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Abstract. Nitrate is a critical ingredient for life in the ocean because, as the mostabundant form of fixed nitrogen in the ocean, it is an essential nutrientfor primary production. The availability of marine nitrate is principallydetermined by biological processes, each having a distinct influence on theN isotopic composition of nitrate (nitrate δ15N) – a propertythat informs much of our understanding of the marine N cycle as well asmarine ecology, fisheries, and past ocean conditions. However, the sparsespatial distribution of nitrate δ15N observations makes itdifficult to apply this useful property in global studies or to facilitaterobust model–data comparisons. Here, we use a compilation of publishednitrate δ15N measurements (n=12 277) and climatological mapsof physical and biogeochemical tracers to create a surface-to-seafloor,1∘ resolution map of nitrate δ15N using an ensembleof artificial neural networks (EANN). The strong correlation (R2>0.87) and small mean difference (<0.05 ‰) between EANN-estimated and observed nitrateδ15N indicate that the EANN provides a good estimate ofclimatological nitrate δ15N without a significant bias. Themagnitude of observation-model residuals is consistent with the magnitude of seasonal to interannual changes in observed nitrate δ15N that are notcaptured by our climatological model. The EANN provides a globally resolved map of mean nitrate δ15Nfor observational and modeling studies ofmore »marine biogeochemistry,paleoceanography, and marine ecology.« less