从旋转水槽实验看蒙克海洋环流理论的非线性解

IF 1 4区 地球科学 Q3 MARINE & FRESHWATER BIOLOGY
P. Polito, O. Sato, D. Napolitano, I. T. Simoes-Sousa, Hélio Almeida, Fabrício R. Lapolli
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引用次数: 0

摘要

摘要Walter Munk于2019年去世,享年101岁。在未来的几年里,他的突破性发现仍将指导和震撼海洋学学生。在这里,我们通过拉格朗日粒子跟踪算法和数值建模,利用Munk的循环理论来感知旋转水箱中的模式。根据视频捕获的信息,我们跟踪漂流者的轨迹,然后客观地映射stream函数,以获得平均环流模式。我们能够再现风驱动的反气旋和不对称环流,包括西部边界增强及其后向反射。后一种现象是由Munk模型的非线性版本预测的,并在作为小环流区的真实副热带环流中观察到。我们配置了两个模拟物理实验的数值模型模拟,分别使用线性项和非线性项。数值实验和物理实验的比较证实了环流非线性畸变的影响。地球物理流体动力学在旋转系统中通常很难可视化,而且与直觉相反。我们将这套实验作为海洋学教学的工具。除了通过实例研究一般的海洋环流理论和观测结果外,该实验还为发展基本的图像处理和地球物理建模技能提供了机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Insights on the non-linear solution of Munk's ocean circulation theory from a rotating tank experiment
Abstract At age 101, Walter Munk passed away in 2019. His groundbreaking discoveries will still guide and amaze oceanography students for years to come. Here, we perceive patterns in rotating tank with Munk's circulation theory aided by a Lagrangian particles tracking algorithm and numerical modeling. From information captured by video, we track the trajectories of drifters, and then objectively mapped the streamfunction to obtain the mean circulation pattern. We were able to reproduce the wind-forced anticyclonic and asymmetric gyre, including the western boundary intensification and its retroflection. The latter phenomenon was predicted by the non-linear version of Munk's model and observed in real subtropical gyres as small recirculation regions. We have configured two numerical model simulations mimicking the physical experiment, with linear and non-linear terms. The comparison between the numerical and physical experiments confirmed the effect of non-linear distortion of the gyre. Geophysical fluid dynamics is often hard to visualize, and counter-intuitive in a rotating system. We present this set of experiments as a tool for oceanography teaching. Besides studying general ocean circulation theories and observations through practical examples, this experiment provides an opportunity to develop basic image processing and geophysical modelling skills.
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来源期刊
CiteScore
1.60
自引率
12.50%
发文量
21
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