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1995 | Buch

The Development of Climate Research Kapitel lesen Erstes Kapitel lesen

Analysis of Climate Variability

Applications of Statistical Techniques

herausgegeben von: Hans von Storch, Antonio Navarra

Verlag: Springer Berlin Heidelberg

Enthalten in: Professional Book Archive

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Über dieses Buch

EUROPEAN SCHOOl OF CLiMATOlOGY AND NATURAL HAZARDS The training of scientific and technical personnel and the development of highly qualified scientists are, and have always been, among the important concerns of the European Commission. Advanced training is an important requirement for the implementation of a common EU policy in science and technology. The European School of Climatology and Natural Hazards was started as apart of the training and education activities of the European Programme on Climatology and Natural Hazards (EPOCH), and is continued under the subsequent research programme (ENVIRONMENT 1990-1994). The school consists of annual courses on specialised subjects within re­ search in climatology and natural hazards, and is open to graduating, grad­ uate and post graduate students in these fields. Each of the courses is organized in cooperation with a European Institu­ tion involved in the current research programme, and is aimed at giving to the students formal lectures and participation in informal discussions with leading researchers. The present volume is based on the lectures given at the course held on the island of Elba from the 30th October to the 6th of November 1993 on Statistical Analysis of Climate Variability. It features selected and extended presentations, and represents an important contribution to advanced studies in climate statistical analysis, supplementing more traditional texts. I trust that all those involved in research related to climate change and climate variability will appreciate this work and will benefit from the com­ prehensive and state-of-the-art information it provides.

Inhaltsverzeichnis

Frontmatter

Introduction

Frontmatter
Chapter 1. The Development of Climate Research
Abstract
The geophysical sciences in general and climate research in particular have always played a special role among the other scientific disciplines for their special relationship with the object of their study. Climate research deals with climate, that is to say the response of the atmosphere, ocean, land and ice systems to the solar forcing over a prescribed time interval. In this respect climatology diverges from the other quantitative “hard” sciences in a simple, but very important detail: the impossibility of performing the “crucial” experiment on which the classic paradigm of investigation of the physical sciences is built.
Antonio Navarra
Chapter 2. Misuses of Statistical Analysis in Climate Research
Abstract
The history of misuses of statistics is as long as the history of statistics itself. The following is a personal assessment about such misuses in our field, climate research. Some people might find my subjective essay of the matter unfair and not balanced. This might be so, but an effective drug sometimes tastes bitter.
Hans von Storch

Analyzing The Observed Climate

Frontmatter
Chapter 3. Climate Spectra and Stochastic Climate Models
Abstract
As the power spectra of extratropical atmospheric variables are essentially white on time scales between about 10 days and a few years, many climatic fluctuations can be understood as the response of the slow climate variables to stochastic forcing by the fast atmospheric fluxes (Hasselmann, 1976). The stochastic climate model explains the statistical properties of mid-latitude sea surface temperature anomalies on time scales of up to a few years, and has been applied to long term climate changes, quasi-geostrophic ocean fluctuations, sea ice variability, soil moisture fluctuations, and ocean circulation variability, providing a simple but powerful framework to investigate climate variability. After briefly describing the spectral characteristics of the atmospheric variables, the concept of the stochastic climate model is introduced in this chapter. Its application to climate variations is then illustrated in the context of the midlatitude sea surface temperature anomalies. The other applications are then briefly discussed.
Claude Frankignoul
Chapter 4. The Instrumental Data Record: Its Accuracy and Use in Attempts to Identify the “CO2 Signal”
Abstract
Although meteorological recording began earlier, the period since the middle of the nineteenth century is that traditionally associated with instrumental records. This review examines in detail the reliability of temperature, precipitation and sea-level pressure data, concentrating on the construction of hemispheric and global average temperature series. The key piece of observational evidence in the “global warming debate” is the “global” temperature series (Jones and Wigley, 1990). How much has the temperature risen? In the last section, pattern correlation statistics are used to search for the greenhouse warming signals predicted by five different General Circulation Models in the observed record of land and ocean surface temperature changes. No trends in the time series of the statistic, R(t), were found.
Phil Jones
Chapter 5. Interpreting High-Resolution Proxy Climate Data — The Example of Dendroclimatology
Abstract
Current scientific concern to establish the reality, the nature and the speed of climate changes, believed by many to be the inevitable consequence of human activities, should serve to reinforce our determination to understand similar details of the “natural” (i.e. non-anthropogenic) variability of climate. Reconstructing past climates on all timescales is clearly important if we hope to understand the mechanisms that control climate (Bradley, 1990). However, the scope and the rapidity of the changes foreseen in many scenarios of an “enhanced-greenhouse” world, highlight the particular relevance of palaeoclimate studies that focus on recent centuries and millennia (Eddy, 1992). When considering the question “Can we detect an enhanced greenhouse signal?”, natural records of past climate variability (so-called “proxy” climate data) that are annually resolved and that capture decadal-tocentury timescale variability, represent an essential basis for comparison with relatively short modern climate records which are rarely longer than a hundred years.
Keith R. Briffa
Chapter 6. Analysing the Boreal Summer Relationship Between Worldwide Sea-Surface Temperature and Atmospheric Variability
Abstract
The ocean and the atmosphere can be viewed as two sub-systems of the earth’s environmental system. The ocean and atmosphere interact at the air-sea boundary. This chapter is orientated towards analysing the impact of the ocean on the atmosphere. The timescales analysed are from the seasonal to the multidecadal. Statistical analysis is playing an important role in advancing our knowledge of air-sea interaction and its influence on worldwide climate variability. So this chapter is particularly well-suited for illustrating an application of statistics in climatology.
M. Neil Ward

Simulating and Predicting Climate

Frontmatter
Chapter 7. The Simulation of Weather Types in GCMs: A Regional Approach to Control-Run Validation
Abstract
General Circulation Models (GCMs) attempt to reproduce the three-dimensional atmospheric processes which result in the surface climate that we experience across different regions of the globe. The time and spatial scales upon which various GCMs operate are much cruder (less-well resolved) than those of the real world. Nevertheless, GCM experiments offer the most promising approach for gaining insight into the physical mechanisms underlying past and potential future climates, particularly at a regional level. It is this regional detail which is needed to develop and test theories about past climates and for making predictions about future climates that might come about as a consequence of increasing greenhouse gases.
Keith R. Briffa
Chapter 8. Statistical Analysis of GCM Output
Abstract
In general circulation model (GCM) studies, statistical methods are needed for a number of purposes: to validate a model with observations, to identify its response to anomalous boundary conditions or its sensitivity to changes in model formulation, and to determine its predictive skill. The first two problems are discussed in this Chapter 8 and in the next Chapter 9, while the evaluation of forecasts is discussed in Chapter 10.
Claude Frankignoul
Chapter 9. Field Intercomparison
Abstract
Frequently analyses of climate data, whether model-generated or observed, require the simultaneous assessment of the significance of multiple statistics. A commonly ocurring situation is the test of a hypothesis for the difference in means of two fields of data, e.g. the average wintertime temperature anomaly pattern over a net of European stations in each of two sets of GCM simulations. Regardless of whether the problem is approached with the multivariate methods described in Chapter 8 or as a collection of individual or local significance tests, the collective or field significance of the results depends crucially on the number of data points or tests and their interdependence.
Robert E. Livezey
Chapter 10. The Evaluation of Forecasts
Abstract
The ultimate goal of climate dynamics is the prediction of climate variability on interannual to interdecadal and longer timescales. Two necessary, but not sufficient, conditions for the usefulness of such forecasts is that they have real skill in discriminating one forecast situation from another and that this skill is known in advance by the user. Two “no-skill” forecast schemes would be random forecasts or constant (e.g. fixed climatology) forecasts. An additional factor relevant to a scheme’s usefulness is its efficiency — in other words its skill with respect to more simple forecast schemes (like persistence of existing conditions).
Robert E. Livezey
Chapter 11. Stochastic Modeling of Precipitation with Applications to Climate Model Downscaling
Abstract
Probabilistic models of precipitation are mathematical representations of the probability distribution of precipitation without regard to time, that is
$$ prob\left( {x < X < x + dx} \right) = \smallint _x^{x + dx}fx\left( r \right)dr, $$
(11.1)
where X might be, for instance, the total annual (or monthly) precipitation occurring at a given station, or the maximum annual precipitation occurring in a 24 hour period. Probabilistic models are useful for assessing risk, which, in its simplest form, is the probability of an undesirable outcome. For instance, in an unirrigated agricultural area, it may be important to know the probability that the growing season precipitation will be less than the threshold required for crop survival. Likewise, the design of small flood protection structures, particularly in urban areas, requires knowledge of what engineers term a probability intensity duration relationship. A probability intensity duration relationship is simply the family of probability distributions of the annual maximum precipitation for duration D where D might take on, for example, values of 1, 2, 3, 6, 12, and 24 hours.
Dennis Lettenmaier

Pattern Analy

Frontmatter
Chapter 12. Teleconnections Patterns
Abstract
Walker and Bliss had devoted their entire life to a pure subjective search for significant statistical relation among the myriads of correlation values that their limited data set was producing, but a breakthrough was achieved in 1981 by two papers by Mike Wallace in collaboration with D. Gutzler and J. Horel (Wallace and Gutzler, 1981; Horel and Wallace, 1981).
Antonio Navarra
Chapter 13. Spatial Patterns: EOFs and CCA
Abstract
Many analyses of climate data sets suffer from high dimensions of the variables representing the state of the system at any given time. Often it is advisable to split the full phase space into two subspaces. The “signal” space is spanned by few characteristic patterns and is supposed to represent the dynamics of the considered process. The “noise subspace”, on the other hand, is high-dimensional and contains all processes which are purportedly irrelevant in their details for the “signal subspace”.
Hans von Storch
Chapter 14. Patterns in Time: SSA and MSSA
Abstract
Singular Spectrum Analysis (SSA) is a particular application of the EOF expansion. In classical EOF analysis, the random field \( \vec X \) to be studied, called also the state vector, contains values measured or estimated at a given time, that is, the coordinates of \( \vec X \) represent different locations in space at the same time. By diagonalising the covariance matrix of \( \vec X \), one tries therefore to capture the dominant spatial patterns. The SSA expansion (Vautard et al., 1992) is an EOF expansion, but the state vector \( \vec X \) now contains values at the same location but at different lags. The leading eigenelements of the corresponding covariance matrix represent thus the leading time patterns of the random field. SSA is a time series analysis, in the sense that a single signal is analysed.
Robert Vautard
Chapter 15. Multivariate Statistical Modeling: POP-Model as a First Order Approximation
Abstract
The study of a time series is a standard exercise in statistical analysis. A time series, which is an ordered set of random variables, and its associated probability distribution are called a stochastic process. This mathematical construct can be applied to time series of climate variables. Strictly speaking, a climate variable is generated by deterministic processes. However since a myriad of processes contribute to the behavior of a climate variable, a climate time series behaves like one generated by a stochastic process. More detailed discussion of this problem is given by H. von Storch and Zwiers (1995).
Jin-Song von Storch
Backmatter
Metadaten
Titel
Analysis of Climate Variability
herausgegeben von
Hans von Storch
Antonio Navarra
Copyright-Jahr
1995
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-662-03167-4
Print ISBN
978-3-662-03169-8
DOI
https://doi.org/10.1007/978-3-662-03167-4