As can be seen from the graphic above, there is a strong correlation between carbon dioxide increases and adjustments to the United States Historical Climatology Network (USHCN) temperature record. And these adjustments to the surface data in turn result in large divergences between surface data sets and satellite data sets.
In the post with April data, the following questions were asked in the conclusion: “Why are the new satellite and ground data sets going in opposite directions? Is there any reason that you can think of where both could simultaneously be correct?”
Professor Robert Brown of Duke University had an excellent response the this question
here.
To give it the exposure it deserves, his comment is reposted in full below. His response ends with rgb.
Rgbatduke June 10, 2015 at 5:52 am
The two data sets should not be diverging, period, unless everything we understand about atmospheric thermal dynamics is wrong. That is, I will add my “opinion” to Werner’s and point out that it is based on simple atmospheric physics taught in any relevant textbook.
This does not mean that they cannot and are not systematically differing; it just means that the growing difference is strong evidence of bias in the computation of the surface record. This bias is not really surprising, given that every new version of HadCRUT and GISS has had the overall effect of cooling the past and/or warming the present! This is as unlikely as flipping a coin (at this point) ten or twelve times each, and having it come up heads every time for both products. In fact, if one formulates the null hypothesis “the global surface temperature anomaly corrections are unbiased”, the p-value of this hypothesis is less than 0.01, let alone 0.05. If one considers both of the major products collectively, it is less than 0.001. IMO, there is absolutely no question that GISS and HadCRUT, at least, are at this point hopelessly corrupted.
One way in which they are corrupted with the well-known Urban Heat Island effect, wherein urban data or data from poorly sited weather stations shows local warming that does not accurately reflect the spatial average surface temperature in the surrounding countryside. This effect is substantial, and clearly visible if you visit e.g. Weather Underground and look at the temperature distributions from personal weather stations in an area that includes both in-town and rural PWSs. The city temperatures (and sometimes a few isolated PWSs) show a consistent temperature 1 to 2 C higher than the surrounding country temperatures. Airport temperatures often have this problem as well, as the temperatures they report come from stations that are deliberately sited right next to large asphalt runways, as they are primarily used by pilots and air traffic controllers to help planes land safely, and only secondarily are the temperatures they report almost invariably used as “the official temperature” of their location. Anthony has done a fair bit of systematic work on this, and it is a serious problem corrupting all of the major ground surface temperature anomalies.
The problem with the UHI is that it continues to systematically increase independent of what the climate is doing. Urban centers continue to grow, more shopping centers continue to be built, more roadway is laid down, more vehicle exhaust and household furnace exhaust and water vapor from watering lawns bumps greenhouse gases in a poorly-mixed blanket over the city and suburbs proper, and their perimeter extends, increasing the distance between the poorly sited official weather stations and the nearest actual unbiased countryside.
HadCRUT does not correct in any way for UHI. If it did, the correction would be the more or less uniform subtraction of a trend proportional to global population across the entire data set. This correction, of course, would be a cooling correction, not a warming correction, and while it is impossible to tell how large it is without working through the unknown details of how HadCRUT is computed and from what data (and without using e.g. the PWS field to build a topological correction field, as UHI corrupts even well-sited official stations compared to the lower troposphere temperatures that are a much better estimator of the true areal average) IMO it would knock at least 0.3 C off of 2015 relative to 1850, and would knock off around 0.1 C off of 2015 relative to 1980 (as the number of corrupted stations and the magnitude of the error is not linear — it is heavily loaded in the recent past as population increases exponentially and global wealth reflected in “urbanization” has outpaced the population).
GISS is even worse. They do correct for UHI, but somehow, after they got through with UHI the correction ended up being neutral to negative. That’s right, UHI, which is the urban heat island effect, something that has to strictly cool present temperatures relative to past ones in unbiased estimation of global temperatures ended up warming them instead. Learning that left me speechless, and in awe of the team that did it. I want them to do my taxes for me. I’ll end up with the government owing me money.
However, in science, this leaves both GISS and HadCRUT (and any of the other temperature estimates that play similar games) with a serious, serious problem. Sure, they can get headlines out of rewriting the present and erasing the hiatus/pause. They might please their political masters and allow them to convince a skeptical (and sensible!) public that we need to spend hundreds of billions of dollars a year to unilaterally eliminate the emission of carbon dioxide, escalating to a trillion a year, sustained, if we decide that we have to “help” the rest of the world do the same. They might get the warm fuzzies themselves from the belief that their scientific mendacity serves the higher purpose of “saving the planet”. But science itself is indifferent to their human wishes or needs! A continuing divergence between any major temperature index and RSS/UAH is inconceivable and simple proof that the major temperature indices are corrupt.
Right now, to be frank, the divergence is already large enough to be raising eyebrows, and is concealed only by the fact that RSS/UAH only have a 35+ year base. If the owners of HadCRUT and GISSTEMP had the sense god gave a goose, they’d be working feverishly to cool the present to better match the satellites, not warm it and increase the already growing divergence because no atmospheric physicist is going to buy a systematic divergence between the two, as Werner has pointed out, given that both are necessarily linked by the Adiabatic Lapse Rate which is both well understood and directly measurable and measured (via e.g. weather balloon soundings) more than often enough to validate that it accurately links surface temperatures and lower troposphere temperatures in a predictable way. The lapse rate is (on average) 6.5 C/km. Lower Troposphere temperatures from e.g. RSS sample predominantly the layer of atmosphere centered roughly 1.5 km above the ground, and by their nature smooth over both height and surrounding area (that is, they don’t measure temperatures at points, they directly measure a volume averaged temperature above an area on the surface. They by their nature give the correct weight to the local warming above urban areas in the actual global anomaly, and really should also be corrected to estimate the CO_2 linked warming, or rather the latter should be estimated only from unbiased rural areas or better yet, completely unpopulated areas like the Sahara desert (where it isn’t likely to be mixed with much confounding water vapor feedback).
RSS and UAH are directly and regularly confirmed by balloon soundings and, over time, each other. They are not unconstrained or unchecked. They are generally accepted as accurate representations of LTT’s (and the atmospheric temperature profile in general).
The question remains as to how accurate/precise they are. RSS uses a sophisticated Monte Carlo process to assess error bounds, and eyeballing it suggests that it is likely to be accurate to 0.1-0.2 C month to month (similar to error claims for HadCRUT4) but much more accurate than this when smoothed over months or years to estimate a trend as the error is generally expected to be unbiased. Again this ought to be true for HadCRUT4, but all this ends up meaning is that a trend difference is a serious problem in the consistency of the two estimators given that they must be linked by the ALR and the precision is adequate even month by month to make it well over 95% certain that they are not, not monthly and not on average.
If they grow any more, I would predict that the current mutter about the anomaly between the anomalies will grow to an absolute roar, and will not go away until the anomaly anomaly is resolved. The resolution process — if the gods are good to us — will involve a serious appraisal of the actual series of “corrections” to HadCRUT and GISSTEMP, reveal to the public eye that they have somehow always been warming ones, reveal the fact that UHI is ignored or computed to be negative, and with any luck find definitive evidence of specific thumbs placed on these important scales. HadCRUT5 might — just might — end up being corrected down by the ~0.3 C that has probably been added to it or erroneously computed in it over time.
rgb
See
here for further information on GISS and UHI.
In the sections below, as in previous posts, we will present you with the latest facts. The information will be presented in three sections and an appendix. The first section will show for how long there has been no warming on some data sets. At the moment, only the satellite data have flat periods of longer than a year. The second section will show for how long there has been no statistically significant warming on several data sets. The third section will show how 2015 so far compares with 2014 and the warmest years and months on record so far. For three of the data sets, 2014 also happens to be the warmest year. The appendix will illustrate sections 1 and 2 in a different way. Graphs and a table will be used to illustrate the data.
Section 1
This analysis uses the latest month for which data is available on WoodForTrees.com (WFT). All of the data on WFT is also available at the specific sources as outlined below. We start with the present date and go to the furthest month in the past where the slope is a least slightly negative on at least one calculation. So if the slope from September is 4 x 10^-4 but it is – 4 x 10^-4 from October, we give the time from October so no one can accuse us of being less than honest if we say the slope is flat from a certain month.
1. For GISS, the slope is not flat for any period that is worth mentioning.
2. For Hadcrut4, the slope is not flat for any period that is worth mentioning.
3. For Hadsst3, the slope is not flat for any period that is worth mentioning.
4. For UAH, the slope is flat since March 1997 or 18 years and 4 months. (goes to June using version 6.0)
5. For RSS, the slope is flat since January 1997 or 18 years and 6 months. (goes to June)
The next graph shows just the lines to illustrate the above. Think of it as a sideways bar graph where the lengths of the lines indicate the relative times where the slope is 0. In addition, the upward sloping blue line at the top indicates that CO2 has steadily increased over this period.
When two things are plotted as I have done, the left only shows a temperature anomaly.
The actual numbers are meaningless since the two slopes are essentially zero. No numbers are given for CO2. Some have asked that the log of the concentration of CO2 be plotted. However WFT does not give this option. The upward sloping CO2 line only shows that while CO2 has been going up over the last 18 years, the temperatures have been flat for varying periods on the two sets.
Section 2
For this analysis, data was retrieved from Nick Stokes’ Trendviewer available on his
website. This analysis indicates for how long there has not been statistically significant warming according to Nick’s criteria. Data go to their latest update for each set. In every case, note that the lower error bar is negative so a slope of 0 cannot be ruled out from the month indicated.
On several different data sets, there has been no statistically significant warming for between 11 and 22 years according to Nick’s criteria. Cl stands for the confidence limits at the 95% level.
The details for several sets are below.
For UAH6.0: Since October 1992: Cl from -0.009 to 1.742
This is 22 years and 9 months.
For RSS: Since January 1993: Cl from -0.000 to 1.676
This is 22 years and 6 months.
For Hadcrut4.3: Since July 2000: Cl from -0.017 to 1.371
This is 14 years and 11 months.
For Hadsst3: Since August 1995: Cl from -0.000 to 1.780
This is 19 years and 11 months.
For GISS: Since August 2003: Cl from -0.000 to 1.336
This is 11 years and 11 months.
Section 3
This section shows data about 2015 and other information in the form of a table. The table shows the five data sources along the top and other places so they should be visible at all times. The sources are
UAH, RSS,
Hadcrut4,
Hadsst3, and
GISS.
Down the column, are the following:
1. 14ra: This is the final ranking for 2014 on each data set.
2. 14a: Here I give the average anomaly for 2014.
3. year: This indicates the warmest year on record so far for that particular data set. Note that the satellite data sets have 1998 as the warmest year and the others have 2014 as the warmest year.
4. ano: This is the average of the monthly anomalies of the warmest year just above.
5. mon: This is the month where that particular data set showed the highest anomaly. The months are identified by the first three letters of the month and the last two numbers of the year.
6. ano: This is the anomaly of the month just above.
7. y/m: This is the longest period of time where the slope is not positive given in years/months. So 16/2 means that for 16 years and 2 months the slope is essentially 0. Periods of under a year are not counted and are shown as “0”.
8. sig: This the first month for which warming is not statistically significant according to Nick’s criteria. The first three letters of the month are followed by the last two numbers of the year.
9. sy/m: This is the years and months for row 8. Depending on when the update was last done, the months may be off by one month.
10. Jan: This is the January 2015 anomaly for that particular data set.
11. Feb: This is the February 2015 anomaly for that particular data set, etc.
16. ave: This is the average anomaly of all months to date taken by adding all numbers and dividing by the number of months.
17. rnk: This is the rank that each particular data set would have for 2015 without regards to error bars and assuming no changes. Think of it as an update 25 minutes into a game.
Source UAH RSS Had4 Sst3 GISS
1.14ra 6th 6th 1st 1st 1st
2.14a 0.170 0.255 0.564 0.479 0.75
3.year 1998 1998 2014 2014 2014
4.ano 0.483 0.55 0.564 0.479 0.75
5.mon Apr98 Apr98 Jan07 Aug14 Jan07
6.ano 0.742 0.857 0.832 0.644 0.97
7.y/m 18/4 18/6 0 0 0
8.sig Oct92 Jan93 Jul00 Aug95 Aug03
9.sy/m 22/9 22/6 14/11 19/11 11/11
Source UAH RSS Had4 Sst3 GISS
10.Jan 0.261 0.367 0.688 0.440 0.82
11.Feb 0.156 0.327 0.660 0.406 0.88
12.Mar 0.139 0.255 0.681 0.424 0.90
13.Apr 0.065 0.175 0.656 0.557 0.74
14.May 0.272 0.310 0.696 0.593 0.76
15.Jun 0.329 0.391 0.728 0.580 0.80
Source UAH RSS Had4 Sst3 GISS
16.ave 0.204 0.304 0.685 0.500 0.82
17.rnk 4th 6th 1st 1st 1st
If you wish to verify all of the latest anomalies, go to the following:
For UAH, version 6.0 was used. Note that WFT uses version 5.6. So to verify the length of the pause on version 6.0, you need to use Nick’s program.
http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tlt/tltglhmam_6.0beta2
For RSS, see: ftp://ftp.ssmi.com/msu/monthly_time...hannel_tlt_anomalies_land_and_ocean_v03_3.txt
For Hadcrut4, see:
http://www.metoffice.gov.uk/hadobs/...ime_series/HadCRUT.4.4.0.0.monthly_ns_avg.txt
For Hadsst3, see:
http://www.cru.uea.ac.uk/cru/data/temperature/HadSST3-gl.dat
For GISS, see:
http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt
To see all points since January 2015 in the form of a graph, see the WFT graph below. Note that UAH version 5.6 is shown. WFT does not show version 6.0 yet.
Steve Goddard @SteveSGoddard 
hockey schtick @hockeyschtick1 
Steve Milloy @JunkScience 
