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We 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., Key Points The models reproduced primary productivity better using in situ chlorophyll‐a than satellite valuesThe models performed well in low‐productivity seasons and in sea ice‐covered/deep‐water regionsNet primary productivity models need to be carefully tuned for the Arctic Ocean

A primary production model, specific to the subtropical North Pacific, was developed using productivity and chlorophyll data collected at Station ALOHA (22.75°N, 158.00°W) and measured phytoplankton physiological parameters. The production algorithm is a mechanistic, full spectral model that provides depth-dependent photosynthesis rates. Six years of phytoplankton pigment data were analyzed to derive seasonal cycles and to parameterize chlorophyll a (Chl; monovinyl and divinyl chlorophyll a) versus depth profiles. The Chl profiles fit a Gaussian-type distribution with depth over a constant background concentration equal to mixed-layer values. Maximum Chl concentrations were always found below the surface, and the parameters displayed distinct monthly and seasonal patterns. Photosynthesis versus irradiance experiments were conducted at a number of locations and seasons in the vicinity of Station ALOHA to calculate phytoplankton physiological parameters needed for the computation of primary production rates. Relationships were explored between environmental conditions and the physiological and profile parameters in order to develop algorithms based on remotely sensed sea-surface variables and time of year. These relationships were used to model primary production rates using data collected during the Hawaii Ocean Time-series (HOT) program. The performance of the model and model parameters were tested by comparing modeled results to those measured directly at Station ALOHA by trace metal-clean, in situ 14C incubation techniques. Monthly averaged chlorophyll profile parameters and a constant maximum quantum yield (0.026 mol C(mol quanta)−1) generates an annual production estimate of 168 g C m−2, which closely resembles that measured at Station ALOHA (181 g C m−2) during the same period. The measured and modeled primary production rates for the subtropical North Pacific are twice as high as values derived from Coastal Zone Color Scanner (CZCS) data (60–90 g C m−2) during the period 1978–1986. If the study area near Station ALOHA is representative of most oligotrophic waters, current global estimates of primary production rates have been significantly underestimated.

Made available in DSpace on 2013-08-28T14:13:50Z (GMT). No. of bitstreams: 1 WOS000287796800018.pdf: 4189122 bytes, checksum: 57872b9405eca4911ea4efd9756f160c (MD5) Made available in DSpace on 2013-09-30T19:22:54Z (GMT). No. of bitstreams: 2 WOS000287796800018.pdf: 4189122 bytes, checksum: 57872b9405eca4911ea4efd9756f160c (MD5) WOS000287796800018.pdf.txt: 74870 bytes, checksum: b4ae21f33d5d23b3ff5f0482dfbc34ee (MD5) Previous issue date: 2011-01-01 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T15:34:00Z No. of bitstreams: 2 WOS000287796800018.pdf: 4189122 bytes, checksum: 57872b9405eca4911ea4efd9756f160c (MD5) WOS000287796800018.pdf.txt: 74870 bytes, checksum: b4ae21f33d5d23b3ff5f0482dfbc34ee (MD5) Made available in DSpace on 2014-05-20T15:34:00Z (GMT). No. of bitstreams: 2 WOS000287796800018.pdf: 4189122 bytes, checksum: 57872b9405eca4911ea4efd9756f160c (MD5) WOS000287796800018.pdf.txt: 74870 bytes, checksum: b4ae21f33d5d23b3ff5f0482dfbc34ee (MD5) Previous issue date: 2011-01-01 National Aeronautics and Space Agency 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 C-14 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. Virginia Inst Marine Sci, Coll William & Mary, Gloucester Point, VA 23062 USA CNRS, LOV, Villefranche Sur Mer, France Univ Paris 06, UMR 7093, Villefranche Sur Mer, France SUNY Stony Brook, Sch Marine & Atmospher Sci, Stony Brook, NY 11794 USA Tokyo Univ Informat Sci, Chiba 2658501, Japan Oregon State Univ, Dept Bot & Plant Pathol, Corvallis, OR 97331 USA Univ Estadual Paulista, BR-11330900 São Paulo, Brazil European Commiss, Joint Res Ctr, I-21027 Ispra, Italy NOAA, NMFS, Narragansett Lab, Narragansett, RI 02882 USA Nagoya Univ, Hydrospher Atmospher Res Ctr, Nagoya, Aichi 4648601, Japan Seikai Natl Fisheries Res Inst, Ishigaki Trop Stn, Okinawa 9070451, Japan CUNY, Brooklyn Coll, Dept Geol, Brooklyn, NY 11210 USA Univ Roma Tor Vergata, Dept Biol, I-00173 Rome, Italy Plymouth Marine Lab, Plymouth PL1 3DH, Devon, England Fisheries & Oceans Canada, Inst Freshwater, Winnipeg, MB R3T 2N6, Canada Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA 92093 USA NOAA, Coastal Serv Ctr, Charleston, SC 29405 USA Univ Estadual Paulista, BR-11330900 São Paulo, Brazil NASA: NNG06GA03G

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.

International audience; 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.

[1] Results of a single-blind round-robin comparison of satellite primary productivity algorithms are presented. The goal of the round-robin exercise was to determine the accuracy of the algorithms in predicting depth-integrated primary production from information amenable to remote sensing. Twelve algorithms, developed by 10 teams, were evaluated by comparing their ability to estimate depth-integrated daily production (IP, mg C m−2) at 89 stations in geographically diverse provinces. Algorithms were furnished information about the surface chlorophyll concentration, temperature, photosynthetic available radiation, latitude, longitude, and day of the year. Algorithm results were then compared with IP estimates derived from 14C uptake measurements at the same stations. Estimates from the best-performing algorithms were generally within a factor of 2 of the 14C-derived estimates. Many algorithms had systematic biases that can possibly be eliminated by reparameterizing underlying relationships. The performance of the algorithms and degree of correlation with each other were independent of the algorithms’ complexity.