Tropical cyclone Wind Pump induced chlorophyll-a enhancement in the South China Sea: A comparison of the open sea and continental shelf

Abstract

The continental shelf and open sea region have different ocean physical and environmental responses to tropical cyclones (TC). The present study firstly compared the grid-based maximum response (GMR) of sea surface chlorophyll a (Chl-a) to TCs Wind Pump between the continental shelf (depths 50-200m) and open sea (depths>200 m) region in the South China Sea (SCS) during 1998-2020, using multiple-satellite data. For all TC events, the average Chl-a increases by 0.13 mg/m3. The comparisons between the pre- and post-TC periods show that Chl-a increases by 0.18 mg/m3 (115%) in the continental shelf and 0.07 mg/m3 (57%) in the open sea region, respectively. In both areas, TC-induced Chl-a enhancement (Chl_en) strongly depends on TC maximum wind speed and translation speed. The strong and fast-moving TCs and weak and slow-moving TCs both induce the maximum Chl_en in the continental shelf region. While in the open sea, the maximum Chl_en was induced by the strong and slow-moving TCs. Overall, our results reveal that the TC wind pump’s effects on Chl-a enhancement are stronger in the continental shelf region than in the open sea.

Oceanic and ecological response to native Typhoons Cempaka and Lupit (2021) along the northern South China Sea continental shelf: comparison and evaluation of global and regional Operational Oceanography Forecasting Systems

“…In general, the Cempaka-induced maximum Chla represented by SCSOFSv2 and MO and the Lupit-induced maximum Chla represented by MO were observed 2-3 days after the typhoon passed. This was 2 days ahead of the average (4-5 days) on the SCS shelf (Li and Tang, 2022), and several days ahead of the timing in the open sea, such as 6 days induced by Typhoon Linfa (Chen and Tang, 2012) and 13 days induced by Typhoon Nangka (Qiu et al, 2019). In addition, Cempaka-and Lupit-induced Chla bloom was significantly stronger than the approximate mean increase of 0.18 mg m −3 on the SCS shelf and 0.07 mg m −3 in the open sea, respectively (Li and Tang, 2022).…”

“…However, the southwest wind strengthened in the northern SCS during the Lupit period August 3-7, when compared to August 2, to produce a long-lasting vertical mixing and stronger upwelling along the continental shelf (Figure S7), uplifting more nutrients to stimulate a phytoplankton bloom and also Chla enhancement. Recent research has also found that slow-moving typhoons induce the most pronounced Chla increase on the continental shelf, consistent with that caused by strong and fast-moving typhoons (Li and Tang, 2022). Heavy rainfall and cloud coverage blocked the satellite observation of Chla during the Lupit period in the northern SCS (Figures 7I, L), so it cannot be used to verify the simulation results.…”

“…Based on decades of typhoon data, researches have pointed out the association of Chla enhancement with water depth, typhoon's intensity, and translational speed (Chen et al, 2017;Wang, 2020;Li and Tang, 2022). By comparing the response to typhoons for areas with three different water depths, Chen et al (2017) found an increase in depth-integrated Chla, mainly in the coastal waters and continental shelf.…”

“…Wang (2020) demonstrated Chla responses to slow-translation-speed typhoons in the SCS, mostly near coastal waters. Furthermore, a recent study revealed that both weak and slow-moving as well as strong and fast-moving typhoons were characterized by maximum Chla bloom with an increase of 0.23 mg m −3 on the continental shelf, whereas strong and slow-moving typhoons induce a larger Chla enhancement with an increase of 0.14 mg m −3 in the open sea (Li and Tang, 2022).…”

Composite Analysis-Based Machine Learning for Prediction of Tropical Cyclone-Induced Sea Surface Height Anomaly

“…At the same time, changes in TC-induced SSHA can be significant in nearshore regions. However, in open sea (e.g., WNPSO), TC movement is not controlled by such topography [12,[42][43][44]. These uncertainties in ML-based predictions may therefore be potentially influenced by the geographical location, in addition to being controlled by TC characteristics and preexisting ocean factors.…”