Comparative study on the characteristics of marine bloom events in two representative areas of the South China Sea
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摘要: 定量海洋浮游植物藻华事件的特征有利于认识海洋生态环境及动力过程，但由于遥感数据易受云层遮蔽的影响，以往对于南海藻华事件的研究多采用不连续的观测数据和遥感数据进行，因此未能系统性地认识南海中的藻华事件发生特征和控制规律。本文借鉴海洋热浪事件的统计分析框架，基于前期研究中构建的2005−2019年逐日、无缺失的遥感叶绿素a浓度资料，提取冬季吕宋西北部海域和夏季越南东南部海域两个代表性海区的藻华事件，分析两个代表性海区中藻华事件的持续时间、强度和长期趋势等特征。结果显示，吕宋西北部海域藻华频率降低，强度增大，且大部分海区趋势显著；越南东南部海域大部分海域趋势不显著。进一步分析两个海区藻华事件的影响因子，发现海面风场（正相关）和海表温度（负相关）对藻华事件影响最大；两个代表性海区藻华事件主要都由上升流控制，风是最重要的影响因子。分析藻华事件的先兆条件，发现温度锋面代表的亚中尺度活动也是一个重要的影响因子。本研究以南海为典型案例，为探讨海洋生态环境提供了新的研究视角。Abstract: Quantitative analysis on the properties of marine phytoplankton bloom events is helpful to understand the marine ecology, environment, and dynamic processes. In the South China Sea, remote sensing is vulnerable to clouds. Previous studies were mostly conducted with discontinuous observation or remote sensing data, which failed to comprehensively understand the characteristics and controlling factors of marine bloom events in the South China Sea. This study applied the statistical framework for defining marine heat waves, based on previously reconstructed daily full coverage remotely sensed chlorophyll a product from 2005 to 2019, to extract marine bloom events in two representative subregions, i.e., the northwestern Luzon in winter and southeastern Vietnam in summer. The duration, intensity, and corresponding long-term trends of marine bloom events in the two representative sea areas were analyzed. The results showed that the bloom frequency in the Luzon Strait has decreased, while the intensity has increased. These trends were significant in most areas for the winter Luzon Strait, while the trends for most of the summer Vietnam coasts were not significant. We further analyzed the influencing factors of marine bloom events and found that the winds (positive correlation) and sea surface temperature (negative correlation) had the greatest impacts on the bloom events. In both two representative sea areas, the marine bloom events were mainly dominated by upwelling, and the wind was the most important influencing factor. Analysis on the precursor conditions of marine bloom events found that the sub-mesoscale activity represented by the temperature front is also an important influencing factor. The study applied in South China Sea as a case study, as well can provide a new perspective to study marine ecosystem and environment.
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图 1 研究区域水深图
灰色为等深线（单位：m）；白色框为后文分析中的两个关注区域
Fig. 1 Bathymetric map of study area
Gray lines represent the isobath （unit: m）; the white boxes are the two areas of concern in the following analysis
3 叶绿素浓度以及各影响因子的季节平均态
白框为后文分析藻华事件叶绿素浓度（区域A和区域B）以及各影响因子进行平均的关键区域
3 Chlorophyll concentration and the seasonal mean state of each influencing factor
The white box shows the key regions for averaging the chlorophyll concentration （Region A and Region B） and influencing factors of marine bloom events in the following analysis
图 4 区域A（a）和区域B（b）平均叶绿素浓度、90%阈值线和气候态的时间序列
紫色阴影分别表示冬季（11月至2月）和夏季（6月至9月）；红色箭头表示藻华事件
Fig. 4 Time series of mean chlorophyll concentrations, 90% threshold lines, and climatic states in regions A （a） and B （b）
Purple shades indicate winter （November to February） and summer （June to September）; red arrows indicate marine bloom events
图 5 提取的区域A（a1−a4）和区域B（b1−b4）藻华事件指标
橘色柱形图代表15年间强度最大的事件
Fig. 5 Marine bloom event indicators extracted from Region A （a1−a4） and Region B （b1−b4）
Orange bars indicate the most intense events during 2005 and 2020
图 6 2005−2019年吕宋西北部海域（a1−a4）和越南东南部海域（b1−b4）藻华事件指标趋势（p<0.1时用黑色点表示）
Fig. 6 Trend of marine bloom event indicators in the northwest Luzon （a1−a4） and southeast Vietnam （b1−b4） from 2005 to 2019 （black dots for p<0.1）
图 7 区域A（a）和区域B（b）2005−2010年和2014−2019年叶绿素浓度异常（减气候态）的频率直方图
红色和蓝色实线为正态分布拟合；虚线为均值μ（单位：mg/m3）；标准差为σ（单位：mg/m3）；黑色实线为2005−2019年90%阈值（单位：mg/m3）；a1代表区域A，2005–2010年；a2代表区域A，2014–2019年；b1代表区域B，2005–2010年；b2代表区域B，2014–2019年
Fig. 7 Frequency histograms of chlorophyll a concentration anomalies （minus climatic states） for regions A （a） and B （b） from 2005 to 2010 and 2014 to 2019
The solid red and blue lines are fitted with normal distribution; the dashed line is the mean value μ （unit: mg/m3）; the standard deviation is σ （unit: mg/m3）; the solid black line is the 90% threshold from 2005 to 2019 （unit: mg/m3）. a1 represents Region A, 2005–2010; a2 represents Region A, 2014–2019; b1 represents Region B, 2005–2010; b2 represents Region B, 2014–2019
图 8 区域A（a1−a6）和区域B（b1−b6）叶绿素浓度和各影响因子关于延迟天数和相关系数茎状图及各影响因子相关性最大延迟天数的茎状图（a7，b7）
实心圆表示通过90%显著性检验，空心圆表示未通过
Fig. 8 Stem-like plots of chlorophyll a concentrations and influencing factors in Region A （a1−a6） and Region B （b1−b6） on days of delay and correlation coefficients, and stem-like graph of maximum delay days of each influencing factor （a7, b7）
Solid circles indicate passing the 90% significance test, hollow circles indicate failing
图 9 区域A（a1−a6）和区域B（b1−b6）归一化藻华事件及对应的影响因子及延迟相关
x轴表示藻华发生的天数（例如x=0代表藻华事件发生的前一天）；各影响因素和叶绿素浓度为12个（区域A）和14个（区域B）藻华事件的平均；图中均列出相关性最大延迟回归天数以及对应的相关系数（99%显著）；风场和温度锋面数据经过5 d的滑动窗口滤波
Fig. 9 Normalized bloom event and corresponding influencing factors in regions A （a1−a6） and B （b1−b6） with lagged correlation
The x-axis represents the number of days of marine bloom （for example, x=0 represents the day before the marine bloom event）; the influencing factors and chlorophyll a concentration are the average of 12 （Region A） and 14 （Region B） marine bloom events. The maximum lagged regression days and the corresponding correlation coefficient （99% significant） are listed above each panel. The wind field and temperature front data are filtered by a 5-day sliding window
图 10 区域A（a1−a5）和区域B（b1−b5）藻华事件发生前5天的平均叶绿素浓度以及各影响因子的空间分布及区域A（a6−a10）和区域B（b6−b10）藻华事件峰值为中心的5 d平均叶绿素浓度以及各影响因子的空间分布
从上到下分别为叶绿素浓度（a1、a6、b1、b6，单位：mg/m3）；海表温度（a2、a7、b2、b7，单位：℃）；海面高度（a3、a8、b3、b8，单位：cm）；海面风场（a4、a9、b4、b9，单位：m/s）；温度锋面（a5、a10、b5、b10，单位：℃/km）
Fig. 10 Spatial plots of averaged chlorophyll a concentration and corresponding influencing factors in regions A （a1−a5） and B （b1−b5） 5-day before the marine bloom event and spatial plots of average chlorophyll a concentration and corresponding influencing factors in regions A （a6−a10 ) and B （b6−b10） of a 5-day window centered at the peak of marine bloom events
From top to bottom: chlorophyll a concentration （a1, a6, b1, b6, unit: mg/m3）; sea surface temperature （a2, a7, b2, b7, unit: ℃）; sea surface height （a3, a8, b3, b8, unit: cm）; sea surface wind vectors and speed （a4, a9, b4, b9, unit: m/s）; and temperature front （a5, a10, b5, b10, unit: ℃/km）
表 1 藻华事件统计指标
Tab. 1 Statistical indicators of marine bloom events
统计指标 定义 单位 持续时间 开始时间−结束时间 d 最大强度 藻华事件期间叶绿素浓度异常的最大值 mg/m3 平均强度 藻华事件期间叶绿素浓度异常的平均值 mg/m3 累积强度 藻华事件期间叶绿素浓度异常的逐天累加和 mg/(m3∙d) 
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[1] Smith V H. Responses of estuarine and coastal marine phytoplankton to nitrogen and phosphorus enrichment[J]. Limnology and Oceanography, 2006, 51(1part2): 377−384. doi: 10.4319/lo.2006.51.1_part_2.0377 [2] Werdell P J, Bailey S W, Franz B A, et al. Regional and seasonal variability of chlorophyll-a in Chesapeake Bay as observed by SeaWiFS and MODIS-Aqua[J]. Remote Sensing of Environment, 2009, 113(6): 1319−1330. doi: 10.1016/j.rse.2009.02.012 [3] 王正, 毛志华, 李晓娟. 气候变化对南海浮游植物藻华形成的影响研究进展[J]. 环境污染与防治, 2017, 39(12): 1384−1390.Wang Zheng, Mao Zhihua, Li Xiaojuan. Research progress in the influence of global change on phytoplankton blooms of the South China Sea[J]. Environmental Pollution & Control, 2017, 39(12): 1384−1390. [4] 刘昕, 王静, 程旭华, 等. 南海叶绿素浓度的时空变化特征分析[J]. 热带海洋学报, 2012, 31(4): 42−48. doi: 10.3969/j.issn.1009-5470.2012.04.008Liu Xin, Wang Jing, Cheng Xuhua, et al. The temporal and spatial evolution of chlorophyll-a concentration in the South China Sea[J]. Journal of Tropical Oceanography, 2012, 31(4): 42−48. doi: 10.3969/j.issn.1009-5470.2012.04.008 [5] Pan Gang, Chai Fei, Tang Danling, et al. Marine phytoplankton biomass responses to typhoon events in the South China Sea based on physical-biogeochemical model[J]. Ecological Modelling, 2017, 356: 38−47. doi: 10.1016/j.ecolmodel.2017.04.013 [6] Huang Lei, Zhao Hui, Pan Jiayi, et al. Remote sensing observations of phytoplankton increases triggered by successive typhoons[J]. Frontiers of Earth Science, 2017, 11(4): 601−608. doi: 10.1007/s11707-016-0608-x [7] Lee J H, Moon J H, Kim T. Typhoon-triggered phytoplankton bloom and associated upper-ocean conditions in the northwestern pacific: evidence from satellite remote sensing, argo profile, and an ocean circulation model[J]. Journal of Marine Science and Engineering, 2020, 8(10): 788. doi: 10.3390/jmse8100788 [8] Qiu Dajun, Zhong Yu, Chen Yongqiang, et al. Short-term phytoplankton dynamics during typhoon season in and near the pearl river estuary, South China Sea[J]. Journal of Geophysical Research:Biogeosciences, 2019, 124(2): 274−292. doi: 10.1029/2018JG004672 [9] Liu K K, Chao S Y, Shaw P T, et al. Monsoon-forced chlorophyll distribution and primary production in the South China Sea: observations and a numerical study[J]. Deep-Sea Research Part I: Oceanographic Research Papers, 2002, 49(8): 1387−1412. doi: 10.1016/S0967-0637(02)00035-3 [10] Siswanto E, Horii T, Iskandar I, et al. Impacts of climate changes on the phytoplankton biomass of the Indonesian Maritime Continent[J]. Journal of Marine Systems, 2020, 212: 103451. doi: 10.1016/j.jmarsys.2020.103451 [11] Zhang Min, Zhang Yuanling, Shu Qi, et al. Spatiotemporal evolution of the chlorophyll a trend in the North Atlantic Ocean[J]. Science of the Total Environment, 2018, 612: 1141−1148. doi: 10.1016/j.scitotenv.2017.08.303 [12] Sharma P, Singh A, Marinov I, et al. Contrasting ENSO types with satellite-derived ocean phytoplankton biomass in the tropical Pacific[J]. Geophysical Research Letters, 2019, 46(11): 5987−5996. doi: 10.1029/2018GL080689 [13] Wei Qinsheng, Fu Mingzhu, Sun Junchuan, et al. Seasonal physical fronts and associated biogeochemical-ecological effects off the Jiangsu shoal in the western Yellow Sea, China[J]. Journal of Geophysical Research: Oceans, 2020, 125(10): e2020JC016304. [14] Li Q P, Zhou Weiwen, Chen Yinchao, et al. Phytoplankton response to a plume front in the northern South China Sea[J]. Biogeosciences, 2018, 15(8): 2551−2563. doi: 10.5194/bg-15-2551-2018 [15] Li Weiqi, Ge Jianzhong, Ding Pingxing, et al. Effects of dual fronts on the spatial pattern of chlorophyll a concentrations in and off the Changjiang River Estuary[J]. Estuaries and Coasts, 2021, 44(5): 1408−1418. doi: 10.1007/s12237-020-00893-z [16] Dawson H R S, Strutton P G, Gaube P. The unusual surface chlorophyll signatures of southern Ocean Eddies[J]. Journal of Geophysical Research: Oceans, 2018, 123(9): 6053−6069. doi: 10.1029/2017JC013628 [17] Guo Mingxian, Xiu Peng, Chai Fei, et al. Mesoscale and submesoscale contributions to high sea surface chlorophyll in subtropical gyres[J]. Geophysical Research Letters, 2019, 46(22): 13217−13226. doi: 10.1029/2019GL085278 [18] Xu Guangjun, Dong Changming, Liu Yu, et al. Chlorophyll rings around ocean eddies in the North Pacific[J]. Scientific Reports, 2019, 9(1): 2056. doi: 10.1038/s41598-018-38457-8 [19] Lakshmi R S, Chatterjee A, Prakash S, et al. Biophysical interactions in driving the summer monsoon chlorophyll bloom off the Somalia coast[J]. Journal of Geophysical Research: Oceans, 2020, 125(3): e2019JC015549. [20] Rintaka W E, Priyono B. Variation of seawater temperature and chlorophyll a prior to and during upwelling event in Bali Strait, Indonesia: from observation and model[J]. IOP Conference Series: Earth and Environmental Science, 2020, 429: 012002. doi: 10.1088/1755-1315/429/1/012002 [21] Hu Qiwei, Chen Xiaoyan, Huang Wanyi, et al. Phytoplankton bloom triggered by eddy-wind interaction in the upwelling region east of Hainan Island[J]. Journal of Marine Systems, 2021, 214: 103470. doi: 10.1016/j.jmarsys.2020.103470 [22] 高慧, 赵辉, 沈春燕, 等. 冬季吕宋岛西北海域叶绿素时空变化特征[J]. 海洋学研究, 2018, 36(1): 75−85. doi: 10.3969/j.issn.1001-909X.2018.01.008Gao Hui, Zhao Hui, Shen Chunyan, et al. Spatial-temporal variation of winter phytoplankton blooms in the northwest of Luzon Island[J]. Journal of Marine Sciences, 2018, 36(1): 75−85. doi: 10.3969/j.issn.1001-909X.2018.01.008 [23] Yuan Yuan, Zhou Wen, Chan J C L, et al. Impacts of the basin-wide Indian Ocean SSTA on the South China Sea summer monsoon onset[J]. International Journal of Climatology, 2008, 28(12): 1579−1587. doi: 10.1002/joc.1671 [24] Liu Xin, Wang Jing, Cheng Xuhua, et al. Abnormal upwelling and chlorophyll-a concentration off South Vietnam in summer 2007[J]. Journal of Geophysical Research: Oceans, 2012, 117(C7): C07021. [25] 乐凤凤, 宁修仁. 南海北部浮游植物生物量的研究特点及影响因素[J]. 海洋学研究, 2006, 24(2): 60−69. doi: 10.3969/j.issn.1001-909X.2006.02.007Le Fengfeng, Ning Xiuren. Variations of the phytoplankton biomass in the northern South China Sea[J]. Journal of Marine Sciences, 2006, 24(2): 60−69. doi: 10.3969/j.issn.1001-909X.2006.02.007 [26] Liu Fenfen, Tang Shilin, Huang Ruixin, et al. The asymmetric distribution of phytoplankton in anticyclonic eddies in the western South China Sea[J]. Deep-Sea Research Part I: Oceanographic Research Papers, 2017, 120: 29−38. doi: 10.1016/j.dsr.2016.12.010 [27] Guo Lin, Xiu Peng, Chai Fei, et al. Enhanced chlorophyll concentrations induced by Kuroshio intrusion fronts in the northern South China Sea[J]. Geophysical Research Letters, 2017, 44(22): 11565−11572. doi: 10.1002/2017GL075336 [28] 连展, 王新怡, 魏泽勋. 中国南海表层叶绿素a季节内变化特征及成因[J]. 海洋科学进展, 2020, 38(4): 649−661. doi: 10.3969/j.issn.1671-6647.2020.04.009Lian Zhan, Wang Xinyi, Wei Zexun. Features and driving mechanisms of the intra-seasonal variation of sea surface chlorophyll a in the South China Sea[J]. Advances in Marine Science, 2020, 38(4): 649−661. doi: 10.3969/j.issn.1671-6647.2020.04.009 [29] 古园园, 王静, 储小青, 等. 夏季南海西部叶绿素浓度高值带的年际变化[J]. 海洋学报, 2017, 39(6): 1−9.Gu Yuanyuan, Wang Jing, Chu Xiaoqing, et al. Interannual variability of the high chlorophyll a concentration strip in the western South China Sea during summer[J]. Haiyang Xuebao, 2017, 39(6): 1−9. [30] 赵健. 上升流区藻华现象成因对比分析: 越南东部与索马里[D]. 湛江: 广东海洋大学, 2018.Zhao Jian. Contrastive analysis of the causes of phytoplankton blooms in tow upwelling areas: Vietnam and Somalia[D]. Zhanjiang: Guangdong Ocean University, 2018. [31] Lu Wenfang, Oey L Y, Liao Enhui, et al. Physical modulation to the biological productivity in the summer Vietnam upwelling system[J]. Ocean Science, 2018, 14(5): 1303−1320. doi: 10.5194/os-14-1303-2018 [32] Zeng Jialing, Liu Chunli, Li Xue, et al. Comparative study of the variability and trends of phytoplankton biomass between spring and winter upwelling systems in the South China Sea[J]. Journal of Marine Systems, 2022, 231: 103738. doi: 10.1016/j.jmarsys.2022.103738 [33] Wang Jiujuan, Tang Danling, Sui Yi. Winter phytoplankton bloom induced by subsurface upwelling and mixed layer entrainment southwest of Luzon Strait[J]. Journal of Marine Systems, 2010, 83(3/4): 141−149. [34] Lu Wenfang, Yan Xiaohai, Jiang Yuwu. Winter bloom and associated upwelling northwest of the Luzon Island: a coupled physical-biological modeling approach[J]. Journal of Geophysical Research: Oceans, 2015, 120(1): 533−546. doi: 10.1002/2014JC010218 [35] Ning X, Chai Fei, Xue Huijie, et al. Physical-biological oceanographic coupling influencing phytoplankton and primary production in the South China Sea[J]. Journal of Geophysical Research: Oceans, 2004, 109(C10): C10005. doi: 10.1029/2004JC002365 [36] Chen Gengxin, Xiu Peng, Chai Fei. Physical and biological controls on the summer chlorophyll bloom to the east of Vietnam[J]. Journal of Oceanography, 2014, 70(3): 323−328. doi: 10.1007/s10872-014-0232-x [37] Tang Danling, Ni I H, Kester D R, et al. Remote sensing observations of winter phytoplankton blooms southwest of the Luzon Strait in the South China Sea[J]. Marine Ecology Progress Series, 1999, 191: 43−51. doi: 10.3354/meps191043 [38] Shang Shaoling, Li Li, Li Jun, et al. Phytoplankton bloom during the northeast monsoon in the Luzon Strait bordering the Kuroshio[J]. Remote Sensing of Environment, 2012, 124: 38−48. doi: 10.1016/j.rse.2012.04.022 [39] Hobday A J, Alexander L V, Perkins S E, et al. A hierarchical approach to defining marine heatwaves[J]. Progress in Oceanography, 2016, 141: 227−238. doi: 10.1016/j.pocean.2015.12.014 [40] Lu Wenfang, Gao Xinyu, Wu Zelun, et al. Framework to extract extreme phytoplankton bloom events with remote sensing datasets: a case study[J]. Remote Sensing, 2022, 14(15): 3557. doi: 10.3390/rs14153557 [41] Wang Tianhao, Yu Peng, Wu Zelun, et al. Revisiting the intraseasonal variability of chlorophyll a in the adjacent Luzon Strait with a new gap-filled remote sensing data set[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 4201311. [42] Belkin I M, O’reilly J E. An algorithm for oceanic front detection in chlorophyll and SST satellite imagery[J]. Journal of Marine Systems, 2009, 78(3): 319−326. doi: 10.1016/j.jmarsys.2008.11.018 [43] Oliver E C J, Benthuysen J A, Darmaraki S, et al. Marine heatwaves[J]. Annual Review of Marine Science, 2021, 13: 313−342. doi: 10.1146/annurev-marine-032720-095144 [44] 陈更新. 南海中尺度涡的时空特征研究[D]. 青岛: 中国科学院研究生院(海洋研究所), 2010.Chen Gengxin. Mesoscale eddies in the South China Sea: mean properties and spatio-temporal variability[D]. Qingdao: Institute of Oceanology, Chinese Academy of Sciences, 2010. [45] Xing Xiaogang, Qiu Guoqiang, Boss E, et al. Temporal and vertical variations of particulate and dissolved optical properties in the South China Sea[J]. Journal of Geophysical Research: Oceans, 2019, 124(6): 3779−3795. doi: 10.1029/2018JC014880 [46] Zhao Hui, Zhao Jian, Sun Xingli, et al. A strong summer phytoplankton bloom southeast of Vietnam in 2007, a transitional year from El Niño to La Niña[J]. PLoS One, 2018, 13(1): e0189926. doi: 10.1371/journal.pone.0189926 [47] 陈莹, 赵辉. 南海中西部叶绿素时空变化特征分析[J]. 海洋学研究, 2021, 39(3): 84−94.Chen Ying, Zhao Hui. Spatio-temporal distribution of chlorophyll in the mid-western South China Sea[J]. Journal of Marine Sciences, 2021, 39(3): 84−94. [48] Chen C C, Shiah F K, Chung S W, et al. Winter phytoplankton blooms in the shallow mixed layer of the South China Sea enhanced by upwelling[J]. Journal of Marine Systems, 2006, 59(1/2): 97−110. [49] Lin Hongyang, Liu Zhiyu, Hu Jianyu, et al. Characterizing meso- to submesoscale features in the South China Sea[J]. Progress in Oceanography, 2020, 188: 102420. doi: 10.1016/j.pocean.2020.102420 [50] 赵辉, 齐义泉, 王东晓, 等. 南海叶绿素浓度季节变化及空间分布特征研究[J]. 海洋学报, 2005, 27(4): 45−52.Zhao Hui, Qi Yiquan, Wang Dongxiao, et al. Study on the features of chlorophyll a derived from SeaWiFS in the South China Sea[J]. Haiyang Xuebao, 2005, 27(4): 45−52. [51] Walker N D, Leben R R, Balasubramanian S. Hurricane-forced upwelling and chlorophyll a enhancement within cold-core cyclones in the Gulf of Mexico[J]. Geophysical Research Letters, 2005, 32(18): L18610. -
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