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Frontiers of Earth Science

Front. Earth Sci.    2017, Vol. 11 Issue (4) : 601-608
Remote sensing observations of phytoplankton increases triggered by successive typhoons
Lei HUANG1,2, Hui ZHAO1(), Jiayi PAN2,3,4, Adam DEVLIN5
1. College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
2. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
3. Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518057, China
4. College of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
5. Department of Civil and Environmental Engineering, Portland State University, Portland, OR 97207, USA
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Phytoplankton blooms in the Western North Pacific, triggered by two successive typhoons with different intensities and translation speeds under different pre-existing oceanic conditions, were observed and analyzed using remotely sensed chlorophyll-a (Chl-a), sea surface temperature (SST), and sea surface height anomaly (SSHA) data, as well as typhoon parameters and CTD (conductivity, temperature, and depth) profiles. Typhoon Sinlaku, with relatively weaker intensity and slower translation speed, induced a stronger phytoplankton bloom than Jangmi with stronger intensity and faster translation speed (Chl-a>0.18 mg·m3 versus Chl-a<0.15 mg·m3) east of Taiwan Island. Translation speed may be one of the important mechanisms that affect phytoplankton blooms in the study area. Pre-existing cyclonic circulations provided a relatively unstable thermodynamic structure for Sinlaku, and therefore cold water with rich nutrients could be brought up easily. The mixed-layer deepening caused by Typhoon Sinlaku, which occurred first, could have triggered an unfavorable condition for the phytoplankton bloom induced by Typhoon Jangmi which followed afterwards. The sea surface temperature cooling by Jangmi was suppressed due to the presence of the thick upper-ocean mixed-layer, which prevented the deeper cold water from being entrained into the upper-ocean mixed layer, leading to a weaker phytoplankton augment. The present study suggests that both wind (including typhoon translation speed and intensity) and pre-existing conditions (e.g., mixed-layer depths, eddies, and nutrients) play important roles in the strong phytoplankton bloom, and are responsible for the stronger phytoplankton bloom after Sinlaku’s passage than that after Jangmi’s passage. A new typhoon-influencing parameter is introduced that combines the effects of the typhoon forcing (including the typhoon intensity and translation speed) and the oceanic pre-condition. This parameter shows that the forcing effect of Sinlaku was stronger than that of Jangmi.

Keywords typhoon      mixed-layer depth      phytoplankton bloom      Northwest Pacific Ocean      upwelling     
Corresponding Author(s): Hui ZHAO   
Just Accepted Date: 19 October 2016   Online First Date: 14 November 2016    Issue Date: 10 November 2017
 Cite this article:   
Lei HUANG,Hui ZHAO,Jiayi PAN, et al. Remote sensing observations of phytoplankton increases triggered by successive typhoons[J]. Front. Earth Sci., 2017, 11(4): 601-608.
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Fig.1  Track and intensity of typhoons (a) Sinlaku (07–16 September 2008) and (b) Jangmi (23 September–01 October 2008) in the western North Pacific (WNP).
Fig.2  (a) Chlorophyll-a (Chl-a) concentration (mg·m?3) before Typhoon Sinlaku; Chl-a concentration (mg·m?3 ) after Typhoon (b) Sinlaku and (c) Jangmi; (d) time series of daily Chl-a concentration averaged over an offshore region (122.5°–125°E, 21°–24°N) shown as the box in (b) and (c).
Fig.3  (a) SST before Typhoon Sinlaku; SST after Typhoon (b) Sinlaku and (c) Jangmi; (d) time series of SST averaged over the offshore region (122.5°–125°E, 21°–24°N) shown as the box in Fig. 2(b) and 2(c).
Fig.4  SSHA (cm) from September to early October, 2008.
Fig.5  Time series of Ekman pumping velocity (10?4 m·s?1) during (a) Sinlaku and (b) Jangmi averaged over the offshore area (122.5°–125°E, 21°–24°N ) for both Sinlaku and Jangmi.
Fig.6  (a) Vertical profiles of the climatological temperature (°C) in September, CTD temperature profiles on the (b) west side and (c) east side of the typhoon tracks.
No. Time Location T/°C Δ T/°C MLD/m ΔMLD/m
1 1-Sep 20.91°N, 123.58°E 29.2 ----- 30 -----
2 11-Sep 20.93°N, 124.00°E 28.2 ?1 42 12
3 21-Sep 20.27°N, 123.91°E 28.5 ----- 60 -----
4 1-Oct 20.23°N, 123.43°E 28.1 ?0.4 70 10
5 1-Sep 22.30°N, 126.44°E 29.5 ----- 30 -----
6 11-Sep 23.31°N, 126.58°E 28.7 ?0.8 60 30
7 16-Sep 24.42°N, 126.31°E 28.2 ?1.3 70 40
8 21-Sep 24.33°N, 126.89°E 28.3 ----- 35 -----
9 1-Oct 24.35°N, 127.41°E 28.6 0.3 55 20
Tab.1  The CTD time and locations, sea surface cooling at the locations, and mixed-layer deepening estimated based on the CTD temperature profiles
1 Babin S M, Carton  J A, Dickey  T D, Wiggert  J D (2004). Satellite evidence of hurricane induced phytoplankton blooms in an oceanic desert. J Geophys Res, 109(C3): 1978–2012
2 Chen C T A ,  Liu C T ,  Chuang W S ,  Yang Y J ,  Shiah F K ,  Tang T Y ,  Chung S W  (2003). Enhanced buoyancy and hence upwelling of subsurface Kuroshio waters after a typhoon in the southern East China Sea. J Mar Syst, 42(1–2): 65–79
3 Furuya K (1990). Subsurface chlorophyll maximum in the tropical and subtropical western Pacific Ocean: vertical profiles of phytoplankton biomass and its relationship with chlorophylla and particulate organic carbon. Mar Biol, 107(3): 529–539
4 Gong X, Shi  J, Gao H W  (2012). Subsurface chlorophyll maximum in ocean: its characteristics and influencing factors. Adv Earth Sci, 27(5): 539–548 (in Chinese)
5 Gong X, Shi  J, Gao H W ,  Yao X H  (2015). Steady-state solutions for subsurface chlorophyll maximum in stratified water columns with a bell-shaped vertical profile of chlorophyll. Biogeosciences, 12(4): 905–919
6 Hu J Y, Hiroshi  K (2004). Detection of cyclonic eddy generated by looping tropical cyclone in the northern South China Sea: a case study. Acta Oceanol Sin, 23(2), 213–224
7 Lin I I, Wu  C C, Emanuel  K A, Lee  I H, Wu  C R, Pun  I F (2005). The interaction of Super typhoon Maemi (2003) with a warm ocean eddy. Mon Weather Rev, 133(9): 2635–2649
8 Lin I, Liu  W T, Wu  C C, Wong  G T F, Hu  C, Chen Z ,  Liang W D ,  Yang Y, Liu  K K (2003). New evidence for enhanced ocean primary production triggered by tropical cyclone. Geophys Res Lett, 30(13)
9 Liu G Q, He  Y J, Shen  H, Qiu Z F  (2010). Submesoscale activity over the shelf of the northern South China Sea in summer: simulation with an embedded model. Chinese Journal of Oceanology and Limnology, 28: 1073–1079
10 Lü H, He  Y J, Shen  H, Cui L M ,  Dou C E  (2010). A new method for the estimation of oceanic mixed-layer depth using shipboard X-band radar images. Chin J Oceanology Limnol, 28(5): 962–967
11 Pan J Y, Sun  Y J (2013). Estimate of ocean mixed layer deepening after a typhoon passage over the south china sea by using satellite data. J Phys Oceanogr, 43(3): 498–506
12 Price J F (1981). Upper ocean response to a hurricane. J Phys Oceanogr, 11(2): 153–175 doi:10.1175/1520-0485(1981)011<0153:UORTAH>2.0.CO;2
13 Stewart R H (2008). Introduction to Physical Oceanography. Texas: Texas A & M University, 49–50
14 Tsai Y, Chern  C S, Wang  J (2008). The upper ocean response to a moving typhoon. J Oceanogr, 64(1): 115–130
15 Wei Z X, Fang  G H, Choi  B H, Fang  Y, He Y J  (2003). Sea surface height and transport stream function of the South China Sea from a variable-grid global ocean circulation model. Sci China Ser D, 46(2): 139–148
16 Yentsch C S (1965). Distribution of chlorophyll and phaeophytin in the open ocean. Deep Sea Research and Oceanographic Abstracts, 12(5): 653–666
17 Zhang B, Perrie  W, Zhang J A ,  Uhlhorn E W ,  He J Y  (2014). High-resolution hurricane vector winds from C-band dual-polarization SAR observations. J Atmos Ocean Technol, 31(2): 272–286
18 Zhao H, Han  G Q, Zhang  S W, Wang  D X (2013). Two phytoplankton blooms near Luzon Strait generated by lingering Typhoon Parma. J Geophys Res Biogeosci, 118(2): 412–421
19 Zhao H, Tang  D L, Wang  Y (2008). Comparison of phytoplankton blooms triggered by two typhoons with different intensities and translation speeds in the South China Sea. Mar Ecol Prog Ser, 365: 57–65
20 Zheng G M, Tang  D L (2007). Offshore and nearshore chlorophyll increases induced by typhoon winds and subsequent terrestrial rainwater runoff. Mar Ecol Prog Ser, 333: 61–74
21 Zheng Z W, Ho  C R, Kuo  N J (2008). Importance of pre-existing oceanic conditions to upper ocean response induced by Super Typhoon Hai-Tang. Geophys Res Lett, 35(20): L20603
22 Zheng Z W, Ho  C R, Zheng  Q, Lo Y T ,  Kuo N J ,  Gopalakrishnan G  (2010). Effects of preexisting cyclonic eddies on upper ocean responses to Category 5 typhoons in the western North Pacific.  J Geophys Res (1978–2012), 115(C9)
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