Time Series Analysis in Climatology and Related Sciences

ISBN-13: 9783030580544 / Angielski / Miękka / 2020 / 245 str.

Victor Privalsky

Spis treści

Chapter 1. Introduction

References        

Chapter 2. Basics of Scalar Random Processes    

2.1. Basic statistical characteristics

2.2 Deterministic process

2.3 Random Process

2.4 Covariance and correlation functions

2.5 Spectral density

2.6 Examples of geophysical time series and their statistics

References

Chapter 3. Time and Frequency Domain Models of Scalar Time Series

3.1 Nonparametric spectral analysis

3.2 Parametric models of time series

3.3 Parametric spectral analysis

3.4 Determining the order of autoregressive models

3.5. Comparison of autoregressive and nonparametric spectral estimates

3.6 Advantages and disadvantages of autoregressive analysis (scalar case)

References

Chapter 4. Practical Analysis of Time Series

4.1 Selecting the sampling interval

4.2 Linear trend and its analysis

4.3 Testing for stationarity and ergodicity

4.4 Linear filtering

4.5 Frequency resolution of autoregressive spectral analysis

4.6 Example of AR analysis in time and frequency domains

Appendix

References

Chapter 5. Stochastic models and spectra of climatic and related time series

5.1 Properties of climate indices

5.2 Properties of time series of spatially averaged surface temperature

5.3. Quasi-Biennial Oscillation

5.4 Other oscillations

Appendix

References

Chapter 6. Statistical Forecasting of Geophysical Processes

6.1 General remarks

6.2 Method of extrapolation

6.2 Example 1. Global annual temperature

6.3 Example 2. Quasi-Biennial Oscillation

6.4 Example 3. ENSO components

6.5 Example 4. Madden-Julian Oscillation

Appendix

References

Chapter 7. Bivariate Time Series Analysis

7.1 Elements of bivariate time series analysis

7.2 Granger causality and feedback

7.3 On properties of software for analysis of multivariate time series

References

Chapter 8. Teleconnection Research and Bivariate Extrapolation

8.1 Example 1. The ENSO Teleconnection

8.2. Example 2. Teleconnections between ENSO and AST               

8.3. Example 3. Bivariate Extrapolation of Madden-Julian Oscillation

Appendix

References

Chapter 9. Reconstruction of Time Series

9.1 Introduction

9.2. Methods of Reconstruction

9.3. Verification of the autoregressive reconstruction method

9.4. Discussion and conclusions

References

Chapter 10. Frequency domain structure and feedbacks in QBO time series

References

Chapter 11. Verification of General Circulation Models

11.1 Verifying the structure of ENSO

11.2 Verification of ENSO influence upon global temperature

11.3 Verifications of properties of surface temperature over CONUS

11.4. Conclusions

Chapter 12. Applications to proxy data

12.1 Introduction

12.2. Greenland Ice Cores

12.3. Antarctic ice cores

12.3 Discussion and conclusions

References

Chapter 13. Application to Sunspot Numbers and Total Solar Irradiance

13.1 Introduction

13.2 Properties of sunspot number time series

13.3 Properties of total solar irradiance time series

References

Chapter 14. Multivariate time and frequency domain analysis

14.1 Time domain analysis

14.2 Frequency domain analysis

14.3. Analysis of a simulated trivariate time series

14.4 Analysis of climatic time series

References

Chapter 15. Summary and Recommendations

References

Blurb

This book gives the reader the basic knowledge of the theory of random processes necessary for applying to study climatic time series. It contains many examples in different areas of time series analysis such as autoregressive modelling and spectral analysis, linear extrapolation, simulation, causality, relations between scalar components of multivariate time series, and reconstructions of climate data.

As an important feature, the book contains many practical examples and recommendations about how to deal and how not to deal with applied problems of time series analysis in climatology or any other science where the time series are short.