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 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 resulted in significant differences in long-term vegetation carbon accumulation (-4.7 to 9.5ĝ€¯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 Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), on NBP. 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 differed strongly between models. In addition, we find that differences in the timing of simulated phenology and fire dynamics are associated with differences in simulated or prescribed vegetation cover and process representation. We further find model disagreement in simulated vegetation carbon, phenology, and apparent carbon residence time, indicating that the models have different types and coverage of vegetation 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.
| Original language | English |
|---|---|
| Pages (from-to) | 5639-5668 |
| Number of pages | 30 |
| Journal | Biogeosciences |
| Volume | 18 |
| Issue number | 20 |
| DOIs | |
| State | Published - Oct 20 2021 |
Funding
Acknowledgements. This work utilized data from the OzFlux network, which is supported by the Australian Terrestrial Ecosystem Research Network (TERN; http://www.tern.org.au, last access: 8 March 2021). Lina Teckentrup, Martin G. De Kauwe, and Andrew J. Pitman acknowledge support from the Australian Research Council (ARC) Centre of Excellence for Climate Extremes (CE170100023). Martin G. De Kauwe and Andrew J. Pitman acknowledge support from the ARC Discovery Grant (DP190101823). Martin G. De Kauwe was also supported from the NSW Research Attraction and Acceleration Program. Sebastian Lienert acknowledges funding from the Swiss National Science Foundation (grant no. 172476) and from the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement no. 821003 (project 4C, Climate\u2013Carbon Interactions in the Current Century). ORNL is managed by UT-Battelle, LLC, for the DOE under contract DE-AC05-1008 00OR22725. Emilie Joetzjer would like to thank the European Union\u2019s Horizon 2020 research and innovation programme with the CRESCENDO project under the grant agreement no. 641816 and the H2020 CONSTRAIN under the grant agreement no. 820829. JSBACH simulations used resources of the Deutsches Klimarechenzentrum (DKRZ) under project ID bm0891. The CESM project is supported primarily by the National Science Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under cooperative agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (https://doi.org/10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. Danica Lombardozzi was supported by the US Department of Agriculture NIFA award 2015-67003-23485. S\u00F6nke Za-ehle was supported by the European Union\u2019s Horizon 2020 research and innovation programme under the grant agreement no. 821003 (4C project). Atul K. Jain acknowledges his funding support by the US Department of Energy (grant no. DE-SC0016323). Daniel S. Goll benefited from support from the Agence Nationale de la Recherche (ANR) grant ANR-16-CONV-0003 (CLAND). We thank Vladislav Bastrikov and Andrew J. Wiltshire for providing model output as part of the TRENDY v8 ensemble.