Challenges of modeling depth‐integrated marine primary productivity over multiple decades: A case study at BATS and HOT

Global Biogeochemical Cycles - Tập 24 Số 3 - 2010
Vincent S. Saba1,2, Marjorie A. M. Friedrichs2, Mary‐Elena Carr3, David Antoine4, Robert A. Armstrong5, Ichio Asanuma6, Olivier Aumont7, Nicholas R. Bates8, Michael J. Behrenfeld9, Val Bennington10, Laurent Bopp11, Jorn Bruggeman12, Erik T. Buitenhuis13, Matthew J. Church14, Áurea Maria Ciotti15, Scott C. Doney16, Mark Dowell17, John P. Dunne18, Stephanie Dutkiewicz19, Watson W. Gregg20, Nicolas Hoepffner17, Kimberly J.W. Hyde21, Joji Ishizaka22, Takahiko Kameda23, David M. Karl14, Ivan D. Lima16, Michael W. Lomas8, John Marra24, Galen A. McKinley10, Frédéric Mélin17, J. Keith Moore25, André Morel4, John E. O’Reilly21, Barış Salihoğlu26, Michele Scardi27, Tim Smyth28, Shilin Tang29, Jerry Tjiputra30, Julia Uitz31, Marcello Vichi32, Kirk Waters33, Toby K. Westberry9, Andrew Yool34
1Now at Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, New Jersey, USA.
2Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, Virginia, USA
3Columbia Climate Center Earth Institute, Columbia University New York New York USA
4Laboratoire d'Océanographie de Villefranche, UMR 7093 Université Pierre et Marie Curie, Paris 06, CNRS Villefranche‐sur‐Mer France
5School of Marine and Atmospheric Sciences State University of New York at Stony Brook Stony Brook New York USA
6Tokyo University of Information Sciences, Chiba, Japan
7Laboratoire d'Océanographie: Expérimentation et Approche Numérique IPSL, UPMC, IRD, CNRS Plouzané France
8Bermuda Institute of Ocean Sciences, St. George’s, Bermuda
9Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, USA
10Department of Atmospheric and Ocean Sciences University of Wisconsin‐Madison Madison Wisconsin USA
11Laboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA, UVSQ, CNRS, Gif-sur-Yvette, France
12Department of Theoretical Biology, Faculty of Earth and Life Sciences Vrije University of Amsterdam Amsterdam Netherlands
13Laboratory for Global Marine and Atmospheric Chemistry, School of Environmental Sciences University of East Anglia Norwich UK
14Department of Oceanography, School of Ocean and Earth Science and Technology University of Hawai'i at Mãnoa Honolulu Hawaii USA
15Campus Experimental do Litoral Paulista UNESP São Vicente Brazil
16Department of Marine Chemistry & Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
17Joint Research Centre, European Commission, Ispra, Italy
18Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, New Jersey, USA
19Earth Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
20NASA Global Modeling and Assimilation Office; Goddard Space Flight Center; Greenbelt; Maryland; USA
21National Marine Fisheries Services Narragansett Laboratory NOAA Narragansett Rhode Island USA
22Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan
23Group of Oceanography National Research Institute of Far Seas Fisheries Shizuoka Japan
24Geology Department, Brooklyn College of the City University of New York, Brooklyn, New York, USA
25Department of Earth System Science, University of California, Irvine, California, USA
26Institute of Marine Sciences, Middle East Technical University, Erdemli, Turkey
27Department of Biology, University of Rome Tor Vergata, Rome, Italy
28Plymouth Marine Laboratory, Plymouth, UK
29Freshwater Institute, Fisheries and Oceans Canada, Winnipeg, Manitoba, Canada
30Geophysical Institute, University of Bergen, Bergen, Norway
31Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California ,USA
32Centro Euro‐Mediterraneo per i Cambiamenti Climatici Instituto Nazionale di Geofisica e Vulcanologia Bologna Italy
33NOAA Coastal Services Center Charleston South Carolina USA
34National Oceanography Centre, Southampton, Southampton, UK

Tóm tắt

The performance of 36 models (22 ocean color models and 14 biogeochemical ocean circulation models (BOGCMs)) that estimate depth‐integrated marine net primary productivity (NPP) was assessed by comparing their output to in situ 14C data at the Bermuda Atlantic Time series Study (BATS) and the Hawaii Ocean Time series (HOT) over nearly two decades. Specifically, skill was assessed based on the models' ability to estimate the observed mean, variability, and trends of NPP. At both sites, more than 90% of the models underestimated mean NPP, with the average bias of the BOGCMs being nearly twice that of the ocean color models. However, the difference in overall skill between the best BOGCM and the best ocean color model at each site was not significant. Between 1989 and 2007, in situ NPP at BATS and HOT increased by an average of nearly 2% per year and was positively correlated to the North Pacific Gyre Oscillation index. The majority of ocean color models produced in situ NPP trends that were closer to the observed trends when chlorophyll‐a was derived from high‐performance liquid chromatography (HPLC), rather than fluorometric or SeaWiFS data. However, this was a function of time such that average trend magnitude was more accurately estimated over longer time periods. Among BOGCMs, only two individual models successfully produced an increasing NPP trend (one model at each site). We caution against the use of models to assess multiannual changes in NPP over short time periods. Ocean color model estimates of NPP trends could improve if more high quality HPLC chlorophyll‐a time series were available.

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