This session focuses on the link between statistical time-series analysis and climatology, including paleoclimatology. Analysis of climatic time series has to deal with measurement errors, spatial representation, proxy quality, dating uncertainties, as well as deterministic noise generated within the climate system. The challenge is to improve and develop methods and obtain results that are robust and reliable also in the presence of (1) nonstationarities, (2) non-normal distributions, (3) autocorrelation, (4) uneven time spacing, (5) uncertain timescales. We welcome novel contributions that address those difficulties and show a sound statistical basis of the methodology.
A particular challenge arises in the context of analysing climate variations at centennial-to-millennial timescales in relatively short records. Therefore, we are inviting contributions in which novel tools or methods are used to analyse such low-frequency variations in Holocene climate records.
Confirmed contribution: Peter Huybers, Department of Earth and Planetary Sciences, Harvard University, USA