Optimizing Mooring Placement to Constrain Southern Ocean Air–Sea Fluxes

• Main Authors: Wei, Yanzhou, Gille, Sarah T, Mazloff, Matthew R, Tamsitt, Veronica, Swart, Sebastiaan, Chen, Dake, Newman, Louise
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: eScholarship, University of California 2020
• Subjects: Earth Sciences; Oceanography; Atmospheric Sciences; Maritime Engineering; Meteorology & Atmospheric Sciences; Southern Ocean
• Online Access: https://escholarship.org/uc/item/6t13p293

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.