The divergent long-term predictions of ecosystem models in the Amazon raises some important questions: How well do models predict processes that we are measuring right now, such as gross primary productivity (GPP), ecosystem respiration, latent heat exchange and surface energy balance? What model structures or mechanisms are responsible for diverging predictions? Which parameters in models are most sensitive to calibration?
Tropical forest ecosystems are arguably the most complex on earth, and much simplification is necessary in order to model them. Ecosystem models are generally regarded as repositories for knowledge of how different plant functional types respond to variation in climate, but more importantly, are comprised of hypotheses as to the dominant ecophysiological or demographic processes controlling seasonal and year-to-year variation in ecosystem-level processes. This project leverages legacy and ongoing measurements made part of the Large Scale Biosphere Atmosphere Experiment in Amazônia (LBA) with eddy covariance towers at eight discrete sites ranging from seasonally wet forests to cerrado, to provide both input meteorological data to drive ecosystem models and the ecosystem-level measurements necessary to evaluate the models. Intercomparing models, both in structure an in predictions, is a powerful tool for testing models as hypotheses, discovering which components of models must be improved, as well as which processes in nature are highest priority for further experimentation or observations in order to reduce our uncertainly in modeling those processes.
Publications: Sakaguchi, K., X. Zeng, B.J. Christoffersen, N. Restrepo-Coupe, S.R. Saleska, and P.M. Brando (2011), Natural and Drought Scenarios in an East-Central Forest: Fidelity of the Community Land Model 3.5 with Three Biogeochemical Models, J. Geophys. Res., doi: 10/1029/2010JG001477, in press.
Students: Brad Christoffersen, PhD Candidate, Ecology and Evolutionary Biology, University of Arizona; Gabriel Moreno, Master's Student, Atmospheric Sciences, University of Arizona; Koichi Sakaguchi, PhD Candidate, Hydrometerology, University of Arizona.
Project Collaborators: Gustavo Gonçalves, Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Brazil; Xubin Zeng, Professor, Atmospheric Sciences, University of Arizona; Paul Moorcroft, Professor, Organismic and Evolutionary Biology, Harvard University; Rafael Rosolem, Research Assistant Professor, Hydrology and Water Resources, University of Arizona.