Statistical Analysis of Climate Extremes

Contents
Preface
1 Introduction
	Reading Material
2 Data
	2.1 Event Times
		2.1.1 Elbe Winter Floods
	2.2 Peaks Over Threshold
		2.2.1 Potsdam Wind Speed
	2.3 Block Extremes
	2.4 Detection of Extremes
		2.4.1 Threshold Placement
		2.4.2 Block Length Selection
	Exercises
	Reading Material
3 Methods
	3.1 Stationary Processes
		3.1.1 GEV Distribution
		3.1.2 GP Distribution
		3.1.3 Return Level, Return Period, and Risk
		3.1.4 Heavy-Tail Distributions
	3.2 Nonstationary Poisson Process
		3.2.1 Kernel Occurrence Rate Estimation
		3.2.2 Bandwidth Selection
		3.2.3 Boundary Bias Correction
		3.2.4 Confidence Band Construction
		3.2.5 Cox–Lewis Test
	Exercises
	Reading Material
4 Floods and Droughts
	4.1 Case Study: Elbe Floods
		4.1.1 Data Quality
		4.1.2 Reservoirs
		4.1.3 Further River Training
		4.1.4 Land Use
		4.1.5 Climate
	4.2 Case Study: Monsoon Droughts
		4.2.1 Detection
		4.2.2 Occurrence Rate Estimation
	4.3 Outlook
	4.4 Summary for the Risk Analyst
	Reading Material
5 Heatwaves and Cold Spells
	5.1 Indices
	5.2 Case Study: European Heatwaves
		5.2.1 Action Measures
		5.2.2 Occurrence Rate Estimation
		5.2.3 Other Measures
		5.2.4 Absolute versus Calendar Day Percentiles
	5.3 Case Study: Cold Spells at Potsdam
		5.3.1 Late Frost
	5.4 Outlook
	5.5 Summary for the Risk Analyst
	Reading Material
6 Hurricanes and Other Storms
	6.1 Case Study: US Landfalling Hurricanes
		6.1.1 Database
		6.1.2 Risk Analysis
		6.1.3 Sensitivity Analysis
	6.2 Case Study: Paleo Hurricanes
		6.2.1 Occurrence Rate Estimation
	6.3 Case Study: Storms at Potsdam
		6.3.1 Stationarity
		6.3.2 Nonstationarity
	6.4 Outlook
	6.5 Summary for the Risk Analyst
	Reading Material
Appendix A Climate Measurements
	A.1 Temperature
	A.2 Precipitation
	A.3 Wind Speed
	Reading Material
Appendix B Natural Climate Archives
	B.1 Speleothems
	B.2 Lake Sediment Cores
	B.3 Tree-Rings
	B.4 Other Natural Archives
	B.5 Dating of Natural Archives
	Reading Material
Appendix C Physical Climate Models
	C.1 Earth System Models
	C.2 Uncertainties and Model–Data Comparisons
	Reading Material
Appendix D Statistical Inference
	D.1 Estimation
		D.1.1 Mean and Standard Deviation, Standardization
		D.1.2 Linear Regression
		D.1.3 Nonlinear Regression
		D.1.4 Monte Carlo Experiments
	D.2 Hypothesis Tests
	Reading Material
Appendix E Numerical Techniques
	E.1 Optimization
	E.2 Parallel Computing
	E.3 Random Numbers
	Reading Material
Appendix F Data and Software
	F.1 Data
	F.2 Software
Appendix G List of Symbols and Abbreviations
References
Index