Adjustments Multiply Warming at US CRN1 Stations

A study of US CRN1 stations, top-rated for their siting quality, shows that GHCN adjusted data produces warming trends several times larger than unadjusted data.

The unadjusted files from ghcn.v3.qcu have been scrutinized for outlier values, and for step changes indicative of non-climatic biases. In no case was the normal variability pattern interrupted by step changes. Coverages were strong, the typical history exceeding 95%, and some achieved 100%.(Measured by the % of months with a reported Tavg value out of the total months in the station’s lifetime.)

The adjusted files are another story. Typically, years of data are deleted, often several years in a row. Entire rows are erased including the year identifier, so finding the missing years is a tedious manual process looking for gaps in the sequence of years. All stations except one lost years of data through adjustments, often in recent years. At one station, four years of data from 2007 to 2010 were deleted; in another case, 5 years of data from 2002 to 2006 went missing. Strikingly, 9 stations that show no 2014 data in the adjusted file have fully reported 2014 in the unadjusted file.

It is instructive to see the effect of adjustments upon individual stations. A prime example is 350412 Baker City, Oregon.

Over 125 years GHCN v.3 unadjusted shows a trend of -0.0051 C/century. The adjusted data shows +1.48C/century. How does the difference arise? The coverage is about the same, though 7 years of data are dropped in the adjusted file. However, the values are systematically lowered in the adjusted version: Average annual temperature is +6C +/-2C for the adjusted file; +9.4C +/-1.7C unadjusted.

How then is a warming trend produced? In the distant past, prior to 1911, adjusted temperatures decade by decade are cooler by more than -2C each month. That adjustment changes to -1.8C 1912-1935, then changes to -2.2 for 1936 to 1943. The rate ranges from -1.2 to -1.5C 1944-1988, then changes to -1C. From 2002 onward, adjusted and unadjusted values are the same.

Some apologists for the adjustments have stated that cooling is done as much as warming. Here it is demonstrated that by cooling selectively in the past, a warming trend can be created, even though the adjusted record ends up cooler on average over the 20th Century.

A different kind of example is provided by 417945 San Antonio, Texas. Here the unadjusted record had a complete 100% coverage, and the adjustments deleted 262 months of data, reducing the coverage to 83%. In addition, the past was cooled, adjustments ranging from -1.2C per month in 1885 gradually coming to -0.2C by 1970. These cooling adjustments were minor, only reducing the average annual temperature by 0.16C. Due to deleted years of data, San Antonio went from an unadjusted trend of +0.30C/century to an adjusted trend of +0.92C/century, tripling the warming at that location.

The overall comparison for the set of CRN1 stations:

History 1874 to 2014
Stations 23
Dataset Unadjusted Adjusted
Average Trend 0.18 0.76 °C/Century
Std. Deviation 0.66 0.54 °C/Century
Max Trend 1.18 1.91 °C/Century
Min Trend -2.00 -0.48 °C/Century
Ave. Length 119 Years

These stations are sited away from urban heat sources, and the unadjusted records reveal a diversity of local climates, as shown by the deviation and contrasting Max and Min results. Six stations showed negative trends over their lifetimes.

Adjusted data reduces the diversity and shifts the results toward warming. The average trend is 4 times warmer, only 2 stations show any cooling, and at smaller rates. Many stations had warming rates increased by multiples from the unadjusted rates. Whereas 4 months had negative trends in the unadjusted dataset, no months show cooling after adjustments.

Periodic Rates from US CRN1 Stations

°C/Century °C/Century
Start End Unadjusted Adjusted
1915 1944 1.22 1.51
1944 1976 -1.48 -0.92
1976 1998 3.12 4.35
1998 2014 -1.67 -1.84
1915 2014 0.005 0.68

Looking at periodic trends within the series, it is clear that adjustments at these stations increased the trend over the last 100 years from flat to +0.68 C/Century. This was achieved by reducing the cooling mid-century and accelerating the warming prior to 1998. provides a list of 23 stations that have the CRN#1 Rating for the quality of the sites. I obtained the records from the latest GHCNv3 monthly qcu report, did my own data quality review and built a Temperature Trend Analysis workbook. I made a companion workbook using the GHCNv3 qca report. Both datasets are available here:

As it happens, the stations are spread out across the continental US (CONUS): NW: Oregon, North Dakota, Montana; SW: California, Nevada, Colorado, Texas; MW: Indiana, Missouri, Arkansas, Louisiana; NE: New York, Rhode Island, Pennsylvania; SE: Georgia, Alabama, Mississippi, Florida.

In conclusion, it is not only a matter of concern that individual station histories are altered by adjustments. But also the adjusted dataset is the one used as input into programs computing global anomalies and averages. This much diminished dataset does not inspire confidence in the temperature reconstruction products built upon it.


In response to a comment, this update shows the effect of GHCN adjustments on each of the 23 stations. The average station was warmed by +0.58 C/Century, from +.18 to +.76, comparing adjusted to unadjusted records.

19 station records were warmed, 6 of them by more than +1 C/century. 4 stations were cooled, most of the total cooling coming at one station, Tallahassee.

So for this set of stations, the chance of adjustments producing warming is 19/23 or 83%.

Unadjusted Adjusted Adjusted – Unadjusted
Years in Stn Trends Stn Trends Stn Trends
Record  °C/Century  °C/Century  °C/Century
351862 CORVALLIS 125 0.38 1.05 0.67
350412 BAKER CITY 125 -0.01 1.48 1.49
51564 CHEYENNE WELLS 118 0.84 1.18 0.34
83186 FT MYERS 121 1.18 1.05 -0.12
121873 CRAWFORDSVILLE 115 -2.00 -0.43 1.57
97847 SAVANNAH 141 -0.09 0.56 0.65
42941 FAIRMONT 93 0.90 1.91 1.01
48702 SUSANVILLE 119 0.04 0.84 0.81
80211 APALACHICOLA 111 -0.07 0.95 1.02
86997 PENSACOLA 135 0.27 0.10 -0.17
88758 TALLAHASSEE 123 0.07 -0.48 -0.54
160549 BATON ROUGE 122 -0.07 0.74 0.81
226177 NATCHEZ 121 -0.78 0.59 1.37
238466 TRUMAN DAM & RSVR 122 -0.56 0.62 1.19
245690 MILES CITY 123 0.28 0.31 0.03
269171 WINNEMUCCA 137 0.39 1.11 0.71
308383 SYRACUSE 112 0.78 0.73 -0.05
322188 DICKINSON 120 0.59 0.69 0.11
325479 MANDAN 102 0.43 0.68 0.25
369728 WILLIAMSPORT 120 0.10 1.01 0.92
376698 PROVIDENCE 130 0.68 1.25 0.56
417945 SAN ANTONIO 130 0.30 0.92 0.62
15749 MUSCLE SHOALS 74 0.54 0.58 0.04
Averages 0.18 0.76 0.58

The excel workbooks with data and analyses are provided for your interest and review.

US CRN1 Adjusted TTA 2014 US CRN1 Unadjusted TTA2 2014


  1. Scott · March 17, 2015

    Good job.


  2. Alf · April 2, 2015

    Ron; are you saying that Mosh and Berkley Earth used adjusted raw data as raw data??


    • Ron Clutz · April 2, 2015

      Alf, I haven’t compared these results to Berkeley Earth. These data come from GHCN v.3 unadjusted and adjusted. Berkeley builds it’s data from various sources, not limited to GHCN. I took these because most observers agree GHCN unadjusted matches closely to reports submitted by National Weather Services. More on the data sets here:

      As I understand the Berkeley process, they break station histories into shorter, separate series at points where they determine a step change due to a non-climatic shift (eg. new siting, instrumentation or procedure). Some say that has the effect of accentuating the rate of a warming series.
      I haven’t done the analysis to compare.


  3. short url · May 30, 2015

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    • Ron Clutz · May 30, 2015

      Thanks short. When I first got interested in the climate issue, I discovered that getting to the facts involved a lot of digging and you had to carefully consider the sources. It’s why I decided to make available here some of the facts and understandings that came out of that searching and reading. Our climate system is complex with many mysteries, but we do know some things, and people should know about them.


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