Increasing atmospheric CO2 overrides the historical legacy of multiple stable biome states in Africa.
Increasing atmospheric CO2 overrides the historical legacy of multiple stable biome states in Africa.
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DOI：
10.1111/nph.12551
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发表时间：
2014-02
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影响因子：
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通讯作者：
Glenn R. Moncrieff;S. Scheiter;W. Bond;S. Higgins
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文献类型：
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作者：
Glenn R. Moncrieff;S. Scheiter;W. Bond;S. Higgins
全球大部分陆地表面的主要植被不仅是由当代气候决定的，而且还受到过去环境条件的影响。这混淆了预测当前和未来生物群分布的尝试，因为即使是一个完美的模型也会在不了解历史植被状态的情况下预测多个可能的生物群。在这里，我们比较了使用动态全球植被模型(DGVM)模拟的以树木和草为主的生物群在整个非洲的分布。我们明确评估当前和预测的未来气候在哪里以及在什么条件下多种稳定的生物群状态是可能的。我们的模拟结果表明，在当前条件下，热带和亚热带非洲的广大地区可能存在多个稳定的生物群状态。在大气二氧化碳增加的推动下，预计在21世纪，多个稳定生物群状态的潜力将普遍丧失。许多目前可能同时以树木和草为主的生物群的地点变得确定地以树木为主。具有多种稳定生物群状态的地区分布广泛，在试图预测未来植被变化时需要加以考虑。测试具有多个稳定平衡的系统的行为特征，如滞后和对历史条件的依赖，以及由此产生的模拟植被的不确定性，将导致对全球变化影响的更好预测。
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.