The South China Sea Operational Oceanography Forecasting System (SCSOFS), constructed and operated by the National Marine Environmental Forecasting Center of China, has been providing daily updated hydrodynamic forecasting in the South China Sea (SCS) for the next 5 d since 2013. This paper presents recent comprehensive updates to the configurations of the physical model and data assimilation scheme in order to improve the forecasting skill of the SCSOFS. This paper highlights three of the most sensitive updates: the sea surface atmospheric forcing method, the discrete tracer advection scheme, and a modification of the data assimilation scheme. Intercomparison and accuracy assessment among the five sub-versions were performed during the entire upgrading process using the OceanPredict Intercomparison and Validation Task Team Class 4 metrics. The results indicate that remarkable improvements have been made to the SCSOFSv2 with respect to the original version (known as SCSOFSv1). The domain-averaged monthly mean root-mean-square errors of the sea surface temperature and sea level anomaly have decreased from 1.21 to 0.52 ^∘C and from 21.6 to 8.5 cm, respectively.

In recent years, advancements in high-resolution digital twin platforms or artificial intelligence marine forecasting have led to the increased requirements of high-resolution oceanic data. However, existing sea surface temperature (SST) products from observations often fail to meet researchers’ resolution requirements. Deep learning models serve as practical techniques for improving the spatial resolution of SST data. In particular, diffusion models (DMs) have attracted widespread attention due to their ability to generate more vivid and realistic results than other neural networks. Despite DMs’ potential, their application in SST spatial downscaling remains largely unexplored. Hence we propose a novel DM-based spatial downscaling model, called DIFFDS, designed to obtain a high-resolution version of the input SST and to restore most of the meso scale processes. Experimental results indicate that DIFFDS is more effective and accurate than baseline neural networks, its downscaled high-resolution SST data are also visually comparable to the ground truth. The DIFFDS achieves an average root-mean-square error of 0.1074 °C and a peak signal-to-noise ratio of 50.48 dB in the 4× scale downscaling task, which shows its accuracy.

The Arabian Sea Warm Pool (ASWP) is a part of the Indian Ocean Warm Pool, formed in the Arabian Sea before the onset of the Indian summer monsoon. The ASWP has a significant impact on climate change in the Indian Peninsula and globally. In this study, we examined the intraseasonal and interannual variability of sea temperature in the ASWP using the latest Simple Ocean Data Assimilation (SODA) reanalysis dataset. We quantified the contributions of sea surface heat flux forcing, horizontal advection, and vertical entrainment to the sea temperature using the mixed-layer heat budget analysis method. We also used a lead–lag correlation method to examine the relationship between the interannual variability of the ASWP and various large-scale modes in the Indo-Pacific Ocean. We found that the ASWP formed in April and decayed in June; its formation and decay processes were asymmetrical, with the decay rate being twice as fast as the formation rate. During the ASWP development phase, the sea surface heat flux forcing had the largest impact on the mixed-layer temperature with a contribution of up to 85 %. Its impact was divided into the net surface heat flux (0.41–0.50 ^∘C per 5 d) and the shortwave radiation loss penetrating the mixed layer (from −0.08 ^∘C per 5 d to −0.17 ^∘C per 5 d). During the decay phase, the cooling effect of the vertical entrainment on the temperature variation increased (from −0.05 ^∘C per 5 d to −0.18 ^∘C per 5 d) and dominated the temperature variation jointly with the sea surface heat flux forcing. We also found that the ASWP has strong interannual variability related to the basin warming of the Indian Ocean. The lead–lag correlation indicated that the ASWP had a good synchronous correlation with the Indian Ocean Dipole. The ASWP had the largest correlation coefficient at a lag of 5–7 months of the Niño3.4 index, showing the characteristics of modulation by the El Niño–Southern Oscillation (ENSO). When the El Niño (La Niña) event peaked in the winter of the previous year, the ASWP that occurred before the summer monsoon was more significant (insignificant) in the following year.

The oceanic surface pressure of CO₂ (pCO₂) is an essential parameter for understanding the global and regional carbon cycle and the oceanic carbon uptake capacity. We constructed a three-dimensional physical-biogeochemical model with a high resolution of 1/30° for the South China Sea (SCS) to compensate for the limited temporal coverage and limited spatial resolution of the observations and numerical models. The model simulated oceanic surface pCO₂ from 1992 to 2021, and the empirical orthogonal function analysis (EOF) of the model results is conducted for a better understanding of the seasonal and interannual variations of oceanic surface pCO₂ in this region. The model results showed that the SCS serves as an atmospheric CO₂ source from March to October and a sink from November to February, with a domain-averaged climatological oceanic surface pCO₂ value that varies between 357 and 408 μatm, and the temporal variation was positively correlated with the variation of sea surface temperature (SST). The majority of the SCS showed a long-term increasing trend for oceanic surface pCO₂ with a value of (1.19±0.60) μatm/a, which is in response to the continuously rising atmospheric CO₂ concentration. The first EOF mode is positively correlated with the Niño 3 index with a correlation coefficient of 0.51 when the Niño 3 leads 5 months, and the second EOF mode is correlated with the PDO index when the PDO leads 7 months, which suggests an influence of climate variability on the carbonate system. Moreover, it was found that the long-term trend rate of oceanic surface pCO₂ was mainly controlled by total CO₂ (TCO₂) through the decomposition of influence factors, and SST variation took a dominant role in seasonal variations of pCO₂. With rapid global warming and continuous release of CO₂, the carbonate system in the SCS may change leading to calcite and aragonite saturation.