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Biogeosciences, 8, 489-503, 2011
www.biogeosciences.net/8/489/2011/
doi:10.5194/bg-8-489-2011
© Author(s) 2011. This work is distributed
under the Creative Commons Attribution 3.0 License.
An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe
V. S. Saba1,2, M. A. M. Friedrichs1, D. Antoine3, R. A. Armstrong4, I. Asanuma5, M. J. Behrenfeld6, A. M. Ciotti7, M. Dowell8, N. Hoepffner8, K. J. W. Hyde9, J. Ishizaka10, T. Kameda11, J. Marra12, F. Mélin8, A. Morel3, J. O'Reilly9, M. Scardi13, W. O. Smith Jr.1, T. J. Smyth14, S. Tang15, J. Uitz16, K. Waters17, and T. K. Westberry6
1Virginia Institute of Marine Science, The College of William & Mary, P.O. Box 1346, Gloucester Point, VA, 23062-1346, USA
2Presently at the Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ, USA
3Laboratoire d'Océanographie de Villefranche, LOV, CNRS et Université Pierre et Marie Curie, Paris 06, UMR 7093, Villefranche-sur-Mer, France
4School of Marine and Atmospheric Sciences, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
5Tokyo University of Information Sciences, 4-1-1, Onaridai, Wakaba, Chiba, 265-8501, Japan
6Department of Botany and Plant Pathology, Cordley Hall 2082, Oregon State University, USA
7UNESP-Campus Experimental do Litoral Paulista, PraçaInfante Dom Henrique S/N, São Vicente, São Paulo CEP 11330-900, Brazil
8European Commission – Joint Research Centre, 21027 Ispra, Italy
9NOAA/NMFS Narragansett Laboratory, 28 Tarzwell Drive, Narragansett, RI, 02882, USA
10Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan
11Ishigaki Tropical Station, Seikai National Fisheries Research Institute 148-446, Fukai-Ohta, Ishigaki-shi Okinawa 907-0451, Japan
12Geology Department, Brooklyn College of the City University of New York, 2900 Bedford Ave., Brooklyn, NY 11210, USA
13Department of Biology, University of Rome "Tor Vergata", Via della Ricerca Scientifica, 00133 Roma, Italy
14Plymouth Marine Laboratory, Prospect Place, Plymouth, Devon PL1 3DH, UK
15Freshwater Institute, Fisheries and Oceans Canada, 501 University Crescent, Winnipeg, Manitoba R3T 2N6, Canada
16Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
17NOAA Coastal Services Center, 2234 South Hobson Ave., Charleston, SC 29405-2413, USA

Abstract. Nearly half of the earth's photosynthetically fixed carbon derives from the oceans. To determine global and region specific rates, we rely on models that estimate marine net primary productivity (NPP) thus it is essential that these models are evaluated to determine their accuracy. Here we assessed the skill of 21 ocean color models by comparing their estimates of depth-integrated NPP to 1156 in situ 14C measurements encompassing ten marine regions including the Sargasso Sea, pelagic North Atlantic, coastal Northeast Atlantic, Black Sea, Mediterranean Sea, Arabian Sea, subtropical North Pacific, Ross Sea, West Antarctic Peninsula, and the Antarctic Polar Frontal Zone. Average model skill, as determined by root-mean square difference calculations, was lowest in the Black and Mediterranean Seas, highest in the pelagic North Atlantic and the Antarctic Polar Frontal Zone, and intermediate in the other six regions. The maximum fraction of model skill that may be attributable to uncertainties in both the input variables and in situ NPP measurements was nearly 72%. On average, the simplest depth/wavelength integrated models performed no worse than the more complex depth/wavelength resolved models. Ocean color models were not highly challenged in extreme conditions of surface chlorophyll-a and sea surface temperature, nor in high-nitrate low-chlorophyll waters. Water column depth was the primary influence on ocean color model performance such that average skill was significantly higher at depths greater than 250 m, suggesting that ocean color models are more challenged in Case-2 waters (coastal) than in Case-1 (pelagic) waters. Given that in situ chlorophyll-a data was used as input data, algorithm improvement is required to eliminate the poor performance of ocean color NPP models in Case-2 waters that are close to coastlines. Finally, ocean color chlorophyll-a algorithms are challenged by optically complex Case-2 waters, thus using satellite-derived chlorophyll-a to estimate NPP in coastal areas would likely further reduce the skill of ocean color models.

Citation: Saba, V. S., Friedrichs, M. A. M., Antoine, D., Armstrong, R. A., Asanuma, I., Behrenfeld, M. J., Ciotti, A. M., Dowell, M., Hoepffner, N., Hyde, K. J. W., Ishizaka, J., Kameda, T., Marra, J., Mélin, F., Morel, A., O'Reilly, J., Scardi, M., Smith Jr., W. O., Smyth, T. J., Tang, S., Uitz, J., Waters, K., and Westberry, T. K.: An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe, Biogeosciences, 8, 489-503, doi:10.5194/bg-8-489-2011, 2011.
 
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