Thursday, December 6, 2012

Jim Cooks says: Thanks for Playing “It’s Global Cooling,” Dan!

Dan Pangburn wrote: Perhaps you are having trouble understanding the equation that has been made public on the web that, using only one independent variable, has calculated average global temperatures since they have been accurately measured world wide (about 1895) with an accuracy of 88%. Including the influence of atmospheric carbon dioxide (a second independent variable) increases the accuracy to 88.5%. 

Actually the trouble is with Dan's manipulation and cherry picking as Tim Cook explained over at IrregularTimes back in July 2011.  Furthermore, it's silly thinking the lay public is in any position to judge Dan's work.  But, that's what Dan seems to expect.

The comments section expose's Dan's ability to side step serious critique.

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Thanks for Playing “It’s Global Cooling,” Dan!

According to Dan Pangburn, we’re encountering Global Cooling:
People have no significant influence on what the climate does no matter how assertively they shout and stamp their feet. As average global temperature continues on its downtrend, the credibility of AGW declines.
See what the average global temperature anomalies are (through Dec, 2010) for the five agencies that report them in the pdf made public 3/10/11 at . The average for May, 2011 is a tiny bit lower than Dec, 2010.
This recent steep decline is coming off an El Nino and will not continue. The huge effective thermal capacitance of the oceans (about 100 times everything else) puts the long term slope of the global temperature decline at only about 0.1 °C per decade; 0.2 °C per decade if sunspots stop completely. Just as it rose faster over land (compared to global), it will decline faster over land.
If you follow Pangburn’s link you’ll see the chart he references:
Let’s take Pangburn’s global cooling claims apart piece by piece. I’ll be referencing NASA GISS Data because it’s handily available; as Pangburn’s graph handily shows, NASA GISS data on global temperature follows the pattern of the other data sources. Read more about NASA GISS temperature data here.
1. The “2011″ graph point contains only information for January 2011 and February 2011.
In the text above Pangburn also mentions global temperature for May 2011, but not March 2011 or April 2011. Why does Pangburn only reference January, February and May? Because the global temperature anomalies for March 2011 and April 2011 were higher. Pangburn’s picking his data points selectively.
If you include all five months’ worth of data for 2011 currently available, you get a warmer result for 2011:
2. Pangburn’s “global cooling” remarks refer to changes since 2010, the hottest year on record.
That’s like watching Usain Bolt set a new world record for sprinting in the Olympics, turning to watch silver medalist Richard Thompson follow him across the finish line, then turning to your friend and saying, “you know, there’s a slowing trend in track and field.”
3. The data are truncated to drop temperature data from before 1998.
Why? Not for a nice round number of say, ten or fifteen years. Pangburn chooses to go back thirteen years because 1998 was the hottest year for its decade, the hottest year on record. If Dan Pangburn truncates the temperature record at 1998, then viewers will be inclined to think that the first few years of the 21st century mark a cooling trend, too.
Let’s look at the long-term trend Dan Pangburn chopped off of his graph, including all available years of the global temperature record:
Some “global temperature downtrend” there.
If the topic interests you, and you want a better understanding of the math involved - here's a good place to start:
Statistics and ClimateSome basics on statistics for beginners:
Statistics and Climate – Part One – independence, sampling, and the central limit theorem
Statistics and Climate – Part Two – sampling, sample size, Type I and Type II errors and Student T-test
Statistics and Climate – Part Three – Autocorrelation – the effect of autocorrelated time-series
Statistics and Climate – Part Four – Autocorrelation – how to handle statistical uncertainty with AR(1) autocorrelated time-series
Statistics and Climate – Part Five – AR(n) - introducing AR(2) and ARMA models and the problems of assuming AR(1) when the model is more complex


Or for something simpler:

How reliable are climate models?

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