Early warning signals of simulated Amazon rainforest dieback
Early warning signals of simulated Amazon rainforest dieback
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DOI：
10.1007/s12080-013-0191-7
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发表时间：
2013-08-01
影响因子：
1.6
通讯作者：
Lenton, Timothy M.
中科院分区：
文献类型：
--
作者：
Boulton, Chris A.;Good, Peter;Lenton, Timothy M.
我们在模拟亚马逊雨林未来枯死的复杂气候模型（HadCM3）中测试了提出的通用临界点预警信号。模型中的树盖度控制方程表明，树盖度的零稳定状态和非零稳定状态共存，并且随着生产力的下降，树盖度趋于一个跨临界分岔。森林枯死是一种非零树木覆盖状态下的非线性变化，随着生产力的下降，它应该表现出临界减速。我们使用一系列版本的HadCM3来测试相应的预警信号。然而，在接近模拟亚马逊枯死时，在树木覆盖、植被碳或净初级生产力中没有看到预期的临界减速的早期预警信号。在自相关中缺乏令人信服的趋势似乎是系统被快速和非线性强迫的结果。随着时间的推移，方差显著上升，但这可以用年际温度和降水变率的增加来解释，年际温度和降水变率的增加迫使森林生长。一般预警指标的失败导致我们在模型中寻求更具体系统的、可观察的森林稳定性变化指标。净生态系统生产力对温度异常的敏感性（负相关）通常随着枯死期的临近而增加，这可归因于生态系统呼吸对温度的非线性敏感性。因此，随着枯死期的临近，大气CO2异常对温度异常的敏感性（正相关）增加。这种稳定性指标的好处是在现实世界中很容易观察到。
We test proposed generic tipping point early warning signals in a complex climate model (HadCM3) which simulates future dieback of the Amazon rainforest. The equation governing tree cover in the model suggests that zero and non-zero stable states of tree cover co-exist, and a transcritical bifurcation is approached as productivity declines. Forest dieback is a non-linear change in the non-zero tree cover state, as productivity declines, which should exhibit critical slowing down. We use an ensemble of versions of HadCM3 to test for the corresponding early warning signals. However, on approaching simulated Amazon dieback, expected early warning signals of critical slowing down are not seen in tree cover, vegetation carbon or net primary productivity. The lack of a convincing trend in autocorrelation appears to be a result of the system being forced rapidly and non-linearly. There is a robust rise in variance with time, but this can be explained by increases in inter-annual temperature and precipitation variability that force the forest. This failure of generic early warning indicators led us to seek more system-specific, observable indicators of changing forest stability in the model. The sensitivity of net ecosystem productivity to temperature anomalies (a negative correlation) generally increases as dieback approaches, which is attributable to a non-linear sensitivity of ecosystem respiration to temperature. As a result, the sensitivity of atmospheric CO2 anomalies to temperature anomalies (a positive correlation) increases as dieback approaches. This stability indicator has the benefit of being readily observable in the real world.