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Abstract A simplified quasigeostrophic (QG) analytical model together with an idealized numerical model are used to study the effect of uneven ice–ocean stress on the temporal evolution of the geostrophic current under sea ice. The tendency of the geostrophic velocity in the QG model is given as a function of the lateral gradient of vertical velocity and is further related to the ice–ocean stress with consideration of a surface boundary layer. Combining the analytical and numerical solutions, we demonstrate that the uneven stress between the ice and an initially surface-intensified, laterally sheared geostrophic current can drive an overturning circulation to trigger the displacement of isopycnals and modify the vertical structure of the geostrophic velocity. When the near-surface isopycnals become tilted in the opposite direction to the deeper ones, a subsurface velocity core is generated (via geostrophic setup). This mechanism should help understand the formation of subsurface currents in the edge of Chukchi and Beaufort Seas seen in observations. Furthermore, our solutions reveal a reversed flow extending from the bottom to the middepth, suggesting that the ice-induced overturning circulation potentially influences the currents in the deep layers of the Arctic Ocean, such as the Atlantic Water boundary current.
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Learning ocean circulation models with reservoir computing
Two elementary models of ocean circulation, the well-known double-gyre stream function model and a single-layer quasi-geostrophic (QG) basin model, are used to generate flow data that sample a range of possible dynamical behavior for particular flow parameters. A reservoir computing (RC) machine learning algorithm then learns these models from the stream function time series. In the case of the QG model, a system of partial differential equations with three physically relevant dimensionless parameters is solved, including Munk- and Stommel-type solutions. The effectiveness of a RC approach to learning these ocean circulation models is evident from its ability to capture the characteristics of these ocean circulation models with limited data including predictive forecasts. Further assessment of the accuracy and usefulness of the RC approach is conducted by evaluating the role of both physical and numerical parameters and by comparison with particle trajectories and with well-established quantitative assessments, including finite-time Lyapunov exponents and proper orthogonal decomposition. The results show the capability of the methods outlined in this article to be applied to key research problems on ocean transport, such as predictive modeling or control.
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- PAR ID:
- 10391030
- Date Published:
- Journal Name:
- Physics of Fluids
- Volume:
- 34
- Issue:
- 11
- ISSN:
- 1070-6631
- Page Range / eLocation ID:
- 116604
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0119061
