Climate consists of many components, for example, atmosphere, hydrosphere, cryosphere, and biosphere. All the components act under mechanisms that relate them in a highly nonlinear way, making the climate a complex system. This complexity is a challenge to study the climate and its implications at various spatiotemporal scales. However, the dependence of anthropogenic activities on the climate has encouraged its study in order, for example, to anticipate its periodic changes and, as far as possible, extreme events that may have adverse effects. As climate study is an intricate task, several approaches have been used to unravel the underlying processes that dominate its behavior. Those approaches range from linear correlation analysis to complex machine learning-based knowledge discovery analysis. This last approach has become more relevant after the introduction of sophisticated climate simulation models and high-tech equipment (e.g., satellite) that allow a climate record of greater coverage (spatial and temporal) and that, together, have generated ubiquitous large databases. One of the knowledge discovery approaches based on this big data is based on climate networks. Nevertheless, causal reasoning methods have also been used recently to infer and characterize these networks, which are called causal climate networks. Several studies have been carried out with climate networks; however, the recent introduction of causality methods makes the study of climate with causal climate networks an opportunity to explore and exploit them more widely. In addition, the particularities of the climate make it necessary to understand specific operational issues that must be taken into account when applying networks. This thesis aims to propose new methodologies and applications of causal climate networks following as a common thread the characterization of physical phenomena that manifest themselves at different spatial scales. For this, different case studies have been taken. They are the climate in South America and a large part of the Pacific and Atlantic oceans, then, reducing the scale, the surrounding factors that influence the rainfall of Ecuador, and, finally, the selection of predictors for downscaling models in an Andean basin. Among the main results are the following three. First, a methodology for evaluating global climate models based on what is called here as causal flows. Second, an approach that studies causal flows and helps trace influence paths in flow fields. Third, the presentation of evidence that shows the effectiveness of methods based on causality in selecting predictors for downscaling models. The thesis contributes to efforts to bridge the gap between the climate science and causal inference communities. This through the study and application of causal reasoning and taking advantage of the enormous amounts of climate data available today.

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

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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

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Introduction

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The complex climate system
(Le Treut et al., 2007)

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Climate Informatics
Easterbrook, S., 2012. What is Climate Informatics? Serendipity.
Climate
Science
Computer
Science
Information
Science
CI

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Causality and climate networks (1)
(Yamasaki et al., 2008)

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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

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(Molkenthin et al., 2014)
“From Dynamics to Topology”
(Ebert-Uphoff and Deng, 2017)

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Unraveling the climate system (1)
(Ebert-Uphoff and Deng, 2012a)

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Unraveling the climate system (2)
(Ebert-Uphoff and Deng, 2012b)
(Kumar, 2020)

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Challenges in the climate system
(Runge et al., 2019)
(Ebert-Uphoff and Deng, 2014)

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The thesis goal
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Knowledge discovery
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Methodologies and applications
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Spatial scales
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Common tasks in climatology
– Model evaluation based on processes
– Trace of flows
– Predictor selection
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Complementary

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A focus
on the
climate
of South
America
Model evaluation
Trace of flows
Predictor selection

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Causal flows and evaluation of
GCMs

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Evaluation of Models by Causal
Flows (EMCaF)
https://www.e-education.psu.edu/worldofweather/node/2029
GMC
Reference

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Evaluation of Models by Causal
Flows (EMCaF)
https://www.e-education.psu.edu/worldofweather/node/2029
GMC
Reference
Causal flows

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Evaluation of Models by Causal
Flows (EMCaF)
NCEP/NCAR
MPI-ESM-LR

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Meaning of the GC strength

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Use of the methodology

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GC strength

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Link length (1)

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Link length (2)
ENSO

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RCP 2.6 and RCP 8.5 (1)

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RCP 2.6 and RCP 8.5 (2)

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A virtual control volume approach to
study climate causal flows

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https://www.flickr.com/photos/globalwaterpartnership/5663389997

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Control volume
Control
Volume
Control
Volume
Control Surface

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Virtual control volume

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Delimitation of areas of influence (1)

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Delimitation of areas of influence (2)
4,200 m a.s.l.
100 m a.s.l.
4,200 m a.s.l.

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Causality-based predictor selection

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Feature selection
https://docs.microsoft.com/es-es/windows/ai/windows-ml/what-is-a-machine-learning-model

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Interpretability and robustness
(Yu et al., 2020)

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General scheme

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Quantitative evaluation

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Qualitative evaluation

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Predictores seleccionados (1)

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Predictores seleccionados (2)

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Conclusions

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Model evaluation
Trace of flows
Predictor selection

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Thank you!

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Questions

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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
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Ebert-Uphoff, I., Deng, Y., 2012b. Causal Discovery for Climate Research
Using Graphical Models. Journal of Climate 25, 5648-5665.
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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.

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References (2)
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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.
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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.
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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.
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Molkenthin et al., 2014. Networks from Flows - From Dynamics to
Topology. Scientific Reports 4, 4119-4123.
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Yu et al., 2020. Causality-based Feature Selection: Methods and
Evaluations. ACM Comput. Surv. 53, 1-36.