Vipin Kumar IIT Mumbai 2010 0 Case Study 2: Dipoles Teleconnections are recurring long distance...

© Vipin Kumar IIT Mumbai 2010 1

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


Importance of dipoles 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.

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.

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 Correlation of Land temperature anomalies with NAO


List of Well Known Climate Indices

Index Description

SOI Southern Oscillation Index: Measures the SLP anomalies between Darwin and Tahiti NAO 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


Background on Complex Networks for Climate Data

Climate Network constructed from the anomaly time series of all locations on the Earth.

Nodes in the Graph represent the regions on the Earth.

Edges in the Graph represent the correlation between the anomaly time series of two locations.

The study of Complex Networks in Climate Data was previously explored by Tsonis, et al, Donges, et al, Steinbach et al.

However prior research focused on positive or absolute values of correlation.

Area weighted degree in a correlation-based climate network (Tsonis, et al.)

longitude

latit

ude

SST Clusters With Relatively High Correlation to Land Temperature

-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180

90

60

30

0

-30

-60

-90

29

75 78 67 94

Steinbach et. al, KDD 03


Challenges in studying dipoles 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.

The number of negative edges around the globe is very high. So an algorithm focusing on negative edges will not scale.

Distribution of edges around the Earth

Distribution of edges > 5000km away

Distribution of negative edges

Distribution of edges around the Earth with abs correlation > 0.5

Distribution of negative edges

Distribution of edges around the Earth having a distance > 5000km and abs correlation > 0.2


Shared Reciprocal Nearest Neighbors

Shared Reciprocal Nearest Neighbors (SRNN) Density

Shared Nearest Neighbor (SNN) defines similarity between pair of objects based upon the list of k-nearest neighbors.

Additional Reciprocity constraint imposed, i.e. two objects must lie on each others nearest neighbor list.

Positive and Negative densities computed by looking at the k nearest neighbors in the positive and the negative correlations.

Overall SRNN density is defined as the product of the two densities.

After spatial clustering over SRNN density, dipoles are identified as cluster pairs with negative correlation between them.

Dipoles from SRNN density


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.


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.


Known SOI connectionsKnown

Known

Vecchi and Wittenberg (2010)

We might find more with the dynamic index!


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.


Comparison of Climate ModelsHindcast 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


Dipole Activity in Future

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).

Hindcast data


Phase Lock of SOI in Future Climate?

Shukla et al. (2009)

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