The book "Stochastic Climate Models" presents the proceedings of the 1999 Chorin workshop on stochastic climate models, highlighting the enthusiasm and progress in this field. It covers a wide range of topics, including the development of stochastic climate models, their mathematical foundations, and their applications in climate research. The book is edited by Peter Imkeller and Jin-Song von Storch, and includes contributions from leading researchers in mathematics and climate science. The book discusses the hierarchy of climate models, from simple models to complex general circulation models (GCMs), and explores the mathematical aspects of these models. It also addresses the emergence of randomness in climate models, the role of stochasticity in climate systems, and the use of stochastic models for climate prediction. The book includes chapters on stochastic resonance, energy balance models, and the application of stochastic partial differential equations in climate modeling. The authors emphasize the importance of stochastic models in understanding climate variability and predictability, particularly in the context of phenomena such as El Niño and the North Atlantic Oscillation. They also discuss the challenges and limitations of stochastic modeling, as well as the need for further research to improve the accuracy and reliability of these models. The book is structured into several chapters, each focusing on different aspects of stochastic climate modeling. It includes contributions from leading experts in the field, and provides a comprehensive overview of the current state of research in stochastic climate modeling. The book is an important resource for researchers and students interested in climate science, mathematics, and stochastic processes.The book "Stochastic Climate Models" presents the proceedings of the 1999 Chorin workshop on stochastic climate models, highlighting the enthusiasm and progress in this field. It covers a wide range of topics, including the development of stochastic climate models, their mathematical foundations, and their applications in climate research. The book is edited by Peter Imkeller and Jin-Song von Storch, and includes contributions from leading researchers in mathematics and climate science. The book discusses the hierarchy of climate models, from simple models to complex general circulation models (GCMs), and explores the mathematical aspects of these models. It also addresses the emergence of randomness in climate models, the role of stochasticity in climate systems, and the use of stochastic models for climate prediction. The book includes chapters on stochastic resonance, energy balance models, and the application of stochastic partial differential equations in climate modeling. The authors emphasize the importance of stochastic models in understanding climate variability and predictability, particularly in the context of phenomena such as El Niño and the North Atlantic Oscillation. They also discuss the challenges and limitations of stochastic modeling, as well as the need for further research to improve the accuracy and reliability of these models. The book is structured into several chapters, each focusing on different aspects of stochastic climate modeling. It includes contributions from leading experts in the field, and provides a comprehensive overview of the current state of research in stochastic climate modeling. The book is an important resource for researchers and students interested in climate science, mathematics, and stochastic processes.