Projected strengthening of Amazonian dry season by constrained climate model simulations

Projected strengthening of Amazonian dry season by constrained climate model simulations
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DOI:
10.1038/nclimate2658
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发表时间:
2015-07-01
影响因子:
30.7
通讯作者:
Guimberteau, Matthieu
Guimberteau, Matthieu
中科院分区:
地球科学1区
文献类型:
--
作者:
Boisier, Juan P.;Ciais, Philippe;Guimberteau, Matthieu

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亚马逊雨林的脆弱性及其提供的生态服务取决于旱季水的充足供应,无论是降水还是土壤储存的水分。因此,承载雨水的南美季风在整个世纪将如何演变,是一个令人感兴趣的重大问题。大规模的天然化,伴随着森林碳储量和吸收能力的损失,是一种极端的、尽管非常不确定的情景(1-6)。我们发现,亚马逊河流域的36个全球气候模型(GCM)模拟的对比降雨预测可以再现经验降水模型,校准与历史GCM数据的大尺度环流的功能。因此,这些简单的模型与观测校准,并用于约束GCM模拟。根据目前的水文趋势(7、8),对21世纪末的预测是,季风季节性周期将加强,亚马逊河流域南部的旱季将延长。用这种方法,在该地区受到的热带稀树草原,倾向于旱季的增加是大大大于GCM模拟的。我们的研究结果证实了最先进的GCM所显示的主导画面,但表明,这些影响的模型民主的观点可能被大大低估。
The vulnerability of Amazonian rainforest, and the ecological services it provides, depends on an adequate supply of dry-season water, either as precipitation or stored soil moisture. How the rain-bearing South American monsoon will evolve across the twenty-first century is thus a question of major interest. Extensive savanization, with its loss of forest carbon stock and uptake capacity, is an extreme although very uncertain scenario(1-6). We show that the contrasting rainfall projections simulated for Amazonia by 36 global climate models (GCMs) can be reproduced with empirical precipitation models, calibrated with historical GCM data as functions of the large-scale circulation. A set of these simple models was therefore calibrated with observations and used to constrain the GCM simulations. In agreement with the current hydrologic trends(7,8), the resulting projection towards the end of the twenty-first century is for a strengthening of the monsoon seasonal cycle, and a dry-season lengthening in southern Amazonia. With this approach, the increase in the area subjected to lengthy-savannah-prone-dry seasons is substantially larger than the GCM-simulated one. Our results confirm the dominant picture shown by the state-of-the-art GCMs, but suggest that the model democracy view of these impacts can be significantly underestimated.