Future Directions

Chapter
Part of the Atmospheric and Oceanographic Sciences Library book series (ATSL, volume 42)

Abstract

What changes may bring the future to climate time series analysis? First we outline (Sections 9.1, 9.2 and 9.3) more short-term objectives of “normal science” (Kuhn 1970), extensions of previous material (Chapters 1, 2, 3, 4, 5, 6, 7 and 8). Then we take a chance (Sections 9.4 and 9.5) and look on paradigm changes in climate data analysis that may be effected by virtue of strongly increased computing power (and storage capacity). Whether this technological achievement comes in the form of grid computing (Allen 1999; Allen et al. 2000; Stainforth et al. 2007) or quantum computing (Nielsen and Chuang 2000; DiCarlo 2009; Lanyon et al. 2009)—the assumption here is the availability of machines that are faster by a factor of ten to the power of, say, twelve, by a mid-term period of, say, less than a few decades.

Keywords

Surrogate Data Bootstrap Resampling Singular Spectrum Analysis Climate Time Series Persistence Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Climate Risk AnalysisHannoverGermany
  2. 2.Alfred Wegener Institute for Polar and Marine ResearchBremerhavenGermany

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