Estimating signal amplitudes in optimal fingerprinting, part I: theory
Estimating signal amplitudes in optimal fingerprinting, part I: theory
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DOI：
10.1007/s00382-003-0313-9
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发表时间：
2003-11-01
期刊：
影响因子：
4.6
通讯作者：
Stott, PA
中科院分区：
文献类型：
--
作者：
Allen, MR;Stott, PA
越来越清楚的证据表明，人类的影响对过去几十年来发生的大规模气候变化起了很大作用。现在人们的注意力正转向新出现的人为信号的物理含义。特别令人感兴趣的问题是，目前的气候模式是否高估或低估了气候系统对外部强迫，包括人为强迫的反应幅度。如果模型模拟的响应振幅存在显著误差，则表明存在模型中未充分表示的放大或阻尼机制。在保持与观测变化一致的同时，我们可以缩放模型模拟变化的因子的不确定性范围提供了基于模型的预测的不确定性估计。任何模型，显示了一个现实水平的内部变异性，估计这个因素的问题是复杂的，因为它代表了两个不完全已知的数量之间的比率：观察和模拟的响应都受到抽样的不确定性，主要是由于内部混沌变异。通过集合模拟，模拟响应中的采样不确定性可以减少，但不能消除。这些比例因子的准确估计需要修改的标准的“最佳指纹”算法的气候变化检测，借鉴传统的“总最小二乘”的方法在统计文献中讨论。最佳指纹识别的两种变体的代码可以在http://www.climateprediction.net/detection上找到。
There is increasingly clear evidence that human influence has contributed substantially to the large-scale climatic changes that have occurred over the past few decades. Attention is now turning to the physical implications of the emerging anthropogenic signal. Of particular interest is the question of whether current climate models may be over- or under-estimating the amplitude of the climate system's response to external forcing, including anthropogenic. Evidence of a significant error in a model-simulated response amplitude would indicate the existence of amplifying or damping mechanisms that are inadequately represented in the model. The range of uncertainty in the factor by which we can scale model-simulated changes while remaining consistent with observed change provides an estimate of uncertainty in model-based predictions. With any model that displays a realistic level of internal variability, the problem of estimating this factor is complicated by the fact that it represents a ratio between two incompletely known quantities: both observed and simulated responses are subject to sampling uncertainty, primarily due to internal chaotic variability. Sampling uncertainty in the simulated response can be reduced, but not eliminated, through ensemble simulations. Accurate estimation of these scaling factors requires a modification of the standard 'optimal fingerprinting' algorithm for climate change detection, drawing on the conventional 'total least squares' approach discussed in the statistical literature. Code for both variants of optimal fingerprinting can be found on http://www.climateprediction.net/detection.