Author Posting. © Elsevier B.V., 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 76 (2009): 95-112, doi:10.1016/j.jmarsys.2008.05.015.
We present a generalized framework for assessing the skill of global upper ocean
ecosystem-biogeochemical models against in-situ field data and satellite observations.
We illustrate the approach utilizing a multi-decade (1979-2004) hindcast experiment
conducted with the Community Climate System Model (CCSM-3) ocean carbon model.
The CCSM-3 ocean carbon model incorporates a multi-nutrient, multi-phytoplankton
functional group ecosystem module coupled with a carbon, oxygen, nitrogen,
phosphorus, silicon, and iron biogeochemistry module embedded in a global, threedimensional
ocean general circulation model. The model is forced with physical climate
forcing from atmospheric reanalysis and satellite data products and time-varying
atmospheric dust deposition. Data-based skill metrics are used to evaluate the simulated
time-mean spatial patterns, seasonal cycle amplitude and phase, and subannual to
interannual variability. Evaluation data include: sea surface temperature and mixed layer
depth; satellite derived surface ocean chlorophyll, primary productivity, phytoplankton
growth rate and carbon biomass; large-scale climatologies of surface nutrients, pCO2, and
air-sea CO2 and O2 flux; and time-series data from the Joint Global Ocean Flux Study
(JGOFS). Where the data is sufficient, we construct quantitative skill metrics using:
model-data residuals, time-space correlation, root mean square error, and Taylor
This work was supported in part by grants from the NSF/ONR National Ocean
Partnership Program (N000140210370), the NASA Ocean Biology and Biogeochemistry
Program (NNX07AL80G), and the NSF Center for Microbial Oceanography Research
and Education (C-MORE).
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