A global eddy-resolving forecast system LFS is developed based on a primitive ocean general circulation model. The system's configuration, forecast experiments, and a preliminary evaluation of the preoperational system are shown. In the preoperational stage, since the full data assimilation has not yet been set up for LFS, the initial state is obtained by nudging the ocean temperature and salinity from an ocean analysis dataset. Despite this, the LFS demonstrates a generally good performance in short-term oceanography forecasting, except for sea level anomaly (SLA). The median values of the 1-day forecast leading root mean square error (RMSE) for the sea surface temperature (SST), SLA, upper 2000 m temperature, and salinity are approximately 0.52°C, 0.10 m, 0.57°C and 0.13 psu, respectively. Although there are slight warm biases in the forecasted SST, the forecasts of temperature and salinity in the thermocline by the LFS are comparable with the results of operational oceanography systems under the framework of the Intercomparison and Validation Task Team. However, the forecast SLA has a relatively large RMSE related to the absence of direct observational constraints in the initial state. Further investigations are needed to improve the performance of LFS.

The performance of eddy‐resolving global ocean–sea ice models in simulating mesoscale eddies is evaluated using six eddy‐resolving experiments forced by different atmospheric reanalysis products. Interestingly, eddy‐resolving ocean general circulation models (OGCMs) tend to simulate more (less) energetic eddy‐rich (eddy‐poor) regions with a smaller (larger) spatial extent than satellite observation, which finally shows that larger (smaller) mesoscale energy intensity (EI) is simulated in the eddy‐rich (eddy‐poor) regions. Quantitatively, there is an approximately 27%–60% overestimation of EI in the eddy‐rich regions, which are mainly located in the Kuroshio–Oyashio Extension, the Gulf Stream, and the Antarctic Circumpolar Currents regions, although the global mean EI is underestimated by 25%–45%. Apparently, the eddy kinetic energy in the eddy‐poor region is underestimated. Further analyses based on coherent mesoscale eddy properties show that the overestimation in the eddy‐rich regions is mainly attributed to mesoscale eddies’ intensity and is more prominent when mesoscale eddies are in their growth stage.

Investigating oceanic variations at multiple spatial and temporal scales is vital for an in-depth understanding of the ocean response to global climate change. However, the available observational datasets contain uncertainties and deficiencies that leave them insufficient for investigating global ocean variability with long temporal scales and/or meso spatial scales. Here, we present a daily and century-long (1901–2010) global oceanic simulation dataset with high resolution (1/10° horizontal resolution and 55 vertical layers) forced by 6-hour atmospheric data from ERA-20C. Preliminary evaluations demonstrate that this simulation can realistically reproduce the large-scale global ocean circulation and capture the essential features of global surface mesoscale eddies. This long-running high-resolution simulation dataset provides temporally highly resolved oceanic and flux variables. Together with its good performance in simulating the global oceanic state, this eddy-resolving simulation has the potential to help toward a better understanding of ocean variability at multiple spatial and temporal scales.

The LICOM Forecast System (LFS), a global eddy-resolving ocean forecasting system, provides 1–15-day forecasts of ocean mesoscale eddies (OMEs). This study conducts a comparative analysis of the forecast results against observational data, thereby evaluating the performance of the LFS. Results show that LFS underestimates the number of eddies by ~10% and their amplitude by ~30%, while overestimating eddy radius by ~5% and velocity by ~25%. Forecasted eddies are matched on a one-to-one basis with observed eddies based on distance, ensuring that those forecasted eddies that can locate corresponding counterparts in the observational dataset yield accurate forecasts. The outcomes reveal that the LFS successfully forecasts 63% of the eddies in the NWP. The characteristics of these accurately forecasted eddies are delineated. The findings indicate that eddies with greater amplitudes are more likely to be accurately predicted. Additionally, we further scrutinized the correlation between eddy attributes and forecast performance, revealing that with an increase in amplitude or a decrease in velocity, the LFS produces forecasts with improved locational accuracy. The mean forecast error in eddy position is 79.94 km for a 1-day lead time, which decreases to 70.97 km for eddies with amplitudes exceeding 1 cm and velocities below 1 km/day.