Different Types of Surface Chlorophyll Patterns of Oceanic Mesoscale Eddies Identified by AI Framework

IF 3.3 2区 地球科学 Q1 OCEANOGRAPHY Journal of Geophysical Research-Oceans Pub Date : 2024-09-17 DOI:10.1029/2024JC021176
Rui Nian, Minghan Yuan, Zhengguang Zhang, Tong Wu, Yajie Ji, Yanmei Wang, Hua Yang, Zhen Fu, Hengfu Xu, Kexin Shi, Bo He
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Abstract

Oceanic mesoscale eddies (with scale 101–102 km) and their submesoscale fine structures (with scale 100–101 km) can effectively induce vertical motions and bring nutrients into the oceanic euphotic layer, which leaves abundant footprints on the ocean surface chlorophyll distributions and have the potential to promote primary productivity of oceanic ecosystem. In return, these surface chlorophyll footprints observed by ocean color satellites can serve as a useful tool to reveal the spatial structures of mesoscale eddies and their submesoscale fine structures. By combining artificial intelligence (AI) algorithms to develop a series of identification strategies for typical surface chlorophyll patterns around mesoscale eddies, we find that over 20% of mesoscale eddy observations exhibit identifiable typical chlorophyll patterns, which tends to regulate an increase of the surface chlorophyll concentration within the corresponding eddies, especially enhancing by about 30% in nutrient-restricted subtropical regions compared with the background values. Based on their geometric features, typical chlorophyll patterns are primarily classified as Core, Spiral, Tail, Ring, Loop, and Eye respectively by clustering algorithm. Further spatial-spectral analysis found that the typical patterns on eddies exhibit a much steeper wave-number spectral slope about −3, compared to the non-typical distributions on eddies and the non-eddy background distribution (about −2.7–−2.2). This implies that the occurrence of different typical chlorophyll patterns may correspond to specific mesoscale and submesoscale dynamic processes.

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用人工智能框架识别海洋中尺度涡旋的不同类型表面叶绿素模式
大洋中尺度漩涡(尺度 101-102 公里)及其次中尺度精细结构(尺度 100-101 公里)能有效地引起垂直运动,将营养物质带入大洋的透光层,在洋面叶绿素分布上留下丰富的足迹,具有促进大洋生态系统初级生产力的潜力。反过来,海洋色彩卫星观测到的这些表层叶绿素足迹可以作为揭示中尺度漩涡空间结构及其亚中尺度精细结构的有用工具。通过结合人工智能(AI)算法,对中尺度漩涡周围的典型表层叶绿素模式制定一系列识别策略,我们发现超过20%的中尺度漩涡观测数据呈现出可识别的典型叶绿素模式,这往往会调节相应漩涡内表层叶绿素浓度的增加,尤其是在营养受限的亚热带地区,与背景值相比增加了约30%。根据叶绿素漩涡的几何特征,利用聚类算法将典型的叶绿素漩涡分为核心漩涡、螺旋漩涡、尾部漩涡、环状漩涡、环状漩涡和眼状漩涡。进一步的空间光谱分析发现,与漩涡上的非典型分布和非漩涡背景分布(约-2.7--2.2)相比,漩涡上的典型模式表现出更陡的波数谱斜率,约为-3。这意味着不同典型叶绿素模式的出现可能与特定的中尺度和亚中尺度动力过程相对应。
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来源期刊
Journal of Geophysical Research-Oceans
Journal of Geophysical Research-Oceans Earth and Planetary Sciences-Oceanography
CiteScore
7.00
自引率
13.90%
发文量
429
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