A Multi-variate Empirical Orthogonal Function Method to Construct Nitrate Maps in the Southern Ocean A Multi-variate Empirical Orthogonal Function Method to Construct Nitrate Maps in the Southern Ocean

• Main Authors: Liang, Yu-Chiao, Mazloff, Matthew R, Rosso, Isabella, Fang, Shih-Wei, Yu, Jin-Yi
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: eScholarship, University of California 2018
• Subjects: Life Below Water; Sampling; Empirical orthogonal functions; Ocean models; Atmospheric Sciences; Oceanography; Maritime Engineering; Meteorology & Atmospheric Sciences; Southern Ocean
• Online Access: https://escholarship.org/uc/item/6zv402vr

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.