Tropical cyclones (TCs) are natural disasters for coastal regions. TCs with maximum wind speeds higher than 32.7 m/s in the north-western Pacific are referred to as typhoons. Typhoons Sarika and Haima successively passed our moored observation array in the northern South China Sea in 2016. Based on the satellite data, the winds (clouds and rainfall) biased to the right (left) sides of the typhoon tracks. Sarika and Haima cooled the sea surface ~4 and ~2 °C and increased the salinity ~1.2 and ~0.6 psu, respectively. The maximum sea surface cooling occurred nearly one day after the two typhoons. Station 2 (S2) was on left side of Sarika’s track and right side of Haima’s track, which is studied because its data was complete. Strong near-inertial currents from the ocean surface toward the bottom were generated at S2, with a maximum mixed-layer speed of ~80 cm/s. The current spectrum also shows weak signal at twice the inertial frequency (2f). Sarika deepened the mixed layer, cooled the sea surface, but warmed the subsurface by ~1 °C. Haima subsequently pushed the subsurface warming anomaly into deeper ocean, causing a temperature increase of ~1.8 °C therein. Sarika and Haima successively increased the heat content anomaly upper than 160 m at S2 to ~50 and ~100 m°C, respectively. Model simulation of the two typhoons shows that mixing and horizontal advection caused surface ocean cooling, mixing and downwelling caused subsurface warming, while downwelling warmed the deeper ocean. It indicates that Sarika and Haima sequentially modulated warm water into deeper ocean and influenced internal ocean heat budget. Upper ocean salinity response was similar to temperature, except that rainfall refreshed sea surface and caused a successive salinity decrease of ~0.03 and ~0.1 psu during the two typhoons, changing the positive subsurface salinity anomaly to negative

Parameter estimation plays an important role in reducing model error and thus is of great significance to improve the simulation and prediction capabilities of the model. However, due to filtering divergence, parameter estimation by ensemble-based filters still faces great challenges. Previous studies have shown that a covariance inflation scheme could alleviate the filtering divergence problem by increasing the signal-to-noise ratio of the state-parameter covariance. In this study, we proposed a new inflation scheme based on a local ensemble transform Kalman filter (LETKF). With the new scheme, the Zebiak–Cane (Z-C) model parameters were estimated by assimilating the sea surface temperature anomaly (SSTA) data. The effectiveness of the parameter estimation and its influence on El Niño–Southern Oscillation (ENSO) prediction were evaluated in an observation system simulation experiments (OSSE) framework and real-world scenario, respectively. With the utilization of the OSSE framework, the results showed that the model parameters were successfully estimated. Parameter estimation reduced the model error when compared with only state estimation (onlySE); however, multiple parameter estimation (MPE) further improved the ENSO prediction skill by providing better initial conditions and parameter values than the single parameter estimation (SPE). Parameter estimation could thus alleviate the spring prediction barrier (SPB) phenomenon of ENSO to a certain extent. In real-world experiments, the optimized parameters significantly improved the ENSO forecasting skill, primarily in prediction of warm events. This study provides an effective parameter estimation strategy to improve climate models and further climate predictions in the real world.