Is It Warmer Now than a Century ago? It Depends.

This study was first published on August 20, 2014 at No Tricks Zone. The dataset ends with 2013 records.

In a previous study of World Class station records, the effects of urban development could not be discounted since the 25 long service records come from European cities. This is a study to see what the best sites in the US can tell us about temperature trends in the last century. There are two principal findings below.

Surfacestations.org 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.

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.

The records themselves vary in quality of coverage, but are all included here because of their CRN#1 rating. The gold medal goes to Savannah for 100% monthly coverage, with a single missing daily observation since 1874. Pensacola was a close second among the four stations with perfect monthly coverage. Most stations were missing less than 20 months with coverages above 95%.

1. First Class US stations show little warming in the last century.

Area FIRST CLASS US STATIONS
History 1874 to 2013
Stations 23
Average Length 118 Years
Average Trend 0.16 °C/Century
Standard Deviation 0.66 °C/Century
Max Trend 1.18 °C/Century
Min Trend -1.93 °C/Century

The average station shows a rise of about 0.16°C/Century. The large deviation, and the fact that multiple stations had cooling rates shows that warming has not been extreme, and varies considerably from place to place. The observed warming for this group is less than half the rate reported in the European study.

2. Temperature trends are local, not global.

Most remarkable about these stations is the extensive local climate diversity that appears when station sites are relatively free of urban heat sources. 35% (8 of 23) stations reported cooling over the century. Indeed, if we remove the 8 warmest records, the rate flips from +0.16°C to -0.14°C.

And the multidecadal patterns of warming and cooling were quite variable from place to place. Averages over 30-year periods suggest how unusual these patterns are.

For the set of stations the results are:

°C/Century Start End
0.78 Start 1920
-1.21 1921 1950
-1.11 1951 1980
1.51 1981 2013
0.99 1950 2013

The first period varied in length from each station’s beginning to 1920. Surprisingly the second period cooled in spite of the 1930s. Warming appears mostly since 1980. As mentioned above, within these averages are many differing local patterns.

Conclusion:

Question: Is it warmer now than 100 years ago?
Answer: It depends upon where you live. The best observations from US stations show a barely noticeable average warming of 0.16°C / Century. And 35% of stations showed cooling at the same time that others were warming more than the average.

Note about Data Quality.

The attached workbook for Truman Dam & Reservoir is an example of my data quality review method. There are sheets showing the incoming qcu values, removal of flags and errors, audit of outliers (values exceeding 2 St. Dev.) and CUSUM and 1st Differences analyses to test for systemic bias. Note that Truman missed out entirely on warming from 1956 to 2002, in contrast to the conventional notion of global warming from the 1970s to 2000.

Truman Dam & Reservoir also provides a cautionary tale about temperature analysis. The station’s annual averages appear to rise dramatically from 2003 to present. On closer inspection, that period is missing values for 6 Decembers, 8 Januarys and 5 Februarys. So the annual warming is mostly the result of missing data-points.

This shows why analyzing the temperatures themselves can be misleading. By relying only on the station’s monthly slopes, TTA analysis effectively places missing values on the trend line of the existing values.

Note about Fall et al. (2011).

This was the first study to use CRN 1 to 5 ratings to look at US temperature trends in relation to station siting quality. Much discussed at the time was the finding of CRN 1&2 showing warming of 0.155 C/Decade for the period 1979 to 2008. The comparable finding from this analysis is 0.151 C/Decade for CRN 1 stations.

Little noticed was Figure 10 on page 10 of Fall et al. That graph shows that CRN 1&2 rate of warming Tavg unadjusted was about 0.2 C/Century for the period 1895 to 2008. This analysis shows a comparable 0.16 C for CRN 1 for the same period up to 2013.

Excel Workbook is here: First Class US TTA

Truman Dam and Reservoir is here: 238466

Auditing the Abuse of Temperature Records

What I don’t get is the disrespect of the adjusters for the reality of micro climates. BEST acknowledges that >30% of US records show a cooling trend over the last 100 years. Why can’t reported cooling be true?

I did a 2013 study of the CRN top rated US surface stations. Most remarkable about them is the extensive local climate diversity that appears when station sites are relatively free of urban heat sources. 35% (8 of 23) of the stations reported cooling over the century. Indeed, if we remove the 8 warmest records, the rate flips from +0.16°C to -0.14°C. In order to respect the intrinsic quality of temperatures, I calculated monthly slopes for each station, and combined them for station trends.

Recently I updated that study with 2014 data and compared adusted to unadjusted records. The analysis shows the effect of GHCN adjustments on each of the 23 stations in the sample. 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%.

In the Quest for the mythical GMST, these records have to be homogenized, and also weighted for grid coverage, resulting in cooling being removed as counter to the overall trend.

The pairwise homogenization technique assumes that two local climates move in tandem. Thus, if one of them diverges, it must be adjusted back in line.  But I question that premise. It’s obvious that a mountaintop site will typically show lower temps than a nearby sea level site. But it is wrong to assume that changes at one should be consistent with changes in the other. Not only are the absolute readings different, the patterns of changes are also different. Infilling does violence to the local climate realities. It is perfectly normal that one place can have a cooling trend at the same time another place is warming.

Weather stations measure the temperature of air in thermal contact with the underlying terrain. Each site has a different terrain, and for a host of landscape features documented by Pielke Sr., the temperature patterns will differ, even in nearby locations. However, if we have station histories (and we do), then trends from different stations can be compared to see similarities and differences..

In summary, temperatures from different stations should not be interchanged or averaged, since they come from different physical realities. The trends can be compiled to tell us about the direction, extent and scope of temperature changes.

What Paul Homewood, Steven Goddard, Booker and others are doing is a well-respected procedure in financial accounting. The Auditors must determine if the aggregate corporation reports are truly representative of the company’s financial condition. In order to test that, samples of component operations are selected and examined to see if the reported results are accurate compared to the facts on the ground.

Discrepancies such as those we’ve seen from NCDC call into question the validity of the entire situation as reported. The stakeholders must be informed that the numbers presented are misrepresenting the reality. The Auditor must say of NCDC something like: “We are of the opinion that NCDC statements of global surface temperatures do not give a true and fair view of the actual climate reported in all of the sites measured.”

The several GHCN samples analyzed so far show that older temperatures have been altered so that the figures are lower than the originals. In some cases, more recent temperatures have been altered to become higher than the originals. Alternatively, recent years of observations are simply deleted. The result is a spurious warming trend of 1-2F, the same magnitude as the claimed warming from rising CO2. How is this acceptable public accountability? More like “creative accounting.” Once a researcher believes that rising CO2 causes rising temperatures, and since CO2 keeps rising, then temperatures must continue to rise, cooling is not an option. In fact 2015 dare not be cooler than 2014.

We are learning from this that GHCN only supports the notion of global warming if you assume that older thermometers ran hot and today’s thermometers run cold. Otherwise the warming does not appear in the original records; they have to be processed, like tree proxies. Not only is the heat hiding in the oceans, even thermometers are hiding some.

Once you accept that facts and figures in the historical record are changeable, then you enter Alice’s Wonderland, or the Soviet Union, where it was said: “The future is certain; only the past keeps changing.” The apologists for NCDC confuse data and analysis. The temperature readings are facts, unchangeable. If someone wants to draw comparisons and interpret similarities and differences, that’s their analysis, and they must make their case from the data to their conclusions. Usually, when people change the record itself it’s because their case is weak.

My submission to the International Temperature Data Review project has gone to the panel.

https://rclutz.wordpress.com/2015/04/26/temperature-data-review-project-my-submission/

About US CRN Station Ratings

The USCRN rating system classifies the sites of weather stations with ratings from 1 to 5 (1 being the best)

From the USHCRN manual:

The USCRN will use the classification scheme below to document the “meteorological measurements representativity” at each site.  This scheme, described by Michel Leroy (1998), is being used by Meteo-France to classify their network of approximately 550 stations. The classification ranges from 1 to 5 for each measured parameter. The errors for the different classes are estimated values.

  • Class 1 – Flat and horizontal ground surrounded by a clear surface with a slope below 1/3 (<19deg). Grass/low vegetation ground cover <10 centimeters high. Sensors located at least 100 meters from artificial heating or reflecting surfaces, such as buildings, concrete surfaces, and parking lots. Far from large bodies of water, except if it is representative of the area, and then located at least 100 meters away. No shading when the sun elevation >3 degrees
  • Class 2 – Same as Class 1 with the following differences. Surrounding Vegetation <25 centimeters. Artificial heating sources within 30m. No shading for a sun elevation >5deg.
  • Class 3 (error 1C) – Same as Class 2, except no artificial heating sources within 10 meters.
  • Class 4 (error >= 2C) – Artificial heating sources <10 meters.
  • Class 5 (error >= 5C) – Temperature sensor located next to/above an artificial heating source, such a building, roof top, parking lot, or concrete surface.

Click to access X030FullDocumentD0.pdf

Starting in 2007, the Surfacestations.org project undertook to make field inspections of weather stations to classify them according to the CRN criteria. The website provides the names of 23 stations that have the CRN#1 Rating for the quality of the sites, along with all stations that have been rated thus far.

http://www.surfacestations.org/USHCN_stationlist.htm

As it happens, the CRN#1 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.

Climate Change Legislation

Recently US Senators considered a number of amendments to their Keystone pipeline bill, but none of them were in the proper format, or actually addressed the issue.

An appropriate legislative motion would read like this:

Whereas, Extent of global sea ice is at or above historical averages;

Whereas, Populations of polar bears are generally growing;

Whereas, Sea levels have been slowly rising at the same rate since the Little Ice Age ended 150 years ago;

Whereas, Oceans will not become acidic due to buffering from extensive mineral deposits and marine life is well adapted to pH fluctuations that do occur;

Whereas, Extreme weather events have not increased in recent decades and such events are more associated to periods of cooling rather than warming;

Whereas, Cold spells, not heat waves, are the greater threat to human life and prosperity;

Therefore, This chamber agrees that climate is variable and prudent public officials should plan for future periods both colder and warmer than the present. Two principle objectives will be robust infrastructure and cheap, reliable energy.

Comment:

The underlying issue is the assumption that the future can only be warmer than the present. Once you accept the notion that CO2 makes the earth’s surface warmer (an unproven conjecture), then temperatures can only go higher since CO2 keeps rising. The present plateau in temperatures is inconvenient, but actual cooling would directly contradict the CO2 doctrine. Some excuses can be fabricated for a time, but an extended period of cooling undermines the whole global warming mantra.

It’s not a matter of fearing a new ice age. That will come eventually, according to our planet’s history, but the warning will come from increasing ice extent in the Northern Hemisphere. Presently US infrastructure is not ready to meet a return of 1950s weather, let alone something unprecedented. Public policy must include preparations for cooling since that is the greater hazard. Cold harms the biosphere: plants, animals and humans. And it is expensive and energy intensive to protect life from the ravages of cold. Society can not afford to be in denial about the prospect of the plateau ending with cooling.

New chemical Element Discovered

The new element is Governmentium (Gv). It has one neutron, 25 assistant neutrons, 88 deputy neutrons and 198 assistant deputy neutrons, giving it an atomic mass of 312, the heaviest of all.  These 312 particles are held together by forces called morons, which are surrounded by vast quantities of lefton-like particles called peons.

Since Governmentium has no electrons or protons, it is inert. However, it can be detected, because it impedes every reaction with which it comes into contact. A tiny amount of Governmentium can cause a reaction normally taking less than a second to take from four days to four years to complete.

Governmentium has a normal half-life of 3-6 years. It does not decay but instead undergoes a reorganization in which a portion of the assistant neutrons and deputy neutrons exchange places.  In fact, Governmentium’s mass will actually increase over time, since each reorganization will cause more morons to become neutrons, forming isodopes.

This characteristic of moron promotion leads some scientists to believe that Governmentium is formed whenever morons reach a critical concentration. This hypothetical quantity is referred to as critical morass.

When catalyzed with money, Governmentium becomes Administratium, an element that radiates just as much energy as Governmentium since it has half as many peons but twice as many morons. All of the money is consumed in the exchange, and no other byproducts are produced. It tends to concentrate at certain points such as government agencies, large corporations, and universities. Usually it can be found in the newest, best appointed, and best maintained buildings.

Scientists point out that administratium is known to be toxic at any level of concentration and can easily destroy any productive reaction where it is allowed to accumulate. Attempts are being made to determine how administratium can be controlled to prevent irreversible damage, but results to date are not promising.

Credit: William DeBuvitz, http://www.lhup.edu/~dsimanek/administ.htm

The Permafrost Bogeyman

The permafrost Methane bogeyman disappears in the light of the facts.

1) When there was warming in places like Alaska, atmospheric methane did not increase.

2) Permafrost depletion in the NH stopped since 2005.

3) When permafrost thaws, vegetation grows and removes more CO2 than is released by the melting. The region acts as a sink, not a source of CO2.

4) Past warm periods (Medieval and Holocene warmings) did not produce increases in methane.

So scientists with models are stirring up alarm about thawing of Siberian permafrost. But there are scientists in Siberia monitoring the situation. What do they say?

“Indeed above at the surface it has gotten warmer, but that’s just part of a normal cycle. The permafrost is rock hard, And that is how it is going to stay. There’s no talk of thawing.” Michali Grigoryev
http://notrickszone.com/2012/11/19/russian-arctic-scientist-permafrost-changes-due-to-natural-factors-its-going-to-be-colder/

“It seems that the permafrost should be melting if the temperature is rising. However, many areas are witnessing the opposite. The average annual temperature is getting higher, but the permafrost remains and has even started to spread. Why? An important factor is the snow cover. Global warming reduces it, therefore making the heat insulator for the permafrost thinner. Then even weak frosts are enough to freeze the ground deeper below the surface.”

Nikolai Osokin is a glaciologist at the Institute of Geography, the Russian Academy of Sciences.

http://en.rian.ru/analysis/20070323/62485608.html
“The Russian Academy of Sciences has found that the annual temperature of soils (with seasonable variations) has been remaining stable despite the increased average annual air temperature caused by climate change. If anything, the depth of seasonal melting has decreased slightly.”

“This is just another scare story . . . This ecological structure is balanced and is not about to harm people with gas discharges.”
Vladimir Melnikov is the director of the world’s only Institute of the Earth’s Cryosphere. The Russian Academy of Sciences’ Institute is located in the Siberian city of Tyumen and investigates the ways in which ground water becomes ice and permafrost.

“The boundaries of the Russian permafrost zone remain virtually unchanged. At the same time, the permafrost is several hundred meters deep. For methane, other gases and hydrates to escape to the surface, it would have to melt at tremendous depths, which is impossible.”
Yuri Izrael, director of the Institute of Climatology and Ecology of the Russian Academy of Sciences.

http://en.rian.ru/analysis/20050822/41201605-print.html

Runaway warming from permafrost thawing has not happened before, is not happening now, but we should believe it will happen if we don’t do something?

Update to Adjustments Warming US CRN#1 Stations

In response to a comment, this post 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

About GHCN Temperature Data

A post below this one presents a comparison between unadjusted and adjusted GHCN temperature data. This article provides some background information on these data and how they are treated in Temperature Trend Analysis methodology.

GHCN acts as a global repository for surface weather station records submitted to it by National Weather Services (NWS). Each NWS reviews local records according to their procedures and certifies that the data accurately represent the weather experienced in their jurisdiction. The GHCN version 3 qcu file is composed of these data (qcu signifies quality controlled unadjusted.) Various bloggers, ranging from E.M. Smith (chiefio) to Nick Stokes (moyhu) are satisfied that this file is close to the data submitted by NWS agencies.

The quality control consists of attaching flags to values appearing in the file. Because my home computer has limited power, I worked with the Taverage monthly datasets. There a monthly value is flagged with an “a” if 1 daily value is missing in calculating the average, “b” is 2 dailies missing, and so on up to 9 omissions. 10 or more missing dailies and the month is assigned a “-9999”, indicating a blank for the month. An additional column beside each month identifies outlier values.

My principle is to include all data unless there is good reason to exclude. The data preparation procedure involves unzipping the downloaded file and opening it as a word document. The station records of interest are copied into a new word document, which my notebook can handle without processing delays. The text data is then put into an excel workbook, spread into cells, -9999s converted to blanks, flags and additional columns are removed.

My data quality assurance practices include scrutinizing each value greater than 2 Standard deviations away from mean. I use CUSUM and first differences to test for step changes in the record, which would suggest a non-climatic change in the data (e.g. Change of equipment, procedure or location). In the US CRN#1 dataset I found no step changes, and the outlier values were few. I tested excluding some high or low values, but found no discernible effect on the slopes

The same procedure was followed for the qca file (quality controlled adjusted). This meant adding two additional sheets into each station’s data workbook, examples of which are provided through links below.

In the US CRN#1 unadjusted workbook, there is a sheet for each station with the data pasted into a template that calculates several measures. The basic analysis is to compute the slopes for each month (Jan, Feb, etc.) over the lifetime of that station. The 12 slopes are then averaged for the station trend. In addition, trends are calculated for several shorter periods of interest, again by combining 12 monthly slopes for that station period.  A summary page brings together results from all the stations and generates averages of trends for the set of stations, by months, and by periods of years.

Data workbooks for two stations are provided here:
350412 Baker City, Oregon 350412
417945 San Antonio, Texas 417945

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:

Area FIRST CLASS US 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.

Surfacestations.org 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:ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v3/

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.

Update

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