Thanks, I haven’t found anything in the station records that correlate with the step-wise transitions that I observe. Therefore, I don’t think they are SHAP-related. Also, I don’t observe positive trends. Instead, the step-wise transitions are followed by intervals that vary from a few years to several decades with essential zero-slope and small variances.

Although graphs would be better, perhaps the following tables will be helpful:

Ft. Collins, CO (053005) NCDC Seasonal Adjustments of
WRCC TMAX (°F)
(+ indicates WRCC>NCDC value)
Interval DJF MAM JJA SON
1900:1905 1.14 0.63 1.28 1.98
1906:1910 0.05 0.00 1.01 0.97
1911:1940 0.01 0.07 0.38 1.00
1941:1960 0.52 0.56 1.17 1.55
1961:1989 0.49 0.61 0.33 0.57
1990:2005 -0.18 -0.04 -0.35 -0.11

Ft. Collins, CO (053005) NCDC Seasonal Adjustments of
WRCC TMIN (°F)
(+ indicates WRCC>NCDC value)
Interval DJF MAM JJA SON
1900:1905 2.26 2.12 0.61 3.39
1906:1910 0.16 2.33 0.00 2.33
1911:1940 -0.29 2.26 0.00 2.26
1941:1960 -1.02 0.96 0.00 0.96
1961:1989 0.53 0.96 0.00 0.96
1990:2005 1.11 1.54 0.00 1.54

Ft. Collins, CO (053005) NCDC Seasonal Adjustments of
NCDC del TAVE (°F)
(+ indicates WRCC>NCDC value)
Interval DJF MAM JJA SON
1900:1905 -0.99 0.69 -0.40 0.23
1906:1910 -0.98 0.70 -0.39 0.23
1911:1940 -0.99 0.70 -0.23 0.23
1941:1960 -0.74 0.02 -0.32 0.49
1961:1989 0.02 0.02 0.03 0.03
1990:2005 0.00 0.00 0.00 0.00

Ft. Collins, CO (053005) NCDC Seasonal Adjustments of
Net Adjustment (°F)
(+ indicates WRCC>NCDC value)
Interval DJF MAM JJA SON
1900:1905 1.56 -0.04 0.98 1.42
1906:1910 0.15 0.02 0.01 0.01
1911:1940 0.00 0.00 0.00 0.00
1941:1960 1.48 -0.03 0.27 1.40
1961:1989 0.08 0.04 0.02 0.03
1990:2005 0.00 0.00 0.00 0.00
Note: Net Adjustment equals (Adj WRCC TMAX + Adj WRCC TMIN)/2 – NCDC del TAVE.)

The varinaces in the intervals between the step-wise for WRCC TMAX, WRCC TMIN and Net Adjustments are less than 0.3 and usually less than 0.1; the variance for NCDC del TAVE is less than 0.0001.

The above tables show quite clearly the dynamics and significance of the adjustments that have been made to the Ft. Collins, CO data set. I believe the asymmetries and trend patterns are remarkable but you will certainly draw your own conclusions. Some might argue that since the Net Adjustment has remained essentially constant since 1933, the bias introduce by NCDC’s adjustments haven’t changed. However, the fact that NCDC TMIN was lower than WRCC TMIN by more than 1.1 °F for the latest interval, 1990:2005, whereas, NCDC TMAX was only slightly above zero than WRCC TMAX and NCDC del TAV was equal to zero is quite interesting when compared to the preceding interval. I imagine those same adjustments are being made currently.

The only possible cause of the observed step-wise adjustments that I have imagined is the splicing of data from other surface stations but I can find no record of that having occured.

It would seem risible, prima facie, that SHAP adjustments could possibly be a positive trend in the first place. This goes counter to what is obvious.

a “sane” environmental movement.

Yes, it’s like having the Boy Who Cried Wolf running the fire dept.

Thanks, I am aware of the referenced NCDC sources. Unfortunately, the explanations by Karl, et al, have not enabled me to understand some of the significant differences that I have observed in the USHCN/NCDC surface station temperature data sets.

I am puzzled by the residual difference between TAVE and TMED, i.e., del TAVE = TAVE – (TMAX + TMIN)/2. A graph of del TAVE for a specific station displays distinct step-wise seasonal and annual trend patterns. The seasonal pattern for DJF includes the prior-year-December anomaly that John Goetz discusses in this post related to GISS data sets. The intervals between the step-wise changes vary from a few years to several decades. For example, Tucumcari 4NE, NM (299156), a graphical display of the 1900:2005 data set show step-wise changes in del TAVE occurred in 1914 and 1982.

Anthony’s 6/30 post on highlighted Tucumcari 4NE. His 7/1 update included a hyperlink to a report, Weather Observations at the Agricultural Science Center at Tucumcari 1905–2002 (http://tucumcarisc.nmsu.edu/documents/rr751.pdf). I posted comments noting that the TMAX, TMIN and TMEAN data contained in the report were quite different in comparison to the corresponding the NCDC data set for Tucumcari 4NE that I downloaded from http://cdiac.ornl.gov. I also compared the NMSU/ASC data set with a data set that I downloaded from http://www.wrcc.dri.edu/summary/Climsmco.html. The NMSU/ASC and WRCC data set were quite similar.

Recently, I have compared NCDC and WRCC data sets for several other surface stations. In all cases, I observed similar step-wise trend patterns. I now believe that I understand the analytical linkages between RCC and NCDC surface station temperature data sets. The analytical details are beyond the scope of this message, except to state that one of the key relationships is:
Adj WRCC TAVE = WRCC TAVE – NCDC
= Adj WRCC TMED + WRCC del TAVE – del NCDC TAVE

This leads to my postulation that:

“NCDC causes adjustment to be made to WRCC TMAX and/or WRCC TMIN on a seasonal basis which, in effect, adjusts WRCC TMED. NCDC then causes an adjustment to Adj WRCC TAVE to be made by subtracting del NCDC TAVE from Adj WRCC TMED which, depending on its sign, compensates for some or increases the adjustments reflected in Adj WRCC TMED.”

I realize this postulation may appear to be quite a reach and a “picture might be better that 1000 words”. However, I haven’t learned how to export Excel graphs to WordPress!!

Although I believe that I now understand the analytical linkages between RCC and NCDC surface station temperature data sets, I have not insights about NCDC’s reasoning as regards the timing or magnitude of the step-wise adjustments.

“I went and checked GISTEMP, and they do explain how they divide up the Earth for their statistical averaging on this page. But one thing snagged at my mind a bit. Step three of the process involves dividing the planet into 8000 grid boxes. This would create boxes of roughly 63,759 square kilometers within which an average temperature anomaly is computed and then supplied to the global computation. It is how the average temperature is computed that bothers me.

That average temperature anomaly is computed from the temperature stations within that grid box, and also any within a 1200 kilometers radius. To give a graphic perspective so people can visualize this, let’s say my grid box is centered in St. Louis, Missouri. My local grid average is determined by not only the stations within my grid (roughly within 125 kilometers of me), but also from stations in Pittsburgh, Atlanta, Dallas, and Minneapolis, to name just a few. Basically, the radius of effect means that my one grid box’s temperature anomaly is not determined from the only 63,759 square kilometers within it, but from the 4.52 million square kilometers around it, an area 71 times as large.

Based upon this computation design, the United States should be represented by roughly two grid boxes if you are looking at total area involved in determining the average temperature anomaly compared to total area of the US (9,826,630 square kilometers total area of the United States divided by the 4,521,600 square kilometers derived from the 1200 kilometer radius of effect). But the US grid sample is still based upon the 63,759 square kilometer grid box determination, so the total US grid boxes are 154. That would seem to me to heavily weight the US sample.

Why the 1200 kilometer area of effect? Doesn’t this mean that certain stations get counted multiple times? Based upon my math above, it would suggest that many stations in the United States get counted over 77 times. Does the temperature in St. Louis really effect the temperature in Atlanta, and vice versa? Surely, we could just use the temperatures provided by the stations within the grid boxes to determine that grid box’s average anomaly and work out a good global average from that.

It does bother me that temperatures from regions separated by natural geologic barriers that would disrupt weather patterns (as an example, mountain ranges) are used to determine one anomaly statistic. I can see Hansen’s problem due to coverage zones for his 8000 grid boxes. There are probably several areas in the South Pacific where he would be lucky to get one reliable temperature reporting station within a 1200 km radius. Even so, if he would admit this, and explain his rationale for his process, I’m sure it would create more understanding. I also think that it makes no sense to have stations count more than once in the total computation.

…Note, my 63,759 square kilometers figure is derived from using the entire Earth’s surface, both land and ocean. If you only use the land surface area (148,940,000 square kilometers), our grid box shrinks to 18,618 square kilometers. The 1200 kilometer radius circle would hold 243 of those boxes. I gave Hansen the benefit of the doubt that he used the entire planet surface when computing his grid. The source for the surface area data of the Earth is wikipedia.”

I wholeheartedly concur.

On the whole, I think the environmental movement is quite misguided (e.g., the Polar bear nonsense, and much other equally bad nonsense such as spotted owls, snail darters, and peregrine falcons). This bothers me because I think a non-misguided environmental movement is necessary. Unfortunately sensible conservationism has been turned into religious idiocy and is just plain wrong about nearly all their “facts”.

Bear in mind that those are Farenheit measurements. The overall correction is around 0.3°C. That’s half the increase.

But even so, note that FILNET is supposed to be neutral and SHAP should definitely be ‘way negative. And it’s ironic that the MMTS adjustment is positive considering that the switchover created massive CRN4 violations.

McKitrick and Michaels (2007) estimate that around half of the global increase of the last century is spurious.

nice touch John G.