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

Discovery of Climate Indices using Clustering

Michael SteinbachSteven Klooster

Christopher Potter

Rohit Bhingare, School of InformaticsUniversity 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 Find global climate patterns of interest to Earth

Scientists Finding connection between the ocean/atmosphere

and land.

Average Monthly Temperature NINO 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 : <G0, G1, G2, G3>

Clusters with correlation to known indices

G0 G1

G2 G3

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