Increasing atmospheric CO2 overrides the historical legacy of multiple stable biome states in Africa. Issue 3 (28th October 2013)

• Record Type: Journal Article
• Authors: Moncrieff, Glenn R.; Scheiter, Simon; Bond, William J.; Higgins, Steven I.
• Is Part Of: New phytologist. Volume 201:Issue 3(2014)
• Page Start: 908
• Page End: 915
• Publication Date: 2013-10-28
• Subjects: adaptive dynamic global vegetation model (aDGVM) -- biome -- climate change -- dynamic vegetation model -- forest -- multiple stable states -- savanna; Botany -- Periodicals; 580
• DOI: 10.1111/nph.12551
• Languages: English
• ISSNs: 0028-646X

Abstract

Summary: The dominant vegetation over much of the global land surface is not predetermined by contemporary climate, but also influenced by past environmental conditions. This confounds attempts to predict current and future biome distributions, because even a perfect model would project multiple possible biomes without knowledge of the historical vegetation state. Here we compare the distribution of tree‐ and grass‐dominated biomes across Africa simulated using a dynamic global vegetation model (DGVM). We explicitly evaluate where and under what conditions multiple stable biome states are possible for current and projected future climates. Our simulation results show that multiple stable biomes states are possible for vast areas of tropical and subtropical Africa under current conditions. Widespread loss of the potential for multiple stable biomes states is projected in the 21st Century, driven by increasing atmospheric CO2 . Many sites where currently both tree‐dominated and grass‐dominated biomes are possible become deterministically tree‐dominated. Regions with multiple stable biome states are widespread and require consideration when attempting to predict future vegetation changes. Testing for behaviour characteristic of systems with multiple stable equilibria, such as hysteresis and dependence on historical conditions, and the resulting uncertainty in simulated vegetation, will lead to improved projections of global change impacts.