Transactions of the Kansas Academy of Science

The present study aims to analyze the available Ocean Model Intercomparison Project Phase-2 (OMIP2) models from the Coupled Model Intercomparison Project Phase-6 (CMIP6) group in representing the seasonal mean variations of temperature and salinity. The skill of the models in representing seasonal mean biases of temperature and salinity is assessed with World Ocean Atlas and Argo observations over the Tropical Indian Ocean (TIO), Bay of Bengal (BoB), Arabian Sea (AS), and Southern Indian Ocean. It is identified that most of the individual models and multi-model mean of OMIP2 models exhibit a cold (surface) and warm (subsurface) temperature bias over the entire TIO. The salinity analysis reveals that most of the TIO (except the equatorial TIO) is dominated by saltier biases, especially predominant over the south AS and the western BoB. Vertical shear of horizontal currents (VSHC) and the Brunt-Väisälä frequency have been analyzed to understand the stability of the Ocean, suggesting that the VSHC contributes to the vertical mixing resulting in weak stratification, is mainly responsible for the persistence of surface cold and subsurface warm biases. In addition, Freshwater transport (FWT) is estimated at different straits, suggesting that FWT can modulate the salinity in the fresh region of TIO. The current study summarizes the improvement and necessity of ocean models to depict vertical hydrodynamic conditions for skillful seasonal forecasts accurately.

Pan et al. (2025) claim to uncover numerous timing and datum errors in the University of Hawai‘i Sea Level Center Research Quality dataset and declare that the dataset is “less reliable than previously assumed”. We argue that Pan et al. overstate the scientific implications of unresolved issues in the dataset and demonstrate that Pan et al. have failed to consider the extensive metadata accompanying the observations, which already document most of the issues Pan et al. claim to expose. We also clarify the nature of the Research Quality dataset, which is not guaranteed to be free of errors, and assert that when data and metadata are used together as intended, the dataset remains a reliable basis for scientific research.

A thermocline inversion model based on multi-head attention mechanism within a neural network framework is developed to estimate and analyze the ocean thermocline features, including depth (updepth and base), thickness, and intensity, in the western Pacific Ocean. This model employs Argo-derived thermocline product alongside various satellite remote sensing observations of ocean surface parameters, such as sea surface height, salinity, temperature, and wind. Specifically, three independent inversion models are executed using a dataset spanning the previous five years for training purposes, with the resulting model parameters used to estimate thermocline features in March, June, September, and December of 2016. The analysis reveals that thermocline updepth mainly located in the east of the Kuroshio extension area occurring in winter and spring; the seasonal distribution of the thermocline base is characterized by deeper depths at higher latitudes in the northern hemisphere during winter and spring, and from summer to winter in the southern hemisphere; the thermocline intensity in tropical regions is observed to be shallower yet stronger, exhibiting significant variations along the latitude with distinct seasonal changes. The seasonal distribution characteristics and variation trends of the updepth, base and intensity of the thermocline calculated by the multi-head attention neural network are generally consistent with the referenced Argo-derived thermocline dataset. Notably, the proposed intelligent inversion model for thermocline could also be utilized under condition of certain position with high flexibility, and exhibits faster convergence and greater accuracy compared to the classic Bi-LSTM model under comparable experimental conditions.

Abstract Long-term observations, which were collected in the Sea of Okhotsk coastal zone under open-water conditions and at times when the sea was ice-covered from mid-January to mid-March 2022, are interpreted. The project augments preceding work using three synchronously-recording, seafloor-mounted, pressure transducers sampling at 1 Hz to acquire time series of inshore wave oscillations. Units are deployed nearer to the shore and closer together than was done during the previous studies. Spectral analyses of open-water sea level oscillations perpendicular to the coast reveal a wide band of energy, suggestive of a propagating edge wave at about 5-min period with an offshore-to-inshore gain up to 1.5. Similar wave characteristics occur alongshore. Intriguingly, the peak edge wave period at 5 min migrates to 6–7 min when the sea becomes covered with ice, and narrower bands at periods from 0.5 to about 3 min emerge. Other period ranges also appear to be affected by the onset, presence and eventual disintegration of sea ice. Whilst a shift of the dominant edge wave period can be attributed to changes in spectral refraction arising from the incoming swells being low-pass filtered by sea ice, because of the observed tidal signal it is speculated in this case that the observed period adjustment and attendant expansion of bandwidth may also be associated with instability around the fundamental frequency. The potential for edge wave solitons to exist is explored.

Abstract Marine heatwaves (MHWs) pose a significant threat to marine ecosystems and economies. Predicting MHWs is essential for mitigating their impact, but remains a challenge. Despite considerable progress having been made in understanding the regional-scale drivers of MHWs, a significant knowledge gap remains when it comes to understanding the synoptic-scale processes associated with these events. In this study, we used self-organising maps to identify the synoptic-scale atmospheric and oceanic patterns associated with MHWs identified in four sub-regions of the Tasman Sea between 1985 and 2014. Our results reveal patterns associated with recurring, as well as distinct extreme warming events. We show that anomalous atmospheric influence is consistently present during MHWs and that the two most recurring patterns are linked to a La Niña climate phase. Distinct synoptic air-sea patterns are also identified in the 1997/98 El Niño event. Furthermore, we identify a ‘reservoir’ of warm subsurface temperatures from 2000-2014, during which MHW frequency increased two-fold. Importantly, we have identified patterns of persistent anomalous conditions before the onset of MHWs with timescales on the order of days for atmospheric conditions and weeks to months for oceanic conditions, providing valuable insight into MHW predictors. These findings highlight the importance of understanding synoptic-scale drivers of MHWs and timescales of recurring patterns for MHW prediction. The temporal variability observed in the lead-up to MHWs underscores the potential significance of factors such as surface-layer temperature and sea-level anomalies in capturing longer-term warming trends, likely influenced by sustained atmospheric stress and oceanic dynamics, whilst atmospheric conditions at onset precipitate the transition to the extreme warming thresholds.

Ocean wave-current interactions are important physical processes at the sea surface, which can potentially cause extreme sea states under certain conditions. Usually, such interactions are more notable in regions with strong waves and background currents. In this study, focusing on the Kuroshio Extension, we used buoy-measured and altimeter-derived wave data to determine variations in wave properties with the background currents. Statistically, the wave height can be underestimated (overestimated) by approximately 4% (3%) when the current and waves are in the opposite (same) direction. In regions with warm (cold) eddies, the wave height and wavelength inside the eddy are larger (smaller) than those outside by approximately 5% and 8% (4% and 4%), respectively, and the wave direction is deflected by 11° anticlockwise (clockwise). The wavenumber spectra of wave height and surface current speed are highly correlated with a power law of k− 2–k− 3 at scales of 20–200 km for swell-dominated cases. Additionally, the convergence and divergence of wave energy resulting from the current-induced refraction of swell are captured. From another perspective, the wave-induced Stokes drift calculated using the directional spectrum accounts for 54% of the reanalysis surface currents, and the accuracy of the estimated surface current can be improved by up to 14% by considering Stokes drift. This study provided quantitative analysis of observed surface wave variations in the Kuroshio Extension region from multiple perspectives.

Abstract Hydrodynamics and physical processes that occur at various length and time scales strongly influence coral reefs. Therefore, understanding the interactions between reefs, hydrodynamics and other physical processes is crucial for the maintenance and survival of reef systems. Coral reefs around the world are under increasing threat to global climate change, and additionally coral bleaching is a major concern for the health and survival of these reefs. Some marginal coral reefs are situated in areas where the complex ocean flow patterns interact with topographical features, providing possible refuges to rising ocean temperatures and coral bleaching. A prominent example is the Sodwana Bay coral reef system which has shown resilience to coral bleaching. This resilience has been attributed to cold water temperature anomalies that cause short-term temperature fluctuations on the reefs. This study explores hydrodynamics at various scales around the Sodwana Bay coral reefs and associated short-term temperature anomalies using a flexible mesh hydrodynamic model of the southwest region of the Indian Ocean, nested within a global ocean model. The nested hydrodynamic model better replicates the observed temperature anomalies when compared to the the reanalysis NEMO global ocean model. The higher model resolution around Sodwana results in less numerical mixing and smoothing of the temperature fields in the nearshore region when compared to the reanalysed NEMO global ocean model leading to a better replication of the local hydrodynamics around the Sodwana region. The anomalies investigated were associated with remote upwelling of cold water near the Delagoa Peninsula, followed by advection from the Delagoa Bight towards the Sodwana region. The separation of the strong intermittent southward stream from the Delagoa Peninsula is strongly linked to the upwelling at the Delagoa Peninsula. An analysis of the hydrodynamic patterns during the anomaly periods reveal that when the strong southward stream reattaches to the coastline, it typically does so south of Sodwana. The reattachment of the stream has an inertial effect and pushes the flow of water against the coastline which deflects the flow northwards up past Sodwana resulting in a northward current reversal along the Sodwana coastline which agrees with observed current reversals during the anomaly periods by insitu measurements taken on the Sodwana reefs. The model also revealed that local upwelling occurs within the Sodwana canyons during this event, making the water in the canyons colder than the surrounding water. When the locally upwelled water spreads over the reef system, the anomaly amplitude is enhanced by approximately 20 %.

Wind energy is a vital renewable energy source, and accurate Wind Speed Prediction (WSP) plays a key role in optimizing wind energy production and managing power grids effectively. However, predicting Wind Speed (WS) remains a significant challenge due to the inherently complex and dynamic behavior of wind flow. This paper introduces WindForecastX, an innovative approach that improves prediction accuracy by leveraging a dynamic unified ensemble learning model combined with advanced data assimilation techniques. The ability to accurately predict WS is vital for wind energy planning and monitoring. The accuracy of WSP has been limited because previous studies predominantly relied on data from a single location to develop models and predictions. The proposed WindForecastX model combines the strengths of ensemble learning and data assimilation techniques to enhance long-term WSPaccuracy. WindForecastX utilizes a Stacked Convolutional Neural Network (CNN) and bidirectional long short-term memory (BiLSTM) with a Data assimilation (SCBLSTM + DA) model, Adaptive Wind Speed Assimilation and Quality (AWAQ) incorporating WS observations from nearby locations. By leveraging these advanced techniques, including the Kalman filter, WindForecastX assimilates data from multiple sources to enhance the accuracy of WSP. To evaluate WindForecastX, we utilize real-world wind speed data collected from nine meteorological stations in the Tirunelveli district of Tamil Nadu, India. These stations are used for training and testing, with two stations designated as target stations for WSP. The results demonstrate that WindForecastX outperforms existing WSPmodels. Furthermore, WindForecastX exhibits reduced sensitivity to changes in the prediction time scale compared to standalone models, enhancing its reliability.

The magnitude of change in Chlorophyll-a (Chl-a) concentration in the Northern Arabian Sea (NAS) and northwestern Arabian Sea (NWAS), associated winds and Sea Surface Temperature (SST) that could potentially contribute to the fisheries management policies were investigated. The aforementioned parameters were examined from 2003 to 2021 by considering the area into 7 regions. Off Oman, monthly average winds delineate the presence of weak anticyclonic circulation during October and another one from February strengthening and migrating southwards till April. This feature is absent in May. For the first time, the warming in different regions was investigated and observed a temperature range from 0.4 °C to 0.8 °C with highest off Iran and least off central Oman. The Ekman Mass Transport (EMT) decreased with the highest off Pakistan (18.91%) and least off central Oman (0.55%). The decrease in Chl-a concentration was highest off Iran and least off Oman. The correlation of Chl-a with SST off Yemen was highest (-0.55) indicating that any change in SST will more conclusively influence Chl-a off Yemen than the rest of the regions. From 2003 to 2021 the wind speed off Oman increased up to 0.46 m/s while the other regions show decreased wind speed. The maximum correlation between winds and Chl-a was observed off Oman, indicating that changes in winds are more likely to affect Chl-a concentration in this region compared to other regions. The study statistically establishes the differential influence of SST and Sea Surface Winds (SSW) in the study area.

Observations of Japanese eel Anguilla japonica recruitment on Hokkaido’s coast in 2020 revealed a poleward shift of the species’ northern limit by several hundred kilometers. Field observations conducted from April to July 2021 in a river in southern Hokkaido, as reported in this study, identified for the first time the potential recruitment period of juvenile glass eels in Hokkaido, suggesting that the recruitment season may have commenced in May and concluded in July. The long-term trend of Japanese eel recruitment to Hokkaido was examined using a three-dimensional particle-tracking model. Virtual larvae were programmed to swim both horizontally and vertically, in addition to being transported by ocean currents, after their release near eastern Taiwan (Scenario 1) and northeastern Japan (Scenario 2). Scenario 1 showed increased recruitment in northern Japan and decreased recruitment in southern Japan during 2014–2023 compared to 1994–2003, which was attributed to the shift in the Kuroshio path. In Scenario 2, focusing on local processes near Hokkaido, the spatial variation in estimated glass eel recruitment exhibited patterns consistent with the natural variation in eel abundance observed across 95 rivers in southern Hokkaido in 2022, with higher recruitment in southeastern Hokkaido and lower recruitment in the Tsugaru Strait. Simulated recruitment trends from 1994 to 2023 indicated an increase in southeastern Hokkaido and a decrease in the Tsugaru Strait. The increased recruitment to southeastern Hokkaido was linked to the northward shifts of the Kuroshio/Kuroshio Extension and Oyashio currents, which weakened the southward currents in the confluence zone of the Kuroshio/Kuroshio Extension and Oyashio. In contrast, reduced recruitment in the Tsugaru Strait was associated with the strengthening of the east-flowing Tsugaru Current. These findings suggest that long-term fluctuations in ocean currents significantly influence the northern limit of anguillid eel habitats, highlighting the impact of changing oceanic conditions on their natural distribution.

This study introduces an open software Lagrangian particle tracking model designed for simulating the transport of microplastics (MPs), which incorporates crucial processes such as horizontal diffusion, beaching, resuspension, and biofouling. A sensitivity analysis for the parametrization of these processes was conducted on a regional scale – in the Gulf of Finland (GoF), the eastern Baltic Sea – employing very high-resolution hydrodynamic model output to drive the particle model. The sensitivity analysis underscores the impact of each process on the number of particles in the water column, sediments, beach areas, and at the domain boundary. The results indicate a significant impact of including or excluding a process and relatively high sensitivity of the parametrization on the simulated MP pathways. Stronger diffusion dispersed particles widely throughout the gulf and enhanced the export of the MPs out from the gulf. Beaching and biofouling were the major contributing factors to particle removal from the water column, while resuspension promoted settling in offshore areas. The number of beached particles rapidly increased during the wind-induced downwelling process. Scenario simulations, including parametrizations favoring or hindering MP transport, showed that a coincidence of several factors could lead to very diverse MP pathways. The analysis offers valuable insights, providing a foundation for tuning the model parameters to improve simulations with realistic loads in the future.