Call number:
PIK M 311-01-0581
;
AWI A13-00-0040
Description / Table of Contents:
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.
Type of Medium:
Monograph available for loan
Pages:
XIII, 282 Seiten
,
Illustrationen
ISBN:
354066310X
,
3-540-66310-X
URL:
https://www.gbv.de/dms/ilmenau/toc/303297816.PDF
Language:
English
Note:
Contents
Introduction
1 Climatic System: Data, Processes, Scales, and Deterministic Models
1.1 Main Components of the Climate System
1.1.1 "Thick" Subsystems
1.1.2 "Thin" Subsystems
1.1.3 Local and Discrete Objects
1.2 Climate Processes
1.2.1 Overview of Climate Processes
1.2.2 External Climate Mechanisms
1.2.3 Internal Mechanisms of Climatie Variations
1.2.4 Transfer-Accumulation Processes
1.3 Scales of Climatic Variability
1.3.1 Spatial Scales
1.3.2 Temporal Scales
1.4 Deterministic Climate Models
1.4.1 General Circulation Models and Coupled Models
1.4.2 Other Types of Climate Models
1.5 Observational Basis for Stochastic Climate Theory
1.5.1 Data on Variability of "Thick" Climatic Subsystems
1.5.1.1 Near-Surface Air Temperature
1.5.1.2 Other Atmospheric Variables
1.5.1.3 Sea Surface Temperature
1.5.1.4 Sea Level
1.5.1.5 lce Sheets
1.5.2 Data on Variables of Thin Earth Covers
1.5.2.1 Snow Cover
1.5.2.2 Sea lce
1.5.2.3 Vegetation Cover
1.5.3 Data on Discrete and Local Climatic Objects
1.5.3.1 River Runoff
1.5.3.2 Lakes
1.5.3.3 Mountain Glaciers
1.5.4 Conclusions on Observational Data
2 Theoretical Foundations of the Stochastic Approach to Climate Variability Studies
2.1 Basic Ideas and Principles of the Stochastic Climate Theory
2.1.1 Mathematical Models and Natural Processes
2.1.2 A Climatic Variable as a Random Variable
2.1.3 Evolution of a Climatic Variable as a Random Function
2.1.4 Stationarity of Climatic Processes
2.2 Introduction to the Theory of Random Functions with Emphasis on Climate Variability
2.2.1 Moments, Mean Value, Correlation Function
2.2.2 The Ergodicity of Climate Variability
2.2.3 Examples of Stationary Random Sequences
2.2.3.1 Uncorrelated Random Variables
2.2.3.2 Moving Averages
2.2.4 Spectral Representation of the Random Process
2.2.5 Climatic Meanings of the Spectral Distribution Function
2.2.6 Spectral Representation of Stationary Sequences
2.2.7 The Markov Sequence
2.2.8 The Discrete Wiener Process
2.2.9 Other Types of Random Functions
2.2.9.1 Autoregressive Models
2.2.9.2 Seasonal Models
2.2.9.3 Threshold Models
2.3 Estimation of Model Parameters
2.3.1 Theoretical Models and the Practice of Model Identification
2.3.2 Informational Approach to the Identification of Stochastic Models
2.3.3 Maximum Entropy Method and Autoregressive Models
2.3.4 Model Identification and Estimation of Model Parameters
2.3.5 An Example ofModel Identification and Parameter Estimation
2.3.6 Frequency Truncation Method of Normalized Spectral Estimates
2.3.7 Other Methods of Time Series Processing
2.3.7.1 Conventional Methods. Moving Average and ARMA models
2.3.7.2 "Deterministic Chaos". Other Methods of Nonlinear Analysis
2.4 Physical Basis of the Stochastic Climate Theory
2.4.1 Atmospheric Forcing ofthe Climate System
2.4.1.1 Observational Evidence
2.4.1.2 Atmospheric Model Results
2.4.1.3 Simple Nonlinear Model as Analog of Atmospheric Forcing
2.4.2 Hasselmann's Stochastic Climate Models
2.4.2.1 Hypothesis on Weather-Climate Two-Scale Separation
2.4.2.2 Classification of Climate Models
2.4.2.3 Analogies with Turbulent Fluid, Brownian Motion, and Other Physical Processes. The Central Limit Theorem
2.4.2.4 Spectra and Correlation Functions of the Stochastic Climate Models. Models Without Feedback
2.4.2.5 Models with Feedback
3 Stochastic Models of Recent Climatic Changes
3.1 Changes in Thick Climatic Subsystems
3.1.1 Local Changes
3.1.1.1 Analysis of Observational Data
3.1.1.2 Local Stochastic Dynamical Models
3.1.2 Regional, Spatially Averaged, and Two-Dimensional Patterns
3.1.2.1 20 Stochastic Patterns of Observational Data
3.1.2.2 Stochastic Dynamical Regional Models
3.1.2.3 Stochastic Models of ENSO Events
3.1.3 Globally Averaged Climate Variables
3.1.3.1 Global Water Mass Exchange. Global Mean Sea Level
3.1.3.2 Global Temperatures
3.1.3.3 "Minus Two" Law of Climatic Variability
3.1.3.4 Stochastic Dynamical Models of Global Temperatures
3.1.3.5 Local-Global Polarization Phenomenon
3.2 Variabilities of Thin Climatic Subsystems
3.2.1 Analyzed Oata
3.2.1.1 37 GHz Polarization Oifference and Related Data
3.2.1.2 Snow and Sea lce Remotely Sensed Data
3.2.1.3 Related Satellite-Based and Conventional Data on Global Air and Sea Temperatures
3.2.2 Comparison of Results for Remotely Sensed and Conventional Data
3.2.2.1 Comparison of Results on Local Scales
3.2.2.2 Globally Averaged 37 GHz Polarization Difference Data. Concentration of Carbon Dioxide in the Atmosphere
3.2.3 Results of Stochastic Analysis of Local and Regional Hydrological Changes
3.2.3.1 Results of 37 GHz PD Data Analysis for Floodable Areas
3.2.3.2 Results for 37 GHz PD Data on Vegetation Cover in Different Natural Zones
3.2.4 Results of Analysis of Global Changes in Hydrological and Related Parameters
3.2.5 Modeling the Dynamics of Thin Subsystems
3.2.6 Local-Global Polarization Phenomenon and Thin Climatic Subsystems
3.2.7 Discussion on the Global Climatic Subsystems
3.3 Changes in Local and Discrete Climatic Objects
3.3.1 Rivers and River Runoff
3.3.2 Mountain Glaciers
4 Stochastic Models for Glacial Cycles
4.1 Stochastic Analysis of Reconstructed Data on Glacial Cycles
4.1.1 Existing Paleoreconstructed Time Series
4.1.2 Results of Stochastic Analysis of the Last Deglaciation Period, 0 - 18 ka B.P.
4.1.3 Analysis of 200 - 300 ka Time Series
4.1.4 Longer Time Series. Features of Cyclicity
4.1.5 High Resolution Paleorecords
4.2 Zero-Dimensional Model of Glacial Cycles
4.2.1 Hypotheses, Assumptions, and Equations
4.2.2 Results of Numerical Experiments
4.3 Two-Dimensional Stochastic Dynamical Model of Glacial Cycles
4.3.1 Mathematical Model, Parameters, and Experiments
4.3.1.1 Computational Area
4.3.1.2 Equations and Parameters of the Model
4.3.1.3 Numerical Experiments
4.3.2 Results
4.3.2.1 Experiments Without External Forcing
4.3.2.2 Experiments With External Forcing. Globally Averaged Results
4.3.2.3 Zonally Averaged Results
4.3.2.4 Regional Results
Conclusion
References
Index
Location:
A 18 - must be ordered
Location:
AWI Reading room
Branch Library:
PIK Library
Branch Library:
AWI Library
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