Information-Theoretic Methods for Identifying Relationships among Climate Variables
This work addresses a specific bottleneck in climate science by enabling more reliable analysis of variable relationships, though it is incremental as it builds on existing information-theoretic methods.
The paper tackles the challenge of accurately estimating information-theoretic quantities like entropy and mutual information from data, developing probabilistic computational techniques that provide uncertainty estimates and demonstrate their application by identifying relationships between climate variables such as cloud data and sea surface temperatures.
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).