The University of Southampton
University of Southampton Institutional Repository

Net primary productivity estimates and environmental variables in the Arctic Ocean:: An assessment of coupled physical-biogeochemical models

Net primary productivity estimates and environmental variables in the Arctic Ocean:: An assessment of coupled physical-biogeochemical models
Net primary productivity estimates and environmental variables in the Arctic Ocean:: An assessment of coupled physical-biogeochemical models
The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959–2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.
2169-9275
8635-8669
Lee, Younjoo J.
9f356f88-8850-4554-be41-2051756a0689
Matrai, Patricia A.
46e93d10-f30b-49e2-9cbd-5219a7bcdeb0
Friedrichs, Marjorie A. M.
00820cbf-8154-4943-8b00-008b265da272
Saba, Vincent S.
d676abd8-4fcb-4a47-8687-e172209f7c18
Aumont, Olivier
6ea5af9d-4c27-42d9-9ba7-749729efa72f
Babin, Marcel
8cff8ea6-a08b-4e3d-9cae-91cead8006c8
Buitenhuis, Erik T.
8be1d414-09e5-4c69-8d0c-be330f53a74e
Chevallier, Matthieu
dfdc7246-5868-4042-985c-53692d57d87d
De Mora, Lee
44005308-d872-4e6c-b57c-d8f15a1530ed
Dessert, Morgane
0df22174-416e-4f39-9bc0-8149c1a4379e
Dunne, John P.
4378d8c4-1e21-4bff-98c6-0d4a4d707794
Ellingsen, Ingrid H.
ed1678b9-53c7-4bed-8daa-d69363310d76
Feldman, Doron
a10b48a5-3cb1-4325-bc14-65af42a4a93a
Frouin, Robert
8146c2d7-5cc9-47ba-b533-ab5725e5daba
Gehlen, Marion
e4883d9b-b726-46b9-abcd-c2915e5cee51
Gorgues, Thomas
a8138c6c-bd72-4c65-a974-76bad8b88b70
Ilyina, Tatiana
1f1d00ed-8ce3-4e6d-a893-42dc3b14c18d
Jin, Meibing
addff87a-f8d8-4381-bc7d-c820ca02aec0
John, Jasmin G.
fe186831-cbe3-4239-9087-b5b504544e4d
Lawrence, Jon
43b953db-8f69-4222-adba-b62bd28ee82d
Manizza, Manfredi
d6a72472-644a-4f45-aae2-c2e8949b7225
Menkes, Christophe E.
6c3b7f35-ac87-42e0-bbd2-862657d9c5df
Perruche, Coralie
8c6f5292-855c-4811-87ef-f903e6d998b6
Le Fouest, Vincent
a858e0fb-dd55-4b3a-9748-a72de9dcea61
Popova, Ekaterina E.
3ea572bd-f37d-4777-894b-b0d86f735820
Romanou, Anastasia
a16375ed-b29d-4139-a509-7643c07a6c3e
Samuelsen, Annette
c2cd182b-6851-41a6-82b4-b2c123fc65a7
Schwinger, Jörg
ab3c0196-8f1a-4dcf-9d60-d30f21d6e533
Séférian, Roland
281466a7-fa1a-4b24-82cf-ee34e801792a
Stock, Charles A.
7944dbbb-d3b5-4abe-b8c0-5c11084260e6
Tjiputra, Jerry
2e95bc26-02b7-4478-b291-4ffc9e4a38fe
Tremblay, L. Bruno
0f2ae67d-ac82-42a5-bce3-f671a49155c4
Ueyoshi, Kyozo
c44ddc6f-b0ec-45c9-9e53-5105343f94b6
Vichi, Marcello
77d9b7f1-1bb3-46ad-8c12-46747f68c973
Yool, Andrew
882aeb0d-dda0-405e-844c-65b68cce5017
Zhang, Jinlun
2fb9623a-24b6-4860-b909-86aac148d8ff
Lee, Younjoo J.
9f356f88-8850-4554-be41-2051756a0689
Matrai, Patricia A.
46e93d10-f30b-49e2-9cbd-5219a7bcdeb0
Friedrichs, Marjorie A. M.
00820cbf-8154-4943-8b00-008b265da272
Saba, Vincent S.
d676abd8-4fcb-4a47-8687-e172209f7c18
Aumont, Olivier
6ea5af9d-4c27-42d9-9ba7-749729efa72f
Babin, Marcel
8cff8ea6-a08b-4e3d-9cae-91cead8006c8
Buitenhuis, Erik T.
8be1d414-09e5-4c69-8d0c-be330f53a74e
Chevallier, Matthieu
dfdc7246-5868-4042-985c-53692d57d87d
De Mora, Lee
44005308-d872-4e6c-b57c-d8f15a1530ed
Dessert, Morgane
0df22174-416e-4f39-9bc0-8149c1a4379e
Dunne, John P.
4378d8c4-1e21-4bff-98c6-0d4a4d707794
Ellingsen, Ingrid H.
ed1678b9-53c7-4bed-8daa-d69363310d76
Feldman, Doron
a10b48a5-3cb1-4325-bc14-65af42a4a93a
Frouin, Robert
8146c2d7-5cc9-47ba-b533-ab5725e5daba
Gehlen, Marion
e4883d9b-b726-46b9-abcd-c2915e5cee51
Gorgues, Thomas
a8138c6c-bd72-4c65-a974-76bad8b88b70
Ilyina, Tatiana
1f1d00ed-8ce3-4e6d-a893-42dc3b14c18d
Jin, Meibing
addff87a-f8d8-4381-bc7d-c820ca02aec0
John, Jasmin G.
fe186831-cbe3-4239-9087-b5b504544e4d
Lawrence, Jon
43b953db-8f69-4222-adba-b62bd28ee82d
Manizza, Manfredi
d6a72472-644a-4f45-aae2-c2e8949b7225
Menkes, Christophe E.
6c3b7f35-ac87-42e0-bbd2-862657d9c5df
Perruche, Coralie
8c6f5292-855c-4811-87ef-f903e6d998b6
Le Fouest, Vincent
a858e0fb-dd55-4b3a-9748-a72de9dcea61
Popova, Ekaterina E.
3ea572bd-f37d-4777-894b-b0d86f735820
Romanou, Anastasia
a16375ed-b29d-4139-a509-7643c07a6c3e
Samuelsen, Annette
c2cd182b-6851-41a6-82b4-b2c123fc65a7
Schwinger, Jörg
ab3c0196-8f1a-4dcf-9d60-d30f21d6e533
Séférian, Roland
281466a7-fa1a-4b24-82cf-ee34e801792a
Stock, Charles A.
7944dbbb-d3b5-4abe-b8c0-5c11084260e6
Tjiputra, Jerry
2e95bc26-02b7-4478-b291-4ffc9e4a38fe
Tremblay, L. Bruno
0f2ae67d-ac82-42a5-bce3-f671a49155c4
Ueyoshi, Kyozo
c44ddc6f-b0ec-45c9-9e53-5105343f94b6
Vichi, Marcello
77d9b7f1-1bb3-46ad-8c12-46747f68c973
Yool, Andrew
882aeb0d-dda0-405e-844c-65b68cce5017
Zhang, Jinlun
2fb9623a-24b6-4860-b909-86aac148d8ff

Lee, Younjoo J., Matrai, Patricia A., Friedrichs, Marjorie A. M., Saba, Vincent S., Aumont, Olivier, Babin, Marcel, Buitenhuis, Erik T., Chevallier, Matthieu, De Mora, Lee, Dessert, Morgane, Dunne, John P., Ellingsen, Ingrid H., Feldman, Doron, Frouin, Robert, Gehlen, Marion, Gorgues, Thomas, Ilyina, Tatiana, Jin, Meibing, John, Jasmin G., Lawrence, Jon, Manizza, Manfredi, Menkes, Christophe E., Perruche, Coralie, Le Fouest, Vincent, Popova, Ekaterina E., Romanou, Anastasia, Samuelsen, Annette, Schwinger, Jörg, Séférian, Roland, Stock, Charles A., Tjiputra, Jerry, Tremblay, L. Bruno, Ueyoshi, Kyozo, Vichi, Marcello, Yool, Andrew and Zhang, Jinlun (2016) Net primary productivity estimates and environmental variables in the Arctic Ocean:: An assessment of coupled physical-biogeochemical models. Journal of Geophysical Research: Oceans, 121 (12), 8635-8669. (doi:10.1002/jgrc.v121.12).

Record type: Article

Abstract

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959–2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

Text
jgrc22024 - Version of Record
Download (2MB)

More information

Accepted/In Press date: 8 November 2016
e-pub ahead of print date: 9 December 2016
Published date: December 2016
Organisations: Marine Systems Modelling, National Oceanography Centre

Identifiers

Local EPrints ID: 407619
URI: http://eprints.soton.ac.uk/id/eprint/407619
ISSN: 2169-9275
PURE UUID: 7378d180-3318-4c7f-b3ca-339da7a2089c

Catalogue record

Date deposited: 16 Apr 2017 17:04
Last modified: 15 Mar 2024 12:43

Export record

Altmetrics

Contributors

Author: Younjoo J. Lee
Author: Patricia A. Matrai
Author: Marjorie A. M. Friedrichs
Author: Vincent S. Saba
Author: Olivier Aumont
Author: Marcel Babin
Author: Erik T. Buitenhuis
Author: Matthieu Chevallier
Author: Lee De Mora
Author: Morgane Dessert
Author: John P. Dunne
Author: Ingrid H. Ellingsen
Author: Doron Feldman
Author: Robert Frouin
Author: Marion Gehlen
Author: Thomas Gorgues
Author: Tatiana Ilyina
Author: Meibing Jin
Author: Jasmin G. John
Author: Jon Lawrence
Author: Manfredi Manizza
Author: Christophe E. Menkes
Author: Coralie Perruche
Author: Vincent Le Fouest
Author: Ekaterina E. Popova
Author: Anastasia Romanou
Author: Annette Samuelsen
Author: Jörg Schwinger
Author: Roland Séférian
Author: Charles A. Stock
Author: Jerry Tjiputra
Author: L. Bruno Tremblay
Author: Kyozo Ueyoshi
Author: Marcello Vichi
Author: Andrew Yool
Author: Jinlun Zhang

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×