As we’ve seen in this report from John Goetz, GISS: worlds airports continue to run warmer than ROW a significant portion of the GHCN (Global Historical Climate Network) surface temperature record is measured in airports, not rural open fields. Airports, airport expansion, and air travel frequency tend to be linked with the population, growth, and wealth trends of a city. It stands to reason that since the majority of thermometers in the GHCN record are at airports, they’d have a broad application of UHI. Joe tries out a simple method of approximating what the signal might look like with a UHI removal. – Anthony
Chasing a More Accurate Global Century Scale Temperature Trend
By Joseph D’Aleo, CCM, AMS Fellow
Hadley Center Annual Mean Temperature since 1895 shows a warming of about 1C since 1895. – Click for larger image
The long term global temperature trends have been shown by numerous peer review papers to be exaggerated by 30%, 50% and in some cases much more by issues such as urbanization, land use changes, bad siting, bad instrumentation, and ocean measurement techniques that changed over time. NOAA made matters worse by removing the satellite ocean temperature measurement which provide more complete coverage and was not subject to the local issues except near the coastlines and islands. The result has been the absurd and bogus claims by NOAA and the alarmists that we are in the warmest decade in 100 or even a 1000 years or more and our oceans are warmest ever. See this earlier story that summarizes the issues.
No one disputes the cyclical warming from 1979 to 1998 that is shown in all the data sets including the satellite, only the cause. These 60-70 year cycles tie in lock step with the ocean temperature cycles and solar Total Solar Irradiance. The annual mean USHCN temperatures are shown below along with the annual TSI and PDO+AMO.
Click for larger image
One needs simply to look at the record highs for the United States and globe to see that the warmest years are not all in the last two decades (although some were to be expected given it is one of two peaks in the cycles). The first image below shows the decadal state record all-time highs. The 1930s still clearly dominates (24 state all time records) with only one state (South Dakota) in the 2000s tying a 1930s all-time heat record.
Click for a larger image
The following image (enlarged here) shows the record monthly highs by individual year. Note the 1930s and 1950s dominate and this decade showing the least record highs than any decade since the 1800s.
Here is the NCDC compilation of the continental all-time records (enlarged here), note for all the populated continents, the records were in the 1800s and early 1900s.
TRYING TO GET AT A BETTER LONG TERM TREND
NCDC removed the UHI effect for the US in 2007 in version 2 of the USHCN. GISS maintains their version of a UHI adjustment of this NCDC USHCN data. By differencing the two, I found the following (enlarged here):
NOAA USHCNV2 -vs- GISS – click for larger image
It shows an artificial warming of about 0.45 C or 0.75F for the NOAA data for removal of the urbanization adjustment. Phil Jones of the Hadley Center, co-authored a paper that showed the UHI contamination of China was 1 degree Celsius (1.8F) for the century, so this contamination appears not to be unreasonable, in fact it may be conservative.
I then took that UHI adjustment for the United States and applied to the global data. The Hadley center data is dominated by land areas with their ocean temperatures mainly coming from ships and in the northern hemisphere. Here’s what Hadley says about marine data “For marine regions sea surface temperature (SST) measurements taken on board merchant and some naval vessels are used. As the majority come from the voluntary observing fleet, coverage is reduced away from the main shipping lanes and is minimal over the Southern Oceans.”
I subtracted the UHI annual contamination from the annual Hadley CRUT3v global temperatures. I got the following (enlarged here):
This gives a much more believable view of global temperatures, consistent with the natural forcings and more in line with records shown. The greatest warming was in the early 20th Century. The warming since 1930s and 1940s was negligible (0.2C). It suggests much to do about nothing in DC and Copenhagen. See PDF here.
UPDATE: This post has been changed to include a raw Hadley CRUT3v global plot, a NOAA-GISS difference plot and a corrected adjusted Hadley plot now all in Celsius. This is a work in progress and an attempt to see what Hadley plot might look like with an adjustment for UHI that numerous peer review papers suggest is necessary. Your suggestions are welcome (jsdaleo at yahoo.com).
Did you convert from F to C first? Why would you apply the effect to land and ocean? Why not estimate the global warm bias directly using satellite data for the last 30 years?
I agree that the temperature datasets probably overstate the warming, but there is no way that extreme corrections like this are right, IMAO.
Whoops. Note the temp for 24/9/09 on the UAH satellite site – 40.13F above this time last year. What’s up?
He talks about big UHI effect in China, that is believable when you look at the sheer size of some urban areas in that country complete with rapid growth.
It’s not CO2 that creates a possible decent chunk man-made warming in some areas, it’s global urbanization, look at the year 1900 and determine how much less land back then was paved over and urbanized.
Also, the Farmer’s Almamac outlook for Fall, you see they’re thinking more towards a chilly Fall season than a warm one unless you’re in the Southern U.S
http://www.farmersalmanac.com/weather/a/farmers-almanac-fall-forecast
Joseph D’Aleo: “I then took that UHI adjustment for the United States and made the leap of faith that it applied to the global data.”
A “leap of faith” that the Urban Heat Island effect applies globally? Like for the sea surface temperatures that represent 70% of the earth’s surface as well as all the non-urban land areas? A “leap of faith” based on just the United States figures, which cover less than two per cent of the earth’s surface?
A “leap of faith” that just happens to provide you with a graph that tells you what you want to see?
Gosh. This is an easy way to do science! When the results don’t tell you what you want to see, just take a leap of faith and hey presto! everything is hunky dory.
Leap of faith? More like a fall into darkness.
REPLY: If you’ll put your snark rifle aside for a moment, bear in mind that the majority of the GHCN surface temperature record is measured in airports, not rural open fields. Airports, airport expansion, and air travel frequency are closely linked with the population, growth, and wealth trends of a city. It stands to reason that since the majority of thermometers in the GHCN record are at airports, they’d have a broad application of UHI.
See: http://wattsupwiththat.com/2009/07/15/giss-worlds-airports-continue-to-run-warmer-than-row/
Joe could have worded that better, I agree. I’ve added a foreword to remind readers of the airport issue and asked Joe to clarify. – Anthony
UPDATE: Joe agreed with the poor choice of words, and has clarified his paragraph. Also some commentators suggested he made an error in F to C conversion or skipped it, he did indeed, and the graph has been updated. – A
Ian George (15:32:57) :
40.13F above this time last year. What’s up?
You are trying to say that Sept. 2009 is more than 40 degrees higher than Sept. 2008 ?
Gee…. Earth has not seen temps like that in about 50 Million years. What a MAJOR rapid temperature change. Historic.
Should have provided the link. It will probably be adjusted soon.
http://discover.itsc.uah.edu/amsutemps/execute.csh?amsutemps+001
“A “leap of faith” that the Urban Heat Island effect applies globally? Like for the sea surface temperatures …?”
Let’s hope he meant to say “to the land-based global data.”
I’ve suspected after we legitimately remove poor siting effects and UHI, we would likely find we never achieved the highs of the 30’s/40’s at any time since. A full blown audit of all records, and corrections for these factors might reveal no net warming at all since that era. A huge task, but, Complete destruction of the AGW theory as a result.
interesting data, interesting times.
Joe D’Aleo: Your graph suggests that the temperature trend between 1979 and 2009 is a rise of ~0.1 C. However, the UAH satellite dataset would say the trend was about 0.125 C/decade, for a total rise of ~0.38 C…and the trend for the RSS analysis is even higher! Doesn’t that perhaps suggest to you that there might be something wrong with your analysis?
Still another correction has to be made. What would the temperature record look like if we were to account for the dense smog of the 19th and early 20th century which blocked a lot more sunlight than the latter half of the 20th century. I reason without that smog temperatures would have been higher in the past. The major climate change therefore is our air has become cleaner, more transparent and more sunlight is reaching ground level.
Slioch (15:43:57) : et al….
Your point begs the question- Since continental land masses are a small portion of the surface, for periods preceding the satellite record, and periods following it where the satellite record is not exclusively used, are not those land mass records then the basis from which vast ocean surface temps must have been calculated? And as was clearly shown here in other work, much of that record is tainted with UHI.
Joel Shore (16:09:35) : There is something wrong. I got distinctly larger trends, although still reduced, with a very simple method. But the issue is not quite as large as you could suggest:
http://www.climateaudit.org/phpBB3/viewtopic.php?f=3&t=740
And RSS has a spurious warm shift in the early nineties. Let’s not drag that into this.
The main issue as far as I can see is that Joe has applied an “UHI correction” to the entire globe, including the oceans. I’m also still not clear whether he converted from F to C.
Joel Shore (16:09:35) :
you forgot to consider that climate models estimate trends at 600 mbar (UAH satellite) to be roughly 2 times higher than on the ground. D’aleo’s estimate is in much much better agreement, than the agencies’ surface temperature data sets.
(if climate models are good for anything, it is contradicting other agw conjecture.)
http://www.climateaudit.org/wp-content/uploads/2008/06/hadat43.gif
The data are very interesting, especially the overlay of the PDO, AMO and TSI on the global temperature track.
However, applying the domestic UHI correction to the HadCRUT global temperature has two major problems which will tend to counter each other:
1. The UHI correction applies to land data only. This will tend to reduce the global UHI effect and increase the apparent warming trend over that shown in your last figure.
2. The US actually appears to have less unaccounted-for UHI effects than land-based measurements for the rest of world, at least since 1980 (see McKitrick and Michaels, JGR 2007). This will tend to decrease the apparent warming trend below your last figure.
I still haven’t found a good thermostat for my house that will allow me to set the temperature to the hundredths degree. Does anyone know where I can get one? I like my house temperature to be 71.68 degrees Fahrenheit. Anything more or less than that is just plain uncomfortable.
Joel Shore (16:09:35) :
Joe D’Aleo: Your graph suggests that the temperature trend between 1979 and 2009 is a rise of ~0.1 C. However, the UAH satellite dataset would say the trend was about 0.125 C/decade, for a total rise of ~0.38 C…and the trend for the RSS analysis is even higher! Doesn’t that perhaps suggest to you that there might be something wrong with your analysis?
Yep, I’m with you on this. We’ve disagreed on other issues, but it doesn’t look as though this analysis has been properly thought through. The “amplification factor” in the troposphere is something like 1.2, so that doesn’t explain the discrepancy between satellite and the “adjusted” Hadley records. This looks to be a case of torturing the data to get a result we like.
What would the temperature record look like if we were to account for the dense smog of the 19th and early 20th century which blocked a lot more sunlight than the latter half of the 20th century.
Indeed the smog, smoke and particulate pollution did block sunlight from reaching the surface. However, the surface temperatures are compiled from just 2 values, minimum temperature and maximum temperatures for the day.
The minimum temperature for the day typically occurs after dawn when the first sunlight of the day overcomes the effect of radiative cooling.
Smoke and smog is mostly close to the surface and would have it’s maximum effect in blocking sunlight when the sun has just risen and sunlight travels at a low angle through the atmosphere to reach the surface.
Most of the observed warming has been in the minimum temperature (about 70%) and reduced smog and smoke would (and probably does) account for the warming from the mid-1970s to the mid-1990s due to clear air legislation in the developing world.
It also accounts for the discrepancy between Joe’s maximum temperature distribution and the compiled surface temperature records (Hadcru. GISS).
Thanks Joseph D’Aleo for the publication and Anthony for posting this.
Despite the remarks, the method and the final graph makes sense to me and it’s a hell of a lot better than the “consensus” based on a hockey stick graph and a bunch of blatant lies.
“Consensus” is all that’s left and it won’t hold.
The 2nd graph, the one with the big red bars, that looks like the records for where I live.
1933 , they don’t make ’em like that anymore.
The 2007-2008 global cooling event, evidence for clouds as the cause!
http://www.drroyspencer.com/2009/09/the-2007-2008-global-cooling-event-evidence-for-clouds-as-the-cause/
Manfred says:
The 2X factor is at one specific height and in the tropics. If you average over the whole globe and consider the fact that the T_2LT effectively averages (in a weighted way) over a large portion of the troposphere (and, maybe even into the stratosphere a bit in the tail), then the expected amplification factor is much smaller. John Finn quotes a value of 1.2, which sounds about right.
If you want to study the sun, go to the moon!
http://omniclimate.wordpress.com/2009/09/25/to-study-the-sun-go-to-the-moon/
Layne Blanchard says:
No…I believe that most of the ocean temperature data are from sea surface temperatures measured by ships.
Not to pile on… but that first graph of PDO+AMO with TSI and temps is simply egregious… Why would you plot US temps when trying to argue ocean cycles/TSI influence on GLOBAL temps? Why not just plot the global temps? Also, the TSI plotted is from the severely outdated Hoyt-Schatten data which Leif describes as “worse than useless”. Its pretty hard to take the rest of the “essay” seriously when you copy and paste that as your first plot… No wonder icecap doesn’t allow comments!
REPLY:I agree it started out a bit confusing. Criticism helped. All of the graphs have been updated. Joe points out that the solar data has been calibrated for Willson’s ACRIM. – A
@John Finn (17:04:19) :
“The “amplification factor” in the troposphere is something like 1.2”
what is your source of 1.2 ?
looking at the picture below it should be around 2 between -45° and +45° latitude, what already covers about 70% of the earth’s surface. Elsewhere it is roughly factor 1 giving a total of approx. 1.7.
http://www.climateaudit.org/wp-content/uploads/2008/06/hadat43.gif
It hit 59 degrees in Antarctica?
Manfred (22:21:02) :
@John Finn (17:04:19) :
“The “amplification factor” in the troposphere is something like 1.2″
what is your source of 1.2 ?
I’ve seen it quoted lots of times. Here’s one source.
http://en.wikipedia.org/wiki/Satellite_temperature_measurements
“Climate models predict that as the surface warms, so should the global troposphere. Globally, the troposphere should warm about 1.2 times more than the surface; in the tropics, the troposphere should warm about 1.5 times more than the surface.”
There’s also a discussion here at CA
http://www.climateaudit.org/?p=3048
which includes the following: “In this respect, the March 2008 satellite data for the tropics is pretty interesting. The graph below shows UAH (black) and RSS (red) for the tropics ( both divided by 1.2 to synchronize to the surface variations – an adjustment factor that John Christy said to use in an email)”
I could be wrong but I assumed the factor of 1.2 related to the expected amplification of the troposphere relative to the surface. Apart from this there are studies which have cited the 1.2 figure which I’m sure I can dig out if needed (and I could be bothered 🙂 )
This graph appears to (possibly) follow extremes of terrestrial polar motion radius, but I can’t make out the x-axis labeling:
http://icecap.us/images/uploads/ANNUALHIGHS.jpg
Any chance the x-axis text-size can be quadrupled in size? … or perhaps a link to the data?
Philip_B (17:19:22) :
“Indeed the smog, smoke and particulate pollution did block sunlight from reaching the surface. However, the surface temperatures are compiled from just 2 values, minimum temperature and maximum temperatures for the day.”
No it’s not. At least in Germany it never was. I don’t know if there is a difference and if, how big it is, but it should differ in some way. Does anyone know how the mean temperatures are compiled in other countries?
http://www.dwd.de/bvbw/appmanager/bvbw/dwdwwwDesktop?_nfpb=true&_windowLabel=dwdwww_main_book&switchLang=en&_pageLabel=dwdwww_start
I’ve wondered about the effect of smoke and smog. It doesn’t affect just the developing world. In Britain there were various Clean Air Acts in the 50’s/60’s that have been very successful. Now our air is far cleaner. I have seen it stated several times that much of the warming Britain and western Europe enjoyed during the 20th century was due to this environmental legislation. If so, it’s a huge irony that this warming, apparently so feared by the greenies, was caused by successful environmental improvements!
As well as cleaner air allowing more heat to reach the surface, I wonder if there’s another important factor. If there are less particulates in the air, would this reduce the amount of clouds by some amount? And if so, could this mechanism also contribute to the warming?
Chris
Ian George (15:32:57) :
Whoops. Note the temp for 24/9/09 on the UAH satellite site – 40.13F above this time last year. What’s up?
http://discover.itsc.uah.edu/amsutemps/execute.csh?amsutemps+001
It seems that we’ve reached the tipping point. Hansen was right, I suggest give him more funding.
“Most of the observed warming has been in the minimum temperature (about 70%) and reduced smog and smoke would (and probably does) account for the warming from the mid-1970s to the mid-1990s due to clear air legislation in the developing world.”
I have never seen charts showing the separate evolution of both the Tmin and Tmax components of the temperature, always the aggregate. I think that such a chart would be extremely informative since most of the alleged effects of AGW seem to be related to increases in Tmax during the summer, while in fact, the reality is that we have experienced less colder nights during the winter (that increases the average temperature, but it is hardly something to worry about, unless you are the owner of a skying resort).
The AGW scam is based in a number of false claims, specially the following three:
1) CO2 (and other greenhouse gases) are the cause of warming. They are not.
2) The warming experienced is unprecedenteted. It is not.
3) The consequence of warming is more extreme weather. It is not, mostly it has resulted in less extreme weather (since the gradient of temperatures has reduced).
I notice that most of the discussions on this blog and similar ones I follow, are focused on the first two issues, but I think that precisely the third one is the most important, because even if the first two were true, it is the third which makes all the difference.
As an example, there is a diagram in the Technical Summary of the IPCC 4AR (Box TS5. Figure 1 – page 53) that shows how the increase in mean temperature translates into more extreme weather. This is an oversimplyfied analysis, very easy to debunk.
We know from life that clouds overhead cool the surface at thermometer height in the tropics and temperate zones. Never been to polar zones. Do we know that the same applies there?
Manfred says:
In addition to having to integrate over the whole globe, you also have to consider the fact that the satellite data does not measure the temperature at one altitude but rather a weighting of temperatures over a broad range of altitudes.
This updated version sure isn’t much better… Just because land areas have better coverage doesn’t mean they are given more “weight” in the global temperature calculation. In other words, nothing changes the fact that you are applying a UHI correction to ~ 70% of the globe that is covered in oceans. Also, I still don’t see the relevance of the copied and pasted “Sun and Ocean Cycles Versus Temperature” chart that uses US temperatures to make an argument about GLOBAL trends… I’d like to see that same chart, but using a global temperature dataset and a more recent TSI record. The last thing I’ll point out… In your “NOAA USCH V2 – GISS US” plot you use the two most extreme values in the chart to derive your “artificial warming”. If I used that same technique to derive the warming during the last 30 years from the UAH global temp time series http://www.woodfortrees.org/graph/uah, I’d get total warming of about 1.2 degrees C, but of course I would have been “cherry-picking” in the worst way.
Joe D’Aleo says:
So, you seem to be justifying using the UHI correction on the entire global temperature because of the sparser coverage for sea surface temperatures. However, I don’t think that they just average all of the measurements that they have. I think they weight them by the area that they represent. So, if sea surface temperatures are sparser, each sea surface temperature measurement will tend to represent a larger area and will thus get weighted more. (Depending on how they do things, it may be that coastal land stations end up representing more area over the oceans than sea surface temps. end up representing area over land…but even if this were true, I imagine it would not be a huge effect, and thus sea surface temperatures would end up representing a significant part of the global temperature record.)
@ timetochooseagain (16:16:07) :
The main issue as far as I can see is that Joe has applied an “UHI correction” to the entire globe, including the oceans.
@ John Finn (17:04:19) :
This looks to be a case of torturing the data to get a result we like.
Takes the words right out of my mouth. This analysis is awful. If Joseph wanted to impress us with record temps, he should have shown how overnight low temps have trended because that will be a much better indicator of AGW than afternoon highs. Of course, choosing the US and a few international locales does not prove much about global temps.
In science, we are supposed to collect the data and then interperate the results to arrive at a conclusion. Joseph began with the conclusion and worked backwards.
Regarding the topic of UHI, has there been a rebuttal to the following articles (among several others) that show that UHI has not affected trends in temperatures?
http://www.ncdc.noaa.gov/oa/about/response-v2.pdf
http://www.agu.org/pubs/crossref/1999/1998GL900322.shtml
Joe D’Aleo: Regarding your “Sun and Ocean Cycles Versus Temperatures” graph that appears in many of your posts and most recently in the one above.
The LST anomalies of the United States do in fact follow the SST of the U.S. Coastal Waters.
http://s5.tinypic.com/209r3t2.jpg
From my post “SST Anomalies of U.S. Coastal Waters”:
http://bobtisdale.blogspot.com/2009/03/sst-anomalies-of-us-coastal-waters.html
But those are SST anomalies. Your graph, however, uses a curious summing of PDO+AMO. The PDO data is the leading Principal Component of the North Pacific SST anomalies, North of 20N, after Global SST anomalies have been removed. The AMO from the NOAA ESRL is simply detrended North Atlantic SST anomalies. If memory serves me well, to sum the PDO and AMO data, you do standardize the AMO data, but one fact remains: you’re adding the leading PC of the North Pacific SST anomaly residuals and detrended SST anomalies. The PDO reflects a pattern of SST anomalies, while the AMO data from NOAA ESRL does not. In effect, you’re comparing apples to the pattern of the “dimples” on the orange rind.
Also as Adam noted above, the Hoyt and Schatten (1993) data is very much outdated. The problem does not lie in the fact that their data ends in 1978 and that you’ve spliced the ACRIM data to it, though there is debate about the ACRIM data, too. The problem with the Hoyt and Schatten TSI data is the variation in the minimums. Recent studies indicate the major variations in TSI minimum do not exist. Here’s a link to a copy of Hoyt and Schatten (1993):
http://www.countyofkings.com/planning/Plan/Dairy/comment%20PDF's/21-3.pdf
In “IPCC 20th Century Simulations Get a Boost from Outdated Solar Forcings”…
http://bobtisdale.blogspot.com/2009/03/ipcc-20th-century-simulations-get-boost.html
…which was also posted here at WUWT…
http://wattsupwiththat.com/2009/03/05/ipcc-20th-century-simulations-get-a-boost-from-outdated-solar-forcings/
…I illustrated the difference between the Hoyt and Schatten [green curve] and the current understanding of TSI variability as represented by Svalgaard [purple curve] in terms of TSI:
http://s5.tinypic.com/fp6qyp.jpg
And I also illustrated the comparison in terms of their impacts on global temperature:
http://s5.tinypic.com/2mg6rll.jpg
Climatologists hindcast 20th century global temperature for the IPCC using outdated TSI reconstructions to help their models reproduce the rise in global temperature from the 1910s to the 1940s. The Global Warming Art website also uses it in their illustration of “Climate Change Attribution”… http://www.globalwarmingart.com/wiki/Image:Climate_Change_Attribution_png
…which was discussed in this post:
http://bobtisdale.blogspot.com/2009/01/agw-proponents-are-two-faced-when-it.html
Looks like both sides want to massage the data.
Thanks for the clarification Bob. I was about to ask for the source of that TSI reconstruction which peaks suspiciously in 1945 (to match temp?). Curious bit of work…
Can you provide a link or two for the recent studies that suggest minimal variation in TSI? I’m very curious about the mechanism for determining that.
Thanks, Ed
Can anyone direct me to a good resource for historic length of day versus long/lat?
Seems there is plenty of data for global, but I’d like to look at hemispheric data to see if it could be used detect variations in obliquity into and out of the little ice age.
Scott A. Mandia (07:13:21) : “If Joseph wanted to impress us with record temps, he should have shown how overnight low temps have trended because that will be a much better indicator of AGW than afternoon highs.”
Actually, using the Minimum temps would be much worse:
http://pielkeclimatesci.wordpress.com/?s=minimum+temperature&searchsubmit=Find+%C2%BB
With regard to the papers-the second is ancient, but the best recent work on issues with the data is:
http://www.climatesci.org/publications/pdf/R-321.pdf
And, with regard to the talking points memo:
http://pielkeclimatesci.wordpress.com/2009/07/03/roger-a-pielke-sr-comments-on-the-ncdc-talking-point-response-to-the-report-%E2%80%9Cis-the-us-surface-temperature-record-reliable%E2%80%9Dby-anthony-watts/
http://wattsupwiththat.com/2009/06/24/ncdc-writes-ghost-talking-points-rebuttal-to-surfacestations-project/
http://www.climateaudit.org/?p=6370
http://wattsupwiththat.com/2009/07/30/on-climate-comedy-copyrights-and-cinematography/
The last link also contains stuff which isn’t so pertinent. The publications arguing that the warming trends in the surface data are exaggerated are extensive.
I tried to answer many of your questions on this post including why I felt you can use the AMO and PDO like I did here: http://icecap.us/index.php/go/joes-blog/why_we_need_a_new_global_data_set/
Unfortunately we can’t do a more elaborate Hadley/IPCC data evaluation because Phil Jones at Hadley has refused to provide raw data and adjustments made and claims to have lost some of the original data for want of storage capabilities. They only have for some regions the homogenized, modified data. See this post for more: http://icecap.us/index.php/go/political-climate/opinion_the_dog_ate_global_warming1/
The data centers want to treat their information as proprietary to them but unlike public corporations, they are repositories of data for use by educational and research institutions and researchers and are obliged to properly maintain the original data, make it available to anyone who is qualified to work with it, AND document each and every adjustment made and why.
Can you imagine what would happen after an audit or investor review of an annual financial report of any public corporation if they hid key financial information and only supplied carefully processed/manipulated information that showed them in the best possible light. Do we have the ENRON(s) of weather here? All three data centers have a vested financial interest in promulgating global warming. They each have reputations and major funding at risk. In other words, the foxes are in charge of the data chicken coups.
The only independent data sources are RSS and UAH with satellite analysis. They work closely together addressing issues raised and make those adjustments in both directions public. Their work has confirmed the warming of the 1979-1998 period but at about half the rate of the data centers, consistent with that up to 50% contamination in the literature.
In all my talks (I give dozens a year), I try and address all the issues with the surface data bases including those uncovered by Anthony and some of you) and point out the fact that numerous peer review papers estimate artificial warming contamination of 30-50% or even higher. But just stating that seems to not easily resonate with people. It is hard to visualize mentally what that means.
I do show the US data before and after UHI was removed in 2007 and that helps, but I wanted to try and find a way to adjust the IPCC’s Hadley source with a UHI adjustment to show what 30-50% means, that the warmest 10 years were not in the last 12 years, and that the changes are cyclical and can be related to natural factors, and importantly in the end, that EPA regulation, US congressional legislation and any Copenhagen action is unnecessary and unwarranted.
You should take a closer look at the problems
http://icecap.us/images/uploads/US_AND_GLOBAL_TEMP_ISSUES.pdf before you dismiss the brief post and the need to somehow get at the real trends and determine if indeed we have a problem before spending trillions of dollars and destroying the world’s economy and our future.
Great. Hey Mods, that one had a lot of links, could you dig it out of the filter for me? Thanks.
Bob Tisdale (07:47:05) : The AMO’s ambiguous definition has been making me think that there has to be a better way to get whatever signal is there. How come nobody has literally done an identical analysis to that of the PDO in the Atlantic?
Joseph D’Aleo (10:13:05) : My only real problem is that the adjustments suggested here are not in accord with my attempt here:
http://www.climateaudit.org/phpBB3/viewtopic.php?f=3&t=740
But I totally agree that the data, especially in the land surface data sets, exaggerates the warming.
See slides eight and nine:
http://searchanddiscovery.com/documents/2009/110119christy/ndx_christy.pdf
Re: Joseph D’Aleo (10:13:05)
“The only independent data sources are RSS and UAH…”
Okay then… so how do the trends from RSS and UAH compare with the 1980 to present trends in your “adjusted” HadCRUT time series? Wouldn’t that comparison be a very simply way to verify your “analysis” using an independent data source?
Joseph D’Aleo claims, “The only independent data sources are RSS and UAH with satellite analysis. … Their work has confirmed the warming of the 1979-1998 period but at about half the rate of the data centers, consistent with that up to 50% contamination in the literature.”
Not according to the linear trends given for the global mean temperatures for the four (two surface, two satellite) series. For example, RSS from Jan 1979 to Dec 1998 inclusive is shown here (click on ‘raw data’ to see the figures):
http://www.woodfortrees.org/plot/rss/from:1979/to:1999/trend
The other series can be accessed by changing the ‘Data source’ appropriately.
The results are as follows:
Jan 1979 to Dec 1998 global mean linear trend:
HADCRUT3 +0.0153C/year
GISSTEMP +0.0143C/year
UAH +0.0113C/year
RSS +0.0154C/year
So, Mr D’Aleo can you tell us:
Do you accept the above figures?
Do you accept that they in no way support your assertion that the satellite series show warming at “about half the rate” of the surface series? (Indeed the highest rate is given, marginally, by RSS).
If you don’t accept the figures please explain why not and provide your figures and their source.
The idea that we should subtract UHI from the world temperature set is a good one.
I’d like to see it done with just the Hadley surface data instead of the whole set.
Slioch (11:34:39) : That’s idiotic. The satellites are expected to show warming at 1.2 times the surface rate, so the real comparison would then be:
HADCRUT3 +0.0153C/year (divided by 2 is about .008)
UAH/1.2 +0.0094C/year
In other words, with the best satellite data set, the surface trend is much larger than would be expected based on the amplification ratio which is commonly cited. Indeed, it is almost twice as large as it should be.
Hadley, NASA GISS and NOAA GHCN v2 graphic plots all show 0.4 to 0.5C warming for the two decades which is double the satellite
http://www.cru.uea.ac.uk/cru/data/temperature/nhshgl.gif
http://data.giss.nasa.gov/gistemp/graphs/Fig.A2.lrg.gif
http://www.ncdc.noaa.gov/gcag/GCAGdealtem?mon1=1&monb1=1&mone1=1&bye1=1979&eye1=1998&graph=Lineplot&klu=1&dat=BLEND&mon2=00&bye2=00&eye2=00&mon3=00&ye=00¶m=Temperature&proce=80&puzo=0&ts=6&non=1&begX=0&begY=0&endX=71&endY=35&sbeX=0&sbeY=0&senX=71&senY=35
Adam S
Hold the blog. It’s all irrelevant. Temps are still increasing – now 40.24F higher than this time last year.
http://discover.itsc.uah.edu/amsutemps/execute.csh?amsutemps+001
Ed: You asked, “Can you provide a link or two for the recent studies that suggest minimal variation in TSI? I’m very curious about the mechanism for determining that.”
Preminger and Walton (2005) “A New Model of Total Solar Irradiance Based on Sunspot Areas”.
http://www.agu.org/pubs/crossref/2005/2005GL022839.shtml
You’d have to ask Leif for others or you could run through his website here:
http://www.leif.org/research/
Re: Joseph D’Aleo (13:02:02)
“Hadley, NASA GISS and NOAA GHCN v2 graphic plots all show 0.4 to 0.5C warming for the two decades which is double the satellite”
Are you kidding me!? This is mind numbing… Try actually plotting the data yourself.
All four major global temp datasets (including RSS and UAH) have very similar trends for the two decades in question. Maybe your talks don’t “resonate” because you’re feeding people a bunch of BS.
http://www.woodfortrees.org/plot/gistemp/from:1980/to:1999/trend/offset:-0.27/plot/gistemp/from:1980/to:1999/offset:-0.27/plot/rss/from:1980/to:1999/trend/plot/rss/from:1980/to:1999/plot/uah/from:1980/to:1999/trend/plot/uah/from:1980/to:1999
And you still haven’t justified applying UHI corrections to the ~ 70% of the globe that is ocean. Land areas don’t get “weighted” more just because there is better coverage! Mind numbing…
timetochooseagain: You asked, “How come nobody has literally done an identical analysis to that of the PDO in the Atlantic?”
I believe that type of evaluation brings out the ENSO signal in the SST data. Refer to Zhang et al (1997) “ENSO-like Interdecadal Variability: 1900–93”. They were first to calculate the PDO (NP in the paper):
http://www.atmos.washington.edu/~david/zwb1997.pdf
The process they use to calculate the PDO is described in my post “Misunderstandings About The PDO – Revised”. It’s right after Figure 2:
http://bobtisdale.blogspot.com/2009/04/misunderstandings-about-pdo-revised.html
On the other hand, Mestas-Nunez and Enfield (1999) “Rotated Global Modes of Non-ENSO Sea Surface Temperature Variability” were researching the non-ENSO related variability and they calculated the AMO in a complex manner:
http://www.aoml.noaa.gov/phod/docs/gmsst2.pdf
Enfield et al (2004) simplified the process in “The Atlantic Multidecadal Oscillation and its Relationship to Rainfall and River Flows in the Continental U.S.” by detrending the North Atlantic SST anomalies:
http://www.sfwmd.gov/portal/page/portal/pg_grp_sfwmd_hesm/portlet_opsplan_2/portlet_subtab_opsplan_clmvar/tab19738261/enfieldmestas_2001.pdf
The NOAA ESRL data and description are found here:
http://www.cdc.noaa.gov/data/timeseries/AMO/
Note how the ESRL uses a 121-month filter to make the AMO nice and smooth.
The unsmoothed AMO data is here:
http://www.cdc.noaa.gov/data/correlation/amon.us.long.data
BTW, you can even make NINO3.4 SST anomalies show their multidecadal variability with a 121-month filter:
http://i43.tinypic.com/33agh3c.jpg
Joseph D’Aleo
Oh, come on!
Show us the figures! Not poor resolution graphs.
You made the claim that “RSS and UAH” … “confirmed the warming of the 1979-1998 period but at about half the rate of the data centers”
So. Show us the data from RSS and UAH.
Show us the data from “the data centres” – HADCRUT3 and GISSTEMP?
What do you mean by rate? It seems from your reply that you are subtracting the temperature anomaly of 1979 from 1998 [“all show 0.4 to 0.5C warming for the two decades”]. Is that really what you are doing? That is egregious cherry-picking that relies on noise for much of any imagined effect.
But even then, for the period I quoted above, Jan 1979 to Dec 1998, your claim does not hold. The figures are as follows:
UAH from -0.146C to +0.289C, a rise of +0.435C.
RSS from -0.227 to +0.312C, a rise of +0.539C.
Both of which “show 0.4 to 0.5C warming for the two decades” which is what you mention for the NASA GISS and HADLEY. So, even cherry-picking doesn’t support your claim.
Show us:
1. the data for all four (or five including NOAA GHCN v2) series that you used to make your claim.
2. what, precisely, you mean by “rate”. Are you using linear regression? If not, why not? If not, what are you using? And over what time interval, precisely? Define what you mean.
You made the claim. Defend it. So far, you have just asserted, and as far as I can see, wrongly.
Adam (14:42:16) : The only one being mind numbing here is you. Sure the trends are similar, but they aren’t supposed to be that similar! And UAH, which has the best product, is still considerably less in the rate it shows. One more time, this time with feeling, eh?
http://www.woodfortrees.org/plot/hadcrut3gl/from:1979/offset:-.15/mean:12/scale:1.2/plot/uah/mean:12
Slioch (15:31:38) : See my response above.
Re: timetochooseagain (15:56:26)
“Sure the trends are similar, but they aren’t supposed to be that similar!”
Please… I don’t give a crap what you think the trends should or shouldn’t be… I care what the data say the trends are. What should be disturbing to you is that a supposedly reputable meteorologist who goes around giving talks on climate change can’t even compare two temperature time series. Seriously, this ranks with some of the worst posts I’ve ever seen on WUWT, and every time Joe responds it gets even worse. I’m with Slioch… Lets see the graphs Joe! And let me emphasize it again… why in the hell would you apply a UHI correction to the 70% of the globe that is ocean?
Anthony, how can you not be embarrassed by this guest post??
REPLY: How can you not be embarrassed to be a representative of Iowa State University with that sort of language? Clean it up. Read the latest response from Joe. – A
More cracks in the AGW foundation: click
I don’t know what planet you are referring to. This is from the latest UAH Monthly summary
Sept. 8, 2009
Vol. 19, No. 4
For Additional Information:
Dr. John Christy, UAH, (256) 961-7763
john.christy@nsstc.uah.edu
Dr. Roy Spencer, UAH, (256) 961-7960
roy.spencer@nsstc.uah.edu
Global Temperature Report: September 2009
Global climate trend since Nov. 16, 1978: +0.13 C per decade
——————————————
That is 0.26 in the 20 year 1978-1998 period.
The Hadley data includes virtually none of the southern hemisphere ocean and the SH is 80% ocean and is limited in the northern hemisphere. In Hadley’s own words
“For marine regions sea surface temperature (SST) measurements taken on board merchant and some naval vessels are used. As the majority come from the voluntary observing fleet, coverage is reduced away from the main shipping lanes and is minimal over the Southern Oceans.”
Also this plot from Klotzbach showing how NOAA GHCN is departing fram UAH and RSS since 1979
http://icecap.us/images/uploads/NCDC-SAT.jpg
Joel Heinrich (02:46:07) :
GISS, HadCRU and all the other climate averages I am aware of use minimum and maximum temperatures exclusively in determining climate averages.
I’d be surprised if Germany uses a different method for climate averages. Perhaps they may report weather averages using different method?
Joe D’Aleo: You wrote, “As for the argument by some posters or commenters that the AMO and PDO are derived differently and represent patterns not simply magnitudes, let me agree but note that the “patterns” in both oceans with the two phases of the indices are the same.”
Your argument after the word “but” does not address the problem. The AMO does not represent a pattern if you’ve derived it differently from the PDO. If you’ve created the AMO by detrending North Atlantic SST anomalies, the AMO only represents the difference between the SST anomalies of the North Atlantic and the linear trend of those anomalies, nothing more. It is used to show the semi-periodic cycle in the North Atlantic SST anomalies.
The PDO is represented in illustrations as the 1st EOF of the North Pacific SST anomalies, North of 20N. Here’s an illustration of the first EOF of the global ocean dataset performed by Atmoz. The pattern in the North Atlantic you are referring to is suppressed in his illustration:
http://atmoz.org/img/first-eof-world-sst.png
From his webpage here:
http://atmoz.org/blog/2008/08/03/on-the-relationship-between-the-pacific-decadal-oscillation-pdo-and-the-global-average-mean-temperature/
And here’s an illustration of the first EOF of the North Atlantic I created using the KNMI Climate Explorer. It shows a pattern that is similar to that of the PDO:
http://i37.tinypic.com/2rwahqx.jpg
So yes, you’re correct that they show similar patterns.
BUT
That map of the North Atlantic SST pattern was created by an EOF analysis, not by detrending the North Atlantic SST anomalies.
I’m not saying that there is not a cycle in the North Pacific SST anomalies that at times coincides with the AMO. There is one. It can be seen if you detrend North Pacific SST anomalies in the same way you detrend North Atlantic SST anomalies for the AMO. Or you could combine them by detrending the SST anomalies of the global SST anomalies between 20N and 65N. All three are illustrated in the following graph:
http://i34.tinypic.com/2ppfnyf.png
You wrote, “Both warm modes are accompanied by general net global warmth. Cold modes have the opposite configurations and results.”
If the North Atlantic SST anomalies are rising (reflected also by a rising AMO, assuming the rise exceeds the linear trend) then North Atlantic SST anomalies are contributing to a rise in global temperature.
But the same cannot be said about the PDO, since it represents the pattern of SST anomalies in the North Pacific, North of 20N. If the PDO is positive, the SST anomalies in the Eastern North Pacific are positive with respect to the SST anomalies in the Western North Pacific. The warm anomalies in the Eastern North Pacific are contributing to the temperature of the Pacific Northwest of North America, but at the same time, the cooler SST anomalies in the western North Pacific are contributing to the cooling of Northeast Asia. I discussed this in great detail in my post “Revisiting ‘Misunderstandings About The PDO – Revised’” under the heading of “The PDO, In And Of Itself, Does Not Raise And Lower Global Temperature According To Its Phase”:
http://bobtisdale.blogspot.com/2009/05/revisiting-misunderstandings-about-pdo.html
Since the PDO is a lagged aftereffect of ENSO, any rise in global temperatures while the PDO is positive would be attributable to ENSO. You can make NINO3.4 SST anomalies show their multidecadal variability with a 121-month filter:
http://i43.tinypic.com/33agh3c.jpg
When the frequency and magnitude of El Nino events exceed the frequency and magnitude of La Nina events, global SST anomalies rise. And the opposite can be said when the frequency and magnitude of La Nina events exceed the frequency and magnitude of El Nino events. I’ve illustrated this in a number of posts here at WUWT and at my website.
Regards.
Adam,
NOAA pulled out the satellite input into the global sea surface temperature because of ‘complaints’ it had a cold bias. That leaves scattered ship reports. I am told they are making no effort to use any input that may be possible from the 3307 ARGO buoys http://www-hrx.ucsd.edu/www-argo/statusbig.gif, so the 70% ocean data isn’t all you think it is.
Anyway, since the adjustment made is less than 1/3 the contamination Phil Jones found for China (1.3C per century), it is like adjusting for land only. Since NOAA, NASA and Hadley seemed to have lost Canada, Brazil, Africa, parts of Russia, Greenland, the UHI contamination for China is probably not an outlier.
Dr. Oke who won the AMS Landsberg award for his pioneer work on UHI in 2006 or 2007, found that even a town of 1000 could have a UHI of 2.2C. See more on that in this post: http://www.warwickhughes.com/hoyt/uhi.htm
See Phil Jones “Great Leap Forward” here http://www.climateaudit.org/?p=1241 for his flip from 0.05C per century global to 1.3C per century for China for the UHI contamination.
I would also suggest that since Adam has co-authored papers, let Adam come up with a better method to remove the UHI effect from a dataset and present it here for review and try-out. That’s what this is all about. Trying out an idea. If Adam would focus a portion of that energy he puts into derision toward application to the problem he might do something valuable and helpful.
– Anthony
Bob Tisdale
I agree with your argument, didn’t want to detour too far down that path. I agree there is the cumulative warming effects of more El Ninos during +PDO and cooling from La Ninas during -PDO. The warming in +PDO and El Ninos is net- there are cool pockets – e.g the southeast US just as there are regional warm pockets in La Nina (again southeast US).
The warm tripole of the +AMO corrrelates with general warmth across most NH continents on an annual basis, though can be offset by -PDO. When they are both negative – we are at our coldest, both positive, warmest.
Regards
——————
Adam, Slioch, et al
This quote from the secind link above
“What constitutes an urban site versus a rural site? Peterson and others who support the IPCC viewpoint consider a town with a population of less than 10,000 people to be rural and not to require any adjustment for urbanization. Nothing could be further from the truth.
Oke (1973) and Torok et al (2001) show that even towns with populations of 1000 people have urban heating of about 2.2 C compared to the nearby rural countryside. Since the UHI increases as the logarithm of the population or as about 0.73 log (pop), a village with a population of 10 has an urban warming of 0.73 C, a village with 100 has a warming of 1.46 C, a town with a population of 1000 people already has an urban warming of 2.2 C, and a large city with a million people has a warming of 4.4 C (Oke, 1973).
Try this thought experiment: In 1900, world population is 1 billion and in 2000, it is 6 billion for an increase of a factor of six. If the surface measuring stations are randomly distributed and respond to this population increase, it would equal 2.2 log (6) or 1.7 C, a number already greater than the observed warming of 0.6 C. If however we note that UHIs occur only on land or 29% of the Earth’s surface, than the net global warming would be 0.29*1.7 or 0.49 C which is close the observed warming. It is not out of the realm of possibility that most of the twentieth century warming was urban heat islands.”
As for the
Anthony, Joseph D’Aleo has published a follow-up article:
ep 27, 2009
Why We Need a New Global Data Set
By Joseph D’Aleo, CCM, AMS Fellow
I believe this is a more accurate (though still not perfect) plot of global temperatures than those produced by Hadley, NOAA, and NASA GISS. It combines data from all three centers, using data from two (NOAA and GISS) to adjust the third (Hadley)
http://www.icecap.us
Yes Adam, I would like to see what you come up with.
Slioch
See the difference NOAA GHCN vs UAH and RSS
http://icecap.us/images/uploads/NCDC-SAT.jpg
Re: Joseph D’Aleo (17:19:16):
“… That is 0.26 in the 20 year 1978-1998 period…”
Okay… now we are getting somewhere. So, you are taking the linear trend from 1978 to present and applying to the years 1978 – 1998. When you use that same method for GISS and Hadcrut you get 0.32, nowhere near twice the warming that you claim. Plus, if you use RSS, you get 0.30. So maybe you ought to rescind that “twice as much warming” claim. Its not even close.
“…since the adjustment made is less than 1/3 the contamination Phil Jones found for China (1.3C per century), it is like adjusting for land only.”
Are you even being serious? You are severely abusing the results of the Jones et al study whose main findings can be summarized as:
– London and Vienna had no UHI signal in temperature trends because the influence of the cities weren’t changing over time.
– Only very small UHI effects were implied for “land-based” datasets in China.
– Urban related warming was 0.1 degree C per decade from 1951–2004.
So, based on those results, you think its logical to argue that land areas of the ENTIRE GLOBE have been impacted the same way URBAN areas of China have? Plus, you extrapolated the 50 years Jones examined to an entire century… how is that justified?
Re: wattsupwiththat (18:02:25)
“I would also suggest that since Adam has co-authored papers, let Adam come up with a better method to remove the UHI effect from a dataset and present it here for review and try-out.”
Sorry, not my area, but plenty of others have used various methods to examine UHI impact on global temp records. Most have found results contradicting those here. So, Anthony, do I get extra credit for not being anonymous (involuntarily)?
REPLY: No you get extra credit when you contribute something besides sniping. – A
Adam (19:18:15) : “You are severely abusing the results of the Jones et al study whose main findings can be summarized as:
– London and Vienna had no UHI signal in temperature trends because the influence of the cities weren’t changing over time.
– Only very small UHI effects were implied for “land-based” datasets in China.
– Urban related warming was 0.1 degree C per decade from 1951–2004. ”
This is grossly misleading 1. No idea why Vienna and London are thrown in (hey, small, unrepresentative sample much?) 2. The warming over China in the entire period before urban warming is taken out is equivalent to .26 degrees C per decade-in line with the supposed global land trend:
http://hadobs.metoffice.com/crutem3/diagnostics/global/nh+sh
3. The after the fact land trend is .16 degrees per decade, which is much less, and is less than the global rate.
4. That’s especially problematic because that part of the world should be warming faster:
Ramanathan, V., M.V. Ramana, G. Roberts, D. Kim, C. Corrigan, C. Chung, and D. Winker, 2007. Warming trends in Asia amplified by brown cloud solar absorption. Nature, 448, 575-578.
The Jones et al. paper is quite frankly devastating.
Joe D’Aleo says:
That graph, which is from your own website, looks incorrect to me. Can you explain how it jives with graphs we can make using WoodForTrees, like this (which is like Adam’s except extended up through the current year): http://www.woodfortrees.org/plot/gistemp/from:1980/to:2009/trend/offset:-0.27/plot/gistemp/from:1980/to:2009/offset:-0.27/plot/rss/from:1980/to:2009/trend/plot/rss/from:1980/to:2009/plot/uah/from:1980/to:2009/trend/plot/uah/from:1980/to:2009
Your graph also makes no sense, as it shows the total deviation between NCDC and the satellite record over that period to be equal to about what the total rise in the NCDC record was! That implies that the satellite record didn’t rise over the period, when in fact even UAH rose at 0.13 C/decade, only modestly less than the NCDC rose. Frankly, I think you screwed up the graph.
By the way, while we have your ear, could I ask you something that has been troubling me for a while: Why do you produce plots (like this: http://icecap.us/images/uploads/MSUCRUCO2.jpg or http://icecap.us/images/uploads/CO2MSU.jpg ) that compare temperature and CO2 on the same graph in such a way that you would expect the temperature trend to have the same slope as the CO2 rise if the transient climate response were about 8 or 9 C / doubling? If you wanted to make a comparison to the IPCC expectations, wouldn’t it make sense to use a scale where the transient climate sensitivity were about 1/4 of that…Or, do you worry that on such a graph it would be much more obvious that, given the obviously large temperature fluctuations, one can’t really say whether or not the temperature trend is following the expectations over such a short time period?
@ timetochooseagain (10:12:23) :
I do not question the results of the paper that Pielke refers to but I do question his statement:
Thus minimum temperatures over land are very sensitive to their immediate local environments. Their use to characterize minimum temperatures as being spatially representative over a larger area, such as used to diagnose global warming and cooling, are not appropriate.
I undertand how actual recorded local temperatures may be influenced, but why would the TREND in temperature at these places be affected?
It is not as if these locations are moving? Overnight temps are rising faster than afternoon temps which is not a surprise because of the convective nature of the afternoon boundary layer. Overnight layers may not be as stable as we have believed but they are certainly more stable than daytime layers. Pielke himself states this in the second link you provided:
In addition to the thermodynamic stability and wind speed, the nocturnal boundary layer is sensitive to changes in land surface characteristics, such as heat capacity [Carlson, 1986; McNider et al., 1995a]. Additionally, it is also much more sensitive to external forcing such as downward longwave radiation from greenhouse gas forcing, water vapor, clouds, or aerosols than is the daytime boundary layer [Eastman et al., 2001; Pielke and Matsui, 2005]. The main reason for this sensitivity is that the nocturnal boundary layer is shallower than the daytime boundary layer. Thus heating of the surface due to infrared radiation or changes in heat capacity or conductivity of heat from the soil is distributed through a smaller air layer.
Then Pielke summarizes with:
In summary, given the lack of observational robustness of minimum temperatures, the fact that the shallow nocturnal boundary layer does not reflect the heat content of the deeper atmosphere, and problems global models have in replicating nocturnal boundary layers, it is suggested that measures of large-scale climate change should only use maximum temperature trends.
Of course, then it means less AGW in that specific respect because max temps have warmed at a much lower rate. Strange that Pielke states how overnight temps are more strongly influenced by downwelling greenhouse gas IR so why would he remove a component that he is trying to measure?!
I still cannot see how TREND would be affected if one considers all of Pielke’s “problems”. For example, wouldn’t wind speed changes average out over the long haul so that the underlying trend sticks out?
Regarding the Peterson paper from 1998 as being ancient. Are we not looking at historical data and not just newer data? So what if we are looking at 1950 to 1990? Comparison between temperature trends of the full corrected land data set used in global temperature trend analysis and a subset of rural stations suggests there is very little residual effect of urbanization remaining in the data. And more importantly, this study was done well before surfacestations.org and parallels the NOAA analysis that some here think is a conspiracy.
Here are some newer articles that also refute Pielke’s claims:
http://hadobs.metoffice.com/urban/Parker_JClimate2006.pdf
Conclusion: The analysis of Tmin demonstrates that neither urbanization nor other local instrumental or thermal effects have systematically exaggerated the observed global warming trends in Tmin.
http://ams.allenpress.com/perlserv/?request=get-abstract&doi=10.1175%2FJCLI3431.1
Conclusion: comparison between U.S. Historical Climatology Network (HCN) time series from the full dataset and a subset excluding the high population sites indicated that the UHI contamination from the high population stations accounted for very little of the recent warming.
And of course IPCC WGI 2007 at http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter3.pdf
Conclusion: Urban heat island effects are real but local, and have not biased the large-scale trends. A number of recent studies indicate that effects of urbanisation and land use change on the land-based temperature record are negligible (0.006ºC per decade) as far as hemispheric- and continental-scale averages are concerned because the very real but local effects are avoided or accounted for in the data sets used.
So far the data suggests that “the rising tide lifted all boats” which seems common sense to me.
Thanks for this reference, Smokey. Check out the Calgary Herald, folks.
Quotes from it:
[In reference to Latif]: “They failed to observe the current cooling for years after it had begun, how then can their predictions for the resumption of dangerous warming be trusted? My point is they cannot. It’s true the supercomputer models Latif and other modellers rely on for their dire predictions are becoming more accurate. But getting the future correct is far trickier. Chances are some unforeseen future changes will throw the current predictions out of whack long before the forecast resumption of warming.”
Chris
Norfolk, VA, USA
By the way, another way of looking at my last point without getting into the transient climate sensitivity is simply this: If you look at what the rise in CO2 in those ICECAP plots corresponds to on the temperature axis, you find that it is about 0.63 to 0.7 C per decade. By contrast, the IPCC AR1 WG1 Summary for Policymakers (SPM), available here http://www.ipcc.ch/ipccreports/ar4-wg1.htm states (on p. 12), “For the next two decades, a warming of about 0.2°C per decade is projected for a range of SRES emission scenarios.”
Philip_B (17:32:47) :
Joel Heinrich (02:46:07) :
GISS, HadCRU and all the other climate averages I am aware of use minimum and maximum temperatures exclusively in determining climate averages.
I’d be surprised if Germany uses a different method for climate averages. Perhaps they may report weather averages using different method?
Average hourly data?
Finally somebody dared to do this.
My observation is, if you combine HadCRUT till 1978 with MSUAH since 1978, it fits excellently with HadSST dataset. Usual explanation of difference between trends in SST and land stations was the ocean eats the warming while land has not, but UHI is much better explanation – satellite data show no significant differences in tropospheric temperatures above oceans or land. So, SST are also a good global temperature proxy. One thing still remains – removing the sharp step down in SST in 1945 caused by improper taking on sampling techniques.
Joseph D’Aleo (17:19:16) :
said, “… This is from the latest UAH Monthly summary
Sept. 8, 2009
Vol. 19, No. 4
Global Temperature Report: September 2009
Global climate trend since Nov. 16, 1978: +0.13 C per decade
——————————————
That is 0.26 in the 20 year 1978-1998 period.”
Earlier you stated, “Hadley, NASA GISS and NOAA GHCN v2 graphic plots all show 0.4 to 0.5C warming for the two decades which is double the satellite”
So, you are comparing that 0.26C obtained from the linear trend for UAH with the beginning and end points of the 1979-1998 graphs of HADCRUT3 and GISSTEMP.
You also say, “I don’t know what planet you are referring to”, so I’ll tell you. It’s a planet called Earth, which grows things called apples and oranges, which anyone with the remotest competence and honesty knows should not be compared.
You have used the linear trend for the UAH (that +0.13 C per decade). Good. So let us use the linear trend for the other series also. I previously gave the linear trends from 1979 to 1988 inclusive, since that was the period to which you referred.
Here they are again:
Jan 1979 to Dec 1998 global mean linear trend:
HADCRUT3 +0.0153C/year
GISTEMP +0.0143C/year
UAH +0.0113C/year
RSS +0.0154C/year
But if you want to use the linear trend from Dec 1978 to the present for the same four series, then here they are:
HADCRUT3 +0.0158C/year
GISTEMP +0.0159C/year
UAH +0.0124C/year
RSS +0.0153C/year
(see: http://www.woodfortrees.org/plot/hadcrut3gl/from:1978.92/trend/plot/gistemp/from:1978.92/trend/plot/uah/from:1978.92/trend/plot/rss/from:1978.92/trend ).
Neither time spans support your claim. It is false.
Incidentally, Joseph D’Aleo’s original article stated, “No one disputes the cyclical warming from 1979 to 1998” which implies the frequent false meme that “global warming stopped in 1998”.
If that were the case why is it that the linear trends are generally higher from c. 1979 to the present than to 1998? (including UAH, but not RSS which is almost the same). (see my previous post for the data).
If the linear trend from 1979 to the present is greater than the linear trend to 1998, even for UAH, who will stand up and say, “global warming stopped in 1998”?
Scott A. Mandia (20:12:49) : It’s called landuse change. I tell you what, why don’t you ask Roger himself your questions and point him to those papers. I could be wrong but I would bet you that he has seen them and is not terribly impressed.
But for you, some reading:
http://wattsupwiththat.files.wordpress.com/2009/07/2009_christynm_eafrica.pdf
http://ams.allenpress.com/perlserv/?request=get-abstract&doi=10.1175%2FJCLI3627.1
Christy, J.R., 2002: When was the hottest summer? A State Climatologist struggles for an answer. Bull. Amer. Met. Soc. 83, 723-734.
The conclusion of all of these studies is that in all three locations the Minimum trends are unrepresentative of the true temperature trends due to land use changes, the selection of stations to include in the records introduces a warm bias, and that the official data sets are significantly erroneous.
Slioch (01:47:07) : You seem to be adept at calculating trends (although you apparently aren’t capable of understanding the statistical oddity you’ve just described which is just that, an oddity) so I’ll tell you what, why don’t you calculate the trend in the last 144 months (twelve years!) and tell us what the values are? And tell us, then, how a negative trend can equal continued warming.
Adam is just being dense when he says “I don’t care what you think they are supposed to be” it’s not what I think it’s the climate models, and the data itself. Trends aloft should be greater than at the surface.
I grow weary of having to repeat this again and again, but what you and now Shore are doing is totally inappropriate. This is the correct comparison:
http://www.woodfortrees.org/plot/hadcrut3gl/from:1979/offset:-.15/mean:12/scale:1.2/plot/uah/mean:12/plot/hadcrut3gl/from:1979/offset:-.15/mean:12/scale:1.2/trend/plot/uah/mean:12/trend
You are all growing increasingly tedious.
@ Andrew (06:44:03) :
Thanks for the links. I agree that land use does affect local temperatures and the links you provide show how land use does in fact affect T and Tmin at those single sites.
However, if land use change were significant at a large percentage of sites, then the data presented in the links I provided would likely not have yielded the results they did. If so, that would imply an urbanization trend that is the same as the land use trend. Seems like quite a coincidence.
Are you aware of any studies that measure temperature trends in areas that are rural and have exhibited no land use change? That would be instructive.
Scott A. Mandia (07:55:15) : The problem is that most weather stations are located where people live, so that they are around for continual observation. So I’m not sure if what you are suggesting is even possible.
Andrew (06:44:03) :
Adam is just being dense when he says “I don’t care what you think they are supposed to be” it’s not what I think it’s the climate models, and the data itself. Trends aloft should be greater than at the surface.
I grow weary of having to repeat this again and again, but what you and now Shore are doing is totally inappropriate. This is the correct comparison:
http://www.woodfortrees.org/plot/hadcrut3gl/from:1979/offset:-.15/mean:12/scale:1.2/plot/uah/mean:12/plot/hadcrut3gl/from:1979/offset:-.15/mean:12/scale:1.2/trend/plot/uah/mean:12/trend
Why is it? Adam is right. The trends are what they are. If you want to argue that the the data doesn’t agree with the models – then fine – but that’s a totally separate issue.
Andrew says:
It is not clear to me how resilient that expected 1.2 amplification factor is. And, of course, there are some reasonably substantial errorbars on the trends, especially for the satellite data. Furthermore, I am not sure why you have chosen to plot just the UAH satellite analysis and not RSS also. In fact, I think there are now 4 different analyses for temperatures on the global scale (the third and fourth being from U of Md and U of Washington if I remember correctly) and I believe it is UAH which has the lowest trend.
John Finn (12:15:56) : Except that if you go over to RC, they will tell you-beat it into your head in fact-that warming simply could not occur without being amplified aloft, whatever the cause:
http://www.realclimate.org/index.php/archives/2007/12/tropical-troposphere-trends/
And not only is the 1.2 ratio what is generally found in models, it is also found in the residuals of the detrended observations ~the same:
http://www.climateaudit.org/phpBB3/viewtopic.php?f=3&t=740
Because it isn’t (ACCORDING TO RC) something the models have wrong which causes this, it’s the nature of the way the system is supposed to behave.
Joel Shore (13:34:16) : Ditto, and RSS is not shown, UMD is not shown, UW is not shown, because those analyses are erroneous (the latter two almost certainly so, but the case against RSS is pretty good to.).
Christy, J. R., and W. B. Norris (2006), Satellite and VIZ-radiosonde intercomparisons for diagnosis of non-climatic influences, J. Atmos. Oceanic Tech., 23, 1181-1194.
Christy, J. R. and W. B. Norris (2009), Discontinuity issues with radiosonde and satellite temperatures in the Australian region: 1979-2006, J. Atmos. Oc. Tech., 25, doi: 10.1175/2008JTECHA1126.1.
Christy, J. R., W. B. Norris, R. W. Spencer, and J. J. Hnilo (2007), Tropospheric temperature change since 1979 from tropical radiosonde and satellite measurements, J. Geophys. Res., 112, D06102. doi: 10.1029/2005JD006881.
Randall, R. M., and B. M. Herman (2008), Using limited time period trends as a means to determine attribution of discrepancies in microwave sounding unit-derived tropospheric temperature time series, J. Geophys. Res., 113, D05105, doi:10.1029/2007JD008864.
But even if you compare surface temperatures to RSS it is still clear that the surface measures have a warm bias:
http://www.woodfortrees.org/plot/hadcrut3gl/from:1979/offset:-.15/mean:12/scale:1.2/plot/uah/mean:12/plot/hadcrut3gl/from:1979/offset:-.15/mean:12/scale:1.2/trend/plot/uah/mean:12/trend/plot/rss/mean:12/plot/rss/mean:12/trend
Again, the evidence that the surface data has a warm bias is extensive. And yet the resistance…the ghost of Kuhn is slowly nodding his head saying “Yup”.
Andrew,
UAH lists their errorbars as +/- 0.05 C/decade (see http://vortex.nsstc.uah.edu/data/msu/t2lt/readme.18Jul2009 , under “Update 7 Aug 2005 *”), at least as of 2003. I don’t know what RSS and HADCRUT list their errorbars as. But, I just wonder if you are taking the data beyond where it can be trusted.
And, by the way, despite John Christy’s papers (and one paper that appears to be written by some independent folks), I don’t think the final word has yet been written on which satellite analysis trends are the most accurate.
Andrew (16:04:13) :
John Finn (12:15:56) : Except that if you go over to RC, they will tell you-beat it into your head in fact-that warming simply could not occur without being amplified aloft, whatever the cause .
I’m not here to defend RC, but my interpretation of their article is that they do expect amlification to occur. They do not say that is happening. In any case you appear to be saying there are 2 wrongs here, i.e. temperature discrepancy and tropospheric hot spot. If the ‘hot spot’ theory is wrong – then there is no discrepancy and vice versa.
Incidentally most of the disagreement between UAH and the others appears to originate in the pre-1992 period. Roy Spencer has commented on the RSS/UAH disagreement on his blog. The trends for all 4 records since ~1992 are remarkably similar. Here’s Joel’s plot using 1992 as a start point (and a slightly different offset)
http://www.woodfortrees.org/plot/gistemp/from:1992/to:2009/trend/offset:-0.28/plot/gistemp/from:1992/to:2009/offset:-0.28/plot/rss/from:1992/to:2009/trend/offset:-0.05/plot/rss/from:1992/to:2009/offset:-0.05/plot/uah/from:1992/to:2009/trend/plot/uah/from:1992
The very slightly higher warming trend in the GISS record is probably due to their arctic extrapolation. The arctic has been warmer over the past few years.
Joel Shore (18:14:39) : eh, whatever.
John Finn (02:48:58) : That trend line is 1. starting right in the middle of Pinatubo, and the amplification factor for volcanoes appears to be even greater than 1.2 (see the plot I showed above) and 2. Right around the time of RSS’s step change from the transition between NOAA 12 and 11 and 10.
timetochooseagain (16:16:07) : The main issue as far as I can see is that Joe has applied an “UHI correction” to the entire globe, including the oceans.
This is not significantly different from what GIStemp does. It UHI adjusts the data (badly, IMHO) and then uses these UHI data to fabricate “boxes and grids” of temperatures over the ocean up to 1200 km from the source thermometer.
That means, for example, that the Military Base at Diego Garcia warms an area the size of 1/2 the United States, and it is almost 100% ocean.
So goose, meet gander.
timetochooseagain (16:16:07) : The main issue as far as I can see is that Joe has applied an “UHI correction” to the entire globe, including the oceans.
This is not significantly different from what GIStemp does. It UHI adjusts the data (badly, IMHO) and then uses these UHI data to fabricate “boxes and grids” of temperatures over the ocean up to 1200 km from the source thermometer.
That means, for example, that the Military Base at Diego Garcia warms an area the size of 1/2 the United States, and it is almost 100% ocean.
So goose, meet gander.
Joel Shore (17:59:07) :
Layne Blanchard says:
“Your point begs the question- Since continental land masses are a small portion of the surface, for periods preceding the satellite record, and periods following it where the satellite record is not exclusively used, are not those land mass records then the basis from which vast ocean surface temps must have been calculated?”
No…I believe that most of the ocean temperature data are from sea surface temperatures measured by ships.
Nope. You have a simplistic and wrong understanding of the process.
GIStemp uses only LAND data up through STEP3 (all the anomaly, boxing, gridding, etc. steps). Only in the optional STEP4_5 does it allow you to bring in a Hadley SST anomaly map (not actual temperatures) and that is based on a variety of things including some completely made up re-imagined data. But since Hadley raw data have become “The Dogs Lunch”, it is rather unclear what value it might add.
http://chiefio.wordpress.com/2009/09/08/gistemp-islands-in-the-sun/
FWIW, STEP4 is pretty ugly:
http://chiefio.wordpress.com/2009/03/07/gistemp-step4-the-process/
Luckily, the STEP3 script makes it clear that it is optional. From:
http://chiefio.wordpress.com/2009/03/07/gistemp-step3-the-process/
echo ; echo “If you don’t want to use ocean data, you may stop at this point”
echo “You may use the utilities provided on our web site to create maps etc”
echo “using to_next_step/SBBX1880.Ts.${label}.$rad as input file” ; echo
echo “In order to combine this with ocean data, proceed as follows:”
echo “move SBBX1880.Ts.${label}.$rad from STEP3/to_next_step to STEP4_5/input_files/.”
echo “create/update the SST-file SBBX.HadR2 and move it to STEP4_5/input_files/.”
echo “You may use do_comb_step4.sh to update an existing SBBX.HadR2 file”
echo “You may use do_comb_step5.sh to create the temperature anomaly tables”
echo “that are based on land and ocean data”
And what sea data is used is hideously mangled by the time it gets to GIStemp as an “anomaly data food product”:
http://chiefio.wordpress.com/2009/02/28/hansen-global-surface-air-temps-1995/
Inclusion of Marine Temperatures
This says that they have had uncertainty in global temperatures due to poor spatial sampling. That is, they don’t cover oceans well.. Add in ships and bouys data and it gets better “but in situ data introduce other errors”. Then they go on to say satellites provide better total surface coverage, but limited time coverage and “The satellite data provide high resolution while the in situ data provide bias correction.” OK, which is it: “introduce other errors” or “provide bias correction”? Please explain how such an error prone data set can be used to correct a new high tech satellite series? This just smells like a cover up of a “Data Food Product Homoginizing Process” coming.
Yup, next paragraph. They talk about “Empirical Orthogonal Functions” used to fill in some South Pacific data… but it uses “Optimal Interpolations” which sure sounds like they are just cooking each datapoint independently… From here on out when they use EOF data they are talking about this synthetic data. It also looks like they use 1982-1993 base years to create the offsets that are used to cook the data for 1950-81. Wonder if any major ocean patterns were different in those two time periods, and just what surface (ship / bouy) readings were used to make the Sea Surface Temp reconstructions? They do say “The SST field reconstructed from these spatial and temporal modes is confined to 59 deg. N – 45 deg S because of limited in situ data at higher latitudes.” OK, got it. You are making up data based on what you hope are decent guesses. But in GIStemp “nearby” can be 1000km away with no consideration for climate differences, so I’m concerned that the same quality of care is being given here.
In short, no real sea data need apply to the GIStemp temperature series. Only a fabricated interpolated filled in sparse modified composite splice anomaly map is used.
Yeah, it’s that good…
Oh, and there is a bit more at:
http://chiefio.wordpress.com/2009/03/05/illudium/
Here is a short quote:
http://www.emc.ncep.noaa.gov/research/cmb/sst_analysis/
Analysis Description and Recent Reanalysis
“The optimum interpolation (OI) sea surface temperature (SST) analysis is produced weekly on a one-degree grid. The analysis uses in situ and satellite SSTs plus SSTs simulated by sea ice cover.”
So here are your first clues. It’s an “analysis” not a reporting of satellite data. It uses “in situ”, that is surface reports from ships, buoys, etc.; along with satellite Sea Surface Temperatures and, my favorite, SSTs simulated by sea ice cover. Given the recent “issues” with sea ice reporting it kinda make you wonder…
So, ok, a stew of ships, buoys, whatever, a dash of satellite data, and some simulations (based on a broken ice cover satellite?) are used to create this analysis product (that some folks want to call “satellite data”…)
“Before the analysis is computed, the satellite data is adjusted for biases using the method of Reynolds (1988) and Reynolds and Marsico (1993). A description of the OI analysis can be found in Reynolds and Smith (1994). The bias correction improves the large scale accuracy of the OI.”
Oh, and the satellite data are adjusted based on an optimal interpolation method. We’re getting even further away from “data” and into the land of processed data food product…
So your “Ocean Temperatures” are not quite temperatures and didn’t quite come from thermometers. These are then BLENDED in with all those boxes and grids made up in STEP3 of GIStemp that are largely based on AIRPORTS on islands. There are dozens of Islands in the Sun, each with an airport warming an area of ocean about 1/2 the size of the continental USA. To the extent a ship wanders into that box, it might contribute a bit to the “average of averages”, but good luck figuring out how much. At this point both anomaly maps are way past the average-the-temperature steps and all you have is to anomaly maps to merge. One data point each per box.
GIStemp is all about smearing airport tarmac into the countryside and oceans.
http://chiefio.wordpress.com/2009/08/26/agw-gistemp-measure-jet-age-airport-growth/
And it’s UHI method is broken too:
http://chiefio.wordpress.com/2009/08/30/gistemp-a-slice-of-pisa/
Adam (05:56:25) : This updated version sure isn’t much better… Just because land areas have better coverage doesn’t mean they are given more “weight” in the global temperature calculation. In other words, nothing changes the fact that you are applying a UHI correction to ~ 70% of the globe that is covered in oceans.
As pointed out in the above links, the “Sea” temperatures in GIStemp are UHI adjusted LAND temperatures that are smeared out over 1200 km radius boxes of OCEAN surface. Then, and only then, may the Hadley SST anomaly map be merged in to fill in any missing bits.
IMHO, the application of that UHI error bias correlation in this analysis is sound in direct proportion to the degree to which GIStemp (and one presumes Hadley, if only they would tell us what their method might be…) does in fact spread UHI land data over the sea.
Basically, you have no sea temperatures to work with only “anomaly boxes” based on land temps from STEP3 with a fudge applied from some simulated interpolated spliced corrected anomaly maps (in optional STEP4_5) for any boxes not already fabricated from the UHI land data in STEP3.
And that, boys and girls, is why “reasoning from what ought to be reasonable” is useless with this AGW game of “hide the data”. You MUST look at the code and see just what shenanigans were really being pulled.
And that is not pretty.
E.M.Smith (10:32:11) : AFAIK the SST is calculated entirely separately-that it has nothing to do with the GISS land analysis-it comes from:
1880-11/1981: Hadley HadISST1 (Rayner 2000),
12/1981-present: Reynolds-Rayner-Smith (2001)
Are you sure about that claim?
Look at these maps:
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2009&month_last=08&sat=-1&sst=1&type=trends&mean_gen=0112&year1=1979&year2=2008&base1=1951&base2=1980&radius=1200&pol=reg
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2009&month_last=08&sat=4&sst=0&type=trends&mean_gen=0112&year1=1979&year2=2008&base1=1951&base2=1980&radius=1200&pol=reg
http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2009&month_last=08&sat=4&sst=1&type=trends&mean_gen=0112&year1=1979&year2=2008&base1=1951&base2=1980&radius=1200&pol=reg
Does it look like the combined land and sea surface looks more in the over lap areas in the land only map or the sea surface only? Note the strong “land” warming in the South Atlantic, which isn’t present in the sea surface only…and isn’t present in the combined either. I suspect that the land surface may be an issue closer to shore, but I don’t see the issue you imply showing up.
Andrew (05:56:52) :
Joel Shore (18:14:39) : eh, whatever.
John Finn (02:48:58) : That trend line is 1. starting right in the middle of Pinatubo, and the amplification factor for volcanoes appears to be even greater than 1.2 (see the plot I showed above) and 2. Right around the time of RSS’s step change from the transition between NOAA 12 and 11 and 10.
I’m not sure I understand your points. I used 1992 because off the discrepancy between RSS and UAH before then. What ever the reason for the discrepancy it no longer seems to exist.
Can we agree that since 1992 RSS and UAH now agree:
http://www.woodfortrees.org/plot/rss/from:1992/to:2009/trend/offset:-0.05/plot/rss/from:1992/to:2009/offset:-0.05/plot/uah/from:1992/to:2009/trend/plot/uah/from:1992
It is also the case that, since 1992, Hadley and GISS are very much in agreement with the 2 satellite records.
Perhaps I’m doing you a disservice but your argument seems to hinge on the tropospheric amplification factor (1.2) being exactly the same as the UHI trend in the surface records.
John Finn (15:15:37) : “Can we agree that since 1992 RSS and UAH now agree” No because the step change in RSS relative to UAH is a serious issue that you are trying to gloss over. RSS Shifted up relative to UAH. Yes, there trends are similar afterward, so what?
The rest of your argument appears to amount to “Such a coincidence seems to unlikely to be true”. You might enjoy an anecdote from Richard Feynman:
“Feynman walks into a class late, and announces to the class that he has encountered something astounding. While walking through a parking lot, he saw a car with the plate number 186CSC. What, he asks the class, do they think the odds are of seeing that precise number?”
More seriously, again unless you account for the fact that the response to volcanic eruptions is DEFINITELY amplified in the LT relative to the surface, OF COURSE you will get a higher satellite trend starting from right after one which diverges less from the surface record.
Now, mind telling me why the response to volcanoes, El Ninos, etc. is clearly amplified and the long term trend isn’t? I’ll tell you it seems clear to me that the best explanation of this difference is that the long term trends in one or more datasets is WRONG.
And for the love of god, why are all issues with the surface data conflated with UHI? Siting (hey, look where we are!) is not UHI, Paint is not UHI (Hey, remember that Anthony? Shame that got derailed by the sorry state of the stations as far as siting goes.), Land Use is not always UHI, lack of coverage in certain areas is not UHI, odd limited selection of stations is not UHI (and this issue has been shown to exaggerate warming in Alabama, California, East Africa, by Christy, who inevitably found many temperature records that weren’t included in the official data sets, usually because they weren’t digitized), the HO-83 hygrothermometer is not UHI….
Andrew (14:37:16) :
E.M.Smith (10:32:11) : AFAIK the SST is calculated entirely separately-that it has nothing to do with the GISS land analysis-it comes from:
1880-11/1981: Hadley HadISST1 (Rayner 2000),
12/1981-present: Reynolds-Rayner-Smith (2001)
Are you sure about that claim?
Absolutely.
Read The Code.
Published papers are fine to get an idea what someone claims or maybe even what they intended but the only thing that actually does anything is the code.
And the code says “ALL LAND ALL THE TIME THROUGH STEP3”. (Which step quite definitely creates fictional sea temperatures in boxes of ocean near islands. See the links I gave and you can see the log file output showing the particular ocean boxes created from particular LAND stations on islands.)
It then says “Optionally, you MAY add SST data via the HadSBBX data from Hadley”.
I have no idea what goes into the links you gave, I do know exactly what GIStemp does. It is also possible that the sea temps you are looking at are via the Hadley file merger in STEP4_5, but that just puts us back at asking what Hadley’s dog did with their homework…