Discovery of Climate Indices using Clustering

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Discovery of Climate Indices using Clustering

• Michael Steinbach
• Steven Klooster
• Christopher Potter

Rohit Bhingare, School of Informatics University
of Edinburgh
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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

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Key Interest

• Find global climate patterns of interest to
  Earth Scientists
• Finding connection between the ocean/atmosphere
  and land.

Average Monthly Temperature
NINO 12 Index
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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.
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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.

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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.

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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
• ltG0, G1, G2, G3gt

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Clusters with correlation to known indices
G1
G0
G2
G3
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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.

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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