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Statistical Analysis in Climate Research
The purpose of this book is to help the climatologist understand the basic precepts of the statistician's art and to provide some of the background needed to apply statistical methodology correctly and usefully. The book is self contained: introductory material, standard advanced techniques, and the specialized techniques used specifically by climatologists are all contained within this one source. There are a wealth of real-world examples drawn from the climate literature to demonstrate the need, power and pitfalls of statistical analysis in climate research.
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Statistical Analysis of Climate Series
Analyzing, Plotting, Modeling, and Predicting with R
- © 2013
- Helmut Pruscha 0
München, Germany
You can also search for this author in PubMed Google Scholar
- Within the context of the general climate discussion, the evaluation of climate series gains growing importance ?
- Provides application of statistical methods to climatological data Techniques for treating series records
- Applying among others ARIMA and GARCH model Programs in R and data sets on climate series are provided at the author's homepage
- Includes supplementary material: sn.pub/extras
25k Accesses
6 Citations
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About this book
The book presents the application of statistical methods to climatological data on temperature and precipitation. It provides specific techniques for treating series of yearly, monthly and daily records. The results’ potential relevance in the climate context is discussed.
The methodical tools are taken from time series analysis, from periodogram and wavelet analysis, from correlation and principal component analysis, and from categorical data and event-time analysis.
The applied models are - among others - the ARIMA and GARCH model, and inhomogeneous Poisson processes.
Further, we deal with a number of special statistical topics, e.g. the problem of trend-, season- and autocorrelation-adjustment, and with simultaneous statistical inference.
Programs in R and data sets on climate series, provided at the author’s homepage, enable readers (statisticians, meteorologists, other natural scientists) to perform their own exercises and discover their own applications.
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Getting It Right Matters: Climate Spectra and Their Estimation
Climate series.
- Computing in R
- Statistical Analysis
Table of contents (8 chapters)
Front matter.
Helmut Pruscha
Trend and Season
Correlation: from yearly to daily data, model and prediction: yearly data, model and prediction: monthly data, analysis of daily data, spectral analysis, complements, back matter, authors and affiliations, about the author.
Helmut Pruscha, Professor for Mathematics, has served as Academic Director at the University of Munich’s Institute of Mathematics. Before doing so, he had worked for many years as a statistician at a Max-Planck-Institute for neurobiology. His research interests include topics concerning applied statistics and mathematical statistics, especially categorical time series and point processes. He has published several textbooks in German.
Bibliographic Information
Book Title : Statistical Analysis of Climate Series
Book Subtitle : Analyzing, Plotting, Modeling, and Predicting with R
Authors : Helmut Pruscha
DOI : https://doi.org/10.1007/978-3-642-32084-2
Publisher : Springer Berlin, Heidelberg
eBook Packages : Mathematics and Statistics , Mathematics and Statistics (R0)
Copyright Information : Springer-Verlag Berlin Heidelberg 2013
Hardcover ISBN : 978-3-642-32083-5 Published: 30 October 2012
Softcover ISBN : 978-3-642-43087-9 Published: 09 November 2014
eBook ISBN : 978-3-642-32084-2 Published: 30 October 2012
Edition Number : 1
Number of Pages : VIII, 176
Topics : Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences , Climatology , Statistical Theory and Methods , Statistics and Computing/Statistics Programs , Atmospheric Sciences
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View all of our climate data analysis tools & methods or use the list below to jump to a certain group.
Climate Model Evaluation
Climate data processing & visualization, climate data formats, statistical methods, evaluation of interannual-to-decadal climate variability in earth system models, model configurations, overview: climate model intercomparison project (cmip), common spectral model grid resolutions, overview: climate data processing, web based visualization and processing for climate and weather, climate data processing software, regridding overview, common climate data formats: overview, text (ascii) files, binary (data format), netcdf overview, statistical & diagnostic methods overview, empirical orthogonal function (eof) analysis and rotated eof analysis, taylor diagrams, trend analysis.
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- Published: 14 May 2024
2023 summer warmth unparalleled over the past 2,000 years
- Jan Esper ORCID: orcid.org/0000-0003-3919-014X 1 , 2 ,
- Max Torbenson ORCID: orcid.org/0000-0003-2720-2238 1 &
- Ulf Büntgen 2 , 3 , 4
Nature ( 2024 ) Cite this article
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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
- Climate change
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Including an exceptionally warm Northern Hemisphere (NH) summer 1 ,2 , 2023 has been reported as the hottest year on record 3-5 . Contextualizing recent anthropogenic warming against past natural variability is nontrivial, however, because the sparse 19 th century meteorological records tend to be too warm 6 . Here, we combine observed and reconstructed June-August (JJA) surface air temperatures to show that 2023 was the warmest NH extra-tropical summer over the past 2000 years exceeding the 95% confidence range of natural climate variability by more than half a degree Celsius. Comparison of the 2023 JJA warming against the coldest reconstructed summer in 536 CE reveals a maximum range of pre-Anthropocene-to-2023 temperatures of 3.93°C. Although 2023 is consistent with a greenhouse gases-induced warming trend 7 that is amplified by an unfolding El Niño event 8 , this extreme emphasizes the urgency to implement international agreements for carbon emission reduction.
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Department of Geography, Johannes Gutenberg University, Mainz, Germany
Jan Esper & Max Torbenson
Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic
Jan Esper & Ulf Büntgen
Department of Geography, University of Cambridge, Cambridge, United Kingdom
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Department of Geography, Masaryk University, Brno, Czech Republic
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Esper, J., Torbenson, M. & Büntgen, U. 2023 summer warmth unparalleled over the past 2,000 years. Nature (2024). https://doi.org/10.1038/s41586-024-07512-y
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Accepted : 02 May 2024
Published : 14 May 2024
DOI : https://doi.org/10.1038/s41586-024-07512-y
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IMAGES
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COMMENTS
There are a wealth of real-world examples drawn from the climate literature to demonstrate the need, power and pitfalls of statistical analysis in climate research. Suitable for graduate courses on statistics for climatic, atmospheric and oceanic science, this book will also be valuable as a reference source for researchers in climatology ...
Instead, we use probabilistic ideas and statistics to describe the 'climate' system. Four factors ensure that the climate system is amenable to statistical thinking. † The climate is controlled by innumerable factors. Only a small proportion of these factors can be considered, while the rest are necessarily interpreted as background noise.
Component Analysis, and Covariance Discriminant Analysis. The specific statistical chal-lenges that arise in climate applications are also discussed, including model selection prob-lems associated with Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis.
J Climatol. 24: 665-680. On the role of statistics in climate research. 2004 •. Hans von Storch. We review the role of statistical analysis in the climate sciences. Special emphasis is given to attempts to construct dynamical knowledge from limited observational evidence, and to the ongoing task of drawing detailed and reliable information ...
Book.pdf. Climatology is, to a large degree, the study of the statistics of our climate. The powerful tools of mathematical statistics therefore find wide application in climatological research, ranging from simple methods for determining the uncertainty of a climatological mean to sophisticated techniques which reveal the dynamics of the ...
This chapter discusses statistical concepts in climate research, as well as time series and stochastic processes, and some of the techniques used to estimate covariance functions and spectra. 1. Introduction Part I. Fundamentals: 2. Probability theory 3. Distributions of climate variables 4. Concepts in statistical inference 5. Estimation Part II. Confirmation and Analysis: 6. The statistical ...
Download Free PDF. Jean Claude Nshimiyimana. 1999. The purpose of this book is to help the climatologist understand the basic precepts of the statistician's art and to provide some of the background needed to apply statistical methodology correctly and usefully. The book is self contained: introductory material, standard advanced techniques ...
Climatology is, to a large degree, the study of the statistics of our climate. The powerful tools of mathematical statistics therefore find wide application in climatological research. The purpose of this book is to help the climatologist understand the basic precepts of the statistician's art and to provide some of the background needed to apply statistical methodology correctly and usefully.
There are a wealth of real-world examples drawn from the climate literature to demonstrate the need, power and pitfalls of statistical analysis in climate research. Suitable for graduate courses on statistics for climatic, atmospheric and oceanic science, this book will also be valuable as a reference source for researchers in climatology ...
Statistical Methods for Climate Scientists. A comprehensive introduction to the most commonly used statistical methods relevant in atmospheric, oceanic and climate sciences. Each method is described step-by-step using plain language, and illustrated with concrete examples, with relevant statistical and scientific concepts explained as needed.
The term meta-analysis was coined by Gene V. Glass in a paper in 1976, in which he defined a meta-analysis as the analysis of analyses . . . [that is,] the statistical analysis of a large collection of analysis results from individual studies for the purpose of integrating the findings (Glass 1976).
Download book PDF. Statistical Analysis of Climate Series Download book PDF. Overview Authors: Helmut Pruscha 0; Helmut Pruscha ... His research interests include topics concerning applied statistics and mathematical statistics, especially categorical time series and point processes. He has published several textbooks in German.
Statistical Analysis in Climate Research, by von Storch and Zwiers, Cambridge Univ Press. Applied Multivariate Statistical Analysis, by Johnson and Wichern. Software Homework computations are expected to be done using the package R. Class Schedule 08/29 field significance 09/05 NO CLASS: attend GMU seminar on August 28
Climate Data Analysis Tools & Methods. View all of our climate data analysis tools & methods or use the list below to jump to a certain group. Climate Model Evaluation. Climate Data Processing & Visualization. Climate Data Formats. Statistical Methods.
Trend analysis of climate variables is the central process in assessing the state of the climate of a region and provides an overall estimate about the variations in the climate variables 16 ...
Statistical analysis in climate research / Hans von Storch and Francis W. Zwiers. p. cm. Includes index. ISBN 0 521 45071 3 1. Climatology - Statistical methods. I. Title. QC981.S735 1998 551.5'072-dc21 98-17416 CIP ISBN 0 521 45071 3 hardback ... Book.pdf Created Date:
Here, we combine observed and reconstructed June-August (JJA) surface air temperatures to show that 2023 was the warmest NH extra-tropical summer over the past 2000 years exceeding the 95% ...
Figure 1.13: The dominant pair of CCA patterns that describe the connection between simultaneous winter (DJF) mean anomalies of sea-level pressure (SLP, top) and sea-surface temperature (SST, bottom) in the North Atlantic. The largest features of the SLP field are indicated by shading in the SST map, and vice versa. See also [14.3.1]. From Zorita et al. [438]. - "Statistical Analysis in ...
Figure 5 illustrates the overall wind shear index characteristics of land surface regions in China. As depicted in Figure 5 a, most sites have α values ranging from 0.06 to 0.2, accounting for approximately 70%. Among them, the interval of 0.14 to 0.16 has the highest proportion, around 11%, followed by 0.08 to 0.12.
working in the climate sciences, as well as risk analysts interested in climate extremes. manfred mudelsee is founder of the research company Climate Risk Analysis, and a visiting scientist at the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research. His research focuses on how climate change is related to extreme climate ...
Access the portal of NASS, the official source of agricultural data and statistics in the US, and explore various reports and products.