Atmospheric circulation as a source of uncertainty in climate change projections

Atmospheric circulation as a source of uncertainty in climate change projections
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DOI:
10.1038/ngeo2253
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发表时间:
2014-10-01
期刊:
影响因子:
18.3
通讯作者:
Shepherd, Theodore G.
Shepherd, Theodore G.
中科院分区:
地球科学1区
文献类型:
--
作者:
Shepherd, Theodore G.

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人为气候变化的证据继续加强,对恶劣天气事件的关注正在增加。因此,科学兴趣正迅速从探测和确定全球气候变化的原因转向预测其在区域范围的影响。然而,在气候变化方面,我们有信心的几乎所有事情都与全球表面温度模式有关,而全球表面温度模式主要由热力学控制。相比之下,我们对气候变化的大气环流方面的信心要小得多,这主要是由动力学控制,并对区域气候施加强有力的控制。包括降水在内的环流相关领域的模式预测显示了广泛的可能结果,即使是在百年时间尺度上。不确定性的来源包括低频混沌变率和环流对气候强迫的响应对模式误差的敏感性。由于环流对外部强迫的响应似乎强烈地投射到现有的变率模式上,变率动态中的误差知识可能会对模式预测提供一些限制。然而,在气候变化与环流有关的方面,很难获得更高的科学信心。为了有效的决策,有必要转向一种更明确的概率性、基于风险的方法。
The evidence for anthropogenic climate change continues to strengthen, and concerns about severe weather events are increasing. As a result, scientific interest is rapidly shifting from detection and attribution of global climate change to prediction of its impacts at the regional scale. However, nearly everything we have any confidence in when it comes to climate change is related to global patterns of surface temperature, which are primarily controlled by thermodynamics. In contrast, we have much less confidence in atmospheric circulation aspects of climate change, which are primarily controlled by dynamics and exert a strong control on regional climate. Model projections of circulation-related fields, including precipitation, show a wide range of possible outcomes, even on centennial timescales. Sources of uncertainty include low-frequency chaotic variability and the sensitivity to model error of the circulation response to climate forcing. As the circulation response to external forcing appears to project strongly onto existing patterns of variability, knowledge of errors in the dynamics of variability may provide some constraints on model projections. Nevertheless, higher scientific confidence in circulation-related aspects of climate change will be difficult to obtain. For effective decision-making, it is necessary to move to a more explicitly probabilistic, risk-based approach.