Nitrate is a critical ingredient for life in the ocean because, as the most abundant form of fixed nitrogen in the ocean, it is an essential nutrient for primary production. The availability of marine nitrate is principally determined by biological processes, each having a distinct influence on the N isotopic composition of nitrate (nitrate I 15N) - a property that informs much of our understanding of the marine N cycle as well as marine ecology, fisheries, and past ocean conditions. However, the sparse spatial distribution of nitrate I 15N observations makes it difficult to apply this useful property in global studies or to facilitate robust model-data comparisons. Here, we use a compilation of published nitrate I 15N measurements ( n Combining double low line12 277) and climatological maps of physical and biogeochemical tracers to create a surface-to-seafloor, 1 ĝ resolution map of nitrate I 15N using an ensemble of artificial neural networks (EANN). The strong correlation ( R 2>0.87) and small mean difference ( <0.05 ‰) between EANN-estimated and observed nitrate I 15N indicate that the EANN provides a good estimate of climatological nitrate I 15N without a significant bias. The magnitude of observation-model residuals is consistent with the magnitude of seasonal to interannual changes in observed nitrate I 15N that are not captured by our climatological model. The EANN provides a globally resolved map of mean nitrate I 15N for observational and modeling studies of marine biogeochemistry, paleoceanography, and marine ecology.
CITATION STYLE
Rafter, P., Bagnell, A., Marconi, D., & Devries, T. (2019). Global trends in marine nitrate N isotopes from observations and a neural network-based climatology. Biogeosciences, 16(13), 2617–2633. https://doi.org/10.5194/bg-16-2617-2019
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