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Correlation is way better than forcings from greehouse gases and feedbacks from clouds (eg Linden Choi paper 2009)

I’d like to know how much calibrated equipment one would need to compare airport weather station temperatures with temperatures in the suburbs.

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Took a look at Birmingham UK weather station data recently released by the UK Met Office. This shows a 1Celsius rise in temperature between 1950 and 2000 if you look at the annual means. But I looked at the monthly means and found that December showed no warming but July and August showed warming greater than 3Celsius over the same period.

What?

But then I realised that the weather station is Birmingham ELMDON. That’s the home of Birmingham International airport. So I thought “Could high passenger numbers in the summer months be the cause of the warming?”. Well here are the approximate figures, passenger numbers vs. temp gradient over the period 1950 to 2000:-

Jan “560,000″ 2
Feb “600,000″ 2.6
March “730,000″ 3.4
April “700,000″ 0.8
May “877,000″ 0.4
June “900,000″ 0.4
July “1,000,000″ 2.8
August “1,000,000″ 3
Sept “960,000″ 1
October “844,000″ 0.4
Nov “600,000″ 0.5
Dec “500,000″ 0

You’ll notice that the months with the highest warming trend correspond to the highest passenger numbers – obviously relevant in 2000 but not so relevant in 1950 I imagine. December has the lowest passenger figures and shows no warming trend over the 50 year period.

Conclusion I came to was that the only clear warming trend over the period 1950 to 2000 was due to increasing passenger numbers at nearby Birmingham airport during the summer months over that period.

Perhaps you could take a closer look at your Australian airport sites showing warming and see if they correlate to increasing passenger numbers over the period of interest, both by year and by month. Naturally your busiest months won’t be the same as for us in the Northern Hemisphere of course.

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onlineuniversalwork

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http://www.ncdc.noaa.gov/oa/climate/ghcn-monthly/images/ghcn_temp_overview.pdf

and also

http://reg.bom.gov.au/amm/docs/2004/dellamarta.pdf

Their procedures should always result in either a step function or the sum of a small number of step functions. There is no part in the procedure that inserts ramp functions or anything similar to that. What’s more, the step functions cannot be closer than 10 years to each other for a given site.

Thus there is absolutely no explanation available for the ramp-like period between 1940 and 1980.

The decision of whether or not to correct for a potential inhomogeneity is often a subjective one. Supporting metadata evidence of a discontinuity makes that decision easier but is not always available. Consequently, it was often impossible to reproduce the exact homogeneity adjustments of Torok and Nicholls (1996). Slightly different techniques, reference stations and source data can apparently produce different results. Rather than correct the entire dataset with new adjustments, the pre-1994 homogeneity adjustments determined by Torok (1996) were generally re-applied for the updated series, except for about 30 adjustments in which an obvious error had been made, such as the wrong sign of adjustment or an incorrect year of adjustment. A few records were completely reassessed due to significant differences between the objective test results based on the two types of reference series used.

This is an admission that the process is not repeatable and that each researcher applies his/her own judgement. The adjustments made by Torok and Nicholls (1996) did not include archived documentation of their justification, not even so they could be understood again in 2004 (a mere 8 years later).

One more example of lack of reproducibility, lack of documentation and no way to trace back where an answer came from.

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You remember the “Very Artificial Fudge Factor”? The array called “valadj”? Knight puts it onto a table of years, which I’ll borrow:


Year . . . Fudge Factor
1904 . . . 0
1909 . . . 0
1914 . . . 0
1919 . . . 0
1924 . . . 0
1929 . . . -0.1
1934 . . . -0.25
1939 . . . -0.3
1944 . . . 0
1949 . . . -0.1
1954 . . . 0.3
1959 . . . 0.8
1964 . . . 1.2
1969 . . . 1.7
1974 . . . 2.5
1979 . . . 2.6
1984 . . . 2.6
1989 . . . 2.6
1994 . . . 2.6
1999 . . . 2.6


Not too far different from the black line above, including the little negative dip in the 20′s and 30′s…

Any stat-nerds want to knock together a probability estimate that some random piece of computer code somewhere in the UK (code that the RealClimate crowd claim was only used for testing) would be a close fit to the historical error of a thermometer in Darwin?

Ye Ha! Boys, we’ve struck consensus.

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http://www.onlineuniversalwork

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My simple thinking said “OK, icecaps on Earth are melting. We are being told that our fossil fuel use is causing this and up to this time I was concerned. But there is no way our pollution would cause Mars icecaps to melt so maybe we are not the cause here”

Since NASA has been shown to delete inconvenient data recently, my guess is that they also did that to this page.

What I now hope, is that someone else will remember seeing this in print and will publish this article somewhere.

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