Back to Search Start Over

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models

Authors :
Patricia A. Matrai
Timothy J Smyth
Bernard Gentili
Frédéric Mélin
Takahiko Kameda
Younjoo Lee
David Antoine
Ichio Asanuma
Toru Hirawake
Michele Scardi
Zhongping Lee
Mathieu Ardyna
Christian Katlein
Toby K. Westberry
Marjorie A. M. Friedrichs
Marcel Babin
Simon Bélanger
Sang Heon Lee
Kevin R. Turpie
Shilin Tang
Emmanuel Devred
Vincent S. Saba
Mar Fernández-Méndez
Kirk Waters
Sung-Ho Kang
Maxime Benoît‐Gagné
Bigelow Laboratory for Ocean Sciences
Virginia Institute of Marine Science (VIMS)
Laboratoire d'océanographie de Villefranche (LOV)
Observatoire océanologique de Villefranche-sur-mer (OOVM)
Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)
Takuvik Joint International Laboratory ULAVAL-CNRS
Université Laval [Québec] (ULaval)-Centre National de la Recherche Scientifique (CNRS)
Université du Québec à Rimouski (UQAR)
Fisheries and Oceans Canada (DFO)
Norwegian Polar Institute
Hokkaido University [Sapporo, Japan]
KIOST
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI)
European Commission - Joint Research Centre [Ispra] (JRC)
Università degli Studi di Roma Tor Vergata [Roma]
Plymouth Marine Laboratory (PML)
Plymouth Marine Laboratory
NASA
Department of Botany and Plant Pathology
Oregon State University (OSU)
Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)
Université Laval [Québec] (ULaval)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)
Source :
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2015, 120 (9), pp.6508-6541. ⟨10.1002/2015JC011018⟩, Journal of Geophysical Research. Oceans, EPIC3Journal of Geophysical Research: Oceans, pp. n/a-n/a, ISSN: 21699275
Publication Year :
2015
Publisher :
HAL CCSD, 2015.

Abstract

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll‐a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed‐layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite‐derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low‐productivity seasons as well as in sea ice‐covered/deep‐water regions. Depth‐resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption‐based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll‐a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic‐relevant parameters.<br />Key Points The models reproduced primary productivity better using in situ chlorophyll‐a than satellite valuesThe models performed well in low‐productivity seasons and in sea ice‐covered/deep‐water regionsNet primary productivity models need to be carefully tuned for the Arctic Ocean

Details

Language :
English
Database :
OpenAIRE
Journal :
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2015, 120 (9), pp.6508-6541. ⟨10.1002/2015JC011018⟩, Journal of Geophysical Research. Oceans, EPIC3Journal of Geophysical Research: Oceans, pp. n/a-n/a, ISSN: 21699275
Accession number :
edsair.doi.dedup.....230bd66aa5c0fcbea7b805e5c6248479
Full Text :
https://doi.org/10.1002/2015JC011018⟩