Multi-resolution time series analysis applied to solar irradiance and climate reconstructions

A better understanding of natural climate variability is crucial for global climate change studies and the evaluation of the sensitivity of the climate system to imposed perturbations. External forcing factors might contribute substantially to both high and low frequency variations in climate but a clear separation of their impact from internally generated fluctuations is difficult. We employ wavelet decomposition to identify common characteristics in forcing and climatic time series of the last four centuries. Here, we focus on solar irradiance variations by applying this statistical method to a selection of widely used proxy-based reconstructions. Major variability components are isolated through time-scale decomposition. Two classical solar modes (85 and 11 years) are not only identified within the limited time period covered by the solar datasets, but their relative influences on climate as represented by hemispheric surface temperature reconstructions are also estimated. While the low-frequency component shows close ties between solar variations and surface climate, a relationship between the 11-year sunspot cycle and temperature reconstructions is more difficult to attribute. However, the statistical multi-resolution analysis appears to be an ideal tool to uncover relationships and their changes at different temporal scales normally hidden by the strong background noise in the climate system.