Multi-pattern fingerprint method for detection and attribution of climate change
Multi-pattern fingerprint method for detection and attribution of climate change
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DOI：
10.1007/s003820050185
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发表时间：
1997-09-01
期刊：
影响因子：
4.6
通讯作者：
Hasselmann, K
中科院分区：
文献类型：
--
作者：
Hasselmann, K
将存在自然内变率的外部强迫气候变化信号的多变量最优指纹检测方法推广到归因问题。为了确定在观测到的气候资料中探测到的气候变化信号是否可归因于某一特定的气候强迫机制或机制组合，必须具体说明候选气候强迫的预测时空相关气候变化信号型。除了信号模式外，该方法还需要输入自然气候变率和预测信号模式误差的时空相关协方差矩阵信息。检测和归因问题被视为一系列单独的一致性检验，适用于所有候选强迫机制，以及在预测的气候变化模式所跨越的相空间内没有发生气候变化的零假设。作为输出，该方法产生了在观测数据中检测气候变化信号的显著性杠杆，以及检索到的气候变化信号与每个强迫机制的一致性的个别置信水平。如果统计置信水平超过给定的临界值，则认为统计上显著的气候变化信号与给定的强迫机制一致，但只有在该置信水平上拒绝所有其他候选气候变化机制（来自有限的提议机制集）时，才将其归因于该强迫。虽然所有的关系都可以很容易地用标准矩阵符号表示，但分析是使用张量符号进行的，其度量由自然变异性协方差矩阵给出。这简化了推导，并澄清了协变信号模式与逆变指纹模式之间的不变关系。信号模式定义了分析气候轨迹的约简向量空间，而指纹则需要将气候轨迹投影到该约简空间上。
The multi-variate optimal fingerprint method for the detection of an externally forced climate change signal in the presence of natural internal variability is extended to the attribution problem. To determine whether a climate change signal which has been detected in observed climate data can be attributed to a particular climate forcing mechanism, or combination of mechanisms, the predicted space-time dependent climate change signal patterns for the candidate climate forcings must be specified. In addition to the signal patterns, the method requires input information on the space-time dependent covariance matrices of the natural climate variability and of the errors of the predicted signal patterns. The detection and attribution problem is treated as a sequence of individual consistency tests applied to all candidate forcing mechanisms, as well as to the null hypothesis that no climate change has taken place, within the phase space spanned by the predicted climate change patterns. As output the method yields a significance lever for the detection of a climate change signal in the observed data and individual confidence levels for the consistency of the retrieved climate change signal with each of the forcing mechanisms. A statistically significant climate change signal is regarded as consistent with a given forcing mechanism if the statistical confidence level exceeds a given critical value, but is attributed to that forcing only if all other candidate climate change mechanisms (from a finite set of proposed mechanisms) are rejected at that confidence level. Although all relations can be readily expressed in standard matrix notation, the analysis is carried out using tensor notation, with a metric given by the natural-variability covariance matrix. This simplifies the derivations and clarifies the invariant relation between the covariant signal patterns and their contravariant fingerprint counterparts. The signal patterns define the reduced Vector space in which the climate trajectories are analyzed, while the fingerprints are needed to project the climate trajectories onto this reduced space.