A comparison of global estimates of marine primary production from ocean color

被引:502
作者
Carr, Mary-Elena
Friedrichs, Marjorie A. M.
Schmeltz, Marjorie
Aita, Maki Noguchi
Antoine, David
Arrigo, Kevin R.
Asanuma, Ichio
Aumont, Olivier
Barber, Richard
Behrenfeld, Michael
Bidigare, Robert
Buitenhuis, Erik T.
Campbell, Janet
Ciotti, Aurea
Dierssen, Heidi
Dowell, Mark
Dunne, John
Esaias, Wayne
Gentili, Bernard
Gregg, Watson
Groom, Steve
Hoepffner, Nicolas
Ishizaka, Joji
Kameda, Takahiko
Le Quere, Corinne
Lohrenz, Steven
Marra, John
Melin, Frederic
Moore, Keith
Morel, Andre
Reddy, Tasha E.
Ryan, John
Scardi, Michele
Smyth, Tim
Turpie, Kevin
Tilstone, Gavin
Waters, Kirk
Yamanaka, Yasuhiro
机构
[1] CALTECH, Jet Propuls Lab, Pasadena, CA 91101 USA
[2] Old Dominion Univ, Ctr Coastal Phys Oceanog, Norfolk, VA 23529 USA
[3] Fontier Res Ctr Global Change, Ecosyst Change Res Program, Yokohama, Kanagawa 2360001, Japan
[4] Oceanog Lab, F-06238 Villefranche Sur Mer, France
[5] Stanford Univ, Dept Geophys, Stanford, CA 94305 USA
[6] Tokyo Univ Informat Sci, Chiba 2658501, Japan
[7] Univ Paris 06, Lab Oceanog Dynam & Climatol, CNRS, MHNN,IRD, F-75252 Paris 05, France
[8] Duke Univ, Marine Lab, Beaufort, NC 28516 USA
[9] Oregon State Univ, Dept Bot & Plant Pathol, Corvallis, OR 97331 USA
[10] Univ Hawaii, Dept Oceanog, Honolulu, HI 96822 USA
[11] Max Planck Inst Biogeochem, D-07701 Jena, Germany
[12] Univ New Hampshire, Durham, NH 03824 USA
[13] Univ Estadual Paulista, BR-11330900 Sao Paulo, Brazil
[14] Univ Connecticut, Dept Marine Sci, Groton, CT 06340 USA
[15] Joint Res Ctr, Inland & Marine Waters Unit, I-21020 Ispra, Italy
[16] NOAA, Geophys Fluid Dynam Lab, Princeton, NJ 08542 USA
[17] NASA, Goddard Space Flight Ctr, Global Modeling & Assimilat Off, Greenbelt, MD 20771 USA
[18] Plymouth Marine Lab, Remote Sensing Grp, Plymouth PL1 3DH, Devon, England
[19] Nagasaki Univ, Fac Fisheries, Nagasaki 8528521, Japan
[20] Natl Res Inst Far Seas Fisheries, Grp Oceanog, Shizuoka 4248633, Japan
[21] Univ E Anglia, Norwich NR4 7TJ, Norfolk, England
[22] British Antarctic Survey, Norwich NR4 7TJ, Norfolk, England
[23] Stennis Space Ctr, Dept Marine Sci, Stennis Space Ctr, MS 39529 USA
[24] Lamont Doherty Earth Observ, Palisades, NY 10964 USA
[25] Univ Calif Irvine, Irvine, CA 92697 USA
[26] MBARI, Moss Landing, CA 95039 USA
[27] Univ Roma Tor Vergata, Dept Biol, I-00133 Rome, Italy
[28] NOAA, Coastal Serv Ctr, Charleston, SC 29405 USA
[29] Coll William & Mary, Virginia Inst Marine Sci, Gloucester Point, VA 23062 USA
基金
英国自然环境研究理事会;
关键词
D O I
10.1016/j.dsr2.2006.01.028
中图分类号
P7 [海洋学];
学科分类号
0707 ;
摘要
The third primary production algorithm round robin (PPARR3) compares output from 24 models that estimate depth-integrated primary production from satellite measurements of ocean color, as well as seven general circulation models (GCMs) coupled with ecosystem or biogeochemical models. Here we compare the global primary production fields corresponding to eight months of 1998 and 1999 as estimated from common input fields of photosynthetically-available radiation (PAR), sea-surface temperature (SST), mixed-layer depth, and chlorophyll concentration. We also quantify the sensitivity of the ocean-color-based models to perturbations in their input variables. The pair-wise correlation between ocean-color models was used to cluster them into groups or related output, which reflect the regions and environmental conditions under which they respond differently. The groups do not follow model complexity with regards to wavelength or depth dependence, though they are related to the manner in which temperature is used to parameterize photosynthesis. Global average PP varies by a factor of two between models. The models diverged the most for the Southern Ocean, SST under 10 degrees C, and chlorophyll concentration exceeding 1 mg Chlm(-3). Based on the conditions under which the model results diverge most, we conclude that current ocean-color-based models are challenged by high-nutrient low-chlorophyll conditions, and extreme temperatures or chlorophyll concentrations. The GCM-based models predict comparable primary production to those based on ocean color: they estimate higher values in the Southern Ocean, at low SST, and in the equatorial band, while they estimate lower values in eutrophic regions (probably because the area of high chlorophyll concentrations is smaller in the GCMs). Further progress in primary production modeling requires improved understanding of the effect of temperature on photosynthesis and better parameterization of the maximum photosynthetic rate. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:741 / 770
页数:30
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