SCSIO OpenIR  > 热带海洋环境国家重点实验室(LTO)
An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models
Lee, Younjoo J.; Matrai, Patricia A.; Friedrichs, Marjorie A. M.; Saba, Vincent S.; Antoine, David; Ardyna, Mathieu; Asanuma, Ichio; Babin, Marcel; Belanger, Simon; Benoit-Gagne, Maxime; Devred, Emmanuel; Fernandez-Mendez, Mar; Gentili, Bernard; Hirawake, Toru; Kang, Sung-Ho; Kameda, Takahiko; Katlein, Christian; Lee, Sang H.; Lee, Zhongping; Melin, Frederic; Scardi, Michele; Smyth, Tim J.; Tang, Shilin; Turpie, Kevin R.; Waters, Kirk J.; Westberry, Toby K.;
AbstractWe 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.
Department[Lee, Younjoo J.; Matrai, Patricia A.] Bigelow Lab Ocean Sci, East Boothbay, ME 04544 USA; [Friedrichs, Marjorie A. M.] Virginia Inst Marine Sci, Coll William & Mary, Gloucester Point, VA 23062 USA; [Saba, Vincent S.] Northeast Fisheries Sci Ctr, NOAA Natl Marine Fisheries Serv, Princeton, NJ USA; [Antoine, David; Gentili, Bernard] Univ Paris 06, Univ Paris 04, Villefranche Sur Mer, France; [Antoine, David; Gentili, Bernard] CNRS, UMR 7093, LOV, Observ Oceanol, Villefranche Sur Mer, France; [Antoine, David] Curtin Univ, Remote Sensing & Satellite Res Grp, Dept Phys Astron & Med Radiat Sci, Perth, WA 6845, Australia; [Ardyna, Mathieu; Babin, Marcel; Benoit-Gagne, Maxime; Devred, Emmanuel] Univ Laval, CNRS, Takuvik Joint Int Lab, Quebec City, PQ, Canada; [Asanuma, Ichio] Tokyo Univ Informat Sci, Chiba, Japan; [Belanger, Simon] Univ Quebec Rimouski, Dept Biol Chem & Geog, Rimouski, PQ, Canada; [Fernandez-Mendez, Mar; Katlein, Christian] Alfred Wegener Inst Helmholtz Zentrum Polarund me, Bremerhaven, Germany; [Hirawake, Toru] Hokkaido Univ, Fac Fisheries Sci, Hakodate, Hokkaido, Japan; [Kang, Sung-Ho] Korea Polar Res Inst, Inchon, South Korea; [Kameda, Takahiko] Fisheries Res Agcy, Seikai Natl Fisheries Res Inst, Nagasaki, Japan; [Lee, Sang H.] Pusan Natl Univ, Dept Oceanog, Busan, South Korea; [Lee, Zhongping] Univ Massachusetts, Sch Environm, Boston, MA 02125 USA; [Melin, Frederic] Inst Environm & Sustainabil, European Commiss Joint Res Ctr, Ispra, Italy; [Scardi, Michele] Univ Roma Tor Vergata, Dept Biol, I-00173 Rome, Italy; [Smyth, Tim J.] Plymouth Marine Lab, Plymouth, Devon, England; [Tang, Shilin] Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Guangzhou, Guangdong, Peoples R China; [Turpie, Kevin R.] Univ Maryland, Baltimore Cty Joint Ctr Earth Syst Technol, Baltimore, MD 21201 USA; [Waters, Kirk J.] NOAA Off Coastal Management, Charleston, SC USA; [Westberry, Toby K.] Oregon State Univ, Dept Bot & Plant Pathol, Corvallis, OR 97331 USA ; LTO
KeywordArctic Ocean Net Primary Productivity Model Skill Assessment Subsurface Chlorophyll-a Maximum Ocean Color Model Remote Sensing
Subject AreaOceanography
Indexed BySCI
Document Type期刊论文
Recommended Citation
GB/T 7714
Lee, Younjoo J.,Matrai, Patricia A.,Friedrichs, Marjorie A. M.,et al. An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models[J]. JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS,2015,120(9):6508-6541.
APA Lee, Younjoo J..,Matrai, Patricia A..,Friedrichs, Marjorie A. M..,Saba, Vincent S..,Antoine, David.,...& assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models.JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS,120(9),6508-6541.
MLA Lee, Younjoo J.,et al."An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models".JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS 120.9(2015):6508-6541.
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