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Evaluating existing ocean glider sampling strategies for submesoscale dynamics

Patmore, R. D. ORCID:, Ferreira, D. ORCID:, Marshall, D. P., du Plessis, M. D., Brearley, J. A. and Swaart, S. (2024) Evaluating existing ocean glider sampling strategies for submesoscale dynamics. Journal of Atmospheric and Oceanic Technology. ISSN 1520-0426 (In Press)

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To link to this item DOI: 10.1175/JTECH-D-23-0055.1


Mixing in the upper ocean is important for biological production and the transfer of heat and carbon between the atmosphere and deep ocean, properties commonly targeted by observational campaigns using ocean gliders. We assess the reliability of ocean gliders to obtain a robust statistical representation of submesocale variability in the ocean mixed layer of the Weddell Sea. A 1/48◦ regional simulation of the Southern Ocean is sampled with virtual ‘bow-tie’ glider deployments, which are then compared against the reference model output. Sampling biases of lateral buoyancy gradients associated with the arbitrary alignment between glider paths and fronts are formally quantified, and the magnitude of the biases are comparable to observational estimates, with a mean error of 52%. The sampling bias leaves errors in the retrieved distribution of buoyancy gradients largely insensitive to deployment length and the deployment of additional gliders. Notable sensitivity to these choices emerges when the biases are removed by sampling perpendicular to fronts at all times. Detecting seasonal change in the magnitude of buoyancy gradients is sensitive to the glider-orientation sampling bias but the change in variance is not. We evaluate the impact of reducing the number of dives and climbs in an observational campaign and find small reductions in the number of dive-climb pairs have a limited effect on the results. Lastly, examining the sensitivity of the sampling bias to path orientation indicates the bias is not dependent on the direction of travel, in our deep ocean study site.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:116391
Publisher:American Meteorological Society

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