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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.
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Optimizing observational arrays for biogeochemistry in the tropical Pacific by estimating correlation lengths
Abstract Global climate change has impacted ocean biogeochemistry and physical dynamics, causing increases in acidity and temperature, among other phenomena. These changes can lead to deleterious effects on marine ecosystems and communities that rely on these ecosystems for their livelihoods. To better quantify these changes, an array of floats fitted with biogeochemical sensors (BGC‐Argo) is being deployed throughout the ocean. This paper presents an algorithm for deriving a deployment strategy that maximizes the information captured by each float. The process involves using a model solution as a proxy for the true ocean state and carrying out an iterative process to identify optimal float deployment locations for constraining the model variance. As an example, we use the algorithm to optimize the array for observing ocean surface dissolved carbon dioxide concentrations (pCO2) in a region of strong air–sea gas exchange currently being targeted for BGC‐Argo float deployment. We conclude that 54% of the pCO2variability in the analysis region could be sampled by an array of 50 Argo floats deployed in specified locations. This implies a relatively coarse average spacing, though we find the optimal spacing is nonuniform, with a denser sampling being required in the eastern equatorial Pacific. We also show that this method could be applied to determine the optimal float deployment along ship tracks, matching the logistics of real float deployment. We envision this software package to be a helpful resource in ocean observational design anywhere in the global oceans.
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- PAR ID:
- 10535194
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Limnology and Oceanography: Methods
- Volume:
- 22
- Issue:
- 11
- ISSN:
- 1541-5856
- Format(s):
- Medium: X Size: p. 840-852
- Size(s):
- p. 840-852
- Sponsoring Org:
- National Science Foundation
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