Abstract
<p>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 CO<sub>2</sub> 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.</p>
Highlights
Decadal variability in the growth rate of atmospheric carbon dioxide (CO2) is strongly influenced by variability in the uptake and release of carbon by the oceans and the terrestrial biosphere (Ballantyne et al, 2012; Raupach et al, 2008)
Oceania has been found to contribute significantly to the uncertainty in global and regional carbon budgets (Bastos et al, 2020), and the important role played by semi-arid ecosystems in explaining the variability in the global carbon cycle was highlighted by the 2011 La Niña event
This carbon uptake enhancement has been associated with the asymmetric response of gross primary production (GPP) to precipitation in combination with vegetation expansion linked to rainfall
Summary
Decadal variability in the growth rate of atmospheric carbon dioxide (CO2) is strongly influenced by variability in the uptake and release of carbon by the oceans and the terrestrial biosphere (Ballantyne et al, 2012; Raupach et al, 2008). ENSO has been shown to explain more than 40 % of satellite-derived net primary production (NPP) variability, mainly driven by the response of Southern Hemisphere ecosystems (Bastos et al, 2013) and in particular semi-arid ecosystems (Zhang et al, 2018). A recent study suggested that the Australian terrestrial carbon sink may be enhanced due to more extreme wet events projected for future decades (Ma et al, 2016). This carbon uptake enhancement has been associated with the asymmetric response of GPP to precipitation (i.e. positive GPP anomalies tend to be larger than negative ones; Haverd et al, 2017) in combination with vegetation expansion linked to rainfall (based on a single dynamic vegetation model; Poulter et al, 2014). A series of studies have identified evidence of rising CO2, leading to a marked greening of the Australian continent (Donohue et al, 2009, 2013; Ukkola et al, 2016b; Trancoso et al, 2017)
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