SINOMA - A new approach for estimating linear relationships between noisy serial data streams

Reconstructions of past climates are based on the calibration of available proxy data. This calibration is usually achieved by means of linear regression models. In the recent paleo-climate literature there is an ongoing discussion on the validity of highly resolved climate reconstructions. The reason for this is that the proxy data are noisy, i.e. in addition to the variability that is related to the climate variable of interest, they contain other sources of variability. Inadequate treatment of such noise leads to a biased estimation of regression slopes, resulting in a wrong representation of the real amplitude of past climate variations. Methods to overcome this problem have had a limited success so far. Here, we present a new approach - SINOMA - for noisy serial data streams that are characterized by different spectral characteristics of signal and noise. SINOMA makes use of specific properties of the data streams temporal or spatial structure and by this is able to deliver a precise estimate of the true regression slope and, simultaneously, of the ratio of noise variances present in the predictor and predictand. The paper introduces the underlying mathematics as well as a general description of the presented algorithm. The validity of SINOMA is illustrated with two test data-sets. Finally we address methodological limitations and further potential applications.

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