Resilience Indicators for Tropical Rainforests in a Dynamic Vegetation Model

Abstract

Tropical forests and particularly the Amazon rainforest have been identified as potential tipping elements in the Earth system. According to a dynamical systems theory, a decline in forest resilience preceding a potential shift to a savanna-like biome could manifest as increasing autocorrelation of biomass time series. Recent satellite records indeed exhibit such a trend and also show larger autocorrelation, indicative of reduced resilience, in drier forest regions. However, it is unclear which processes underlie these observational findings and on which scales they operate. Here, we investigate which processes determine tropical forest resilience in the stand-alone, state-of-the-art dynamic global vegetation model LPJmL. We find that autocorrelation is higher in dry climates than wet climates (approx. 0.75 vs. 0.2, for a lag of 10 years), which qualitatively agrees with observations. By constructing a reduced version of LPJmL and by disabling and enabling certain processes in the model, we show that (i) this pattern is associated with population dynamics operating on different time scales in different climates and (ii) that the pattern is sensitive to the allocation of carbon to different pools, especially in years of stress. Both processes are highly uncertain, oversimplified or even lacking in most Earth system models. Our results indicate that the observed spatial variations and trends in vegetation resilience indicators may be explained by local physiological and ecological mechanisms alone, without climate-vegetation feedbacks. In principle, this is consistent with the view that the Amazon rainforest is responding to climate change locally and does not necessarily need to approach one large-scale tipping point, although the latter cannot be ruled out based on our findings.

Keywords: Amazon rainforest; climate change; critical slowing down; dynamic global vegetation models; early warning signals; ecosystem resilience; tipping points.