Tucson Then and Now

In the discussion of my previous post, a reader posted a link to a fascinating picture of Tucson in 1923 – a picture complete with Stevenson screen in the foreground, if you can imagine, clearly visible on the right.

tucson27.jpg
Figure 1. Tucson c. 1923. From left to right: Agriculture Building, Old Main, and the Mines and Engineering Building. Note the

There have been some ongoing discussions of Tucson stations at various blogs, originally initiated by the observation that the Tucson U of Arizona had the highest TOBS of all USHCN stations. I reviewed the status of this about 10 days ago, which has posted more jibes from Atmoz and others regarding the ASOS station at Tucson International. Continue reading

Porto Velho and Londrina

Porto Velho and Londrina are two somewhat similar sized Brazilian cities (populations 335,000 and 500,000 respectively) which have remarkably different Hansen adjustments. One is adjusted up by 2 deg C and one is adjusted down by 2 deg C. It’s pretty strange to see. Continue reading

Revkin on the Hansen Fiasco

Andrew Revkin of the New York Times writes here in a compacted story. Me versus Jor-El. I spent quite a bit of time saying that the errors mattered a lot at the individual station level and were “significant” for U.S. temperature. For example, consider this page at NASA which shows a comparison between temperatures by individual stations for 2000 and after. The majority of values on this graphic are wrong and the entire graphic will have to be replaced.

In an earlier note on this, also cross-posted at Anthony’s blog, I tried to hew my own line between the exaggerated claims in the right-wing blogosphere and the NASA claims of immateriality. Revkin mentioned in passing to me that Gavin Schmidt, while denying the more extreme blogosphere characterizations of the error, had admitted that everything that I had said about the matter had been accurate. Including the diagnosis of the Jor-El Complex, I presume.

Update:As to the claim of Schmidt and Hansen that 0.15 deg C does not “matter”, if they are prepared to stipulate this, then I would submit the graphic below showing U.S. temperature history (new NASA version) since the 1920s showing a trend increase of only 0.21 deg C in the 87 year period.

nasa217.gif
Source: New NASA Fig D data, August 2007. The t-value for the trend coefficient is only 1.24, which is not 95% significant even for i.i.d. (and there is lots of autocorrelation here.) There is a temperature increase before 1920 which would increase the trend estimate to 0.42 deg C.

Values in the 2000s are elevated and 95% “statistical significance” is not the only relevant metric for data analysis. However, if people are making claims of statistical significance, it’s fair enough to analyze these claims. The issues remaining to the validity of the NOAA and NASA adjustments remain outstanding. What impact do HO-83 hygrothermometers have on this? Has urbanization and microsite effects been properly accounted for – not just in the U.S. record but in Brazil and China and elsewhere? These issues remain outstanding.

Actually, if one plays with this a little, one can even sharpen Gavin’s point a little further. Gavin and Hansen say that a change of 0.15 deg C is not significant. The trend increase in the U.S. from 1930-2006 is 0.13 deg C., which, according to them, is not “significant”. I myself am expressing no views on the matter at this time – I’m simply reporting the implications of their claims.

nasa218.gif

Brazil GHCN Station Population

Bernie has collated the population of the Brazil GHCN stations (used in GISS) located here. OF 20 GHCN/GISS “rural” stations, only 3 currently have populations under 5,000 and some are now cities. Of 7 GHCN/GISS “small” towns, 6 are currently cities.

Collated GISS Versions Online

I’ve loaded R-tables for the dset=1 and dset=2 versions. The R-tables are lists each 7364 long, each item is a station time series. The files are about 8 MB in size. I have a variety of little scripts to retrieve and analyze things. The data is located here:

http://data.climateaudit.org/data/giss/giss.dset1.tab
http://data.climateaudit.org/data/giss/giss.adj.tab

Each file can be downloaded as follows:

con < – url("http://data.climateaudit.org/data/giss/giss.dset1.tab&quot;)
load(con) #giss.dset1
length(giss.dset1) #7364

It took about 15 seconds to load on my cable which is high-speed.

The names are the station identifications 3-digit country plus 9-digit station identifier. So you can pull out a station as follows:

test=giss.dset1[[paste(“42574500003”)]] ;
tsp(test) # 1898.000 2006.917 12.000

It would be easy to make a NetCDF file from this and I’ll post one up if someone sends me a conversion script. I thought about posting up an ASCII version but it’s about 4 times larger and NOBODY should be using Excel for this type of thing. Get with the R-program.

Hansen and the Great White North

Here’s something interesting: I’ve collated the GISS raw(dset=1) and GISS adjusted (dset=2) versions and then calculated the range of adjustments. The largest positive adjustment was over 8 deg C and the largest negative adjustment is greater than -6 deg C. I separated out the stations that had no adjustments (max adjustments under 0.01 deg C either way) and plotted their locations in the first figure. I then plotted figures showing stations with adjustments. (Nearly 40% of the 5990 stations with adjustments had zero adjustment.) Continue reading

Notes on GISS Station Data

I’ve spent some time (an inordinate amount of time) trying to figure out why GISS uses some GHCN stations and not others. Doing so has required a lot of work on GISS data sets which are nastily organized and with many seemingly ad hoc inclusions, exclusions and sloppinesses. Does any of this matter to world peace? Probably not. But it matters to anyone who’s trying to see what they did and I’ve documented some of the relevant information here, mostly to ensure that I don’t forget it. Continue reading

The HO-83 Hygro- thermometer

In the discussion of the Tucson weather station, Ben Herman of the U of Arizona observed that there were serious biases with the HO-83 hygrothermometer – introduced in the early 1990s – which was said to be a contributor to the uptick to Tucson values. Although USHCN has implemented adjustments to U.S. data to deal with time-of-observation bias and station history, both of which resulted in significant upward adjustments of recent data relative to earlier data, I have been unable to see any evidence that either NOAA or NASA made any attempt to adjust for the upward bias of recent readings using the HO-83 thermometer, although its problems are thoroughly discussed in the specialist literature. Continue reading

Replication Policy Re-Posted

Here’s a discussion of replication policy posted up in the relatively early days of the blog, which I’ve re-posted in light of NASA spokesman Gavin Schmidt’s attempts to justify Hansen’s refusal to provide the source code used in his temperature calculations. It seems that these calculations are important enough to prompt a concern over the “destruction of Creation” but apparently only the elect will be permitted to see these calculations. The discussion of replication is based on experience in economics and social science unrelated to the present controversy but fully applicable to it. Continue reading

An oldie but goodie – Microsite and UHI in 1952

Well before the current debate over the value of the near surface temperature record and its many possible biases, and well before Parker’s UHI studies sought to minimize the effect based on windy -vs- non windy days, J. Murray Mitchell published a paper in 1952 titled: On the Causes of Instrumentally Observed Secular Temperature Trends which was a quality study on the numerous possible effects of localized micro-site effects, as well as broader UHI effects related to population growth in cities. He created a tree chart of the known influences at the time:

Diagram of known effects on weather stations, from 1952

He looked at a variety of possible influences, and attempted to quantify them, both for rural and urban stations. Curiously, he discovered an effect that I’ve never heard of before, the day of the week effect:
Days of the week effect
While not a fully comprehensive study, it did hint at the fact that in the USA, there was a greater percentage of the population and business at the time that observed Sunday as a day of prayer and rest.

This paper is actually a summary of three different studies, examining New Haven, CT, and the Blue Hill Meteorological Observatory for UHI related issues, plus a broader study of 77 stations examining the effect of UHI on those stations then.

It’s a good read, and provides some grounding for the current discussions on the issues. Note: A hat tip to Roger Pielke, Junior, for bringing this paper to my attention.