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A Complex Network Approach to Understand Climate Variability
Q.Y. Feng
Sub Physical Oceanography
Marine and Atmospheric Research
Research output
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Thesis
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Doctoral thesis 1 (Research UU / Graduation UU)
Overview
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Dive into the research topics of 'A Complex Network Approach to Understand Climate Variability'. Together they form a unique fingerprint.
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Keyphrases
El Nio
100%
Climate Variability
100%
Complex Network Approach
100%
Pacific Climate
66%
Sea Surface Temperature
50%
Complex Network Theory
50%
Atlantic Meridional Overturning Circulation
33%
Spatial Correlation
33%
Westward Propagation
33%
Climate Networks
33%
Interannual Variability
16%
Negative Consequences
16%
Complex Networks
16%
General Circulation Models
16%
Southern Oscillation
16%
Temperature Signal
16%
North Atlantic
16%
Ocean Models
16%
Multidecadal
16%
Temperature Data
16%
Pearson Correlation
16%
Climate Research
16%
Observation Location
16%
Meridional Overturning Circulation
16%
Dynamical Systems
16%
Niño 3.4
16%
Crucial Aspect
16%
Temperature Observations
16%
Topological Properties
16%
Interannual Variation
16%
Quality Indicators
16%
Correlation Measure
16%
Slowly Varying
16%
Early Warning Indicators
16%
Atlantic Multidecadal Oscillation
16%
North Atlantic Sea Surface Temperature
16%
Topological Features
16%
Quality Measures
16%
Stability Index
16%
Surface Temperature Change
16%
Optimal Observation
16%
Multiple Sections
16%
Sea Surface Temperature Anomaly
16%
Machine Learning Approach
16%
Climate Stability
16%
Index Forecast
16%
Nonlinear Measures
16%
Informed Decision-making
16%
Mutual Information Method
16%
Engineering
Sea Surface
100%
Complex Networks
100%
Circuit Theory
60%
Spatial Correlation
40%
Temperature Data
20%
Powerful Tool
20%
Lead Time
20%
Negative Consequence
20%
Learning Approach
20%
Quality Measure
20%
Pearsons Linear Correlation Coefficient
20%
North Atlantic Sea
20%
Mutual Information
20%
Earth and Planetary Sciences
Sea Surface Temperature
100%
Climate Variation
100%
North Atlantic
40%
General Circulation Model
20%
Southern Oscillation
20%
Ocean Models
20%
Temperature Anomaly
20%
Dynamical System
20%
Annual Variation
20%
Temperature Variation
20%
Atlantic Multidecadal Oscillation
20%
Machine Learning
20%