Statistical Methods for Climate Scientists

An accessible introduction to statistical methods for students in the climate sciences.

Timothy DelSole (Author), Michael Tippett (Author)

9781108472418, Cambridge University Press

Hardback, published 24 February 2022

542 pages
25 x 17.3 x 2.8 cm, 1.17 kg

'… Timothy DelSole and Michael Tippett aim to streamline students' mathematical training by collecting the most important methods into a single textbook … As more climate scientists venture into such subtle problems as whether to attribute extreme events like prolonged heat waves to climate change, the critical statistical thinking skills fostered in Statistical Methods for Climate Scientists will be of increasing importance.' Brad Marston, Physics Today

A comprehensive introduction to the most commonly used statistical methods relevant in atmospheric, oceanic and climate sciences. Each method is described step-by-step using plain language, and illustrated with concrete examples, with relevant statistical and scientific concepts explained as needed. Particular attention is paid to nuances and pitfalls, with sufficient detail to enable the reader to write relevant code. Topics covered include hypothesis testing, time series analysis, linear regression, data assimilation, extreme value analysis, Principal Component Analysis, Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. The specific statistical challenges that arise in climate applications are also discussed, including model selection problems associated with Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. Requiring no previous background in statistics, this is a highly accessible textbook and reference for students and early-career researchers in the climate sciences.

1. Basic Concepts in Probability and Statistics
2. Hypothesis Tests
3. Confidence Intervals
4. Statistical Tests Based on Ranks
5. Introduction to Stochastic Processes
6. The Power Spectrum
7. Introduction to Multivariate Methods
8. Linear Regression: Least Squares Estimation
9. Linear Regression: Inference
10. Model Selection
11. Screening: A Pitfall in Statistics
12. Principal Component Analysis
13. Field Significance
14. Multivariate Linear Regression
15. Canonical Correlation Analysis
16. Covariance Discriminant Analysis
17. Analysis of Variance and Predictability
18. Predictable Component Analysis
19. Extreme Value Theory
20. Data Assimilation
21. Ensemble Square Root Filters
22. Appendix
References
Index.

Subject Areas: Climate change [RNPG], Meteorology & climatology [RBP], Oceanography [seas RBKC], Probability & statistics [PBT], Numerical analysis [PBKS]