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Long memory in temperature reconstructions

机译：温度重建中的长记忆

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Ever since H. E. Hurst brought the concept of long memory time series to prominence in his study of river flows the origins of the so-called Hurst phenomena have remained elusive. Two sets of competing models have been proposed. The fractional Gaussian noises and their discrete time counter-part, the fractionally integrated processes, possess genuine long memory in the sense that the present state of a system has a temporal dependence on all past states. The alternative to these genuine long memory models are models which are non-stationary in the mean but for physical reasons are constrained to lie in a bounded range, hence on visual inspection appear to be stationary. In these models the long memory is merely an artifact of the method of analysis. There are now a growing number of millenial scale temperature reconstructions available. In this paper we present a new way of looking at long memory in these reconstructions and proxies, which gives support to them being described by the non-stationary models. The implications for climatic change are that the temperature time series are not mean reverting. There is no evidence to support the idea that the observed rise in global temperatures are a natural fluctuation which will reverse in the near future.
机译：自从H. E. Hurst在研究河流流动以来就提出了长记忆时间序列的概念以来，所谓的Hurst现象的起源一直难以捉摸。已经提出了两组竞争模型。从系统的当前状态对所有过去状态都具有时间依赖性的意义上讲，分数高斯噪声及其离散时间对应部分（分数积分过程）具有真正的长记忆。这些真正的长记忆模型的替代方案是平均不是平稳的模型，但由于物理原因而被限制在一定范围内，因此在目视检查中看起来是静止的。在这些模型中，长记忆仅仅是分析方法的产物。现在，越来越多的千年规模温度重建可用。在本文中，我们提出了一种在这些重构和代理中查看长记忆的新方法，这为由非平稳模型描述的它们提供了支持。气候变化的含义是温度时间序列不是均值回复。没有证据支持这一观点，即观测到的全球温度升高是自然波动，这种波动将在不久的将来逆转。

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