In this study, a global-ocean-data-assimilation system based on the three-dimensional variational (3DVAR) scheme is built for operational oceanography. The available observations include satellite altimetry; the satellite-measured sea-surface temperature (SST); and T/S profiles from Argo floats, which are assimilated to provide the initial condition of the global-ocean forecasting. The statistical analysis methods are designed to assess the performance of the data-assimilation scheme, and the results show that the analysis SST fields agree well with OSTIA and MGDSST, and the corresponding root-mean-square errors are, respectively, 0.523 and 0.548 °C. Moreover, the analysis sea-surface-height fields are well represented at the middle and low latitudes and have a slightly greater difference in the regions with strong mesoscale eddies. The variations in the vertical distribution of the forecasting temperature profiles resemble those of the GTS buoy observation. The forecasting salinity profiles correspond well to GTS observations, except with a weaker cold bias between the depths 100 and 200 m (about 0.2 PSU) at buoy station 2901494. Overall, our 3DVAR assimilation system plays a significant role in improving the accuracy of analysis and forecasting fields for operational oceanography.

In this study, a global analysis and forecasting system at 1/12° is built for operational oceanography at the National Marine Environmental Forecasting Center (NMEFC) by using the NEMO ocean model (NMEFC-NEMO). First, statistical analysis methods are designed to evaluate the performance of sea level anomaly (SLA) forecasting. The results indicate that the NMEFC-NEMO performs well in SLA forecasting when compared with the Mercator-PSY4, Mercator-PSY3, UK-FOAM, CONCEPTS-GIOPS and Bluelink-OceanMAPS forecasting systems. The respective root-mean-squared errors (RMSEs) of NMEFC-NEMO (Mercator PSY4) are 0.0654 m (0.0663 m) and 0.0797 m (0.0767 m) for the lead times of 1 and 7 days. The anomaly correlation coefficients between forecasting and observations exceed 0.8 for the NMEFC-NEMO and Mercator-PSY4 systems, suggesting that the accuracy of SLA predicted using NMEFC-NEMO is comparable to Mercator PSY4 and superior to other forecasting systems. Moreover, the global spatial distribution of oceanic eddies are effectively represented in NMEFC-NEMO when compared to that in the HYCOM reanalysis, and the detection rate reaches more than 90% relative to HYCOM reanalysis. Regarding the strong eddies in the Kuroshio region, the NMEFC-NEMO reproduces the characteristic for anticyclonic and cyclonic eddies merging and splitting alternatively. As for the detective eddies in the Gulf Stream, NMEFC-NEMO effectively represents the spatial distribution of mesoscale eddies from different seasons. The amplitude of oceanic eddies, including both cyclones and anticyclones, were much stronger on 1 July 2019 than 1 January 2019. Overall, NMEFC-NEMO has a superior performance in SLA forecasting and effectively represents the oceanic mesoscale eddies for operational oceanography.

In order to figure out the associated underlying dynamical processes of the 2014–2015 warming event, we used the ECCO (Estimating the Circulation and Climate of the Ocean) reanalysis from 1993 to 2016 and two combined scatterometers, QuikSCAT and ASCAT, to analysis hydrodynamic condition and ocean heat budget balance process in the equatorial tropical pacific. The spatiotemporal characteristics of that warming event were revealed by comparing the results with a composite El Niño. The results showed that the significant differences between the 2014 and 2015 warming periods were the magnitudes and positions of the equatorial easterly wind anomalies during the summer months. The abruptly easterly wind anomalies of 2014 that spread across the entire equatorial Pacific triggered the upwelling of the equatorial Kelvin waves and pushed the eastern edge of the warm pool back westward. These combined effects caused abrupt decreases in the sea surface temperatures (SST) and upper ocean heat content (OHC) and damped the 2014 warming process into an El Niño. In addition, the ocean budget of the upper 300 m of the El Niño 3.4 region showed that different dynamical processes were responsible for different warming phases. For example, at the beginning of 2014 and 2015, the U advection and subsurface processes played dominant roles in the positive ocean heat content tendency. During the easterly wind anomalies period of 2014, the U advection process mainly caused a negative tendency and halted the development of the warming phase. In regard to the easterly wind anomalies of 2015, the U advection and subsurface processes were weaker negatively when compared with that in 2014. However, the V advection processes were consistently positive, taking a leading role in the positive trends observed in the middle of 2015.