It is now widely recognized that the climate system is governed by nonlinear, multi-scale processes, whereby memory effects and stochastic forcing by fast processes, such as weather and convective systems, can induce regime behavior. Motivated by present difficulties in understanding the climate system and to aid the improvement of numerical weather and climate models, this book gathers contributions from mathematics, physics and climate science to highlight the latest developments and current research questions in nonlinear and stochastic climate dynamics. Leading researchers discuss some of the most challenging and exciting areas of research in the mathematical geosciences, such as the theory of tipping points and of extreme events including spatial extremes, climate networks, data assimilation and dynamical systems. This book provides graduate students and researchers with a broad overview of the physical climate system and introduces powerful data analysis and modeling methods for climate scientists and applied mathematicians.

The author describes the stochastic (probabilistic) approach to the study of changes in the climate system. Climatic data and theoretical considerations suggest that a large part of climatic variation/variability has a random nature and can be analyzed using the theory of stochastic processes. This work summarizes the results of processing existing records of climatic parameters as well as appropriate theories: from the theory of random processes (based on the results of Kolmogorov and Yaglom) and Hasselmann's "stochastic climate model theory" to recently obtained results.

A collection of articles written by mathematicians and physicists, designed to describe the state of the art in climate models with stochastic input. Mathematicians will benefit from a survey of simple models, while physicists will encounter mathematically relevant techniques at work.

This book introduces stochastic dynamical systems theory in order to synthesize our current knowledge of climate variability. Nonlinear processes, such as advection, radiation and turbulent mixing, play a central role in climate variability. These processes can give rise to transition phenomena, associated with tipping or bifurcation points, once external conditions are changed. The theory of dynamical systems provides a systematic way to study these transition phenomena. Its stochastic extension also forms the basis of modern (nonlinear) data analysis techniques, predictability studies and data assimilation methods. Early chapters apply the stochastic dynamical systems framework to a hierarchy of climate models to synthesize current knowledge of climate variability. Later chapters analyse phenomena such as the North Atlantic Oscillation, El Niño/Southern Oscillation, Atlantic Multidecadal Variability, Dansgaard–Oeschger events, Pleistocene ice ages and climate predictability. This book will prove invaluable for graduate students and researchers in climate dynamics, physical oceanography, meteorology and paleoclimatology.

Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. This book presents bootstrap resampling as a computing-intensive method able to meet the challenge. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation. This book is written for climatologists and applied statisticians. It explains step by step the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. It tests the accuracy of the algorithms by means of Monte Carlo experiments. It analyses a large array of climate time series, giving a detailed account on the data and the associated climatological questions. This makes the book self-contained for graduate students and researchers.

The definitive textbook on stochastic processes, written by one of the world's leading information theorists, covering both theory and applications.

Some developing biotechnologies challenge accepted legal and ethical norms because of the risks they pose. Xenotransplantation (cross-species transplantation) may prolong life but may also harm the xeno-recipient and the public due to its potential to transmit infectious diseases. These trans-boundary diseases emphasise the global nature of advances in health care and highlight the difficulties of identifying, monitoring and regulating such risks and thereby protecting individual and public health. Xenotransplantation raises questions about how uncertainty and risk are understood and accepted, and exposes tensions between private benefit and public health. Where public health is at risk, a precautionary approach informed by the harm principle supports prioritising the latter, but the issues raised by genetically engineered solid organ xenotransplants have not, as yet, been sufficiently discussed. This must occur prior to their clinical introduction because of the necessary changes to accepted norms which are needed to appropriately safeguard individual and public health.