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.
Papildus informācija
This edited volume discusses the recent developments and current research questions in nonlinear and stochastic climate dynamics.
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vii | |
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xxiv | |
| Preface |
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xxxi | |
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1 Challenges for Ice Age Dynamics: A Dynamical Systems Perspective |
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1 | (32) |
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2 Tipping Points in the Climate System |
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33 | (21) |
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3 Atmospheric Teleconnection Patterns |
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54 | (51) |
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4 Atmospheric Regimes: The Link between Weather and the Large-Scale Circulation |
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105 | (31) |
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5 Low-Frequency Regime Transitions and Predictability of Regimes in a Barotropic Model |
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136 | (23) |
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6 Complex Network Techniques for Climatological Data Analysis |
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159 | (25) |
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7 On Inference and Validation of Causality Relations in Climate Teleconnections |
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184 | (25) |
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8 Stochastic Climate Theory |
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209 | (32) |
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9 Stochastic Subgrid Modelling for Geophysical and Three-Dimensional Turbulence |
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241 | (35) |
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10 Model Error in Data Assimilation |
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276 | (42) |
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11 Long-Term Memory in Climate: Detection, Extreme Events, and Significance of Trends |
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318 | (22) |
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12 Fractional Stochastic Models for Heavy Tailed, and Long-Range Dependent, Fluctuations in Physical Systems |
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340 | (29) |
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13 Modelling Spatial Extremes Using Max-Stable Processes |
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369 | (23) |
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14 Extreme Value Analysis in Dynamical Systems: Two Case Studies |
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392 | (38) |
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| Index |
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430 | |
Christian L. E. Franzke is a research scientist at Universität Hamburg. His research interests include nonlinear atmospheric and climate dynamics, weather and climate risks, dynamics of extreme events, and stochastic and multi-scale modelling. He has developed new methods for the nonlinear analysis of paleoclimate data, station data and climate model data, and has developed nonlinear stochastic climate models. Terence J. O'Kane is an Australian Research Council Future Fellow, a principal research scientist at the Commonwealth Scientific and Industrial Research Organisation, Canberra, and Adjunct Professor in Mathematics at the University of Tasmania. His research interests include the statistical mechanics and dynamics of geophysical flows, climate dynamics and variability, ensemble prediction and data assimilation, and time series analysis. He has worked on all aspects of weather prediction including the theory, modelling and operational implementation of ensemble systems. In 2013 he was awarded the J. H. Michell Medal by the Australian Mathematics Society for outstanding research.
Biežāk uzdotie jautājumi par e-grāmatām