Determining the role of linear and nonlinear interactions in records of climate change: possible solar influences in annual to century-scale records

Time series analysis methods have traditionally helped in identifying the role of various forcing mechanisms in influencing climate change. A challenge to understanding decadal and century-scale climate change has been that the linkages between climate changes and potential forcing mechanisms such as solar variability are often uncertain. However, most studies have focused on the role of climate forcing and climate response within a strictly linear framework. Nonlinear time series analysis procedures provide the opportunity to analyze the role of climate forcing and climate responses between different time scales of climate change. An example is provided by the possible nonlinear response of paleo-ENSO-scale climate changes as identified from coral records to forcing by the solar cycle at longer time scales.

PARTIALLY COHERENT DYNAMIC PROGRAMMING ALGORITHM FOR CPM DETECTION.

A dynamic programming algorithm for joint data detection and carrier phase estimation of continuous-phase-modulated signal is presented. The intent is to combine the robustness of noncoherent detectors with the superior performance of coherent ones. The algorithm differs from the Viterbi algorithm only in the metric that it maximizes over the possible transmitted data sequences. This metric is influenced both by the correlation with the received signal and the current estimate of the carrier phase. Carrier-phase estimation is based on decision guiding, but there is no external phase-locked loop. Instead, the phase of the best complex correlation with the received signal over the last few signaling intervals is used. The algorithm is slightly more complex than the coherent Viterbi algorithm but does not require narrowband filtering of the recovered carrier, as earlier appproaches did, to achieve the same level of performance.