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Case Study 2: Dipoles
• Teleconnections are recurring long distance patterns of climate anomalies. Typically,
teleconnections are represented by time series known as climate indices.
•Pressure dipoles represent a class of teleconnections characterized by pressure anomalies of
opposite polarity at two locations at the same time.
E.g. Southern Oscillation is characterized by anomalies of opposite polarity at Tahiti and Darwin,
Australia. North Atlantic Oscillation is characterized by opposite polarity at Iceland and Azores.
NAO/SOI on the Globe
© Vipin Kumar
IIT Mumbai 2010
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Importance of dipoles
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Identification of these oscillations is crucial for understanding the climate system, especially
for weather and climate forecast simulations within the context of global climate change,
where the emphasis lies in the distinction between long-term trends in climate patterns due
to human activity compared to naturally oscillating dipoles.
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The NAO accounts for much inter-annual and decadal variance over the Atlantic region in
seasonal means of many meteorological variables such as sea level pressure, surface air
temperature and precipitation.
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SOI comprises the dominant mode of tropical climate variability with additional influence on
extra tropics. E.g. the recent cold winter over North America can be attributed to the phases
of SOI and NAO.
Correlation of Land temperature anomalies with
SOI
© Vipin Kumar
IIT Mumbai 2010
Correlation of Land temperature anomalies
with NAO
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List of Well Known Climate Indices
Index
Description
SOI
NAO
Southern Oscillation Index: Measures the SLP anomalies between Darwin and Tahiti
North Atlantic Oscillation: Normalized SLP differences between Ponta Delgada, Azores
and Stykkisholmur, Iceland
AO
Arctic Oscillation: Defined as the _first principal component of SLP poleward of 20 N
PDO
Pacific Decadel Oscillation: Derived as the leading principal component of monthly SST
anomalies in the North Pacific Ocean, poleward of 20 N
QBO
Quasi-Biennial Oscillation Index: Measures the regular variation of zonal (i.e. east-west)
strato-spheric winds above the equator
CTI
Cold Tongue Index: Captures SST variations in the cold tongue region of the equatorial
Pacific Ocean (6 N-6 S, 180 -90 W)
WP
Western Pacific: Represents a low-frequency temporal function of the ‘zonal dipole' SLP
spatial pattern involving the Kamchatka Peninsula, southeastern Asia and far western
tropical and subtropical North Pacific
NINO1+2 Sea surface temperature anomalies in the region bounded by 80 W-90 W and 0 -10 S
NINO3
Sea surface temperature anomalies in the region bounded by 90 W-150 W and 5 S-5 N
NINO3.4 Sea surface temperature anomalies in the region bounded by 120 W-170 W and 5 S-5 N
NINO4
Sea surface temperature anomalies in the region bounded by 150 W-160 W and 5 S-5 N
Discovered primarily by human observation
© Vipin Kumar
IIT Mumbai 2010
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Background on Complex Networks for Climate Data
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Climate Network constructed from the
anomaly time series of all locations on the
Earth.
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Nodes in the Graph represent the regions
on the Earth.
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Edges in the Graph represent the
correlation between the anomaly time series
of two locations.
Area weighted degree in a correlationbased climate network (Tsonis, et al.)
SST Clusters With Relatively High Correlation to Land Temperature
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The study of Complex Networks in Climate
Data was previously explored by Tsonis, et
al, Donges, et al, Steinbach et al.
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30
29
latitude
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0
75
78
67
94
-30
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However prior research focused on positive
or absolute values of correlation.
-60
-90
-180 -150 -120
-90
-60
-30
0
30
60
90
longitude
Steinbach et. al, KDD 03
© Vipin Kumar
IIT Mumbai 2010
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120
150
180
Challenges in studying dipoles
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l
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The distribution of positive
and negative edges around
the Earth is uneven as most
of the highly positive edges
come from nearby locations
due to spatial
autocorrelation. The area
weighted correlation shows
that the equator is dominant.
If we remove all edges <
5000km away the
distribution is balanced.
Distribution of edges around the Earth with
abs correlation > 0.5
Distribution of edges around the Earth
Distribution of edges around the Earth having a
distance > 5000km and abs correlation > 0.2
Distribution of edges > 5000km away
The number of negative
edges around the globe is
very high. So an algorithm
focusing on negative edges
will not scale.
Distribution of negative edges
© Vipin Kumar
IIT Mumbai 2010
Distribution of negative edges
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Shared Reciprocal Nearest Neighbors
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Shared Nearest Neighbor (SNN) defines similarity between pair of objects based upon the list of k-nearest
neighbors.
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Additional Reciprocity constraint imposed, i.e. two objects must lie on each others nearest neighbor list.
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Positive and Negative densities computed by looking at the k nearest neighbors in the positive and the
negative correlations.
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Overall SRNN density is defined as the product of the two densities.
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After spatial clustering over SRNN density, dipoles are identified as cluster pairs with negative correlation
between them.
Shared Reciprocal Nearest Neighbors
(SRNN) Density
© Vipin Kumar
Dipoles from SRNN density
IIT Mumbai 2010
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Static vs Dynamic NAO Index: Impact on land temperature
NCEP Reanalysis (Observations are assimilated
into a forecast model to produce a global dataset )
From the top figures, we see that both the patterns
are similar but the dynamic index generates a
stronger impact on land temperature anomalies as
compared to the static index.
Figure to the right shows the aggregate area
weighted correlation for the all the network periods.
© Vipin Kumar
IIT Mumbai 2010
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Static vs Dynamic SO Index: Impact on land temperature
NCEP Reanalysis (Observations are assimilated
into a forecast model to produce a global dataset )
From the top figures, we see that both the patterns
are similar but the dynamic index generates a
stronger impact on land temperature anomalies as
compared to the static index.
Figure to the right shows the aggregate area
weighted correlation for the all the network periods.
© Vipin Kumar
IIT Mumbai 2010
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Known SOI connections
Known
Known
Vecchi and
Wittenberg (2010)
We might find more with the dynamic index!
© Vipin Kumar
IIT Mumbai 2010
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Detection of Global Dipole Structure
•
Helps in studying the interconnections between dipoles.
•
•
Helps in studying changes in dipole structure over a period of time.
Helps in discovering new connections and dipoles.
© Vipin Kumar
IIT Mumbai 2010
‹#›
Detection of Global Dipole Structure
•
Helps in studying the interconnections between dipoles.
•
•
Helps in studying changes in dipole structure over a period of time.
Helps in discovering new connections and dipoles.
© Vipin Kumar
IIT Mumbai 2010
‹#›
Detection of Global Dipole Structure
•
Helps in studying the interconnections between dipoles.
•
•
Helps in studying changes in dipole structure over a period of time.
Helps in discovering new connections and dipoles.
© Vipin Kumar
IIT Mumbai 2010
‹#›
Detection of Global Dipole Structure
•
Helps in studying the interconnections between dipoles.
•
•
Helps in studying changes in dipole structure over a period of time.
Helps in discovering new connections and dipoles.
© Vipin Kumar
IIT Mumbai 2010
‹#›
Detection of Global Dipole Structure
•
Helps in studying the interconnections between dipoles.
•
•
Helps in studying changes in dipole structure over a period of time.
Helps in discovering new connections and dipoles.
© Vipin Kumar
IIT Mumbai 2010
‹#›
Comparison of Climate Models
Hindcast data
• Useful for quantifying the performance of different climate models.
• Strength of the dipoles varies in different climate models.
• SOI is not captured by all models. Fig. shows that both the models show most
dipoles as NCEP, but SOI is only simulated by GFDL 2.1 and not by BCM 2.0.
• Helpful for making regional prediction from models
© Vipin Kumar
IIT Mumbai 2010
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Dipole Activity in Future
Hindcast data
Forecast data
• Dipole connections in forecast data provide insights about dipole activity in future.
• For e.g. both forecasts for 2080-2100 show continuing dipole activity in the extratropics but decreased activity in the
tropics. SOI activity is reduced in GFDL2.1 and activity over Africa is reduced in BCM 2.0. This is consistent with
archaeological data from 3 mil. years ago, when climate was 2-3°C warmer (Shukla, et. al).
© Vipin Kumar
IIT Mumbai 2010
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Phase Lock of SOI in Future Climate?
© Vipin Kumar
IIT Mumbai 2010
Shukla
‹#› et al. (2009)