Sea Surface Height Research Articles

Assimilation of synthetic and real SWOT observations for the North Atlantic Ocean and Canadian east coast using the regional ice ocean prediction system

The Surface Water Ocean Topography (SWOT) mission significantly improves on the capabilities of current nadir altimeters by enabling two-dimensional mapping. Assimilating this advanced data into high-resolution models poses challenges. To address this, Observing System Simulation Experiments (OSSEs) were conducted to evaluate the effects of both simulated and actual SWOT data on the Regional Ice Ocean Prediction System (RIOPS). This study examines the OSSEs’ design, focusing on the simulated observations and assimilation systems used. The validity of the OSSE designs is confirmed by ensuring the deviations between the assimilation system and the Nature Run (NR) align with discrepancies observed between actual oceanic data and OSSE simulations. The study measures the impact of assimilating SWOT and two nadir altimeters by calculating root mean square forecast error for sea surface height (SSH), temperature, and velocities, along with performing wave-number spectra and coherence analyses of SSH errors. The inclusion of SWOT data is found to reduce RMS SSH errors by 16% and RMS velocity errors by 6% in OSSEs. The SSH error spectrum shows that the most notable improvements are for scales associated with the largest errors in the range of 200-400 km, with a 33% reduction compared to traditional data assimilation. Additionally, spectral coherence analysis shows that the limit of constrained scales is reduced from 280 km for conventional observations to 195 km when SWOT is assimilated as well. This study also represents our first attempt at assimilating early-release SWOT data. A set of Observing System (data denial) experiments using early-release SWOT measurements shows similar (but smaller) responses to OSSE experiments in a two nadir-altimeter context. In a six-altimeter constellation setup, a positive impact of SWOT is also noted, but of significantly diminished amplitude. These findings robustly advocate for the integration of SWOT observations into RIOPS and similar ocean analysis and forecasting frameworks.

Statistical analysis of the association between El Niño and the biological carbon pump in the East Sea (Japan Sea)

Understanding the impacts of climate change on oceanic carbon cycling is important from a carbon sequestration perspective. A sediment trap study focused on the biological carbon pump system in the Ulleung Basin (UB) in the southwestern part of the East Sea (Japan Sea) was conducted from 2011 to 2017. Particulate organic carbon (POC) flux significantly increased by 37, 56, and 43% from 2014 to 2016 during the El Niño phase. We examined data related to water current variability, such as sea surface height, current velocity, and eddy frequency to understand their roles in particle transport. In addition, a Martin curve was employed to analyze the rate of vertical attenuation of POC flux in the UB. Current variability could be the most important factor influencing the increase in sinking-particle flux during the El Niño phase.

Weighted Fusion Method of Marine Gravity Field Model Based on Water Depth Segmentation

Among the marine gravity field models derived from satellite altimetry, the Scripps Institution of Oceanography (SIO) series and Denmark Technical University (DTU) series models are the most representative and are often used to integrate global gravity field models, which were inverted by the deflection of vertical method and sea surface height method, respectively. The fusion method based on the offshore distance used in the EGM2008 model is just model stitching, which cannot realize the true fusion of the two types of marine gravity field models. In the paper, a new fusion method based on water depth segmentation is proposed, which established the Precision–Depth relationship of each model in each water depth segment in the investigated area, then constructed the FUSION model by weighted fusion based on the precision predicted from the Precision–Depth relationship at each grid in the whole region. The application in the South China Sea shows that the FUSION model built by the new fusion method has better accuracy than SIO28 and DTU17, especially in shallow water and offshore areas. Within 20 km offshore, the RMS of the FUSION model is 5.10 mGal, which is 8% and 4% better than original models, respectively. Within 100 m of shallow water, the accuracy of the FUSION model is 4.01 mGal, which is 14% and 12% higher than the original models, respectively. A further analysis shows that the fusion model is in better agreement with the seabed topography than original models. The new fusion method can blend the effective information of original models to provide a higher-precision marine gravity field.

Effects of Oceanographic Conditions on Fishery Distribution: A Case Study of Chub Mackerel (Scomber japonicus) in Northeastern Taiwan

Changes in oceanographic conditions can affect species distribution in marine habitats. Global climate change may negatively influence the oceanographic factor–species distribution relationship. Here, we assessed the influence of oceanographic conditions on chub mackerel (Scomber japonicus) distribution in northeastern Taiwan by constructing and using a habitat ensemble model incorporating chub mackerel fishery, climatic oscillation, and oceanography data. Our results indicated that the chub mackerel catch was mainly influenced by the Western Pacific Oscillation. Moreover, sea-surface height and mixed-layer depth exerted the most and least significant effects on chub mackerel distribution, respectively. The chub mackerel catch rate peaked in the study area with a sea-surface temperature of 29°C, sea-surface chlorophyll of 0.25 mg/m3, sea-surface salinity of 33.7 psu, and SSH of 0.575 m. Chub mackerel was the most widely distributed in the area between 25°N, 120.5°E and 26.2°N, 121.5°E. Our findings can be used to develop critical adaptation plans for managing chub mackerel fisheries in the northeastern waters of Taiwan. Considering changing climate conditions globally, the incorporation of this knowledge into managerial strategies may aid decision-makers in protecting not only other ocean fisheries but also individuals dependent on them.

A two-stage deep learning architecture for detection global coastal and offshore submesoscale ocean eddy using SDGSAT-1 multispectral imagery

Submesoscale ocean eddies are essential oceanic phenomenon that control and influence the ocean energy cascade. Most existing eddy detection methods rely on low-resolution satellite altimeter data, which fail to capture submesoscale ocean features and oceanographic phenomena in shallow water. Introducing high-resolution multispectral data can alleviate these problems, yet it has been largely overlooked. A generalized and efficient deep learning architecture that combines developments in deep learning with Sustainable Development Goals Science Satellite 1 (SDGSAT-1) multispectral data from earth observations offers a potential pathway for more fine detection of ocean eddies. Considering that oceanic eddy exhibits spatially sparse characteristics on high-resolution remote sensing scenes, the oceanic eddy detection (OED) model suitable for global coastal and offshore regions is divided into two stages: eddy information judgment and eddy position determination. Correspondingly, SDGSAT-1 multispectral data from November 2021 to December 2022 were carried out to construct two submesoscale eddy datasets for training and testing each stage model. The union validation of multiple metrics demonstrates that the proposed OED model and its stage models achieve state-of-the-art (SOTA) performance, especially in optically complex coastal and offshore waters. We applied the model to real-world scenes captured by SDGSAT-1 in 2023, and found that the detected results were mainly located at the water depth below 200 m. The authenticity of the recognition results is validated using sea surface chlorophyll concentration, temperature, and topography data, indicating that the OED model has achieved remarkable effectiveness under various sea conditions. In addition, the temporal distributions and statistical characteristics of detected submesoscale eddies are analyzed over an extended period (November 2021 to November 2023). Finally, HISEA-2, Landsat-9, and Sentinel-2 served as testing grounds to validate the generalization of the proposed methodology, with experimental results demonstrating that the OED model possesses significant developmental potential for multi-source remote sensing data. This paper presents a comprehensive deep learning framework for the global-scale detection of submesoscale eddies and underscores the pivotal role of high-resolution multispectral imagery as an innovative data source for global coastal and offshore eddy identification.

Coastal vertical land motion across Southeast Asia derived from combining tide gauge and satellite altimetry observations

Vertical land motion (VLM) is complex in Southeast Asia because this region is subject to a range of natural processes (e.g., earthquakes) and anthropogenic activities (e.g., groundwater withdrawal) that can change land heights. To aid in coastal management, long-term observations of VLM are as crucial as observations for climate-induced sea surface height changes; however, such long-term observations are sparse for Southeast Asian coasts. To fill this observational gap, here we derive monthly VLM time series from 1993 to 2020 at 50 coastal sites across Southeast Asia by combining tide-gauge records and newly generated satellite altimetry observations. These altimetry observations are reproduced sea-level products using new altimetry standards and more accurate geophysical corrections. Our 27-year-long VLM dataset shows high spatial variability and non-linear temporal changes in VLM across Southeast Asia. We identify several major sources that dominate the regional land-height changes, which include large subsidence due to groundwater extraction in Manila and Bangkok, land uplift in Indonesia and subsidence in Thailand from postseismic deformation resulting from the sequence of large Sumatran earthquakes since 2004, and land subsidence as a result of sediment compaction in Malaysia. Those signals are quantitatively or qualitatively consistent with observations from other sources. This VLM dataset can be used to advance our understanding of the physical mechanisms behind land-height changes and to improve sea level projections in the region.

Strengthening of the Equatorial Pacific Upper‐Ocean Circulation Over the Past Three Decades

AbstractThirty years (1993–2022) of concurrent satellite and in‐situ observations show a long‐term strengthening of the equatorial Pacific upper‐ocean circulation. Enhanced southeasterly and cross‐equatorial winds have caused an annual mean, basin‐wide acceleration of the equatorial westward near‐surface currents by ∼20% and an acceleration of poleward flow north (south) of the equator by ∼60% (∼20%). Additional moored velocity data reveal a deepening of the Equatorial Undercurrent (EUC) core at 170°W and significant shoaling at 140°W and 110°W, but no significant changes in EUC core velocity. The strongest subsurface zonal velocity trends are observed above the EUC core in the central to eastern Pacific and occur before and after the seasonal maximum of EUC core velocity, causing enhanced upper‐ocean vertical current shear. Consistent with trends of the 20°C isotherm depth along the equatorial Pacific, a significant basin‐wide steepening of the equatorial thermocline is observed. Both the accelerating equatorial current system and the enhanced thermocline slope are consistent with an observed steepening of the zonal sea surface height gradient due to increased wind‐driven westward mass transport at the surface. During February‐March, both surface and subsurface currents along the equator show eastward velocity trends, in contrast to westward near‐surface current trends during the remainder of the year. The trend reversal is attributed to both a long‐term shift in equatorial Kelvin wave activity and to the impact of strong interannual variability due to El Niño Southern Oscillation and other modes of natural variability on decadal to multidecadal time scales.

The international multi-system OSEs/OSSEs by the UN Ocean Decade Project SynObs and its early results

“Synergistic Observing Network for Ocean Prediction (SynObs)” was launched in 2022 as a project of the United Nations Decade of Ocean Science for Sustainable Development to evaluate the importance of ocean observation systems and co-design the future evolution of the ocean observing network. SynObs is currently leading the flagship OSEs/OSSEs, an internationally coordinated activity in which observing system experiments (OSEs) and observing system simulation experiments (OSSEs) are conducted using a variety of ocean and coupled atmosphere–ocean prediction systems to evaluate ocean observation impacts consistent across most prediction systems. The flagship OSEs/OSSEs comprises the ocean prediction (OP) OSEs for high-resolution ocean predictions, the subseasonal-to-seasonal (S2S) OSEs for long-term lead-time coupled ocean–atmosphere predictions, and the OP OSSEs for evaluating new and future observing systems. SynObs plans to use the results of the flagship OSEs to contribute to the reports on the ocean observing network design made by international organizations and projects. Here, we introduce this initiative, and we report on some initial results. Some observation impacts consistent across four ocean prediction systems are found by a preliminary analysis of the analysis runs for the OP OSEs. For example, impacts of the altimetry data on the assimilated sea surface height (SSH) field are generally large in the westerly boundary current regions and around Antarctic Circumpolar Currents where SSH has large variability but are small in the tropical regions, despite the relatively large SSH variability there. The analysis also indicates the possibility that there are some characteristic differences in the observation impacts between low-resolution and eddy-resolving ocean prediction systems. Although OSE outputs of only four ocean prediction systems are available now, we will make further investigation, adding OSE outputs of other prediction systems that will be submitted in the near future.

Monthly and Interannual Variations in Upwelling and Chlorophyll off the Vietnam Coast in Summer Based on Deep Learning

AbstractThe deep learning technique is used to automatically identify upwelling regions off the Vietnam coast using Sea Surface Temperature (SST) data from satellite imagery. After delineating these upwelling areas, this study is pioneering in deriving the upwelling probability off the Vietnam coast, pinpointing two significant upwelling hotspots. To deepen the understanding of the individual impacts of wind and sea surface height anomalies on monthly and spatial variations in upwelling probability and chlorophyll distribution, the Empirical Orthogonal Function method was employed to analyze this data set. The upwelling probability demonstrates a close relationship with wind and sea surface height anomalies, while chlorophyll concentration correlates with the strength of the southwesterly wind. The southwesterly wind, Ekman pumping, eddy dipole, and northeastward jet have been identified as key drivers of the formation and spatial variability of upwelling in summer, albeit with varying contributions to different parts of the coastal region. The distribution of upwelling probability correlates with chlorophyll variation off the northern Vietnam coast. High chlorophyll concentration off the southern Vietnam coast is primarily influenced by the southwesterly wind that carries the Mekong River plume eastward. Furthermore, two abnormal scenarios in upwelling and chlorophyll concentration during 2010 and 2018 were analyzed, attributed to the abnormal southwesterly monsoon and coastal circulation influenced by an El Niño event and a weak La Niña event with a positive Indian Ocean Dipole, respectively. This study elucidates the dynamic processes underlying the intricate chlorophyll distribution over the continental shelf, which is influenced by both the river plume and upwelling.

Prediction of the Relative Resource Abundance of the Argentine Shortfin Squid Illex argentinus in the High Sea in the Southwest Atlantic Based on a Deep Learning Model.

To analyze the impact of the marine environment on the relative abundance of Illex argentinus (high and low categories) in the southwest Atlantic, this study collected logbook data from Chinese pelagic trawlers from December 2014 to June 2024, including vessel position data and oceanographic variables such as sea surface temperature, 50 m and 100 m water temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and mixed layer depth. Vessel positions were used to enhance the logbook data quality, allowing an analysis of the annual trends in the resource center of this squid at a spatial resolution of 0.1° × 0.1° and a temporal resolution of ten days. The findings showed that the resource center is primarily located around 42° S in the north and between 45° S and 47° S in the south, with a trend of northward movement during the study period. Additionally, we constructed two ensemble learning models based on decision trees-AdaBoost and PSO-RF-aiming to identify the most critical environmental factors that affect its resource abundance; we found that the optimal model was the PSO-RF model with max_depth of 5 and n_estimators of 46. The importance analysis revealed that sea surface temperature, mixed layer depth, sea surface height, sea surface salinity, and 50 m water temperature are critical environmental factors affecting this species' resources. Given that deep learning models generally have shorter running times and higher accuracy than other models, we developed a CNN-Attention model based on the five most important input factors. This model achieved an accuracy of 73.6% in forecasting this squid for 2024, predicting that the population would first appear near the Argentine exclusive economic zone around mid-December 2023 and gradually move east and south thereafter. The predictions of the model, validated through log data, maintained over 70% accuracy during most periods at a time scale of ten days. The successful construction of the resource abundance forecasting model and its accuracy improvements can help enterprises save fuel and time costs associated with blind searches for target species. Moreover, this research contributes to improving resource utilization efficiency and reducing fishing duration, thereby aiding in lowering carbon emissions from pelagic trawling activities, offering valuable insights for the sustainable development of this species' resources.

Feasibility Analysis for Predicting Indian Ocean Bigeye Tuna (Thunnus obesus) Fishing Grounds Based on Temporal Characteristics of FY-3 Microwave Radiation Imager Data

Efficient and accurate fishery forecasting is of great significance in ensuring the efficiency of fishery operations. This paper proposes a fishery forecasting method using a brightness temperature (TB) time series spatial feature extraction and fusion model. Using Indian Ocean bigeye tuna fishery data from 2009 to 2021 as a reference, this paper discusses the feasibility of fishery forecasting using FY-3 Microwave Radiation Imager (MWRI) Level 1 TB data. For this paper, we designed a deep learning network model for radiometer TB time series feature extraction (TimeTB-FishNet) based on the Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Attention mechanism. After expanding the dimensions of TB features, the model uses them together with spatiotemporal feature factors (year, month, longitude, and latitude) as features. By adding the GRU and Attention to the CNN, the CNN-GRU-Attention model architecture is established and can extract deep time series spatial features from the data to achieve the best results. In the model validation experiments, the TimeTB-FishNet model performed optimally, with a coefficient of determination (R2) of 0.6643. In the generalization experiments, the R2 also reached 0.6261, with a root mean square error (RMSE) of 46.6031 kg/1000 hook. When the sea surface height (SSH) was introduced, the R2 further reached 0.6463, with a lower RMSE of 45.1318 kg/1000 hook. The experimental results show that the proposed method and model are feasible and effective. The proposed model can directly use enhanced radiometer TB data without relying on lagging ocean environmental product data, performing deep temporal and spatial feature extraction for fishery forecasting. This method can provide a reference for the fishing of bigeye tuna in the Indian Ocean.

Improving ocean analyses in the ensemble-based data assimilation system using the Community Earth System Model by assimilating satellite sea surface salinity

ABSTRACT This study evaluates the impact of assimilating satellite Sea Surface Salinity (SSS) data into the Community Earth System Model (CESM), a fully coupled general circulation model, using the Ensemble Adjustment Kalman Filter (EAKF). Two data assimilation experiments spanning from January 2010 to December 2019 are compared: EXP1, assimilating Sea Surface Temperature (SST) and Temperature-Salinity (T-S) profiles, and EXP2, which adds satellite SSS assimilation. Results highlight EXP2′s notable enhancement in sea surface salinity accuracy and variability, particularly in coastal and tropical regions, compared to EXP1. Moreover, the assimilation of satellite SSS contributes to improved sea surface velocity in the analysis, indicating enhanced ocean dynamics. However, the impact of extra assimilation of SSS on seawater temperature, sea surface height, and sea ice is relatively minor when other data is assimilated. This study pioneers the evaluation of satellite SSS assimilation within the ensemble-based data assimilation system using CESM, promising advancements in both data assimilation and numerical forecasting based on CESM.

Influence of CyGNSS L2 wind data on tropical cyclone analysis and forecasts in the coupled HAFS/HYCOM system

This study examines the influence of NASA Cyclone Global Navigation Satellite System (CyGNSS) Level 2-derived 10 m (near-surface) wind speed over the ocean on analyses and forecasts within the NOAA operational Hurricane Analysis and Forecast System (HAFS). HAFS is coupled with a regional configuration of the HYCOM ocean model. The primary advantages of data from the CyGNSS constellation of satellites include higher revisit frequency compared to polar-orbiting satellites, and the availability of reliable wind observations over the ocean surface during convective precipitation. CyGNSS data are available early in the life cycle of tropical cyclones (TCs) when aerial reconnaissance observations are not available. We focus on TCs whose forecasts were initialized when the TC was a depression or tropical storm. In the present study, we find first, that assimilation of CyGNSS near-surface winds improves storm track, intensity, and structure statistics in the analysis and early in the forecast, for many cases. Second, we find that assimilation of CyGNSS observations provides additional insights into the evolution of air-sea interaction in intensifying TCs: In effect, the ocean responds in the coupled model to modifications in the initial 10 m wind field, thereby impacting forecasts of intensity, storm structure, and sea surface height, as demonstrated by two case studies. We also discuss some forecasts where assimilating CYGNSS appears to degrade performance for either intensity or structure.

Detecting marine heatwaves below the sea surface globally using dynamics-guided statistical learning

Extreme warm water events, known as marine heatwaves, cause a variety of adverse impacts on the marine ecosystem. They are occurring more and more frequently across the global ocean. Yet monitoring marine heatwaves below the sea surface is still challenging due to the sparsity of in situ temperature observations. Here, we propose a statistical learning method guided by ocean dynamics and optimal prediction theory, to detect subsurface marine heatwaves based on the observable sea surface temperature and sea surface height. This dynamics-guided statistical learning method shows good skills in detecting subsurface marine heatwaves in the oceanic epipelagic zone over many parts of the global ocean. It outperforms both the classical ordinary least square regression and popular deep learning methods that do not effectively exploit ocean dynamics, with clear dynamical interpretation for its outperformance. Our study provides a useful statistical learning method for near real-time monitoring of subsurface marine heatwaves at a global scale and highlights the importance of exploiting ocean dynamics for enhancing the efficiency and interpretability of statistical learning.

Prediction on Yellowfin Tuna (Thunnus albacares) Fishing Ground in Waters Near the Marshall Islands Based on SMOTETomek‐RF

ABSTRACTThis study monitored 37 longliners fishing in waters near the Marshall Islands from 2020 to 2022 by Liancheng Overseas Fishery (Shenzhen) Co., Ltd.'s operation management system. This study developed nine predictive models on the relationship between catch per unit effort (CPUE) data for yellowfin tuna (Thunnus albacares) and the environmental data. The environmental data integrate 48 variables, including eddy kinetic energy, chlorophyll a concentration, sea surface height, and additional measures of vertical oceanic conditions, alongside spatiotemporal parameters (year, month, day, longitude, and latitude). This study employed four spatial resolutions (0.25° × 0.25°, 0.5° × 0.5°, 1° × 1°, and 2° × 2°) to develop nine predictive models: KNN, RF, GBDT, CART, LightGBM, XGBoost, CatBoost, AdaBoost, and Stacking (RF, KNN, GBDT, and LR). These models, with a daily time resolution, were trained using 75% of the data and tested with the remaining 25%. The optimal spatial resolution and model were determined through a comprehensive comparison of model evaluation metrics across these spatial resolutions. The SMOTETomek algorithm was then applied to resample 75% of the data at the optimal spatial resolution, forming a new training dataset. This dataset was used to refine the model, subsequently tested with the remaining 25% of the data. Results indicated that (1) the optimal spatial resolution is 0.25° × 0.25° and the optimal model is RF; (2) the SMOTETomek algorithm enhances the model's predictive performance; and (3) the developed SMK‐RF model, exhibiting Acc and AUC values of 76.73% and 82.47%, respectively, accurately predicts the central fishing grounds for yellowfin tuna, consisting closely with actual fishing activity.

Response of the upper ocean to northeast Pacific atmospheric rivers under climate change

Atmospheric rivers are important transport vehicles for Earth’s water cycle. Using a high-resolution, eddy-resolving Earth System Model, atmospheric river impacts on the upper ocean are investigated by analyzing historical and climate change simulations. For atmospheric rivers along the North American coastline, strong winds cause significant dynamic and thermodynamic upper ocean responses. They push ocean water towards the coast, measured by sea surface height, a process that is amplified under climate change. Mixed layers are deeper upstream of atmospheric rivers, and shallower downstream, however for climate change, shoaling downstream is subdued. Air-sea heat fluxes tend to promote ocean cooling upstream and warming downstream, although different regions have different climate change heat flux signals. Southern California heat flux changes due to warming are driven by evaporative processes and strengthen the ocean responses seen in historical simulations. The regions north are primarily dominated by sensible heat flux changes and counter the historical patterns.

The rate of global sea level rise doubled during the past three decades

The rise in globally averaged sea level—or global mean sea level—is one of the most unambiguous indicators of climate change. Over the past three decades, satellites have provided continuous, accurate measurements of sea level on near-global scales. Here, we show that since satellites began observing sea surface heights in 1993 until the end of 2023, global mean sea level has risen by 111 mm. In addition, the rate of global mean sea level rise over those three decades has increased from ~2.1 mm/year in 1993 to ~4.5 mm/year in 2023. If this trajectory of sea level rise continues over the next three decades, sea levels will increase by an additional 169 mm globally, comparable to mid-range sea level projections from the IPCC AR6.

Seasonal Distribution of Key Small-Sized Fish in the South Inshore of Zhejiang, China

Small-sized fish are a vital food source for large predatory commercial fish and play a key role in marine food webs, bridging lower and higher trophic levels. They are indispensable in maintaining the energy flow and material cycling within aquatic ecosystems. This study utilized bottom-trawl survey data from 2017 to 2020 along the south inshore of Zhejiang, China, complemented by concurrent environmental data, to examine the influence of environmental factors on the resource density and seasonal distribution patterns of four dominant small-sized fish species. The research findings indicated that SSH (sea surface height) and Chl (chlorophyll-a concentration) emerged as the key environmental factors influencing resource densities, with all four species exhibiting similar preferences toward these variables. However, significant disparities were observed in their preferences for SST (sea surface temperature), SSS (sea surface salinity), and DO (dissolved oxygen). The various species’ resource density and distribution patterns underwent significant seasonal variations. Additionally, the seasons and regions with the highest resource densities consistently aligned, occurring predominantly in autumn within the northern waters of the study area. This research further elucidated the environmental predilections and seasonal spatial distribution traits of small-sized fish in the south inshore of Zhejiang, an important feeding ground for economic fish species in the East China Sea. This provides scientific backing for forecasting alterations in coastal fishery resources under environmental and climate change scenarios and supports ecosystem-based fisheries management strategies.

A Study of Free Surface Agitation in a Shipyard Using Numerical Modeling

In recent years, the Port of Topolobampo, Sinaloa, Mexico, has experienced unusual free sea surface elevations, particularly during the months of November and December, affecting the shipyard areas, service docks, and berthing locations. This study focuses on analyzing the oscillatory behavior of free surface elevations in shipyard regions. A hydrodynamic model was employed to simulate the circulation and sea surface agitation, aiming to quantify the elevation magnitudes based on oceanographic and meteorological data from November of the preceding year. A 30-day numerical simulation was conducted, revealing the velocity fields associated with coastal currents and tides during November, as well as the interaction between incident waves and wave transformations due to protective structures. The results demonstrated accurate behavior in 95% of the simulation period, while anomalous elevations exceeding those specified in the design and operational guidelines of the Port of Topolobampo were observed during the final five days of the simulation. An ANOVA test was performed between the surface elevation and vertically integrated velocity to assess whether the deviations in the last five days were statistically significant compared to the rest of the simulation period. With a P-value of less than 0.05, the null hypothesis of no difference was rejected, confirming a significant variation. These findings suggest that the extreme values recorded should be considered for the potential redesign of shipyard infrastructure.

A slower North Equatorial Countercurrent but faster Equatorial Undercurrent in a warming climate

Abstract We analyze century-end projections for the tropical Pacific upper-ocean currents simulated within the Climate Model Intercomparison Project Phase 6 (CMIP6) under global warming. We find that while the intensity of precipitation within the Intertropical Convergence Zone (ITCZ) increases, the ITCZ also shifts towards the equator and broadens, which reduces wind stress curl north of the equator. Consequently, the North Equatorial Countercurrent (NECC) shifts equatorward, following the ITCZ, and weakens, despite the more intense ITCZ. The strength of the North Equatorial Current (NEC) and the South Equatorial Current (SEC) also decreases due to the weakening of the Walker circulation and the corresponding wind stress. However, despite the weaker winds, the Equatorial Undercurrent (EUC) intensifies as it shoals due to stronger vertical stratification induced by surface warming. Furthermore, we find a slightly stronger zonal pressure gradient along the core of the EUC, instead of a weaker one expected from weaker wind stress and sea surface height gradient along the equator. Ultimately, we argue that it is reduced vertical friction theory due to increased ocean stratification and hence larger Richardson number that explains the faster EUC. These intricate balances control future changes in equatorial currents, and the uncertainties of projected changes need to be further examined.