基于CMIP6模式分析北极典型海区浮游植物藻华模拟误差

杨美晴; 冯志轩; 宋洪军

doi:10.12284/hyxb2023115

基于CMIP6模式分析北极典型海区浮游植物藻华模拟误差

doi: 10.12284/hyxb2023115

杨美晴^1,,
冯志轩^{1, 2, ,},
宋洪军³

1.
华东师范大学河口海岸学国家重点实验室，上海 200241
2.
华东师范大学崇明生态研究院，上海 202162
3.
自然资源部第一海洋研究所海洋生态环境科学与技术重点实验室，山东青岛 266061

基金项目: 国家自然科学基金面上项目（42176225）；上海市浦江人才计划（20PJ1403100）；上海市“科技创新行动计划”自然科学基金（20ZR416300）；上海市科学技术委员会重点项目（21JC402500）。

详细信息

作者简介:
杨美晴（1998-），女，吉林省吉林市人，从事北冰洋生态系统过程与机制研究。E-mail：yangmeiqing9@163.com

通讯作者:
冯志轩，男，研究员，从事海洋多尺度物理与生态耦合过程的观测和模拟研究。E-mail： zxfeng@sklec.ecnu.edu.cn

中图分类号: S963.21⁺3；P941.62
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出版历程
- 收稿日期: 2022-11-07
- 修回日期: 2023-02-27
- 网络出版日期: 2023-08-08
- 刊出日期: 2023-07-01

Analyze simulation errors of phytoplankton blooms in typical Arctic seas based on CMIP6 models

1.
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China
2.
Institute of Eco-Chongming, East China Normal University, Shanghai 202162, China
3.
Key Laboratory of Marine Ecological Environment Science and Technology, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China

摘要

摘要: 在海冰覆盖的极地海区，浮游植物季节性藻华变化呈现典型的单峰特征。由于藻华过程受控于海冰、光照、混合层深度和营养盐供给等多个因素，其发生时间和强度在地球系统模式模拟结果中存在较大的不确定性。本研究选取11种CMIP6地球系统模式结果，以多种类型的观测资料和产品作为判断参考值，评估各模式结果能否准确模拟北极典型海区（巴伦支海、楚科奇海及白令海）浮游植物藻华动态的变化规律。通过计算能表征光照和营养盐限制的多个指标，分析表层叶绿素a浓度模拟结果的误差来源。结果表明，依据冰下光照时长、混合层变化速率、表层硝酸盐指标将11种模式分为3组，与参考值指标差异较小组别中的模式在藻华模拟方面明显占优，而其余模式在表层硝酸盐或混合层变化的模拟上存在较大误差，导致表层叶绿素a浓度峰值的发生时间延后且峰值浓度误差大。总体而言，地球系统模式配置中除要考虑光照和营养盐这两种基础限制条件外，也需关注由温盐控制的上混合层深度，从而准确模拟出表层叶绿素a浓度的季节性变化规律，上述研究为地球系统模式中相关参数化方案的改进提供了参考。
- 北冰洋 /
- 海冰 /
- 浮游植物藻华 /
- 上混合层 /
- 地球系统模式 /
- 第六次耦合模式比较计划
Abstract: Phytoplankton blooms in polar regions with seasonal sea ice cover show a unimodal seasonality. However, the bloom processes are controlled by multiple physical and biogeochemical factors, including sea ice, light availability, mixed layer depth, and nutrients; those may result in great uncertainties in simulating phytoplankton bloom by the Earth System Models (ESMs). In this study, the results of 11 Coupled Model Intercomparison Phase-6 (CMIP6) ESMs were analyzed and evaluated with various types of observational products in order to determine whether those ESMs can correctly model the phytoplankton blooms in three Arctic shelf seas, Barents Sea, Chukchi Sea, and Bering Sea. By calculating multiple indices that represent light and nutrient limitations, the error sources of simulated surface chlorophyll a concentrations were comprehensively analyzed. Our results show that the 11 ESMs can be divided into three groups based on ice-adjusted photoperiod, rate of change of mixed layer depth, and surface nitrate concentration. Some groups are characterized by the smallest bias between modeled indices and observation-based reference, and those ESMs perform best in simulating phytoplankton bloom characteristics. The other groups of ESMs differ significantly from the reference values in terms of surface nitrate and/or rate of change of mixed layer depth, resulting in delayed occurrences of annual chlorophyll a peak concentration and greater differences in corresponding peak values. In general, in addition to the two primary constraints of light and nutrients, the ESMs should also well represent the upper mixed layer controlled by temperature and salinity distributions, so as to accurately simulate the seasonal variation of surface chlorophyll a concentration. The above analyses indicate ESMs can be used in assessing polar planktonic ecosystems, and there is room for improving ecosystem-related parametrization in future ESM development.
- Arctic Ocean /
- sea ice /
- phytoplankton bloom /
- upper mixed layer /
- earth system models /
- CMIP6

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图 1 研究区域划分

Fig. 1 Division of the study area

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图 2 本研究的技术方法

Fig. 2 Technology road of this study

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图 3 巴伦支海多年平均海冰密集度分布

Fig. 3 Mean sea ice concentration in the Barents Sea

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图 4 楚科奇海（65°～70°N）及白令海（55°～65°N）多年平均海冰密集度分布

Fig. 4 Mean sea ice concentration in the Chukchi Sea (65°−70°N) and Bering Sea (55°−65°N)

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图 5 CMIP6模式模拟巴伦支海（a）、楚科奇海（b）和白令海（c）30年平均海冰空间能力的泰勒图

Fig. 5 Taylor diagram of CMIP6 model simulations of 30-year seasonal mean sea ice distribution in the Barents Sea (a), Chukchi Sea (b), and Bering Sea (c)

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图 6 海冰面积、冰下光照时长、混合层深度、表层硝酸盐浓度和表层叶绿素a浓度在巴伦支海、楚科奇海和白令海的气候态月平均曲线

Fig. 6 Monthly climatology of sea ice area, light duration under ice, mixed layer depth, surface nitrate concentration, and surface chlorophyll a concentration in the Barents Sea, Chukchi Sea, and Bering Sea

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图 7 巴伦支海（a）、楚科奇海（b）和白令海（c）地球系统模式参数模拟情况与分组

Fig. 7 Simulation and grouping of earth system model parameters in the Barents Sea (a), Chukchi Sea (b), and Bering Sea (c)

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表 1 本研究所选取的11种地球系统模式及其配置特点

Tab. 1 List of eleven earth system models and their configuration characteristics

模式名称	国家	主要耦合模块	网格数（经向 × 纬向 × 垂向）	参考文献
ACCESS-ESM1-5	澳大利亚	大气、气溶胶、海洋、陆地、海冰、海洋生地化	360 × 300 × 50	Ziehn等^[21]
CESM2	美国	大气、气溶胶、大气化学、海洋、陆地、海冰、海洋生地化、冰架	320 × 384 × 60	Danabasoglu^[22]
CMCC-ESM2	意大利	大气、气溶胶、海洋、陆地、海冰、海洋生地化	362 × 292 × 50	Lovato等^[23]
CNRM-ESM2-1	法国	大气、气溶胶、大气化学、海洋、陆地、海冰、海洋生地化	362 × 294 × 75	Séférian等^[24]
CanESM5	加拿大	大气、气溶胶、大气化学、海洋、陆地、海冰、海洋生地化、冰架	360 × 290 × 45	Sospedra-Alfonso等^[25]
GFDL-ESM4	美国	大气、气溶胶、大气化学、海洋、陆地、海冰、海洋生地化、冰架	720 × 576 × 75	Dunne等^[26]
IPSL-CM6A-LR	法国	大气、海洋、陆地、海冰、海洋生地化	362 × 332 × 75	Boucher等^[27]
MIROC-ES2L	日本	大气、气溶胶、大气化学、海洋、陆地、海冰、海洋生地化	360 × 256 × 63	Hajima等^[28]
MPI-ESM1-2-HR	德国	大气、海洋、陆地、海冰、海洋生地化	802 × 404 × 40	Müller等^[29]
MPI-ESM1-2-LR	德国	大气、海洋、陆地、海冰、海洋生地化	256 × 220 × 40	Müller等^[29]
UKESM1-0-LL	英国	大气、气溶胶、大气化学、海洋、陆地、海冰、海洋生地化	360 × 330 × 75	Froster等^[30]

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表 2 CMIP6地球系统模式月平均指标的均方根误差

Tab. 2 Root mean square errors of monthly mean indices derived from CMIP6 ESMs

海区	模式名称	冰下日照时长/h	表层硝酸盐浓度/ （mmol·m⁻³）	混合层变化速率/ （m·mon⁻¹）	表层叶绿素a浓度/ （mg·m⁻³）	叶绿素a浓度峰值出现月份
巴伦支海	ACCESS-ESM1-5	0.518	5.63	37.95	0.47	7
	CESM2	1.878	1.15	28.47	0.73	6−7
	CMCC-ESM2	2.568	1.56	48.98	0.47	5
	CNRM-ESM2-1	0.349	5.35	32.76	0.25	6
	CanESM5	3.190	3.51	28.04	0.23	5
	GFDL-ESM4	0.789	1.00	30.05	0.18	5
	IPSL-CM6A-LR	0.769	1.47	29.84	0.12	5
	MIROC-ES2L	0.904	3.01	51.43	0.40	6
	MPI-ESM1-2-HR	0.888	1.18	35.75	0.90	6−7
	MPI-ESM1-2-LR	0.878	4.21	38.91	0.49	5
	UKESM1-0-LL	4.513	1.29	38.36	0.26	6
楚科奇海	ACCESS-ESM1-5	0.819	16.89	16.61	0.32	7
	CESM2	0.864	2.95	11.87	0.78	6
	CMCC-ESM2	2.599	5.11	12.92	0.75	6
	CNRM-ESM2-1	1.576	3.81	11.20	0.46	7−8
	CanESM5	3.070	4.43	10.76	0.18	6
	GFDL-ESM4	2.016	6.33	10.25	1.76	6
	IPSL-CM6A-LR	0.693	4.80	10.73	0.24	9
	MIROC-ES2L	1.776	3.08	12.29	0.19	6
	MPI-ESM1-2-HR	1.086	4.46	11.33	1.56	7
	MPI-ESM1-2-LR	2.654	6.22	10.82	0.45	7
	UKESM1-0-LL	3.438	19.04	14.17	0.55	6
白令海	ACCESS-ESM1-5	0.642	13.31	14.48	0.53	7
	CESM2	1.327	4.61	12.42	1.33	5
	CMCC-ESM2	1.439	8.58	15.95	0.36	5
	CNRM-ESM2-1	0.666	7.48	14.38	0.20	5
	CanESM5	1.861	6.15	16.56	0.40	4
	GFDL-ESM4	0.630	5.57	11.76	0.51	5
	IPSL-CM6A-LR	0.867	9.30	11.87	0.28	5
	MIROC-ES2L	0.525	4.55	13.76	0.32	6
	MPI-ESM1-2-HR	0.485	2.86	14.55	1.81	6
	MPI-ESM1-2-LR	1.340	3.99	11.50	0.59	5
	UKESM1-0-LL	3.635	9.04	15.64	0.39	5

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表 3 K均值聚类对CMIP6模式分组结果与相应质心

Tab. 3 Grouping of CMIP6 ESMs based on K-means clustering and corresponding centroids

海区	组别	模式	质心坐标
巴伦支海	第一组	ACCESS-ESM1-5、CNRM-ESM2-1、 MPI-ESM1-2-HR、MPI-ESM1-2-LR、 UKESM1-0-LL	（1.429，3.532，36.743）
	第二组	CMCC-ESM2、MIROC-ES2L	（1.736，2.286，50.208）
	第三组	CESM2、CanESM5、GFDL-ESM4、 IPSL-CM6A-LR	（1.657，1.779，29.097）
楚科奇海	第一组	ACCESS-ESM1-5、UKESM1-0-LL	（1.432，4.035，11.725）
	第二组	CESM2、CMCC-ESM2、CNRM-ESM2-1、IPSL-CM6A-LR、MIROC-ES2L、 MPI-ESM1-2-HR	（2.128，17.961，15.394）
	第三组	CanESM5、GFDL-ESM4、 MPI-ESM1-2-LR	（2.583，5.523，10.615）
白令海	第一组	CMCC-ESM2、CNRM-ESM2-1、 CanESM5、UKESM1-0-LL	（1.900，7.812，15.631）
	第二组	ACCESS-ESM1-5、IPSL-CM6A-LR	（0.755，11.305，13.178）
	第三组	CESM2、GFDL-ESM4、MIROC-ES2L、 MPI-ESM1-2-HR、MPI-ESM1-2-LR	（0.861，4.318，12.798）

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