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ArticleChallenges of modeling depth-integrated marine primary productivity over multiple decades : a case study at BATS and HOT(American Geophysical Union, 2010-09-15) Saba, Vincent S. ; Friedrichs, Marjorie A. M. ; Carr, Mary-Elena ; Antoine, David ; Armstrong, Robert A. ; Asanuma, Ichio ; Aumont, Olivier ; Bates, Nicholas R. ; Behrenfeld, Michael J. ; Bennington, Val ; Bopp, Laurent ; Bruggeman, Jorn ; Buitenhuis, Erik T. ; Church, Matthew J. ; Ciotti, Aurea M. ; Doney, Scott C. ; Dowell, Mark ; Dunne, John P. ; Dutkiewicz, Stephanie ; Gregg, Watson ; Hoepffner, Nicolas ; Hyde, Kimberly J. W. ; Ishizaka, Joji ; Kameda, Takahiko ; Karl, David M. ; Lima, Ivan D. ; Lomas, Michael W. ; Marra, John F. ; McKinley, Galen A. ; Melin, Frederic ; Moore, J. Keith ; Morel, Andre ; O'Reilly, John ; Salihoglu, Baris ; Scardi, Michele ; Smyth, Tim J. ; Tang, Shilin ; Tjiputra, Jerry ; Uitz, Julia ; Vichi, Marcello ; Waters, Kirk ; Westberry, Toby K. ; Yool, AndrewThe 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.
PreprintAssessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean( 2008-03) Friedrichs, Marjorie A. M. ; Carr, Mary-Elena ; Barber, Richard T. ; Scardi, Michele ; Antoine, David ; Armstrong, Robert A. ; Asanuma, Ichio ; Behrenfeld, Michael J. ; Buitenhuis, Erik T. ; Chai, Fei ; Christian, James R. ; Ciotti, Aurea M. ; Doney, Scott C. ; Dowell, Mark ; Dunne, John P. ; Gentili, Bernard ; Gregg, Watson ; Hoepffner, Nicolas ; Ishizaka, Joji ; Kameda, Takahiko ; Lima, Ivan D. ; Marra, John F. ; Melin, Frederic ; Moore, J. Keith ; Morel, Andre ; O'Malley, Robert T. ; O'Reilly, Jay ; Saba, Vincent S. ; Schmeltz, Marjorie ; Smyth, Tim J. ; Tjiputra, Jerry ; Waters, Kirk ; Westberry, Toby K. ; Winguth, ArneDepth-integrated primary productivity (PP) estimates obtained from satellite ocean color based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BOGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of ~1000 14C measurements spanning more than a decade (1983- 1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PP, specifically yielding too few low PP (< 0.2 gC m-2d-2) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomass-normalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140°W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison six years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill.