It is often stated that global temperature has increased over some specific time frame. Few realize there are different ways to answer this question, and the increase may not actually be significant, particularly in view of persistent correlation between temperature over long time scales (LTP).
In Statistical analysis of hydroclimatic time series: Uncertainty and insights Koutsoyiannis evaluates two publications using two different approaches to this issue: the evaluation of trends as done in Cohn,
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The Draft Garnaut Report is to be commended for commissioning a study Global temperature trends - Breusch and Vahid (BV) by two prominent Australian National University (ANU) econometricians to examine global temperature series. The approach they take to modeling temperature has a long history. See for example at RealClimate,
Rybski, and Koutsoyiannis. Their findings that the significance barely reaches the 95% level with these kinds of models is not inconsistent with any of them. Even if there
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Long-range dependence is being identified many disciplines such as, networking, databases, economics, climate and biodiversity. LTP is competing with the sexy "long tail" for top spot as a theory of cultural consumption. Thus, the need for software offering complete long-range dependence analysis is crucial.
While there are some steps towards this direction, none are yet completely satisfactory. For one, the Hurst exponent cannot be calculated in a definitive way, it can only be estimated. Second,
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Here is a summary of the chapters in my upcoming book Niche Modeling to be published by CRC Press. Many of the topics have been introduced as posts on the blog. My deepest thanks to everyone who has commented and so helped in the refinement of ideas, and particularly in providing motivation and focus.
Writing a book is a huge task, much of it a slog, and its not over yet. But I hope to get it to the publishers so it will be available at the end of this year. Here is the dustjacket blurb:
Through
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Previously "A New Temperature Reconstruction" used random data with long term persistence (LTP) to illustrate the circular reasoning behind the 'hockey stick' reconstruction of past temperatures. This one shows the potential for false positives due to the statistics used in the 'hockey stick'. The dynamic simulation below shows future temperatures predicted using a random fractional differencing algorithm that generates realistic LTP behavior. Future temperatures and validation statistics are
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Mathematical shapes can affect our lives and the decisions we make.
The
hockey stick graph describing the earths average temperature over the last millennia has been the subject of a controversial debate over reliability of methods of statistical analysis.
From this to this ...
The long tail is another new icon, described in a new book, developed in the Blogosphere, by Chris Anderson called "The Long Tail":
Forget squeezing millions from a few megahits at the top of the charts. The future
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The Australian Institute of Geoscientists News has published online my article "Reconstruction of past climate using series with red noise" on page 14. Many thanks to Louis Hissink the editor for the rapidity of this publication. It is actually a very interesting newsletter with articles on the IPCC, and a summary of the state of the hockey stick (or hokey stick). There are articles on the K-T boundary controversy and how to set up an exploration company.
Reconstructing the hokey stick with random
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To follow up on the last post, I have calculated the RE as well as the R2 statsitics for the reconstruction from the random series. The same approach was used, i.e. generate 1000 sequences with LTP, select those with positive slope and R2>0.1, calibrate on linear model, and average. Here is the reconstruction again, with the test and training periods marked with a horizontal dashed line (test period to the left, training to right of temperature values):
The table below adds the RE statistic
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Below is an investigation of scale invariance or long term persistence (LTP) in time series including tree-ring proxies – the recognition, quantification and implications for analysis – drawn largely from Koutsoyiannis [2] (preprints available here). In researching this topic, I found a lot of misconceptions about LTP phenomena, such as LTP implying a long term memory process, and a lack of recognition of the implications of LTP. As to implications, the standard error of the mean of
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