Unadjusted data of long period stations in GISS show a virtually flat century scale trend

A fine extraction of genuine information from an egregiously abused data set. The retro-chilling of old records and The Great Dying of The Thermometers (actually about 1990, they went from ~6000 to ~1600) are so brazen as to defy belief.

Unadjusted data???? Are you crazy? Don’t you go and challenge Dr. Jones et al now. They run this game, sonny, and you’d better get used to it.

To the untrained eye this looks to have two warming cooling cycles in it. The first in the early to mid 20th century, which corresponds with the well attested long hot summer that made the Battle of Britain possible and the second in the late part of the 20th Century which corresponds with the well undertood warming cycle that began in approximately 1974/76 following the 1974 La Nina with a step change in global rainfall.
Just a word of warning though – last week this website was going off the deep end at BEST for using a non-standard period for assessing climate change. Whilst this assessment is useful, it is important that the limitations of the data to fully inform the current debate are fully understood

@Michael Palmer
“At some point they are going to run out of tricks to create a warming signal”
I appreciate very much that you just put it as it is. We sceptics sometimes want to sound extremely nuanced etc. simply to be taken serious, and thus, when someone just say the truth we all know just like that, its a great relief.
Here RUTI (Rural Unadjusted Temperature Index) versus BEST global land trend:
http://hidethedecline.eu/media/ARUTI/GlobalTrends/Est1Global/fig1c.jpg
The ONLY difference between the 2 datasets happens in the years 1950-78 (just before satellite data starts) :
BEST adds 0,55 K to the warm trend 1950-78 compared to RUTI.
RUTI global taken from:
http://hidethedecline.eu/pages/posts/ruti-global-land-temperatures-1880-2010-part-1-244.php
RUTI will grow stronger and stronger and even though all beginning is tough, I hope everone will help collecting even more original temperature data to me to make RUTI even better.
Tricks: Coastal stations.
One (important) trick from Hadcrut is to use rural coastal stations so that they do have rural data aboard.
Problem is, that coastal stations world wide has around 0,6K more heat trend 1925-2010 than near by non-coastal stations, see
Joanne Nova/RUTI :
http://hidethedecline.eu/pages/posts/ruti-coastal-temperature-stations-242.php
K.R. Frank

Interesting.
But does it stack up?
Interested to know …

So, where we have continuous, reliable, non-manipulated data, there is no warming at all. QED
Strange that many “skeptics” seem to have reconciled themselves with the notion that “there was some global warming in the 20th century.”
Repetitive, all-pervasive lie, if non constantly resisted, in time would appear as containing at least some truth in it. I remember that in the 1980s one could hardly find a person in Russia, however opposed to the regime, who would not believe in some part of the Soviet propaganda. It has been nailed into people’s brains for 70 years, from the womb to the tomb, and only a few were stubborn enough to see it through.

Michael
I had some stuff you might have been interested in reading but Anthony snipped the link. Interesting, it isn’t the Mod’s here at WUWT snipping this, it is Anthony himself
Look at posts at SkS during May this year or my posts, under my authors name. Unless Anthony snips this as well.
Also, Anthony, if you want to talk about how people are treated, seriously read all the exchanges between SkS and Dr P Snr. Please note the civil tone of it all.
Unless you want to snip this too.
Note that I have copied this post so we can show what you have snipped Anthony
{see these: http://wattsupwiththat.com/2011/09/25/a-modest-proposal-to-skeptical-science/ and http://wattsupwiththat.com/2011/10/11/on-skepticalscience-%e2%80%93-rewriting-history/ and explain why that sort of behavior is OK for SkS How do you justify changing/deleting user comments months and years later? ~mod}

Unfortunately the adding of all temperatures and dividing by the total number does not actually produce a correct answer.
Many inputs can affect these temperatures between the times of reading which will skew the average without any knowledge that this has happened. A continuous recording, like a barograph for pressure, would be much more accurate. Whether this is done I have no idea.
See:- Does a Global Temperature Exist? Essex, McKitrick and Andresen 2006.

Please forgive my ignorance if I am being way off here, but I have had this niggling thought for some time that there is something missing when recording a temperature reading alone. I have this sense that there are parameters missing such as humidity, cloud cover and perhaps wind speed/direction that should also be recorded and used collectively when compiling a record. My feeling is that a more complete “measure” would be accomplished, EG an average temp of 15c/70% humidity/45% cloud/20km/hr/NW – in short, a sort of micro-climate record, expanded to regional and then global if that’s possible. This thing with averages of averages of averages of data points (numbers) bothers me. Perhaps my thinking is off so please straighten me out. Thanks

Have the stations gone away, or are the stations still there, but just no longer counted?

KPO says:
October 24, 2011 at 2:17 am
Please forgive my ignorance if I am being way off here, ….

I had the same thought. The use of Google for about 20 minutes fixed the ignorance. It is something to do with “wet build temperature” or similar. Basically the relative humidity is taken into account. (I am sure others with infinitely more knowledge can explain or correct me).
If you mean that the ‘temperature’ itself is not a good reading, because what we need to measure is ‘energy’, you have my vote. This view has been expounded on this site often, but I apologise for forgetting by whom. Records of weather such as could would also be extremely useful, IIRC Willis has posted a few essays on the matter.

Glenn Tamblyn says:
October 24, 2011 at 12:42 am
“Also, in your post there is no mention of how you handle area weighting of averages. Without this your results will be hugely biased towards the trends in regions where temperature stations are denser. ”
But if you do area weighting your result will be hugely biased towards the trends of isolated thermometers with no second data source a thousand miles around, like in GISS. Is this what you consider a better approach? It also makes manipulations much simpler. The PNS approach. Drop the thermometers that don’t confess.

KPO says:
October 24, 2011 at 2:17 am
“This thing with averages of averages of averages of data points (numbers) bothers me. ”
xxxxxxxxxxxxxx
Well said!

I just don’t get it with this site. On some posts (e.g. a recent one by Willis Eschenbach), there’s the argument that skeptics have never doubted that the world is warming. And then this article comes along to doubt that warming. Which is it? Maybe Anthony Watts can shed some light on this?
Also, Anthony and others were quite annoyed that the recent BEST results were publicized before peer-review, and here we have a biochemist being given free reign to post what amounts to a back-of-the-envelope calculation of average ground temps. Michael Palmer or Anthony Watts could have, at the very least, got a statistician or near equivalent to verify the averaging analysis, which I suspect is particularly well suited to giving an average trend of zero.
Cheers.

@ Glen Tamblyn
“Also, in your post there is no mention of how you handle area weighting of averages. Without this your results will be hugely biased towards the trends in regions where temperature stations are denser.”
This article never claims to be working out the average temperature of America or any region in particular – it is only looking at long term trends in individual stations, so this point is not valid.
It is also petty and peevish to complain about being “snipped” here when you have not had your comment disappeared down a black hole so that no other people may know you posted something. Most readers of this blog, if interested, would be savvy enough to find your work anyway from the comment Anthony left stating where your work was. You have been invited to place your specific work here so we can read it easily in context. Could you please do that?

KPO: you’ve caught the flavor of my frustration with the (understandable, inevitable) enterprise of reducing the complexity of a system such as local weather or (its big sister, integrated over time and space) climate, into a single parameter called “temperature” and even that not measuring heat content but just a dry bulb thermometer. We lose so much information in this process. It would be nice if the final statistic came with a label reminding us of the magnitude of what has been left on the cutting-room floor and how subjective the cutting has been. Just use a five-star or color code. I know, I know, error bars are a good nudge in that direction; and this post by Michael Palmer contains, in Figure 1, another excellent pointer for quality, namely the number of stations. It tells the reader that the data before 1900 and after about 1990 is “thin” and “different.”. I use those words to suggest the orthogonality of the information-space we are trying to explore if not capture in the temperature time series for the entire US “represented” by GISS numbers.
That dying of the thermometers is the real story for me, and Michael Palmer adds a valuable chapter to the story. Many thanks.

Hi, Michael. One needed adjustment has to do with time-of-observation bias. There are literature references on this available via the internet. You may want to check Steve McIntyre’s Climate Audit for discussions of this.
I believe that the raw data needs this adjustment.

Please forgive my ignorance if I am being way off here, but I have had this niggling thought for some time that there is something missing when recording a temperature reading alone.

Indeed. The mainstream datasets are based upon a daily temperature reading.
The problem here is what does this temperature reading actually represent?
For the moment forget all about all the problems associated with the accuracy of the thermometer… forget the Urban Heat Island effect and all about the known location issues associated with thermometers… and lets take a look at what daily data is actually being recorded.
If we used a basic data logging thermometer to record the temperature at the end of every minute during a single calendar day we would accumulate 1,440 temperature data points… the maths is simple: 60 minutes x 24 hours = 1,440.
From these 1,440 data points we could then very easily calculate an Average Daily Temperature for that day.
Unfortunately, this is not how the mainstream dataset their daily temperature reading.
This is what they actually do.
First:
They capture the data point with the highest temperature and call it their Daily High Temperature.
Although this is fairly reasonable it is important to remember that this is the high outlier value from the 1,440 data points for that day.
Second:
They capture the data point with the lowest temperature and call it their Daily Low Temperature.
Although this is fairly reasonable it is important to remember that this is the low outlier value from the 1,440 data points for that day.
Third:
They add the Daily High Temperature outlier value to the Daily Low Temperature outlier value and then divide this intermediate number by two. So what would a rational person call this number? By definition (i.e. the maths) it is the mid-point between the daily extreme outlier temperatures. However, climatologists by some bizarre logic call this number the Daily Average Temperature. It is only in climatology that the average of 1,440 data points is calculated by just using the two extreme outlier values for the day… no wonder climate science is regarded as a weird science.
To underline just how weird this weird science really is lets ask ourselves:
Question: What data would a rational person use to demonstrate rising temperatures?
Rational Answer: The series of Daily High Temperatures.
Climatology Answer: The series of mid-points between the daily extreme outlier temperatures.

Final result of adjustments:
http://cdiac.ornl.gov/epubs/ndp/ushcn/ts.ushcn_anom25_diffs_urb-raw_pg.gif
As you can see the entire twencen warming is indeed manmade. Made by the adjustments applied to the thermometer readings that is…

I have been lurking here and several other sites for over a year now, trying to make sense of all this from both sides of the fence. In answer to this simple question, Is the earth’s temperature rising? depending upon whom you ask you get the differing responses YES!, NO!, DEPENDS! IT WAS BUT NO LONGER IS! I think I have learned a great deal. Of one thing I am now fairly certain. Nobody knows.

First hand example of how data sets get messed up…
This morning, as part of my first cuppa routine, I checked the weather online using Intellicast’s site. 47deg. Let my dogs out…hmmmm …seems much cooler than 47deg, checked Accu. 47deg. Then checked Wunderground, 47deg. Used Wunderground’s “station select” feature, saw that it had defaulted to APRSWXNET – Milford, Ma.. All of the surrounding stations were reporting 34-37degs, and they appeared to be amateur stations, whereas I’m guessing that APRSWXNET is an “official” station of some sort.
Whatever it is, multiple services are using it, and it’s wrong by about 12deg.
JimB

Garrett Curley (@ga2re2t) says:
“I just don’t get it with this site. On some posts (e.g. a recent one by Willis Eschenbach), there’s the argument that skeptics have never doubted that the world is warming. And then this article comes along to doubt that warming. Which is it?”
It depends on what time frame you’re talking about. I’ve no doubt the average temperature of the globe has been rising (with substantial positive benefits!!!) beginning in 1970. This is confirmed by satellite data beginning in 1979. From 1940-1970 the globe was cooling. I’m old enough to recall climate scientists becoming alarmed by possible catastrophic anthropogenic global cooling in the early 1970’s.
The question is whether the last 30-40 years is unusual or not. Roald Amundsen was able navigate the Northwest Passage around 1906 so Arctic Sea Ice extent today doesn’t seem out of line with where it was in the past. Retreating glaciers around the world are constantly revealing human artifacts on the newly exposed ground giving concrete proof that the glaciers have not retreated as far back as they’ve retreated in the past. Greenland today still has a colder climate than when the Vikings were farming it and named it Greenland.
So if there appears to be some dissonance about global warming or not this is why. There surely has been some warming in recent decades but it doesn’t appear to be anything really out of the ordinary compared to other warming episodes in recorded history.

@Steve C
You write: “Has anyone drawn a graph in which 99% weighting was given to the best of the rural stations, and 1% to the urban?”
RUTI is : “Rural Unadjusted Temperature Index”, and thus the goal is exactly what you look for.
In several areas it is hardly possible to get real rural data, but all possibilities are tried to get best data as possible.
In many areas, there are not long rural stations 1900-2010 uninterrupted, but very often it was possible to look at a larger area where many mostly rural or small-urban sites combined made a VERY solid mostly rural trend for the whole area. This is important beacause many even sceptics believes that a mostly rural temperature index is impossible just because there are not many long rural stations public available.
Check it out:
http://hidethedecline.eu/pages/ruti.php
Thanks for comment.
K.R. Frank

@Dave Springer
Frank Lansner: “The ONLY difference between the 2 datasets happens in the years 1950-78 (just before satellite data starts) :
BEST adds 0,55 K to the warm trend 1950-78 compared to RUTI.”
Dave Springer:”Hi Frank. I had a look at the graphs and recognized the source of the difference. That’s the infamous Time of Observation adjustment. It’s the biggest upward adjustment they make.”
Thanks for comment. Yes , the time of observsation… its amazing.
So across the world from country to country, from culture to culture, continent to continent then thermometers NOT meant for climate purposes, just to tell people about their local temperatures, we have this synchronous TOBS.
Everywhere, the time of observation has systematically been changed in one direction causing too cold temperature data, that “must” be corrected massively.
If this is mostly TOBS (or similar) then I find it interesting that global warming is not really measured, but is created on the desk.
K.R. Frank

In answer to this simple question, Is the earth’s temperature rising? depending upon whom you ask you get the differing responses YES!, NO!, DEPENDS! IT WAS BUT NO LONGER IS! I think I have learned a great deal. Of one thing I am now fairly certain. Nobody knows.

Correct. NOBODY KNOWS.
Lots of people believe different things… and say different things… but in the end: nobody knows.
It is possible that people were beginning to get a handle on the situation back in the 1970s… at that time they said: the world is cooling… so they threw more money at climate research… unfortunately this money was spent on manipulating data and inventing the Global Warming myth… we are no further forward… and none the wiser regarding that specific question… we are actually a whole lot dumber overall regarding climate.
However, we have discovered that the concept of a Global Average Temperature is intangible (and largely irrelevant)… the temperature profile of the Earth is fractal in nature and cannot actually be measured in a meaningful way… additionally Climate and Weather is regional in nature… for example: the occurrence of an El Niña (or La Niña) impacts different regional areas in different ways… therefore one-size does not fit all geographic regions in the same way… warming can be beneficial in one place while it is detrimental in another.
Additionally, it is easy to support the following statements:
1) Global Warming is generally a good thing… more people die when it is cold… living organisms flourish when it is warmer… Additionally, convection dominates the daily energy flows when there is water in the environment. Therefore, the extent of any Global Warming is limited by convection and can only every become a problem for arid and desert locations that remain deprived of water.
2) CO2 is vital for plants and increasing levels of CO2 result in increasing crop yields and overall promote the greening of the earth… again water dominates any greenhouse gas effect that may exist in the atmosphere… and CO2 is basically irrelevant as a greenhouse gas because it only constitutes 0.039% of the atmosphere.
3) The Global Warming myth is just scare mongering… as you say: nobody knows… so just forget about it until you are asked to pay for some crackpot Global Warming scam / scheme… in that case just say NO and walk away – nothing to see here – just another snake oil salesman.

Excellent. I’ve never understood why anyone wasted any time on interpolating, or on urban locations, when so many continuous rural locations are available. It’s just common sense to use good sensors (if available) and totally ignore dubious sensors. In this case good sensors are available, so we should only be paying attention to them.

There is one part of the AGW theory that I am not skeptical about. That’s the part that says that there is such a thing as “The Urban Heat Island Effect” (UHIE) which is all due to us anthropoids are getting more numerous as we reap the benefits of burning more fossil fuels.
There is no reason, in my opinion, to assume that just because air temperatures have gone up in cities and other urban areas air temperatures in the surrounding areas have gone down in some kind of response – they probably have not as there is no reason for them to do so. However there is absolutely no reason to try to “fiddle figures” in order to wish any UHIE away. That goes for both “warmists and skeptics” alike.
Therefore if for a hundred or so years we measured air temperatures (T) at say 3000 stations, all in rural surroundings, and we are happy to equal the average (T) of those 3000 stations with “the average global T” (15° C), then if, say 100 or so stations have become “urbanized” resulting in each of them experiencing a T rise of say a couple of degrees C each then yes, AGW is happening but only in our paperwork, and is a kind of warming that can only be detected locally. Furthermore the UHIE has got nothing to do with “Back- radiation” from CO2.
If, the trend for the last century (1900 – 2000), in spite of the UHIE, is flat – then that should tell us that, in spite of a couple of “warming spikes” the world outside our windows is getting cooler.
And by the way – now that, allegedly, most skeptical scientists as well as “Real Climate scientists” are pandering to the notion that AGW is due to CO2 back radiation, I am wondering why I still cannot find any actual data that proves it. – Is the “proof” needed yet another “Consensus”?

oMan says:
October 24, 2011 at 3:53 am
…. “reducing the complexity of a system such as local weather or (its big sister, integrated over time and space) climate, into a single parameter called “temperature” Also
Jer0me says:
October 24, 2011 at 2:47 am
…”If you mean that the ‘temperature’ itself is not a good reading, because what we need to measure is ‘energy’, you have my vote.”
Yes, both your replies are what I’m getting at. I would however like an “expert” explanation as to why they do what they do, so I’ll do some digging – if anyone here has a quick ‘n easy – thanks.
I have this mental picture of a future 300 year old Mom and Son phone call on Mothers day “ …what’s the climate like there Johnny? “Oh an average of 21C” ????

I didn’t mention it in my reply above, but after reading Doug’s comments on the statistics, I put together these notes on the detection of “global warming”. Read the whole thing and tell me what’s wrong with it.
“Pull text”:

The heat content of the climate system isn’t just in the dry air over time. One has to measure moisture content and soil-/water-surface temperature for a start. Then, for each component, calculate enthalpy over each area (specifically, the thermal mass of each component). That gives the “instantaneous” heat content for the measured region.
Do that for the whole globe. Then sum for the global total at that instant.
It’s that simple. Meticulous and rigourous, but simple.

Unlike “global temperature”, the enthalpy figure is a real thing.
Plot the real thing over time. look at the graph. Then try to figure out what’s happening in the real world.

Logic is a scary thing!

That would be “Willis”

So looking at :
– No area weighting – a single station in Montana perhaps hundreds of miles from any other has the same weighting as a pair of stations that might be less than five miles apart. This is a serious biasing of the data used.
– Averaging raw temperatures (which vary hugely over short distances) rather than anomalies (which don’t – a mountaintop and a nearby pass/beach have different raw temperatures, but see roughly the same weather patterns).
– Throwing out 90% of the temperature records, when even a quick examination shows 1/3 of stations with a negative trend, 2/3 with a positive trend, making any conclusions from 10% poorly supported. I’m not surprised by a low trend – I would be equally unsurprised by a huge trend given the poor data treatment.
This article by Michael Palmer really says nothing meaningful, due to bad data handling – it’s like a compilation of “Never Do This” methods stuck together.
Michael Palmer – For the correlation of nearby station anomalies and area weighting, I would recommend Hansen & Lebedeff 1987 (http://pubs.giss.nasa.gov/cgi-bin/abstract.cgi?id=ha00700d), which discusses this issue (and a lot more) in terms of trying to compute these trends.

Would anyone care to explain to me why the graph linked by Mr Palmer for Hohenpiessenberg at http://climatereason.com/LittleIceAgeThermometers/Hohenpeissenberg_Germany.html shows a virtual flat line and then one that MFK Boulder links to at http://preview.tinyurl.com/Hohenpeissenberg shows our favourite hockey stick, preferably without using the word “baloney” in the text even if you do spell it correctly.

MFKBoulder October 24, 2011 at 6:11 am says: “Look at the Hohenpeissenerbg Graph and you see the statement quoted is nothig but belony.”
The link you provided does not suggest whether the graph uses raw or adjusted data.
Furthermore, the link mentions that, “In March 1950, the status of the Hohenpeissenberg station was upgraded to that of a meteorological observatory.”
Did it switch in 1950 from “Mannheim hours” (at 700, 1400 and 2100 hours local mean time) to hourly readings? If so, does the graph use only the data taken at “Mannheim hours” for accurate comparison?
John M Reynolds

KR says:
October 24, 2011 at 8:18 am
“Averaging raw temperatures (which vary hugely over short distances) rather than anomalies (which don’t – a mountaintop and a nearby pass/beach have different raw temperatures, but see roughly the same weather patterns).”
In fact, my method amounts to averaging anomalies rather than raw temperatures.
“Throwing out 90% of the temperature records, when even a quick examination shows 1/3 of stations with a negative trend, 2/3 with a positive trend, making any conclusions from 10% poorly supported.”
The 10% were selected not for some trend or for location but purely based on continuity. If you think that criterion meaningless, fine, I just don’t agree with you.
“For the correlation of nearby station anomalies and area weighting, I would recommend Hansen & Lebedeff 1987 …”
I’m not claiming to have calculated The One True Average Temperature Trend. The only point I make is that long-running stations trend differently from ones that are not, and for that I don’t need area weighting.
Thanks for playing.

Frank Lansner says:
October 24, 2011 at 5:40 am

@Dave Springer
Frank Lansner: “The ONLY difference between the 2 datasets happens in the years 1950-78 (just before satellite data starts) :
BEST adds 0,55 K to the warm trend 1950-78 compared to RUTI.”
Dave Springer:”Hi Frank. I had a look at the graphs and recognized the source of the difference. That’s the infamous Time of Observation adjustment. It’s the biggest upward adjustment they make.”
Thanks for comment. Yes , the time of observsation… its amazing.
So across the world from country to country, from culture to culture, continent to continent then thermometers NOT meant for climate purposes, just to tell people about their local temperatures, we have this synchronous TOBS.
Everywhere, the time of observation has systematically been changed in one direction causing too cold temperature data, that “must” be corrected massively.
If this is mostly TOBS (or similar) then I find it interesting that global warming is not really measured, but is created on the desk.

As James Woods said at the end of the film “Contact”… “Yes, that is interesting, isn’t it.”
The strength of the global warming narrative is in the satellite data beginning in 1979. There is little doubt that the data is reliable, accurate to the degree necessary, and coverage is near global and around the clock. The earth’s temp was rising between 1979 and 1999 and has leveled off since then.
But that’s not a long enough period of time for a “climate trend” which is defined as 30 years of weather. Even 30 years is questionable for being long enough because we know for a fact that there are climate cycles that go far beyond a mere 30 years. Interglaical periods for instance are on a cycle of 100,000 years. The AMDO (Atlantic Multi-Decadal Oscillation) is a 60-year cycle. In fact many of us believe that the past several decades are simply the warm side of the AMDO being measured by satellites and nothing more.

Michael Palmer – Disclaimer: I am not a climate scientist and claim no expertise relevant to this subject other than basic arithmetics. In case I have overlooked equivalent previous work, this is due to my ignorance of the field
nuff said!

Dave Springer asks:Hi Ivor. Just curious about how cloud height is determined aboard ship.
Hi Dave,
We used the mark 1. uncorrected Eyeball, a large chart supplied by the Met office, vast amounts of experience and a lot of guesswork. As you know the various types of clouds have different base levels so it was always the starting point to decide what type of cloud you were looking at. The low clouds were fairly easy to determine but high clouds were largely a matter of looking at the type on a chart and then trying to see if it had a positive base i.e. at the lowest end of its height range; or blurred baseline, possibly higher. In those days we did Met as part of the Board of Trade exams. I don’t recall a course called “staring at computer screens” ,we actually had to look out of the bridge windows! Vertical sextant angle would not work unfortunately unless God dropped a plumb line to give a reference point between horizon and cloud base. Cloud cover was estimated by quartering the sky and using percentage estimates. You can understand my chagrin at the way our guesstimated data are now refined to multiple decimal points.
I too took the nuclear defence course. We were told to take cover in the shaft tunnel by an enthusiastic Lt Commander. When we pointed out the lack of such on a 200,000 ton tanker we were told to turn our backs to the flash, close our eyes, bend over and …………you can guess the rest.

“KPO says:
October 24, 2011 at 2:17 am
I have this sense that there are parameters missing such as humidity,”
I understand where you are coming from. However how much difference does it really make in the end? The percent water vapor in the atmosphere can vary from close to 0 to about 4%. Let us assume that in a dry year, the humidity averages 1% and in a humid year, it averages 3%. The specific heat capacity of air is 1.0. Let us assume the specific heat capacity of water vapor is 2.0. So if the air has 1% water vapor, the average specific heat capacity is 1.01. And if the air has 3% water vapor, the average specific heat capacity is 1.03. I know the molar mass of water is 18 and not 29, but if we just assume they are the same, then the mass of the atmosphere with 3% water vapor is 2% larger than if there is 1% water vapor. (I am also generously assuming water vapor exists evenly throughout the atmosphere and does not condense out.) Then applying mct(moist air) = mct(dry air), we find that the mc for the moist air is 4% larger than for dry air. So to balance things out, the dry air has to have a temperature change that is 4% larger than the moist air. In other words, if moist air goes up by 1.00 degrees C, the dry air, with the same energy input, would go up by 1.04 degrees C. So I would say the difference is very small. Perhaps the error bars need to be made just a wee bit larger to account for the unknown average humidity values? Note that I am not addressing phase changes that may occur due to humidity which is a separate topic.

Stephen Wilde says:
“From our perspective there seems to be a warming or cooling at the recording sites when averaged out overall but in reality all that is being recorded is the rate of energy flow past the recording sites as the speed of energy flow through the system changes in an inevitable negative response to a forcing agent whether it be sun sea or GHGs.In effect the positions of the surface sensors vary in relation to the position of the climate zones and they record that variance and NOT any change in energy content for the system as a whole.”
A most welcome summary of the starting point that is a temperature record. For many, a temperature record is the end of the thought process but it really is the beginning. Thanks for reminding us what these data points actually mean!

Michael Palmer’s article is an important one and we need to focus on the Big Picture. The article introduces two topics, one about station quality and the other about calculation. The station quality topic is logically prior to the other topic and raises the very important question about station quality and the empirical evidence for it.
Palmer describes the stations reporting continuously since 1900 as follows:
“Here, I selected for stations that had continuously reported at least one monthly average value (but usually many more) for each year between 1900 and 2000. This criterion yielded 335 rural stations and 278 non-rural ones.”
Graphing these stations, he concludes that:
“While the sequence and the amplitudes of upward and downward peaks are closely similar to those seen in Figure 2, the trends for both rural and non-rural stations are virtually zero. Therefore, the average temperature anomaly reported by long-running stations in the GISS dataset does not show any evidence of long-term warming.”
From Palmer’s observations, we need to ask what we can infer about the stations. I suggest that the most important and telling inference that can be drawn is that these stations have been well managed. The stations that do not fall into the category of “well managed” can then be graded on various levels of “poor management.” The levels of poor management can be determined by searching for causes of gaps or bumps and similar matters. (Bumps occur when there is a sudden and large continuous change in temperatures reported.)
I emphasize poor management for a very important reason. The only reasonable inference that can be made about stations with numerous gaps and bumps is that the readings that come from them are flaky. Yes, flaky, as in usually inaccurate and maybe in several different ways. The inferences that Warmista want to draw at this point are that errors offset one another, that errors are one-time shifts that do not affect trends, that surrounding stations are not flaky, and so on. Obviously, none of those inferences are justifiable without the results of empirical research done on the ground. Because Warmista adamantly refuse to engage in such empirical research, they are making wholly unjustified assumptions.
For the last thirty years, Anthony Watts and others have gathered information about siting which could explain many gaps and bumps and which could be used in grading poor management. Watts’ factual information goes far beyond what has been described here.
When cornered, the Warmista response is that all of these empirical matters are unimportant because their incredibly sophisticated statistical techniques enable them to compensate for all flakiness in all weather station records. The breath taking boldness of this claim makes it highly suspect. It raises the question whether Warmista could specify any degree of flakiness that could not be accommodated within their statistical techniques. (Please note that questions of calculation are separate from and can be in conflict with empirical knowledge of stations.)
The practical conclusion of all this is that the records of well managed weather stations should be privileged over those of poorly managed stations in calculations of average temperatures. Palmer’s claim that the well managed stations show no temperature trend at all should be the accepted baseline among climate scientists and deviations from it should require justification from empirical research about particular poorly managed stations.

****
Frank Lansner says:
October 24, 2011 at 5:40 am
Thanks for comment. Yes , the time of observsation… its amazing.
So across the world from country to country, from culture to culture, continent to continent then thermometers NOT meant for climate purposes, just to tell people about their local temperatures, we have this synchronous TOBS.
Everywhere, the time of observation has systematically been changed in one direction causing too cold temperature data, that “must” be corrected massively.
****
Technically, TOBS is a legit correction, but then how could corrections to so many stations (thousands) produce a TOBS correction so lopsided upward? One would think that such a correction applied globally over so many stations would end up nearly random — near zero. And the TOBS correction applied isn’t even done by each individual stations’ data, it’s done by a TOBS “model” (algorithm).
I assume TOBS “models” are as trustworthy as climate models, until shown otherwise.

The strength of the global warming narrative is in the satellite data beginning in 1979. There is little doubt that the data is reliable, accurate to the degree necessary, and coverage is near global and around the clock.

I admire your faith in satellite data that is not calibrated against earth based thermometers… satellite data that cannot be independently verified / processed / checked… although the satellite data starts in 1979 the data series has been accumulated from various satellites with differing equipment and differing failure rates… I do not share your unquestioning faith in it the accuracy of the data, the reliability of the equipment, the scope & timeliness of the coverage… let alone the subsequent processing of the raw data by the usual suspects.

@Dave Springer
“The strength of the global warming narrative is in the satellite data beginning in 1979. ”
Yes, the great argument is that land temperature data is not too far from the satelite data, some say.
But the largest difference occured 1950-78 it seems.
K.R. Frank

KR says:
October 24, 2011 at 8:47 am
“The lack of area weighting and discarding of 90% of the data, on the other hand, are quite serious issues.”
You persevere in misunderstanding the intention of my post. But, if throwing away 90% of the stations is so terrible: Why does GISS do the same, then? They axed 90% of their stations themselves.
“Area weighting data simply allows you to use the other 90% of the available data.”
Thank you. I already suspected that you were clueless, but now I’m sure of it.
‘“Thanks for playing” – Oh? You consider this a game?’
Yes. For me, it is – my day job is in real science.

Dave Springer says:
October 24, 2011 at 9:49 am
The difficulty is in deciding which point of the cloud base is directly above the horizon. Due to the curvature of the Earth the cloud base continues over the horizon until eventually it appears to meet the horizon. Without the plumb line to indicate exactly where the point of measurement should be you can pick the observed height all the way down to zero. We have corrections for refraction, parallax and dip(height of eye) and vertical sextant angle can be used to determine the distance of an object of known height, or the height of an object of known distance.
(I still use rule of thumb when yachting!)

Anthony, et al,
I would love to see you address the following in a post on your site.
An even easier way to demonstrate a long term flat USA trend is to simply insist that the date range begin and end at similar points in the AMO cycle. See the following post:
http://sbvor.blogspot.com/2011/10/amo-as-driver-of-climate-change.html

I am one of those who is sceptical as to whether there has been any increase in average temperature over the last century or so. I am one of those who is sceptical as to whether an average can be arrived at and whether it would have anything other than a conceptual meaning if it could. I am one of those that deny that an average can be calculated using existing resources.
Having regard for the width of the error bars arising from the shortcomings in the metrology, from the hodgepodge availability and distribution of data and the inconsistency of the data sources, the current temperature number is no more than a guess, and I can see no reason to believe that it is correct to within 2 degrees.
How is it that lacking any qualification regarding climate science, I can be so adamant in what I say? It is because I am blessed (or cursed) with being numerate. If the data is dodgy one cannot manipulate it in a way that would resolve to a valid answer.

malagaview:
While not a new idea, the mixing of max and min temp can be justified on the basis of consistancy with practices when there were no data loggers. You do want you can with the information you have. However, The real ‘no no’ is collating daily readings into monthy and calling it monthy raw data. Months are not all the same length. Over much of the recorded period, the months did not even start on within the same week in different countries. Working with months, you have infilling and dropping as days in the month, and again months in years.
Raw means what you saw, That means, the max and the min, or the reading and the time of day as written down – with the gaps where there are gaps.

This discard of stations that, in effect,
are the ones reporting level or declining temperatures:
Didn’t I read here at WUWT the same thing about sea-level?
Before they calculate the ‘global’ sea-level,
they throw out all stations showing decline or no rise,
since they ‘know’
that something they call ‘global sea level’
is on the bring of catastrophically rising.
They can’t have those meaningless outliers contaminating their message.

Brian says:
October 24, 2011 at 10:45 am
Interesting that posts like this keep popping up when Anthony has admitted the earth is warming, just not that humans are causing it. Are you going back on your pledge to “accept whatever result they produce, even if it proves my premise wrong.”?
Has Anthony ever detailed what exactly it would take for him to accept AGW? How high to temperatures have to get? Who has to do the analysis? Because after his reversal on the BEST study, it seems that any study that supports AGW must be wrong for some reason or another.

No matter high the temperatures get, it’s still not evidence of AGW! Global warming is not AGW! We could set a record high temperature everywhere on the globe from now until the sun burns out and it still wouldn’t be evidence of AGW because global warming does not mean AGW.
Remember these two things
1) Evidence of global warming is not evidence of anthropegenic global warming.
2) Repeat #1 until you get it.

@Soylent Green
But once it’s clear the earth is warming (really it already is) you need to suggest a cause. Either it’s the human emissions of gasses that are known to have warming effects, or something else. The possible list of “something else” shrinks as temperatures keep going up. Claiming that it’s a coincidence is pretty hard to swallow.

Michael Palmer says: Yes. For me, it is – my day job is in real science.

BRAVO! Give this man a cigar 🙂

@DirkH says:
October 24, 2011 at 10:27 am
“One of the longest running records in Europe is Berlin; nearly no trend over 300 years”
Berlin Dahlem is also one of the sites which has been removed from the GISS record last year

It isn’t “global warming” that anyone is skeptical of. It is whether it is caused by anything people are doing or if anything people are doing have a significant impact on the rate/amount of warming. The globe is pretty much always “warming” or “cooling” and is rarely stable for any great length of time. The point is that nobody has shown that any current warming is at any greater rate than has occurred naturally before the industrial age.
In fact, the post-industrial warming is simply the only major warming trend we have had since the LIA. What we have measured is the recovery from the LIA till 1933, then cooling until 1976, then another period of warming till about 1998.
What is so frustrating is the constant adjusting of the adjustments. When we have a warm year that is close to 1933, it gets adjusted upward and 1933 gets adjusted downward to make a new “hottest” year. It simply would not fly in any other field of science. There is too much subjective “adjustment” applied to the records. I would want to look at rural stations that are still rural (continuously reporting notwithstanding) and see what the raw data show.
I believe the “adjustments” are corrupt and are agenda-driven.

Michael Palmer
‘“Thanks for playing” – Oh? You consider this a game?’
Yes. For me, it is – my day job is in real science.
That’s a very telling statement. If you don’t consider studying the climate worthy of actual scientific effort, then, well, never mind. I’ll just take your post as seriously as you apparently do.
Adieu

This is interesting. The use of long station series seems a very good idea.
But isn’t this a similar method of taking first differences that was discussed and criticised before? See Hu McCulloch’s 2010 post at Climate Audit http://climateaudit.org/2010/08/19/the-first-difference-method/. I can’t say I understand all the arguments in that thread, but is there a better way of dealing with missing years?

Brian, what about the list of possible causes of the Medieval Warm Period, the Roman, etc, or the fact that even the BEST team shows a steeper temperature rise in the early 1800’s than anything in the 20th century? Since the list of natural causes is so diminished, would you suggest pirates, jousting tournaments, and goat entrails being sacrificed to Apollo as likely forcings for those events?
Most of the apocalyptic hand waving dismissed solar effects, as the variation in visual magnitude solar output is rather small. But now we’re finding strong links between UV output and upper atmospheric temperatures, along with a possibly huge influence on cloud coverage because of cosmic ray cloud seeding, which is modulated by the strength of the solar wind, and that strength does vary strongly with solar cycles. Then they often pretend that the AMO, PDO, AO, and other natural oscillations don’t exist, and pretend that the world has statistically significant warming in the past 15 years, which it hasn’t.
If you follow some of the adjustments they make to the temperature record, we should worry less about the present getting warmer than the alarming rate at which the past keeps getting cooler. If present trends continue, millions of extra people in the 1940’s are going to freeze to death.

Smokey,
This is the problem with arguing with [SNIP: – Policy Violation -REP] , they all have different arguments, and are willing to change them at the drop of a hat. “The earth isn’t warming!” “Ok maybe it is but it’s not humans!” “Ok it’s humans but it’s not harmful!” “Ignore the fact that I was wrong on my first two premises!”
Most scientists reject catastrophic AGW? I like that you slip “catastrophic in there. Make up your mind, are you denying global warming, AGW, or that AGW is harmful? Changing your position from argument to argument is not acceptable. Technically you may be correct, most scientists do not believe AGW will lead to armaggedon. But denying the scientific consensus on man-made climate change is absurd. http://en.wikipedia.org/wiki/Scientific_opinion_on_climate_change

“The upshot of this is that missing monthly Δtemperature values are simply dropped and replaced by the average (Δtemperature) from the other stations.”
Why do we need to do that replacement? Isn’t it possible to calculate the global temperature trend by using just the real observations?
1. Calculate the trend of one site (station) in one day of year. For example the 25th October trend in 1900..2011.
2. Do that for every site
3. Do that for every day of the year
What is the impact of changing 2. and 3. ?
If the site has been moved or there is some other changes, just consider the site closed and start a new one.

@ Bob Kutz :October 24, 2011 at 8:30 am
Thanks for a thoughtful and well-constructed post.

KR says: October 24, 2011 at 1:19 pm
Michael Palmer: ‘“Thanks for playing” – Oh? You consider this a game?’ Yes. For me, it is – my day job is in real science.
That’s a very telling statement. If you don’t consider studying the climate worthy of actual scientific effort, then, well, never mind. I’ll just take your post as seriously as you apparently do.
Adieu
_______________________________________________________________________
KR has got Michael’s point upside down. Thank goodness there are still real scientists like Michael who choose, in their free time, to visit an area of science that has become so corrupted that its gatekeepers are corrupt mad, proven by the fact that they proclaim they have 97% support, whilst gagging dissenters and failing to count their true number.
With mad gatekeepers, I guess entry to this domain often has to be played as a game.

KR says:
October 24, 2011 at 1:19 pm
“If you don’t consider studying the climate worthy of actual scientific effort, then, well, never mind.”
—
I find studying the climate eminently worthy of scientific effort. What I cannot take seriously is the “reconstruction” of the “true” temperature record from a woefully incomplete and corrupted database. No amount of adjusting, correcting, weighting, averaging, extrapolating, smoothing, roughing, digesting and regurgitating will get us past the garbage in, garbage out problem.

@ Lucy Skywalker
Thanks very much for your comments. I have seen your posts here and also recently perused your own page from top to bottom and enjoyed it. I look forward to your further posts!
Best wishes, Michael

phlogiston says:
October 24, 2011 at 2:02 pm
“BTW do mobile phones have thermometers?”
Yes they do. Too bad they have an internal heat source and are carried around much of the time in physical contact with a 98.6F warm body or inside heated/air conditioned buildings and vehicles.
You didn’t think about that question very much.
Some people say there’s no such thing as a stupid question but this one proves them wrong.

Theo Goodwin says:
October 24, 2011 at 2:26 pm
That was a brutal drubbing of the one trick pony. Took him to school with that one you did. Wished I’d have written it!

@Smokey
So you basically accept the findings of climate research except for the consequences. So do you agree that 90% of the self-proclaimed “skeptics” are crazy for denying the earth is warming and that humans are causing (at least) much of it? What other scientific consensus’ do you deny? Evolution? The germ theory of disease? Do you think the evidence that smoking causes lung cancer is just a political ploy?
Climate-denial-gate has robbed the “skepticism” movement of whatever credibility it had.

DR_UK says:
October 24, 2011 at 1:22 pm
“But isn’t this a similar method of taking first differences that was discussed and criticised before? See Hu McCulloch’s 2010 post at Climate Audit http://climateaudit.org/2010/08/19/the-first-difference-method/.”
—
Thanks for the link. The idea is indeed similar. There is one difference, however: The CA post states: “Missing observations may simply be interpolated for the purposes of computing first differences (thereby splitting the 2 or more year observed difference into 2 or more equal interpolated differences).” In contrast to this approach, I did not fill the gaps with interpolated numbers.

George Turner says:
October 24, 2011 at 1:29 pm
If you follow some of the adjustments they make to the temperature record, we should worry less about the present getting warmer than the alarming rate at which the past keeps getting cooler. If present trends continue, millions of extra people in the 1940′s are going to freeze to death.
Thank you George!! This was the comment of the day for me. ( I hope my mum and dad aren’t affected because then I wouldn’t be here)

Brian says:
October 24, 2011 at 2:49 pm
“What other scientific consensus’ do you deny? Evolution? The germ theory of disease? Do you think the evidence that smoking causes lung cancer is just a political ploy?”
—
None of these, actually. You may also be surprised to learn that not all of us own a gun.
Anyone who likens the profundity of our understanding of global warming, and the quality of the evidence to those pertaining to any of the above theories only proves their ignorance.

Hansen’s own GISStemp data show that it is actually COOLER than his infamous “Scenario C” forecast, which was based on NO (i.e. ZERO, NONE, NOT ANY) man-made CO2 emissions after 2000!
http://www.real-science.com/doubt-temperatures-rising-fast-hansens-emissions
If Hansen paid any attention at all to HIS OWN DATA, as well as his own predictions, pronouncements, prognositcations and various other bloviations, he would have concluded years ago that CO2-based warming simply DOES NOT HAPPEN!

As Requested:
Off Averages & Anomalies Part 1A
In recent years a number of claims have been made about ‘problems’ with the surface temperature record: that it is faulty, biased, or even ‘being manipulated’. Many of the criticisms often seem to revolve around misunderstandings of how the calculations are done and thus exaggerated ideas of how vulnerable to error the analysis of the record is. In this series I intend to look at how the temperature records are built and why they are actually quite robust. In this first post (Part 1A) I am going to discuss the basic principles of how a reasonable surface temperature record should be assembled, Then in Part 1B I will look at how the major temperature products are built. Finally in Parts 2A and 2B I will then look at a number of the claims of ‘faults’ against this to see if they hold water or are exaggerated based on misconceptions.
How NOT to calculate the Surface Temperature
So, we have records from a whole bunch of meteorological stations from all around the world. They have measurements of daily maximum and minimum temperatures for various parts of the last century and beyond. And we want to know how much the world has warmed or not.
Sounds simple enough. Each day we add up all these station’s daily average temperatures together, divide by the number of stations and, voilá, we have the average temperature for the world that day. Then do that for the next day and the next and…. Now we know the world’s average temperature, each day, for all that measurement period. Then compare the first and last days and we know how much warming has happened – how big the ‘Temperature Anomaly’ is – between the two days. We are calculating the ‘Anomaly of the Averages’. Sounds fairly simple doesn’t it? What could go wrong?
Absolutely everything.
So what is wrong with the method I described above?
1. Every station may not have data for the entire period covered by the record. They have come and gone over the years for all sorts of reasons. Or a station may not have a continuous record. It may not be measured on weekends because there wasn’t the budget for someone to read the station then. Or it couldn’t be reached in the dead of winter.
Imagine we have 5 measuring stations, A to E that have the following temperatures on a Friday:
A = 15, B = 10, C = 5, D = 20 & E = 25
The average of these is (15+10+5+20+25)/5 = 15
Then on Saturday, the temperature at each station is 2 °C colder because a weather system is passing over. But nobody reads station C because it is high in the mountains and there is no budget for someone to go up there at the weekend. So the average we calculate from the data we have available on Saturday is:
(13+8+18+23)/4 = 15.5.
But if station C had been read as well it would have been:
(13+8+3+18+23)/5 = 13
This is what we should be calculating! So our missing reading has distorted the result.
We can’t just average stations together! If we do, every time a station from a warmer climate drops off the record, our average drops. Every time a station from a colder climate drops off, our average rises. And the reverse for adding stations. If stations report erratically then our record bounces erratically. We can’t have a consistent temperature record if our station list fluctuates and we are just averaging them. We need another answer!
2. Our temperature measurements aren’t from locations spaced evenly around the world. Much of the world isn’t covered at all – the 70% that is oceans. And even on land our stations are not evenly spread. How many stations are there in the roughly 1000 km between Maine and Washington DC, compared to the number in the roughly 4000 km between Perth & Darwin?
We need to allow for the fact that each station may represent the temperature of very different size regions. Just doing a simple average of all of them will mean that readings from areas with a higher station density will bias the result. Again, we can’t just average stations together!
We need to use what is called an Area Weighted Average. Do something like: take each station’s value, multiply it by the area it is covering, add all these together, and then divide by the total area. Now the world isn’t colder just because the New England states are having a bad winter!
3. And how good an indicator of its region is each station anyway? A station might be in a wind or rain shadow. It might be on a warm plain or higher in adjacent mountains, or in a deep valley that cools quicker as the Sun sets. It might get a lot more cloud cover at night or be prone to fogs that cause night-time insulation. So don’t we need a lot of stations to sample all these micro-climates to get a good reliable average? How small does each station’s ‘region’ need to be before its readings are a good indicator of that region? If we are averaging stations together we need a lot of stations!
4. Many sources of bias and errors can exist in the records. Were the samples always taken at the same time of day? If Daylight Savings Time was introduced, was the sampling time adjusted for this? Where log sheets for a station (in the good old days before new fangled electronic recording gizmos) written by someone with bad handwriting – is that a 7 or a 9? Did the measurement technology or their calibrations change? Has the station moved, or changed altitude? Are there local sources of biasing around the station? And do these biases cause one-off changes or a time-varying bias?
We can’t take the reading from a station at face-value. We need to check for problems. And if we find them we need to decide whether we can correct for the problem or need to throw that reading or maybe all that station’s data away. But each reading is a precious resource – we don’t have a time-machine to go back and take another reading. We shouldn’t reject it unless there is no alternative.
So, we have a Big Problem. If we just average the temperatures of stations together, even with the Area Weighting answer to problem #2, this doesn’t solve problems #1, #3 or #4. It seems we need a very large detailed network, which has existed for all of the history of the network, with no variations in stations, measurement instruments etc, and without any measurement problems or biases.
And we just don’t have that. Our station record is what it is. We don’t have that time machine. So do we give up? No!
How do stations’ climates change?
Let’s consider a few key questions. If we look at just one location over its entire measurement history, say down on the plains, what will the numbers look like? Seasons come and go; there are colder and warmer years. But what is the longer term average for this location? What is meant by ‘long term’? The World Meteorological Organisation (WMO) defines Climate as the average of Weather over a 30 year period. So if we look at a location averaged over something like a 30 year period and compare the same location averaged over a different 30 year period, the difference between the two is how much the average temperature for that location has changed. And what we find is that they don’t change by very much at all. Short term changes may be huge but the long term average is actually pretty stable.
And if we then look at a nearby location, say up in the mountains, we see the same thing: lots of variation but a fairly stable average with only a small long term change. But their averages are very different from each other. So although a station’s average change over time is quite small, an adjacent station can have a very different average even though its change is small as well. Something like this:
Comparing two adjacent stations
Next question: if each of our two stations averages only change by a small amount, how similar are the changes in their averages? This is not an idle question. It can be investigated, and the answer is: mostly by very little. Nearby locations will tend to have similar variations in their long term averages. If the plains warm long term by 0.5°C, it is likely that the nearby mountains will warm by say 0.4–0.6°C in the long term. Not by 1.5 or -1.5°C.
It is easy to see why this would tend to be the case. Adjacent stations will tend to have the same weather systems passing over them. So their individual weather patterns will tend to change in lockstep. And thus their long term averages will tend to be in lock-step as well. Santiago in Chile is down near sea level while the Andes right at its doorstep are huge mountains. But the same weather systems pass over both. The weather that Adelaide, Australia gets today, Melbourne will tend to get tomorrow.
Station Correlation Scatter Plots (HL87)Final question. If nearby locations have similar variations in their climate, irrespective of each station’s local climate, what do we mean by ‘nearby’? This too isn’t an idle question; it can be investigated, and the answer is many 100’s of kilometres at low latitudes, up to 1000 kilometres or more at high latitudes. In Climatology this is the concept of ‘Teleconnection’ – that the climates of different locations are correlated to each other over long distances.
Figure 3, from Hansen & Lebedeff 1987 (apologies for the poor quality, this is an older paper) plots the correlation coefficients versus separation for the annual mean temperature changes between randomly selected pairs of stations with at least 50 common years in their records. Each dot represents one station pair. They are plotted according to latitude zones: 64.2-90N, 44.4-64.2N, 23.6-44.4N, 0-23.6N, 0-23,6S, 23.6-44.4S, 44.4-64.2S.
Notice how the correlation coefficients are highest for stations closer together and less so as they stretch farther apart. These relationships are most clearly defined at mid to high northern latitudes and mid southern latitudes – the regions of the Earth with higher proportions of land to ocean.
This makes intuitive sense since surface air temperatures of the oceanic regions are influenced also by water temperatures, ocean currents etc instead of just air masses passing over them, while land temperatures don’t have this other factor. So land temperatures would be expected to have better correlation since movement of weather systems over them is a stronger factor in their local weather.
This is direct observational evidence of Teleconnection. Not just climatological theory but observation.
A better answer
So what if we do the following? Rather than averaging all our stations together, instead we start out by looking at each station separately. We calculate its long term average over some suitable reference period. Then we recalculate every reading for that station as a difference from that reference period average. We are comparing every reading from that station against its own long term average. Instead of a series of temperatures for a station, we now have a series of ‘Temperature Anomalies’ for that station. And then we repeat this for each individual station, using the same reference period to produce the long term average for each separate station.
Then, and only then, do we start calculating the Area Weighted Average of these Anomalies. We are now calculating the ‘Area Average of the Anomalies’ rather than the ‘Anomaly of the Area Averages’ – now there’s a mouthful. Think about this. We are averaging the changes, not averaging the absolute temperatures.
Does this give us a better result? In our imaginary ideal world where we have lots of stations, always reporting all the time, no missing readings, etc., then these two methods will give the same result.
The difference arises when we work in an imperfect world. Here is an example (for simplicity I am only doing simple averages here rather than area weighted averages):
Let’s look at stations A to E. Let’s say their individual long term reference average temperatures are:
A = 15, B = 10, C = 5, D = 20 & E = 25
Then for one day’s data their individual readings are:
A = 15.8, B = 10.4, C = 5.7, D = 20.4 & E = 25.3
Using the simple Anomaly of Averages method from earlier we have:
(15.8+10.4+5.7+20.9+25.3)/5 – (15+10+5+20+25)/5 = 0.52
While using our Average of Anomalies method we get:
((15.8-15) + (10.4-10) + (5.7-5) + (20.4-20) + (25.3-25))/5 = 0.52
Exactly the same!
However, if we remove station C as in our earlier example, things look very different. Anomaly of Averages gives us:
(15.8+10.4+20.4+25.3)/4 – (15+10+5+20+25)/5 = 2.975 !!
While Average of Anomalies gives us:
((15.8-15) + (10.4-10) + (20.4-20) + (25.3-25))/4 = 0.475
Obviously both values don’t match what the correct value would be if station C were included, but the second method is much closer to the correct value. Bearing in mind that Teleconnection means that adjacent stations will have similar changes in anomaly anyway, this ‘Average of Anomalies’ method is much less sensitive to variations in station availability.
Now let’s consider how this approach could be used when looking at station histories over long periods of time. Consider 3 stations in ‘adjacent’ locations. A has readings from 1900 to 1960. B has reading from 1930 to 2000 and C has readings from 1970 to today. A overlaps with B, B overlaps with C. But C doesn’t overlap with A. If our reference period is say 1930 – 1960, we can use the readings from A & B. But C doesn’t have any readings from our reference period. So how can we splice together A, B, & C to give a continuous record for this location?
Doesn’t this mean we can’t use C since we can’t reference it to out 1930-1960 baseline? And if we use a more recent reference period we lose A. Do we have to ignore C’s readings entirely? Surely that means that as the years roll by and the old stations disappear, eventually we will have no continuity to our record at all? That’s not good enough.
However there is a way we can ‘splice’ them together.
A & B have a common period from 1930-1960. And B & C have a common period from 1970-2000. So if we take the average of B from 1930 to 1960 and compare it to the same average from A for the same period we know how much their averages differ. Similarly we can compare the average of B from 1930-1960 to the average for B from 1970-2000 to see how much B has changed over the intervening period. Then we can compare B vs C over the 1970-2000 period to relate them together. Knowing these three differences, we can build a chain of relationships that links C1970-2000 to B1970-2000 to B1930-1960 to A1930-1960
Something like this:
‘Chaining’ station histories together
If we have this sort of overlap we can ‘stitch together’ a time series stretching beyond more than one station’s data. We have the means to carry forward our data series beyond the life (and death) of any one station, as long as there is enough time overlap between them. But we can only do this if we are using our Average of Anomalies method. The Anomaly of Averages method doesn’t allow us to do this.
So where has this got us in looking at our problems? The Average of Anomalies approach directly addresses problem #1. Area Weighted Averaging addresses problem #2. Teleconnection and comparing a station to itself helps us hugely with problem #3 – if fog provides local insulation, it probably always had, so any changes are less related to the local conditions and more to underlying climate changes. Local station bias issues still need to be investigated but if they don’t change over time, then they don’t introduce ongoing problems. For example, if a station is too close to an artificial heat source, then this biases that station’s temperature. But if this heat source has been a constant bias over the life of the station, then it cancels out when calculate the anomaly for the station. So this method also helps us with (although doesn’t completely solve) problem #4. In contrast, using the Anomaly of Averages method, local station biases and erratic station availability will compound each other making things worse.
So this looks like a better method.
Which is why all the surface temperature analyses use it!
The Average of Anomalies approach is used precisely because it avoids many of the problems and pitfalls.
In Part 1B I will look at how the main temperature records actually compile their trends.

Off Averages & Anomalies, Part 1B
In Part 1A we looked at how a reasonable temperature record needs to be compiled. If you haven’t already read part 1A, it might be worth reading it before 1B.
There are four major surface temperature analysis products produced at present: GISTemp from the Goddard Institute of Space Sciences (GISS); HadCRUT, a collaboration between the Hadley Research Center and the University of East Anglia Climate Research Unit (HadCRUT); The US National Oceanic And Atmospheric Administration’s (NOAA) National Climatic Data Center (NCDC); and the Japanese Meteorological Agency (JMA). Another major analysis effort is currently underway: the Berkeley Earth Surface Temperature Project (BEST), but as yet their results are preliminary.
GISTemp
We will look first specifically at the product from GISS, at how they do their Average of Anomalies, and their Area Weighting scheme. This product dates back to work undertaken at GISS in the early 1980s with the principle paper describing the method being Hansen & Lebedeff 1987 (HL87).
The following diagram illustrates the Average of Anomalies method used by HL87
Reference Station method for comparing stations series
This method is called the ‘Reference Station Method’. One station in the region to be analysed is chosen as station 1, the reference station. The next stations are 2, 3, 4, etc., to ‘N’. The average for each pair of stations (T1, T2), (T1, T3), etc. is calculated over the common reference period using the data series for each station T1(t), T2(t), etc., where “t” is the time of the temperature reading. So for each station their anomaly series is the individual readings – Tn(t) – minus the average value of Tn.
“δT” is the difference between their two averages. Simply calculating the two averages is sufficient to produce two series of anomalies, but GISTemp then shifts T2(t) down by δT, combines the values of T1(t) and T2(t) to produce a modified T1(t), and generates a new average for this (the diagram doesn’t show this, but the paper does describe it). Why are they doing this? Because this is where their Area Averaging scheme is included.
When combining T1(t) and T2(t) together, after adjusting for the difference in their averages, they still can’t just add them because that wouldn’t include any Area Weighting. Instead, each reading is multiplied by an Area Weighting factor based on the location of each station; these two values are then added together and divided by the combined area weighting for the two stations. So series T1(t) is now modified to be the area weighted average of series T1(t) and T2(t). Series T1(t) now needs to be averaged again since the values will have changed. Then they are ready to start incorporating data from station 3 etc. Finally, when all the stations have been combined together, the average is subtracted from the now heavily-modified T1(t) series, giving us a single series of Temperature Anomalies for the region being analysed.
So how are the Area Weighting values calculated? And how does GISTemp then average out larger regions or the entire globe?
They divide up the Earth into 80 equal area boxes – this means each box has sides of around 2500km. Then within each box they divide these up into 100 equal area smaller sub-boxes.
GISTemp Grids
They then calculate an anomaly for each sub-box using the method above. Which stations get included in this calculation? Every station within 1200 km of the centre of the sub-box. And the weighting for each station used simply diminishes in proportion to its distance from the centre of the sub-box. So a station 10km from the centre will have a weighting of 1190/1200 = 0.99167, while a station 1190 km from the centre will have a weighting of 10/1200 = 0.00833. In this way, stations closer to the centre have a much larger influence while those farther away an ever smaller influence. And this method can be used even if there are no stations directly in the sub-box, inferring its result from surrounding stations.
In the event that stations are extremely sparse and there were only 1 station within 1200 km, then that reading would be used for a sub-box. But as soon as you have even a handful of stations within range, their values will quickly start to balance out the result. And closer stations will tend to predominate. Then the sub-boxes are simply averaged together to produce an average for the larger box – we can do this without any further area averaging because we have already used area averaging within the sub-box and they are all of equal area. Then in turn the larger boxes can be averaged to produce results for latitude bands, hemispheres, or globally. Finally these results are then averaged over long time periods.
Remember our previous discussion of Teleconnection, and that long term climates are linked over significant distances. This is why this process can produce a meaningful result even when data is sparse. On the other hand, if we were trying to use this method to estimate daily temperatures in a sub-box, the results would be meaningless. The short term chaotic nature of daily weather would swamp any longer range relationships. But averaged out over longer time periods and larger areas, the noise starts to cancel out and underlying trends emerge. For this reason, the analysis used here will be inherently more accurate when looked at over larger times and distances. The monthly anomaly for one sub-box will be much less meaningful than the annual anomaly for the planet. And the 10-year average will be more meaningful again.
And why the range of 1200 km? This was determined in HL87 based on the correlation coefficients between stations shown in the earlier chart. The paper explains this choice:
“The 1200-km limit is the distance at which the average correlation coefficient of temperature variations falls to 0.5 at middle and high latitudes and 0.33 at low latitudes. Note that the global coverage defined in this way does not reach 50% until about 1900; the northern hemisphere obtains 50% coverage in about 1880 and the southern hemisphere in about 1940. Although the number of stations doubled in about 1950, this increased the area coverage by only about 10%, because the principal deficiency is ocean areas which remain uncovered even with the greater number of stations. For the same reason, the decrease in the number of stations in the early 1960s, (due to the shift from Smithsonian to Weather Bureau records), does not decrease the area coverage very much. If the 1200-km limit described above, which is somewhat arbitrary, is reduced to 800 km, the global area coverage by the stations in recent decades is reduced from about 80% to about 65%.”
Effect of station count on area coverage
It’s a trade-off between how much coverage we have of the land area and how good the correlation coefficient is. Note that the large increase in contributing station numbers in the 1950s and subsequent drop off in the mid-1970s does not have much of an impact on percentage station coverage – once you have enough stations, more doesn’t improve things much. And remember, this method only applies to calculating surface temperatures on land; ocean temperatures are calculated quite separately. When calculating the combined Land-Ocean temperature product, GISTemp uses land-based data in preference as long as there is a station within 100 km. Otherwise it uses ocean data. So in large land areas with sparse station coverage, it still calculates using the land-based method out to 1200 km. However, for an island in the middle of a large ocean, the land-based data from that island is only used out to 100 km. After that point, the ocean based data prevails. In this way data from small islands don’t influence the anomalies reported for large areas of ocean when ocean temperature data is available.
One aspect of this method is the order in which stations are merged together. This is done by ordering the list of stations used in calculating a sub-box by those that have the longest history of data first, with the stations with shorter histories last. So they are merging progressively shorter data series into a longer series. In principle, the method used to select the order in which the stations are being processed could have a small effect on the result. Selecting stations closer to the centre of the sub-box first is an alternative approach. HL87 considered this and found that the two techniques produced differences that were two orders of magnitude smaller than the observed temperature trends. And their chosen method was found to produce the lowest estimate of errors. They also looked at the 1200 km weighting radius and considered alternatives. Although this produced variations in temperature trends for smaller areas, it made no noticeable difference to zonal, hemispheric, or global trends.
The Others
The other temperature products use somewhat simpler methods.
HadCRUT (or really CRU, since the Hadley Centre contribution is Sea Surface Temperature data) calculate based on grid boxes that are xº by xº, with default value being 5º by 5º. At the equator, this means they are approximately 550 x 550 km, although much smaller at the polar regions. They then take a simple average all the anomalies for every station within that grid box. This is a much simpler area averaging scheme. Because they aren’t interpolating data like GISS, they are limited by the availability of stations as to how small their grid size can go, otherwise too many of their grids may have no station at all. And in grid boxes where there is no data available, they do not calculate anything. So they aren’t extrapolating / interpolating data. But equally, any large-scale temperature anomaly calculation such as for a hemisphere or the globe is effectively assuming that any uncalculated grid boxes are all actually at the calculated average temperature. However, to then build results for larger areas, they need to area weight the averages for the differing sizes of the grid boxes depending on the latitude they are at.
NCDC and JMA also use a 5º by 5º grid size and simply average anomalies for all stations within that grid then area weighted averaging is used to average grid boxes together. All three also combine land and sea anomaly data. Unlike HadCRU & NCDC, which use the same ocean data, JMA maintain their own separate database of SST data.
In this article and the Part 1A, I have tried to give you an overview of how and why surface temperatures are calculated, and why calculating anomalies then averaging them is far more robust than what might seem the more intuitive method of averaging then calculating the anomaly.
In parts 2A & 2B we will look at the implications of this for the various questions and criticisms that have been raised about the temperature record.

Of Averages & Anomalies Part 2B
In Part 1A and Part 1B we looked at how surface temperature trends are calculated, the importance of using Temperature Anomalies as your starting point before doing any averaging and why this can make our temperature record more robust.
In Part 2A and in this Part 2B we will look at a number of the claims made about ‘problems’ in the record, and how misperceptions about how the record is calculated can lead us to think that it is more fragile than it actually is. This should also be read in conjunction with earlier posts here at SkS on the evidence here, here & here that these ‘problems’ don’t have much impact. In this post I will focus on why they don’t have much impact.
If you hear a statement such as ‘They have dropped stations from cold locations so the result is now give a false warming bias’ and your first reaction is, yes, that would have that effect, then please, if you haven’t done so already, go and read Part 1A and Part 1B then come back here and continue.
Part 2A focused on issues of broader station location. Part 2B focuses on issues related to the immediate station locale.
Now to the issues. What are the possible problems?
Problems with ‘bad’ stations
One issue that has received considerable attention is the question of the ‘quality’ of surface observation stations, particularly in the US. How well do the stations in the observation network meet quality standards with respect to location and avoidance of local biasing issues, and how much might this impact on the accuracy of the temperature record.
The upshot of investigations into this is that, at least in the US, a substantial proportion of stations have poor location quality ratings. However, analysis of the impact of the site quality problems by a number of independent analysts suggests that these problems have had almost no impact on the accuracy of the long term temperature record. How could this be? Surely that is the whole point of these quality rankings – poor quality sites can give bad results. So why wouldn’t they?
The definition of the best quality sites, Category 1 is as follows:
“Flat and horizontal ground surrounded by a clear surface with a slope below 1/3 (<19º). Grass/low vegetation ground cover 3 degrees.”
Down to Category 5:
“(error ≥ 5ºC) – Temperature sensor located next to/above an artificial heating source, such a building, roof top, parking lot, or concrete surface”
Lets consider a few of these factors. And remember, we are interested in factors that have an impact on long-term changes in the temperature readings at a site. If a factor results in a bias in the reading but this bias does not change over time, then it will not impact on the analysis since we are interested in changes – static biases get cancelled out in the analysis and have no long-term impact. Firstly, let’s look at the standard enclosure used for a meterological measurement station – the Stevenson Screen:
Stevenson Screen
The screen is designed to isolate the instruments inside from outside influences, particularly radiant effects from its surrounds and rain. It is usually made from a material such as wood or similar that is a fairly good insulator and isn’t going to change temperature too much because of radiant heating/cooling from its surroundings. The double-slatted design suppresses air movement from wind through the enclosure, minimising wind chill effects and restricting rain entry onto the instruments. The double-slatted design also means that any air rising from beneath the enclosure isn’t being preferentially drawn into or out of the box. And the design of the base allows air movement from below while shielding from radiation from below.
So what are the problems which can change the category of a temperature monitoring station to lower than 1?
Slope > 19º
A problem may arise if the station isn’t located on sufficiently flat ground. This can produce air movements that are caused by temperature, resulting in warmer air possibly moving towards the station. However, unless there have been really major earthworks around the site, this factor doesn’t change over time and is unlikely to have a long-term changing impact.
Grass/low vegetation ground cover >10 centimeters high
This can impact on air movements around the station. Also, if the vegetation changes substantially – low grass to shrubs and trees – then this could change water evaporation rates around the station and alter air temperatures. Major increases in vegetation might have a cooling effect on the station due to evaporative effects, while declines in vegetation back to Category 1 standards might have a warming impact. However, unless there is a regular and progressive change in the vegetation pattern around the station, this would not produce an ongoing change of any bias. If maintenance of vegetation around the station over its lifetime has been poor or erratic, then the bias may fluctuate up and down. This would create shorter term fluctuations in the bias but this would tend to cancel out in the longer term.
Shading when the sun elevation >3 degrees
If the degree to which the station and its surrounds are shaded over the course of the day changes, this can alter local heating. Primarily this is going to impact as a result of shading causing differing heating/cooling of the ground under/around the enclosure, resulting in changes in the temperature and flow rate of rising air up through the enclosure. Unless the cause of the shading varies over long, multi-year time frames such as trees growing or buildings rising, the shading effect is not a long-term changing biasing factor. Depending on the cause of the shading, this may cause changes in the bias over the course of a day and over the seasons, but as a multi-year bias, this would remain constant.
Not far enough from large bodies of water
This too is a static bias. The body of water would have a cooling effect due to evaporation that would vary with daily weather conditions and the seasons but would not be a multi-year biasing factor.
Static artificial heating sources
Essentially surfaces such a brick, concrete, bitumen, etc. that can act as local heat stores, greater than normal grass covered earth would be, that can then release heat either radiantly or by heating the surrounding air. These can be vertical structures, horizontal surfaces away from the enclosure, or a horizontal surface beneath the enclosure. The enclosures are designed to minimise radiant heat penetration into the enclosure from its surrounds, so the major impact of such static heating sources is going to be from heating surrounding air which may then pass through the enclosure. This will be worst when such a surface is very close to the enclosure, particularly beneath it, generating rising warmer air into the box.
Also an important factor will be the extent to which any such surfaces tend to form a partial ‘room’ around the enclosure, restricting horizontal air movement. Any such surface will tend to heat the air near/above it, causing that air to rise. More air is then drawn in to replace this, potentially flowing over or through the enclosure. If the distances involved and the geometry of the site result in this new air being warmer than the general surroundings, this could provide a warming bias for the site. Conversely if this replacement air is being drawn from a location that isn’t warmer then there may be no bias at all, possibly even a cooling effect. Ambient winds may also blow warmed air towards, or away from enclosure, depending on wind direction. And the effect of any such bias will vary over the course of the day and the seasons.
However, since the main source of any such bias is the amount and layout of such surfaces and sunlight, these biases won’t change over multi-year time frames unless the area of the surfaces is changing. This could be due to construction, or changes in shading of these surfaces such as by trees growing or building construction nearby. And some of these shading changes could actual reduce the bias over time, resulting in a long-term cooling trend. Also to be considered is whether the site is included within a region that is or becomes urban, in which case the UHI adjustments mentioned previously may cancel out any bias completely. And we still have to allow for area weighting of data from such a site when averaged over the Earth’s land surface. And this doesn’t affect the oceans at all.
Dynamic artificial heating sources
These are similar to the static surfaces, but they are things that actively pump heated air into the environment. Things such as Air Conditioner condensers, Exhaust fans, Heater flues, Cooling towers, Vehicle exhausts, etc. As with the static sources, a key issue here is geometry. They are generating hot air which will tend to rise unless winds blow it towards the enclosure. Does any such device actively blow warm air towards the enclosure? Or does its operation tend to draw air in from elsewhere and over the enclosure? How distant is the device and what is the geometry?
Also how long does the device operate for; 24/7 or intermittently? A station may be next to a large car park, but unless there is continuous activity, even thousands of cars have no extra impact if they are all parked and empty. Does an Air-conditioner run 24/7 or just 9-5 weekdays? Is it a reverse cycle A/C unit also used for heating in winter or at night, in which case it will pump out colder air then that doesn’t rise? How much do these activities vary with the seasons? And ultimately do these activities grow in magnitude over multi-year time frames? Otherwise they again contribute to short-term intra-annual biasing but not multi-year effects. And they may be cancelled out anyway by UHI compensations.
Conclusions about ‘Bad’ Stations
The US network certainly isn’t as good as it should be. There are certainly factors operating there that influence short term daily and seasonal readings and these may have important implications for use in daily Meteorological forecasting which rely on absolute temperatures. However, for long term multi-year Climatological uses, it is perhaps easy to overestimate the impact of these problems.
It easy to understand how our subjective impressions, standing near a poor quality site, seeing an A/C roaring away or feeling the radiant heat from a concrete parking lot nearby, could lead us to think this is a big issue. But the combination of the screening properties of the enclosures, long-term averaging, anomaly-based averaging, and UHI compensation will certainly tend to remove many biases that do not have long term-trend changes. And area averaging over the Earth’s land surface combined with the fact that most of the Earth is water reduces any impact even further.
So it isn’t surprising that the long term temperature trend data doesn’t seem to be significantly affected by station quality issues. That is not to say that there may not be noticeable impacts on shorter term measures – local and seasonal trends and possibly daily temperature range (DTR) effects for example. But for the headline Global Temperature Anomaly, which is a main indicator of Climate Change, station quality issues appear to be a very minor issue, something that ‘all comes out in the wash’.
Station Homogenisation issues
Finally we come to ‘Station Homogenisation’ – the process of reviewing station data records looking for errors that are a result of how the measurement was taken, rather than what the temperature actually was.
A common misconception is that ‘the thermometer never lies’. That the raw data is the gold standard. As anyone who works in any field involving instrumentation knows, this isn’t true; there are always ‘issues’ that you have to monitor for. Any instrument, even a simple thermometer will have its own built in biases.
Sometimes there will be readings that are just plain whacky. And surrounding influences can have an impact. A thermometer out in the sunshine will have a different reading from one shaded by your hand for a few minutes. A caretaker who can work quickly taking the readings when the enclosure door is open will produce a different bias from one who works slowly, or reads the instruments in a different order. Bias and error is everywhere.
If readings at a station weren’t always taken at the same time of day, this can introduce biases. Changes in the instruments used can introduce a bias. Some readings can be just plain wrong. Imagine some scenarios:
The caretaker of a station may have had a ‘big night out’ and not read the thermometer very accurately. There is an error there but we probably can’t detect it.
The caretaker of a station may have a ‘big night out’ every Friday night. Now there might be a regular error in Saturday’s readings. With a pattern like this, we might be able to detect it with statistical analysis. We might be able to correct it but only if we are certain enough.
That caretaker might have had one ‘REALLY big night out’ and next morning broke the thermometer. He replaced it but did he record that fact in the station log? If he did, we know that a change of bias has been introduced between the two thermometers. Then we can compare readings from before and after and try to find the change. But only after we have years’ worth of data from both thermometers. And if he didn’t log it, then we only spot a problem if that station seems to have a strange change compared to nearby stations.
Over time the Stevenson Screen may have fallen into disrepair, resulting in a slow changing bias as outside influences start to penetrate. Then the site is updated with a new screen. Biases removed, although the new screen may have its own small bias. If we now about the change we can try to compensate for it. Eventually when we have enough data from before and after.
The caretaker at the station in Ushuaia right at the southern tip of Argentina records data through the early 1900’s. In Spanish, with poor handwriting – really hard to tell 7’s and 9’s apart. The log sheets are sent to Buenos Aires where the data from this and many other stations are collated and typed up onto summary sheets by a clerk with an old battered typewriter. Then they are filed away; 40 years later they are extracted, faded and old, photocopied on a poor quality early copier and mailed to the US for incorporation into climate databases. Where they must be copied into the database again by hand. How many errors have crept in during that process?
So, we can’t simply take the raw data at face value. It has noise in it. We need to analyse this data looking for problems and correcting them when we are confident enough of the correction. But also being careful that we don’t introduce errors through unjustified corrections. This requires care and judgment and it is sometimes a real detective story. And often corrections cannot be made until many, many years later because you need lots of data before you can spot changes in bias.
So this process of working through the data, trying to make it more accurate is ongoing.
But what of its impact on the temperature record? Again, if the biases at a station don’t change over time, they don’t affect our analysis. Individual errors matter but they will tend to be random, some higher, some lower so when we average over large areas and long time periods, they tend to cancel out. Again, it is problems that cause changing biases that matter. And analysis of changes due to Homogenisation in the record indicate that there are as many cooling changes as warming ones. Such as this from Brohan et al 2006:
Homogenisation Distribution
Conclusion
So, Part 1A looked at how we should calculate the temperature record and why the method used is very important to the result. And that this doesn’t necessarily match our intuitive idea of how it should be done; in this our intuition is often wrong. In Part 1B looked at how we DO calculate the temperature record, that is using the method outlined in part one and that the area weighting scheme used by one record is based on empirical evidence. In Part 2A we looked at some of the areas where the temperature record has been criticised with respect to its broader locale. And in this post we have explored issues related to the immediate surrounds of the station.
I think we have seen that there are many reasons why we tend to overestimate the effect of these problems. This conclusion is consistent with the evidence here, here & here from various analyses that show that these possible problems haven’t had any significant effect on the result.
My conclusion is that we can have a strong confidence in the results produced for the global temperature trend. Any problems will show up more in short-term patterns such as seasonal, monthly and daily trends. But the headline global numbers look pretty robust.
You will have to make up your own mind but I hope I have been able to give you some food for thought when you are thinking about this.

I just don’t get it with this site. On some posts (e.g. a recent one by Willis Eschenbach), there’s the argument that skeptics have never doubted that the world is warming. And then this article comes along to doubt that warming. Which is it? Maybe Anthony Watts can shed some light on this?

Have you heard of the Little Ice Age? Click that link and you will see two things. The first is the obligatory “I believe in global warming like you, so don’t cut off my funding!” message (see Climategate and it’s emails showing what is done to those who fail to support AGW). At the very least, this shows that you cannot say that the scientist here has an anti AGW bias. Second, there is this, yet another site (Wikipedia) that starts with the obligatory “we support AGW!” message, trying to say that the little ice age (according to the IPCC) was not worldwide, and then is followed by actual scientific evidence from , respectively, Europe, North America, Central America, Africa, Antarctica, Australia, New Zealand and Pacific Islands, and South America, which shows that the LIA was, indeed, worldwide, and that the IPCC was wrong or flat out lying. And if you look here, you see a 300 year record of 5 European cities showing about 150 years of cooling, followed by 150 years of warming. We also have records of the cold people wrote about then, such as the frost fairs held on the top of the ice of the frozen river Thames in England, and how George Washington dragged cannon across the frozen Hudson River. Conclusion, it was colder then. Then about 150 years ago, it started to warm up, and warmed up rapidly.
Thus, by the time we get to 1900 or so, it has warmed up from it’s formerly cold temperature. and since then the temperature has remained about the same. Thus we can clearly say “the world is warming”, since it is clearly warmer than the little ice age, and doubt modern, unprecedented warming since 1950, since we can see that for the last 100 years or a bit more, the temperature has not changed nearly as much as it did when the climate changed from the little ice age to the modern warm period.
To sum up:
There was a Little Ice Age, so called because it was cold.
It was consistently colder than now for quite some time, perhaps 300 years.
The Little Ice Age ended, and it warmed up rapidly.
For the last 100 years or more, in the USA at least, it has remained about the same, warmer than the Little Ice Age.
If global warming were true, it would not have remained consistently the same, it would have warmed rapidly after 1950, it did not.
This is according to the most reliable temperature measurement station we have, the ones that have remained constant for 100+ years.
Or in one sentence, it has definitely warmed up since the Little Ice Age, and has remained fairly warm for over 100 years, but has not warmed much more in that 100 years from what it was 100 years ago.
You just need a little perspective, compared to 200 years ago, the world has warmed, compared to 100 years ago, the world has not warmed.

Legatus says:
October 24, 2011 at 3:45 pm
Or in one sentence, it has definitely warmed up since the Little Ice Age, and has remained fairly warm for over 100 years, but has not warmed much more in that 100 years from what it was 100 years ago.
You just need a little perspective, compared to 200 years ago, the world has warmed, compared to 100 years ago, the world has not warmed.
It’s the last hundred years temperature data they’d been playing with as in New Zealand, the second mentions Salinger’s connection with CRU: http://climaterealists.com/index.php?id=6151
http://www.climateconversation.wordshine.co.nz/tag/nz-temperature-records/
It’s taken around forty years to put the record straight..

One thing which I find intriguing is that global temperatures in 1959 are shown to be somewhere in the region of 287.22 K. – I say in the region of 287.22 K because there are a few different figures “on the market” -. The chart I chose, at random, started in 1959 and ended in 2004 and showed a global temperature of 287.77 K for that last of the two years.
There were a few “ups and downs” mainly behind the decimal point during those years but the 287 K bit was reproduced for every year (almost).
So, the intriguing bit for me is; if thermometers placed at a few places on the Earth’s dry surface plus a lot of highly unreliable temperature reports from the world’s merchant and military shipping can be accurate to within 0,55 K or better, then why the Ken-Nell do we spend not just millions but billions or more of $€£ on satellite measurements?

Well with all this talk about missing data, and thermometers dying (none of Mother Gaia’s thermometers die; so she always knows the Temperature) and methods of diddling the averages to substitute for the missing data.
It’s kind of a lost cause; there’s this thing called the Nyquist Sampling Theorem, and it says you don’t have anywhere near enough global stations ; and never have had, to either reconstruct the original continuous Temperature map (don’t need to do); but you do need the ability to reconstruct the original continuous Temperature function; in order to even extract a correct average of the values; whether you recreate the values or not.
So whatever you have as a calculation for the average of the data set, whether data samples are missing or not; the zero frequency signal; which is another name for the average value, is itslef corrupted with aliassing noise; so what one does to fix it is somehwat irrelevent..
And the twice a day, min-max Temperature readings, are already in violation of the Nyquist criterion for the temporal sampling, since the daily temperature variation is not a simple sinusoid with no harmonic content, so at least a second harmonic with a 12 hour period must be present, and twice in 24 hours sampling, will result in the daily average calculation also containing aliassing noise. Not to mention that any varying cloud cover during the day, will totally bamboozle the min-max thermometer (but not Mother Gaia’s thermometers)..

Looks like we have an un-closed italics tag up above.
[Fixed. ~dbs]

Dave Springer @ here
And others talking about adjustments…
Are you aware that many studies, including Fall et al (Anthony Watts’ study), have corroborated the average temperature record for the US? There have also been many blog initiatives taking raw, adjusted, rural, airport, urban data and processed them in different ways and basically corroborated that the US record for average temperatures is robust?
Here is an excellent breakdown of US adjustment choices, focussing on UHI, over at The Blackboard, Lucia’s blog.
Last year Steve Mosher and Zeke Hausfather wrote a seminal post at WUWT discussing numerous attempts at reconstructing global temperatures.
http://wattsupwiththat.com/2010/07/13/calculating-global-temperature/
These were worked up from raw data and from adjusted data, and with various filters and processes, the general result being that the official temperature records are robust. Mosh and Zeke made an excellent case to move on from questions about the need for adjustments to more incisive enquiry about the robustness of some of them (like UHI).
(Check the link above, because it points to numerous projects from both sides of the aisle reaching pretty good agreement on basic ideas.)
So many issues have been investigated – we must not lose sight of all the work that has been done. For example, here is a link to an experiment taking 60 rural stations from around the globe with at least 90 years of uninterrupted data. Result? Good agreement with official (land-only) records.
For US rural/urban comparisons, there have been several blog attempts, which conclude that the difference is negligible.
Recall also the global temperature record from raw data at The Air Vent (just one of the skeptical sites I have cited here). Time and again both sides of the aisle have tested the data thoroughly and found the official records to be fairly robust.
Regarding the top post, there is good agreement between rural and urban temps, from independent analyses, in the literature, and even according to Michael Palmer’s work above.. There is no need to discard recent data, although it would be good to learn why rural stations have dropped off lately – and remember the last time station drop out was thought to be an issue and it turned out it wasn’t anything nefarious, and that it didn’t make a difference to the temp records anyway.

Interestingly, Dr Miskolczi’s estimate of climate sensitivity is… zero.

Yes, it would appear the good doctor does not believe we’ve experienced global ice ages over the past million years.

barry says:
October 24, 2011 at 5:48 pm
Very interesting post, barry, but it is incomplete. Lucy Skywalker sets forth what you need to address as follows:
“Glenn, we’re questioning your unstated assumptions: that the basic data, including the so-called rural, are free of UHI / or that the UHI is accurately enough known – and that the corrections applied are appropriate.
The longstanding records that are the subject of this post, are very strong evidence against all your assumptions. They constitute a statistically significant result that bears out all the UHI-testing surveys I collected here. Stop avoiding this issue. Be a real scientist and face these uncomfortable facts that challenge the BEST work, the Jones-Wang UHI papers, and all the consensus work.”
In addition, I am questioning the entire framework that Mosher and friends inherited but never questioned. As scientists, it is not enough to draw your diagrams across maps of Earth’s surface and apply your inherited statistical techniques when you have empirical evidence of huger importance staring you in the face. The longstanding records from what I call the “well managed” stations are powerful evidence that the records from the other stations are questionable. Shame on you for ignoring those long standing stations. I take it that you cannot think outside the box enough to address this empirical matter. But it is your duty now as scientists to explain empirically the differences between the long standing stations and the others.

My dear Smokey,
I’d rather lick my way to the centre of the earth than attempt to make points through your endlessly shifting goal posts. Been there before, bubba. Bought the T-shirt, got back on the boat.
You’re welcome to respond to the content of my post rather than snarking about a word in it. It would be on-topic to boot. I may even reply with more consideration.
🙂

BTW, one thing I would surely like to see, as an amendment and addition to this article, see if there are stations like these, with guaranteed long and accurate records, in countries other than the US. Yes, I know that others have shown in this thread that there are other very long records, what I am looking for is
1) How accurate and reliable are they? This would require them to be multiple stations, rather than just, say, at one spot. The GISS record hare is of 600 stations, are there any such records from other countries?
2)I would like to see it over the same 100 year time frame, apples to apples.
3) Unadjusted data, of course.

peetee says:
October 24, 2011 at 3:31 pm
“uhhh… the article references to ‘abstract’ – where’s the actual published paper to be found? What journal? Uhhh… what peer-reviewed journal? Surely, surely…. this can’t be a pre-release!!!”
—
peetee, this IS the FINAL release, and the paper is peer-reviewed right here. That means you and your peers!
“But wait”, I hear you saying, “you can’t be serious! My troll posts count for peer review?” To which I reply: “Why yes, for sure! You have no idea what real academic peer review can be like.”
Glad we could clear that up.

Theo @ here
Included in my post are links to posts on the questions that Lucy Skywalker raised, testing for UHI in the records and in the data. This work has been done under the auspices of skeptic websites and others.I have not included the literature references as I assumed these would be disregarded out of hand. I took pains only to reference sources that have the trust, or seem to have the trust, of the skeptical milieu.
Lucy’s riposte seems to be that there is doubt that stations have not been properly screened to distinguish rural from urban. There are a number of different methods, Zeke works with three in his post on UHI in the US. At Residual Analysis, the screening methods tested are (1) GHCN classification, then from population density, and then from (2) ‘vegetation’ metadata,from which he sourced actually rural (no settlement nearby) stations.
Other parameters have been tested – airport trends (example, example) is a good example, where the results are not much different than urban, rural or all sites.
Unsure about GHCN data? Then use the GSOD data set, which shows a similar profile,/a> to the other records – this has been compiled outside the official channels by citizens bloggers. Here is a comparison to GHCN data from 1950 (caveat – GSOD has poorer coverage before 1974, at least that was the case last time I read up on it a year ago).
My original post to this one is aimed more at the people here who are impugning the methods and motives of the official compilers of temperature data and records. There is no call for that when there is so much material – from skeptics as well – corroborating the official records. Even Anthony Watts paper (Fall et al) corroborates the averaged temperature record for the US.
In reply to your comment to me, Lucy’s queries are of course valid, but it is wrong to suggest that they have not been addressed.

Garrett Curley (@ga2re2t) (October 24, 2011 at 3:13 am)
What’s your pleasure?
A world which is warming, cooling or static?
Just tell me your preference and I’ll give you a time frame to match your desires.
But, as for human contribution…
See my previous comment.