Reconstructing past hydroclimatic variability using climate-sensitive paleoclimate proxies provides context to our relatively short instrumental climate records and a baseline from which to assess the impacts of human-induced climate change. However, many approaches to reconstructing climate are limited in their ability to address sampling variability inherent in different climate proxies. We iteratively optimise an ensemble of possible reconstruction data series to maximise the Gaussian kernel correlation of Rehfeld et al. (2011) which reconciles differences in the temporal resolution of both the target variable and proxies or covariates. The reconstruction method is evaluated using synthetic data with different degrees of sampling variability and noise. Two examples using paleoclimate proxy records and a third using instrumental rainfall data with missing values are used to demonstrate the utility of the method. While the Gaussian kernel correlation method is relatively computationally expensive, it is shown to be robust under a range of data characteristics and will therefore be valuable in analyses seeking to employ multiple input proxies or covariates.

中文翻译：

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协调不均匀采样的古气候代理：高斯核相关多代理重建

使用气候敏感的古气候代理重建过去的水文气候变异为我们相对较短的仪器气候记录提供了背景，并提供了评估人为气候变化影响的基线。然而，许多重建气候的方法在解决不同气候代理中固有的采样变异性方面的能力有限。我们迭代优化可能的重建数据系列的集合，以最大化 Rehfeld 等人的高斯核相关性。(2011) 协调目标变量和代理或协变量的时间分辨率差异。重建方法使用具有不同程度的采样变异性和噪声的合成数据进行评估。使用古气候代理记录的两个示例和使用具有缺失值的仪器降雨数据的第三个示例用于证明该方法的实用性。虽然高斯核相关方法的计算成本相对较高，但它在一系列数据特征下表现出稳健性，因此在寻求采用多个输入代理或协变量的分析中非常有价值。