Effects of tropical cyclones (TCs) on dissolved oxygen (DO) in subsurface waters (20-200 m) over the Oxygen Minimum Zones (OMZs) in the Bay of Bengal (BoB) are examined based on Argo and satellite data. Five TCs (Hudhud, Five, Vardah, Maarutha and Mora) during 2013-2018 are considered. Analyses reveal three types of DO temporal variability caused by the storm-induced mixing and upwelling. The first type features temporal DO increases in subsurface waters (37-70 m) caused mainly by intense vertical mixing and downwelling. The second type features DO reductions in subsurface waters after the storms attributed to storm-induced upwelling. The third type features temporal DO increases at depths between 40 and 79 m and decreases at depths between 80 and 150 m due to the combined effect of strong vertical mixing and upwelling. These three types of DO responses can occur in different areas, depending on TC intensity, translational speed and Ekman pumping. The temporal DO variability is also influenced by the shallow oxycline (58.3 ± 16.7 m), mesoscale eddies and biochemical processes. Due to TC intensification, a pre-existing oceanic cyclonic eddy produced a large upwelling and induced a long time of DO decrease in the subsurface layer. This study suggests three different types of DO responses along the TC track in the OMZ, which is useful to evaluate the influence of TCs on the OMZ.

This paper uses the Argo sea surface salinity (SSS_Argo) before and after the passage of 25 tropical cyclones (TCs) in the Bay of Bengal from 2015 to 2019 to evaluate the sea surface salinity (SSS) of the Soil Moisture Active Passive (SMAP) remote sensing satellite (SSS_SMAP). First, SSS_Argo data were used to evaluate the accuracy of the 8-day SMAP SSS data, and the correlations and biases between SSS_SMAP and SSS_Argo were calculated. The results show good correlations between SSS_SMAP and SSS_Argo before and after TCs (before: SSS_SMAP = 1.09SSS_Argo−3.08 (R² = 0.69); after: SSS_SMAP = 1.11SSS_Argo−3.61 (R² = 0.65)). A stronger negative bias (−0.23) and larger root-mean-square error (RMSE, 0.95) between the SSS_SMAP and SSS_Argo were observed before the passage of 25 TCs, which were compared to the bias (−0.13) and RMSE (0.75) after the passage of 25 TCs. Then, two specific TCs were selected from 25 TCs to analyze the impact of TCs on the SSS. The results show the significant SSS increase up to the maximum 5.92 psu after TC Kyant (2016), which was mainly owing to vertical mixing and strong Ekman pumping caused by TC and high-salinity waters in the deep layer that were transported to the sea surface. The SSS_SMAP agreed well with SSS_Argo in both coastal and offshore waters before and after TC Roanu (2016) and TC Kyant (2016) in the Bay of Bengal.

Severe typhoon Rammasun (2014) was strengthened (super typhoon) in the east of the Leizhou Peninsula before moving into the Beibu Gulf. This study used the empirical orthogonal function (EOF) method to analyze the spatiotemporal variations in the sea surface wind (SSW), surface level anomaly (SLA), sea surface temperature (SST), mixed layer depth (MLD) and precipitation of the SCS. The study found that this typhoon had a significant influence on the SSW, SLA, SST, MLD and precipitation changes. During the typhoon Rammasun, the spatial distribution of precipitation showed obvious ‘left-hand-side intensification’ relative to the typhoon track. However, the SST decreased rapidly, and cooling showed a clear ‘right-hand-side intensification’, and two cyclonic eddies in the southwestward were observed from the mode of EOF-1 of the SLA. The spatiotemporal variations of SST, MLD, SLA, SSW and precipitation during the period of super typhoon Rammasun were excellently reflected by the EOF method. The variabilities of SST, MLD, SLA, SSW and precipitation in the SCS during the typhoon period and the relationship between the variabilities were further analyzed.