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
- 4Université Paris Saclay, CEA-CNRS-UVSQ, LSCE/IPSL, Gif sur Yvette, France
- 5CSIRO Oceans and Atmosphere, G.P.O. Box 1700, Canberra, ACT 2601, Australia
- 6Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61821, USA
- 7CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France, Unite mixte de recherche 3589 Meteo-France/CNRS, 42 Avenue Gaspard Coriolis, 31100 Toulouse, France
- 8Institute of Applied Energy (IAE), Minato-ku, Tokyo 105-0003, Japan
- 9Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
- 10National Center for Atmospheric Research, Climate and Global Dynamics, Terrestrial Sciences Section, Boulder, CO 80305, USA
- 11Department of Meteorology, Department of Geography & Environmental Science, National Centre for Atmospheric Science, University of Reading, Reading, UK
- 12Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada
- 13Max Planck Institute for Meteorology, Hamburg, Germany
- 14Department of Geography, LMU, Munich, Germany
- 15College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK
- 16Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
- 17Max Planck Institute for Biogeochemistry, P.O. Box 600164, Hans-Knöll-Str. 10, 07745 Jena, Germany
- deceased, 19 January 2021
Abstract. 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 such as the global carbon budget, the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations of net biome production (NBP) and the carbon stored in vegetation between 1901 to 2018 from 13 DGVMs (TRENDY v8 ensemble). We focused our analysis on both Australia's short-term (inter-annual) and long-term (decadal to centennial) terrestrial carbon dynamics. The TRENDY models simulated differing magnitudes of NBP on inter-annual timescales, and these differences contributed to carbon accumulation in the vegetation on decadal to centennial timescales (−4.7–9.5 PgC). We compared the TRENDY ensemble to several satellite-derived datasets and showed that the spread in the models' simulated carbon storage resulted from varying changes in 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 carbon dioxide (CO2) concentration, although notably, the models with nutrient cycles did not simulate the smallest NBP response to CO2. Our results suggest that a change in the climate forcing did not have a large impact on the carbon cycle on long timescales. However, 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 Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on NBP. While the DGVMs agreed on sign of the response of NBP to El Nino and La Nina, and to positive and negative IOD events, the magnitude of inter-annual variability in NBP differed strongly between models. In addition, we identified differences in the timing of simulated phenology and fire dynamics associated with differences in simulated/prescribed vegetation composition and process representation. Model disagreement in simulated vegetation carbon, phenology and apparent carbon residence time, indicates the models have different types of vegetation cover across Australia (whether prescribed or emergent). Our study highlights the need to evaluate parameter assumptions and the key processes that drive vegetation dynamics, such as phenology, mortality and fire, in an Australian context to reduce uncertainty across models.
Lina Teckentrup et al.
Status: final response (author comments only)
Lina Teckentrup et al.
Lina Teckentrup et al.
Viewed (geographical distribution)