Tamino: Cheaper by the Decade offers a good post about recent global temperature trends.

The post states that “The cause of the swings in decadal rate is random events like el Nino… They can change the rate of temperature increase dramatically, and they are (as far as we know) random”. I would disagree. El Nino is not random. It is certainly pseudorandom, meaning that it appears to be random, but is an entirely deterministic causal process which is not fully understood yet. We can look at the Fourier transform of the Southern Oscillation Index (SOI; a measure of El Nino) to see on what temporal scales that El Nino repeats itself.

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Via Climate Audit

Despite the fact that this paper is two years old, it’s getting tossed around now like it’s breaking news. Courtesy WSJ, of course. Can You Believe What Scientists Publish? by Jacob Goldstein and Most Science Studies Appear to Be Tainted By Sloppy Analysis by Robert Lee Hotz. The original paper is Why Most Published Research Findings Are False [Ioannidis, 2005].

Firstly, this paper is published in PLoS Medicine which should give a hint to its subject matter. If a paper was published in Science or Nature it might be interesting. As it stands, this paper is related only to the medical field.

Paraphrasing one of the comments on the PLoS site: If over half of the studies published are wrong, then odds are this one is too. And that means that half of the studies published aren’t wrong. But would that mean this study was right? I like the apparent paradox there.

Anyway, I’d be willing to wager than most of the conclusions published in peer-reviewed journals are not wrong. Not wager a lot mind you, I don’t have much disposable income.

References:
Ioannidis, J.P.A., (2005), Why Most Published Research Findings Are False, PLoS Medicine Vol. 2, No. 8, e124 doi:10.1371/journal.pmed.0020124

It’s not really news any more, but there’s more drought information from NASA: Earth Observatory

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Unlike many geophysical data, precipitation is not normally distributed about a mean value. A gamma distribution fits the data much better than a Gaussian distribution.

α is called the shape parameter and β is the inverse of the scale parameter. The larger α is, the more the distribution resembles the Gaussian distribution. The closer β is to zero, the more spread out the distribution is. That means that a gamma distribution with a small β is highly skewed. Below is a map of the inverse scale parameter (β) plotted for the US and Mexico during the years 1950-2000 for only the spring months: March, April, and May.

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