留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

南极罗斯海区域高分辨率海洋-海冰-生态系统耦合模式构建与评估

朱龙兴 罗晓凡 赵伟 张永莉 魏皓

朱龙兴,罗晓凡,赵伟,等. 南极罗斯海区域高分辨率海洋-海冰-生态系统耦合模式构建与评估[J]. 海洋学报,2026,48(x):1–18 doi: 10.12284/hyxb20260000
引用本文: 朱龙兴,罗晓凡,赵伟,等. 南极罗斯海区域高分辨率海洋-海冰-生态系统耦合模式构建与评估[J]. 海洋学报,2026,48(x):1–18 doi: 10.12284/hyxb20260000
Longxing Zhu,Xiaofan Luo,Wei Zhao, et al. Development and evaluation of a regional high-resolution coupled ocean-sea ice-ecosystem model for the Ross Sea, Antarctica[J]. Haiyang Xuebao,2026, 48(x):1–18 doi: 10.12284/hyxb20260000
Citation: Longxing Zhu,Xiaofan Luo,Wei Zhao, et al. Development and evaluation of a regional high-resolution coupled ocean-sea ice-ecosystem model for the Ross Sea, Antarctica[J]. Haiyang Xuebao,2026, 48(x):1–18 doi: 10.12284/hyxb20260000

南极罗斯海区域高分辨率海洋-海冰-生态系统耦合模式构建与评估

doi: 10.12284/hyxb20260000
基金项目: 国家重点研发计划(2023YFC3107702);国家自然科学基金项目(41941008,42476265)。
详细信息
    作者简介:

    朱龙兴(2000—),男,江西省赣州市人,从事罗斯海海洋生态动力学模拟方面研究。E-mail:zlxybd@tju.edu.cn

    通讯作者:

    罗晓凡(1987—),女,副教授,主要从事极地冰区海洋生态动力学模拟方面研究。E-mail:xiaofan.luo@tju.edu.cn

    魏皓(1964—),女,教授,主要从事物理海洋学、海洋生态动力学研究。E-mail:hao.wei@tju.edu.cn

  • 中图分类号: XXXX

Development and evaluation of a regional high-resolution coupled ocean-sea ice-ecosystem model for the Ross Sea, Antarctica

  • 摘要: 深入理解低营养层生态系统对环境变化的响应机理是制定科学合理的海洋保护区规划、预测海洋生态系统未来演变的重要前提。数值模拟作为探究机理的有效工具之一,因其涉及多参数化过程,仍需持续优化与改进。本研究旨在发展并评估一套适用于南极罗斯海区域的高分辨率三维海洋-海冰-生态系统耦合模式(Ross Sea coupled Ocean-Sea ice-Ecosystem model,简称ROSE)。在系统梳理现有罗斯海生态模式发展的基础上,基于NEMOv3.6(version 3.6 of the Nucleus for European Modeling of the Ocean)海洋模式、LIM3(version 3 of the Louvain-la-Neuve Sea Ice Model)海冰模式和PISCESv2(the Pelagic Interactions Scheme for Carbon and Ecosystem Study - volume 2)海洋碳循环-生态模式构建了ROSE模式。通过对海冰动力学相关参数的系统调试,发现调整冰-海拖曳系数可显著提升ROSE模式对罗斯海典型生境特征—沿岸冰间湖的模拟效果。目前,基于ROSE模式已完成了2010–2020年的后报模拟,本文结合观测数据及已有研究成果,对ROSE模拟的海冰时空变化、海洋水文特征、溶解铁和叶绿素a浓度开展了较为细致的评估。结果表明,ROSE模式能够较为准确地再现上述关键状态变量的典型特征,具备进一步解析罗斯海近期环境变化驱动机制及低营养层生态过程响应规律的能力。
  • 图  1  罗斯海水深及5个航次观测站位示意图

    来自NBP1310航次的断面a和NBP1201航次的断面b分别以绿线和红线标记。灰色线表示500、1000、2000和4000 m等深线。VL(Victoria Land)为维多利亚地,CI(Coulman Island)为库尔曼岛,AIT(Aviator ice tongue)为飞行员冰舌,TNB(Terry Nova Bay)为特拉诺瓦湾,DT(Drygalski Trough)为德里加尔斯基海槽,MB(Mawson Bank)为莫森浅滩,JT(Joides Trough)为卓艾德斯海槽,PB(Pennell Bank)为彭内尔浅滩,RI(Ross Island)为罗斯岛,IB(Iselin Bank)为伊斯林浅滩,GCT(Glomar Challenger Trough)为格洛玛挑战者海槽,HaB(Hayes Bank)为海斯浅滩,HoB(Houtz Bank)为霍茨浅滩,CC(Cape Colbeck)为科尔贝克角

    Fig.  1  Bathymetry in the Ross Sea with locations of in-situ measurements from five cruises

    Transects a and b from cruises of NBP1310 and NBP1201 are marked with green and red lines, respectively. Grey contours denote the 500, 1 000, 2 000, and 4 000 m isobaths. VL: Victoria Land, CI: Coulman Island, AIT: Aviator ice tongue, TNB: Terra Nova Bay, DT: Drygalski Trough, MB: Mawson Bank, JT: Joides Trough, PB: Pennnell Bank, RI: Ross Island, IB: Iselin Bank, GCT: Glomar Changer Trough, HaB: Hayes Bank, HoB: Houtz Bank, CC: Cape Colbeck

    图  2  以LIM3中参数默认值(rn_cio = 5×10−3,rn_pstar = 2×104 N·m−2,rn_crhg = 20)20%为间隔,对(a)rn_cio,(b)rn_pstar和rn_crhg进行参数值调整的敏感实验

    在图(a)中,红色条和蓝色条分别表示偏差和均方根误差;在图(b)中,黑色和圈内颜色表示偏差,蓝色数字表示均方根误差

    Fig.  2  Sensitive experiments with varying (a) rn_cio, (b) rn_pstar and rn_crhg at an interval of 20% of their default values (rn_cio = 5×10−3, rn_pstar = 2×104 N m−2, rn_crhg = 20) used in LIM3

    Red and blue bars in (a) and black (and color shading spots) and blue numbers in (b) indicate the bias and RMSE, respectively

    图  3  ROSE模式生态模块示意图(改编自Zhang等[17];缩写含义见正文)

    Fig.  3  Schematic diagram of the ecology module in ROSE (Adapted from Zhang et al., 2023; Full meaning of abbreviations can be found in maintext)

    图  4  2010–2010年罗斯海海冰覆盖面积(a)多年平均周年循环,(b)逐月变化,(c)12月的年际变化,以及(d–f)基于OSTIA,ROSE和SOSE多年平均的12月海冰密集度的空间分布

    Fig.  4  (a) Annual cycle of multi-year averaged, (b) monthly time series, and (c) yearly variations of December sea ice area over 2010–2010 in the Ross Sea. Spatial distribution of December sea ice concentration based on (d) OSTIA, (e) ROSE and (f) SOSE

    图  5  航次观测数据及与之时空对应的ROSE模式结果的(a)温度和(b)盐度的泰勒图,(c)航次观测和(d)ROSE模式水深2 000 m以浅的温盐点聚图,沿断面a(见图1)的(e)温度和(f)盐度垂直分布

    在图(a)和(b)中,红色文本标注了模式温度和盐度的偏差范围。图(c–d)中散点颜色表示水深,黑色实线为中性密度28.00和28.27 kg m−3,蓝色虚线为海水冰点。图(e–f)中背景颜色为ROSE模拟结果,散点为NBP1310航次观测数据,红色虚线框区域表示OSTIA反演的12月冰间湖(海冰密集度SIC < 25%)

    Fig.  5  Taylor diagrams of (a) temperature and (b) salinity between cruise measurements and corresponding model results. Potential temperature-salinity scatter plot based on (c) cruise measurements and (d) corresponding ROSE simulations within water column shallower than 2000 m in the Ross Sea. Vertical distribution of (e) temperature and (f) salinity along the transect a

    Biases for model simulated temperature and salinity are respectively shown in red in (a) and (b). In (c–d), color shading of scatters represents the water depth. Solid black curves show the neutral density of 28.00 and 28.27 kg m−3. The dashed blue line indicates the freezing point of seawater. In (e–f), color shading represents the ROSE simulation. Scatters denote the observations from the NBP1310 cruise. The red dashed lines highlight the OSTIA-reconstructed polynya in December SIC <25%)

    图  6  模式与TPXO数据K1和M2分潮对比

    黑色等值线代表迟角,灰色等值线代表水深,背景颜色代表振幅

    Fig.  6  Comparison of K1 and M2 tidal constituents between Rose and TPXO

    Black contours denote the phase lag, gray contours denote the depth, and the color shading indicates amplitude

    图  7  2010–2020年水柱平均的多年平均流场

    在陆架区域,出流用红色表示、入流用蓝色表示。颜色阴影和箭头分别指示流速大小和流向。灰色等高线为1 000 m和3 000 m等深线,粗黑色等高线为500 m等深线,缩写含义见图1

    Fig.  7  Multi-year mean flow field averaged in the whole water column over 2010–2020

    Outflow is indicated in red and inflow in blue over the shelf. Color shading and arrow respectively indicate the speed magnitude and flow direction. Gray contours denote the 1 000 and 3 000 m isobaths. Bold black contour denotes the 500 m isobath. Full meaning of abbreviations can be found in Fig.1 caption

    图  8  (a)观测和(b)ROSE模拟的沿NBP1310航次断面a的DFe偏离值垂直分布,(c)为观测与模拟DFe的散点图及两者相关系数和均方根误差。(d–f)为NBP1201航次断面b的结果

    偏离值定义为真实值与区域平均值的差值。表层浓度(红色标出)为25 m以浅水域深度平均的DFe浓度

    Fig.  8  Vertical distribution of (a) observed and (b) the corresponding simulated DFe anomalies along transect a from the NBP1310 cruise. (c) Scatter plots of observed and simulated DFe concentrations (true values) with correlation coefficient and RMSE. (d–f) Same as (a–c) but for transect b from the NBP1201 cruise.

    Anomaly is the difference between the true value and regional mean. Surface concentration (shown in red) is defined as the average DFe concentration within shallow water of 25 m

    图  9  (a)基于OC-CCI的(黑色虚线)和ROSE模拟的(黑色实线)RISP内Chl-a浓度月变化。(b)ROSE模拟的RISP内南极棕囊藻(PHA)到硅藻(DIA)的季节性演替。(c–d)分别为航次观测和ROSE模拟的50 m以浅水域深度平均Chl-a浓度。(e–f)分别为NBP1201航次断面b和ROSE模拟的Chl-a浓度偏离值的垂直结构

    图(c–d)中,红色线表示NBP1201航次的断面b,黑色虚线为NBP1302航次中与断面b相近的一条断面。偏离值为真实值与区域平均值的差值

    Fig.  9  (a) OC-CCI satellite-based (black dashed lines) and the ROSE-simulated (black solid lines) annual cycle of Chlorophyll-a concentrations, and (b) ROSE-simulated seasonal succession from Phaeocystis antarctica to diatoms in the Ross Ice Shelf Polynya (RISP). (c–d) Depth-mean Chl-a concentrations in the upper 50 m layer based on cruise measurements and corresponding results from the ROSE model. (e–f) Vertical distribution of the observed and the simulated Chl-a anomalies along transect b from the NBP1201 cruise

    Red lines in (c) and (d) indicate transect b, while black dashed lines indicate the transect from the NBP1302 cruise. Anomaly is the difference between the true value and regional mean

    表  1  南大洋部分生态模式

    Tab.  1  Ecological modelling applications covering the Ross Sea

    模式名称 特点 主要状态变量 研究问题 参考文献
    RISPEM 箱式模式 4种营养盐(2种N,1种Si,1种P),DFe,2种Phyto,2种Zoo,2种碎屑,碳化学模块 浮游植物水华和群落演替的影响因素 文献[17]
    POOZ 一维模式 3种营养盐(2种N,1种Si),2种Phyto,2种Zoo,1种细菌,2种碎屑 开阔水域的Si和N循环 文献[40]
    SWAMCO 一维模式 4种营养盐(2种N,1种Si,1种P),DFe,2种Phyto(SWAMCO4为4种),1种Zoo,细菌 充足的光和DFe是水华的必要条件 文献[4041]
    MEDUSA-RS 一维模式 2种营养盐(1种N,1种Si),DFe,2种Phyto,2种Zoo,2种碎屑 21世纪罗斯海初级生产和深海碳
    输出的变化
    文献[42]
    ERSEM 一维模式 4种营养盐(2种N,1种Si,1种P),DFe,4种Phyto,3种Zoo,3种碎屑,碳化学模块 光和铁对初级生产和浮游植物群落结构的影响 文献[4344]
    CIAO 三维模式,分辨率25–180 km,垂向23层 1种营养盐(1种N),DFe,2种Phyto,1种Zoo,1种碎屑 DFe在罗斯海初级生产和浮游植物
    群落结构时空变化中的作用
    文献[4849, 53]
    PlankTOM10 三维全球模式,经向分辨率2°纬向分辨率平均1.5°,垂向30层 3种营养盐(1种Si,1种P,1种N),DFe,6种Phyto,4种Zoo,3种碎屑,碳化学模块 上行控制和下行控制对浮游植物的
    影响
    文献[54]
    SIESTA 三维模式,水平分25 km,垂向最低0.02 m 4种营养盐(2种N,1种Si,1种P),1种冰藻,1种碎屑 冰藻的冰下生产 文献[5556]
    BEC 三维全球模式,经向分辨率3.6°,纬向分辨率0.9–2.0°,垂向25层 4种营养盐(2种N,1种Si,1种P),DFe,5种Phyto,1种Zoo,1种碎屑 不同浮游植物对初级生产的贡献,
    及其面临的上行控制
    文献[4546]
    MEDUSA 三维全球模式,水平分辨率1°,垂向64层 2种营养盐(1种N,1种Si),DFe,2种Phyto,2种Zoo,2种碎屑(2.0版本加入DIC,TA,DO和POC) 全球变化下的生物泵和深海碳输出 文献[5758]
    ROMS-BEC 三维模式,水平分辨率1/4°,垂向64层 4种营养盐(2种N,1种Si,1种P),DFe,5种Phyto,1种Zoo,1种碎屑,碳化学模块 棕囊藻对碳循环的贡献,以及下行
    控制对浮游植物影响
    文献[16, 47]
    KMBM3 三维全球模式,水平分辨率1.8°×3.6°,垂向19层 3种营养盐(1种N,1种Si,1种P),DFe,4种Phyto,1种Zoo,2种碎屑,碳化学模块 南大洋初级生产和DFe的未来变化 文献[5960]
      注:N表示氮盐,Si表示硅酸盐,P表示磷酸盐,DFe为溶解铁,Phyto表示浮游植物,Zoo表示浮游动物
      Note: N denotes nitrate, Si denotes silicate, P denotes phosphate, DFe denotes dissolved iron, Phyto denotes phytoplankton, Zoo denotes zooplankton.
    下载: 导出CSV

    表  2  ROSE构建所用数据集

    Tab.  2  Dataset used for ROSE configuration

    数据集 所用变量 时空分辨率 数据来源
    EAR5 风速,气温和湿度,短波与长波辐射,降水及降雪 1/4°×1/4°,逐时 https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5
    Bedmap2 水深和冰架 1 km https://www.bas.ac.uk/project/bedmap-2/#data
    HYCOM 海洋温度,盐度,海冰,海面高度和水平流场 1/12°×1/12°,41层,逐日 https://www.hycom.org/
    Glo-BGC 营养盐,溶解氧和溶解铁 1/4°×1/4°,75层,逐日 https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_BGC_001_029/description
    GLODAP 溶解无机碳和总碱度 1°×1°,33层,逐月 https://glodap.info/
    TPXO9 五个分潮潮流 1/30°×1/30° https://www.tpxo.net/tpxo-products-and-registration
    下载: 导出CSV

    表  3  ROSE海冰模块和生态模块参数,P和D代表南极棕囊藻和硅藻,Z和M代表小型浮游动物和中型浮游动物

    Tab.  3  Key parameters for sea ice and planktonic organisms and their values in ROSE (P: Phaeocystis antarctica, D: Diatoms, Z: Microzooplankton, M: Mesozooplankton)

    参数符号 单位 参数名称
    海冰模块
    m_cio 3×10−3 / 冰-海拖曳系数
    m_pstar 1.2×104 N m−2 冰抗压强度
    m_crhg 12 / 冰强度衰减常数
    P,D
    ${ \mu_{\max }^{0}} $ 2.0,1.2 d−1 0°C生长率
    $ {{\alpha}^I} $ 1.2,0.6 (W m−2)−1 d−1 P-I斜率
    ${ {S}_{rat}^{I} }$ 4.5,3.0 / 细胞大小比例
    $ {{K}_{{Fe}}^{I,\min }} $ 1.0,0.4 nmol Fe L−1 铁最小半饱和常数
    $ {\theta_{\max }^{{Fe,I}} }$ 40,20 µmol Fe (mol C)−1 最大铁碳比
    $ {\theta_{{opt}}^{{Fe,I}}} $ 17,7 µmol Fe (mol C)−1 最优铁碳比
    ${ {P}_I^Z }$ 0.9,0.9 / 小型浮游动物偏好
    $ {{P}_I^M} $ 0.65,2.00 / 中型浮游动物偏好
    ZM
    ${ {g}_{\max }^{{X}}} $ 1.80,0.575 d−1 最大摄食速率
    $ {{P}_{{Z}}^{{M}}} $ 2.5 / 中型对微型浮游动物偏好
    ${ {K}_{{G}}^{{X}} }$ 10,20 µmol C L−1 摄食半饱和常数
    下载: 导出CSV

    表  4  ROSE评估所用数据集

    Tab.  4  Dataset used for ROSE evaluation

    覆盖时间 评估对象 来源
    数据集 类别
    SOSE 模式(含同化) 2010.1.1–2020.12.31 逐日 海冰密集度 http://sose.ucsd.edu/
    OSTIA 卫星 2010.1.1–2020.12.31 逐日 海冰密集度 https://doi.org/10.48670/moi-00168
    OC-CCI 卫星 2010.1.1–2020.12.31 逐日 叶绿素a https://climate.esa.int/en/projects/ocean-colour/data/
    TPXO 模式 2010.1.1–2020.12.31 逐日 潮汐 https://www.tpxo.net/tpxo-products-and-registration
    巡航项目 航次数据
    PRISM-RS NBP1201 2012.1.6–2012.2.5 温盐,叶绿素a,溶解铁 https://www.bco-dmo.org/project/2155
    TRACERS NBP1302 2013.2.3–2013.3.18 温盐,叶绿素a https://www.bco-dmo.org/project/547771
    Phantastic01 NBP1310 2013.12.20–2014.1.5 温盐,溶解铁 https://dataportal.nioz.nl/doi/10.25850/nioz/7b.b.r
    PIPERS NBP1704 2017.4.23–2017.5.28 温盐 https://www.bco-dmo.org/project/815403
    CICLOPS NBP1801 2017.12.30–2018.2.18 温盐,叶绿素a https://www.bco-dmo.org/project/774945
    下载: 导出CSV
  • [1] Arrigo K R, van Dijken G L, Bushinsky S. Primary production in the Southern Ocean, 1997-2006[J]. Journal of Geophysical Research: Oceans, 2008, 113(C8): C08004.
    [2] Goffart A, Catalano G, Hecq J H. Factors controlling the distribution of diatoms and Phaeocystis in the Ross Sea[J]. Journal of Marine Systems, 2000, 27(1/3): 161−175.
    [3] Smith Jr W O, Ainley D G, Arrigo K R, et al. The oceanography and ecology of the Ross Sea[J]. Annual Review of Marine Science, 2014, 6(1): 469−487. doi: 10.1146/annurev-marine-010213-135114
    [4] Arrigo K R, van Dijken G, Long M. Coastal Southern Ocean: a strong anthropogenic CO2 sink[J]. Geophysical Research Letters, 2008, 35(21): L21602. doi: 10.1029/2008gl035624
    [5] Arrigo K R, Weiss A M, Smith Jr W O. Physical forcing of phytoplankton dynamics in the southwestern Ross Sea[J]. Journal of Geophysical Research: Oceans, 1998, 103(C1): 1007−1021. doi: 10.1029/97JC02326
    [6] Smith Jr W O. Primary productivity measurements in the Ross Sea, Antarctica: a regional synthesis[J]. Earth System Science Data, 2022, 14(6): 2737−2747. doi: 10.5194/essd-14-2737-2022
    [7] Schoemann V, Becquevort S, Stefels J, et al. Phaeocystis blooms in the global ocean and their controlling mechanisms: a review[J]. Journal of Sea Research, 2005, 53(1/2): 43−66. doi: 10.1016/j.seares.2004.01.008
    [8] Sweeney C, Hansell D A, Carlson C A, et al. Biogeochemical regimes, net community production and carbon export in the Ross Sea, Antarctica[J]. Deep Sea Research Part II: Topical Studies in Oceanography, 2000, 47(15/16): 3369−3394. doi: 10.1016/s0967-0645(00)00072-2
    [9] Richardson K, Beardall J, Raven J A. Adaptation of unicellular algae to irradiance: an analysis of strategies[J]. New Phytologist, 1983, 93(2): 157−191. doi: 10.1111/j.1469-8137.1983.tb03422.x
    [10] van Hilst C M, Smith Jr W O. Photosynthesis/irradiance relationships in the Ross Sea, Antarctica, and their control by phytoplankton assemblage composition and environmental factors[J]. Marine Ecology Progress Series, 2002, 226: 1−12. doi: 10.3354/meps226001
    [11] Alderkamp A C, van Dijken G L, Lowry K E, et al. Effects of iron and light availability on phytoplankton photosynthetic properties in the Ross Sea[J]. Marine Ecology Progress Series, 2019, 621: 33−50. doi: 10.3354/meps13000
    [12] Smith W O, Nelson D M. Phytoplankton bloom produced by a receding ice edge in the Ross Sea: spatial coherence with the density field[J]. Science, 1985, 227(4683): 163−166. doi: 10.1126/science.227.4683.163
    [13] McGillicuddy Jr D J, Sedwick P N, Dinniman M S, et al. Iron supply and demand in an Antarctic shelf ecosystem[J]. Geophysical Research Letters, 2015, 42(19): 8088−8097. doi: 10.1002/2015GL065727
    [14] Kustka A B, Kohut J T, White A E, et al. The roles of MCDW and deep water iron supply in sustaining a recurrent phytoplankton bloom on central Pennell Bank (Ross Sea)[J]. Deep Sea Research Part I: Oceanographic Research Papers, 2015, 105: 171−185. doi: 10.1016/j.dsr.2015.08.012
    [15] Marsay C M, Barrett P M, McGillicuddy Jr D J, et al. Distributions, sources, and transformations of dissolved and particulate iron on the Ross Sea continental shelf during summer[J]. Journal of Geophysical Research: Oceans, 2017, 122(8): 6371−6393. doi: 10.1002/2017JC013068
    [16] Nissen C, Vogt M. Factors controlling the competition between Phaeocystis and diatoms in the Southern Ocean and implications for carbon export fluxes[J]. Biogeosciences, 2021, 18(1): 251−283. doi: 10.5194/bg-18-251-2021
    [17] Zhang Yongli, Zhao Wei, Wei Hao, et al. Iron limitation and uneven grazing pressure on phytoplankton co-lead the seasonal species succession in the Ross Ice Shelf Polynya[J]. Journal of Geophysical Research: Oceans, 2023, 128(3): e2022JC019026. doi: 10.1029/2022JC019026
    [18] Arteaga L A, Boss E, Behrenfeld M J, et al. Seasonal modulation of phytoplankton biomass in the Southern Ocean[J]. Nature Communications, 2020, 11(1): 5364. doi: 10.1038/s41467-020-19157-2
    [19] Parkinson C L. A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic[J]. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116(29): 14414−14423. doi: 10.3410/f.736084233.793562060
    [20] DuVivier A K, Molina M J, Deppenmeier A L, et al. Projections of winter polynyas and their biophysical impacts in the Ross Sea Antarctica[J]. Climate Dynamics, 2024, 62(2): 989−1012. doi: 10.1007/s00382-023-06951-z
    [21] Castagno P, Falco P, Dinniman M S, et al. Temporal variability of the Circumpolar Deep Water inflow onto the Ross Sea continental shelf[J]. Journal of Marine Systems, 2017, 166: 37−49. doi: 10.1016/j.jmarsys.2016.05.006
    [22] Montes-Hugo M, Doney S C, Ducklow H W, et al. Recent changes in phytoplankton communities associated with rapid regional climate change along the western Antarctic Peninsula[J]. Science, 2009, 323(5920): 1470−1473. doi: 10.1126/science.1164533
    [23] Smith Jr W O, Sedwick P N, Arrigo K R, et al. The Ross Sea in a sea of change[J]. Oceanography, 2012, 25(3): 90−103. doi: 10.5670/oceanog.2012.80
    [24] Porter D F, Springer S R, Padman L, et al. Evolution of the seasonal surface mixed layer of the Ross Sea, Antarctica, observed with autonomous profiling floats[J]. Journal of Geophysical Research: Oceans, 2019, 124(7): 4934−4953. doi: 10.1029/2018JC014683
    [25] Jacobs S S, Giulivi C F, Dutrieux P. Persistent Ross Sea freshening from imbalance West Antarctic ice shelf melting[J]. Journal of Geophysical Research: Oceans, 2022, 127(3): e2021JC017808. doi: 10.1029/2021JC017808
    [26] Smith Jr W O, Dinniman M S, Hofmann E E, et al. The effects of changing winds and temperatures on the oceanography of the Ross Sea in the 21st century[J]. Geophysical Research Letters, 2014, 41(5): 1624−1631. doi: 10.1002/2014GL059311
    [27] Wang Zhaomin. On the response of Southern Hemisphere subpolar gyres to climate change in coupled climate models[J]. Journal of Geophysical Research: Oceans, 2013, 118(3): 1070−1086. doi: 10.1002/jgrc.20111
    [28] Sullivan C W, Arrigo K R, McClain C R, et al. Distributions of phytoplankton blooms in the Southern Ocean[J]. Science, 1993, 262(5141): 1832−1837. doi: 10.1126/science.262.5141.1832
    [29] Peloquin J A, Smith Jr W O. Phytoplankton blooms in the Ross Sea, Antarctica: interannual variability in magnitude, temporal patterns, and composition[J]. Journal of Geophysical Research: Oceans, 2007, 112(C8): C08013. doi: 10.1029/2006jc003816
    [30] Smith Jr W O, Asper V, Tozzi S, et al. Surface layer variability in the Ross Sea, Antarctica as assessed by in situ fluorescence measurements[J]. Progress in Oceanography, 2011, 88(1/4): 28−45. doi: 10.1016/j.pocean.2010.08.002
    [31] Arrigo K R, Robinson D H, Worthen D L, et al. Phytoplankton community structure and the drawdown of nutrients and CO2 in the Southern Ocean[J]. Science, 1999, 283(5400): 365−367. doi: 10.1126/science.283.5400.365
    [32] Liu Xiao, Smith Jr W O. Physiochemical controls on phytoplankton distributions in the Ross Sea, Antarctica[J]. Journal of Marine Systems, 2012, 94: 135−144. doi: 10.1016/j.jmarsys.2011.11.013
    [33] Bolinesi F, Saggiomo M, Ardini F, et al. Spatial-related community structure and dynamics in phytoplankton of the Ross Sea, Antarctica[J]. Frontiers in Marine Science, 2020, 7: 574963. doi: 10.3389/fmars.2020.574963
    [34] Maier-Reimer E. Geochemical cycles in an ocean general circulation model. Preindustrial tracer distributions[J]. Global Biogeochemical Cycles, 1993, 7(3): 645−677. doi: 10.1029/93GB01355
    [35] Six K D, Maier-Reimer E. Effects of plankton dynamics on seasonal carbon fluxes in an ocean general circulation model[J]. Global Biogeochemical Cycles, 1996, 10(4): 559−583. doi: 10.1029/96GB02561
    [36] Friedlingstein P, Dufresne J L, Cox P M, et al. How positive is the feedback between climate change and the carbon cycle?[J]. Tellus B, 2003, 55(2): 692−700. doi: 10.3402/tellusb.v55i2.16765
    [37] Pasquer B, Metzl N, Goosse H, et al. What drives the seasonality of air-sea CO2 fluxes in the ice-free zone of the Southern Ocean: a 1D coupled physical-biogeochemical model approach[J]. Marine Chemistry, 2015, 177(Pt 3): 554-565.
    [38] Pondaven P, Fravalo C, Ruiz-Pino D, et al. Modelling the silica pump in the Permanently Open Ocean Zone of the Southern Ocean[J]. Journal of Marine Systems, 1998, 17(1/4): 587−619. doi: 10.1016/s0924-7963(98)00066-9
    [39] Martin J H, Gordon R M, Fitzwater S E. Iron in Antarctic waters[J]. Nature, 1990, 345(6271): 156−158. doi: 10.1038/345156a0
    [40] Lancelot C, Hannon E, Becquevort S, et al. Modeling phytoplankton blooms and carbon export production in the Southern Ocean: dominant controls by light and iron in the Atlantic sector in Austral spring 1992[J]. Deep Sea Research Part I: Oceanographic Research Papers, 2000, 47(9): 1621−1662. doi: 10.1016/S0967-0637(00)00005-4
    [41] Pasquer B, Laruelle G, Becquevort S, et al. Linking ocean biogeochemical cycles and ecosystem structure and function: results of the complex SWAMCO-4 model[J]. Journal of Sea Research, 2005, 53(1/2): 93−108. doi: 10.1016/j.seares.2004.07.001
    [42] Kaufman D E, Friedrichs M A M, Smith Jr W O, et al. Climate change impacts on southern Ross Sea phytoplankton composition, productivity, and export[J]. Journal of Geophysical Research: Oceans, 2017, 122(3): 2339−2359. doi: 10.1002/2016JC012514
    [43] Kwon Y S, La H S, Jung J, et al. Exploring the roles of iron and irradiance in dynamics of diatoms and Phaeocystis in the Amundsen Sea continental shelf water[J]. Journal of Geophysical Research: Oceans, 2021, 126(3): e2020JC016673. doi: 10.1029/2020JC016673
    [44] Kwon Y S, La H S, Kang H W, et al. A regional-scale approach for modeling primary production and biogenic silica export in the Southern Ocean[J]. Environmental Research, 2023, 217: 114811. doi: 10.1016/j.envres.2022.114811
    [45] Wang Shanlin, Moore J K. Incorporating Phaeocystis into a Southern Ocean ecosystem model[J]. Journal of Geophysical Research: Oceans, 2011, 116(C1): C01019.
    [46] Wang Shanlin, Moore J K. Variability of primary production and air-sea CO2 flux in the Southern Ocean[J]. Global Biogeochemical Cycles, 2012, 26(1): GB1008.
    [47] Nissen C, Vogt M, Münnich M, et al. Factors controlling coccolithophore biogeography in the Southern Ocean[J]. Biogeosciences, 2018, 15(22): 6997−7024. doi: 10.5194/bg-15-6997-2018
    [48] Worthen D L, Arrigo K R. A coupled ocean-ecosystem model of the Ross Sea. Part 1: interannual variability of primary production and phytoplankton community structure[M]//Ditullio G R, Dunbar R B. Biogeochemistry of the Ross Sea. Washington: American Geophysical Union, 2003: 93-105.
    [49] Arrigo K R, Worthen D L, Robinson D H. A coupled ocean-ecosystem model of the Ross Sea: 2. Iron regulation of phytoplankton taxonomic variability and primary production[J]. Journal of Geophysical Research: Oceans, 2003, 108(C7): 3231. doi: 10.1029/2001jc000856
    [50] Madec G, The NEMO Team. NEMO ocean engine[R]. The NEMO Team, 2008. (查阅网上资料, 未找到本条文献出版地信息, 不确定文献类型是否正确, 请确认)
    [51] Rousset C, Vancoppenolle M, Madec G, et al. The Louvain-La-Neuve sea ice model LIM3.6: global and regional capabilities[J]. Geoscientific Model Development, 2015, 8(10): 2991−3005. doi: 10.5194/gmd-8-2991-2015
    [52] Aumont O, Ethé C, Tagliabue A, et al. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies[J]. Geoscientific Model Development Discussions, 2015, 8(2): 1375−1509. doi: 10.5194/gmdd-8-1375-2015
    [53] Tagliabue A, Arrigo K R. Anomalously low zooplankton abundance in the Ross Sea: an alternative explanation[J]. Limnology and Oceanography, 2003, 48(2): 686−699. doi: 10.4319/lo.2003.48.2.0686
    [54] Le Quéré C, Rödenbeck C, Buitenhuis E T, et al. Saturation of the Southern Ocean CO2 sink due to recent climate change[J]. Science, 2007, 316(5832): 1735−1738. doi: 10.1126/science.1136188
    [55] Saenz B T, Arrigo K R. Annual primary production in Antarctic sea ice during 2005-2006 from a sea ice state estimate[J]. Journal of Geophysical Research: Oceans, 2014, 119(6): 3645−3678. doi: 10.1002/2013JC009677
    [56] Saenz B T, Arrigo K R. Simulation of a sea ice ecosystem using a hybrid model for slush layer desalination[J]. Journal of Geophysical Research: Oceans, 2012, 117(C5): C05007. doi: 10.1029/2011jc007544
    [57] Yool A, Popova E E, Anderson T R. Medusa-1.0: a new intermediate complexity plankton ecosystem model for the global domain[J]. Geoscientific Model Development, 2011, 4(2): 381−417. doi: 10.5194/gmd-4-381-2011
    [58] Yool A, Popova E E, Anderson T R. MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies[J]. Geoscientific Model Development, 2013, 6(5): 1767−1811. doi: 10.5194/gmd-6-1767-2013
    [59] Kvale K, Keller D P, Koeve W, et al. Explicit silicate cycling in the Kiel Marine Biogeochemistry Model version 3 (KMBM3) embedded in the UVic ESCM version 2.9[J]. Geoscientific Model Development Discussions, 2020, 2020: 1-46. (查阅网上资料, 未找到本条文献卷期页码信息, 请确认)
    [60] Saini H, Kvale K, Chase Z, et al. Southern Ocean ecosystem response to Last Glacial Maximum boundary conditions[J]. Paleoceanography and Paleoclimatology, 2021, 36(7): e2020PA004075. doi: 10.1029/2020PA004075
    [61] Zwally H J, Comiso J C, Gordon A L. Antarctic offshore leads and polynyas and oceanographic effects[M]//Jacobs S S. Oceanology of the Antarctic Continental Shelf. Washington: American Geophysical Union, 1985: 203-226.
    [62] Parish T R, Cassano J J, Seefeldt M W. Characteristics of the Ross Ice Shelf air stream as depicted in Antarctic Mesoscale Prediction System simulations[J]. Journal of Geophysical Research: Atmospheres, 2006, 111(D12): D12109. doi: 10.1029/2005jd006185
    [63] Docquier D, Massonnet F, Barthélemy A, et al. Relationships between Arctic sea ice drift and strength modelled by NEMO-LIM3.6[J]. The Cryosphere, 2017, 11(6): 2829−2846. doi: 10.5194/tc-11-2829-2017
    [64] Hibler III W D. A dynamic thermodynamic sea ice model[J]. Journal of Physical Oceanography, 1979, 9(4): 815−846. doi: 10.5194/tcd-3-1023-2009
    [65] Chikhar K, Lemieux J F, Dupont F, et al. Sensitivity of ice drift to form drag and ice strength parameterization in a coupled ice–ocean model[J]. Atmosphere-Ocean, 2019, 57(5): 329−349. doi: 10.1080/07055900.2019.1694859
    [66] Dong Chunming, Luo Xiaofan, Nie Hongtao, et al. Effect of compressive strength on the performance of the NEMO-LIM model in Arctic Sea ice simulation[J]. Journal of Oceanology and Limnology, 2023, 41(1): 1−16. doi: 10.1007/s00343-022-1241-z
    [67] Mazloff M R, Heimbach P, Wunsch C. An eddy-permitting Southern Ocean state estimate[J]. Journal of Physical Oceanography, 2010, 40(5): 880−899. doi: 10.1175/2009JPO4236.1
    [68] Donlon C J, Martin M, Stark J, et al. The operational sea surface temperature and sea ice analysis (OSTIA) system[J]. Remote Sensing of Environment, 2012, 116: 140−158. doi: 10.1016/j.rse.2010.10.017
    [69] Egbert G D, Erofeeva S Y. Efficient inverse modeling of barotropic ocean tides[J]. Journal of Atmospheric and Oceanic Technology, 2002, 19(2): 183−204. doi: 10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2
    [70] Sathyendranath S, Brewin R J W, Brockmann C, et al. An ocean-colour time series for use in climate studies: the experience of the ocean-colour climate change initiative (OC-CCI)[J]. Sensors, 2019, 19(19): 4285. doi: 10.3390/s19194285
    [71] Belo Couto A, Brotas V, Mélin F, et al. Inter-comparison of OC-CCI chlorophyll-a estimates with precursor data sets[J]. International Journal of Remote Sensing, 2016, 37(18): 4337−4355. doi: 10.1080/01431161.2016.1209313
    [72] Zhai Dongran, Beaulieu C, Kudela R M. Long-term trends in the distribution of ocean chlorophyll[J]. Geophysical Research Letters, 2024, 51(7): e2023GL106577. doi: 10.1029/2023GL106577
    [73] Martinez E, Gorgues T, Lengaigne M, et al. Reconstructing global chlorophyll-a variations using a non-linear statistical approach[J]. Frontiers in Marine Science, 2020, 7: 464. doi: 10.3389/fmars.2020.00464
    [74] Taylor K E. Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research: Atmospheres, 2001, 106(D7): 7183−7192. doi: 10.1029/2000JD900719
    [75] Radach G, Moll A. Review of three-dimensional ecological modeling related to the North Sea shelf system. Part II: model validation and data needs[J]. Oceanography and Marine Biology, 2006, 44: 1−60. doi: 10.1201/9781420006391-4
    [76] Dinniman M S, Klinck J M, Smith Jr W O. A model study of Circumpolar Deep Water on the West Antarctic Peninsula and Ross Sea continental shelves[J]. Deep Sea Research Part II: Topical Studies in Oceanography, 2011, 58(13/16): 1508−1523. doi: 10.1016/j.dsr2.2010.11.013
    [77] Dinniman M S, Klinck J M, Hofmann E E, et al. Effects of projected changes in wind, atmospheric temperature, and freshwater inflow on the Ross Sea[J]. Journal of Climate, 2018, 31(4): 1619−1635. doi: 10.1175/JCLI-D-17-0351.1
    [78] Wang Yufei, Zhou Meng, Zhang Zhaoru, et al. Seasonal variations in Circumpolar Deep Water intrusions into the Ross Sea continental shelf[J]. Frontiers in Marine Science, 2023, 10: 1020791. doi: 10.3389/fmars.2023.1020791
    [79] Orsi A H, Wiederwohl C L. A recount of Ross Sea waters[J]. Deep Sea Research Part II: Topical Studies in Oceanography, 2009, 56(13/14): 778−795. doi: 10.1016/j.dsr2.2008.10.033
    [80] Yan Liangjun, Wang Zhaomin, Liu Chengyan, et al. The salinity budget of the Ross Sea continental shelf, Antarctica[J]. Journal of Geophysical Research: Oceans, 2023, 128(3): e2022JC018979. doi: 10.1029/2022JC018979
    [81] Zhang Zhaoru, Xie Chuan, Wang Chuning, et al. The Ross Sea and Amundsen Sea Ice-Sea Model (RAISE v1.0): a high-resolution ocean-sea ice-ice shelf coupling model for simulating the Dense Shelf Water and Antarctic Bottom Water in the Ross Sea, Antarctica[J]. Geoscientific Model Development, 2024, 18(5): 1375−1393. doi: 10.5194/gmd-2024-128
    [82] Assmann K, Hellmer H H, Beckmann A. Seasonal variation in circulation and water mass distribution on the Ross Sea continental shelf[J]. Antarctic Science, 2003, 15(1): 3−11. doi: 10.1017/s0954102003001007
    [83] Chen Yuanjie, Zhang Zhaoru, Wang Xuezhu, et al. Interannual variations of heat budget over the eastern Ross Sea shelf and the forcing mechanisms[J]. 2022, 43(11): 5055-5076. (查阅网上资料, 未找到本条文献刊名和卷期页码信息, 请确认)
    [84] Xie Chuan, Zhang Zhaoru, Chen Yuanjie, et al. The response of Ross Sea shelf water properties to enhanced Amundsen Sea ice shelf melting[J]. Journal of Geophysical Research: Oceans, 2024, 129(7): e2024JC020919. doi: 10.1029/2024JC020919
    [85] Kohut J, Hunter E, Huber B. Small-scale variability of the cross-shelf flow over the outer shelf of the Ross Sea[J]. Journal of Geophysical Research: Oceans, 2013, 118(4): 1863−1876. doi: 10.1002/jgrc.20090
    [86] St-Laurent P, Klinck J M, Dinniman M S. On the role of coastal troughs in the circulation of warm Circumpolar Deep Water on Antarctic shelves[J]. Journal of Physical Oceanography, 2013, 43(1): 51−64. doi: 10.1175/JPO-D-11-0237.1
    [87] Wang Xiaoqiao, Zhang Zhaoru, Dinniman M S, et al. The response of sea ice and high-salinity shelf water in the Ross Ice Shelf Polynya to cyclonic atmosphere circulations[J]. The Cryosphere, 2023, 17(3): 1107−1126. doi: 10.5194/tc-17-1107-2023
    [88] Gordon A L, Zambianchi E, Orsi A, et al. Energetic plumes over the western Ross Sea continental slope[J]. Geophysical Research Letters, 2004, 31(21): L21302. doi: 10.1029/2004gl020785
    [89] Gordon A L, Orsi A H, Muench R, et al. Western Ross Sea continental slope gravity currents[J]. Deep Sea Research Part II: Topical Studies in Oceanography, 2009, 56(13/14): 796−817. doi: 10.1016/j.dsr2.2008.10.037
    [90] Lewis E L, Perkin R G. The winter oceanography of McMurdo Sound, Antarctica[M]//Jacobs S S. Oceanology of the Antarctic Continental Shelf. Washington: American Geophysical Union, 1985: 145-165.
    [91] Keys H, Jacobs S S, Barnett D. The calving and drift of iceberg B-9 in the Ross Sea, Antarctica[J]. Antarctic Science, 1990, 2(3): 243−257. doi: 10.1017/s0954102090000335
    [92] Picco P, Amici L, Meloni R, et al. Temporal variability of currents in the Ross Sea (Antarctica)[M]//Spezie G, Manzella G M R. Oceanography of the Ross Sea Antarctica. Milano: Springer, 1999: 103-117.
    [93] Picco P, Bergamasco A, Demicheli L, et al. Large-scale circulation features in the central and western Ross Sea (Antarctica)[M]//Faranda F M, Guglielmo L, Ianora A. Ross Sea Ecology: Italiantartide Expeditions (1987-1995). Berlin: Springer, 2000: 95-105.
    [94] Muench R D, Wåhlin A K, Özgökmen T M, et al. Impacts of bottom corrugations on a dense Antarctic outflow: NW Ross Sea[J]. Geophysical Research Letters, 2009, 36(23): L23607. doi: 10.1029/2009gl041347
    [95] Jendersie S, Williams M J M, Langhorne P J, et al. The density-driven winter intensification of the Ross Sea circulation[J]. Journal of Geophysical Research: Oceans, 2018, 123(11): 7702−7724. doi: 10.1029/2018JC013965
    [96] Sedwick P N, Marsay C M, Sohst B M, et al. Early season depletion of dissolved iron in the Ross Sea polynya: implications for iron dynamics on the Antarctic continental shelf[J]. Journal of Geophysical Research: Oceans, 2011, 116(C12): C12019. doi: 10.1029/2010JC006553
    [97] Marsay C M, Sedwick P N, Dinniman M S, et al. Estimating the benthic efflux of dissolved iron on the Ross Sea continental shelf[J]. Geophysical Research Letters, 2014, 41(21): 7576−7583. doi: 10.1002/2014GL061684
    [98] Gerringa L J A, Laan P, van Dijken G L, et al. Sources of iron in the Ross Sea Polynya in early summer[J]. Marine Chemistry, 2015, 177(Pt 3): 447-459.
    [99] Cao Ruobing, Smith Jr W O, Zhong Yisen, et al. The seasonal patterns of hydrographic and biogeochemical variables in the Ross Sea: a BGC-Argo analysis[J]. Deep Sea Research Part II: Topical Studies in Oceanography, 2025, 219: 105436. doi: 10.1016/j.dsr2.2024.105436
    [100] Gerringa L J A, Alderkamp A C, van Dijken G, et al. Dissolved trace metals in the Ross Sea[J]. Frontiers in Marine Science, 2020, 7: 577098. doi: 10.3389/fmars.2020.577098
    [101] Salmon E, Hofmann E E, Dinniman M S, et al. Evaluation of iron sources in the Ross Sea[J]. Journal of Marine Systems, 2020, 212: 103429. doi: 10.1016/j.jmarsys.2020.103429
    [102] Chen Shuangling, Smith Jr W O, Yu Xiaolei. Revisiting the ocean color algorithms for particulate organic carbon and chlorophyll‐a concentrations in the Ross Sea[J]. Journal of Geophysical Research: Oceans, 2021, 126(8): e2021JC017749. doi: 10.1029/2021JC017749
    [103] Park J, Kim J H, Kim H C, et al. Environmental forcings on the remotely sensed phytoplankton bloom phenology in the central Ross Sea Polynya[J]. Journal of Geophysical Research: Oceans, 2019, 124(8): 5400−5417. doi: 10.1029/2019JC015222
  • 加载中
图(9) / 表(4)
计量
  • 文章访问数:  43
  • HTML全文浏览量:  22
  • PDF下载量:  4
  • 被引次数: 0
出版历程
  • 收稿日期:  2025-10-27
  • 修回日期:  2025-12-29
  • 网络出版日期:  2026-01-21

目录

    /

    返回文章
    返回