A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China

A new nudging scheme is proposed for the operational prediction system of the National Marine Environmental Forecasting Center(NMEFC)of China,mainly aimed at improving El Niño–Southern Oscillation(ENSO)and Indian Ocean Dipole(IOD)predictions.Compared with the origin nudging scheme of NMEFC,the new scheme adds a nudge assimilation for wind components,and increases the nudging weight at the subsurface.Increasing the nudging weight at the subsurface directly improved the simulation performance of the ocean component,while assimilating low-level wind components not only affected the atmospheric component but also benefited the oceanic simulation.Hindcast experiments showed that the new scheme remarkably improved both ENSO and IOD prediction skills.The skillful prediction lead time of ENSO was up to 11 months,1 month longer than a hindcast using the original nudging scheme.Skillful prediction of IOD could be made 4–5 months ahead by the new scheme,with a 0.2 higher correlation at a 3-month lead time.These prediction skills approach the level of some of the best state-of-the-art coupled general circulation models.Improved ENSO and IOD predictions occurred across all seasons,but mainly for target months in the boreal spring for the ENSO and the boreal spring and summer for the IOD.

An intermediate coupled model for the tropical ocean-atmosphere system

An intermediate ocean-atmosphere coupled model is developed to simulate and predict the tropical interannual variability. Originating from the basic physical framework of the Zebiak-Cane(ZC) model, this tropical intermediate couple model(TICM) extends to the entire global tropics, with a surface heat flux parameterization and a surface wind bias correction added to improve model performance and inter-basin connections. The model well reproduces the variabilities in the tropical Pacific and Indian basins. The simulated El Ni?o-Southern Oscillation(ENSO) shows a period of 3–4 years and an amplitude of about 2°C, similar to those observed. The variabilities in the Indian Ocean, including the Indian Ocean basin mode(IOBM) and the Indian Ocean Dipole(IOD), are also reasonably captured with a realistic relationship to the Pacific. However, the tropical Atlantic variability in the TICM has a westward bias and is overly influenced by the tropical Pacific. A 47-year hindcast experiment using the TICM for the period of 1970–2016 indicates that ENSO is the most predictable mode in the tropics. Skillful predictions of ENSO can be made one year ahead, similar to the skill of the latest version of the ZC model, while a "spring predictability barrier" still exists as in other models. In the tropical Indian Ocean, the predictability seems much higher in the west than in the east. The correlation skill of IOD prediction reaches 0.5 at a 5-month lead, which is comparable to that of the state-of-the-art coupled general circulation models. The prediction of IOD shows a significant "winter-spring predictability barrier", implying combined influences from the tropical Pacific and the local sea-air interaction in the eastern Indian Ocean. The TICM has little predictive skill in the equatorial Atlantic for lead times longer than 3 months, which is a common problem of current climate models badly in need of further investigation.

A multi-model prediction system for ENSO

The El Niño and Southern Oscillation(ENSO)is the primary source of predictability for seasonal climate prediction.To improve the ENSO prediction skill,we established a multi-model ensemble(MME)prediction system,which consists of 5 dynamical coupled models with various complexities,parameterizations,resolutions,initializations and ensemble strategies,to account for the uncertainties as sufficiently as possible.Our results demonstrated the superiority of the MME over individual models,with dramatically reduced the root mean square error and improved the anomaly correlation skill,which can compete with,or even exceed the skill of the North American Multi-Model Ensemble.In addition,the MME suffered less from the spring predictability barrier and offered more reliable probabilistic prediction.The real-time MME prediction adequately captured the latest successive La Niña events and the secondary cooling trend six months ahead.Our MME prediction has,since April 2022,forecasted the possible occurrence of a third-year La Niña event.Overall,our MME prediction system offers better skill for both deterministic and probabilistic ENSO prediction than all participating models.These improvements are probably due to the complementary contributions of multiple models to provide additive predictive information,as well as the large ensemble size that covers a more reasonable uncertainty distribution.