Global trends in marine nitrate N isotopes from observations and a neural network-based climatology

Rafter, Patrick A.; Bagnell, Aaron; Marconi, Dario; DeVries, Timothy

doi:https://doi.org/10.5194/bg-16-2617-2019

Articles | Volume 16, issue 13

https://doi.org/10.5194/bg-16-2617-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-16-2617-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 13

Research article

|

05 Jul 2019

Research article |

| 05 Jul 2019

Global trends in marine nitrate N isotopes from observations and a neural network-based climatology

Patrick A. Rafter, Aaron Bagnell, Dario Marconi, and Timothy DeVries

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Final revised paper (published on 05 Jul 2019)
Preprint (discussion started on 11 Jan 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Global trends in marine nitrate N isotopes', Anonymous Referee #1, 01 Feb 2019
- AC1: 'Reply to Reviewer #1 by all authors', Patrick Rafter, 07 Mar 2019
RC2: 'review', Anonymous Referee #2, 16 Feb 2019
- AC2: 'Response to Reviewer #2's comments.', Patrick Rafter, 07 Mar 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (13 Mar 2019) by Perran Cook

AR by Patrick Rafter on behalf of the Authors (15 Mar 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (20 Mar 2019) by Perran Cook

RR by Anonymous Referee #2 (15 Apr 2019)

ED: Publish as is (23 Apr 2019) by Perran Cook

AR by Patrick Rafter on behalf of the Authors (12 May 2019) Manuscript

Post-review adjustments

AA: Author's adjustment | EA: Editor approval

AA by Patrick Rafter on behalf of the Authors (19 Jun 2019) Author's adjustment Manuscript

EA: Adjustments approved (24 Jun 2019) by Perran Cook

Short summary

The N isotopic composition of nitrate (nitrate δ15N) is a useful tracer of ocean N cycling and many other ocean processes. Here, we use a global compilation of marine nitrate δ15N as an input, training, and validating dataset for an artificial neural network (a.k.a., machine learning) and examine basin-scale trends in marine nitrate δ15N from the surface to the seafloor.