Skill metrics for confronting global upper ocean ecosystem-biogeochemistry models against field and remote sensing data
Doney, Scott C.
Lima, Ivan D.
Moore, J. Keith
Behrenfeld, Michael J.
Westberry, Toby K.
Mahowald, Natalie M.
Glover, David M.
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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 diagrams.
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.
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