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Abstract Float trajectories are simulated using Lagrangian particle tracking software and eddy‐permitting ocean model output from the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) project. We find that Argo‐like particles near strong mean flows tend to accelerate while at their parking depth. This effect is pronounced in western boundary current regions and in the Antarctic Circumpolar Current system. The acceleration is associated with eddy‐mean flow interactions: Eddies converge particles toward regions with stronger mean currents. Particles do not accelerate when they are advected by the eddy or mean flow alone. During a 9‐day parking period, speed increases induced by the eddy‐mean flow interactions can be as large as 2 cm s−1, representing roughly 10% of the mean velocity. If unaccounted for, this acceleration could bias velocities inferred from observed Argo float trajectories.
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Scripps Argo Trajectory-Based Velocity Product: Global Estimates of Absolute Velocity Derived from Core, Biogeochemical, and Deep Argo Float Trajectories at Parking Depth
Abstract Global estimates of absolute velocities can be derived from Argo float trajectories during drift at parking depth. A new velocity dataset developed and maintained at Scripps Institution of Oceanography is presented based on all Core, Biogeochemical, and Deep Argo float trajectories collected between 2001 and 2020. Discrepancies between velocity estimates from the Scripps dataset and other existing products including YoMaHa and ANDRO are associated with quality control criteria, as well as selected parking depth and cycle time. In the Scripps product, over 1.3 million velocity estimates are used to reconstruct a time-mean velocity field for the 800–1200 dbar layer at 1° horizontal resolution. This dataset provides a benchmark to evaluate the veracity of the BRAN2020 reanalysis in representing the observed variability of absolute velocities and offers a compelling opportunity for improved characterization and representation in forecast and reanalysis systems. Significance Statement The aim of this study is to provide observation-based estimates of the large-scale, subsurface ocean circulation. We exploit the drift of autonomous profiling floats to carefully isolate the inferred circulation at the parking depth, and combine observations from over 11 000 floats, sampling between 2001 and 2020, to deliver a new dataset with unprecedented accuracy. The new estimates of subsurface currents are suitable for assessing global models, reanalyses, and forecasts, and for constraining ocean circulation in data-assimilating models.
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- PAR ID:
- 10406628
- Date Published:
- Journal Name:
- Journal of Atmospheric and Oceanic Technology
- Volume:
- 40
- Issue:
- 3
- ISSN:
- 0739-0572
- Page Range / eLocation ID:
- 361 to 374
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The core Argo array has operated with the design goal of uniform spatial distribution of 3° in latitude and longitude. Recent studies have acknowledged that spatial and temporal scales of variability in some parts of the ocean are not resolved by 3° sampling and have recommended increased core Argo density in the equatorial region, boundary currents, and marginal seas with an integrated vision of other Argo variants. Biogeochemical (BGC) Argo floats currently observe the ocean from a collection of pilot arrays, but recently funded proposals will transition these pilot arrays to a global array. The current BGC Argo implementation plan recommends uniform spatial distribution of BGC Argo floats. For the first time, we estimate the effectiveness of the existing BGC Argo array to resolve the anomaly from the mean using a subset of modeled, full-depth BGC fields. We also study the effectiveness of uniformly distributed BGC Argo arrays with varying float densities at observing the ocean. Then, using previous Argo trajectories, we estimate the Argo array’s future distribution and quantify how well it observes the ocean. Finally, using a novel technique for sequentially identifying the best deployment locations, we suggest the optimal array distribution for BGC Argo floats to minimize objective mapping uncertainty in a subset of BGC fields and to best constrain BGC temporal variability.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/JTECH-D-22-0065.1
