Title of Presentation: Information-Theoretic Methods for Identifying Relationships among Climate Variables
Primary (Corresponding) Author: Kevin Knuth
Organization of Primary Author: University at Albany
Co-Authors: Deniz Gencaga, William B. Rossow
Abstract: The field of Earth-science is currently in the difficult stage of identifying relevant variables. How do we identify candidate variables and indices and ensure that they are maximally relevant to the phenomena we wish to describe or predict? To establish relevance between a variable and a phenomenon, one needs to demonstrate that the variable provides information about the phenomenon. To quantify this, we turn to information theory.
Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extremely challenging. We have developed a set of computational techniques that allow one to accurately compute marginal and joint entropies. These algorithms are probabilistic in nature and thus provide information on the uncertainty in our estimates, which enable us to establish statistical significance of our findings.
We demonstrate these methods by identifying relations between cloud data from the International Satellite Cloud Climatology Project (ISCCP) and data from other sources, such as equatorial pacific sea surface temperatures (SST). We are currently working to use this direct estimation of entropy from data sets to address deeper thermodynamic issues surrounding the radiation budget and its relation to atmospheric circulation and cloud formation.