A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning
A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning
复制标题
南大洋超级环流作为物理信息机器学习识别的统一动力框架
DOI：
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Redouane Lguensat
中科院分区：
文献类型：
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作者：
M. Sonnewald;K. Reeve;Redouane Lguensat
南大洋关闭了全球翻转环流，是调节碳、热量、生物生产和海平面的关键。然而，对大气环流和上升流路径的动力学仍然知之甚少。在这里，使用了使用原则性约束的物理信息无监督机器学习框架。一个统一的框架，提出了调用南极绕极流南部的半绕极超级环流：一个巨大的系列泄漏子环流跨越威德尔和罗斯海连接和维护通过粗糙的地形，作为脚手架。超级环流框架挑战了传统的观点，有单独的流通结构在威德尔和罗斯海，并建议理想化的模式和纬向平均框架可能是有限的气候应用的效用。机器学习用于揭示基于涡度的分析中的连贯驱动力区域。超级环流框架的预测得到了现有观测的支持，可以帮助观测和建模工作，以研究这一正在发生快速变化的气候关键区域。
The Southern Ocean closes the global overturning circulation and is key to the regulation of carbon, heat, biological production, and sea level. However, the dynamics of the general circulation and upwelling pathways remain poorly understood. Here, a physics-informed unsupervised machine learning framework using principled constraints is used. A unifying framework is proposed invoking a semi-circumpolar supergyre south of the Antarctic circumpolar current: a massive series of leaking sub-gyres spanning the Weddell and Ross seas that are connected and maintained via rough topography that acts as scaffolding. The supergyre framework challenges the conventional view of having separate circulation structures in the Weddell and Ross seas and suggests that idealized models and zonally-averaged frameworks may be of limited utility for climate applications. Machine learning was used to reveal areas of coherent driving forces within a vorticity-based analysis. Predictions from the supergyre framework are supported by available observations and could aid observational and modelling efforts to study this climatologically key region undergoing rapid change.
影响因子：
23.8
作者：
Irrgang, Christopher;Boers, Niklas;Saynisch-Wagner, Jan
通讯作者：
Saynisch-Wagner, Jan
DOI：
10.1016/j.dsr.2009.06.005
发表时间：
2009-11
期刊：
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影响因子：
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作者：
I. Núñez‐Riboni;E. Fahrbach
通讯作者：
I. Núñez‐Riboni;E. Fahrbach