Assessing the representation of the Australian carbon cycle in global vegetation models
- 1ARC Centre of Excellence for Climate Extremes, Sydney, NSW, Australia
- 2Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia
- 3Evolution & Ecology Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
- 4Laboratoire des Sciences du Climat et de l’Environnement, Institut Pierre-Simon Laplace, CEA-CNRS-UVSQ, CE Orme des Merisiers, 91191 Gif-sur-Yvette CEDEX, France
- 5Université Paris Saclay, CEA-CNRS-UVSQ, LSCE/IPSL, Gif sur Yvette, France
- 6CSIRO Oceans and Atmosphere, G.P.O. Box 1700, Canberra, ACT 2601, Australia
- 7Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61821, USA
- 8Centre National de Recherche Meteorologique, Unite mixte de recherche 3589 Meteo-France/CNRS, 42 Avenue Gaspard Coriolis, 31100 Toulouse, France
- 9Institute of Applied Energy (IAE), Minato-ku, Tokyo 105-0003, Japan
- 10Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
- 11National Center for Atmospheric Research, Climate and Global Dynamics, Terrestrial Sciences Section, Boulder, CO 80305, USA
- 12Department of Meteorology, Department of Geography & Environmental Science, National Centre for Atmospheric Science, University of Reading, Reading, UK
- 13Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada
- 14Max Planck Institute for Meteorology, Land in the Earth System, Bundesstraße 53, Hamburg, Germany
- 15Department of Geography, LMU, Munich, Germany
- 16College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK
- 17Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
- 18Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK
- 19Max Planck Institute for Biogeochemistry, P.O. Box 600164, Hans-Knöll-Str. 10, 07745 Jena, Germany
Australia plays an important role in the global terrestrial carbon cycle on inter-annual timescales. While the Australian continent is included in global assessments of the carbon cycle, the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations of net biome productivity (NBP) and the carbon stored in vegetation between 1901 to 2018 from 13 DGVMs (TRENDY v8 ensemble). The TRENDY models simulated differing magnitudes of NBP on inter-annual timescales, leading to marked differences in carbon accumulation in the vegetation on decadal to centennial timescales. We showed that the spread in carbon storage resulted from differences in simulated carbon residence time rather than differences in net carbon uptake. Differences in simulated long-term accumulated NBP between models were mostly due to model responses to land-use change. The DGVMs also simulated different sensitivities to atmospheric CO2 concentration. Notably, models with nutrient cycles did not simulate the smallest response. While our results suggested that changes in the climate forcing do not have a large impact on the carbon cycle on long timescales, the inter-annual variability in precipitation drives the year-to-year variability in NBP. We analysed the impact of key modes of climate variability, including the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). While the DGVMs agreed on sign of the response of NBP to El Niño and La Niña, and to positive and negative IOD events, the magnitude of inter-annual variability in NBP differs strongly between models. In addition, we identified simulated phenology and fire as associated with high model uncertainty, indicating differences in simulated vegetation composition and process representation. Model disagreement in simulated vegetation carbon, phenology and carbon residence time imply different types of vegetation cover across Australia between models, whether prescribed or resulting from model assumptions. Our study highlights the need to evaluate parameter assumptions and key processes that drive vegetation dynamics, such as phenology, mortality and fire, in an Australian context to reduce uncertainty across models.
How to cite: Teckentrup, L., De Kauwe, M., Pitman, A., Bastrikov, V., Goll, D., Haverd, V., Jain, A., Joetzjer, E., Kato, E., Lienert, S., Lombardozzi, D., McGuire12, P., Melton, J., Nabel, J., Pongratz, J., Sitch, S., Walker, A., Wiltshire, A., and Zaehle, S.: Assessing the representation of the Australian carbon cycle in global vegetation models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3620, https://doi.org/10.5194/egusphere-egu21-3620, 2021.
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