Abstract
AbstractThe 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.
Highlights
Nitrate, mostly in its dissolved form NO23, is an essential element for supplying and sustaining marine biological productivity in the global oceans (Moore et al 2013)
To evaluate the relationships between the number of multivariate empirical orthogonal functions (MEOFs) modes and the capability to reproduce the reference nitrate anomaly field, we show the snapshots of the mapped nitrate anomalies for one random August in the biogeochemical general circulation model (GCM) simulation from using the first five MEOF modes to using total 720 modes (Figs. 4a–e)
The first 500 MEOF modes are projected onto these synthetic N-S-T datasets to construct the Southern Ocean (SO) nitrate maps
Summary
Mostly in its dissolved form NO23 , is an essential element for supplying and sustaining marine biological productivity in the global oceans (Moore et al 2013). The amount of nitrate serves as an important limiting nutrient, altering the structure and function of phytoplankton communities (Dugdale and Goering 1967; Church et al 2000; Moore et al 2013); and studies have been suggested to regulate the strength of the biological pump (Elderfield 2006, chapter 6; Ducklow et al 2001; Ardyna et al 2017), which is a pivotal part of the global biogeochemical cycles For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
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