南海北部次表层叶绿素最大值年际变化特征分析*

doi:10.11978/2020151

摘要/Abstract

摘要：

次表层叶绿素最大值(subsurface chlorophyll maxima, SCMs)广泛存在于全球各海域, 该最大值层往往具有较高的海洋初级生产力和新生产力, 因此研究其年际变化特征对深入理解气候变化影响下海洋生态系统变化有重要意义。本文采用一维物理-生态耦合模型(one-dimensional physical-biological coupled model, MEM-1D)较好地模拟了1994—2019年南海北部海盆区海温、盐度、营养盐和叶绿素的垂向分布, 并采用3种统计方法, 分别从整体趋势、不同时间尺度及显著变化三方面分析了SCMs特征因子(强度、深度和厚度)的年际变化特征。总体而言1994—2019年SCMs强度整体减小趋势较弱(趋势斜率S<0), 具体表现为先减小(1994—2004年)后增大(2005—2012年)再减小(2013—2019年), 其中1999—2004年显著变小; SCMs深度呈变深趋势(趋势斜率S>0), 1994—2011年逐渐变深, 之后逐渐变浅, 但变化不显著; SCMs厚度整体呈增大趋势, 1999年起显著变大。相关分析发现, 海表面温度在年际变化上与SCMs特征因子间不存在相关性(P>0.05); 海表面温度对SCMs的影响主要表现在季节尺度上, SCMs深度和强度均与海表面温度呈一致性变化。季节性差分自回归滑动平均模型对SCMs三个特征因子的拟合效果较好, 平均绝对百分比误差分别为5.33%(强度)、0.62%(深度)、2.49%(厚度), 模型可用于对SCMs特征因子变化趋势的预测。

关键词: 次表层叶绿素最大值, 趋势统计分析, 时间序列模型, 数值模拟, 南海北部

Abstract:

Subsurface chlorophyll maxima (SCMs) are widely found in the global oceans. The layer of SCM accounts for a great proportion of marine primary productivity and new productivity. Studying the interannual variation of SCMs can deepen our understanding of marine ecosystems under climatic change. In this study, a one-dimensional physical-biological coupled model (MEM-1D) was used to simulate the vertical distribution of ocean temperature, salinity, nutrients, and chlorophyll in the northern South China Sea from 1994 to 2019. Based on numerical modeling results, we analyzed the interannual variations of SCM characteristics (intensity, depth and thickness) using three statistical methods from different aspects, i.e., overall trend, different time scales and significant changes. In general, the intensity of SCMs from 1994 to 2019 shows a slightly decreasing trend (the trend slope S<0). Specifically, the SCM intensity decreased from 1994 to 2004 ( decreased significantly from 1999 to 2004), while it increased from 2005 to 2012 and then decreased until 2019. The depth of SCMs showed a deepening tendency between 1994 and 2019 (the trend slope S>0), gradually becoming deeper from 1994 to 2011, and then shoaling; but the changing trend was not significant. The thickness of SCMs increased as a whole, and it had increased significantly since 1999. Correlation analysis showed no relationship between annually averaged sea-surface temperature (SST) and the characteristics of SCMs (P>0.05). The influence of SST on SCMs was mainly on the seasonal scale, and the depth and intensity of SCMs changed consistently with SST. The seasonal, autoregressively integrated moving average model has a good fit for the three characteristics of SCMs, with the mean absolute percentage errors of 5.33% for intensity, 0.62% for depth and 2.49% for thickness, indicating that the model can be used to predict the trend of SCM characteristics.

Key words: subsurface chlorophyll maximum, trend statistical analysis, time series model, numerical simulation, northern South China Sea

中图分类号:

X145

王仁政, 单正垛, 孟思雨, 宫响. 南海北部次表层叶绿素最大值年际变化特征分析^*[J]. 热带海洋学报, 2021, 40(6): 63-75.

WANG Renzheng, SHAN Zhengduo, MENG Siyu, GONG Xiang. Interannual variation of subsurface chlorophyll maximum in the northern South China Sea[J]. Journal of Tropical Oceanography, 2021, 40(6): 63-75.

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参考文献 38

[1]	陈国鼎, 荆海晓, 李小宝, 等, 2018. 基于经验模态分解与传统水文分析法的降雨序列研究[J]. 水利水电技术, 49(11):8-14.
	CHEN GUODING, JING HAIXIAO, LI XIAOBAO, et al, 2018. Study on rainfall time series based on empirical mode decomposition and traditional hydrological methods[J]. Water Resources and Hydropower Engineering, 49(11):8-14 (in Chinese with English abstract).
[2]	姜浩, 赵中阔, 樊伟, 等, 2018. 基于经验模态分解和小波分解估算海气通量涡相关计算中的截断时间尺度[J]. 海洋与湖沼, 49(6):1138-1150.
	JIANG HAO, ZHAO ZHONGKUO, FAN WEI, et al, 2018. Estimate of cutoff time scale in the eddy covariance method for air-sea flux based on empirical mode decomposition and wavelet decomposition[J]. Oceanologia et Limnologia Sinica, 49(6):1138-1150 (in Chinese with English abstract).
[3]	柯志新, 黄良民, 谭烨辉, 等, 2013. 2008年夏末南海北部叶绿素a的空间分布特征及其影响因素[J]. 热带海洋学报, 32(4):51-57.
	KE ZHIXIAN, HUANG LIANGMIN, TAN YEHUI, et al, 2013. Spatial distribution of chlorophyll a and its relationships with environmental factors in northern South China Sea in late summer 2008[J]. Journal of Tropical Oceanography, 32(4):51-57 (in Chinese with English abstract).
[4]	乐凤凤, 宁修仁, 2006. 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, 24(2):60-69.
	LE FENGFENG, NING XIUREN, 2006. Variations of the phytoplankton biomass in the northern South China Sea[J]. Journal of Marine Sciences, 24(2):60-69 (in Chinese with English abstract).
[5]	夏洁, 高会旺, 2006. 南黄海东部海域浮游生态系统要素季节变化的模拟研究[J]. 安全与环境学报, 6(4):59-65.
	XIA JIE, GAO HUIWANG, 2006. Simulation on seasonal cycle vertical structure of plankton ecosystem in eastern area of South Yellow Sea[J]. Journal of Safety and Environment, 6(4):59-65 (in Chinese with English abstract).
[6]	张冲, 史洁, 高会旺, 等, 2011. 南海北部浮游生态系统要素季节变化的模拟研究[J]. 中国海洋大学学报, 41(3):11-18.
	ZHANG CHONG, SHI JIE, GAO HUIWANG, et al, 2011. Numerical study on seasonal variations of the vertical structures of the plankton ecosystem in the northern area of South China Sea[J]. Periodical of Ocean University of China, 41(3):11-18 (in Chinese with English abstract).
[7]	张钒, 阮五崎, 黄邦钦, 2000. 台湾海峡南部叶绿素A最大值的研究[J]. 厦门大学学报(自然科学版), 39(5):664-668.
	ZHANG FAN, RUAN WUQI, HUANG BANGQIN, 2000. A study of chlorophyll a maximum in the southern part of the Taiwan Strait[J]. Journal of Xiamen University (Natural Science), 39(5):664-668 (in Chinese with English abstract).
[8]	ALLEN J I, BLACKFORD J C, RADFORD P J, 1998. An 1-d vertically resolved modelling study of the ecosystem dynamics of the middle and southern Adriatic sea[J]. Journal of Marine Systems, 18(1-3):265-286. doi: 10.1016/S0924-7963(98)00015-3
[9]	BECKMANN A, HENSE I, 2007. Beneath the surface: characteristics of oceanic ecosystems under weak mixing conditions - a theoretical investigation[J]. Progress in Oceanography, 75(4):771-796. doi: 10.1016/j.pocean.2007.09.002
[10]	BOX G E P, JENKINS G M, 1970. Time series analysis: forecasting and control[M]. San Francisco: Holden-Day: 131-133.
[11]	BUTENSCHÖN M, CLARK J, ALDRIDGE J A, et al, 2016. ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels[J]. Geoscientific Model Development, 9(4):1293-1339. doi: 10.5194/gmd-9-1293-2016
[12]	CHEN Y L L, CHEN H Y, KARL D M, et al, 2004. Nitrogen modulates phytoplankton growth in spring in the south china sea[J]. Continental Shelf Research, 24(4-5):527-541. doi: 10.1016/j.csr.2003.12.006
[13]	CHEN Y L L, 2005. Spatial and seasonal variations of nitrate-based new production and primary production in the south china sea[J]. Deep Sea Research Part I: Oceanographic Research Papers, 52(2):319-340. doi: 10.1016/j.dsr.2004.11.001
[14]	FAIRALL C W, BRADLEY E F, ROGERS D P, et al, 1996. Bulk parameterization of air‐sea fluxes for tropical ocean‐global atmosphere coupled‐ocean atmosphere response experiment[J]. Journal of Geophysical Research: Oceans, 101(C2):3747-3764.
[15]	FENNEL K, BOSS E, 2003. Subsurface maxima of phytoplankton and chlorophyll: steady-state solutions from a simple model[J]. Limnology and Oceanography, 48(4):1521-1534. doi: 10.4319/lo.2003.48.4.1521
[16]	FLANDRIN P, GONÇALVÈS P, 2004. Empirical mode decompositions as data-driven wavelet-like expansions[J]. International Journal of Wavelets, Multiresolution and Information Processing, 2(4):477-496. doi: 10.1142/S0219691304000561
[17]	GONG X, SHI J, GAO H W, et al, 2015. Steady-state solutions for subsurface chlorophyll maximum in stratified water columns with a bell-shaped vertical profile of chlorophyll[J]. Biogeosciences, 12(4):905-919. doi: 10.5194/bg-12-905-2015
[18]	GONG XIANG, SHI JIE, GAO HUIWANG, 2014. Modeling seasonal variations of subsurface chlorophyll maximum in South China Sea[J]. Journal of Ocean University of China, 13(4):561-571. doi: 10.1007/s11802-014-2060-4
[19]	HANSON C E, WAITE A M, THOMPSON P A, et al, 2007. Phytoplankton community structure and nitrogen nutrition in Leeuwin Current and coastal waters off the Gascoyne region of Western Australia[J]. Deep Sea Research Part Ⅱ: Topical Studies in Oceanography, 54(8-10):902-924.
[20]	HODGES B A, RUDNICK D L, 2004. Simple models of steady deep maxima in chlorophyll and biomass[J]. Deep Sea Research Part I: Oceanographic Research Papers, 51(8):999-1015. doi: 10.1016/j.dsr.2004.02.009
[21]	HUANG N E, SHEN ZHENG, LONG S R, et al, 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971):903-995. doi: 10.1098/rspa.1998.0193
[22]	KENDALL M G, 1975. Rank correlation methods[M]. 4th ed. London: Griffin.
[23]	LEWANDOWSKA A M, BOYCE D G, HOFMANN M, et al, 2014. Effects of sea surface warming on marine plankton[J]. Ecology Letters, 17(5):614-623. doi: 10.1111/ele.2014.17.issue-5
[24]	LEWIS M R, CULLEN J J, PLATT T, 1983. Phytoplankton and thermal structure in the upper ocean: consequences of nonuniformity in chlorophyll profile[J]. Journal of Geophysical Research: Oceans, 88(C4):2565-2570.
[25]	LIU K K, CHAO S Y, SHAW P T, et al, 2002. 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, 49(8):1387-1412. doi: 10.1016/S0967-0637(02)00035-3
[26]	LIU K K, CHEN YINGJIE, TSENG C M, et al, 2007. The significance of phytoplankton photo-adaptation and benthic-pelagic coupling to primary production in the South China Sea: observations and numerical investigations[J]. Deep Sea Research Part Ⅱ: Topical Studies in Oceanography, 54(14-15):1546-1574.
[27]	LU ZHONGMING, GAN JIANPING, DAI MINHAN, et al, 2010. The influence of coastal upwelling and a river plume on the subsurface chlorophyll maximum over the shelf of the northeastern South China Sea[J]. Journal of Marine Systems, 82(1-2):35-46. doi: 10.1016/j.jmarsys.2010.03.002
[28]	MANN H B, 1945. Nonparametric tests against trend[J]. Econometrica, 13(3):245-259. doi: 10.2307/1907187
[29]	MELLOR G L, YAMADA T, 1982. Development of a turbulence closure model for geophysical fluid problems[J]. Reviews of Geophysics, 20(4):851-875. doi: 10.1029/RG020i004p00851
[30]	MENG SIYU, GONG XUN, YU YANG, et al, 2021. Strengthened ocean-desert process in the North Pacific over the past two decades[J]. Environmental Research Letters, 16(2):024034. doi: 10.1088/1748-9326/abd96f
[31]	MIDHUN SHAH H, SMITHA B R, HATHA A A M, et al, 2020. Subsurface chlorophyll maxima in the North Eastern Arabian sea: simulation on impact of warming[J]. Ecological Indicators, 110:105858. doi: 10.1016/j.ecolind.2019.105858
[32]	PAN SHANSHAN, SHI JIE, GAO HUIWANG, et al, 2017. Contributions of physical and biogeochemical processes to phytoplankton biomass enhancement in the surface and subsurface layers during the passage of Typhoon Damrey[J]. Journal of Geophysical Research: Biogeosciences, 122(1):212-229. doi: 10.1002/2016JG003331
[33]	RICCI L, PINARDI N, ZAVATARELLI M, et al, 2000. Numerical modelling of the Mediterranean Sapropel S1 ecosystem structure[J]. GeoLines Journal, 11:20-23.
[34]	ŞEN Z, 2012. Innovative trend analysis methodology[J]. Journal of Hydrologic Engineering, 17(9):1042-1046. doi: 10.1061/(ASCE)HE.1943-5584.0000556
[35]	TAKAHASHI M, HORI T, 1984. Abundance of picophytoplankton in the subsurface chlorophyll maximum layer in subtropical and tropical waters[J]. Marine Biology, 79(2):177-186. doi: 10.1007/BF00951826
[36]	TSENG C M, WONG G T F, LIN I I, et al, 2005. A unique seasonal pattern in phytoplankton biomass in low-latitude waters in the South China Sea[J]. Geophysical Research Letters, 32(8):L08608.
[37]	VICHI M, PINARDI N, MASINA S, 2007. A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory[J]. Journal of Marine Systems, 64(1-4):89-109. doi: 10.1016/j.jmarsys.2006.03.006
[38]	WU HAO, QIAN HUI, 2017. Innovative trend analysis of annual and seasonal rainfall and extreme values in Shaanxi, China, since the 1950s[J]. International Journal of Climatology, 37(5):2582-2592. doi: 10.1002/joc.2017.37.issue-5

参数	单位	取值	参考文献
光透射系数k	m^-1	0.05	Chen et al, 2004; Chen, 2005
温度系数Q₁₀	d^-1	2.0	Vichi et al, 2007
Von Karman常数	–	0.4	Vichi et al, 2007
科氏参数	–	5.592×10^-5	–
Redfield N:C	Mmol N·mg^-1C^-1	0.0126	Vichi et al, 2007
Redfield P:C	mmol P·mg^-C^-1	0.768×10^-3	Vichi et al, 2007

参数	单位	取值
参数	单位	硅藻	鞭毛藻	微微型浮游植物	参考文献
最大生长率(10℃)	d^-1	2.1	2.1	2.1	Liu et al, 2002; Liu et al, 2007
呼吸率(10℃)	d^-1	0.15	0.1	0.1	Allen et al, 1998
排泄率	–	0.05	0.2	0.2	Vichi et al, 2007
最小N:C	mmol N·mg^-1 C^-1	0.00687	0.00687	0.00687	Vichi et al, 2007
最大N:C	mmol N·mg^-1 C^-1	0.0252	0.0252	0.0252	Vichi et al, 2007
最小P:C	mmol P·mg^-1 C^-1	0.428×10^-3	0.428×10^-3	0.428×10^-3	Vichi et al, 2007
最大P:C	mmol P·mg^-1 C^-1	0.157×10^-2	0.157×10^-2	0.157×10^-2	Vichi et al, 2007
对NO₃的吸收速率	mg^-1 C^-1·d^-1	0.0025	0.0025	0.0025	Vichi et al, 2007
对NH₄的吸收速率	mg^-1 C^-1·d^-1	0.0025	0.01	0.02	Vichi et al, 2007
对PO₄的吸收速率	mg^-1 C^-1·d^-1	0.0025	0.0025	0.0025	Vichi et al, 2007
沉降速率	m·d^-1	1.0	0.5	0.1	Lu et al, 2010
最小溶解率	d^-1	0.05	0.05	0.05	Vichi et al, 2007
Si摄入常数	mmol Si·m^-3	0.3	–	–	Vichi et al, 2007
标准Si:C	mmol Si·mg^-1 C^-1	0.03	–	–	Vichi et al, 2007
叶绿素a的转换因子	mg C·mg^-1 Chl-a^-1	25.0	50.0	50.0	Vichi et al, 2007

参数	单位	取值			参考文献
参数		异养鞭毛藻	微型浮游动物	中型浮游动物	参考文献
最大生长率(10℃)	d^-1	1.0	0.5	1.0	Liu et al, 2007; Vichi et al, 2007
同化作用效率	–	0.4	0.5	0.5	Vichi et al, 2007
呼吸率(10℃)	d^-1	0.02	0.02	0.02	Vichi et al, 2007
排泄率	–	0.5	0.5	0.5	Vichi et al, 2007
死亡率	d^-1	0.25	0.25	0.2	Vichi et al, 2007
最大N:C	mmol N·mg^-1 C^-1	0.0167	0.0167	–	Vichi et al, 2007
最大P:C	mmol P·mg^-1 C^-1	0.001	0.001	–	Vichi et al, 2007

参数	单位	取值	参考文献
细菌最大生长率	d^-1	8.38	Vichi et al, 2007
细菌呼吸率(10℃)	d^-1	0.02	Vichi et al, 2007
细菌磷元素摄入的m氏常数	mmol P·m^-3	0.05	Vichi et al, 2007
细菌氮元素摄入的m氏常数	mmol N·m^-3	0.5	Vichi et al, 2007
细菌氧限制的半饱和值	–	0.31	Vichi et al, 2007
细菌死亡率	d^-1	0.05	Vichi et al, 2007
碎屑分解率	d^-1	0.01	Vichi et al, 2007
碎屑相对硝化率	d^-1	0.05	Vichi et al, 2007
碎屑C:N(Redfield)	–	6.62	Vichi et al, 2007

季节	SCMs特征因子	与SST相关性参数
季节	SCMs特征因子	P	r
春季	强度	< 0.001	0.51^*
	深度	< 0.001	0.86^*
	厚度	–	–
夏季	强度	0.06	–
	深度	0.02	0.26^*
	厚度	–	–
秋季	强度	< 0.001	0.69^*
	深度	< 0.001	0.81^*
	厚度	–	–