Data guided discovery of dynamic climate dipoles

Jaya Kawale, Stefan Liess, Arjun Kumar, Michael Steinbach, Auroop Ganguly, Nagiza F. Samatova, Fred Semazzi, Peter Snyder, Vipin Kumar

Research output: Contribution to conferencePaperpeer-review

12 Scopus citations


Pressure dipoles in global climate data capture recurring and persistent, large-scale patterns of pressure and circulation anomalies that span distant geographical areas (teleconnections). In this paper, we present a novel graph based approach called shared reciprocal nearest neighbors that considers only reciprocal positive and negative edges in the shared nearest neighbor graph to find dipoles in pressure data. To show the utility of finding dipoles using our approach, we show that the data driven dynamic climate indices generated from our algorithm always perform better than static indices formed from the fixed locations used by climate scientists in terms of capturing impact on land temperature and precipitation. Another salient point of this approach is that it can generate a single snapshot picture of all the dipole interconnections on the globe in a given dataset making it possible to differentiate between various climate model simulations via data driven dipole analysis. Given the importance of teleconnections in climate and the importance of model simulations in understanding the impact of climate change, this methodology has the potential to provide significant insights.

Original languageEnglish (US)
Number of pages15
StatePublished - Dec 1 2011
EventNASA Conference on Intelligent Data Understanding, CIDU 2011 - Mountain View, CA, United States
Duration: Oct 19 2011Oct 21 2011


OtherNASA Conference on Intelligent Data Understanding, CIDU 2011
Country/TerritoryUnited States
CityMountain View, CA


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