Temperature Analysis: It’s Climate Science’s Own Pepsi Challenge

One thing Phil Jones, the CRU and others like to point out is how well X dataset matches to Y’s “independent” dataset. Well that is at best only a half truth, half lie statement. 

They are not independent since they all work from the same basic “raw” datasets. The CRU, GISS and NCDC did not set up their own thermometers all over the globe and take readings, they all rely on the same readings, from the same thermometers for their analysis. 

Where they are “independent” is how they do their analysis. CRU claims they use the methods spelled out in the multiple Jones et al papers and the Brohan et al 2006 paper. NCDC as of right now does their global analysis on the papers published by Dr. Petersen. GISS does their work based on the the Hansen et al 1987 and Hansen et al 2001 papers. 

The one I’m most familiar with is the Hansen 2001 paper and in it Dr. Hansen spells out clearly that each of these analysis methods are different each with their own inherent strengths and weaknesses. 

Since they treat the data differently they can and do reach different results. Then to top it off each country that supplies the raw data via CLIMAT reports do their own analysis of the data as well using their own methods, which leads to an important question? 

Is what these various countries sending in true “raw” data or data that they themselves have done work on such as preliminary homogenization adjustments? 

Who knows. NCDC can’t be 100% sure because they are not the ones doing the readings they just get what is sent to them and if they are not sent the correct metadata they wouldn’t necessarily know if they are getting “raw” or pre-processed data. 

So in the end when you look at for example the outputs from these various groups most people are “picking” one over the others as being the “best” or “most accurate”. As an example of the differences between datasets just head over to the Blackboard and follow Lucia’s posts on the monthly temperature anomalies. Example HadCrut has the Jan. Anomaly at .47° C, GISS had it at .71° C and NCDC had it at .6° C. 

So people seem to pick and choose which analysis they believe on what they usually see in a gridded map or in a line graph, not on the method of how the numbers were derived. They are placing faith that one method is “better and more accurate” then the others. 

To hopefully show this I’m taking a part of the work I’m doing going through the Canadian records and I will and put up two comparative charts that will contain anomalies computed on the 1961-90 baseline. These two charts will have three time series on them, one will be the GISS analysis, another the NCDC analysis and the third will be the Canadian numbers. The catch is I won’t tell you on the graph which is which. Then I will show you a graph of all the different thermometer series used for each station in the GHCN “raw” data file in a seperate graph to see what they all started with. 

Finally at the end I will reveal which is which and let people see how their own internal bias is working. Will they pick the same analysis for both graphs or will they switch analysis based on other factors (visual factors)? Basically this is the old 1980’s Pepsi Challenge where the person tried both Coke and Pepsi without knowing which was which and at the end was shown which “taste” they truly preferred. 

First lets start with Alert NWT Canada WMO # 71802: 

Figure 1

I’m not giving you trend lines either, you pick the series from what you see there. Now I will give you this from the GHCN “raw” file: 

Figure 2

Now I bet you noticed one feature from the “raw” data that corresponds very nicely with one of the adjusted anomaly series: That great big dip in 1973 but did you notice that all the analysis dismiss that huge increase in 1963 on the same thermometer as the 1973 dip? Also notice that the increase in 1990 seems to have vanished as well from the adjustments. I’ll let you think on how those two omissions might have effected the trend lines. 

Now we move onto Eureka NWT Canada WMO # 71917: 

Figure 3

Oh I may or my not have switched the analysis around between figure 1 and figure 3 so that what was Choice A in figure 1 may or may not be Choice A in figure 3. 

 Now lets look at the GCHN raw from this WMO ID:

Figure 4

Now I bet you noticed that my comparison graph didn’t go include the later years of the 2000’s but the raw shows that there is data in those years. The problem is that one of the analysis methods drops those years so I had to cut off the others so they matched.

So make your picks which one you think is the closet to the real temperature and see if you picked the same analysis for both comparison with out knowing before hand who did the analysis. 

Here is the answers: 

Figure 1 A = Canadian, B = GISS, C = NCDC

Figure 3 A = NCDC, B = Canada, C = GISS

Advertisements

4 responses to “Temperature Analysis: It’s Climate Science’s Own Pepsi Challenge

  1. E.M.Smith February 25, 2010 at 11:06 pm

    Nice. Very nice.

    I like the way Eureka also has a “splice” coming to a new hockey stick as the ASOS kicks in. Built in warming ‘in the pipeline’ 😉

    I wonder if anyone else has noticed the “lift” from the ASOS conversion?

  2. Margaret February 26, 2010 at 3:07 am

    Thanks for linking to this – Its great to see people pushing on the individual countries — especially those that have been flag sticks for warming.

    Sorry the file was so big (I didn’t bother downloading it as I wouldn’t have known what to do with it if I had!) They do say in the link in my second post at chiefio that it includes monthly data for the entire period as well as daily. I will copy the bit below:

    Q2. Are data available in an easily downloadable file?

    A. Data from our National Climate Archives Online can be downloaded as XML or as a CSV (comma delimited) file through the “Bulk Data” tool located in the Navigation Options box below any data table you are viewing. Hourly data can be downloaded for a period of 1 month at a time, daily data for a period of 1 year at a time, and monthly data for the entire period of record.

  3. boballab February 26, 2010 at 7:18 am

    Besides having all the daily Temp data, the file contains Snow and Precip daily data and a DOS (OMG!) program to run the thing. The Dos program will compute the data and get you a monthly average (as long as the data meets the 3/5 rule). From there it can compute the yearly average, however it won’t do that if even one month of average is missing.

    So overall that is why it’s such a big file and the program is kinda of clunky. I haven’t found a way yet to backtrack out of one stations data and select another yet withoout closing and restarting the program. Thre nice thing is that I don’ have to keep bopping through all the select screens from EC.

  4. woottaffift March 26, 2010 at 7:04 pm

    i truly adore your own posting type, very exciting,
    don’t give up and keep posting seeing that it simply well worth to follow it.
    looking forward to look into more of your own writing, goodbye 🙂

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: