Abstract
Abstract. 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 δ15N) – 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 δ15N 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 δ15N measurements (n=12 277) and climatological maps
of physical and biogeochemical tracers to create a surface-to-seafloor,
1∘ resolution map of nitrate δ15N using an ensemble
of artificial neural networks (EANN). The strong correlation (R2>0.87) and small mean difference (<0.05 ‰) between EANN-estimated and observed nitrate
δ15N indicate that the EANN provides a good estimate of
climatological nitrate δ15N without a significant bias. The
magnitude of observation-model residuals is consistent with the magnitude of seasonal to interannual changes in observed nitrate δ15N that are not
captured by our climatological model. The EANN provides a globally resolved map of mean nitrate δ15N
for observational and modeling studies of marine biogeochemistry,
paleoceanography, and marine ecology.
Subject
Earth-Surface Processes,Ecology, Evolution, Behavior and Systematics
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