This interdisciplinary session welcomes contributions on novel conceptual approaches and methods for the analysis of observational as well as model time series and associated uncertainties from all geoscientific disciplines.

Methods to be discussed include, but are not limited to:
- linear and nonlinear methods of time series analysis
- time-frequency methods
- predictive approaches
- statistical inference for nonlinear time series
- nonlinear statistical decomposition and related techniques for multivariate and spatio-temporal data
- nonlinear correlation analysis and synchronisation
- surrogate data techniques
- filtering approaches and nonlinear methods of noise reduction

We particularly encourage submissions addressing the problem of uncertainty of geoscientific time series and its treatment in the context of statistical and dynamical analysis, including:
- representation of time series with uncertain dating (in particular paleoclimatic records from ice cores, sediments, speleothems etc.)
- uncertainties in change point / transition detection
- uncertainty propagation in time series methods like correlation, synchronization, spectral analysis, PCA, networks, and similar techniques
- uncertainty propagation in empirical (i.e., data-derived) inverse models

Co-organized as AS5.17/CL5.24/HS3.7/NH11.5/SM7.7
Convener: Reik Donner | Co-conveners: Andrea Toreti, Niklas Boers, Bedartha Goswami, Aljoscha Rheinwalt
| Mon, 08 Apr, 08:30–10:15
Room L3
| Attendance Tue, 09 Apr, 14:00–15:45
Hall X4

Monday, 8 April 2019 | Room L3

08:30–08:45 |
| solicited
Sandra Chapman, Lauren Orr, Jesper Gjerloev, and Nicholas Watkins
08:45–09:00 |
Jaqueline Lekscha and Reik V. Donner
09:00–09:15 |
| presentation
Holger Lange, Marlene Schramm, Sebastian Sippel, and Junbin Zhao
09:15–09:30 |
Roya Mousavian, Christof Lorenz, Benjamin Fersch, Harald Kunstmann, and Masoud Mashhadi Hossainali
10:00–10:15 |
Daniel Bowden, Michael Coughlin, Jan Harms, Andreas Fichtner, Victor Tsai, Vuk Mandic, Gary Pavlis, Pat Meyers, Ross Caton, and Tanner Prestegard