基于Omega方程的南海垂向流速诊断与对比研究

洪坤强; 谢玲玲; 黄家辉

doi:10.12284/hyxb20250109

基于Omega方程的南海垂向流速诊断与对比研究

doi: 10.12284/hyxb20250109

洪坤强^1,,
谢玲玲^{1, 2, 3},
黄家辉^{1, 4, ,}

1.
广东海洋大学海洋与气象学院近海海洋变化与灾害预警实验室，广东湛江 524088
2.
广东省高等学校陆架及深远海气候资源与环境重点实验室，广东湛江 524088
3.
粤西热带海洋生态环境野外观测研究站，广东湛江 524088
4.
自然资源部湛江海洋中心，广东湛江 524005

基金项目: 国家自然科学基金面上项目（42276019）；国家重点研发课题（2024YFC2817003）；广东省科技计划项目（2024B1212040008）。

详细信息

作者简介:
洪坤强（2002—），男，河南省固始县人，研究方向为海洋中小尺度过程。E-mail：hongkq_gdou@163.com

通讯作者:
黄家辉，男，研究方向为海洋中小尺度过程。E-mail：Iron_man1224@163.com

中图分类号: P731
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- 文章访问数: 164
- HTML全文浏览量: 41
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- 被引次数: 0
出版历程
- 收稿日期: 2025-07-11
- 修回日期: 2025-11-05
- 网络出版日期: 2025-12-04
- 刊出日期: 2025-12-31

A comparative study on Omega equation-based vertical velocity diagnosis for the South China Sea

Hong Kunqiang^1
,,
Xie Lingling^{1, 2, 3},
Huang Jiahui^{1, 4
, ,}

1.
Laboratory of Coastal Ocean Variation and Disaster Prediction, College of Oceanology and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
2.
Guangdong Key Laboratory of Climate, Resource and Environment in Continental Shelf Sea and Deep Ocean, Zhanjiang 524088, China
3.
Guangdong Western Tropical Marine Ecological and Environmental Field Observation Research Station, Zhanjiang 524088, China
4.
Zhanjiang Marine Center, Ministry of Natural Resources, Zhanjiang 524005, China

摘要

摘要: 本文基于OFES（Ocean general circulation model For the Earth Simulator）模式0.1° × 0.1°高分辨率温盐和三维流场数据，分析Omega方程在南海垂向流速诊断中的适用性和南海垂向流速的时空变化特征。结果表明，Omega方程诊断垂向流速w_Omega与OFES模式垂向流速w_OFEES在南海海盆大部分区域量级相当，约为 O （10⁻⁵ m/s），南海北部陆架区则小一个量级。w_Omega和w_OFEES的空间相关系数r^s在台湾西南部（R1区）和越南以东（R2区）较大，在菲律宾西（R3区）、南海南部（R4区）以及海南岛东北部（R5区）较小。季节变化上，R1、R2和R4区r^s冬季大、夏季小，R3和R5区r^s无明显季节特征。R1和R2区是Omega方程的适用区，其w_Omega与w_OFEES的时间相关系数r^t较大。各区域中形变项（$ {S}_{{\mathrm{DEF}}} $）的贡献率均超过50%，整体大于平流项（$ {S}_{{\mathrm{ADV}}} $），并呈现出“上层$ {S}_{{\mathrm{ADV}}} $主导，下层$ {S}_{{\mathrm{DEF}}} $增强”的共同垂向结构，其临界深度在20～70 m之间。对比eSQG（effective Surface Quasi-Geostrophy）诊断垂向流速结果，Omega方程明显更适用于南海垂向流速的诊断。
- 南海 /
- 垂向流速 /
- Omega方程 /
- OFES /
- 时空变化 /
- eSQG
Abstract: Based on 0.1° × 0.1° high-resolution temperature, salinity, and three-dimensional velocity data from the Ocean general circulation model For the Earth Simulator (OFES), this study analyzes the applicability of the Omega equation for diagnosing vertical velocity in the South China Sea (SCS) and the spatiotemporal variation characteristics of vertical velocity in the SCS. The results show that the vertical velocity diagnosed by the Omega equation (w_Omega) and the OFES model vertical velocity (w_OFES) are of comparable magnitude in most areas of the SCS basin, approximately O(10⁻⁵ m/s), while w_Omega is one order of magnitude smaller than w_OFES on the northern continental shelf of the SCS. The spatial correlation coefficient (r^s) between w_Omega and w_OFES is larger in the region southwestern of Taiwan (R1) and east of Vietnam (R2), and smaller in the western Philippines (R3), the southern SCS (R4), and the northeastern region of Hainan Island (R5). In terms of seasonal variation, r^s is larger in winter and smaller in summer in regions R1, R2, and R4, while regions R3 and R5 show no significant seasonal characteristics in r^s. Regions R1 and R2 are applicable areas for the Omega equation, where the temporal correlation coefficient r^t between w_Omega and w_OFES is larger. In all regions, the contribution of the deformation term ($ {S}_{{\mathrm{DEF}}} $) exceeds 50%, generally greater than the contribution of the advection term ($ {S}_{{\mathrm{ADV}}} $), and they exhibit a common vertical structure of “$ {S}_{{\mathrm{ADV}}} $ dominance in the upper layer and $ {S}_{{\mathrm{DEF}}} $ enhancement in the lower layer”, with the critical depth ranging from 20−70 m. Comparing the results of vertical velocity diagnosed by effective Surface Quasi-Geostrophy (eSQG) and the Omega equation, the Omega equation is significantly better adapted for diagnosing vertical velocity in the SCS.
- South China Sea /
- vertical velocity /
- Omega equation /
- OFES /
- spatio-temporal variation /
- eSQG

HTML全文

图 1 全年、夏季和冬季平均的EKE与N²水平分布

Fig. 1 2D distribution (x−y) of mean EKE and N² in whole year, summer and winter

下载: 全尺寸图片幻灯片

图 2 全年、夏季和冬季R1区100 m层w_Omega、w_OFES和Δw的水平分布

黑色线表示100 m等深线

Fig. 2 2D distribution (x−y) of w_Omega, w_OFES and Δw at 100 m in the R1 region in whole year, summer and winter

Black lines denote the 100 m isobath

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图 6 全年、夏季和冬季R5区50 m层w_Omega、w_OFES和Δw的水平分布

黑色线表示50 m等深线

Fig. 6 2D distribution (x−y) of w_Omega, w_OFES and Δw at 100 m in the R5 region in whole year, summer and winter.

Black lines denote the 50 m isobath

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图 3 全年、夏季和冬季R2区100 m层w_Omega、w_OFES和Δw的水平分布

Fig. 3 2D distribution (x−y) of w_Omega, w_OFES and Δw at 100 m in the R2 region in whole year, summer and winter

下载: 全尺寸图片幻灯片

图 4 全年、夏季和冬季R3区100 m层w_Omega、w_OFES和Δw的水平分布

Fig. 4 2D distribution (x−y) of w_Omega, w_OFES and Δw at 100 m in the R3 region in whole year, summer and winter

下载: 全尺寸图片幻灯片

图 5 全年、夏季和冬季R4区100 m层w_Omega、w_OFES和Δw的水平分布

黑色线表示100 m等深线

Fig. 5 2D distribution (x−y) of w_Omega, w_OFES and Δw at 100 m in the R4 region in whole year, summer and winter

Black lines denote the 100 m isobath

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图 7 全年、夏季和冬季R1～R5区w_Omega和w_OFES的均方根（RMS）剖面图

Fig. 7 Profile of root mean square of w_Omega and w_OFES in the R1 to R5 regions in whole year, summer and winter

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图 8 R1～R5区w_Omega和w_OFES的空间相关系数r^s随时间和深度的变化及其时间平均

Fig. 8 Correlation r^s as a function of time and depth between the w_Omega and w_OFES in the R1 to R5 regions and their temporal averaged correlation values

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图 9 R1～R5区w_Omega和w_OFES的时间相关系数r^t的水平分布

Fig. 9 2D distribution (x−y) of temporal correlation r^t of w_Omega and w_OFES in the R1 to R5 regions

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图 10 南海区域平均海表高度异常（SLA）时间序列对比

蓝线表示OFES模式结果，红线表示CMEMS卫星高度计数据

Fig. 10 Comparison of the regional mean sea level anomaly (SLA) time series in the South China Sea

The blue line represents the OFES simulation, and the red line represents the CMEMS satellite altimetry data

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图 11 南海年平均SSH与ADT的空间分布对比

a. OFES模式输出的年平均SSH；b. CMEMS提供的年平均ADT

Fig. 11 Spatial comparison of the annual mean SSH and ADT in the South China Sea

a. Annual mean SSH from the OFES simulation; b. annual mean ADT from CMEMS

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图 12 全年、夏季和冬季R1～R5区$ {S}_{{\mathrm{DEF}}} $和$ {S}_{{\mathrm{ADV}}} $剖面图

Fig. 12 Profile of $ {S}_{{\mathrm{DEF}}} $ and $ {S}_{{\mathrm{ADV}}} $ in the R1 to R5 regions in whole year, summer and winter

下载: 全尺寸图片幻灯片

表 1 全年、夏季和冬季R1～R5区w_Omega和w_OFES的空间相关系数r^s

Tab. 1 Spatial correlation r^s of w_Omega and w_OFES in the R1 to R5 regions in whole year, summer and winter

	全年	夏季	冬季
R1 (100 m)	0.45（p < 0.01）	0.52（p < 0.01）	0.64（p < 0.01）
R2 (100 m)	0.73（p < 0.01）	0.70（p < 0.01）	0.77（p < 0.01）
R3 (100 m)	0.61（p < 0.01）	0.51（p < 0.01）	0.55（p < 0.01）
R4 (100 m)	0.66（p < 0.01）	0.20（p < 0.01）	0.60（p < 0.01）
R5 (50 m)	0.15（P = 0.02）	0.41（p < 0.01）	0.09（P = 0.20）

下载: 导出CSV

表 2 全年、夏季和冬季R1～R5区w_eSQG和w_OFES的空间相关系数r^s

Tab. 2 Spatial correlation r^s of w_eSQG and w_OFES in the R1 to R5 regions in whole year, summer and winter

	全年	夏季	冬季
R1 (100 m)	0.30（p < 0.01）	0.32（p < 0.01）	0.04（p = 0.15）
R2 (100 m)	0.41（p < 0.01）	0.49（p < 0.01）	0.35（p < 0.01）
R3 (100 m)	0.18（p < 0.01）	0.14（p < 0.01）	0.42（p < 0.01）
R4 (100 m)	0.64（p < 0.01）	0.69（p < 0.01）	0.51（p < 0.01）
R5 (50 m)	0.14（p < 0.01）	0.27（p < 0.01）	0.07（p = 0.16）

下载: 导出CSV

表 3 R1～R5区w_Omega（w_eSQG）和w_OFES的时间相关系数r^t超过0.4的比例

Tab. 3 The proportion of regions R1～R5 where the temporal correlation coefficient (r^t) between w_Omega (w_eSQG) and w_OFES exceeds 0.4

	w_Omega和w_OFES的r^t_Omega>0.4	w_eSQG和w_OFES的r^t_eSQG>0.4
R1 (100 m)	81%	73%
R2 (100 m)	87%	55%
R3 (100 m)	2%	0
R4 (100 m)	33%	10%
R5 (50 m)	9%	7%

下载: 导出CSV

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