U.S. Department of Energy Office of Biological and Environmental Research

PI-Submitted Research Highlights for
Terrestrial Ecosystem Science Program

Do Dynamic Global Vegetation Models Capture The Seasonality Of Carbon Fluxes In The Amazon Basin? A Data-Model Intercomparison

Brad Christoffersen
Los Alamos National Laboratory


Figure. Annual cycle of daily average ecosystem-scale photosynthesis (GPP; gC m2 day1) near Manaus, Brazil (K34 Tower). Observed (black + dark gray uncertainty) and simulated by models (colors).

(figure provided by Brad Christoffersen, included in publication)

08 Mar 2017

Seasonal Carbon Fluxes in Amazon Forests

The Science             
We compared and contrasted the observed and modeled seasonality of ecosystem photosynthesis (GPP), leaf, and wood production (NPPleaf, 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, we found that most DGVMs poorly represented the annual cycle of gross primary productivity (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.

Contacts (BER PM)
Daniel Stover
Daniel.Stover@science.doe.gov (301-903-0289)

(PI Contact)
Brad Christoffersen
Los Alamos National Laboratory
bradley@lanl.gov, 505-665-9118 

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 US DOE (BER) NGEE-Tropics project to LANL and by the Next-Generation Ecosystem Experiment (NGEE-Tropics) project from the US DOE, Office of Science, Office of Biological and Environmental Research and through contract #DESC00112704 to Brookhaven National Laboratory, respectively.

Restrepo-Coupe, N. 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, 191-208, doi:10.1111/gcb.13442 (2017).


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