The international Argo Program was launched at the turn of the millennium. It has since collected over 2 million vertical profiles of temperature and salinity from the upper 2000 m of the global ocean. Gridded interpolation is a technology that gives full play to the advantages of these profiles because they are scattered. This study develops a global gridded Argo dataset, called GDCSM-Argo, by using an improved gradient-dependent correlation scale method. The dataset is theoretically verified, its error-related statistics are recorded, and it is compared with other datasets to establish its reliability. The results show that the maximum mean RMSEs are 0.8 °C for temperature and 0.1 for salinity, and more than 90% of the analysis results are reliable under the statistical probability of 95%. Not only can GDCSM-Argo adequately preserve large-scale signals in the ocean but also retain more mesoscale features than other gridded Argo datasets. Preliminary applications also verify that GDCSM-Argo can systematically describe the spatio-temporal features of multiple elements in the global ocean, and is a useful tool in many areas of research.

A better understanding of the relationships between oceanic environments and fishing conditions could make the utilization of fish more efficient, profitable, and sustainable. The current lack of high-precision subsurface seawater information has long been a constraint on fishery research. Using near-real-time Argo observations, this paper presents a new approach called gradient-dependent optimal interpolation. This approach provides daily subsurface oceanic environmental information according to fishery dates and locations. An experiment was conducted in the western and central Pacific Ocean using yellowfin tuna (YFT) catch data in August 2017. The results of seawater temperature and salinity represented differences of less than ±0.5°C and ±0.05, respectively, according to verification of error analysis and truth-finding comparisons. After applying the constructed temperature and salinity profiles, we described the relationship between subsurface information and yellowfin tuna catch distribution. Statistical analysis revealed that yellowfin tuna were more adapted to warmer and saltier seawater. At the near-surface (<5 m), the most suitable temperature was 28-29°C, although yellowfin tuna can endure a temperature range from 11 to 12°C at a depth of 300 m. The corresponding upper boundary of the thermocline was approximately 75 m, with a mean strength of 0.074°C/m, and the most suitable salinity for yellowfin tuna was 34.5-36.0 at depths shallower than 300 m. These results indicated that the constructed subsurface information was very close to the true values and they had high spatial and temporal accuracy.

Argo has become an important constituent of the global ocean observation system. However, due to the lack of sea surface measurements from most Argo profiles, the application of Argo data is still limited. In this study, a thermocline model was constructed based on three key thermocline parameters, i.e, thermocline upper depth, the thermocline bottom depth, and thermocline temperature gradient. Following the model, we estimated the sea surface temperature of Argo profiles by providing the relationship between sea surface and subsurface temperature. We tested the effectiveness of our proposed model using statistical analysis and by comparing the sea surface temperature with the results obtained from traditional methods and in situ observations in the Pacific Ocean. The root mean square errors of results obtained from thermocline model were found to be significantly reduced compared to the extrapolation results and satellite retrieved temperature results. The correlation coefficient between the estimation result and in situ observation was 0.967. Argo surface temperature, estimated by the thermocline model, has been theoretically proved to be reliable. Thus, our model generates theoretically feasible data present the mesoscale phenomenon in more detail. Overall, this study compensates for the lack surface observation of Argo, and provides a new tool to establish complete Argo data sets.