Characterization of South America Climate System Based on Causal Discovery Techniques from Time Series

Doctorado en Recursos Hídricos
Doctoral Thesis Proposal
Characterization of South America Climate System
Based on Causal Discovery Techniques from Time Series
Angel Vázquez-Patiño
angel.vazquezp@ucuenca.edu.ec
Departamento de Ciencias de la Computación
Departamento de RR HH y Ciencias Ambientales
Universidad de Cuenca
November 25, 2016

24/11/16 Angel Vázquez-Patiño 2/34
Content
Introduction
Climate and graph theory
Climate networks
Causal discovery networks
Work packages
Causal connections on regional climate
Change of causality strength over time
Causes of anomalies (extreme events)

Introduction

Climate and graph theory (I)
Small-world networks
● Regular networks ‘rewired’ to introduce
increasing amounts of disorder (randomness)
● These systems can be highly clustered, like
regular lattices, yet have small characteristic
path lengths, like random graphs

Climate and graph theory (II)
Watts and Strogatz (1998)

Climate and graph theory (III)
Introduction of graph theory in climate
Tsonis and Roebber (2004)
● Climate as a network of many dynamical
systems
● Ideas from graph theory to a global data set to
study its collective behavior
● Network need to have properties of ‘small-
world’ networks

Climate and graph theory (IV)
Tsonis and Roebber (2004)
Total number of links

Climate networks (I)
General used methods
● Correlation networks, MI networks,
synchronization networks, event
synchronization networks
● Link between two nodes
Aims
● Clustering/regionalization, forecasting, decision
making tools purposes

Climate networks (II)
MaximumInter-RegionalCorrelation
Dendrogram

Climate networks (III)
R: 14 R: 5

Causal discovery networks (I)
● Ebert-Uphoff and
Deng (2010, 2012)
● Isotropic points
● Fully connected
network
● PC algorithm
● Causal sufficiency
Bendito et al. (2007)

Causal discovery networks (II)
● Conditional
independence test
● Necessary condition
● No sufficient condition
● Expert knowledge

● Conditional
independence test

Causal discovery networks (III)
Ebert-Uphoff and Deng (2012)

Causal discovery networks (IV)
● Lack of ground truth
● Synthetic data, Ebert-Uphoff and Deng (2017)
– Dynamical processes in the atmosphere
– Advection and diffusion
– Temperature

Causal discovery networks (V)

Causal discovery networks (VI)

Causal discovery networks (VII)
Uses of the approach
1) Test very specific hypotheses on data or
further details
2) Generation of new hypotheses
3) Causal signatures of models
a) error detection
b) effect of compression
c) classify ensemble members
d) compare climate models
Hammerling et al. (2015)

Work packages

Work package 1
Finding teleconnections affecting
regional climate in South America

Causal connections on regional
climate (I)
Data
● Reanalysis as observations
Main points to deal with
● Studies at global scale only
●
Space domain boundaries, Ebert-Uphoff and Deng (2014)
– Incidences where we cannot avoid violating causal sufficiency
– The model usually needs a few out-of-boundary grid points to converge to a
proper independence model
– Initialization problem: to determine the causal flow originating in a grid point,
it is crucial to have information on the causal flow into that grid point
– Since the first few grid point are lacking that information they often yield
erroneous links
– For the nodes in the boundaries the common causes in any prior slices
are not included, thus violating the causal sufficiency condition to an extend
that renders the boundary grid points useless

Causal connections on regional
climate (II)
Data
● Reanalysis as observations
Main points to deal with
● Studies at global scale only
● Space domain boundaries
● New conditional independence tests
– MIT, Runge et al. (2012)
– Isolating source of entropies

Work package 2
Analyzing the change of causality
strength over time: present vs future

Change of causality strength over
time (I)
Data
● Geopotential height
● Temperature
● Precipitation
Method
● Warming climate scenario
● New causality strength metrics, Runge (2014)
● Not only information theory measures

time (II)
Objective
● Future work, Deng and Ebert-Uphoff (2014)
● Study changing characteristics of atmospheric
information flow
● Weakening of information flow in a future
● Northern hemisphere midtropospheric
● Reduced intrinsic predictability → difficult short-
term weather prediction

time (III)
1950-2000 2050-2100
Deng and Ebert-Uphoff (2014)

Work package 3
Finding causes of anomalies (extreme
events) in climate networks

Causes of anomalies (extreme
events) (I)
● Social impact
Data
● Precipitation and temperature
● GCMs, reanalysis and satellite data
Method
● Model nodes of information
● Model extreme events

Causes of anomalies (extreme
events) (II)
Objective
● Identify causes of extreme events
● Spatial and temporal connections
● Early warning forecasting system, mitigation
plans
Even further in the future
● How well climate models represent the cause-
effect links
● Future change of cause-effect links in models

References
●
Watts, D.J., Strogatz, S.H., 1998. Collective dynamics of “small-world” networks. Nature 393, 440–442.
doi:10.1038/30918
●
Tsonis, A.A., Roebber, P.J., 2004. The Architecture of the Climate Network. Physica A: Statistical Mechanics and its
Applications 333, 497–504. doi:10.1016/j.physa.2003.10.045
●
Ebert-Uphoff, I., Deng, Y., 2010. Causal Discovery Methods for Climate Networks (Research Report No. GT-ME-2010-
001). Georgia Institute of Technology, Atlanta, USA.
●
Ebert-Uphoff, I., Deng, Y., 2012. Causal Discovery for Climate Research Using Graphical Models. Journal of Climate 25,
5648–5665. doi:10.1175/JCLI-D-11-00387.1
● Ebert-Uphoff, I., Deng, Y., 2012. A New Type of Climate Network Based on Probabilistic Graphical Models: Results of
Boreal Winter Versus Summer. Geophysical Research Letters 39, 7. doi:10.1029/2012GL053269
●
Runge, J., Heitzig, J., Marwan, N., Kurths, J., 2012. Quantifying causal coupling strength: A lag-specific measure for
multivariate time series related to transfer entropy. Physical Review E 86. doi:10.1103/PhysRevE.86.061121
● Deng, Y., Ebert-Uphoff, I., 2014. Weakening of atmospheric information flow in a warming climate in the Community
Climate System Model. Geophysical Research Letters 41, 193–200. doi:10.1002/2013GL058646
● Ebert-Uphoff, I., Deng, Y., 2014. Causal Discovery from Spatio-Temporal Data with Applications to Climate Science, in:
Proceedings of the 13th International Conference on Machine Learning and Applications. IEEE, Detroit, USA, pp. 606–
613. doi:10.1109/ICMLA.2014.96
●
Runge, J., 2014. Detecting and Quantifying Causality form Time Series of Complex Systems (Ph.D. Thesis). Humboldt
University of Berlin, Berlin, Germany.
● Hammerling, D., Baker, A.H., Ebert-Uphoff, I., 2015. What can we learn about climate model runs from their causal
signatures?, in: Proceedings of the Fifth International Workshop on Climate Informatics (CI2015). Boulder, Colorado,
USA.
● Ebert-Uphoff, I., Deng, Y., 2017. Causal Discovery in the Geosciences - Using Synthetic Data to Learn How to Interpret
Results. Computers & Geosciences 99, 50–60. doi:10.1016/j.cageo.2016.10.008

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Characterization of South America Climate System Based on Causal Discovery Techniques from Time Series

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