Discovery of Climate Indices using Clustering Michael Steinbach Steven Klooster Christopher Potter Rohit Bhingare, School of Informatics University of Edinburgh

Overview Aim: Applying Clustering to the task of finding interesting patterns in earth science data. Key interests and research goals Climate Indices Using SVD analysis to find Spatial/Temporal Patterns Using Clustering for discovery of indices Conclusion and Future Work

Key Interest l Find global climate patterns of interest to Earth Scientists l Finding connection between the ocean/atmosphere and land. Average Monthly TemperatureNINO 1+2 Index

The El Nino Climate Phenomenon El Nino is the anomalous warming of the eastern tropical region of the Pacific. Normal Year: Trade winds push warm ocean water west, cool water rises in its place El Nino Year: Trade winds ease, switch direction, warmest water moves east.

Climate Indices A climate index is a time series of temperature or pressure –Connecting the Ocean/Atmosphere and the Land –Commonly based on Sea Surface Temperature (SST) or Sea Level Pressure (SLP) Why climate indices? –They extract climate variability at a regional or global scale into a single time series. –They are well-accepted by Earth scientists. –They are related to well-known climate phenomena such as El Nino.

Finding Patterns using SVD and Clustering SVD Analysis: –Impressive for finding the strongest patterns falling into independent subspaces. –All discovered signals must be orthogonal (difficult to attach physical interpretation) –Weaker signals may be masked by stronger signals. Use of Clustering: –The centroids of clusters summarize the behaviour of the ocean/atmosphere in those regions.

Clustering Based Methodology The SNN Procedure: –Apply the SNN clustering on the SST (or SLP) data over a specific time period. –Eliminate all the clusters with poor area- weighted correlation. –The cluster centroids of remaining clusters are potential climate indices :

Clusters with correlation to known indices G0 G1 G2G3

Conclusion Clustering plays a useful role in the discovery of interesting ecosystem patterns. Clustering is used to discover previously unknown relationships between regions of the land and sea.

Future Work Can all climate indices be represented using clusters? Extending the research to land and ocean variables - Many more opportunities for data mining/data analysis in Earth Science data. Earth Observing System: Detecting patterns such as finding relationships between fire frequency and elevation as well as topographic position