of the western Pacific Ocean using high-throughput sequencing targeting the hypervariable V4 region of the 18S rRNA. The pico-sized community exhibited significantly higher diversity than the nano-sized community. Community composition varied significantly between size fractions, driven primarily by the genera Chrysochromulina and Syracosphaera. Furthermore, the nano-sized community was more strongly influenced by environmental variables than the pico-sized community, although neither size fraction displayed a clear coastal-to-open-ocean distribution pattern. Null and neutral community model analyses indicated that both size-fractionated communities were primarily regulated by stochastic processes, while deterministic processes exerted a greater influence on the nano-sized community. Co-occurrence network analysis revealed stronger interconnections and a higher number of keystone species within the nano-sized community. In both networks, intermediate taxa (relative abundances of 0.01% to 0.1%) exhibited the highest diversity and abundance among keystone species, highlighting their pivotal role in shaping the network structure and stability. Additionally, phylogenetic analyses revealed that while the majority of ZOTUs clustered with known taxa, multiple deep-branching, uncultured lineages were identified across both size fractions, indicating substantial uncharacterized genetic diversity. This study underscores the variability and hidden diversity of size-fractionated haptophyte community structures in oligotrophic open oceans, providing valuable insights into their functional significance in global biogeochemical cycles. Full article

This study examines the 3D attributes of submesoscale eddies identified over a 12-month period within the Western Pacific Ocean. Composite parameters of cyclonic submesoscale eddies (CSMEs) occurring within and away from the Kuroshio Current system are compared and analyzed for their surface and subsurface features, as well as the seasonality of their core properties. Within the Kuroshio Current (KC) region, CSMEs are faster, stronger and deeper than in the open water (OW) region, with composite eddy depths of 97.5 m and 77.5 m, or 2.8 and 2.0 times the mixed layer depth, respectively. Prominent dipolar divergence patterns both at the surface and at depth reveal the presence of ageostrophic influence, with KC CSME cores deviating 48% and OW CSMEs deviating 40% from geostrophic balance at the surface. This imbalance drives strong vertical motion with maximum upward velocities of 19.2 m day⁻¹ at 57.7 m and 9.3 m day⁻¹ at 157.1 m within the KC and OW region CSME cores, respectively. Subsurface extrema analysis reveals structural differences in CSMEs between dynamic regions. These results provide a useful model-based estimate for subsurface CSME features which are difficult to quantify with observations. Full article

Metro Manila, one of the world’s most densely populated megacities, is highly vulnerable to sea-level rise because of its low-lying deltaic location, frequent tropical cyclones, and rapid anthropogenic subsidence caused mainly by groundwater extraction. This study brings together historical tide-gauge records from the Port of Manila (PSMSL) with the Sixth Assessment Report of Intergovernmental Panel on Climate Change (IPCC AR6) projections under Shared Socioeconomic Pathways, adding in vertical land motion (VLM) and sea-level fingerprints to work out local relative sea-level (RSL) changes. Assuming a constant subsidence rate, cumulative VLM reaches ~0.785 m by 2100 and ~1.289 m by 2150. When you factor in climatic contributions (amplified 10–20% by fingerprints, especially under high-emission scenarios thanks to far-field Antarctic ice-loss effects in the western Pacific), projected RSL ranges from 1.09–1.42 m (SSP1-2.6) to 1.51–2.00 m (SSP5-8.5) by 2100, and from 1.70–2.28 m to 2.41–3.54 m by 2150. Results show that 7.95–11.15 km² (1.2–1.8% of land area under SSP5-8.5) could face permanent inundation, mostly in Malabon (~18%), Navotas (~20%), and Manila (~7%). Our conservative estimates (permanent ocean-connected flooding, excluding existing aquaculture areas) come in much lower than earlier mid-century projections of up to a 30% area affected. All this will worsen chronic tidal flooding, erosion, saltwater intrusion, and risks to millions in low-lying districts. We urgently need integrated adaptation, better groundwater regulation, and a mix of nature-based and engineered solutions. 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

In the context of global warming, compound dry-hot events (CDHEs) are intensifying in Guangdong, yet CMIP6 projections remain uncertain. This study employs CMIP6 data and the Standardized Compound Event Indicator (SCEI) to quantify CDHEs severity, applying an observational constraint approach to reduce inter-model uncertainty. The results show that, after observational constraint, uncertainties decrease by about 63% and 77% in Period I and II under SSP126 and by about 57% and 59% under SSP585, greatly improving projection robustness. CDHE risk is highest in SSP585-Period II. Future dry-hot intensification in Guangdong generally increases from north to south, with western Guangdong most strongly affected. Although CDHEs weaken in other periods, western Guangdong shows persistent aggravation. Mechanism analyses indicate that SSP585-Period I is mainly linked to cold sea surface temperature (SST) anomalies in the South Atlantic and waters near Australia. After correction, dry-hot conditions show a marked weakening across Guangdong, although slight intensification persists over the Leizhou Peninsula. SSP585-Period II is primarily influenced by warm SST anomalies in the eastern Pacific and South Atlantic and cold anomalies in the North Pacific. The two SSP126 periods are associated with warm SST anomalies in the South Atlantic and waters near Australia and with cold anomalies in the South Atlantic, North Pacific, and North Atlantic, respectively. After correction, CDHEs generally weaken across Guangdong, although southern and south-central areas remain relatively severe. These findings indicate that historical key SST biases can strongly influence future CDHEs projections in Guangdong by modulating large-scale atmospheric circulation, including the Pacific-South American wave train, Indian Ocean SST anomalies, and the Western North Pacific Subtropical Anticyclone. Full article

Background/Objectives: A comprehensive analysis of the mitochondrial genomes of Japetella diaphana and Amphitretus pelagicus was conducted to investigate their genomic composition, gene size, sequence characteristics, and phylogenetic positioning within the Amphitretidae family. Methods: A rigorous phylogenetic analysis was performed utilizing a dataset comprising 13 protein-coding genes, two ribosomal RNAs, and 22 transfer RNAs derived from 26 cephalopod mitochondrial genomes, representing 25 species across seven families, Vampyroteuthidae, Tremoctopodidae, Octopodidae, Enteroctopodidae, Bolitaenidae, Argonautidae, and Amphitretidae, along with outgroup Nautilus macromphalus. Results: Notably, both focal species demonstrated a pronounced adenine–thymine bias in their mitochondrial genomes, with A. pelagicus exhibiting gene rearrangements and two extensive non-coding regions. The analysis, employing both the maximum likelihood and Bayesian inference methodologies, revealed a monophyletic relationship between Bolitaenidae and Vitreledonellidae, as well as a sister taxon relationship between Amphitretidae and Tremoctopodidae. The majority of species were classified into the Amphitretidae and Bolitaenidae clades, with numerous species exhibiting close phylogenetic relationships. Conclusions: This study provides novel insights into the evolutionary relationships within Octopodiformes, underscoring the significance of mitochondrial genome data in resolving phylogenetic relationships among cephalopods. The findings contribute to our understanding of the evolutionary history of octopi and pose implications for their classification and conservation. Furthermore, the results underscore the necessity for continued research into the evolutionary relationships among cephalopod taxa. Full article

Understanding temporal variation in population productivity is critical for effective assessment and management of pelagic fish stocks under a changing climate. In this study, we applied a stochastic surplus production model in continuous time (SPiCT) with time-varying parameters to evaluate the productivity dynamics of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean and to examine the influence of environmental variability on productivity. Multiple time-varying parameterization scenarios were explored to characterize uncertainties in productivity estimates and associated biological reference points. Generalized additive models were subsequently used to quantify the relationships between environmental variables and time-varying productivity. Results indicate that productivity estimates exhibit consistent temporal patterns across alternative modeling scenarios, while their magnitude and associated uncertainty are sensitive to model structure. Among the environmental factors examined, the Pacific Decadal Oscillation (PDO) and mixed layer thickness (MLT) showed consistent and statistically significant associations with maximum net productivity. Higher PDO values and greater MLT were both positively associated with population productivity. Overall, the results highlight the importance of environmental variability in shaping time-varying productivity of yellowfin tuna and demonstrate the feasibility of incorporating key environmental indicators into a state-space model. This approach provides a complementary framework for interpreting stock dynamics and supports the development of ecosystem-based fisheries management strategies in the western and central Pacific. Full article

The characteristics and causes of the long-term trends of diurnal variation of sea surface temperature (DSST) in the South China Sea (SCS) are investigated in this study based on the global hourly sea surface temperature data generated by the mixed layer model (MLSST) from the National Marine Environmental Forecasting Center (NMEFC) of China. Validation of the MLSST dataset demonstrates excellent agreement with in-situ buoy observations in the SCS with a correlation coefficient of 0.951, confirming its reliability in the SCS. Based on this dataset, the long-term trend of DSST in the SCS exhibits significant seasonal variations with the strongest magnitude in spring and the weakest in winter. Specifically, a significant decreasing trend of −0.0014 °C yr⁻¹ during 1982–2009 transitioned to a pronounced increasing trend of 0.0057 °C yr⁻¹ from 2010–2019. Both climatic factors and local atmospheric variables jointly modulate the DSST in the SCS. On the long-term timescale, the Pacific Decadal Oscillation (PDO) served as the dominant factor driving DSST changes in most areas of the SCS. After 2010, the PDO shifted to a persistent positive phase, providing a crucial climatic background for the basin-wide DSST increase. While the El Niño–Southern Oscillation (ENSO) showed enhanced correlation with DSST post-2010, the Indian Ocean Dipole (IOD) had negligible influence overall. In addition, the SCS summer monsoon played an important regulatory role in shaping the long-term trend of summer DSST by altering air–sea heat exchange processes. Among local atmospheric variables, sea surface wind speed was significantly negatively correlated with DSST, and net heat flux was significantly positively correlated with DSST, with their effects showing regional differentiation. The regulatory role of wind speed dominated in the western SCS, whereas the net heat flux exerted a more prominent impact in parts of the eastern SCS. This work clarifies the spatiotemporal patterns and multi-driver framework governing DSST variability in the SCS, providing a basis for understanding regional ocean–atmosphere interactions. Full article

Accurate prediction of tuna distribution is essential for sustainable fisheries management. This study develops a two-stage hybrid model combining Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Random Forest (RF) to predict tuna distribution around drifting fish aggregating devices (DFAD) in the Western and Central Pacific Ocean (WCPO). Echo-sounder buoy data from DFAD were aggregated into 2° × 2° grid cells and matched with oceanographic variables from the Copernicus Marine Service. Random Forest-based variable importance analysis identified primary productivity (27%), chlorophyll-a (22%), and dissolved oxygen (18%) as the three dominant environmental drivers. The CNN-RNN component extracts spatiotemporal features from multi-layer ocean data, while the RF classifier performs binary classification of tuna aggregation zones (high-yield vs. low-yield). All five models (Decision Tree, RF, CNN, Transformer, and CNN-RNN-RF) were evaluated on 557 samples using 5-fold stratified cross-validation, with each fold further split 80:20 for training and validation. The proposed CNN-RNN-RF model achieved the highest performance with an AUC of 0.830, accuracy of 82.6%, and F1-scores of 86.3% (high-yield) and 76.2% (low-yield), outperforming the best baseline model (RF: AUC 0.761, accuracy 75.4%). Predicted high-yield zones showed strong consistency with fishing log records, demonstrating the potential of integrating echo-sounder data with hybrid deep learning for data-driven tuna fisheries management. Full article

In the rapidly advancing field of geological oceanography, the Pacific Ocean and its marginal basins—encompassing key regions like the Sea of Japan, Bohai Bay Basin, South China Sea, and western Pacific seamounts—serve as a critical arena for unlocking Earth’s marine geological mysteries and resource potential [...] Full article

Four Euro-Atlantic (EA) circulation regimes are identified using cluster analysis applied to 500 hPa geopotential heights from the ERA-Interim (ERAI) reanalysis. These are the positive and negative phases of the North Atlantic Oscillation (NAO+, NAO−), Scandinavian Blocking (SB), and the Atlantic Ridge (AR). This paper studies transitions between these four regimes, the signature of tropical heating preceding these transitions, and the identification of transitions for which this forcing plays a role. The findings can further our understanding of when transitions occur. To address these questions, we examine the relationship of heating to the Madden–Julian Oscillation (MJO), the El Niño Southern Oscillation (ENSO), shifts in the Intertropical Convergence Zone (ITCZ), and possible stratospheric influences. Mid-latitude diabatic heating is also examined to determine shifts in the storm tracks. We use the ERAI reanalysis to estimate diabatic heating, streamfunction, Rossby wave activity, and stratospheric zonal winds. We find that Indian Ocean tropical heating enhances the transition from the SB regime to the NAO+ regime. In contrast, western Pacific heating seems to force transitions from all other regimes into the NAO− regime. The flux of Rossby wave activity indicates that in some transitions, mid-latitudes play a role in forcing tropical heating. The majority of the transitions examined show indications of tropically forced behavior. Less than half showed evidence that mid-latitude dynamics were the primary cause of the transition. Nearly half of the transitions appeared to be related to phases of the MJO. We also found that intensification of heating in the eastern equatorial Pacific and equatorial Atlantic (ITCZ) plays a role. Transitions during the early and late parts of the season, along with the role of ENSO, are found to be modest factors. Full article

Since it was first observed in Israel in the 1970s, and due to its subsequent negative impact on human activities, the nomad jellyfish Rhopilema nomadica has earned itself a spot on the list of the 100 Worst Invasive Alien Species in the Mediterranean. It was assumed to originate in the Red Sea, or in the Indo-Pacific region, but in the absence of additional reports of live specimens outside the Mediterranean, its origins have remained a mystery. Here, via molecular analysis, we present the first verified results of the existence of R. nomadica in the Western Indian Ocean. Moreover, using additional evidence from Cassiopea andromeda and R. nomadica, we propose that the construction of the Aswan High Dam may have led to the proliferation of R. nomadica in the Levantine Basin. Full article

Wide-swath satellite altimetry from the Surface Water and Ocean Topography (SWOT) mission provides an unprecedented opportunity to directly observe kilometer-scale ocean dynamics in two dimensions. In this study, we identify an atypical bimodal seasonal cycle of submesoscale processes in the Kuroshio Large Meander (KLM) region south of Japan using SWOT observations during 2023–2025. Submesoscale eddy kinetic energy (EKE) displays a pronounced winter maximum (December–January) as expected for midlatitude oceans, but also a distinct secondary maximum in late summer (August–September) that coincides with the Northwest Pacific typhoon season. SWOT-based eddy statistics reveal that cyclonic and anticyclonic eddies exhibit enhanced occurrence and intensity in winter and late summer. MITgcm LLC4320 outputs demonstrate that the late-summer EKE peak is primarily driven by typhoons, which rapidly deepen the mixed layer and intensify frontal gradients, leading to an intensification of submesoscale eddies. The Kuroshio path further modulates this response. During the KLM state, buoyancy gradients and mixed-layer available potential energy are amplified, allowing storm forcing to generate strong submesoscale activity. Together, typhoon forcing and current-path variability modify the traditionally winter-dominated submesoscale regime. These findings highlight the unique capability of SWOT to resolve submesoscale processes in western boundary currents during extreme weather events. Full article

The background error covariance matrix (BE) is a fundamental component of data assimilation (DA) systems. Its impact on both the DA process and subsequent forecast performance depends on model configuration and the types of observations assimilated. However, few studies have specifically examined BE behavior in the context of satellite DA for regional tropical cyclone (TC) prediction. In this study, we develop the BE and evaluate its structure for a TC forecasting system over the western North Pacific. A total of six BEs are modeled using three control variable (CV) schemes (aligned with the CV5, CV6, and CV7 options available in the Weather Research and Forecasting DA system (WRFDA)) with training data from two distinct periods: the TC season and the winter season. Results demonstrate that the BE structure is sensitive to the training data used. The performance of TC-season BEs derived from different CV schemes is assessed for TC track forecasting through the assimilation of microwave sounder satellite brightness temperature data. The evaluation is based on a set of 14 cases from 2018 that exhibited large official track forecast errors. The CV7 BE, which uses the x- and y-direction wind components as CVs, captures finer small-scale momentum error features and yields greater forecast improvement at shorter lead-times (24 h). In contrast, the CV6 BE, which employs stream function (ψ) and unbalanced velocity potential (χ_u) as CVs, incorporates more large-scale momentum error information. The inherent multivariate couplings among analysis variables in this scheme also allow for closer fits to satellite microwave brightness temperature data, which is particularly crucial for forecasting TCs that primarily develop over oceans where conventional observations are scarce. Consequently, it enhances the large-scale environmental field more effectively and delivers superior forecast skill at longer lead times (48 h and 72 h). Full article