An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-abased models.
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-aconcentration (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-amaximum (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.
Bigelow Laboratory for Ocean Sciences East Boothbay Maine USA.
Younjoo J
YJ
Lee
Bigelow Laboratory for Ocean Sciences East Boothbay Maine USA.
Patricia A
PA
Matrai
Virginia Institute of Marine Science, College of William and Mary Gloucester Point Virginia USA.
Marjorie A M
MA
Friedrichs
NOAA National Marine Fisheries Service, Northeast Fisheries Science Center Princeton New Jersey USA.
Vincent S
VS
Saba
Sorbonne Universités, UPMC Univ Paris 06 and CNRS, UMR 7093, LOV, Observatoire océanologique Villefranche/mer France; Remote Sensing and Satellite Research Group, Department of Physics, Astronomy and Medical Radiation Sciences Curtin University Perth Western Australia Australia.
David
D
Antoine
Takuvik Joint International Laboratory CNRS - Université Laval Québec Canada.
Mathieu
M
Ardyna
Tokyo University of Information Sciences Chiba Japan.
Ichio
I
Asanuma
Takuvik Joint International Laboratory CNRS - Université Laval Québec Canada.
Marcel
M
Babin
Department of Biology, Chemistry and Geography Université du Québec à Rimouski Rimouski Québec Canada.
Simon
S
Bélanger
Takuvik Joint International Laboratory CNRS - Université Laval Québec Canada.
Maxime
M
Benoît-Gagné
Takuvik Joint International Laboratory CNRS - Université Laval Québec Canada.
Emmanuel
E
Devred
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung Bremerhaven Germany.
Mar
M
Fernández-Méndez
Sorbonne Universités, UPMC Univ Paris 06 and CNRS, UMR 7093, LOV, Observatoire océanologique Villefranche/mer France.
Bernard
B
Gentili
Faculty of Fisheries Sciences Hokkaido University Hakodate Japan.
Toru
T
Hirawake
Korea Polar Research Institute Incheon Republic of Korea.
Sung-Ho
SH
Kang
Seikai National Fisheries Research Institute, Fisheries Research Agency Nagasaki Japan.
Takahiko
T
Kameda
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung Bremerhaven Germany.
Christian
C
Katlein
Department of Oceanography Pusan National University Busan Republic of Korea.
Sang H
SH
Lee
School for the Environment, University of Massachusetts-Boston Boston Massachusetts USA.
Zhongping
Z
Lee
European Commission, Joint Research Centre, Institute for Environment and Sustainability Ispra Italy.
Frédéric
F
Mélin
Department of Biology 'Tor Vergata' University Rome Italy.
Michele
M
Scardi
Plymouth Marine Laboratory Plymouth UK.
Tim J
TJ
Smyth
State Key Laboratory of Tropical Oceanography South China Sea Institute of Oceanology, Chinese Academy of Sciences Guangzhou China.
Shilin
S
Tang
Baltimore County-Joint Center for Earth System Technology, University of Maryland Baltimore Maryland USA.
Kevin R
KR
Turpie
NOAA Office for Coastal Management Charleston South Carolina USA.
Kirk J
KJ
Waters
Department of Botany and Plant Pathology Oregon State University Corvallis Oregon USA.
Toby K
TK
Westberry
Arctic Ocean; model skill assessment; net primary productivity; ocean color model; remote sensing; subsurface chlorophyll‐a maximum
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