Monthly Archives: February 2013


A recently published paper by Ludecke et al. in Climate of the Past claims, as its main result, that “the climate dynamics is governed at present by periodic oscillations.”

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Once is not enough

In the past I’ve explored simple energy balance models for the evolution of global average temperature. One of the important things to note is that a “1-box” energy balance model — in which the entire climate system is considered to have a single time constant — isn’t really sufficient. It can give a pretty good fit, but for a more realistic estimate you need at least two boxes. One represents rapid response to climate forcing — think of it as the “atmosphere” if you wish. The other is for slower response — think of it as “ocean” if you wish, or as “upper ocean,” or as “everything else.” One could be even more realistic with more than two boxes, after all the deep ocean certainly effects climate but with a longer time scale still, and there’s the cryosphere on top of it all, but rather than go the way of the full-blown computer simulation model, let’s see what happens if we just use a 2-box model with two time constants. We’ll think of box 1 as the atmosphere, so that it should correspond to the surface temperature we’re all familiar with.

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Cryosat-2 Confirms Stunning Arctic Ice Loss

As ClimateProgress and others report, initial results are in from Cryosat-2. Since 2010, this European Space Agency satellite has surveyed polar ice to estimate its thickness, and by extension, its volume. It was a replacement for Cryosat-1, which was unfortunately destroyed in a launch failure. But the European Space Agency (ESA) considered this mission important enough to construct and deploy a replacement promptly, approving Cryosat-2 less than five months after the failure of Cryosat-1.

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UHI in the U.S.A.

Zeke Hausfather has contributed a guest post at RealClimate about his latest publication studying the impact of the “urban heat island” (UHI) effect on temperature trends, and how effective present correction methods are. The two-sentence summary:

The simple take-away is that while UHI and other urban-correlated biases are real (and can have a big effect), current methods of detecting and correcting localized breakpoints are generally effective in removing that bias. Blog claims that UHI explains any substantial fraction of the recent warming in the US are just not supported by the data.

Do take a look at the RC post, it has much more detail, and at the paper.

Some people can’t be reasoned with

If you keep an eye on global warming denier blogs, you expect to see some pretty stupid stuff. But every now and then they exceed expectation. Sometimes they even take it to a new level. This particular bit was featured by Anthony Watts, but it originates with Steve Goddard.

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2012 Updates to Trend-Observation Comparisons

RealClimate has published their latest 2012 Updates to model-observation comparisons. I’ll take a different twist. Instead of comparing observations to computer model projections, I’ll compare them to very simple statistical projections.

In a year gone by I posted about what kind of betting terms I might consider appropriate for the reality of global warming (incidentally, that post contained an error which was corrected in an update, but the archived copy doesn’t include the update). The idea is to take annual average data (for global temperature) from 1975 through the end of 1999, then fit a trend line by linear regression. If the trend continues, then future data should probably be within two standard deviations of the extrapolated trend line. This is the “projected range” according to the existing trend. The “projected range” according to the not-still-warming theory is that future values should be within two standard deviations of the existing average (in that case, from 2001 through 2007).

I also mentioned that since it would be unlikely but far from shocking if a single future value were outside either range, I would require two (not necessarily consecutive) future years outside the range to decided against either claim — if I were a betting man.

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Fun with averages and trends

Lots of time series, especially in geophysics, exhibit the phenomenon of autocorrelation. This means that not just the signal (if nontrivial signal is present), even the noise is more complicated than the simple kind in which each noise value is independent of the others. Specifically, nearby (in time) noise values tend to be correlated, hence the term “autocorrelation.”

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Death by Chartsmanship

Willard Tony has a new post by Willis Eschenbach accusing Chris Mooney of “chartsmanship” to achieve “raw, pure, visceral alarmism.” But it’s Willis who has turned sniper to show off his “chartsmanship.”

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New Book

My new book, Understanding Statistics: Basic Theory and Practice, is now available. You can get it here.

This is an introductory text, assumes no prior knowledge of statistics, and doesn’t require calculus. Those of you who already practice statistics will find it rather elementary. Those of you who have always wanted to learn something about the topic but never really got around to it — enjoy!


Commenters requested a table of contents, which is given below. I’ve also added the table of contents to the “preview” (which includes the TOC and the first 10 pages of chapter 1).

Someone mentioned getting it to brush up on time series analysis. That is not covered — this is really “Statistics 101.” I do have an elementary introduction to time series available here. Don’t let the title put you off, its focus is astronomical data but the methods are quite general and you don’t need to know anything about astronomy. It’s not your usual time series text, it’s geared to physical science data and doesn’t really cover topics like autocorrelation or ARIMA models, but I think it’s an excellent intro to studying time series data, especially for those who’ve never done it before. I’m also working on a graduate-level text about time series (which will include the usual stuff covered in such texts, and more to boot).

And, I have more books in the works, including an Introduction to Fourier Analysis, and a Brief Introduction to Bayesian Statistics.

There are requests for an e-reader version. I haven’t quite figured out how to do that yet, but I’m looking into it.


Preface: the Value of Data

I. Basic Theory

1. What Is Statistics?
2. Average
3. Histograms
4. Densities and Distributions
5. Probability
6. Dispersion
7. Box and Whiskers
8. Expected Value
9. Expected Values
10. A Miracle Happens
11. Normal
12. Uncertainty
13. Hypothesis Testing
14. Descriptive Statistics

II. Basic Practice

15. Binomial Distribution
16. Uniform Distribution
17. Chi-Square Distributions
18. Multinomial Distribution
19. Student’s t Distribution
20. F Distribution
22. Covariance and Correlation
23. Regression
24. Smoothing
25. Testing the Distribution
26. Outliers
27. Non-Parametric Statistics
28. Case Studies
29. Linear Models
30. Bayesian Statistics


Welcome to the Jungle

A reader recently commented that he had moved to the coutnry and taken steps to arrange an independent and sustainable life. His reason: that he expects, when the climate shit hits the fan, that people may place their own survival above the rules of ethics, that we may attempt to scratch and claw our way to the top of the heap at the expense of others, that life for most people will become a “law of the jungle.” He was providing for his own escape from what he viewed as a future not unlike the post-apocalyptic nightmare of a Mad Max movie.

I sometimes have similar thoughts.

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