Do Dynamic Global Vegetation Models Capture the Seasonality of Carbon Fluxes in the Amazon Basin? A Data-Model Intercomparison

Seasonal carbon fluxes in Amazon forests.

The Science

This study compared and contrasted the observed and modeled seasonality of ecosystem photosynthesis (gross primary productivity, GPP), leaf, and wood production (NPPleaf and NPPwood) at four sites across the Amazon basin, spanning dry season lengths of 1 to 6 months. Observations came from a network of eddy covariance towers and associated ground-based measurements; models were IBIS, ED2, JULES, and CLM3.5, many of which are used in coupled climate–carbon cycle simulations.

The Impact

Observations in Amazonian forests consistently show that seasonality in GPP is driven by endogenous biological cycles of leaf flushing and associated age-related trends in leaf-level photosynthetic capacity. This intercomparison makes an important link between model deficiencies in seasonal carbon flux dynamics with the missing biological mechanisms driving photosynthesis and leaf and stem growth in seasonal Amazon forests. It therefore guides model development with these seasonal carbon flux benchmarks and by highlighting leaf age and carbon sink limitation as key mechanisms underlying these patterns.


Using dynamic global vegetation models (DGVMs) for prediction requires that they be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. In this data-model intercomparison of DGVMs and observations of carbon fluxes at four forests in the Amazon basin, the scientists found that most DGVMs poorly represented the annual cycle of GPP, of photosynthetic capacity (Pc), and of leaf and stem growth. Because these mechanisms are absent from models, modeled GPP seasonality usually follows that of soil moisture availability, which only agrees with observations at the driest, southernmost site. Furthermore, observations suggest that seasonality in growth (NPP) arises from lags or other processes limiting the allocation of GPP to leaves and stems, mechanisms also absent from models. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.

Principal Investigator

Brad Christoffersen
Los Alamos National Laboratory
[email protected]

Program Manager

Daniel Stover
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
[email protected]


This research was funded by the Gordon and Betty Moore Foundation “Simulations from the Interactions Between Climate, Forests, and Land Use in the Amazon Basin: Modeling and Mitigating Large Scale Savannization” project and the NASA LBADMIP project (# NNX09AL52G). N.R.C. acknowledges the Plant Functional Biology and Climate Change Cluster at the University of Technology Sydney, the National Aeronautics and Space Administration (NASA) LBA investigation CD-32, the National Science Foundation’s Partnerships for International Research and Education (PIRE) (#OISE-0730305). B.O.C. and J.W. were funded in part by the Office of Biological and Environmental Research (BER), within the U.S. Department of Energy (DOE) Office of Science, through the Next-Generation Ecosystem Experiments (NGEE)Tropics project to Los Alamos National Laboratory and by the NGEE-Tropics project through contract #DESC00112704 to Brookhaven National Laboratory, respectively.


Restrepo-Coupe, N., N. M. Levine, B. O. Christoffersen, and L. P. Albert, et al. "Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison." Global Change Biology 23 (1), 191–208  (2017).