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Title: Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison

Abstract

To predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, ‘soil water stress’ and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA)more » exhibited dry-season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. In conclusion, correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3];  [4];  [5];  [6];  [7];  [6];  [8];  [9];  [10];  [11];  [12];  [4]
  1. Univ. of Technology Sydney, Sydney, NSW (Australia). Plant Functional Biology and Climate Change Cluster; Univ. of Arizona, Tucson, AZ (United States). Dept. of Ecology and Evolutionary Biology
  2. Univ. of Southern California, Los Angeles, CA (United States). Dept. of Biological Sciences; Harvard Univ., Cambridge, MA (United States). Dept. of Organismic and Evolutionary Biology
  3. Univ. of Arizona, Tucson, AZ (United States). Dept. of Ecology and Evolutionary Biology; Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Arizona, Tucson, AZ (United States). Dept. of Atmospheric Sciences
  4. Univ. of Arizona, Tucson, AZ (United States). Dept. of Ecology and Evolutionary Biology
  5. Univ. of Arizona, Tucson, AZ (United States). Dept. of Ecology and Evolutionary Biology; Brookhaven National Lab, Upton, NY (United States). Biological, Environmental & Climate Sciences Dept.
  6. Federal Univ. of Vicosa, Vicosa (Brazil). Dept. of Agricultural Engineering
  7. Univ. of Leeds, Leeds (United Kingdom). School of Geography
  8. Inst. Nacional de Pesquisas da Amazonia (INPA), Manaus (Brazil)
  9. Inst. Nacional de Pesquisas da Amazonia (INPA), Manaus (Braz; Embrapa Amazonia Oriental, Belem (Brazil)
  10. Univ. of Oxford (United Kingdom). Environmental Change Inst.
  11. Univ. of Arizona, Tucson, AZ (United States). Dept. of of Atmospheric Sciences
  12. Harvard Univ., Cambridge, MA (United States). Dept. of Organismic and Evolutionary Biology
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1341704
Report Number(s):
BNL-113452-2017-JA
Journal ID: ISSN 1354-1013; R&D Project: 21087; YN0100000
Grant/Contract Number:
SC00112704
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Global Change Biology
Additional Journal Information:
Journal Volume: 23; Journal Issue: 1; Journal ID: ISSN 1354-1013
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Amazonia; carbon dynamics; dynamic global vegetation models; ecosystem–climate interactions; eddy covariance; seasonality; tropical forests phenology

Citation Formats

Restrepo-Coupe, Natalia, Levine, Naomi M., Christoffersen, Bradley O., Albert, Loren P., Wu, Jin, Costa, Marcos H., Galbraith, David, Imbuzeiro, Hewlley, Martins, Giordane, da Araujo, Alessandro C., Malhi, Yadvinder S., Zeng, Xubin, Moorcroft, Paul, and Saleska, Scott R.. Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison. United States: N. p., 2016. Web. doi:10.1111/gcb.13442.
Restrepo-Coupe, Natalia, Levine, Naomi M., Christoffersen, Bradley O., Albert, Loren P., Wu, Jin, Costa, Marcos H., Galbraith, David, Imbuzeiro, Hewlley, Martins, Giordane, da Araujo, Alessandro C., Malhi, Yadvinder S., Zeng, Xubin, Moorcroft, Paul, & Saleska, Scott R.. Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison. United States. doi:10.1111/gcb.13442.
Restrepo-Coupe, Natalia, Levine, Naomi M., Christoffersen, Bradley O., Albert, Loren P., Wu, Jin, Costa, Marcos H., Galbraith, David, Imbuzeiro, Hewlley, Martins, Giordane, da Araujo, Alessandro C., Malhi, Yadvinder S., Zeng, Xubin, Moorcroft, Paul, and Saleska, Scott R.. 2016. "Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison". United States. doi:10.1111/gcb.13442. https://www.osti.gov/servlets/purl/1341704.
@article{osti_1341704,
title = {Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison},
author = {Restrepo-Coupe, Natalia and Levine, Naomi M. and Christoffersen, Bradley O. and Albert, Loren P. and Wu, Jin and Costa, Marcos H. and Galbraith, David and Imbuzeiro, Hewlley and Martins, Giordane and da Araujo, Alessandro C. and Malhi, Yadvinder S. and Zeng, Xubin and Moorcroft, Paul and Saleska, Scott R.},
abstractNote = {To predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, ‘soil water stress’ and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry-season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. In conclusion, correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.},
doi = {10.1111/gcb.13442},
journal = {Global Change Biology},
number = 1,
volume = 23,
place = {United States},
year = 2016,
month = 8
}

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