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AbstractA novel tidal analysis package (red_tide) has been developed to characterize low-amplitude non-phase-locked tidal energy and dominant tidal peaks in noisy, irregularly sampled, or gap-prone time series. We recover tidal information by expanding conventional harmonic analysis to include prior information and assumptions about the statistics of a process, such as the assumption of a spectrally colored background, treated as nontidal noise. This is implemented using Bayesian maximum posterior estimation and assuming Gaussian prior distributions. We utilize a hierarchy of test cases, including synthetic data and observations, to evaluate this method and its relevance to analysis of data with a tidal component and an energetic nontidal background. Analysis of synthetic test cases shows that the methodology provides robust tidal estimates. When the background energy spectrum is nearly spectrally white, red_tide results replicate results from ordinary least squares (OLS) commonly used in other tidal packages. When background spectra are red (a spectral slope of −2 or steeper), red_tide’s estimates represent a measurable improvement over OLS. The approach highlights the presence of tidal variability and low-amplitude constituents in observations by allowing arbitrarily configurable fitted frequencies and prior statistics that constrain solutions. These techniques have been implemented in MATLAB in order to analyze tidal data with non-phase-locked components and an energetic background that pose challenges to the commonly used OLS approach.

Abstract: Proposals from multiple nations to deploy air–sea flux moorings in the Southern Ocean have raised the question of how to optimize the placement of these moorings in order to maximize their utility, both as contributors to the network of observations assimilated in numerical weather prediction and also as a means to study a broad range of processes driving air–sea fluxes. This study, developed as a contribution to the Southern Ocean Observing System (SOOS), proposes criteria that can be used to determine mooring siting to obtain best estimates of net air–sea heat flux (Qnet). Flux moorings are envisioned as one component of a multiplatform observing system, providing valuable in situ point time series measurements to be used alongside satellite data and observations from autonomous platforms and ships. Assimilating models (e.g., numerical weather prediction and reanalysis products) then offer the ability to synthesize the observing system and map properties between observations. This paper develops a framework for designing mooring array configurations to maximize the independence and utility of observations. As a test case, within the meridional band from 35° to 65°S we select eight mooring sites optimized to explain the largest fraction of the total variance (and thus to ensure the least variance of residual components) in the area south of 20°S. Results yield different optimal mooring sites for low-frequency interannual heat fluxes compared with higher-frequency subseasonal fluxes. With eight moorings, we could explain a maximum of 24.6% of high-frequency Qnet variability or 44.7% of low-frequency Qnet variability.

AbstractArgo float trajectories are simulated in the southwest Pacific basin (25°–45°S, 170°E–165°W) using velocity fields from a 1/12° Southern Ocean model and a Lagrangian particle tracking model programmed to represent the vertical motions of profiling Argo floats. The system is applied to simulate both core Argo floats (typically parked at 1000-m depth and profiling to 2000-m depth) and Deep Argo floats (parked 500 m above the seafloor). The goal is to estimate probability density functions (PDFs) predicting future float positions. Differences are expected in the trajectory statistics, largely because of limitations in the temporal and spatial resolution of the model fields and uncertainties associated with a random walk component included in the particle advection scheme to represent this unresolved variability. Nonetheless, the core Argo float displacements over ~100-day time intervals are mostly consistent with the derived PDFs, particularly in regions with stable midlayer flows. For the Deep Argo floats, which are released into the open ocean and parked near the bottom, the simulations predict an average total displacement of less than 50 km within 100 days, in good agreement with the Deep Argo floats deployed as part of a pilot study. The study explores both the representativeness and the predictability of float displacements, with an aim to contribute to planning for the float observing system.

The ability to construct nitrate maps in the Southern Ocean (SO) from sparse observations is important for marine biogeochemistry research, as it offers a geographical estimate of biological productivity. The goal of this study is to infer the skill of constructed SO nitrate maps using varying data sampling strategies. The mapping method uses multivariate empirical orthogonal functions (MEOFs) constructed from nitrate, salinity, and potential temperature (N-S-T) fields from a biogeochemical general circulation model simulation Synthetic N-S-T datasets are created by sampling modeled N-S-T fields in specific regions, determined either by random selection or by selecting regions over a certain threshold of nitrate temporal variances. The first 500 MEOF modes, determined by their capability to reconstruct the original N-S-T fields, are projected onto these synthetic N-S-T data to construct time-varying nitrate maps. Normalized root-mean-square errors (NRMSEs) are calculated between the constructed nitrate maps and the original modeled fields for different sampling strategies. The sampling strategy according to nitrate variances is shown to yield maps with lower NRMSEs than mapping adopting random sampling. A k-means cluster method that considers the N-S-T combined variances to identify key regions to insert data is most effective in reducing the mapping errors. These findings are further quantified by a series of mapping error analyses that also address the significance of data sampling density. The results provide a sampling framework to prioritize the deployment of biogeochemical Argo floats for constructing nitrate maps.

Abstract: Argo float trajectories are simulated in the southwest Pacific basin (25°–45°S, 170°E–165°W) using velocity fields from a 1/12° Southern Ocean model and a Lagrangian particle tracking model programmed to represent the vertical motions of profiling Argo floats. The system is applied to simulate both core Argo floats (typically parked at 1000-m depth and profiling to 2000-m depth) and Deep Argo floats (parked 500 m above the seafloor). The goal is to estimate probability density functions (PDFs) predicting future float positions. Differences are expected in the trajectory statistics, largely because of limitations in the temporal and spatial resolution of the model fields and uncertainties associated with a random walk component included in the particle advection scheme to represent this unresolved variability. Nonetheless, the core Argo float displacements over ~100-day time intervals are mostly consistent with the derived PDFs, particularly in regions with stable midlayer flows. For the Deep Argo floats, which are released into the open ocean and parked near the bottom, the simulations predict an average total displacement of less than 50 km within 100 days, in good agreement with the Deep Argo floats deployed as part of a pilot study. The study explores both the representativeness and the predictability of float displacements, with an aim to contribute to planning for the float observing system.