Marine Group II (MGII) archaea are abundant and constitute an ecologically essential portion of marine microbial communities in the upper layers of the ocean, where they make a substantial contribution to organic matter degradation and carbon cycling. Aoguangviridae, which is one of the earliest recognized viral groups linked to MGII archaea, has been detected in a range of marine environments, but its diversity, evolution, and ecological distribution remain unclear. In this work, the diversity and ecology of Aoguangviridae across the global oceans are systematically characterized by integrating global virome resources with metagenomic and metatranscriptomic datasets. Furthermore, a nonredundant genomic dataset compiled from datasets with 59 to 227 genomes, including 168 newly identified uncultivated viral genomes, was expanded. The viruses in this dataset were resolved into 22 subfamilies and 157 genera that displayed conserved modular genome organization. Comparative genomic and phylogenetic analyses identified 7 types of putative auxiliary viral genes and suggested a phylogenetic affinity between representative viral genes and MGII archaeal homologs, which is consistent with a possible host–virus gene exchange. Aoguangviridae are widely distributed across marine and coastal habitats worldwide, with the highest abundance in epipelagic and polar environments. Transcriptional activity was found to vary markedly among lineages and to generally be the highest in the deep chlorophyll maximum layer. Taken together, these findings expand our understanding of the diversity, evolution, biogeography, and transcriptional activity of Aoguangviridae across global oceans.
Coastal shorelines are among the most dynamic and socioeconomically valuable environments on Earth. However, predictions of shoreline change remain constrained by a lack of interdisciplinary integration, poor spatiotemporal validation, insufficient quantification of uncertainties, and spatial data biases. This systematic review, conducted in accordance with PRISMA 2020 guidelines, synthesizes 581 peer-reviewed studies on shoreline prediction indexed in Scopus between 2019 and 2025. The review framework spans influencing factors, monitoring and data acquisition, shoreline extraction methods, quantification frameworks, predictive modeling approaches, and operational applications. Medium-resolution optical satellite platforms dominate observational practice, accounting for 61% of all sensing platforms used in the literature, whereas synthetic aperture radar (7.9%) and unmanned aerial systems (20.6%) remain underutilized, despite their complementary capabilities in dynamic coastal environments. These findings indicate that statistical quantification frameworks remain structurally dependent on linear assumptions, which are fundamentally incompatible with the threshold-driven behavior of real coastal systems. Process-based numerical models are led by XBeach, which accounts for 22.5% of models and demonstrates strong calibration performance but limited forward predictive transferability across sites and forcing conditions. Artificial intelligence applications expanded by 162.5% over the study period, converging toward physics-informed neural networks that directly embed physical conservation laws into model training and achieve the optimal balance between predictive accuracy, interpretability, and computational efficiency. Geographic analysis revealed a pronounced research focus on Asian coastal environments, whereas African and South American coastlines received minimal attention, despite their disproportionate vulnerability to erosion and population exposure. These findings highlight research priorities oriented toward standardized benchmarking, probabilistic forecasting, and transferable hybrid modeling architectures aligned with the United Nations Sustainable Development Goals for climate action, ocean conservation, and sustainable coastal communities.
Storm tracks play a vital role in modulating midlatitude weather and global heat, momentum, and moisture transport. Under global warming, their position and intensity are projected to undergo substantial changes. Using Coupled Model Intercomparison Project Phase 6 simulations, this study examines future storm track responses and the underlying dynamics. Storm tracks are diagnosed using a classical Eulerian metric—synoptic-band (2.5- to 6-d) band-pass-filtered geopotential-height variance—while the mechanisms are quantified through a full-form local eddy kinetic energy budget that partitions baroclinic conversion, barotropic conversion, energy redistribution, and geopotential (pressure) flux convergence. Results show a weakening tendency of Northern Hemisphere storm tracks in June–July–August, consistent with recent evidence of boreal-summer storm track weakening, but a more regionally structured intensification accompanied by a poleward displacement in December–January–February. In the Southern Hemisphere, storm tracks intensify in June–July–August and shift poleward in December–January–February. These changes are linked to enhanced background baroclinicity measured by the Eady growth rate, arising mainly from strengthened meridional temperature gradients with a secondary contribution from changes in static stability. Interpreted through the eddy kinetic energy budget, strengthened high-latitude baroclinic conversion, together with weakened barotropic damping and energy redistribution by flux convergence, supports an upper-tropospheric increase and poleward displacement of transient eddy activity, highlighting the central role of eddy energy conversion and redistribution processes in shaping storm tracks in a warming climate.
Topography plays an important role in shaping Earth’s climate system; however, the relative contributions of bathymetry, land–sea distribution, and continental topography remain uncertain. To isolate the impact of ocean topography on the present oceanic and atmospheric system, we conducted an aquaplanet simulation with realistic bathymetry using the fully coupled Community Earth System Model v1.2.2, in which land was replaced by a 10-m-deep ocean, while realistic seafloor bathymetry was preserved (hereafter BATHY). The model results show that, compared with the real Earth, BATHY retains a similar large-scale ocean circulation but exhibits an overall strengthening. The strengthened subtropical cell, weakened upper circulation in the Southern Ocean, and deepened Atlantic meridional overturning circulation lead to a warmer Antarctic and a colder Arctic, resulting in larger temperature gradients and stronger westerlies in the Northern Hemisphere but smaller gradients and weaker westerlies in the Southern Hemisphere. Consequently, a more hemispherically symmetric climate relative to the real Earth is established. However, hemispheric asymmetry persists in the BATHY simulation, with a surface pattern similar to that of the real Earth. The BATHY experiment reveals the role of ocean topography in shaping climate and modulating hemispheric asymmetries in a land-free coupled system. Ocean topography not only regulates the oceanic and atmospheric systems through ocean circulation but also contributes to hemispheric asymmetry in the current climate system. These findings also have implications for interactions among ocean circulations and for understanding climate variability.
Through rapid advances in the Internet of Things, Big Data, artificial intelligence, and visualization technologies, digital twin technology has emerged as a transformative paradigm in the marine domain. A digital twin of the ocean (DTO) is a dynamic virtual replica of physical marine systems, enabling real-time monitoring, predictive analysis, and bidirectional optimization through integrated observational data, advanced modeling, and high-performance computing. Despite the potential for sustainable ocean governance, climate adaptation, and disaster mitigation, a systematic synthesis of DTO technologies remains lacking. In this study, we conducted a comprehensive literature review of DTO technology in terms of its foundational concepts, architecture, enabling technologies, challenges, future directions, and regional and international initiatives over the past 2 decades. The key functionalities of the DTO include real-time synchronization with marine environments, predictive modeling simulations for “what-if” scenario analysis, and decision support for disaster mitigation, resource management, and climate adaptation. The DTO system architecture generally involves 4 layers: physical entity, data, model, and visualization layers, enabled by technologies including the marine Internet of Things, Big Data, artificial intelligence, predictive models, high-performance computing, and visualization technology. However, challenges persist in terms of data sparsity, lack of standardization, and disparities in digital literacy. By bridging the gaps among research, policy, and industry, we provide a roadmap for future DTO development that emphasizes cross-disciplinary collaboration and ethical governance.
Accurate characterization of wind-speed probability distributions underpins offshore wind resource assessment and ocean–atmosphere forcing estimates in coastal seas. Although the Weibull distribution is widely used, its limited shape flexibility often cannot represent the combination of a sharp central peak and a heavy right tail commonly observed across coastal–inland transition zones. Here, we introduce a physically consistent truncated proportional hazard logistic (TPHL) distribution. The model was obtained by conditioning the proportional hazard logistic family on nonnegative support, ensuring physical realism while retaining a closed-form, analytically tractable, 3-parameter structure with independent control of the typical wind level, the spread of the distribution core, and tail heaviness. We evaluated the TPHL distribution using long-term observations from 12 stations in the Hadley Centre Integrated Surface Database (HadISD) and a high-resolution wind dataset reconstructed from the fifth-generation atmospheric reanalysis (ERA5) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) for 2020 to 2024 across the Guangdong–Hong Kong–Macao Greater Bay Area. Benchmarked against the Weibull, gamma, and lognormal distributions, the TPHL distribution yielded superior goodness of fit with markedly lower Kolmogorov–Smirnov distances. Crucially, the TPHL distribution resolved the extreme value underestimation inherent in conventional models, reducing errors in wind-power density evaluation from approximately 30% to within 10% for most stations. The regionally fitted fields revealed a coherent inland–coastal contrast under typical conditions and variability. Overall, the TPHL distribution provides a unified distributional framework for wind-forcing characterization in complex land–sea environments, with direct value for coastal hazards and marine energy applications.
Stratospheric ozone measurements in the Arctic show record-high values in March 2024 compared with those during 1979–2024. This increase is particularly evident in total column ozone measurements from merged satellite data [477 Dobson units (DU)] and ground-based observations at Lerwick (415 DU), Oslo (400 DU), Sodankylä (412 DU), and Scoresbysund (427 DU). Ozonesonde measurements at Ny-Ålesund, Scoresbysund, Eureka, and Churchill also show elevated ozone levels in the lower (100 to 25 hPa) and middle stratosphere (25 to 5 hPa) in March 2024. In addition, observations from the Microwave Limb Sounder aboard the Aura satellite indicate higher ozone (24 mPa) in the lower stratosphere (100 to 25 hPa) during the same period. The Eliassen–Palm flux analysis reveals strong upward propagation of planetary waves into the lower and middle stratosphere in March 2024. The average polar-cap temperature was also record high because of this intense wave activity, which triggered 3 warming events that substantially disturbed the polar vortex during winter. The early March warming, driven by wave 1 and wave 2, was further enhanced by the combined influence of a strong El Niño, the Madden–Julian Oscillation, and the Quasi-Biennial Oscillation. This unusual and unprecedented dynamical activity consequently led to record-high ozone values in the Arctic stratosphere in March 2024. This study extends previous work by providing a detailed dynamical analysis of the winter and quantifying the contributions of planetary waves, tropospheric forcing, and large-scale variability. This integrated wave diagnostics provides a clear and more robust attribution of the observed ozone changes.
Ocean alkalinity enhancement (OAE) is a leading climate mitigation strategy for atmospheric carbon dioxide removal, with theoretical potential to sequester gigatons of CO2 annually while counteracting ocean acidification. However, as a deliberate anthropogenic perturbation to the marine carbonate system, the ecological consequences for the microbial communities that underpin marine biogeochemical cycles remain incompletely understood. Alterations in seawater pH and carbonate saturation states could influence microbial assemblages, with implications for ecosystem functioning and stability under global climate change. In this study, we experimentally assessed the responses of marine prokaryotic communities to unequilibrated alkalinity additions (85 to 495 μmol·kg−1) using Mg(OH)2 in the subtropical South China Sea. Across both microcosm (55 l) and mesocosm (50,000 l) scales during the wet and dry seasons, a total alkalinity increase of 80 to 427 μmol·kg−1 substantially elevated pH (8.57 to 8.77) and carbonate saturation states. Despite these pronounced chemical perturbations, the prokaryotic diversity, community structure, and predicted metabolic functions remained remarkably stable. This stability persisted across different experimental scales and contrasting seasonal conditions. Instead, seasonal variability and nutrient availability were the dominant drivers of microbial community shifts, overwhelming the minor effects of alkalinity addition. Our findings align with recent studies from the North Atlantic and the Equatorial Pacific, suggesting that the resilience of prokaryotic communities to moderate, unequilibrated OAE is a robust, globally relevant phenomenon. This study emphasizes that even under relatively intense OAE scenarios, microbial community stability may help sustain ecosystem functioning amid emerging ocean-based carbon dioxide removal interventions, a key finding for developing effective monitoring, reporting, and verification (MRV) frameworks.
The Yin–He global spectral model (YHGSM) is a dry-mass-conserving hydrostatic global spectral model that employs spectral transforms to compute horizontal derivatives. This study investigated the performance of the mass conservation ocean model (MaCOM) coupled with the YHGSM (YHGSM_MaCOM), focusing on its application in forecasting Super Typhoon Hinnamnor (2022) and its interaction with the upper ocean over the northwest Pacific. Hinnamnor presented a marked forecasting challenge due to its abnormal track and extreme intensity changes, including rapid intensification along a westward track followed by rapid weakening along a northward sudden-turning track. The results demonstrate that YHGSM_MaCOM significantly improved Typhoon Hinnamnor intensity forecasts, particularly during its sharp-turning period. This improvement is attributed to the coupled model’s ability to capture sea surface cooling during the typhoon event (5.60 °C, whereas satellite observations recorded 5.57 °C), thereby more accurately assessing oceanic feedback on the typhoon, which corresponded to a more accurate forecast of typhoon intensity. Compared with the pure YHGSM, the mean absolute error of the 0- to 5-d intensity forecasts using YHGSM_MaCOM was reduced by 14.9% to 18.3% across different coupling schemes. When the coupling frequency was increased from 3 to 1 h, the typhoon intensity forecast was further improved; however, the typhoon track errors increased. Overall, YHGSM_MaCOM improved Typhoon Hinnamnor track and intensity forecasting, regardless of the coupling scheme, compared to the pure atmospheric model, demonstrating superiority of the ocean model.
This study investigated the seasonal dynamics of deep-water overflow in the Luzon Strait (LS) based on numerical simulations. The results suggest a strong interplay between the upper- and deeper-layer processes that modulate overflow seasonality. The bottom pressure gradient force (PGFalong) across the Bashi Channel and Luzon Trough appears to be the primary factor shaping these dynamics. In general, both the upper (above 500 m) and deeper (below 2,000 m) layers of the LS contribute to deep PGFalong and overflow. Upper PGFalong is influenced by sea-level differences crossing the strait, whereas deep PGFalong is largely related to the density difference between the Pacific Ocean and South China Sea. Seasonally, variations in the intensity and pathways of the Kuroshio Current affect the surface sea levels, leading to oscillations in the isopycnal surfaces. These surface oscillations are transmitted downward and affect bottom PGFalong over the Bashi Channel. Together with density oscillations originating from the deep Pacific Ocean and the deep South China Sea, these processes contribute to the variability of the deep-water overflow in the LS. Weakening wind stress or reductions in Kuroshio strength could potentially reduce the variability of this overflow, underscoring the interconnectedness of atmospheric influences and oceanic responses in this region.
Human-perceived temperature changes, quantified as apparent temperature (APT), have been studied extensively over land but are less well understood over the oceans. APT combines temperature, humidity, and wind speed to provide a thermal comfort index. Oceanic APT trends and their drivers, particularly the role of near-surface wind speed (NSWS), remain unclear despite widespread air temperature (AT) increases. In this study, ECMWF Reanalysis v5 data for 1950 to 2023 are analyzed using a linear sensitivity framework to quantify the contributions of AT, relative humidity (RH), and NSWS to APT trends. Over land, AT is the dominant driver of global APT changes. Over the oceans, by contrast, both AT and NSWS are important, with their relative dominance varying by region: AT warming prevails in most areas, while NSWS cooling dominates across large parts of the Southern Ocean and the Eastern Tropical Pacific. A pronounced land–sea contrast is found: terrestrial APT rises faster than AT over 96% of the land, while oceanic APT warms significantly less than AT, with more than 30% of the ocean showing nonwarming or cooling APT trends. This divergence is primarily due to oceanic NSWS-driven cooling, especially strong in the Southern Hemisphere (−71%) compared to the Northern Hemisphere (−14%). These results reveal that oceanic thermal comfort is governed by the interplay between AT warming and NSWS cooling, with the role of wind shifting from beneficial moderator in the tropics to hazard amplifier in the Southern Ocean, highlighting the need to incorporate wind dynamics in marine heat stress and cold stress assessments.
This study investigates the distinct patterns of winter precipitation variability across southern China, a region encompassing the climatically vital Southwest China and South China subregions. Accurately diagnosing their coordinated variability is fundamental for advancing climate dynamics understanding, yet traditional approaches are often limited by inherent spatial heterogeneity in precipitation characteristics. To address this, we introduce a local standardization procedure prior to empirical orthogonal function analysis, effectively isolating the regionally coherent signal. This refined methodology reveals 2 statistically robust and physically distinct modes. The leading mode (41.4% of variance) is a zonal in-phase pattern, characterized by simultaneous precipitation anomalies across Southwest China and South China. It is synergistically modulated by the Eurasian wave train (EU2), which weakens the East Asian winter monsoon, the India–Burma Trough (IBT), enhancing moisture advection, and the tropical Indian–Pacific tripole sea surface temperature pattern, which intensifies ascending motion. The second mode (14.3% of variance) exhibits an east–west dipole pattern, predominantly governed by the Scandinavian teleconnection (SCAND). The negative phase of SCAND induces blocking high-pressure anomalies near Lake Baikal, prompting cold-air outbreaks that suppress precipitation over South China while concurrently blocking the eastward extension of the IBT, thereby concentrating moisture and increasing precipitation over Southwest China. Our findings elucidate the complex interplay of extratropical teleconnections and tropical forcing in shaping regional climate, and the methodological framework offers an improved approach for analyzing coordinated variability within heterogeneous domains.
The 4-dimensional multigrid analysis (4D-MGA) data assimilation system was developed for the 1/12° global Mass Conservation Ocean Model (MaCOM). 4D-MGA is a variational data assimilation method that accounts for error evolution across concurrent spatial and temporal scales. Dynamic height integration was incorporated into the cost function, thereby imposing a physical constraint for assimilating sea level anomaly observations. To evaluate the 4D-MGA system, sequential data assimilation was performed from 2024 April 5 to May 9. The analysis fields show that 4D-MGA effectively reduces large-scale model biases and improves the spatial representation of mesoscale eddies and surface currents. Both the root mean square error (RMSE) and bias were substantially reduced, and the sea surface height and current fields show substantially improved agreement with satellite observations. Forecasts initialized from the 4D-MGA analysis fields demonstrate consistent and moderate improvements in accuracy. The 1- and 7-d forecasts exhibit lower RMSE and bias than the free-run simulation and maintain stable performance. An intercomparison based on the GODAE Intercomparison and Validation Task Team metrics was conducted. The results place MaCOM with 4D-MGA above the average of the systems compared. MaCOM demonstrates relatively low RMSE and greater stability in salinity, sea surface temperature, and sea level anomaly. Although temperature forecasts rank below average during the first 3 d, they improve to a moderate level over the final 3 d. However, given the short duration of the current experiment, longer-term evaluation is required to fully assess the system’s sustained assimilation performance.
