Jorge Sarmiento (PI)
Princeton University
jls@princeton.edu

Application of Novel Satellite Carbon Biomass to Develop Ecosystem Models Capable of Predicting Climate Change

Ocean ecosystem models are actively being developed to predict the biological response to and impact on climate change through their influence on the atmosphere-ocean distribution of CO2. Up to now, NASA’s satellite-derived observations of chlorophyll have been the most relevant global data set available for the development, evaluation, and retooling of such models. However, the variable of greatest importance to the global carbon cycle and its impact on climate is the carbon biomass of phytoplankton, not chlorophyll concentration. Consequently, interpreting the discrepancies between simulations and observations is confounded by the fact that the relationship between chlorophyll and carbon biomass is not well understood.  A solution to this problem has been found. Through recent advances in ocean color data analysis it is now possible to simultaneously retrieve global distributions of phytoplankton carbon biomass and chlorophyll concentration (Behrenfeld et al., 2004, in press, attached). Based on this new data set, we propose to do the following:  (1) Convert the new datasets into a form that would be useful to us and others in the field by constructing global 8-day and monthly datasets of carbon biomass and chlorophyll-to-carbon ratio (Chl:C), as well as additional variables that can be derived from these datasets including phytoplankton growth rate, mixed layer net primary production, and mixed layer loss.   (2) Use the resulting global carbon biomass dataset to (a) directly compare observations with model predictions of phytoplankton biomass and growth rate, and to (b) improve our existing ecosystem models and (c) develop a new generation of ''inductive'' models that will be based on the biomass and chlorophyll observations.  (3) In collaboration with our colleagues at NOAA’s Geophysical Fluid Dynamics Lab (GFDL), deploy our ecosystem models in coupled atmosphere-ocean global circulation climate models (AOGCMs).  Run concurrently in AOGCMs, independent carbon biomass-based ecosystem models will make it possible to predict future climate change better than has been possible so far. Based on our previous experience with ecosystem models and our analyses of preliminary carbon biomass estimates, we expect that the response of our new models to global warming will be significantly different from our existing models.

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