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 pCO₂ variability 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.