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The Luzon Strait (LS) is a key region for estimating carbon sources and sinks in the South China Sea (SCS) and is highly influenced by the Kuroshio Current (KC) and typhoons. Understanding the variations in the sea surface partial pressure of carbon dioxide (pCO2-sw) under the combined effects of typhoons and KC in this region is crucial for estimating local and regional changes in ocean carbon flux. Based on valuable in situ pCO2-sw and remote sensing data, this study aimed to reveal the temporal variations and the physical mechanisms of pCO2-sw variations under the comprehensive effects of both typhoons and Kuroshio Intrusion (KI) in the LS. One week after the passage of the tropical cyclone (TC) Nanmadol, the concentration in the pCO2-sw and the influencing mechanisms varied in three different regions (W1–W3) on Transect A (120°E). In the region dominated by SCS waters (W1), the average pCO2-sw increased by 5.1 μatm after TC, which was mainly due to the TC "Wind Pump" inducing strong vertical mixing, which brought dissolved inorganic carbon (DIC)-rich deeper water up to the surface. In the region affected by KC (W2 and W3), pCO2-sw decreased after the TC (−8.2 μatm and −1.8 μatm, respectively) with TC-enhanced KI because the invasion of lower pCO2-sw of Kuroshio waters inhibited the TC-induced upwelling. More significant TC-induced upwelling (W3) would alleviate the decrease in pCO2-sw caused by the TC-enhanced KI. This study is a rare case providing a better understanding of the variations in pCO2-sw under TC-enhanced KI, which provides support for regional climate change prediction and carbon flux estimation in the western boundary current regions. [ABSTRACT FROM AUTHOR]

This study proposes to construct a model using random forest method, an efficient machine learning‐based method, to predict the spatial structure and temporal evolution of the sea surface temperature (SST) cooling induced by northwest Pacific tropical cyclones (TCs), a process of the so‐called wind pump. The predictors in use include 12 predictors related to TC characteristics and pre‐storm ocean conditions. The model is shown to skillfully predict the spatiotemporal evolutions of the cold wake generated by TCs of different intensity groups, and capture the cross‐case variance in the observed SST response. Another model is further built based on the same method to assess the relative importance of the 12 predictors in determining the magnitude of the maximum cooling. Computations of feature scores of those predictors show that TC intensity, translation speed and size, and pre‐storm mixed layer depth and SST dominate, depending on the area where the cooling is considered. Plain Language Summary: While many studies have been devoted to understanding the processes and mechanisms underlying the sea surface temperature (SST) cooling induced by tropical cyclones (TCs), few studies have attempted to predict the spatial and temporal evolution of the sea surface temperature (SST) cooling triggered by TCs. In this study, we proposed to achieve this goal by building a model using an efficient and robust machine learning‐based method. The constructed model uses 12 predictors associated with TC characteristics (e.g., intensity, and translation speed) and pre‐storm ocean states (e.g., mixed layer depth). The model performs well in producing the TC‐induced spatial structure and temporal evolution of the cold wake and can capture most of the variance in the observed SST response. We quantified the relative importance of the 12 predictors, and found that TC intensity, translation speed and size, and pre‐storm mixed layer depth and SST dominate in deciding the magnitude of the SST response. The results and proposed method have important implications for predicting the response of ocean primary production to the TC wind pump effects. Key Points: A machine learning‐based model is built to predict the spatiotemporal evolution of the tropical cyclone‐induced sea surface temperature responseThe model well predicts the spatial structure and temporal evolution of the observed response and captures the observed cross‐case varianceFeature scores are computed to assess the relative importance of the predictors in determining the magnitude of the SST response [ABSTRACT FROM AUTHOR]

The Bermuda Testbed Mooring (BTM) and Bay of Bengal Ocean Acidification (BOBOA) mooring measurements were used to identify changes in the partial pressure of CO2 at the sea surface (pCO2sea) and air-sea CO2 fluxes (FCO2) associated with passage of two tropical cyclones (TCs), Florence and Hudhud. TC Florence passed about 165 km off the BTM mooring site with strong wind speeds of 24.8 m s–1 and translation speed of 7.23 m s–1. TC Hudhud passed about 178 km off the BOBOA mooring site with wind speeds of 14.0 m s–1 and translation speed of 2.58 m s–1. The present study examined the effect of temperature, salinity, dissolved inorganic carbon (DIC), total alkalinity (TA), air-sea CO2 flux, and phytoplankton chlorophyll a change on pCO2sea as a response to TCs. Enhanced mixed layer depths were observed due to TCs-induced vertical mixing at both mooring sites. Decreased pCO2sea (–15.16±5.60 μatm) at the BTM mooring site and enhanced pCO2sea (14.81±7.03 μatm) at the BOBOA mooring site were observed after the passage of Florence and Hudhud, respectively. Both DIC and TA are strongly correlated with salinity in the upper layer of the isothermal layer depth (ILD). Strong (weak) vertical gradient in salinity is accompanied by strong (weak) vertical gradients in DIC and TA. Strong vertical salinity gradient in the upper layer of the ILD (0.031 psu m–1), that supply much salinity, dissolved inorganic carbon and total alkalinity from the thermocline was the cause of the increased pCO2sea in the BOBOA mooring water. Weak vertical salinity gradient in the upper layer of the ILD (0.003 psu m–1) was responsible for decreasing pCO2sea in the BTM mooring water. The results of this study showed that the vertical salinity gradient in the upper layer of the ILD is a good indicator of the pCO2sea variation after the passages of TCs. [ABSTRACT FROM AUTHOR]

This study examines how dissolved oxygen (DO) responds to tropical cyclones (TCs) "Wind Pump" in a pre-existing cyclonic and an anticyclonic eddy over the Bay of Bengal (BoB) based on Argo and satellite data. Both TCs induced DO temporal decreases in the subsurface waters (Deep Depression BOB 04 with a pre-existing cyclonic eddy and cyclonic storm Roanu with a pre-existing anticyclonic eddy) owing mainly to the storm-induced upwelling. The deeper oxycline caused by the pre-existing anticyclonic eddy relieved the storm-induced shallow of oxycline during Roanu. On the contrary, the pre-existing cyclonic eddy shoaled the oxycline, intensifying the storm-induced shallow of oxycline during BOB 04. Furthermore, the pre-existing cyclonic eddy induced a long time of DO decrease after BOB 04. This study suggests that the subsurface DO concentrations in the BoB are affected mainly by storm-induced physical processes, and the mesoscale eddies also play an important role. • The intense storm-induced upwelling caused the DO decrease in the subsurface layers. • With a pre-existing cyclonic eddy, storm led to a much shallower oxycline and a long time of DO decrease. • The physical processes played a more important role than the biological processes in the DO distributions under the influence of storms. [ABSTRACT FROM AUTHOR]

The impact of tropical cyclones (TCs) on the CO2 partial pressure at the sea surface (pCO2sea) and air‐sea CO2 flux (FCO2) in the Bay of Bengal (BoB) was quantified based on satellite data and in situ observations between November 2013 and January 2017. The in situ observations were made at the BoB Ocean Acidification mooring buoy. A weak time‐mean net source of 55.78 ± 11.16 mmol CO2 m−2 year−1 at the BoB Ocean Acidification site was estimated during this period. A wide range in increases of pCO2sea (1.0–14.8 μatm) induced by TCs occurred in postmonsoon (October–December), and large decreases of pCO2sea (−14.0 μatm) occurred in premonsoon (March–May). Large vertical differences in the ratio of dissolved inorganic carbon (DIC) to total alkalinity (TA) in the upper layer (ΔDIC/TA) were responsible for increasing pCO2sea in postmonsoon. Relatively small values of ΔDIC/TA were responsible for decreasing pCO2sea in premonsoon. Five TCs (Hudhud, Five, Kyant, Vardah, and Roanu) were considered. Hudhud significantly enhanced CO2 efflux (18.49 ± 3.70 mmol CO2/m2) in oversaturated areas due to the wind effect during the storm and wind‐pump effects after the storm. Vardah insignificantly changed FCO2 (1.22 ± 0.24 mmol CO2/m2) in undersaturated areas because of the counteraction of these two effects. Roanu significantly enhanced CO2 efflux (19.08 ± 3.82 mmol CO2/m2) in highly oversaturated conditions (ΔpCO2 > 20 μatm) since the wind effect greatly exceeded the wind‐pump effects. These five TCs were estimated to account for 55 ± 23% of the annual‐mean CO2 annual efflux, suggesting that TCs have significant impacts on the carbon cycle in the BoB. Plain Language Summary: We examined the impact by five tropical cyclones on the sea surface pCO2 and air‐sea CO2 exchange using Bay of Bengal Ocean Acidification moored buoy measurements over the Bay of Bengal. To our knowledge, this is the first study on the different effects of tropical cyclones and vertical differences in the ratio of dissolved inorganic carbon to total alkalinity in the upper layer on the sea surface pCO2 and local air‐sea CO2 flux. Key Points: TCs tended to increase the pCO2sea in the postmonsoon and to decrease the pCO2sea in the premonsoon in the Bay of BengalLarge vertical differences in the ratio of DIC to TA in the upper layer were responsible for increasing pCO2sea induced by TCsTCs were estimated to account for 55 ± 23% of the annual‐mean CO2 annual efflux [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) disaster loss assessment is an important and difficult problem in TC prevention and disaster mitigation. Few studies have focused on combined model in this area. This study introduced a new combination model method to predict TC disaster loss, taking Guangdong province as an example. We analysed and collected 67 TC data from 1993 to 2009, which had impact on Guangdong province, in which 60 were randomly for training data and another 7 were for testing data. We conducted three models - GA-Elman neural networks, support vector regression (SVR) and generalized regression neural networks (GRNN), and the root mean square error (RMSE) value we got are 5.05, 7.85 and 3.82, respectively. Then the three models are combined into a comprehensive evaluation model by model combination method. The RMSE of the test results is 3.30. The results show that the combined model is superior to one individual model and it is a more accurate and stable method. [ABSTRACT FROM AUTHOR]

Abstract: It is well known that tropical cyclones can cause upwelling, decrease of sea surface temperature, increase of chlorophyll-a (Chl-a) concentration and enhancement of primary production. But little is known about the response of dissolved oxygen (DO) concentration to a typhoon in the open ocean. This paper investigates the impact of a typhoon on DO concentration and related ecological parameters using in situ and remote sensing data. The in situ data were collected 1week after the passage of the super-typhoon Nanmadol in the northern South China Sea in 2011. An increase in DO concentration, accompanied by a decrease in water temperature and an increase in salinity and Chl-a concentration, was measured at sampling stations close to the typhoon track. At these stations, maximum DO concentration was found at a depth of around 5m and maximum Chl-a concentration at depths between 50 and 75m. The layer of high DO concentration extends from the surface to a depth of 35m and the concentrations stay almost constant down to this depth. Due to the passage of the typhoon, also a large sea level anomaly (21.6cm) and a high value of Ekman pumping velocity (4.0×10−4 ms−1) are observed, indicating upwelling phenomenon. At the same time, also intrusion of Kuroshio waters in the form of a loop current into the South China Sea (SCS) was observed. We attribute the increase of DO concentration after the passage of the typhoon to three effects: (1) entrainment of oxygen from the air into the upper water layer and strong vertical mixing of the water body due to the typhoon winds, (2) upwelling of cold nutrient-rich water which stimulates photosynthesis of phytoplankton and thus the generation of oxygen, which also increases the DO concentration due to cold water since the solubility of oxygen increase with decreasing water temperature, and, possibly, (3) transport of DO enriched waters from the Western Pacific to the SCS via the intrusion of Kuroshio waters. [Copyright &y& Elsevier]

This is the first report on an eddy-feature phytoplankton bloom induced by a tropical cyclone (the spiral structure of the bloom was coincident with that of a cold eddy) in the South China Sea (SCS). Applying satellite data, this report can furnish fresh evidence of the relationship between the bloom and the cold eddy. Tropical cyclone Linfa passed over the northern SCS from 16 to 21 June 2009. While it looped over for 2 days (from 17 to 19 June), a cold eddy, which lasted for 11 days in the looping area, was observed on 18 June. Subsequently, an eddy-feature phytoplankton bloom (central location: 18° N, 117.5° E) was detected on 22 June and it remained for 17 days. The character of both the cold eddy and the bloom was rotating counterclockwise, with two arms. High Ekman pumping velocity (>4.5 × 10−4 m s−1) was estimated during the passage of Linfa (from 17 to 19 June). The monthly climatology of the mixed layer depth was about 20 m in June, and the maximum Ekman layer depth was about 346 m after Linfa. The analysis indicated that Linfa may have induced the cold eddy, where it looped around. With upwelling and entrainment, the eddy potentially provided nutrients to the bloom in the surface water. The results suggested that the sea surface current changes that Linfa induced had caused the bloom in an eddy feature. Tropical cyclones appear frequently in the SCS, which may affect the activities of mesoscale eddies in this area. [ABSTRACT FROM AUTHOR]

The China-France Oceanography SATellite (CFOSAT), launched on 29 October 2018, is a joint mission developed by China and France. To evaluate the CFOSAT wind product, L2B swath data with a spatial resolution of 25 × 25 km were compared with in situ measurements between December 2018 and December 2020. The in situ measurements were collected from 217 buoys. All buoy winds were adjusted to 10 m height using a simple logarithmic correction method. The temporal and spatial separations between the CFOSAT and in situ measurements were restricted to less than 30 min and 0.25°. The results indicate that the CFOSAT wind retrievals agree well with the buoy measurements. The root mean square errors (RMSEs) of wind vectors were 1.39 m s−1 and 34.32° and negligible biases were found. In the near shore under rain-free conditions, the RMSEs were enhanced to 1.42 m s−1 and 33.43°. Similarly, the RMSEs were reduced to 1.16 m s−1 and 30.41° offshore after the rain effect was removed. After winds less than 4 m s−1 were removed, the RMSE of wind directions was reduced to 19.69°. The effects of significant wave height, air-sea temperature difference, sea surface temperature, atmospheric pressure and ocean surface current on the wind residuals were assessed. The performance of wind retrievals under the passage of tropical cyclones was evaluated. The evaluation results show that the CFOSAT wind retrievals satisfy the accuracy requirements of scientific research, although some improvements are needed to enhance the performance. [ABSTRACT FROM AUTHOR]

This paper uses the Argo sea surface salinity (SSSArgo) 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 (SSSSMAP). First, SSSArgo data were used to evaluate the accuracy of the 8-day SMAP SSS data, and the correlations and biases between SSSSMAP and SSSArgo were calculated. The results show good correlations between SSSSMAP and SSSArgo before and after TCs (before: SSSSMAP = 1.09SSSArgo−3.08 (R2 = 0.69); after: SSSSMAP = 1.11SSSArgo−3.61 (R2 = 0.65)). A stronger negative bias (−0.23) and larger root-mean-square error (RMSE, 0.95) between the SSSSMAP and SSSArgo 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 SSSSMAP agreed well with SSSArgo in both coastal and offshore waters before and after TC Roanu (2016) and TC Kyant (2016) in the Bay of Bengal. [ABSTRACT FROM AUTHOR]