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Numerous tidal phenomena, including river tides, internal tides, and tides in ice‐covered bay, are nonstationary, which pose a great challenge for traditional tidal analysis methods. Based on the independent point scheme and cubic spline interpolation, a new approach, namely the enhanced harmonic analysis, is developed to deal with nonstationary tides. A MATLAB toolbox, S_TIDE, developed from the widely used T_TIDE, is used to realize the approach. The efficiency of S_TIDE is validated by analyzing a set of hourly water level observations from stations on the lower Columbia River. In all stations, the hindcast of S_TIDE is more accurate than NS_TIDE that is a powerful nonstationary tidal analysis tool adapted to river tides. The changing mean water level and tidal constituent properties obtained by S_TIDE are similar to those obtained by NS_TIDE, continuous wavelet transform, and empirical mode decomposition and highly consistent with theory on river tides. Moreover, different from NS_TIDE that only can be applied to river tides, enhanced harmonic analysis is free of dynamic content, assuming only known tidal frequencies. Therefore, S_TIDE can be applied to all kinds of nonstationary tides theoretically. Though powerful, S_TIDE also has some limitations: S_TIDE cannot be used for prediction and too many independent points in S_TIDE may induce computational memory overflow and unrealistic results. Based on the independent point scheme and cubic spline interpolation, a new approach, enhanced harmonic analysis, was developed to deal with nonstationary tides. Enhanced harmonic analysis is realized by a MATLAB toolbox, S_TIDE, which is developed from the widely used T_TIDE. S_TIDE assumes only known tidal frequencies and theoretically can be applied to all kinds of nonstationary tides and stationary tides. In this study, S_TIDE is applied to analyzing records of river rides that is one of the simplest kinds of nonstationary tides for which ample data are available. The method is compared with other methods to show its efficiency. A MATLAB toolbox, S_TIDE, is used to realize enhanced harmonic analysis (EHA) for nonstationary tidesS_TIDE can separate oscillations in MWL, amplitudes, and phases on different time scales within a given frequency band using different number of IPsFor D4constituents, not only the time variations but also the along‐channel variations are dominated by river flow

Nonstationary internal tides (ITs) are formed from their interactions with background currents. Harmonic analysis (HA), which cannot be used to estimate the incoherent component, is almost exclusively used method to predict ITs from observations. This remains ITs prediction challenge. In this study, we establish a deep learning framework to predict semidiurnal ITs. The model is established and trained with observed semidiurnal internal tidal currents that are 172 days long, and then ITs over the next 42 days are forecasted. The prediction accuracy is greatly improved using the deep learning framework. The magnitudes of errors using the deep learning framework are approximately 35% of those obtained using HA. Most temporal and spatial variations in baroclinic currents can successfully be forecasted using deep learning. In addition, the kinetic energy and incoherent components of ITs can be accurately predicted. Moreover, the relatively high adoptability of the established deep learning model is shown. Internal tides (ITs) can be evidently influenced by their interactions with background currents and horizontal variations in the density field during propagation. ITs become nonstationary and uncorrelated to the spring neap forcing of surface tides. Harmonic analysis (HA), which is used to predict stationary ITs, cannot be used to forecast nonstationary ITs. Thus, IT forecasting is challenging. Many researchers have categorized ITs as unpredictable motions. The adoption of deep learning techniques that do not rely on any dynamic processes related to IT propagation and generation is one possible approach that can be used to improve the predictability of ITs. In this study, we establish a deep learning framework that can be used to significantly increase the predictability of ITs. Thus, the vertical displacement and temporal variation trends of ITs can be successfully forecasted. Baroclinic currents forecasted using deep learning are much closer to the observed currents than the currents obtained using HA. In addition to internal tidal currents, deep learning can also be used to predict the kinetic energy of ITs and the incoherent components, which have been extremely difficult to estimate in the past. Moreover, an established deep learning model can be used to predict ITs in nearby locations. A deep learning approach can be used to overcome internal tide (IT) prediction challengesThe baroclinic current, kinetic energy and incoherent components can be accurately forecasted using deep learning techniquesThe relatively high adoptability of the established deep learning model for the prediction of ITs in nearby locations is demonstrated A deep learning approach can be used to overcome internal tide (IT) prediction challenges The baroclinic current, kinetic energy and incoherent components can be accurately forecasted using deep learning techniques The relatively high adoptability of the established deep learning model for the prediction of ITs in nearby locations is demonstrated

The detection of water transparency (Zsd) changes over multiple timescales is an important task that has wide implications from water quality to climate change research. However, to date, the understanding of Zsdvariations and their driving factors in the Eastern China Seas is fairly limited. Using the Data Interpolating Empirical Orthogonal Functions (DINEOF) method and a semi‐analytic algorithm, monthly cloud‐free Zsdproducts over the period 1997–2019 are retrieved from the European Space Agency Ocean Color Climate Change Initiative datasets. Significant Zsdvariations on seasonal, interannual, and long‐term timescales, as well as their spatial discrepancies, are quantified in the Eastern China Seas. The first EOF mode of Zsdaccounts for 57.46% of the total variance, which is dominated by a seasonal cycle in response to seasonal changes in oceanic and atmospheric factors (e.g., surface winds, sea surface temperature and ocean stratification). The second and third EOF modes are likely associated with seasonal phytoplankton blooms and ocean circulations, respectively. Factors that affect the interannual variation in Zsd, including sea surface temperature, wind speed, river discharge, and the Oceanic Niño Index, are quantitatively evaluated. The long‐term trends suggest different potential driving forces for the Zsdpatterns at a regional scale, such as climate‐driven increased sea surface temperatures, weakened surface winds, reduced sediment discharge, and eutrophication of coastal waters. Overall, this study presents the first comprehensive investigation of Zsdvariations in the Eastern China Seas at multiple timescales and an analysis of their underlying physical and environmental drivers. Water transparency, often quantitatively measured by the Secchi disk depth (Zsd), is an important parameter of water quality and primary productivity in marine ecosystems. With the development of satellite technology, public ocean color datasets provide large‐scale and repetitive measurements for estimating Zsd. Using 23 years of gap‐free satellite products, Zsdvariations on multiple timescales (seasonal, interannual, and long‐term timescales) and their driving forces are quantified in the Eastern China Seas. Physical and environmental factors related to the Zsdvariability, such as surface winds, sea surface temperature, river discharge, the El Niño‐Southern Oscillation, and eutrophication of coastal waters, are also revealed. The conclusions of this work show that satellite Zsdproducts are reliable resources for interpreting the responses of marine ecosystems to natural and anthropogenic drivers. Multi‐sensor satellite data are used to quantify water transparency (Zsd) variations on seasonal, interannual, and long‐term timescalesMajor modes of Zsdvariability are related to seasonal climate change, phytoplankton blooms, and ocean circulations, respectivelyThe effects of surface winds, sea surface temperature, river discharge, and El Niño Southern Oscillation on Zsdvariations are quantified Multi‐sensor satellite data are used to quantify water transparency (Zsd) variations on seasonal, interannual, and long‐term timescales Major modes of Zsdvariability are related to seasonal climate change, phytoplankton blooms, and ocean circulations, respectively The effects of surface winds, sea surface temperature, river discharge, and El Niño Southern Oscillation on Zsdvariations are quantified

The global ocean mass has significantly changed during recent decades, which may induce changes in the abyssal ocean circulation. Due to the lack of in situ observations, the response of the abyssal South China Sea (SCS) circulation to ocean mass changes remains unclear. The ocean state estimate, Estimating the Circulation and Climate of the Ocean (ECCO), provides the long‐term ocean mass changes in terms of ocean bottom pressure (OBP). Here we use the ECCO OBP data to quantify decadal changes in the abyssal SCS circulation and reveal a weakening trend. The OBP gradient trend manifests in forms of an anomalous anticyclonic circulation that is intensified by topographic effects along the continental slopes to reduce the existing abyssal circulation. The weakening abyssal circulation corresponds to the weakening Luzon Strait overflow that yields a decreasing vorticity budget in the abyssal SCS. Caused by global warming, ice melt and long‐term atmospheric forcing changes, ocean mass has been increased and redistributed over the past decades. The changes in ocean mass distribution may lead to changes in abyssal ocean circulation. The changes in the abyssal South China Sea (SCS) circulation remains unclear due to the lack of in situ observations. This study uses the Estimating the Circulation and Climate of the Ocean (ECCO) ocean state estimate to quantify decadal changes in the abyssal SCS circulation. Our analysis of the ECCO ocean bottom pressure data reveals a weakening trend in the abyssal SCS circulation that is consistent with the recently ascertained weakening Luzon Strait overflow. Trends in the ECCO ocean bottom pressure (OBP) gradients indicate a decadal weakening abyssal South China Sea (SCS) circulationThe weakening circulation coincides with the weakening Luzon Strait overflow that yields a decreasing vorticity budget in the abyssal SCSThe anomalous anticyclonic circulation is dominated by OBP gradient trends and intensified by topographic effects along continental slopes Trends in the ECCO ocean bottom pressure (OBP) gradients indicate a decadal weakening abyssal South China Sea (SCS) circulation The weakening circulation coincides with the weakening Luzon Strait overflow that yields a decreasing vorticity budget in the abyssal SCS The anomalous anticyclonic circulation is dominated by OBP gradient trends and intensified by topographic effects along continental slopes