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Abstract In tidewater glacier fjords, subglacial discharge drives a significant mixing mechanism near glacier fronts and drives a strong exchange flow. Numerous studies (Cowton et al., 2015,https://doi.org/10.1002/2014jc010324; Slater et al., 2017,https://doi.org/10.1002/2016gl072374) have utilized a parameterization for buoyant plume theory to force fjord scales systems, but neglect to parameterize the outflowing of the plume away from the glacial wall after it has reached its neutral density. In this study, a new model framework, ROMS‐ICEPLUME, is developed to parameterize the rising and initial outflowing stage of subglacial discharge plumes in the Regional Ocean Modeling System. The coupled model applies a novel parameterization algorithm to prescribe the velocity and vertical extent of the outflowing plume, which reduces numerical instability and improves model performance. The model framework is tested with a quasi‐realistic forcing using observations of a subglacial discharge plume hydrographic surveys collected from a Greenland fjord. We find that the new model framework is able to reproduce the strong outflowing plume and the compensating inflow at depth, with a spatial structure that correlates well with in‐situ observations. On the other hand, the model framework without the new parameterization algorithm fails to capture the outflowing plume structure. Thus, our new framework for parameterizing subglacial discharge plumes is an improvement from previous coupled model frameworks, and is a promising tool toward advancing our understanding of circulation in tidewater glacier fjords.
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Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization Into a Numerical Ocean Circulation Model
Abstract We address the question of how to use a machine learned (ML) parameterization in a general circulation model (GCM), and assess its performance both computationally and physically. We take one particular ML parameterization (Guillaumin & Zanna, 2021,https://doi.org/10.1002/essoar.10506419.1) and evaluate the online performance in a different model from which it was previously tested. This parameterization is a deep convolutional network that predicts parameters for a stochastic model of subgrid momentum forcing by mesoscale eddies. We treat the parameterization as we would a conventional parameterization once implemented in the numerical model. This includes trying the parameterization in a different flow regime from that in which it was trained, at different spatial resolutions, and with other differences, all to test generalization. We assess whether tuning is possible, which is a common practice in GCM development. We find the parameterization, without modification or special treatment, to be stable and that the action of the parameterization to be diminishing as spatial resolution is refined. We also find some limitations of the machine learning model in implementation: (a) tuning of the outputs from the parameterization at various depths is necessary; (b) the forcing near boundaries is not predicted as well as in the open ocean; (c) the cost of the parameterization is prohibitively high on central processing units. We discuss these limitations, present some solutions to problems, and conclude that this particular ML parameterization does inject energy, and improve backscatter, as intended but it might need further refinement before we can use it in production mode in contemporary climate models.
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- Award ID(s):
- 2009752
- PAR ID:
- 10471023
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Volume:
- 15
- Issue:
- 10
- ISSN:
- 1942-2466
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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