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Causality and climate networks
approaches for evaluating climate
models, tracing flows, and selecting
physically meaningful predictors
Angel Vázquez-Patiño
angel.vazquezp@ucuenca.edu.ec
April 29, 2022
PhD thesis defense Angel Vázquez-Patiño 2/48
Content
Introduction
Causal flows and evaluation of GCMs
A virtual control volume approach to study
climate causal flows
Causality-based predictor selection for robust
and interpretable models
Conclusions
PhD thesis defense Angel Vázquez-Patiño 3/48
Introduction
PhD thesis defense Angel Vázquez-Patiño 4/48
The complex climate system
(Le Treut et al., 2007)
PhD thesis defense Angel Vázquez-Patiño 5/48
Climate Informatics
Easterbrook, S., 2012. What is Climate Informatics? Serendipity.
Climate
Science
Computer
Science
Information
Science
CI
PhD thesis defense Angel Vázquez-Patiño 6/48
Causality and climate networks (1)
(Yamasaki et al., 2008)
PhD thesis defense Angel Vázquez-Patiño 7/48
Causality and climate networks (2)
Y
t
t−1
t−2
t−3
present
past |
ϵY, Y
X
t
t−1
t−2
ϵY, XY
t−3
PhD thesis defense Angel Vázquez-Patiño 8/48
(Molkenthin et al., 2014)
“From Dynamics to Topology”
(Ebert-Uphoff and Deng, 2017)
PhD thesis defense Angel Vázquez-Patiño 9/48
Unraveling the climate system (1)
(Ebert-Uphoff and Deng, 2012a)
PhD thesis defense Angel Vázquez-Patiño 10/48
Unraveling the climate system (2)
(Ebert-Uphoff and Deng, 2012b)
(Kumar, 2020)
PhD thesis defense Angel Vázquez-Patiño 11/48
Challenges in the climate system
(Runge et al., 2019)
(Ebert-Uphoff and Deng, 2014)
PhD thesis defense Angel Vázquez-Patiño 12/48
The thesis goal
●
Knowledge discovery
●
Methodologies and applications
●
Spatial scales
●
Common tasks in climatology
– Model evaluation based on processes
– Trace of flows
– Predictor selection
●
Complementary
PhD thesis defense Angel Vázquez-Patiño 13/48
A focus
on the
climate
of South
America
Model evaluation
Trace of flows
Predictor selection
PhD thesis defense Angel Vázquez-Patiño 14/48
Causal flows and evaluation of
GCMs
PhD thesis defense Angel Vázquez-Patiño 15/48
Evaluation of Models by Causal
Flows (EMCaF)
https://www.e-education.psu.edu/worldofweather/node/2029
GMC
Reference
PhD thesis defense Angel Vázquez-Patiño 16/48
Evaluation of Models by Causal
Flows (EMCaF)
https://www.e-education.psu.edu/worldofweather/node/2029
GMC
Reference
Causal flows
PhD thesis defense Angel Vázquez-Patiño 17/48
Evaluation of Models by Causal
Flows (EMCaF)
NCEP/NCAR
MPI-ESM-LR
PhD thesis defense Angel Vázquez-Patiño 18/48
Meaning of the GC strength
PhD thesis defense Angel Vázquez-Patiño 19/48
Use of the methodology
PhD thesis defense Angel Vázquez-Patiño 20/48
GC strength
PhD thesis defense Angel Vázquez-Patiño 21/48
Link length (1)
PhD thesis defense Angel Vázquez-Patiño 22/48
Link length (2)
ENSO
PhD thesis defense Angel Vázquez-Patiño 23/48
RCP 2.6 and RCP 8.5 (1)
PhD thesis defense Angel Vázquez-Patiño 24/48
RCP 2.6 and RCP 8.5 (2)
PhD thesis defense Angel Vázquez-Patiño 25/48
A virtual control volume approach to
study climate causal flows
PhD thesis defense Angel Vázquez-Patiño 26/48
https://www.flickr.com/photos/globalwaterpartnership/5663389997
PhD thesis defense Angel Vázquez-Patiño 27/48
Control volume
Control
Volume
Control
Volume
Control Surface
PhD thesis defense Angel Vázquez-Patiño 28/48
Virtual control volume
PhD thesis defense Angel Vázquez-Patiño 29/48
Delimitation of areas of influence (1)
PhD thesis defense Angel Vázquez-Patiño 30/48
Delimitation of areas of influence (2)
4,200 m a.s.l.
100 m a.s.l.
4,200 m a.s.l.
PhD thesis defense Angel Vázquez-Patiño 31/48
Causality-based predictor selection
PhD thesis defense Angel Vázquez-Patiño 32/48
Feature selection
https://docs.microsoft.com/es-es/windows/ai/windows-ml/what-is-a-machine-learning-model
PhD thesis defense Angel Vázquez-Patiño 33/48
Interpretability and robustness
(Yu et al., 2020)
PhD thesis defense Angel Vázquez-Patiño 34/48
General scheme
PhD thesis defense Angel Vázquez-Patiño 35/48
Quantitative evaluation
PhD thesis defense Angel Vázquez-Patiño 36/48
Qualitative evaluation
PhD thesis defense Angel Vázquez-Patiño 37/48
Predictores seleccionados (1)
PhD thesis defense Angel Vázquez-Patiño 38/48
Predictores seleccionados (2)
PhD thesis defense Angel Vázquez-Patiño 39/48
Conclusions
PhD thesis defense Angel Vázquez-Patiño 40/48
Model evaluation
Trace of flows
Predictor selection
PhD thesis defense Angel Vázquez-Patiño 41/48
PhD thesis defense Angel Vázquez-Patiño 42/48
Thank you!
PhD thesis defense Angel Vázquez-Patiño 43/48
Questions
PhD thesis defense Angel Vázquez-Patiño 44/48
PhD thesis defense Angel Vázquez-Patiño 45/48
PhD thesis defense Angel Vázquez-Patiño 46/48
PhD thesis defense Angel Vázquez-Patiño 47/48
References (1)
●
Le Treut et al., 2007. Historical Overview of Climate Change Science, in:
Climate Change 2007: The Physical Science Basis. Contribution of Working
Group I to the Fourth Assessment Report of the IPCC. Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, pp. 93-127.
●
Ebert-Uphoff, I., Deng, Y., 2012a. A New Type of Climate Network Based on
Probabilistic Graphical Models: Results of Boreal Winter Versus Summer.
Geophysical Research Letters 39, 7.
●
Kumar, V. Development of Precise Indices for Assessing the Potential
Impacts of Climate Change. Atmosphere 2020, 11, 1231.
https://doi.org/10.3390/atmos11111231
●
Ebert-Uphoff, I., Deng, Y., 2012b. Causal Discovery for Climate Research
Using Graphical Models. Journal of Climate 25, 5648-5665.
●
Yamasaki et al., 2008. Climate Networks around the Globe are Significantly
Affected by El Niño. Physical Review Letters 100.
●
Runge et al., 2019. Inferring causation from time series in Earth system
sciences. Nat Commun 10, 2553.
PhD thesis defense Angel Vázquez-Patiño 48/48
References (2)
●
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.
●
Dutta, R., Maity, R., 2020. Identification of potential causal
variables for statistical downscaling models: effectiveness of
graphical modeling approach. Theor Appl Climatol 142, 1255-1269.
●
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.
●
Molkenthin et al., 2014. Networks from Flows - From Dynamics to
Topology. Scientific Reports 4, 4119-4123.
●
Yu et al., 2020. Causality-based Feature Selection: Methods and
Evaluations. ACM Comput. Surv. 53, 1-36.

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Causality and climate networks approaches for evaluating climate models, tracing flows, and selecting physically meaningful predictors

  • 1. Causality and climate networks approaches for evaluating climate models, tracing flows, and selecting physically meaningful predictors Angel Vázquez-Patiño angel.vazquezp@ucuenca.edu.ec April 29, 2022
  • 2. PhD thesis defense Angel Vázquez-Patiño 2/48 Content Introduction Causal flows and evaluation of GCMs A virtual control volume approach to study climate causal flows Causality-based predictor selection for robust and interpretable models Conclusions
  • 3. PhD thesis defense Angel Vázquez-Patiño 3/48 Introduction
  • 4. PhD thesis defense Angel Vázquez-Patiño 4/48 The complex climate system (Le Treut et al., 2007)
  • 5. PhD thesis defense Angel Vázquez-Patiño 5/48 Climate Informatics Easterbrook, S., 2012. What is Climate Informatics? Serendipity. Climate Science Computer Science Information Science CI
  • 6. PhD thesis defense Angel Vázquez-Patiño 6/48 Causality and climate networks (1) (Yamasaki et al., 2008)
  • 7. PhD thesis defense Angel Vázquez-Patiño 7/48 Causality and climate networks (2) Y t t−1 t−2 t−3 present past | ϵY, Y X t t−1 t−2 ϵY, XY t−3
  • 8. PhD thesis defense Angel Vázquez-Patiño 8/48 (Molkenthin et al., 2014) “From Dynamics to Topology” (Ebert-Uphoff and Deng, 2017)
  • 9. PhD thesis defense Angel Vázquez-Patiño 9/48 Unraveling the climate system (1) (Ebert-Uphoff and Deng, 2012a)
  • 10. PhD thesis defense Angel Vázquez-Patiño 10/48 Unraveling the climate system (2) (Ebert-Uphoff and Deng, 2012b) (Kumar, 2020)
  • 11. PhD thesis defense Angel Vázquez-Patiño 11/48 Challenges in the climate system (Runge et al., 2019) (Ebert-Uphoff and Deng, 2014)
  • 12. PhD thesis defense Angel Vázquez-Patiño 12/48 The thesis goal ● Knowledge discovery ● Methodologies and applications ● Spatial scales ● Common tasks in climatology – Model evaluation based on processes – Trace of flows – Predictor selection ● Complementary
  • 13. PhD thesis defense Angel Vázquez-Patiño 13/48 A focus on the climate of South America Model evaluation Trace of flows Predictor selection
  • 14. PhD thesis defense Angel Vázquez-Patiño 14/48 Causal flows and evaluation of GCMs
  • 15. PhD thesis defense Angel Vázquez-Patiño 15/48 Evaluation of Models by Causal Flows (EMCaF) https://www.e-education.psu.edu/worldofweather/node/2029 GMC Reference
  • 16. PhD thesis defense Angel Vázquez-Patiño 16/48 Evaluation of Models by Causal Flows (EMCaF) https://www.e-education.psu.edu/worldofweather/node/2029 GMC Reference Causal flows
  • 17. PhD thesis defense Angel Vázquez-Patiño 17/48 Evaluation of Models by Causal Flows (EMCaF) NCEP/NCAR MPI-ESM-LR
  • 18. PhD thesis defense Angel Vázquez-Patiño 18/48 Meaning of the GC strength
  • 19. PhD thesis defense Angel Vázquez-Patiño 19/48 Use of the methodology
  • 20. PhD thesis defense Angel Vázquez-Patiño 20/48 GC strength
  • 21. PhD thesis defense Angel Vázquez-Patiño 21/48 Link length (1)
  • 22. PhD thesis defense Angel Vázquez-Patiño 22/48 Link length (2) ENSO
  • 23. PhD thesis defense Angel Vázquez-Patiño 23/48 RCP 2.6 and RCP 8.5 (1)
  • 24. PhD thesis defense Angel Vázquez-Patiño 24/48 RCP 2.6 and RCP 8.5 (2)
  • 25. PhD thesis defense Angel Vázquez-Patiño 25/48 A virtual control volume approach to study climate causal flows
  • 26. PhD thesis defense Angel Vázquez-Patiño 26/48 https://www.flickr.com/photos/globalwaterpartnership/5663389997
  • 27. PhD thesis defense Angel Vázquez-Patiño 27/48 Control volume Control Volume Control Volume Control Surface
  • 28. PhD thesis defense Angel Vázquez-Patiño 28/48 Virtual control volume
  • 29. PhD thesis defense Angel Vázquez-Patiño 29/48 Delimitation of areas of influence (1)
  • 30. PhD thesis defense Angel Vázquez-Patiño 30/48 Delimitation of areas of influence (2) 4,200 m a.s.l. 100 m a.s.l. 4,200 m a.s.l.
  • 31. PhD thesis defense Angel Vázquez-Patiño 31/48 Causality-based predictor selection
  • 32. PhD thesis defense Angel Vázquez-Patiño 32/48 Feature selection https://docs.microsoft.com/es-es/windows/ai/windows-ml/what-is-a-machine-learning-model
  • 33. PhD thesis defense Angel Vázquez-Patiño 33/48 Interpretability and robustness (Yu et al., 2020)
  • 34. PhD thesis defense Angel Vázquez-Patiño 34/48 General scheme
  • 35. PhD thesis defense Angel Vázquez-Patiño 35/48 Quantitative evaluation
  • 36. PhD thesis defense Angel Vázquez-Patiño 36/48 Qualitative evaluation
  • 37. PhD thesis defense Angel Vázquez-Patiño 37/48 Predictores seleccionados (1)
  • 38. PhD thesis defense Angel Vázquez-Patiño 38/48 Predictores seleccionados (2)
  • 39. PhD thesis defense Angel Vázquez-Patiño 39/48 Conclusions
  • 40. PhD thesis defense Angel Vázquez-Patiño 40/48 Model evaluation Trace of flows Predictor selection
  • 41. PhD thesis defense Angel Vázquez-Patiño 41/48
  • 42. PhD thesis defense Angel Vázquez-Patiño 42/48 Thank you!
  • 43. PhD thesis defense Angel Vázquez-Patiño 43/48 Questions
  • 44. PhD thesis defense Angel Vázquez-Patiño 44/48
  • 45. PhD thesis defense Angel Vázquez-Patiño 45/48
  • 46. PhD thesis defense Angel Vázquez-Patiño 46/48
  • 47. PhD thesis defense Angel Vázquez-Patiño 47/48 References (1) ● Le Treut et al., 2007. Historical Overview of Climate Change Science, in: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 93-127. ● Ebert-Uphoff, I., Deng, Y., 2012a. A New Type of Climate Network Based on Probabilistic Graphical Models: Results of Boreal Winter Versus Summer. Geophysical Research Letters 39, 7. ● Kumar, V. Development of Precise Indices for Assessing the Potential Impacts of Climate Change. Atmosphere 2020, 11, 1231. https://doi.org/10.3390/atmos11111231 ● Ebert-Uphoff, I., Deng, Y., 2012b. Causal Discovery for Climate Research Using Graphical Models. Journal of Climate 25, 5648-5665. ● Yamasaki et al., 2008. Climate Networks around the Globe are Significantly Affected by El Niño. Physical Review Letters 100. ● Runge et al., 2019. Inferring causation from time series in Earth system sciences. Nat Commun 10, 2553.
  • 48. PhD thesis defense Angel Vázquez-Patiño 48/48 References (2) ● 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. ● Dutta, R., Maity, R., 2020. Identification of potential causal variables for statistical downscaling models: effectiveness of graphical modeling approach. Theor Appl Climatol 142, 1255-1269. ● 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. ● Molkenthin et al., 2014. Networks from Flows - From Dynamics to Topology. Scientific Reports 4, 4119-4123. ● Yu et al., 2020. Causality-based Feature Selection: Methods and Evaluations. ACM Comput. Surv. 53, 1-36.