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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2019-491
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2019-491
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 10 Feb 2020

Submitted as: research article | 10 Feb 2020

Review status
A revised version of this preprint was accepted for the journal BG and is expected to appear here in due course.

Understanding the uncertainty in global forest carbon turnover

Thomas A. M. Pugh1, Tim T. Rademacher2,3,4, Sarah L. Shafer5, Jörg Steinkamp6,7, Jonathan Barichivich8,9, Brian Beckage10, Vanessa Haverd11, Anna Harper12, Jens Heinke13, Kazuya Nishina14, Anja Rammig15, Hisashi Sato16, Almut Arneth17, Stijn Hantson18, Thomas Hickler6,19, Markus Kautz20, Benjamin Quesada17,21, Benjamin Smith22,23, and Kirsten Thonicke13 Thomas A. M. Pugh et al.
  • 1School of Geography, Earth & Environmental Sciences and Birmingham Institute of Forest Research, University of Birmingham, Birmingham, B15 2TT, United Kingdom
  • 2Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
  • 3School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
  • 4Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA
  • 5Geosciences and Environmental Change Science Center, U.S. Geological Survey, 3200 SW Jefferson Way, Corvallis, OR 97331, USA
  • 6Senckenberg Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage 25, 60325 Frankfurt/Main, Germany
  • 7Johannes Gutenberg-University Mainz, Anselm-Franz-von-Bentzel-Weg 12, 55128 Mainz, Germany
  • 8Instituto de Conservación Biodiversidad y Territorio, Universidad Austral de Chile, Valdivia, Chile
  • 9Laboratoire des Sciences du Climat et de l’Environnement, IPSL, CNRS/CEA/UVSQ, 91191 Gif sur Yvette, France
  • 10Department of Plant Biology & Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
  • 11CSIRO Oceans and Atmosphere, PO Box 3023, Canberra, ACT 2601, Australia
  • 12College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter, UK
  • 13Potsdam-Institute for Climate Impact Research (PIK), Telegraphenberg, 14473 Potsdam, Germany
  • 14Center for Regional Environmental Studies, National Institute for Environmental Studies (NIES), 16-2, Onogawa, Tsukuba, Japan
  • 15Technical University of Munich (TUM), School of Life Sciences Weihenstephan, Freising, Germany
  • 16Institute of Arctic Climate and Environment Research (IACE), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showamachi, Kanazawa-ku, Yokohama, 236-0001, Japan
  • 17Karlsruhe Institute of Technology, Institute of Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstrasse 19, 82467, Garmisch-Partenkirchen, Germany
  • 18Department of Earth System Science, University of California, Irvine, CA, USA
  • 19Department of Physical Geography, Goethe University, Altenhöferallee 1, 60348 Frankfurt/Main, Germany
  • 20Department of Forest Protection, Forest Research Institute Baden-Württemberg, 79100 Freiburg, Germany
  • 21Universidad del Rosario, Faculty of Natural Sciences and Mathematics, Research Group “Interactions Climate-Ecosystems (ICE)”, Cra 26 63b-48, 111221, Bogotá, Colombia
  • 22Department of Physical Geography and Ecosystem Science, Lund University, 22362 Lund, Sweden
  • 23Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith NSW 2751, Australia

Abstract. The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle, with both recent-historical baselines and future responses to environmental change poorly constrained by available observations. In the absence of large-scale observations, models tend to fall back on simplified assumptions of the turnover rates of biomass and soil carbon pools to make global assessments. In this study, the biomass carbon turnover times calculated by an ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to assess their current capability to accurately estimate biomass carbon turnover times in forests and how these times are anticipated to change in the future. Modelled baseline 1985–2014 global forest biomass turnover times vary from 12.2 to 23.5 years between models. TBM differences in phenological processes, which control allocation to and turnover rate of leaves and fine roots, are as important as tree mortality with regard to explaining the variation in total turnover among TBMs. The different governing mechanisms exhibited by each TBM result in a wide range of plausible turnover time projections for the end of the century. Based on these simulations, it is not possible to draw robust conclusions regarding likely future changes in turnover time for different regions. Both spatial and temporal uncertainty in turnover time are strongly linked to model assumptions concerning plant functional type distributions and their controls. Twelve model-based hypotheses are identified, along with recommendations for pragmatic steps to test them using existing and novel observations, which would help to reduce both spatial and temporal uncertainty in turnover time. Efforts to resolve uncertainty in turnover time will need to address both mortality and establishment components of forest demography, as well as key drivers of demography such as allocation of carbon to woody versus non-woody biomass growth.

Thomas A. M. Pugh et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Thomas A. M. Pugh et al.

Data sets

Simulations from the JULES dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison A. Harper https://doi.org/10.5281/zenodo.3579375

Simulations from the CABLE-POP land surface model for the Vegetation Carbon Turnover Intercomparison V. Haverd https://doi.org/10.5281/zenodo.3579407

Simulations from the LPJmL3.5 dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison J. Heinke, An. Rammig, and K. Thonicke https://doi.org/10.5281/zenodo.3579396

Simulations from the ORCHIDEE dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison J. Barichivich https://doi.org/10.5281/zenodo.3579402

Simulations from the SEIB-DGVM dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison H. Sato and K. Nishina https://doi.org/10.5281/zenodo.3579384

Simulations from the LPJ-GUESS dynamic global vegetation model v3.0 for the Vegetation Carbon Turnover Intercomparison T. A. M. Pugh and B. Beckage https://doi.org/10.5281/zenodo.3576036

Thomas A. M. Pugh et al.

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Short summary
The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle. Estimates from six contemporary models found this time to range from 12.2 to 23.5 years in the global mean for 1985–2014. Future projections do not give consistent results, but twelve model-based hypotheses are identified, along with recommendations for pragmatic steps to test them using existing and novel observations, which would help to reduce current large uncertainty.
The length of time that carbon remains in forest biomass is one of the largest uncertainties in...
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