Discovering dynamic dipoles in climate data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

Pressure dipoles are important long distance climate phenomena (teleconnection) characterized by pressure anomalies of opposite polarity appearing at two different locations at the same time. Such dipoles have proven important for understanding and explaining the variability in climate in many regions of the world, e.g., the El Niño climate phenomenon is known to be responsible for precipitation and temperature anomalies worldwide. This paper presents a novel approach for dipole discovery that outperforms existing state of the art algorithms. Our approach is based on a climate anomaly network that is constructed using the correlation of time series of climate variables at all the locations on the Earth. One novel aspect of our approach to the analysis of such networks is a careful treatment of negative correlations, whose proper consideration is critical for finding dipoles. Another key insight provided by our work is the importance of modeling the time dependent patterns of the dipoles in order to better capture the impact of important climate phenomena on land. The results presented in this paper show that these innovations allow our approach to produce better results than previous approaches in terms of matching existing climate indices with high correlation and capturing the impact of climate indices on land.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
PublisherSociety for Industrial and Applied Mathematics Publications
Pages107-118
Number of pages12
ISBN (Print)9780898719925
DOIs
StatePublished - 2011
Event11th SIAM International Conference on Data Mining, SDM 2011 - Mesa, AZ, United States
Duration: Apr 28 2011Apr 30 2011

Publication series

NameProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Other

Other11th SIAM International Conference on Data Mining, SDM 2011
CountryUnited States
CityMesa, AZ
Period4/28/114/30/11

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