Climate Indices: Connecting the OceanAtmosphere and the Land

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Climate Indices Connecting the Ocean/Atmosphere
and the Land

• A climate index is a time series of sea surface
  temperature or sea level pressure
• Climate indices capture teleconnections
• The simultaneous variation in climate and related
  processes over widely separated points on the
  Earth

El Nino Events
Nino 12 Index
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Discovery of Climate Indices Using Clustering
A novel clustering technique was developed to
identify regions of uniform behavior in
spatio-temporal data. The use of clustering for
discovering climate indices is driven by the
intuition that a climate phenomenon is expected
to involve a significant region of the ocean or
atmosphere where the behavior is relatively
uniform over the entire area. A cluster-based
approach for discovering climate indices provides
better physical interpretation than those based
on the SVD/EOF paradigm, and provide candidate
indices with better predictive power than known
indices for some land areas. Some SST clusters
reproduce well-known climate indices. In
particular, we were able to replicate the four El
Nino SST-based indices cluster 94 corresponds to
NINO 12, 67 to NINO 3, 78 to NINO 3.4, and 75 to
NINO 4. The correlations of these clusters to
their corresponding indices are higher than
0.9. Some SST clusters, e.g., cluster 29, are
significantly different than known indices, but
provide better correlation with land climate
variables than known indices for many parts of
the globe. The bottom figure shows the
difference in correlation to land temperature
between cluster 29 and the El Nino indices. Areas
in yellow indicate where cluster 29 has higher
correlation.
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Pairs of SLP Clusters That Correspond to Climate
Indices
NAO
AO
Cluster centroid 20 13 versus SOI
SOI
SOI
DMI
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Finding New PatternsIndian Monsoon Dipole Mode
Index

• Recently a new index, the Indian Ocean Dipole
  Mode index (DMI), has been discovered.
• DMI is defined as the difference in SST anomaly
  between the region 5S-5N, 55E-75E and the region
  0-10S, 85E-95E.
• DMI and is an indicator of a weak monsoon over
  the Indian subcontinent and heavy rainfall over
  East Africa.
• We can reproduce this index as a difference of
  pressure indices of clusters 16 and 22.

Plot of cluster 16 cluster 22 versus the Indian
Ocean Dipole Mode index. (Indices smoothed using
12 month moving average.)
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Discovery of Patterns in Earth Science Data

• NASA ESE questions
• How is the global Earth system changing?
• What are the primary forcings?
• How does Earth system respond to natural
  human-induced changes?
• What are the consequences of changes in the Earth
  system?
• How well can we predict future changes?

• Global snapshots of values for a number of
  variables on land surfaces or water
• Data sources
• weather observation stations
• earth orbiting satellites (since 1981)
• modeled-based data

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High Resolution EOS Data

• EOS satellites provide high resolution
  measurements
• Finer spatial grids
• 8 km ? 8 km grid produces 10,848,672 data points
• 1 km ? 1 km grid produces 694,315,008 data points
• More frequent measurements
• Multiple instruments
• Generates terabytes of day per day
• High resolution data allows us to answer more
  detailed questions
• Detecting patterns such as trajectories, fronts,
  and movements of regions with uniform properties
• Finding relationships between leaf area index
  (LAI) and topography of a river drainage basin
• Finding relationships between fire frequency and
  elevation as well as topographic position

Earth Observing System (e.g., Terra and Aqua
satellites)
http//www.crh.noaa.gov/lmk/soo/docu/basicwx.htm
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Global River Discharge Data

• Global River Discharge Data
• 30 rivers, 0.5 degree resolution
• Two measurement stations mouth and source of
  river system/basin
• Minimum of ten continuous years of monthly
  station discharge records
• Interesting associations
• e.g., Amazon discharge is highly correlated with
  the Climate Index ANOM3.4 (r -0.5)

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Relationship Between River Basin PREC and Climate
Indices
Amazon
Parana

• Correlation between PREC aggregation on river
  basins and climate indices (OCI) is shown in
  left figure
• Interesting Observations
• The Amazon and Parana rivers are close to each
  other, however, the correlation to climate
  indices is almost reversed for these two rivers

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Efficient Query Processing Techniques

Proposed Approach
Performance Evaluation

• Workload
• NASA Earth science data
• Monthly USA Net Primary Product data at 0.5
  degree by 0.5 degree resolution in 1982-93
• Monthly Eastern Pacific Sea Surface Temp data at
  0.5 degree at 0.5 degree resolution in 1982-93
• Experimental results
• Range Queries
• save 46-89
• Join Queries
• save 40-98

• Spatial Cone tree
• Normalized time series is located on the surface
  of hypersphere
• Cone containing multiple normalized time series
  in hypersphere
• Grouping similar time series together based on
  spatial proximity
• Query processing on cone-level

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Discovery of Changes from the Global Carbon Cycle
and Climate System Using Data Mining Publications

• Potter, C., Tan, P., Steinbach, M., Klooster,
  S., Kumar, V., Myneni, R., Genovese, V., 2003.
  Major disturbance events in terrestrial
  ecosystems detected using global satellite data
  sets. Global Change Biology, July, 2003.
• Potter, C., Klooster, S. A., Myneni, R.,
  Genovese, V., Tan, P., Kumar,V. 2003. Continental
  scale comparisons of terrestrial carbon sinks
  estimated from satellite data and ecosystem
  modeling 1982-98. Global and Planetary Change (in
  press)
• Potter, C., Klooster, S. A., Steinbach, M., Tan,
  P., Kumar, V., Shekhar, S., Nemani, R., Myneni,
  R., 2003. Global teleconnections of climate to
  terrestrial carbon flux. Geophys J. Res.-
  Atmospheres (in press).
• Potter, C., Klooster, S., Steinbach, M., Tan, P.,
  Kumar, V., Myneni, R., Genovese, V., 2003.
  Variability in Terrestrial Carbon Sinks Over Two
  Decades Part 1 North America. Geophysical
  Research Letters (in press)
• Potter, C. Klooster, S., Steinbach, M., Tan, P.,
  Kumar, V., Shekhar, S. and C. Carvalho, 2002.
  Understanding Global Teleconnections of Climate
  to Regional Model Estimates of Amazon Ecosystem
  Carbon Fluxes. Global Change Biology (in press)
• Potter, C., Zhang, P., Shekhar, S., Kumar, V.,
  Klooster, S., and Genovese, V., 2002.
  Understanding the Controls of Historical River
  Discharge Data on Largest River Basins. (in
  preparation)
• Steinbach, M., Tan, P. Kumar, V., Potter, C. and
  Klooster, S., 2003. Discovery of Climate Indices
  Using Clustering, KDD 2003, Washington, D.C.,
  August 24-27, 2003.
• Zhang, P., Shekhar, S., Huang, Y., and Kumar, V.,
  2003, Spatial Cone Tree An Index Structure for
  Correlation-based Queries on Spatial Time Series
  Data, to appear in the Proc. Of the Intl
  Workshop on Next Generation Geospatial
  Information, Boston, MA
• Zhang, P., Huang, Y., Shekhar, S., and Kumar, V.,
  2003. Exploiting Spatial Autocorrelation to
  Efficiently Process Correlation-Based Similarity
  Queries , Proc. of the 8th Intl. Symp. on Spatial
  and Temporal Databases (SSTD '03)
• Zhang, P., Huang, Y., Shekhar, S., and Kumar, V.,
  2003. Correlation Analysis of Spatial Time Series
  Datasets A Filter-And-Refine Approach, Proc. of
  the Seventh Pacific-Asia Conference on Knowledge
  Discovery and Data Mining (PAKDD '03)
• Ertoz, L., Steinbach, M., and Kumar, V., 2003.
  Finding Clusters of Different Sizes, Shapes, and
  Densities in Noisy, High Dimensional Data, Proc.
  of Third SIAM International Conference on Data
  Mining.
• Tan, P., Steinbach, M., Kumar, V., Potter, C.,
  Klooster, S., and Torregrosa, A., 2001. Finding
  Spatio-Temporal Patterns in Earth Science Data,
  KDD 2001 Workshop on Temporal Data Mining, San
  Francisco
• Kumar, V., Steinbach, M., Tan, P., Klooster, S.,
  Potter, C., and Torregrosa, A., 2001. Mining
  Scientific Data Discovery of Patterns in the
  Global Climate System, Proc. of the 2001 Joint
  Statistical Meeting, Atlanta

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Mining the Climate Data Associations

• min support 0.001, min confidence10

1 FPAR-HI PET-HI PREC-HI SOLAR-HI TEMP-HI gt
NPP-HI (support count145, confidence100) 2
FPAR-HI PET-HI PREC-HI TEMP-HI gt NPP-HI
(support count933, confidence99.3) 3 FPAR-HI
PET-HI PREC-HI gt NPP-HI (support count1655,
confidence98.8) 4 FPAR-HI PET-HI PREC-HI
SOLAR-HI gt NPP-HI (support count268,
confidence98.2) 5 FPAR-HI PET-HI PREC-HI
SOLAR-LO TEMP-HI gt NPP-HI (support count44,
confidence97.8) 6 FPAR-LO PET-LO PREC-LO
SOLAR-LO gt NPP-LO (support count216,
confidence96.9) 7 FPAR-LO PREC-LO SOLAR-LO
TEMP-HI gt NPP-LO (support count152,
confidence96.2) 8 FPAR-LO PET-LO PREC-LO
SOLAR-LO TEMP-LO gt NPP-LO (support count47,
confidence95.9) 9 FPAR-LO PREC-LO SOLAR-LO
TEMP-LO gt NPP-LO (support count49,
confidence94.2) 10 FPAR-LO PREC-LO SOLAR-LO gt
NPP-LO (support count595, confidence93.7)
75 FPAR-HI gt NPP-HI (support count
216924, confidence 55.7)
NPP Solar FPAR ? Temperature Moisture
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Mining the Climate Data Associations
Ref Tan et al 2001
FPAR-Hi gt NPP-Hi (sup5.9, conf55.7)
Grassland/Shrubland areas
Association rule is interesting because it
appears mainly in regions with grassland/shrubland
vegetation type
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Mining the Climate Data Associations
Ref Tan et al 2001
FPAR-Hi gt NPP-Hi (sup5.9, conf55.7)
Grassland/Shrubland areas
Association rule is interesting because it
appears mainly in regions with grassland/shrubland
vegetation type
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Detection of Ecosystem Disturbances
Detection of sudden changes in greenness over
extensive areas from these large global satellite
data sets required development of automated
techniques that take into account the timing,
location, and magnitude of such changes. An
algorithm was designed to identify any
significant and sustained declines in FPAR during
an 18 year time period. This algorithm transforms
a non-stationary time series to a sequence of
disturbance events. Techniques were also
developed to discover associations between
ecosystem disturbance regimes and historical
climate anomalies.
These algorithms and techniques have allowed
Earth Science researchers to gain a deeper
insight into the interplay among natural
disasters, human activities and the rise of
carbon dioxide in Earth's atmosphere during two
recent decades.
Release 03-51AR          NASA DATA MINING
REVEALS A NEW HISTORY OF NATURAL DISASTERS NASA
is using satellite data to paint a detailed
global picture of the interplay among natural
disasters, human activities and the rise of
carbon dioxide in the Earth's atmosphere during
the past 20 years.
http//amesnews.arc.nasa.gov/releases/2003/03_51AR
.html
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Understanding Global Teleconnections of Climate
to Regional Model Estimates of Amazon Ecosystem
Carbon Fluxes
Discovered, using correlation analysis, a strong
connection between the rainfall patterns
generated by the South American monsoon system
and terrestrial greenness over a large section of
the southern Amazon region. This is the first
direct evidence of large-scale effects of the
Atlantic Ocean rainfall systems on yearly
greenness changes in the Amazon region, and the
finding has important implications for the
impacts of "slash and burn" deforestation on this
crucial ecosystem of the world.
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Climate Indices
17
Association Analysis
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Mining Sc Discovery of Patterns in
University of Minnesota Vipin Kumar, Shashi
Shekhar, George Karypis Shyam Boriah, Varun
Chadola Sridhar Iyer, Michael Steinbach Gyorgy
Simon, Pusheng Zhang
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ientific Data the Global Climate Cycle
Michigan State University Pang-Ning Tan NASA
Ames Research Center Christopher
Potter California State University, Monterey Bay
Steve Klooster
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http//www.ahpcrc.umn.edu/nasa-umn/