The book "Stochastic Climate Models" is a collection of papers presented at a workshop held in Chorin, Germany, in 1999. It aims to capture the spirit of enthusiasm and recent developments in the field of stochastic climate modeling. The editors, Peter Imkeller and Jin-Song von Storch, highlight the importance of stochastic models in understanding climate variability and their role within the broader framework of climate research and mathematical modeling. The book is structured into several chapters, each focusing on different aspects of stochastic climate models. Chapter 1 provides an overview of various types of climate models, ranging from simple models to complex models. Chapter 2 delves into the mathematical and physical origins of stochasticity in climate models, discussing techniques such as averaging, normal deviations, and large deviations. Chapter 3 introduces mathematical tools essential for treating stochastic climate models, including stochastic differential equations, evolution equations, and random dynamical systems. Chapter 4 discusses specific reduced climate models and the techniques used to analyze them. The contributors, including both mathematicians and climate physicists, present a range of topics, from the derivation of stochastic models from first principles to the application of these models in understanding complex climate dynamics. The book also includes detailed lectures by leading researchers, providing insights into the latest research and future directions in the field. Overall, the book serves as a valuable resource for researchers and students interested in the intersection of stochastic processes, climate dynamics, and mathematical modeling. It emphasizes the importance of interdisciplinary collaboration and the need for further research to better understand the complex interactions within the climate system.The book "Stochastic Climate Models" is a collection of papers presented at a workshop held in Chorin, Germany, in 1999. It aims to capture the spirit of enthusiasm and recent developments in the field of stochastic climate modeling. The editors, Peter Imkeller and Jin-Song von Storch, highlight the importance of stochastic models in understanding climate variability and their role within the broader framework of climate research and mathematical modeling. The book is structured into several chapters, each focusing on different aspects of stochastic climate models. Chapter 1 provides an overview of various types of climate models, ranging from simple models to complex models. Chapter 2 delves into the mathematical and physical origins of stochasticity in climate models, discussing techniques such as averaging, normal deviations, and large deviations. Chapter 3 introduces mathematical tools essential for treating stochastic climate models, including stochastic differential equations, evolution equations, and random dynamical systems. Chapter 4 discusses specific reduced climate models and the techniques used to analyze them. The contributors, including both mathematicians and climate physicists, present a range of topics, from the derivation of stochastic models from first principles to the application of these models in understanding complex climate dynamics. The book also includes detailed lectures by leading researchers, providing insights into the latest research and future directions in the field. Overall, the book serves as a valuable resource for researchers and students interested in the intersection of stochastic processes, climate dynamics, and mathematical modeling. It emphasizes the importance of interdisciplinary collaboration and the need for further research to better understand the complex interactions within the climate system.