Climate and Statistics
Preface
Foreword
Technical Setup
1
Introduction
1.1
Intro About the Seminar Topic
1.2
Outline of the Booklet
2
Natural Variability by internal variability
2.1
Abstract
2.2
Climate Model Ensemble
2.3
Internal Variability
2.4
Introduction to climate modelling and climate variability
2.5
Basics of climate models
2.6
Types of climate models: A comparison
2.7
Introduction to climate model ensembles
2.8
Definition and meaning of climate model ensembles
2.9
Methods for calculating climate model ensembles
2.10
Example of a climate model ensemble
2.11
Internal variability in the climate system
2.12
Definition and causes of internal variability
2.13
Effects of internal variability on climate projections
2.14
Application and meaning of climate model ensembles in research
2.15
Use of climate model ensembles for robust climate predictions
2.16
Application examples: regional and seasonal climate trends
2.17
Conclusion
3
Standard Precipitation Evapotranspiration Index
3.1
Introduction
3.2
Data and Methods
3.2.1
Data
3.2.2
Implementation of the SPEI
3.3
Results
3.3.1
Comparison of Water Balance Distributions
3.3.2
SPEI in Bavaria
3.4
Discussion
4
Compound events
4.1
Abstract
4.2
Introduction
4.3
Theoretical Background
4.3.1
Copula
4.3.2
Empirical Copula
4.3.3
Archimedean Copulas
4.3.4
Intuitive Hazard Scenarios
4.4
Descriptive Analysis
4.4.1
Climate Map of Germany
4.4.2
Development of Average Data in Germany as a whole
4.4.3
Annual Global Mean Temperature Anomalies
4.5
Model
4.5.1
Copula Models using Original data
4.5.2
Copula Models using Anomalies
4.6
Rarity of 2018
4.6.1
From March to November
4.6.2
From June to August
4.7
Prediction
4.8
Conclusion
5
Asymmetric bivariate copulas for compounds
5.1
Introduction
5.2
Theoretical background on copulas
5.2.1
Definition of d-variate copulas and the special case of bivariate copulas
5.2.2
Sklar’s theorem and the probability integral transformation
5.2.3
Distinction between symmetric and asymmetric copulas
5.3
Theoretical background on measures of asymmetry in a copula and their application to hydrological data
5.3.1
Measures of asymmetry in a copula
5.3.2
Bounds for measures of asymmetry in a copula and the resulting possibility for normalization
5.3.3
Example of an application of a selection of the asymmetry measures described above to hydrological data
5.4
Archimax copulas
5.4.1
Definition
5.4.2
Archimedean and extreme value copulas as special cases of Archimax copulas
5.4.3
Asymmetry
5.5
Conclusion
6
Teleconnections North Atlantic Oscillation
6.1
Abstract
6.2
Introduction
6.3
The spatial and temporal structure of the NAO
6.3.1
Data
6.3.2
One-point correlation maps
6.3.3
EOF Analysis
6.3.4
Cluster Analysis
6.3.5
NAO positive and negative Index
6.4
The Implications of the North Atlantic Oscillation
6.4.1
Temperature, wind speed and direction
6.4.2
Storm and precipitation
6.4.3
Ocean and Sea Ice
6.5
Conclusion
7
Low Flow Events
7.1
Abstract
7.2
Introduction
7.3
Data Analysis
7.3.1
Observed Hydrological Data
7.3.2
Observed Meteorological Data
7.3.3
Reanalysis Data
7.3.4
Global Circulation Model Outputs
7.3.5
Reservoir Indices
7.4
Stationarity vs. Nonstationarity in Low-flow Analysis
7.5
Detecting Nonstationarity
7.5.1
Change Point Analysis
7.5.2
Temporal Trend Analysis
7.6
Univariate Statistical Analysis
7.6.1
Exceedance Probability of a Low-flow Event
7.6.2
Return Period
7.6.3
Hydrological Risk
7.6.4
Nonstationary frequency analysis of low-flow series
7.7
Bivariate Statistical Analysis
7.7.1
Joint Return Period Under Nonstationary Framework
7.7.2
Time-Varying Copula Model
7.7.3
Model Selection and Goodness-of-fit
7.8
Hybrid Dynamical–Statistical Approaches
7.8.1
Statistical Downscaling Model (SDSM)
7.9
Conclusion
8
Statistical streamflow modelling
8.1
Abstract
8.2
Introduction
8.3
Data
8.3.1
Preperation
8.3.2
Preprocessing
8.4
Models
8.4.1
LSTM
8.4.2
Temporal Fusion Transformer
8.4.3
Kling Gupta Efficiency
8.5
Results
8.5.1
Training Setup
8.5.2
Results
8.5.3
Feature Importance
8.6
Conclusion
8.7
Outlook
9
The Lancet Report 2023
9.1
Introduction
9.2
Background
9.3
Selected Indicators
9.3.1
Indicator 1.1.2: Exposure of vulnerable populations to heatwaves
9.3.2
Indicator 1.1.5: Heat-related mortality
9.3.3
Indicator 1.4: Food insecurity and undernutrition
9.4
Discussion
10
Epidemiologic studies on the heat effects
10.1
Introduction
10.2
Excess Mortality Attributed to Heat and Cold
10.2.1
Background for Study
10.2.2
Added Value
10.2.3
Limitations
10.2.4
Findings
10.2.5
Conclusion
10.2.6
Data and Framework
10.2.7
Modelling Framework
10.2.8
Partial Least Squares (PLS) Regression
10.2.9
Akaike Information Criterion (AIC) and Model Selection
10.2.10
Application in the Study
10.2.11
Application in the Study
10.2.12
Model
10.2.13
Assessing Overdispersion
10.2.14
Findings and Interpretation
10.3
Joint Effect of Heat and Air Pollution on Mortality
10.3.1
Findings
10.3.2
Model
10.3.3
Comparison of Findings: Masselot et al. (2023) vs. Stafoggia et al. (2023)
10.3.4
Conclusion
11
Controversial issue : heat and humidity
11.1
Abstract
11.2
Introduction
11.2.1
Background
11.2.2
Epidemiological Studies
11.2.3
Physiological knowledge
11.3
Heat and Humidity in Studies
11.3.1
Humidity definitions
11.3.2
Composite Indicators
11.3.3
Confounder
11.3.4
Effect Modification and Interaction
11.3.5
Data Limitations
11.4
Examples
11.5
Discussion
12
Risk Projections
13
Climate Crisis and Mental Health
13.1
Introduction
13.2
Challenges of Quantifying Climate Change Impact on Mental Health
13.3
Data Sources and Methods Used to Investigate Climate Change Impact
13.4
The Impact of Hurricanes on Posttraumatic Stress Disorder (PTSD)
13.5
Adaption and Mitigation Strategies
13.6
Conclusion
14
References
15
Open issue
16
Acknowledgements
References
Published with bookdown
Climate And Statistics
Chapter 14
References