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Sea surface salinity (SSS) observations play a crucial role in the study of ocean circulation, climate variability, and marine ecosystems. However, current satellite SSS products suffer from systematic biases due to factors such as radio frequency interference (RFI) and land contamination, resulting in fundamental limitations to their application for SSS monitoring. To address this issue, we propose a physics-informed neural network (PINN) approach that directly integrates radiative transfer physical processes into the neural network architecture for SMAP L2 SSS bias correction. This method ensures oceanographically consistent corrections by embedding physical constraints into the forward propagation model. The results demonstrate that PINN achieved a root mean square error (RMSE) of 0.249 PSU, representing a 5.3% to 8.5% relative performance improvement compared to conventional methods—GBRT, ANN, and XGBoost. Further temporal stability analysis reveals that PINN exhibits significantly reduced RMSE variations over multi-year periods, demonstrating exceptional long-term correction stability. Meanwhile, this method achieves more uniform bias improvement in contaminated nearshore regions, showing distinct advantages over the inconsistent correction patterns of conventional methods. This study establishes a physics-constrained machine learning framework for satellite SSS data correction by integrating oceanographic domain knowledge, providing a novel technical pathway for reliable enhancement of Earth observation data.

Satellite sea surface salinity (SSS) products suffer from coarse spatiotemporal resolution, limiting their utility for mesoscale ocean monitoring. To address this, we proposed the Transformer-based satellite SSS super-resolution (SR) model (TSR) coupled with a progressive transfer learning (PTL) strategy. TSR improved the resolution of the salinity satellite SMOS from 1/4° and 10 days to 1/12° and daily. Leveraging Transformer, TSR captured long-range dependencies critical for reconstructing fine-scale structures. PTL effectively balanced structural detail acquisition and local accuracy correction by combining the gridded reanalysis products with scattered in situ observations as training labels. Validated against independent in situ measurements, TSR outperformed existing L3 salinity satellite products, as well as convolutional neural network and generative adversarial network-based SR models, particularly reducing the root mean square error (RMSE) by 33% and the mean bias (MB) by 81% compared to the SMOS input. More importantly, TSR demonstrated an enhanced capability in resolving mesoscale eddies, which were previously obscured by noise in salinity satellite products. Compared to training with a single label type or switching label types non-progressively, PTL achieved a 3%–66% lower RMSE and a 73–92% lower MB. TSR enables higher-resolution satellite monitoring of SSS, contributing to the study of ocean dynamics and climate change.

Marine heatwaves (MHWs) can significantly impact marine ecosystems and socio-economic systems, and their severity may increase with global warming. Nevertheless, research on the onset and decline rates of MHWs remains limited, and their historical and future variations are not yet fully understood. This study, therefore, analyzes the spatiotemporal characteristics of MHW onset and decline rates by using historical and future sea surface temperature data from OISSTv2.1 and CMIP6. The results indicate that during the historical period from 1982 to 2014, MHW onset and decline rates were higher in eddy-active mid-latitude current systems and the western tropical region but lower in subtropical gyres. A remarkably high correlation (0.94) exists between the onset and decline rates; regions with higher onset rates also tend to have higher decline rates. Approximately 49.69% of the global ocean exhibits an increasing trend in MHW onset rates, with significant increases observed in the Eastern Equatorial Pacific. Meanwhile, 92.87% of oceanic regions exhibit an increase in decline rates. Looking ahead to the future (2015~2100), both the SSP245 and SSP585 scenarios display consistent spatial patterns of MHW onset and decline rates. The Kuroshio-Oyashio Extension, Gulf Stream, Antarctic Circumpolar Current, and Brazil-Malvinas Confluence regions exhibit relatively higher onset and decline rates. Under the SSP585 scenario, both the onset and decline rates of MHWs are higher than those under the SSP245 scenario. This indicates that as global warming intensifies, more extreme MHWs are likely to occur. This finding indicates that it is necessary to pay attention to the rate of global warming when mitigating its potential impacts.

The spatial and temporal decorrelation scales of sea surface salinity (SSS) have been calculated in the tropical Indian ocean from the satellite measurements including Soil Moisture and Ocean Salinity (SMOS), Aquarius, Soil Moisture Active Salinity (SMAP), and the model output data for the period of 2011–2017. The differences in spatial and temporal scales from different products are discussed and the physical interpretations of the scales of SSS variability are analysed. The results show that, despite the differences in spatial and temporal resolution, there is good agreement between the spatial and temporal scales of SSS field among all products. Large zonal scales (> 2000 km) and temporal scales with strong anisotropy appear in the central of the equatorial Indian Ocean (8° S–15 °S). In addition, the large meridional decorrelation lengths (~ 800 km) and temporal scales with low anisotropy are found in the southern region of the Arabian Sea (0–12 °N) for all products. The decorrelation scales of SSS in these two areas are mainly caused by freshwater flux and salinity advection, respectively. Our results provide new insights into the controlling mechanisms of SSS variability in different regions.

How to extract the causal relations in climate–cyclone interactions is an important problem in atmospheric science. Traditionally, the most commonly used research methodology in this field is time-delayed correlation analysis. This may be not appropriate, since a correlation cannot imply causality, as it lacks the needed asymmetry or directedness between dynamical events. This study introduces a recently developed and very concise but rigorous formula—that is, a formula for information flow (IF)—to fulfill the purpose. A new way to normalize the IF is proposed and then the normalized IF (NIF) is used to detect the causal relation between the tropical cyclone (TC) genesis over the western North Pacific (WNP) and a variety of climate modes. It is shown that El Niño–Southern Oscillation and Pacific decadal oscillation are the dominant factors that modulate the WNP TC genesis. The western Pacific subtropical high and the monsoon trough are also playing important roles in affecting the TCs in the western and eastern regions of the WNP, respectively. With these selected climate indices as predictors, a method of fuzzy graph evolved from a nonparametric Bayesian process (BNP-FG), which is capable of handling situations with insufficient samples, is employed to perform a seasonal TC forecast. A forecast with the classic Poisson regression is also conducted for comparison. The BNP-FG model and the causality analysis are found to provide a satisfactory estimation of the number of TC genesis observed in recent years. Considering its generality, it is expected to be applicable in other climate-related predictions.

Validating Sea Surface Salinity (SSS) data has become a key component of the Soil Moisture Ocean Salinity (SMOS) satellite mission. In this study, the gridded SMOS SSS products are compared with in situ SSS data from analyzed products, a ship-based thermosalinograph and a tropical moored buoy array. The comparison was conducted at different spatial and temporal scales. A regional comparison in the Baltic Sea shows that SMOS slightly underestimates the mean SSS values. The influence of river discharge overrides the temperature in the Baltic Sea, bringing larger biases near river mouths in warm seasons. The global comparison with two Optimal Interpolated (OI) gridded in situ products shows consistent large-scale structures. Excluding regions with large SSS biases, the mean ΔSSS between monthly gridded SMOS data and OI in situ data is −0.01 PSU in most open sea areas between 60°S and 60°N, with a mean Root Mean Square Deviation (RMSD) of 0.2 PSU and a mean correlation coefficient of 0.50. An interannual tendency of mean ΔSSS shifting from negative to positive between satellite SSS and in situ SSS has been identified in tropical to mid-latitude seas, especially across the tropical eastern Pacific Ocean. A comparison with collocated buoy salinity shows that on weekly and interannual scales, the SMOS Level 3 (L3) product well captures the SSS variations at the locations of tropical moored buoy arrays and shows similar performance with in situ gridded products. Excluding suspicious buoys, the synergetic analysis of SMOS, SMAP and gridded in situ products is capable of identifying the erroneous data, implying that satellite SSS has the potential to act as a real-time 27 Quality Control (QC) for buoy data.

Satellite observations play important roles in ocean operational forecasting systems, however, the direct assimilation of satellite observations cannot provide sufficient constraints on the model underwater structure. This study adopted the indirect assimilation method. First, we created a 3D temperature and salinity reconstruction model that took into account the advantage of the nonlinear regression of the generalized regression neural network with the fruit fly optimization (abbreviated as FOAGRNN). Compared with the reanalysis product and the WOA13 climatology data, the synthetic T/S (temperature and salinity) profiles had sufficient accuracy and could better describe the characteristics of mesoscale eddies. Then, the synthetic T/S profiles were assimilated into the Regional Ocean Model System (ROMS) using the Incremental Strong constraint 4D Variational (I4D-Var) data assimilation algorithm. The quantitative and qualitative analysis results indicated that compared with the direct assimilation of satellite observations, the root mean square errors (RMSEs) of temperature and salinity were reduced by 26.0% and 23.1% respectively by assimilating the synthetic T/S profiles. Furthermore, this method significantly improved the simulation effect of the model underwater structure, especially in the 300 m to 500 m water layer. Compared with the National Marine Data Center’s real-time analysis data, the machine learning-based assimilation system demonstrated a significant advantage in the simulation of underwater salinity structure, while showing a similar performance in the simulation of underwater temperature structure.

The algorithms based on Surface Quasi-Geostrophic (SQG) dynamics have been developed and validated by many researchers through model products, however it is still doubtful whether these SQG-based algorithms are worth using in terms of observed data. This paper analyzes the factors impeding the practical application of SQG and makes amends by a simple “first-guess (FG) framework”. The proposed framework includes the correction of satellite salinity and the estimation of the FG background, making the SQG-based algorithms applicable in realistic circumstances. The dynamical-statistical method SQG-mEOF-R is thereafter applied to satellite data for the first time. The results are compared with two dynamical algorithms, SQG and isQG, and three empirical algorithms, multivariate linear regression (MLR), random forest (RF), and mEOF-R. The validation against Argo profiles showed that the SQG-mEOF-R presents a robust performance in mesoscale reconstruction and outperforms the other five algorithms in the upper layers. It is promising that the SQG-mEOF-R and the FG framework are applicable to operational reconstruction.

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Accurately predicting the spatio-temporal evolution trends and long-term dynamics of three-dimensional ocean temperature and salinity plays a crucial role in monitoring climate system changes and conducting fundamental oceanographic research. Numerical models are the most prevalent of the traditional approaches, which are often too complex and lack of generality. Recently, with the rise of AI, many data-driven methods are proposed. However, most of them take no consideration of natural physical laws that may cause issues of physical inconsistency among different variables. In this paper, we proposed PGTransNet, a novel physics-guided transformer network for 3D Ocean temperature and salinity forecasting. This model is based on Vision Transformer, and to enhance the performance we have three aspects of improvements. Firstly, we design a loss function that deliveries the physical relationship among temperature, salinity and density by fusing the Thermodynamic Equation. Secondly, to capture global and long-term dependencies effectively, we add the Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation (NPGO) in the embedding layer. Thirdly, we adopted the Laplacian sparse positional encodings to alleviate the artifacts caused by high-norm tokens. The former two are the core components to leverage the physical information. Finally, to comprehensively evaluate PGTransnet, we conduct rich experiments in metrics RMSE, Anomoly Correlation Coefficients, Bias and physical consistency. Our proposal demonstrates higher prediction accuracy with fast convergence, and the metrics and visualizations show that our model is insensitive to hyperparameter tuning, ensuring better generalization and adherence to physical consistency. Moreover, as observed from the spatial distribution of the anomaly correlation coefficient, the model exhibits higher forecasting accuracy for coastal and marginal sea regions.