Isolating Secular Signals in Observations and Climate Models Using M-SSA Based Wiener Filtering

Christian Grimm
University of Wisconsin-Milwaukee
MS Graduate Student

“In this thesis, Wiener filtering of gridded surface-temperature time series from observations and climate model simulations is performed by using multi-channel singular spectrum analysis (M-SSA) in order to isolate non-stationary climate signals. The contributions to the singular spectrum from shorter-term internal climate variability – treated in this context as noise – are estimated by fitting to the data spatially extended stochastic models, which are subsequently used to produce synthetic M-SSA spectra. The full spectra are weighted by the signal-to-noise ratios and transformed back to physical space to obtain reconstructions of the non-stationary signal. This methodology was first tested using the twentieth century simulations from the Community Earth System Model Large Ensemble Project, then applied to individual model ensembles as well as the overall ensemble from the Coupled Model Intercomparison Project Phase 5 and, finally, to the observational surface-temperature time series from Twentieth Century Reanalysis.”

Committee Members: 
Profs. Sergey Kravtsov (Advisor); Vytaras Brazauskas & Clark Evans