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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)
24/11/16 Angel Vázquez-Patiño 3/34
Introduction
24/11/16 Angel Vázquez-Patiño 4/34
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
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Climate and graph theory (II)
Watts and Strogatz (1998)
24/11/16 Angel Vázquez-Patiño 6/34
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
24/11/16 Angel Vázquez-Patiño 7/34
Climate and graph theory (IV)
Tsonis and Roebber (2004)
Total number of links
24/11/16 Angel Vázquez-Patiño 8/34
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
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Climate networks (II)
MaximumInter-RegionalCorrelation
Dendrogram
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Climate networks (III)
R: 14 R: 5
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Causal discovery networks (I)
● Ebert-Uphoff and
Deng (2010, 2012)
● Isotropic points
● Fully connected
network
● PC algorithm
● Causal sufficiency
Bendito et al. (2007)
24/11/16 Angel Vázquez-Patiño 12/34
Causal discovery networks (II)
● Conditional
independence test
● Necessary condition
● No sufficient condition
● Expert knowledge
24/11/16 Angel Vázquez-Patiño 13/34
Causal discovery networks (II)
● Conditional
independence test
● Necessary condition
● No sufficient condition
● Expert knowledge
24/11/16 Angel Vázquez-Patiño 14/34
Causal discovery networks (II)
● Conditional
independence test
● Necessary condition
● No sufficient condition
● Expert knowledge
24/11/16 Angel Vázquez-Patiño 15/34
Causal discovery networks (III)
Ebert-Uphoff and Deng (2012)
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Causal discovery networks (IV)
● Lack of ground truth
● Expert knowledge
● Synthetic data, Ebert-Uphoff and Deng (2017)
– Dynamical processes in the atmosphere
– Advection and diffusion
– Temperature
24/11/16 Angel Vázquez-Patiño 17/34
Causal discovery networks (V)
Ebert-Uphoff and Deng (2017)
24/11/16 Angel Vázquez-Patiño 18/34
Causal discovery networks (V)
Ebert-Uphoff and Deng (2017)
24/11/16 Angel Vázquez-Patiño 19/34
Causal discovery networks (VI)
24/11/16 Angel Vázquez-Patiño 20/34
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)
24/11/16 Angel Vázquez-Patiño 21/34
Work packages
24/11/16 Angel Vázquez-Patiño 22/34
Work package 1
Finding teleconnections affecting
regional climate in South America
24/11/16 Angel Vázquez-Patiño 23/34
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
24/11/16 Angel Vázquez-Patiño 24/34
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
24/11/16 Angel Vázquez-Patiño 25/34
Work package 2
Analyzing the change of causality
strength over time: present vs future
24/11/16 Angel Vázquez-Patiño 26/34
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
24/11/16 Angel Vázquez-Patiño 27/34
Change of causality strength over
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
24/11/16 Angel Vázquez-Patiño 28/34
Change of causality strength over
time (III)
1950-2000 2050-2100
Deng and Ebert-Uphoff (2014)
24/11/16 Angel Vázquez-Patiño 29/34
Work package 3
Finding causes of anomalies (extreme
events) in climate networks
24/11/16 Angel Vázquez-Patiño 30/34
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
24/11/16 Angel Vázquez-Patiño 31/34
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
24/11/16 Angel Vázquez-Patiño 32/34
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
24/11/16 Angel Vázquez-Patiño 33/34
Questions
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

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

  • 1. 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
  • 2. 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)
  • 4. 24/11/16 Angel Vázquez-Patiño 4/34 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
  • 5. 24/11/16 Angel Vázquez-Patiño 5/34 Climate and graph theory (II) Watts and Strogatz (1998)
  • 6. 24/11/16 Angel Vázquez-Patiño 6/34 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
  • 7. 24/11/16 Angel Vázquez-Patiño 7/34 Climate and graph theory (IV) Tsonis and Roebber (2004) Total number of links
  • 8. 24/11/16 Angel Vázquez-Patiño 8/34 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
  • 9. 24/11/16 Angel Vázquez-Patiño 9/34 Climate networks (II) MaximumInter-RegionalCorrelation Dendrogram
  • 10. 24/11/16 Angel Vázquez-Patiño 10/34 Climate networks (III) R: 14 R: 5
  • 11. 24/11/16 Angel Vázquez-Patiño 11/34 Causal discovery networks (I) ● Ebert-Uphoff and Deng (2010, 2012) ● Isotropic points ● Fully connected network ● PC algorithm ● Causal sufficiency Bendito et al. (2007)
  • 12. 24/11/16 Angel Vázquez-Patiño 12/34 Causal discovery networks (II) ● Conditional independence test ● Necessary condition ● No sufficient condition ● Expert knowledge
  • 13. 24/11/16 Angel Vázquez-Patiño 13/34 Causal discovery networks (II) ● Conditional independence test ● Necessary condition ● No sufficient condition ● Expert knowledge
  • 14. 24/11/16 Angel Vázquez-Patiño 14/34 Causal discovery networks (II) ● Conditional independence test ● Necessary condition ● No sufficient condition ● Expert knowledge
  • 15. 24/11/16 Angel Vázquez-Patiño 15/34 Causal discovery networks (III) Ebert-Uphoff and Deng (2012)
  • 16. 24/11/16 Angel Vázquez-Patiño 16/34 Causal discovery networks (IV) ● Lack of ground truth ● Expert knowledge ● Synthetic data, Ebert-Uphoff and Deng (2017) – Dynamical processes in the atmosphere – Advection and diffusion – Temperature
  • 17. 24/11/16 Angel Vázquez-Patiño 17/34 Causal discovery networks (V) Ebert-Uphoff and Deng (2017)
  • 18. 24/11/16 Angel Vázquez-Patiño 18/34 Causal discovery networks (V) Ebert-Uphoff and Deng (2017)
  • 19. 24/11/16 Angel Vázquez-Patiño 19/34 Causal discovery networks (VI)
  • 20. 24/11/16 Angel Vázquez-Patiño 20/34 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)
  • 21. 24/11/16 Angel Vázquez-Patiño 21/34 Work packages
  • 22. 24/11/16 Angel Vázquez-Patiño 22/34 Work package 1 Finding teleconnections affecting regional climate in South America
  • 23. 24/11/16 Angel Vázquez-Patiño 23/34 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
  • 24. 24/11/16 Angel Vázquez-Patiño 24/34 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
  • 25. 24/11/16 Angel Vázquez-Patiño 25/34 Work package 2 Analyzing the change of causality strength over time: present vs future
  • 26. 24/11/16 Angel Vázquez-Patiño 26/34 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
  • 27. 24/11/16 Angel Vázquez-Patiño 27/34 Change of causality strength over 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
  • 28. 24/11/16 Angel Vázquez-Patiño 28/34 Change of causality strength over time (III) 1950-2000 2050-2100 Deng and Ebert-Uphoff (2014)
  • 29. 24/11/16 Angel Vázquez-Patiño 29/34 Work package 3 Finding causes of anomalies (extreme events) in climate networks
  • 30. 24/11/16 Angel Vázquez-Patiño 30/34 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
  • 31. 24/11/16 Angel Vázquez-Patiño 31/34 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
  • 32. 24/11/16 Angel Vázquez-Patiño 32/34 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
  • 34. 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