Abstract
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, three-dimensional 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, pCO 2, and air–sea CO 2 and O 2 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.
Highlights
The last two decades witnessed a dramatic increase in the volume of global ocean biogeochemical and ecological observations due, in part, to coordinated international field programs (e.g., Joint Global Ocean Flux Study JGOFS; WorldOcean Circulation Experiment WOCE), satellite ocean color sensors, and emerging and ongoing ocean observing systems (e.g., Fasham et al, 2001; Doney and Hood, 2002; McClain et al, 2004)
Physical fields include sea surface temperature (SST), which is important for biological growth and respiration rates as well as air–sea gas exchange, and mixed layer depth (MLD), which influences nutrient entrainment and the average light field observed by the phytoplankton
A systematic and quantitative approach for assessing model–data skill is an essential tool in model development, evaluation, and data assimilation (Gregg et al, this volume), and emerging global-scale field and satellite data sets provide invaluable opportunities for testing upper ocean coupled ecosystem-biogeochemistry-physical models
Summary
The last two decades witnessed a dramatic increase in the volume of global ocean biogeochemical and ecological observations due, in part, to coordinated international field programs Data availability combined with increasing computational power stimulated a rapid growth in basin to global upper ocean ecosystem-biogeochemistry models (e.g., Sarmiento et al, 1993; Six and Maier-Reimer, 1996; Oschlies and Garcon, 1998; Doney, 1999; Gregg et al, 2003; Aumont et al, 2003; Moore et al, 2004; Le Quéré et al, 2005; Doney and Ducklow, 2006) Such models are widely applied to questions from seasonal and interannual climate variability (e.g., Le Quéré et al, 2000; McKinley et al, 2004; Wetzel et al, 2005; McKinley et al, 2006; Lovenduski et al, 2007; Le Quéré et al, 2007) to anthropogenic climate change (e.g., Bopp et al., S.C. Doney et al / Journal of Marine Systems 76 (2009) 95–112. Where the data is sufficient, we construct quantitative skill scores (time– space correlation and model and data rms variability) (Lima and Doney, 2004)
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