The Ecosystem Demography model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical, ecological, and biogeochemical dynamics of vertically and horizontally heterogeneous terrestrial ecosystems. In a companion paper (Longo et al., 2019a), we described how the model solves the energy, water, and carbon cycles, and verified the high degree of conservation of these properties in long-term simulations that include long-term (multi-decadal) vegetation dynamics. Here, we present a detailed assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles in Amazon forests. We use multiple measurements from eddy covariance towers, forest inventory plots, and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple timescales, ranging from subdaily to century long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes, and the storage of water, energy, and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 qualitatively captures several emergent properties of the ecosystem found in observations, specifically observed relationships between aboveground biomass, mortality rates, and wood density; however, the slopes of these relationships were not accurately captured. We also identified several limitations, including the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in Earth system models (ESMs) and to realistically represent their impacts on energy, water, and carbon cycles. We also identify several priorities for further model development.
Bibliographical noteFunding Information:
Financial support. This research has been supported by the Con-
Acknowledgements. The research was partially carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. We thank the two reviewers, Miriam Johnston and Luciana Alves, for suggestions that improved the manuscript; Luciana Alves, Bruce Daube, David Fitzjarrald, Elaine Gottlieb, Elizabeth Hammond-Pyle, Lucy Hutyra, Natalia Restrepo-Coupe, Raphael Tapajós, Scott Stark, and Kenia Wiedemann for the management and data processing; and Valerio Avitabile, Alessandro Baccini, and Sassan Saatchi for providing remote-sensing estimates of biomass. Mar-cos Longo was supported the NASA Postdoctoral Program, administered by Universities Space Research Association under contract with NASA. The model simulations were carried out at the Odyssey cluster, supported by the FAS Division of Science, Research Computing Group at Harvard University. Abigail L. S. Swann was supported as a Giorgio Ruffolo Fellow in the Sustainability Science Program at Harvard University, for which support from Italy’s Ministry for Environment, Land and Sea is gratefully acknowledged.
© 2019 Author(s).
All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Earth and Planetary Sciences(all)