ALBERT

Note: Contents Preface 1 Data Analysis in Earth Sciences 1.1 Introduction 1.2 Collecting Data 1.3 Types of Data 1.4 Methods of Data Analysis Recommended Reading 2 Introduction to MATLAB 2.1 MATLAB in Earth Sciences 2.2 Getting Started 2.3 The Syntax 2.4 Data Storage 2.5 Data Handling 2.6 Scripts and Functions 2.7 Basic Visualization Tools Recommended Reading 3 Univariate Statistics 3.1 Introduction 3.2 Empirical Distributions Measures of Central Tendency Measures of Dispersion 3.3 Example of Empirical Distributions 3.4 Theoretical Distributions Uniform Distribution Binomial or Bernoulli Distribution Poisson Distribution Normal or Gaussian Distribution Logarithmic Normal or Log-Normal Distribution Student's t Distribution Fisher's F Distribution Χ2 or Chi-Squared Distribution 3.5 Example ofTheoretical Distributions 3.6 Thet-Test 3.7 TheF-Test 3.8 The Χ2-Test Recommended Reading 4 Bivariate Statistics 4.1 Introduction 4.2 Pearson's Correlation Coefficient 4.3 Classical Linear Regression Analysis and Prediction 4.4 Analyzing the Residuals 4.5 Bootstrap Estimates of the Regression Coefficients 4.6 Jackknife Estimates of the Regression Coefficients 4.7 Cross Validation 4.8 Reduced Major Axis Regression 4.9 Curvilinear Regression Recommended Reading 5 Time-Series Analysis 5.1 Introduction 5.2 Generating Signals 5.3 Blackman-Tukey Autospectral Analysis 5.4 Blackman-Tukey Crossspectral Analysis 5.5 Interpolating and Analyzing Unevenly-Spaced Data 5.6 Evolutionary Blackman-Tukey Powerspectrum 5.7 Lomb-Scargle Powerspectrum 5.8 Wavelet Powerspectrum 5.9 Nonlinear Time-Series Analysis (by N. Marwarn) Phase Space Portrait Recurrence Plots Recommended Reading 6 Signal Processing 6.1 Introduction 6.2 Generating Signals 6.3 Linear Time-Invariant Systems 6.4 Convolution and Filtering 6.5 Comparing Functions for Filtering Data Series 6.6 Recursive and Nonrecursive Filters 6.7 Impulse Response 6.8 Frequency Response 6.9 Filter Design 6.10 Adaptive Filtering Recommended Reading 7 Spatial Data 7.1 Types of Spatial Data 7.2 The GSHHS Shoreline Data Set 7.3 The 2-Minute Gridded Global Elevation Data ETOPO2 7.4 The 30-Arc Seconds Elevation Model GTOPO30 7.5 The Shuttle Radar Topography Mission SRTM 7.6 Gridding and Contouring Background 7.7 Gridding Example 7.8 Comparison of Methods and Potential Artifacts 7.9 Statistics of Point Distributions Test for Uniform Distribution Test for Random Distribution Test for Clustering 7.10 Analysis of Digital Elevation Models (by R. Gebbers) 7.11 Geostatistics and Kriging (by R. Gebbers) Theorical Background Preceding Analysis Variography with the Classical Variogram Kriging Discussion of Kriging Recommended Reading 8 Image Processing 8.1 Introduction 8.2 Datastorage 8.3 Importing, Processing and Exporting Images 8.4 Importing, Processing and Exporting Satellite Images 8.5 Georeferencing Satellite Images 8.6 Digitizing from the Screen Recommended Reading 9 Multivariate Statistics 9.1 Introduction 9.2 Principal Component Analysis 9.3 Independent Component Analysis (by N. Marwan) 9.4 Cluster Analysis Recommended Reading 10 Statistics on Directional Data 10.1 Introduction 10.2 Graphical Representation 10.3 Empirical Distributions 10.4 Theoretical Distributions 10.5 Test for Randomness of Directional Data 10.6 Test for the Significance of a Mean Direction 10.7 Test for the Difference of Two Sets of Directions Recommended Reading General Index

Note: Inhaltsverzeichnis 1 Einführung 1.1 Übersicht 1.2 Modernes naturwissenschaftliches Klimaverständnis 1.3 Modelle in der Klimaforschung 2 Klimarelevante Prozesse 2.1 Energie und Strahlung 2.1.1 Strahlung 2.1.2 Wärmetrausporte 15 2.1.3 Transport von Energie im Wasserkreislauf 2.2 Dynamik der Atmosphäre 2.2.1 Erzeugung von Bewegung 2.2.2 Vertikalstruktur der Atmosphäre 2.2.3 Allgemeine Zirkulation 2.2.4 Regionale Strukturen 2.2.5 Turbulenz 2.2.6 Aerosolpartikel 2.2.7 Wolken und Niederschlag 2.3 Zirkulation des Ozeans 2.3.1 Meeresoberflächenströmungen 2.3.2 Tiefenzirkulation 2.3.3 Wellen und Wirbel 2.4 Spurenstoffkreisläufe 2.4.1 Wasserdampf 2.4.2 Kohlendioxid 2.4.3 Methan 2.4.4 Stickstoffverbindungen 2.5 Kryosphäre 3 Natürliche Klimavariabilität 3.1 Jahres- und Tagesgang 3.2 Wetter 3.3 Interannuale Klimaschwankungen 3.3..1 ENSO-Phänomen 3.3.2 Nordatlantische Oszillation 3.3.3 Temperaturentwicklung seit 1900 3.3.4 Die Frage der Sonnenflecken 3.1 Homogenitätsproblematik 3.5 Historische Klimavariationen 3.6 Paläoklimatologie 3.6.1 Vereisungen 3.6.2 Klimarekonstruktion der Kalt- und Warmzeiten 3.6.3 Milanković-Theorie 4 Konzeptionelle Modelle 4.1 Klimazonen 4.2 Ein exemplarisches Energiebilanzmodell 4.2.1 Vereinfachte Bilanzgleichung für Energie 4.2.2 Diskretisierung 4.2.3 Schließung der Gleichung 4.2.4 Berechnungen: Integration 4.3 Physikalisch orientierte Modelle 4.4 Nichtlinearität und Chaos 4.5 Fluktuationen als stochastische Vorgänge 4.6 Wechselwirkungen verschiedener Prozesse 4.6.1 Gedämpftes System mit Störungen 4.6.2 Wirkung von positiven Rückkopplungen 5 Grundlagen von Strömungsmodellen 5.1 Grundgleichungen der Strömungs- und Thermodynamik 5.1.1 Zustandsvariablen 5.1.2 Gesetz der Massenerhaltung 5.1.3 Prinzip der Energieerhaltung 5.1.4 Impulserhaltung 5.1.5 Massenbilanzen für Beimengungen 5.1.6 Zustandsgleichungen 5.1.7 Zusammenfassung 5.2 Diskretisierung 5.2.1 Räumliche Diskretisierung 5.2.2 Zeitliche Diskretisierung 5.3 Parametrisierung und subskalige Prozesse 5.3.1 Schließungsproblem 5.3.2 Beispiel 1: Turbulenz 5.3.3 Beispiel 2: Konvektion und Wolkenbildung 5.3.4 Kritische Übersicht 5.4 Numerische Integration 6 Realitätsnahe Modelle des Klimasystems 6.1 Wettervorhersagemodelle 6.2 Modelle zur Klimasimulation 6.2.1 Methodik von Simulationen 6.2.2 Wechselwirkung von Atmosphäre und Ozean 6.2.3 Klimadrift und Flußkorrektur 6.2.4 Technische Details 6.2.5 Modellierung von Stoffkreisläufen und Biosphäre 6.3 Simulationen von Klimazuständen 6.3.1 Kontrollsimulationen des derzeitigen Klimas 6.3.2 Rekonstruktion von Paläoklimaten 6.3.3 Klimate anderer Planeten 6.3.1 Regionale und lokale Strukturen 6.4 Numerische Experimente mit Modellen 6.1.1 Zielsetzung 6.4.2 Wirksamkeit von Prozessen 6.4.3 Einschwingzeit der Atmosphäre 6.4.4 Sensitivität gegenüber Randbedingungen 6.5 Anwendung zur Klimavorhersage 6.5.1 Prognosen des ENSO-Phänomens 6.5.2 Großskalige Ölbrände in Kuwait 6.6 Beurteilung der Klimamodelle 7 Anthropogene Klimänderung 7.1 Übersicht 7.2 Emissions- und Konzentrations-Szenarien 7.2.1 Szenarien zukünftiger Emissionen 7.2.2 Erwartete Konzentrationen der Treibhausgase 7.3 Klimaszenarien realitätsnaher Modelle 7.3.1 Transiente Szenarienrechnungen 7.3.2 Ergebnisse eines exemplarischen Klima-Szenarios 7.3.3 Problem Kaltstart 7.3.4 2 x CO2-Simulationen 7.3.5 Informationswert von Szenarienrechnungen 7.3.6 Kritische Bewertung der Szenarien 7.4 Nachweis anthropogener Klimabeeinflussung 7.4.1 Zielsetzung 7.4.2 Natürliche Variabilität 7.4.3 Gewichtungsmuster und Nachweisvariable 7.4.4 Nachweis 7.4.5 Beurteilung 7.5 Lokale und regionale Szenarien 7.5.1 Hochaufgelöste Zeitscheibenexperimente 7.5.2 Regionalmodelle 7.5.3 Empirische Modelle 7.5.4 Implikationen 8 Klima und Gesellschaft 8.1 Übersicht 8.2 Historischer Überblick : gesellschaftliche Vorstellungen zum Einfluß von Klima 8.3 Klimafolgenforschung 8.3.1 Grundproblematik 8.3.2 Direkt beeinflußte Systeme 8.3.3 Indirekt beeinflußte Systeme 8.4 Ökonomische Aspekte des Klimawandels 8.4.1 Klimaänderung als Kostenfaktor 8.4.2 Ein zeitabhängiges Sechs-Komponenten-Modell 8.4.3 Beurteilung 8.4.4 Übersicht Klimapolitik 8.5 Vorstellungen von Klimawandel 8.5.1 Problemstellung 8.5.2 Natürliche Variabilität versus Kausalitätsdenken 8.5.3 Die Kempton-Studie 8.5.4 Soziale Interpretationsmechanismen 9 Résumé 10 Anhang 11 Literatur Stichwortverzeichnis

Note: Contents Foreword Preface Contributors I Introduction 1 The Development of Climate Research / by ANTONIO NAVARRA 1.1 The Nature of Climate Studies 1.1.1 The Big Storm Controversy 1.1.2 The Great Planetary Oscillations 1.2 The Components of Climate Research 1.2.1 Dynamical Theory 1.2.2 Numerical Experimentation 1.2.3 Statistical Analysis 2 Misuses of Statistical Analysis in Climate Research / by HANS VON STORCH 2.1 Prologue 2.2 Mandatory Testing and the Mexican Hat 2.3 Neglecting Serial Correlation 2.4 Misleading Names: The Case of the Decorrelation Time 2.5 Use of Advanced Techniques 2.6 Epilogue II Analyzing The Observed Climate 3 Climate Spectra and Stochastic Climate Models / by CLAUDE FRANKIGNOUL 3.1 Introduction 3.2 Spectral Characteristics of Atmospheric Variables 3.3 Stochastic Climate Model 3.4 Sea Surface Temperature Anomalies 3.5 Variability of Other Surface Variables 3.6 Variability in the Ocean Interior 3.7 Long Term Climate Changes 4 The Instrumental Data Record: Its Accuracy and Use in Attempts to Identify the "CO2 Signal" / by PHIL JONES 4.1 Introduction 4.2 Homogeneity 4.2.1 Changes in Instrumentation, Exposure and Measuring Techniques 4.2.2 Changes in Station Locations 4.2.3 Changes in Observation Time and the Methods Used to Calculate Monthly Averages 4.2.4 Changes in the Station Environment 4.2.5 Precipitation and Pressure Homogeneity 4.2.6 Data Homogenization Techniques 4.3 Surface Climate Analysis 4.3.1 Temperature 4.3.2 Precipitation 4.3.3 Pressure 4.4 The Greenhouse Detection Problem 4.4.1 Definition of Detection Vector and Data Used 4.4.2 Spatial Correlation Methods 4.5 Conclusions 5 Interpreting High-Resolution Proxy Climate Data - The Example of Dendr о climatology / by KEITH R. BRIFFA 5.1 Introduction 5.2 Background 5.3 Site Selection and Dating 5.4 Chronology Confidence 5.4.1 Chronology Signal 5.4.2 Expressed Population Signal 5.4.3 Subsample Signal Strength 5.4.4 Wider Relevance of Chronology Signal 5.5 "Standardization" and Its Implications for Judging Theoretical Signal 5.5.1 Theoretical Chronology Signal 5.5.2 Standardization of "Raw" Data Measurements 5.5.3 General Relevance of the "Standardization" Problem 5.6 Quantifying Climate Signals in Chronologies 5.6.1 Calibration of Theoretical Signal 5.6.2 Verification of Calibrated Relationships 5.7 Discussion 5.8 Conclusions 6 Analysing the Boreal Summer Relationship Between World wide Sea-Surface Temperature and Atmospheric Variability / by M. NEIL WARD 6.1 Introduction 6.2 Physical Basis for Sea-Surface Temperature Forcing of the Atmosphere 6.2.1 Tropics 6.2.2 Extratropics 6.3 Characteristic Patterns of Global Sea Surface Temperature: EOFs and Rotated EOFs 6.3.1 Introduction 6.3.2 SST Data 6.3.3 EOF method 6.3.4 EOFs p^→1 - p^→3 6.3.5 Rotation of EOFs 6.4 Characteristic Features in the Marine Atmosphere Associated with the SST Patterns p^→2, p ^→3 and p^→2R in JAS 6.4.1 Data and Methods 6.4.2 Patterns in the Marine Atmosphere Associated with EOF p^→2 6.4.3 Patterns in the Marine Atmosphere Associated with EOF p^→3 6.4.4 Patterns in the Marine Atmosphere Associated with Rotated EOF p^→2R 6.5 JAS Sahel Rainfall Links with Sea-Surface Temperature and Marine Atmosphere 6.5.1 Introduction 6.5.2 Rainfall in the Sahel of Africa 6.5.3 High Frequency Sahel Rainfall Variations 6.5.4 Low Frequency Sahel Rainfall Variations 6.6 Conclusions III Simulating and Predicting Climate 7 The Simulation of Weather Types in GCMs : A Regional Approach to Control-Run Validation / by KEITH R. BRIFFA 7.1 Introduction 7.2 The Lamb Catalogue 7.3 An "Objective" Lamb Classification 7.4 Details of the Selected GCM Experiments 7.5 Comparing Observed and GCM Climates 7.5.1 Lamb Types 7.5.2 Temperature and Precipitation 7.5.3 Relationships Between Circulation Frequencies and Temperature and Precipitation 7.5.4 Weather-Type Spell Lengths and Storm Frequencies 7.6 Conclusions 7.6.1 Specific Conclusions 7.6.2 General Conclusions 8 Statistical Analysis of GCM Output / by CLAUDE FRANKIGNOUL 8.1 Introduction 8.2 Univariate Analysis 8.2.1 The i-Test on the Mean of a Normal Variable 8.2.2 Tests for Autocorrelated Variables 8.2.3 Field Significance 8.2.4 Example: GCM Response to a Sea Surface Temperature Anomaly 8.3 Multivariate Analysis 8.3.1 Test on Means of Multidimensional Normal Variables 8.3.2 Application to Response Studies 8.3.3 Application to Model Testing and Intercomparison 9 Field Intercomparison / by ROBERT E . LIVEZEY 9.1 Introduction 9.2 Motivation for Permutation and Monte Carlo Testing 9.2.1 Local vs. Field Significance 9.2.2 Test Example 9.3 Permutation Procedures 9.3.1 Test Environment 9.3.2 Permutation (PP) and Bootstrap (BP) Procedures 9.3.3 Properties 9.3.4 Interdependence Among Field Variables 9.4 Serial Correlation 9.4.1 Local Probability Matching 9.4.2 Times Series and Monte Carlo Methods 9.4.3 Independent Samples 9.4.4 Conservatism 9.5 Concluding Remarks 10 The Evaluation of Forecasts / by ROBERT E. LIVEZEY 10.1 Introduction 10.2 Considerations for Objective Verification 10.2.1 Quantification 10.2.2 Authentication 10.2.3 Description of Probability Distributions 10.2.4 Comparison of Forecasts 10.3 Measures and Relationships: Categorical Forecasts 10.3.1 Contingency and Definitions 10.3.2 Some Scores Based on the Contingency Table 10.4 Measures and Relationships: Continuous Forecasts 10.4.1 Mean Squared Error and Correlation 10.4.2 Pattern Verification (the Murphy-Epstein Decomposition) 10.5 Hindcasts and Cross-Validation 10.5.1 Cross-Validation Procedure 10.5.2 Key Constraints in Cross-Validation 11 Stochastic Modeling of Precipitation with Applications to Climate Model Downscaling / by DENNIS LETTENMAIER 11.1 Introduction 11.2 Probabilistic Characteristics of Precipitation 11.3 Stochastic Models of Precipitation 11.3.1 Background 11.3.2 Applications to Global Change 11.4 Stochastic Precipitation Models with External Forcing 11.4.1 Weather Classification Schemes 11.4.2 Conditional Stochastic Precipitation Models 11.5 Applications to Alternative Climate Simulation 11.6 Conclusions IV Pattern Analysis 12 Teleconnections Patterns / by ANTONIO NAVARRA 12.1 Objective Teleconnections 12.2 Singular Value Decomposition 12.3 Teleconnections in the Ocean-Atmosphere System 12.4 Concluding Remarks 13 Spatial Patterns: EOFs and CCA / by HANS VON STORCH 13.1 Introduction 13.2 Expansion into a Few Guess Patterns 13.2.1 Guess Patterns, Expansion Coefficients and Explained Variance 13.2.2 Example: Temperature Distribution in the Mediterranean Sea 13.2.3 Specification of Guess Patterns 13.2.4 Rotation of Guess Patterns 13.3 Empirical Orthogonal Functions 13.3.1 Definition of EOFs 13.3.2 What EOFs Are Not Designed for 13.3.3 Estimating EOFs 13.3.4 Example: Central European Temperature 13.4 Canonical Correlation Analysis 13.4.1 Definition of Canonical Correlation Patterns 13.4.2 CCA in EOF Coordinates 13.4.3 Estimation: CCA of Finite Samples 13.4.4 Example: Central European Temperature 14 Patterns in Time : SSA and MSSA / by ROBERT VAUTARD 14.1 Introduction 14.2 Reconstruction and Approximation of Attractors 14.2.1 The Embedding Problem 14.2.2 Dimension and Noise 14.2.3 The Macroscopic Approximation 14.3 Singular Spectrum Analysis 14.3.1 Time EOFs 14.3.2 Space-Time EOFs 14.3.3 Oscillatory Pairs 14.3.4 Spectral Properties 14.3.5 Choice of the Embedding Dimension 14.3.6 Estimating Time and Space-Time Patterns 14.4 Climatic Applications of SSA 14.4.1 The Analysis of Intraseasonal Oscillations 14.4.2 Empirical Long-Range Forecasts Using MSSA Predictors 14.5 Conclusions 15 Multivariate Statistical Modeling : POP-Model as a First Order Approximation / by JIN-SONG VON STORCH 15.1 Introduction 15.2 The Cross-Covariance Matrix and the Cross-Spectrum Matrix 15.3 Multivariate AR(1) Process and its Cross-Covariance and Cross-Spectrum Matrices 15.3.1 The System Matrix A and its POPs 15.3.2 Cross-Spectrum Matrix in POP-Basis: Its Matrix Formulation 15.3.3 Cross-Spectrum Matrix in POP-Basis: Its Diagonal Components 15.3.4