## Visualizing Climate Variability with Time-Dependent Probability Density Functions, Detecting it with Information Theory

Title | Visualizing Climate Variability with Time-Dependent Probability Density Functions, Detecting it with Information Theory |

Publication Type | Journal Article |

Year of Publication | 2012 |

Authors | Larson, JW |

Journal | Procedia Computer Science |

Volume | 9 |

Pagination | 917-926 |

Date Published | 06/2012 |

Other Numbers | ANL/MCS-P2069-0312 |

Abstract | A framework is presented for visualizing and detecting climate variability and change based on time-dependent probability density functions (PDFs). The PDFs show how the distribution of values in the sample window changes over time and show more detail than do timeseries of windowed moments. A set of information-theoretic statistics based on the Shannon entropy and the Kullback-Leibler divergence (KLD) are defined to assess PDF complexity and temporal variability. The KLD-based measures quantify the representativeness of a 30-year sampling window of a larger climatic record: how well a long sample can predict a smaller samples PDF, and how well one 30-year sample matches a similar sample shifted in time. These information-theoretic statistics constitute a new type of climate variability, informatic variability. These techniques are applied to the Central England Temperature record, the longest continuous meteorological observational record. |

URL | http://www.sciencedirect.com/science/article/pii/S1877050912002190 |

http://www.mcs.anl.gov/papers/P2069-0312.pdf |