Finding Climate Indices and Dipoles Using Data Mining Michael Steinbach, Computer Science

Finding Climate Indices and
Dipoles Using Data Mining
Michael Steinbach, Computer Science
Contributors: Jaya Kawale, Stefan Liess, Arjun
Kumar, Karsten Steinhauser, Dominic Ormsby,
Vipin Kumar
Climate Indices: Connecting the Ocean/Atmosphere
and the Land
• A climate index is a time series
of temperature or pressure
– Similar to business or economic
indices
– Based on Sea Surface Temperature
(SST) or Sea Level Pressure (SLP)
• Climate indices are important because
Dow Jones Index
(from Yahoo)
– They distill climate variability at a regional
or global scale into a single time series.
– They are a way to capture teleconnections, i.e., climate
phenomena occurring in one location that can affect the
climate at a faraway location
– They are well-accepted by Earth scientists.
– They are related to well-known climate phenomena such
as El Niño.
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A Temperature Based Climate Index: NINO1+2
Correlation
Between
ANOM
Land
Temp (>0.2)
Correlation
Between
Nino
1+21+2
andand
Land
Temperature
(>0.2)1
90
90
0.8
0.9
El Nino
Events
60
60
0.6
0.8
0.40.7
30
30
0.6
latitude
latitude
0.2
0
0.5
0
0
0.4
-0.2
-30
0.3
-30
-0.4
0.2
Nino 1+2 Index
-60
-60
-0.6
0.1
-90
-180
-150
-120
-90
-180 -150 -120 -90
-90
-60
-60
-30
-30
0
longitude
0
30
30
60
60
90
90
120
120
150
150
0
-0.8
180
180
longitude
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Pressure Based Climate Indices: Dipoles
Dipoles represent a class of teleconnections
characterized by anomalies of opposite
polarity at two locations at the same time.
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Importance of Climate Indices and Dipoles
Crucial for understanding the climate system, especially for weather and climate
forecast simulations within the context of global climate change.
NAO influences sea level pressure (SLP) over
most of the Northern Hemisphere. Strong positive
NAO phase (strong Islandic Low and strong
Azores High) are associated with above-average
temperatures in the eastern US.
SOI dominates tropical climate with floodings
over East Asia and Australia, and droughts
over America. Also has influence on global
climate.
Correlation of Land temperature
anomalies with NAO
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Correlation of Land temperature
anomalies with SOI
Understanding Climate Change Workshop
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Teleconnection Patterns
As Defined by the Climate Prediction Center
Discovered primarily by human observation and EOF Analysis
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
Southern Oscillation Index (SOI – also defined as ENSO in SSTA)
Antarctic Oscillation (AAO – also known as Southern Annular Mode)
Arctic Oscillation (AO – AO&NAO: also known as Northern Annular Mode)
North Atlantic Oscillation (NAO)
NAO
AAO
East Atlantic (EA)
East Atlantic/Western Russia (WR)
Scandinavia (SCAND)
Polar/Eurasia (PE)
West Pacific (WP)
East Pacific-North Pacific (EP-NP)
Pacific/North American (PNA)
Tropical/Northern Hemisphere (TNH)
Pacific Transition (PT)
http://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml
Motivation for Automatic Discovery of
Climate Indices and Dipoles
• The known dipoles are
defined by static locations
but the underlying
phenomenon is dynamic
Dynamic behavior of the
high and low pressure fields
corresponding to NAO
climate index (Portis et al,
2001)
• Manual discovery can miss
many dipoles
• EOF and other types of
eigenvector analysis finds
the strongest signals and the
physical interpretation of
those can be difficult.
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AO: EOF
Analysis of 20N90N Latitude
AAO: EOF
Analysis of 20S90S Latitude
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Shared Nearest Neighbor (SNN)
Density Based Clustering
SNN Density of SLP Time Series Data
• Density based clustering approach
90
60
•
Density is high for points with which you
share most of the same neighbors
latitude
– Determine the density of each point
(time series)
30
0
-30
-60
-90
-180 -150 -120 -90
– Perform the clustering using the
density
• Identify and eliminate noise and
outliers, which are points with low
density.
• Identify core points, which are time
series with high density.
• Build clusters around the core
points.
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-60
-30
0
30
longitude
60
90
120
150
180
SNN density of points on the globe computed using
Sea Level Pressure. Red areas are high density.
25 SLP Clusters
8
Key Extensions
• Consider negative correlations
– Negative correlations are useful for finding dipoles
• Dynamic climate index
– Captures the fact that the climate system changes over
time and even persistent phenomena will change in
some ways
• These two improvements yield indices that can
better capture the impact on land and better
match current climate indices
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Understanding Climate Change Workshop
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Discovering Dipoles
Shared Reciprocal Nearest Neighbors
(SRNN) Density
Climate Network
Nodes in the Graph correspond
to grid points on the globe.
Edge weight corresponds to correlation
between the two anomaly timeseries
Dipoles from SRNN density
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Benefits of Automatic Discovery
of Climate Indices and Dipoles
 Detection of Global Dipole Structure
 Most known dipoles discovered
 New dipoles may represent previously unknown phenomenon.
 Enables analysis of relationships between different dipoles
 Location based definition possible for some known indices that
are defined using EOF analysis.
 Dynamic versions are often better than static
 Dipole structure provides an alternate method to analyze GCM
performance
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Influence of Climate Indices on Land:
Area Weighted Correlation
• For each grid point, compute the correlation of the climate index
with a time series representing the temperature at that point.
– Use absolute correlation
• The area-weighted correlation is the weighted average of these
correlations.
– The weights are the areas of the grid points.
– The area of a grid cell varies by latitude.
Correlation Between Nino 1+2 and Land Temperature (>0.2)
Correlation Between ANOM 1+2 and Land Temp (>0.2)
90
0.8
0.6
60
0.4
30
latitude
0.2
0
0
-0.2
-30
-0.4
-60
-0.6
-0.8
Nino 1+2 Index
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-90
-180 -150 -120 -90
-60
-30
0
30
60
90
120
150
180
longitude
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Detection of Global Dipole Structure
Dipoles found using NCEP (National Centers for Environmental
Prediction) Reanalysis Data For Pressure
Without detrending
With detrending
 Most known dipoles discovered
 Location based definition possible for some known indices that are defined
using EOF analysis.
 New dipoles may represent previously unknown phenomenon.
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