SC1.28 Temporal and spatial uncertainties in climate data analysis 
Convener: Niklas Boers  CoConveners: Bedartha Goswami , Aljoscha Rheinwalt 
Tue, 10 Apr, 13:30–15:00
/ Room 2.31

In this short course, we aim to put forward the idea of considering the temporal evolution of an observed system not as sequences of pointwise estimates like the mean, but as sequences of probability density functions that reflect the inherent uncertainties. Such probability density functions describe the system in much greater detail than simple point estimates.
We will first briefly introduce the elements of probability theory and statistics that are relevant for an accurate description of uncertainties in time series analysis. Thereafter, we will walk the audience through some realworld examples, where we demonstrate the advantages of considering the temporal evolution of a system in terms of a sequence of probability density functions rather than as a sequence of points (and error bars). These examples will cover paleoclimatic applications, where the uncertainties mainly result from the dating processes, but also applications investigating the evolution of the El Nino Southern Oscillation, where the uncertainties stem from the spatial aggregation of the data. Furthermore, we will provide explicit examples of how to generalize traditional time series analysis methods to the analysis of probability density sequences. The final part of the course is planned as an open discussion, where we hope to learn about similar problems that people in the audience face in their research, and discuss further methodological developments needed for a more thorough treatment of uncertainties in geoscientific time series.
To register your interest in the course, simply send an email to: goswami(at)pikpotsdam(dot)de