ALBERT

Description / Table of Contents: Contents: PART 1 FUNDAMENTAL CONCEPTS. - 1 Introduction. - 1.1 Climate archives, variables and dating. - 1.2 Noise and statistical distribution. - 1.3 Persistence. - 1.4 Spacing. - 1.5 Aim and structure of this book. - 1.6 Background material. - 2 Persistence models. - 2.1 First-Order Autoregressive Model. - 2.1.1 Even spacing. - 2.1.2 Uneven Spacing. - 2.2 Second-Order Autoregressive Model. - 2.3 Mixed Autoregressive Moving Average Model. - 2.4 Other models. - 2.4.1 Long-memory process. - 2.4.2 Nonlinear and non-gaussian models. - 2.5 Climate theory. - 2.5.1 Stochastic climate models. - 2.5.2 Long memory of temperature fluctuations?. - 2.5.3 Long memory of river runoff. - 2.6 Background material. - 2.7 Technical issues. - 3 Bootstrap confidence intervals. - 3.1 Error bars and confidence intervals. - 3.1.1 Theoretical example: Mean estimation of Gaussian White Noise. - 3.1.2 Theoretical example: Standard deviation estimation of Gaussian White Noise. - 3.1.3 Real world. - 3.2 Bootstrap principle. - 3.3 Bootstrap resampling. - 3.3.1 Nonparametric: Moving block bootstrap. - 3.3.2 Parametric: Autoregressive Bootstrap. - 3.3.3 Parametric: Surrogate Data. - 3.4 Bootstrap Confidence Intervals. - 3.4.1 Normal confidence interval. - 3.4.2 Student's t confidence interval. - 3.4.3 Percentile confidence interval. - 3.4.4 BCa Confidence Interval. - 3.5 Examples. - 3.6 Bootstrap hypothesis tests. - 3.7 Notation. - 3.8 Background material. - 3.9 Technical issues. - PART 2 UNIVARIATE TIME SERIES. - 4 Regression I. - 4.1 Linear regression. - 4.1.1 Weighted least-squares and ordinary least-squares estimation. - 4.1.2 Generalized least-squares estimation. - 4.1.3 Other estimation types. - 4.1.4 Classical confidence intervals. - 4.1.5 Bootstrap confidence intervals. - 4.1.6 Monte Carlo Experiments: Ordinary least-squares estimation. - 4.1.7 Timescale errors. - 4.2 Nonlinear regression. - 4.2.1 Climate Transition Model: Ramp. - 4.2.2 Trend-Change Model: Break. - 4.3 Nonparametric regression or smoothing. - 4.3.1 Kernel estimation. - 4.3.2 Bootstrap confidence intervals and bands. - 4.3.3 Extremes or outlier detection. - 4.4 Background material. - 4.5 Technical issues. - 5 Spectral analysis. - 5.1 Spectrum. - 5.1.1 Example: AR(1) process, discrete time. - 5.1.2 Example: AR(2) process, discrete time. - 5.1.3 Physical meaning. - 5.2 Spectral estimation. - 5.2.1 Periodogram. - 5.2.2 Welch's overlapped segment averaging. - 5.2.3 Multitaper estimation. - 5.2.4 Lomb-Scargle estimation. - 5.2.5 Peak detection: red-noise hypthesis. - 5.2.6 Example: Peaks in monsoon spectrum. - 5.2.7 Aliasing. - 5.2.8 Timescale errors. - 5.2.9 Example: Peaks in monsoon spectrum (continued). - 5.3 Background material. - 5.4 Technical Issues. - 6 Extreme value time series. - 6.1 Data types. - 6.1.1 Event times. - 6.1.2 Peaks over threshold. - 6.1.3 Block extremes. - 6.1.4 Remarks on data selection. - 6.2 Stationary models. - 6.2.1 Generalized extreme value distribution. - 6.2.2 Generalized pareto distribution. - 6.2.3 Bootstrap confidence intervals. - 6.2.4 Example: Elbe summer floods, 1852-2002. - 6.2.5 Persisitence. - 6.2.6 Remark: Tail estimation. - 6.2.7 Remark: Optimal estimation. - 6.3 Nonstationary models. - 6.3.1 Time-dependent generalized extreme value distribution. - 6.3.2 Inhomogenous poisson process. - 6.3.3 Hybrid: Poisson-Extreme value distribution. - 6.4 Sampling and time spacing. - 6.5 Background material. - 6.6 Technical issues. - PART 3 BIVARIATE TIME SERIES. - 7. Correlation. - 7.1 Pearson's Correlation Coefficient. - 7.1.1 Remark: Alternative correlation measures. - 7.1.2 Classical confidence intervals, nonpersistent processes. - 7.1.3 Bivariate time series models. - 7 .1.4 Classical Confidence Intervals, Persistent Processes. - 7.1.5 Bootstrap Confidence Intervals. - 7.2 Spearman's Rank Correlation Coefficient. - 7.2.1 Classical Confidence Intervals, Nonpersistent Processes. - 7.2.2 Classical Confidence Intervals, Persistent Processes. - 7.2.3 Bootstrap Confidence Intervals. - 7.3 Monte Carlo Experiments. - 7.4 Example: Elbe Runoff Variations. - 7.5 Unequal Timescales. - 7.5.1 Binned Correlation. - 7.5.2 Synchrony Correlation. - 7.5.3 Monte Carlo Experiments. - 7.5.4 Example: Vostok Ice Core Records. - 7.6 Background Material. - 7. 7 Technical Issues. - 8 Regression II. - 8.1 Linear Regression. - 8.1.1 Ordinary Least-Squares Estimation. - 8.1.2 Weighted Least-Squares for Both Variables Estimation. - 8.1.3 Wald-Bartlett Procedure. - 8.2 Bootstrap Confidence lntervals. - 8.2.1 Simulating Incomplete Prior Knowledge. - 8.3 Monte Carlo Experiments. - 8.3.1 Easy Setting. - 8.3.2 Realistic Setting: Incomplete Prior Knowledge. - 8.3.3 Dependence on Accuracy of Prior Knowledge. - 8.3.4 Mis-Specified Prior Knowledge. - 8.4 Example: Climate Sensitivity. - 8.5 Prediction. - 8.5.1 Example: Calibration of a Proxy Variable. - 8.6 Lagged Regression. - 8.6.1 Example: CO2 and Temperature Variations in the Pleistocene. - 8.7 Background Material. - 8.8 Technical Issues. - PART 4 OUTLOOK. - 9 Future Directions. - 9 .1 Timescale Modeling. - 9.2 Novel Estimation Problems. - 9.3 Higher Dimensions. - 9.4 Climate Models. - 9.4.1 Fitting Climate Models to Observations. - 9.4.2 Forecasting with Climate Models. - 9.4.3 Design of the Cost Function. - 9.4.4 Climate Model Bias. -9.5 Optimal Estimation. - 9.6 Background Material. - References. - Author Index. - Subject Index.

Description / Table of Contents: 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.

Description / Table of Contents: The Arctic is now experiencing some of the most rapid and severe climate change on earth. Over the next 100 years, climate change is expected to accelerate, contributing to major physical, ecological, social, and economic changes, many of which have already begun. Changes in arctic climate will also affect the rest of the world through increased global warming and rising sea levels. The volume addresses the following major topics: research results in observing aspects of the Arctic climate system and its processes across a range of time and space scales; representation of cryospheric, atmospheric, and oceanic processes in models, including simulation of their interaction with coupled models; our understanding of the role of the arctic in the global climate system, its response to large-scale climate variations, and the processes involved.

Description / Table of Contents: Contents: Absorption of Sunlight in the Ocean. - Light Absorption by Water Molecules and InorganicSubstances Dissolved in Sea Water. - The Interaction of Light with Organic MoleculesPresent in Sea Water: Physical Principles. - Light Absorption by Dissolved Organic Matter (DOM) in Sea Water. - Light Absorption by Suspended Particulate Matter(SPM) in Sea Water. - Light Absorption by Phytoplankton in the Sea.

Description / Table of Contents: This monograph presents a comprehensive synthesis of our current state of knowledge concerning the climate of the Arctic, using the latest meteorological data. All meteorological elements are described in detail for the first time and an up-to-date review of the available literature for each element is given. Climatic regions are distinguished and described. The monograph also provides an account of the present state of research on climate change and variability in the Arctic for three time scales: the Holocene, the last Millennium, and the 20th century. The book concludes with a presentation of the scenarios of the Arctic climate in the 21st century. This monograph is intended for all those with a general interest in the fields of meteorology, climatology, and with a knowledge of the application of statistics in these areas.

Description / Table of Contents: Contents: 1. Introduction ; 2. Persistence models ; 3. Bootstrap confidence intervals ; 4. Regression I ; 5. Spectral analysis ; 6. Extreme value time series ; 7. Correlation ; 8. Regression II ; 9. Future directions

Description / Table of Contents: This book provides the most comprehensive treatment to date of the microphysical processes which lead to cloud and precipitation formation. Emphasis is placed on presenting a quantitative description of the various mechanisms (e.g. nucleation, diffusional growth and evaporation, collisional growth and breakup) which lead to the formation of cloud and precipitation particles, individually and in populations. The second edition of this highly acclaimed book has been extensively updated to incorporate new research reported in a large number of articles and dozens of books and conference proceedings published since the first edition. Some highlights include: a new chapter on cloud chemistry; improved statistical mechanics models for homogeneous and heterogeneous ice nucleation; better data on aerosol size distribution and on fundamental properties of water; new data and models for asymmetric flow past particles of various shapes and for drop oscillation and breakup; extended treatment of drop coalescence and particle collision cross-sections, including some new effects of turbulence; more complete simulations of stochastic growth; expanded treatment of growth modes of graupel, rime and hailstones; new field data and modelling concerning effects of electric fields and charges on cloud microphysics.