In situ observation of a buoys/moorings array and a model simulation were used to study the modulation of upper ocean thermal structure by Typhoon Kalmaegi in September 2014. The inertial period signals were significant after forcing of Kalmaegi, but they did not account for the net heat change. Removing the inertial period signals showed that the net thermal response biased to the right of Kalmaegi's track. Vertical mixing caused surface cooling with an inverted‐cone structure and subsurface warming with a double‐wing structure. Net upwelling converted the left wing of the subsurface warming to cooling, while net downwelling warmed the upper ocean in front and on both sides of the net upwelling zone. Horizontal advection was not as important as vertical mixing and vertical advection in modulating the thermal structure but contributed to the net outward advection of thermal anomaly in the mixed layer during the forced stage and also in the net along‐track recovery of subsurface anomaly during the relaxation stage. In general, horizontal and vertical advection modulated thermal anomalies in the upper ocean across a broader horizontal range and into the deeper ocean compared with the effect of vertical mixing. Our results indicate the need to consider both mixing and advection (rather than only mixing) when studying the effects of tropical cyclones on local ocean heat uptake and global ocean heat transport.

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

An unmanned surface vehicle (USV) was designed and constructed to operate continuously for covering both flood and ebb and preferably a complete tidal cycle (e.g., ~24 h) to measure the vertical profiles of horizontal flow velocity. It was applied in a tidal channel at Port Fourchon, Louisiana. A bottom-mounted ADCP was deployed for 515 days. The first EOF mode of the velocity profiles showed a barotropic type of flow that explained more than 98.2% of the variability. The second mode showed a typical estuarine flow with two layers, which explained 0.47% of the variability. Using a linear regression of the total transport from the USV with the vertically averaged velocity from the bottom-mounted ADCP, with an R-squared value of 98%, the total along-channel transport throughout the deployment was calculated. A low-pass filtering of the transport allowed for examining the impact of 76 events with cold, warm, or combined cold–warm fronts passing the area. The top seven most severe events were discussed, as their associated transports obviously stood out in the time series, indicating the importance of weather. It is shown that large-scale weather systems with frontal lines of ~1500–3000-km horizontal length scale control the subtidal transport in the area. Cold (warm) fronts tend to generate outward (inward) transports, followed by a rebound. The maximum coherence between the atmospheric forcing and the ocean response reached ~71%–84%, which occurred at about a frequency f of ~0.29 cycle per day or T of ~3.4 days in the period, consistent with the atmospheric frontal return periods (~3–7 days).

Lekima was a devastating super typhoon hitting China in 2019. Here, we use a high-resolution wave–current coupling model to investigate the impacts of wave–current interaction during Lekima on wave height, storm surge, ocean currents, and momentum balance. The model results were in good agreement with observations. It was found that, in the open waters, the strong currents generated by the typhoon winds reduced the typhoon-induced maximum significant wave heights (MSWHs) by 6%–15%. The baroclinicity of seawater also slightly reduced the MSWHs by approximately 3%. In the coastal waters, the MSWHs were increased by 6%–15% when feedbacks from water levels were considered. The typhoon-induced highest storm surge occurred in the coastal waters right of the typhoon’s landing position. The nonconservative wave forces contributed by approximately 0.1–0.4 m to the most severe storm surge (3 m), with this effect being most prominent in coastal waters. The baroclinicity of seawater generally increased the storm surge but had little influence on very shallow waters. Tides tend to exacerbate storm surge in most nearshore waters, except in a small bay. Waves generally increased the velocity of offshore ocean currents via the wave-breaking-induced acceleration. A cross-shore momentum balance analysis shows that when the typhoon was near the shore, the dominant terms in the momentum equation were the horizontal pressure gradient force and the surface wind stress, and the contribution of wave breaking had similar pattern to that of the wind stress but a lower magnitude. Our findings have significant implications for the numerical modeling of typhoons and the prediction of their impacts in the coastal environment.

Typhoon Kalmaegi (2014) in the South China Sea (SCS) is simulated using a fully coupled atmosphere–ocean–wave model (COAWST). A set of sensitivity experiments are conducted to investigate the effects of different model coupling combinations on the typhoon simulation. Model results are validated by employing in-situ data at four locations in the SCS, and best-track and satellite data. Correlation and root-mean-square difference are used to assess the simulation quality. A skill score system is defined from these two statistical criteria to evaluate the performance of model experiments relative to a baseline. Atmosphere–ocean feedback is crucial for accurate simulations. Our baseline experiment successfully reconstructs the atmospheric and oceanic conditions during Typhoon Kalmaegi. Typhoon-induced sea surface cooling that weakens the system due to less heat and moisture availability is captured best in a Regional Ocean Modeling System (ROMS)-coupled run. The Simulated Wave Nearshore (SWAN)-coupled run has demonstrated the ability to estimate sea surface roughness better. Intense winds lead to a larger surface roughness where more heat and momentum are exchanged, while the rougher surface causes more friction, slowing down surface winds. From our experiments, we show that these intricate interactions require a fully coupled Weather Research and Forecasting (WRF)–ROMS–SWAN model to best reproduce the environment during a typhoon.