A Considered Critique of Berkeley Temperature Series

When is all the raw data and code going to be released? My understanding is that what has been released thus far is of very limited value.
I know some people have said it’s just preliminary stuff and the real stuff is coming but if the papers are ready for peer review and Muller is all over the news it seems odd that what was released is not particularly useable to people who want to get to the heart of the methodolgy that was used.

Concern #1 speaks loudest. Once you start hacking at the data, you basically add a bias. Mother Nature doesn’t do that, nor does Anthropogenophecles, the god of significant figures.

David says:
November 3, 2011 at 7:06 pm
“…it seems odd that what was released is not particularly useable to people who want to get to the heart of the methodolgy that was used.”
People who frequent “preprint libraries”, although vast in number, are not qualified to critique methodology.

Well spoken. The words you use are the same as the thoughts we are thinking as we read along. Succinct. And to the point. Thanks for what you and Anthony do.

“Admin: just some housekeeping. Can you please perform a replace of Berkley with Berkeley? Thanks.”
It’s actually Berzerkely.

Matt,
“there is significant UHI but cities only account for a small fraction of land surface and have only a small impact on global averaged trends”. Cities, towns, villages, suburbs, neighborhoods, hamlets, etc. are where the thermometers are. Population centers are over-represented in the temperature records. Read the BEST paper on UHI and tell us how they dealt with that issue.

I will play Steve. If you are asserting that a reasonable upper limit on UHI is .1C per decade, I will take that. I only hope that this time your guessing game has and ending.

Matt says:
November 3, 2011 at 7:34 pm
“Someone should point out to Jeff that there is a difference between saying “there is no UHI” and saying “there is significant UHI but cities only account for a small fraction of land surface and have only a small impact on global averaged trends”. Look, I’m not interested in debating whether or not the latter statement is true. But, the fact that “kid’s can measure” local urban heat islands doesn’t mean it has a significant impact on globally averaged means.”
Your comment contains the equivocation on the phrase “no UHI” that is found in BEST’s work. You seem to be aware of the problem but unaware that BEST uses the equivocation in a fallacious argument. They begin talking about UHI as a local phenomenon, which is the topic they took from Anthony but conclude that UHI has no impact on global average temperatures, a new topic that was not addressed by Anthony. In addition, introducing the topic of “global averaged trends” at all is a beautiful example of a Red Herring; that is, they introduced a topic that might sound like the actual topic but is actually irrelevant to it.
Anthony’s claim is that there is local UHI and that it has a disproportionate effect On The Measurement of global average trends because thermometers are disproportionately found in settings affected by UHI. Anthony does not claim that UHI has a causal effect on global temperature; rather, his claim is about the measurements that feed into measurements of global temperature.
In not addressing Anthony’s concerns about local UHI (and in changing from Anthony’s 30 year period for which there is metadata to a 60 year period for which there is none for the first 30 years) BEST betrayed Anthony’s trust yet chose to give the impression that they did address his concerns. That is plain old deception for the purpose of appearing to attain a success that was not attained, not earned, and not deserved.

Steven Mosher says:
November 3, 2011 at 7:57 pm
“1. We accept the UAH trend say .18C per decade
2. we look at CRU warming more at .28C per decade”
Where did you get these numbers? Does CRU claim that temperatures have risen at .28C per decade? For how many years? Let’s take 60 years as it is very important in some of BEST’s work, especially that having to do with Anthony and local UHI.
Let’s see, .28 per decade times 6 decades yields 1.68C for the most recent sixty years. In turn, that is equivalent to about three degrees Fahrenheit. Does CRU claim that global average temperature has risen 3F in the last 60 years?

Mike Bromley the Kurd says:
People who frequent “preprint libraries”, although vast in number, are not qualified to critique methodology.
Maybe you should have looked at the data yourself.
A cursory look at the preliminary data (plain text version) shows how useless it is.
File data.txt contains adjusted monthly averages, including over 3000 values that are either below -90 Celsius or above 57 Celsius. Some values are below absolute zero (-273), and some are in the many thousands (I saw values above 28000 Celsius).
In site_summary.txt over 600 sites have no known location.
If this is the data set used for the papers, they must retract them.

Doesn’t basic atmospheric physics demand the troposphere warm at a significantly higher rate than the ground? Why wouldn’t this be point #4? Am I missing something?

Since UHI is a real and undeniable FACT, if we cannot measure/detect UHI then there is something wrong with the resolution of the measuring equipment (algorithyms used) PERIOD.
I would be very sceptical of a data set that is unable to detect UHI. I acknowledge that on a trend basis it could be rather small but it should there and should be identifiable.

Slightly OT, but apparently Anthony is scheduled to be the guest during the first hour of tonight’s Coast to Coast radio broadcast (in the US), starting in about twenty minutes.
I wonder whether a discussion of BEST is planned. The only description given on the website is: First Hour: Meteorologist Anthony Watts comments on the global warming issue.
See http://www.coasttocoastam.com/

Uri says:
November 3, 2011 at 8:55 pm
Mike Bromley the Kurd says:
People who frequent “preprint libraries”, although vast in number, are not qualified to critique methodology.
Maybe you should have looked at the data yourself.
I don’t have to. Dang those pesky /sarc tags. My comment on the masses in the PP libraries? First of all, you missed it, Uri. What masses? (read Muller’s spin on the thread).

(1) and (3) Both of these should be testable with the full (presnipped) BEST dataset. Temporal snips do not necessarily include vertical temperature shifts. Vertical temperature snips can use an asymmetrical offset criteria in line with the mean decadal (or a shorter or a longer baseline) trend line (by ineration if the underlying trendline is unknown to begin with). The main goal being to minimize snipping biases, if they do in fact exist at all.
(3) If the subsample via Monte Carlo (random) selection truely represents the total sample, across all groups, statistically speaking (no major under-/over-sampling), and both populations are quite large (1/8 of ~40,000 is 5,000), then the BEST method for station uncertainties may, in fact, scale quite accurtely (but they could do 1/7, 1/5, 1/5. 1/4, 1/3, 1/2 as a check, if necessary).
(2) The BEST data did not say that there was no UHI. What they did say is that the UHI had an insignificant effect on the global land temperature time series, simply due to the fact that urban areas are a relatively small precentage of the total land area. This was the BEST preliminary result for global versus rural only, -0.0019 ± 0.0019 °C/yr (meaning that rural had just a barely higher trend line than global.
So for instance, one could cherry pick and rank all temperature stations showing the highest rates of increase, then remove all rural stations as outliers, and voilà UHI proven (but that still won’t change the global temperature record, as again, urban areas are a very small part of the total global land area).
In the end this is what BEST wanted in the beginning, open peer review. Just don’t expect the BEST efforts to go about chasing all the various hundreds and/or thousands of leads sent their way.

It is obvious many are treating this BEST stuff as serious if flawed work. It is work, it is flawed, but serious, I think not. At best this is simply an attempt at getting attention. At worst an attempt at obfuscation. I think this foolishness needs to be completely ignored until all the raw data and codes is released and the papers are properly published.

richard verney,
And if you look at figure 4 in the BEST UHI paper you will see that the distribution of the cooling trend stations is homogeneously distributed among the warming stations. Look at the almost continuous urban area on the North Atlantic coast of the US. Don’t those blue dots among all that red look weird? There is something wrong with the data, or the methods of analyzing the data. You can also see the red dot concentrations where major cities are located.
http://berkeleyearth.org/Resources/Berkeley_Earth_UHI.pdf
Looking at figure 2, you can see where their “very-rural” sites are located. Match that up with this population density map.
http://modernsurvivalblog.com/wp-content/uploads/2010/06/world-population-density-map.gif

EFS_Junior
(2) The BEST data did not say that there was no UHI. What they did say is that the UHI had an insignificant effect on the global land temperature time series, simply due to the fact that urban areas are a relatively small precentage of the total land area.
Wrong metric. The issue is not that urban areas are a small proportion of global area. The question is -> is the influence of urban sited thermometers on the temperature record an equally small proportion of the influence of all thermometers on that record.
Further, looking only at “urbanization” on the grand scale of cities is to play semantic games with the concept involved. The problem is not merely the several degree rise in temps across the profile of a megalopolis. It is equally (if not more so) the problem of ‘rural’ thermometers being impacted by anthropomorphic warming on very local scales – impacts that have orders of magnitude less effect on global temps than do big cities, but which can have a much larger effect on the microclimate that the thermometer is measuring. And, of course, how those locally warmed thermometers’ readings end up affecting the homegenization adjustments applied to the data.
Thermometers have always been placed near houses, even in rural areas, simply because they have to be convenient to the people who monitor them. With MMTS, they tend to be placed even closer to buildings, as they are tethered by a cable. Such “UHI” doesn’t even require any development to grow in magnitude, it merely requires that MMTS cables of any length be relatively expensive …

Max Hugoson: We have NO assurance, without DAY LONG RECORDINGS that peaks and troughs were properly recorded.
I think you are concerned with Time of Observation bias, which has a long history of study. Don’t entirely understand it myself, but a good start might be W.F. Rumbaugh’s article in the October 1934 issue of Monthly Weather Report. He used a 6 year thermograph record (those would be DAY LONG RECORDINGS) to evaluate the accuracy of min/max measurements.
Which gives me occasion to ask a question that has been bugging me for some time. Perhaps some reader will know. The weather service was taking thermograph recordings in quite a number of stations from the 1800’s and well into the 20th century. What happened to those records? What contribution would they make to climate studies (if any)?

BEST is not testing for the existence of the Urban Heat Island effect. That is not controversial. It is looking for increased warming at urban sites, not higher absolute temperatures. More specifically:
“Time series of the Earth’s average land temperature are estimated using the Berkeley Earth methodology applied to the full dataset and the rural subset; the difference of these shows a slight negative slope over the period 1950 to 2010, with a slope of -0.19°C ± 0.19 / 100yr (95% confidence), opposite in sign to that expected if the urban heat island
effect was adding anomalous warming to the record. The small size, and its negative sign, supports the key conclusion of prior groups that urban warming does not unduly bias estimates of recent global temperature change.”
Setting aside the exact details of their analysis, if “very rural” sites are warming at the same rate as all of the sites, it’s difficult to argue that urbanization is exaggerating “global” warming. The fact that “very rural” sites make up a minority of all sites is irrelevent provided they are weighted according to their spatial distribution.

I see Leif beat me to the minmax thermometer point.
The troposphere is warmed by and from the ground up
Excepting aerosols, which warm the troposphere by absorbing and scattering incoming solar irradiance.
One study from India
http://www.agu.org/pubs/crossref/2011/2011GL046654.shtml

Has anyone done a study on what time the minimum temperature is reached? If I heat one pot to 300 F, and another to 400 F, and I set both pots outside overnight, they will both be the same temperature at some point, but the 300 F pot will get there more quickly.

Why is it that we cannot seem to agree that UHI (which is a well observed fact and easily visible in the data) is different to dUHI and dRural. BEST did not attempt to investigate UHI, they did (possibly rather poorly) attempt to detect the difference betwen dUHI and dRural. They conclude dUHI/dRural shows no divergence but that any long term warming signal is present in both.
I am not happy about quite a bit in the BEST study but I do wish people would stop conflating UHI and dUHI and thereby create Straw Man arguments.

Don Monfort says:
November 3, 2011 at 10:25 pm
“Did you actually read the BEST UHI paper? They stated they could not use MODIS data to separate urban from rural areas, so they broke areas down into rural, and very rural. […] They state: “Rather than compare urban sites to non-urban, thereby explicitly estimating UHI effects, we split sites into very-rural and not very-rural.”
Looks like the “best” climate science of 2011 still cannot account for the development of UHI over time.
Maybe, if we give them ALL of our money, that’ll suffice so that they can find out how human settlements around temperature stations developed over time.
They could use historic maps.
BEST was obviously an attempt at fabricating a publication with a media campaign attached that they could rush out the door in the run-up to Durban. Rename it to “FASTEST, CHEAPEST AND WORST”.

This is my simple approximation (or perhaps lower limit) for UHI related global temperature increase since 1900:
Spencer has computed a nice graph drawing UHI over population density.for the year 2000.
http://www.drroyspencer.com/wp-content/uploads/ISH-station-warming-vs-pop-density-with-lowest-bin-full.jpg
The curve is close to a logarithmic function, a quadrupling of population density increases UHI by about 0.3-0.4 deg Celcius independant of the initial population. I call this increase in UHI subsequently dUHI. The curve in itself already explains why BEST did not work, as dUHI for a population increase from 10 to 20 is about the same as for an increase from 1 million to 2 million. Very rural and NOT very rural is then no criteria to identify locations with low/strong dUHI.
Now since 1900, the world population has risen from 1.7 to 7 billion. According to Spencer’s curve such an increase in population density would result in an UHI increase dUHI of approx. 0.4 degrees.
2 notes:
1. The graph covers only the USA, it may differ elsewhere..
2. The graph allows only an estimation for an instant population increase in a thought experiment. Other factors contributing over time as well to UHI are not included, such as ever increasing energy consumption, paving of roads, installation of air conditioning, deforestation etc. Most of these factors should further increase UHI.

Jeff, I agree about UHI, B-est is a total fail on that issue. More on that later.
#1 is a big problem too. A far as I can tell they have done zero assessment of the effects of this splicing technique. They certainly don’t publish any and the only relevant comments in the paper seems to show they think the effects “should be trend neutral”. To my reading that is a fairly honest and clear statement that they have not looked.
So let’s look for them.
Since Dr Muller has chosen to make very public comments about the level of rise since 1950 I looked at how much that result depended on the time scale and start date. I looked at the simple OLS “trend” over a given interval for all possible start dates.
I did not get what I expected.
50y trends:
http://tinypic.com/r/10i5is4/5
Oh, oh! What are those flat bits about? Decade long periods with ZERO trend. That’s not climate.
Let’s check 10y trends:
http://tinypic.com/r/21ophd/5
OK, that’s more like it.
Now let’s look at , say, 12y trends.
http://tinypic.com/r/1449ol/5
WATTSUPWITHTHAT !???
According to BerkeleyEST there was virtually no 12 year period in the last 200 years that had a non zero trend.
This explains why their record shows less variation than other records.
Not only have they removed the noise , they’ve removed half the signal as well.
(I plot the resulting trend against the date of the middle of the range, so “last 50 years” for the most recent last 50 years gets logged at it’s midpoint of 1985)
Careful with that scalpel Doctor !!

I thought Jeff Id was a smart guy but this is sounds either wrong or badly expressed.
———
– UHI effect.  The Berkeley study not only failed to determine the magnitude of UHI, a known effect on city temperatures that even kids can detect, it failed to detect UHI at all. 
———-
There is a distinction between
1. the existence of the UHI which is considered to be measurable by everyone
2. The TREND in the UHI which is commonly positive, but not necessarily always.
3. The effect of the TREND in UHI which may contribute to the trend, and possibly bias, the surface temperature trend.
There are some if buts and maybes around 2 and 3.
As far as I can tell BEST is saying 3 is not turning out as expected here. And confirms previous research.
And gut-feelings don’t count here.

Just seconding the observation made by Richard Courtney upthread about land-use change.
The land has changed dramatically here in Southern Minnesota and Northern Iowa. I blame my father-in-law for that. He owns a D-9 Caterpillar bull-dozer and every time he purchased a new farm over the last few decades, the first thing he would do is level the woodlots and building sites to plant corn and bean.
So how do you measure the effect of transforming a landscaped checker-boarded by green into one that is uniformly black (or near black) for six months of the year?
Also, what exactly defines “urban”. I know, I know population. But seriously, are all urban centers equal? Compare a mature neighborhood shaded by large elms to twenty thousand acres of new suburb. In other words, growing secondary cities are hotter than more established urban centers. How do we account for that?
Even the rural areas are hotter. Our local GHCN station at Zubrota (72644003) is located at a sewage treatment plant that has added a lot of concrete over the last couple decades as well as a new neighborhood – upwind.
So how much warming can be attributed to these factors? .05C? .1C? Does anyone care?

Another top notch analysis Jeff !!!
Great work !

I suppose that the fact that BEST were unable to distinguish any different trends between dUHI and dRural can be interpreted another way. We know that trend figures produced from a mixture of dUHI and dRural (the Estimated Global Temperature and its associated trend) differ from trend figures obtained from other sources, i.e. satellites. Perhaps both dUHI and dRural are truely showing the same contamination to the basic signal that their difference from these other sources imply.
Therefore, possibly, this surprising result in identical trends for both dUHI and dRural can be used to determine the magnitude of the different trends between satellite and ground observations.

Dear Jeff,
How do you know that all those surface measurements reflect the earth and not the policy of the WMO? Here is a recipe for data fraud: each year you may include some new stations and some others may be dropped. Open stations as you like. However, before closing stations you should compute the slope of their private regressions. Even in a small area warming and cooling stations may co-exist. You only have to close more cooling stations than warming ones in order to create a warming world. It’s better to invest in methods of detecting data manipulation even for BEST. One method is survival analysis. Compute as dependent variable for each station the number of years it was on duty. Define as independent variables (1) latitude category, and (2) station’s melody. I defined the latter on 8000 GHCN stations as follows: compute regression slopes for the periods 1930-1969 and 1970-2009. Dichotomize them as up or down. You get four melodies, down-down, down-up, up-down, and up-up. It’s not surprising that stations on the northern hemisphere ‘live’ longer than in the tropics. But I found that their life expectancy also depends on melody, differences of more than six times the sum of the associated standard deviations. This is just some private exploration, but you may understand that I do not trust the data even if they are called BEST.

@Stephen Rasey says: November 3, 2011 at 9:56 pm
Thank you! That was a wonderfully understandable post about how low frequency info can be disappeared and high frequency info emphasized.

If rural and UHI are trending the same…
…look for a reason
It could be something as simple as CO2 makes plants darker green……..

Richard S Courtney says:
I write to point out a confusion that seems to exist in the minds of several posters to this thread;
i.e. UHI and local anthropogenic effects on temperature are not the same thing.
Yes, they very often are, with the only difference being one of scale. A ‘rural’ thermometer may yet be influence by proximity to ashphalt and other heat collecting surfaces, heat dissipating equipment, heated buildings, etc. UHI is nothing more or less than those same impacts aggregated over a larger volume.
This is why I stated that limiting analysis of UHI to only the largest aggregations of the effect – cities – was a effectively a semantic arguement. They are only looking for the UHI effect in ‘urban’ areas. Nonsense. The importance of the UHI mechnaism is its proximity to thermometers, not how close it is to hip-hop artists.

A second trial (perhaps I used a forbidden word). Has the BEST team done a survival analysis? I did it for 8000 GHCN stations. Define as dependent variable the number of years a station was on duty. Define as independent variables (1) latitude category, and (2) stations melody. Compute for the relevant stations the regression slopes for 1930-1969 and 1970-2009. Dichotomize these as up or down. You get four melodies. It’s not surprising that stations life expectancy depends on latitude: on the northern hemisphere stations are for more years on duty than in the tropics. Having controlled for that, suppose the expectancy depended on melody. What would you conclude? Before applying sophisticated statistics, quality controls are needed. I have my reasons not to trust these data.

WORST not BEST. Without Objective or Robust Statistical Techniques.

Thanks Jeff,
Will look at your material. However, might not have time to look at everything in depth and I couldn’t find any analysis that points to the UHI as the source of discrepancy between satellite and earth-based measurement or excludes the possibility of other affects? Could you point me specifically to that analysis?
Also, one point of clarification:
We are talking about a few percent discrepancy in the slopes, right? As I’ve suggested, measures of statistical significance are a little hairy. If you don’t account for systematics, I’d be a little skeptical. But, let’s assume that there is a statistically significant affect. It looks like the affect amounts to a 3% difference in slope between satellite and earth-based reconstruction over the lifetime of the satellite record. Am I right on that? Personally (and I acknowledge that this is a subjective statement) I’d say that it is pretty impressive that such vastly different methods as satellite and thermometer reconstructions come within 3% of each other. I also feel that a 3% affect is not large enough to justify Anthony’s claims that “it cannot be credibly asserted there has been any significant ‘global warming’ in the 20th century” or that “all terrestrial surface-temperature databases exhibit signs of urban heat pollution and post measurement adjustments that render them unreliable for determining accurate long-term temperature trends” (http://scienceandpublicpolicy.org/images/stories/papers/originals/surface_temp.pdf).

Dang, I hate making trivial errors. “SHOW ME” not “So me”.
Max

Steve Mosher,
What’s wrong with the map? It was offered as an eyeball comparison with the lousy map in the BEST UHI paper (figure 2). Do you think that figure 2 belongs in a scientific paper? It’s mislabled (the black dots are not the rural sites but the very-rural sites), and what’s with the totally black USA? Yes I have seen their explanation, but I am sure you could have done much better than that. And anyone should be able to compare the maps and see that the allegedly very-rural stations (black dots) are not in major areas that are obviously the least populated places on the planet. Look at Africa, S. America and Australia. Where are the black dots that represent stations far from urban areas located? Look at Australia, clearly the black dots are not concentrated in the sparsely populated interior, but in the populated coastal areas. Same story in Africa and S America. Not too many black dots in the Sahara Desert, or the Amazon. Go to the cities to find the black dots. The paper says that the US appears to be very black, but only 18% of the sites are very-rural. Again look at Africa, S America, Australia and other places with large sparsely populated areas. See much black? Are you following me here, Steve?
Look, the point is not that they picked relatively less populated areas out of their 39,000 stations. The problem is that urban, one-horse town, burg, hamlet, suburban and blah…blah…blah areas with human influence on local temperature are heavily overrepresented in the data. The truly very-rural sites are generally not there, because there are very few stations in those places. Do you believe that this study has done what Muller claims for it? That it has laid to rest climate skeptics’ doubts regarding UHI effect in estimating global warming?
Oh, but some geniuses are claiming that this paper was not about UHI. Read the title of the paper, or actually read the paper, if you have a few minutes. And read Muller’s article in the WSJ, in which he falsely claimed that BEST compared urban to very-rural, distant from urban areas, and the climate skeptics got no case on UHI any more. See how many times UHI, or references to UHI are found in those things, which you have not yet read.

Steve Mosher,
I forgot to address this:
“you are misunderstanding what they mean.
what they mean is that sites with No built pixels are very rural ( 16,000)
all other sites are called NOT very rural.. that includes urban”
I know that includes urban. It also includes rural, because with the tool they chose to use they cannot distinguish urban from rural. Is that the right tool to use in trying to find out something about UHI? Isn’t this BEST UHI study rather amateurish? Jeff’s criticism is valid, and that is why he hasn’t heard back from Prof. Dr. Muller.

Matt says:
November 3, 2011 at 9:09 pm
The strongest thing Anthony’s paper seems to be saying is that the poor quality stations tend to amplify diurnal variation. That just adds to the noise, but as Anthony admits, the noise cancels out when you get an average trend. It seems that in Anthony’s own summary of the Fall et al paper, he admits that including the poor stations does not bias the average daily temperature trends towards more warming (if anything, towards less warming).

It said the opposite: “While the 30-year and 115-year trends, and all groups of stations, showed warming trends over those periods, we found that the minimum temperature trends appeared to be overestimated and the maximum warming trends underestimated at the poorer sites.” See http://wattsupwiththat.com/2011/05/11/the-long-awaited-surfacestations-paper/
In other words, minimum temps are rising faster than trend and maximum temps lower than trend at the poorer sites, giving a smaller diurnal variation. This will reduce noise in the data, giving erroneously reduced error bars and confidence intervals, unless this is catered for through UHI/siting adjustments/corrections. Reduced diurnal variation, particularly in winter, is a key prediction of CO2 warming thoery, so poor siting gives a false degree of confidence that CO2 is indeed having this forecast effect.
Too many people seem to have misunderstood and failed to pick up this key finding from the Fell et al paper, instead proclaiming that the trend of ‘average’ (min-max/2) is unaffected by siting issues and that therefore the Surfacestations project was a waste of time. None so blind, etc…

HenryP,
In your article linked above you say:
“In the wavelengths areas where absorption takes place, the molecule starts acting like a little mirror, the strength of which depends on the amount of absorption taking place inside the molecule. Because the molecule is like a sphere, we may assume that ca. 62,5% of a certain amount of light (radiation) is sent back in a radius of 180 degrees in the direction where it came from. This is the warming or cooling effect of a gas hit by radiation. Same effect is also observed when car lights are put on bright in humid, moist conditions: your light is returned to you!!”
I think you are confusing absorption and scattering. Reflection of light off of a molecule is different from absorption and reemission. Absorption represents a change in the quantum state of a molecule. When a photon of the appropriate energy is absorbed by a molecule, that molecule is excited to a higher energy state. Eventually, the molecule will return to the the “ground state” (lowest allowable energy state) and reemit the photon. But, the direction of the reemission depends on the particular excitation and is not strongly correlated with the original direction of the photon. I am simplifying a bit, but that’s the general picture. Reflection, on the other hand, is elastic scattering…and it is specular (angle of reflection = angle of incidence).
Your approximation of molecules as spherical is problematic. CO2 and O2 are very much non-spherical. Organics like Methane are very long chains of atoms and even less spherical.
Also, even if you make this spherical approximation, your claim that the light is reflected 180 degrees is incorrect. The reflection angle depends on the incidence angle. If a ray of light hits a reflecting sphere off-center, the light will not be reflected back along the same direction it came. Sit down and draw the ray optics. Your argument about the light from your headlights “coming back to you” drives the point home. If the light were reflected at 180 degrees it would come back to the headlights…not to you. You are seeing scattered and reflected light at a different angle from 180 degrees. And, you are only seeing a small fraction of the light. The bulk of it is absorbed, forward scattered, or transmitted.
In the end, it is true that atmospheric gasses reflect some light back into space. This is a component of the earth’s “albedo” and it does have a slight cooling affect. However, ice and clouds are much more important to the earth’s albedo than atmospheric gasses.
When greenhouse gasses absorb IR radiation from the sun, the direction of reemission is not specular (mirror image reflection). It is randomized. And it is this randomized reemission that contributes to the warming affect of GHGs. At particular IR wavelengths the warming from absorption and reemission is much more significant than the cooling from back reflection of the gasses.

“At particular IR wavelengths the warming from absorption and reemission is much more significant than the cooling from back reflection of the gasses.”
You are doing the same thing he did; conflating absorption and re-emission, and reflection.
Doesn’t some of the randomized re-emission of absorbed IR radiation from the sun, go back into space?

Richard says:
November 4, 2011 at 2:24 am
“Why is it that we cannot seem to agree that UHI (which is a well observed fact and easily visible in the data) is different to dUHI and dRural.”
=========================================================================
Richard has this right, assuming he means dT(UHI)/dt and dT(Rural)/dt. Too many in this discussion seem to be confusing the difference between the trend in both urban and rural temperatures over time, with the differences between urban and rural temperatures at a given time (the UHI effect).
It would seem to me that the effect on a weather station being engulfed in the UHI is not initially a constant, but something that would rise to some maximum level over time, then remain constant over time.
Consider two stations A and B, a couple of hundred kilometers apart. Both stations were rural at some time in the past. At that time temperatures at both stations were the same. Now assume that station B starts to be engulfed in urbanization from a nearby city. Station B temperature starts being greater than A because of the urban heat island. As the city overtakes Station B the difference between Station A and Station B becomes larger. Finally Station B is fully urban, and (assuming no change in heat added by urbanization) the temperature difference between Station A and Station B now becomes a constant. The temperature anomaly graph with time will look different for the two stations if the time covered begins when they were both rural. But if it begins when Station B was fully urban there will be no difference.
Of course anthropogenic UHI heat content is not constant, or the same for any two urban areas.. Which makes land based historic data very difficult to sort out.

Matt says:
November 3, 2011 at 7:34 pm
“Someone should point out to Jeff that there is a difference between saying “there is no UHI” and saying “there is significant UHI but cities only account for a small fraction of land surface and have only a small impact on global averaged trends”.
Note that the second quote is -not- an exculpatory factor. It’s a -compounding- factor. If cities are a miniscule faction of land mass (true) –but the majority of temperature measurements happen to be within (or near enough) to the cities– then your entire data set has an exceedingly skewed sample.
That is: Cities -do- have a ” a small impact on –actual– global averaged trends”, but the –measurements– are unfortunately concentrated in exactly those areas — and thus the UHI –effect– can have an overwhelming effect on the global –measurements– of the average trend.
IOW:
We’d be better off flat-out excluding data from anywhere within 10 miles of a city. That is excluding a –small– portion of the landmass with known-corrupted data. They do happen to be a vastly disproportionate fraction of the available surface stations
1) CRN1 is a -prerequisite- for competent data, not a sufficient condition.
2) UHI is not a microsite issue, is unfixed by CRN1 quality stations, and happens at -far- more stations than “1% of land mass” would predict – and thus is non-negligible.
3) A calibrated point-source measurement is not a calibrated grid-cell average measurement.

Steven Mosher: Can we conclude ( as Christy, Spencer and Steve do) that the difference
.28 – .18C or .1C per decade could be UHI?
In Steve McIntyre’s wording, I think that is reasonable.
There have been a number of good critiques of the BEST analyses, and this is one of them. point #2 can’t be decided (imo) can’t really be addressed without some study of the particular surfacestations: what distinguishes the “warming” from the “nearly constant” from the “cooling” stations. But the comparison of the satellite trend to the surface station trend is reasonable.

old engineer,
Can you name some of those who are disagreeing with Richard?

Stephen Rasey: The splices contain no low frequency information in the spectrum where we expect GW and UHI signals to exist. But when they glue the spices together, low frequencies return – but from WHERE? It can only come from the glue. It is not in the data anymore.
They cut and splice where there is a jump discontinuity in the data. It is the act that produced the jump discontinuity (which may have been relocating the thermostat, or putting an asphalt runway near it) that perturbed the low frequency signal. Cutting and slicing restores the low frequency signal that the jump discontinuity perverted.

You say Berkeley, I say Berkley. Probably best to spell it correctly because Berkley is Cockney rhyming slang. English is a rich and fascinating language dear to my heart, my only reason for offering this. Anyone who does something extremely stupid can be called a “great steaming birk”, (Berkley contracted) with little chance of causing offence. This seems odd once you appreciate the true meaning of the word.
For those not au-fait with old East London Cockney rhyming slang… Tit for tat rhymes with hat, so the Cockney’s hat is his “titfer”. China plate rhymes with mate, so his buddy is his “China”. Richard the third rhymes with bird and they gave our children the “Dicky birds”. True Cockneys have to be born withing the sound of Bow bells. They greet each with “Wotcha” which comes straight from the high language of chivalry, “What cheer Sir knight?” But I digress.
“Berkley Hunt” is the female, anatomical equivalent of the male “Hampton Wick”, so I will quite understand if this reply gets moderated out of existance.

So, essential, your reasoning is that the temperature dataset and quality of that dataset and the statistics and its methods and the quality of said statistics and methods is absolutely positively in all shapes and forms completely crap and can’t be trusted to be used to prove anything diddley squat, but you believe that the temperature has gone up anyway.
Exactly how does that not make you sound like an utter global warming fundamentalist too deep into the bubbles of his own bong? :p

Verity
,my view from a number of blogs is that Steven Mosher is about 14 years old…and is going through puberty. ignore him. he is worried about his pimples…he has a telescope and a laptop and he thinks he knows things.

Spen says:
November 4, 2011 at 8:51 am
I am still puzzled by confidence levels/error range. I assume the accuracy of the older temperature measurements was no better than +/- 0.5 deg C. Shouldn’t that degree of accuracy apply to the anomaly?
_________________________________
You might want to look at AJ Strata’s article about error in the temp record.
http://strata-sphere.com/blog/index.php/archives/11420

well done Verity for resisting the passive aggressive teenager that Lord Stephen Mosher is trying to become – he is obviously becoming more and more opposed to the idea of truth as he grows ever younger and more immature

@Septic Matthew says: They cut and splice where there is a jump discontinuity in the data. It is the act that produced the jump discontinuity (which may have been relocating the thermostat, or putting an asphalt runway near it) that perturbed the low frequency signal. Cutting and slicing restores the low frequency signal that the jump discontinuity perverted.
It is a legitimate intent to preserve the low frequency (GW and UHI) part of the spectrum. But does the suture do that? Have I made the point clear enough that the splices cannot contain the low frequencies? The low frequencies must be part of the gluing process. And just what data do they use to tune the glue? Hmmm?
Let me give you an example. One of the discontinuities you cite is a station move. Now station moves can be for many different reasons, but I’ll wager that a disproportionate number are positively related to urban pressure. At a station that used to be a Class 1, building modifications, parking lot paving, build up of neighbors, the station class has grown to 4. We can’t have that! So we move the station to a new Class 1 location. The right thing to do, but…. Discontinuity! Over the course of a century, this happens 4 times, once per 20 year span. We have a temperature record that is a bit saw-toothed with a UHI rise in each tooth. Deft use of the scalpel slices this 100 year record with 4 discontinuities into 5 “clean” pieces.
They are glued together… HOW? Eliminate the saw-tooth and make a ramp? If “the trend is the only thing you can trust” how can they do anything else?
I say the use of the scalpel and suture has made a bad data situation much worse. In the example I presented above, which of these is closer to the truth of the real regional temperature record? A) To remove the discontinuities? OR B) LEAVE THEM IN? I say use the entire record, uncut, unaltered. Leave the low frequency in. Isn’t the very act of moving the station an exemplary way to account and correct for a UHI problem? The ‘discontinuities” are not noise. They are an important part of the signal and must not be removed. The discontinuities are themselves the removal of a great deal of UHI from the record.
If you use one long saw-toothed record, you will have a strong low frequency signal that will be a combination of GW and UHI. Yes, UHI will contaminate the signal. But the UHI component is in the teeth of the wave, the gradual buildup between moves. By moving the station, the base temperature ought to drop back down due to lower UHI effects to the GW region trend. The overall century-long trend in the station might be (GW + ½ UHI). At least that is an upper bound on GW. But if you cut at discontinuities, treat the trend as your friend, and discount the absolute temperatures, then after splicing out the discontinuities your long term reconstructed signal will probably be (GW + 3.5 UHI), vastly overestimating the real GW trend.
I have used this station-move-because-of-UHI example to make an illustration of my point. Is it contrived? I think not. I think it represents many discontinuity problems, but not all. Be that as it may, my main point is that the scalpel destroys the very data we seek in the GW argument. The suture, the glue, is no guarantee the original signal is preserved. Indeed, the glue can be a major source of corruption or counterfeiting.

Mike Bromley the Kurd,
“People who frequent “preprint libraries”, although vast in number, are not qualified to critique methodology.”
People who submit to those libraries apparently are often not qualified to critique their own methods either!

Stephen Rasey says:
November 4, 2011 at 4:52 pm
Let me give you an example. One of the discontinuities you cite is a station move. Now station moves can be for many different reasons, but I’ll wager that a disproportionate number are positively related to urban pressure. At a station that used to be a Class 1, building modifications, parking lot paving, build up of neighbors, the station class has grown to 4. We can’t have that! So we move the station to a new Class 1 location. The right thing to do, but…. Discontinuity! Over the course of a century, this happens 4 times, once per 20 year span. We have a temperature record that is a bit saw-toothed with a UHI rise in each tooth. Deft use of the scalpel slices this 100 year record with 4 discontinuities into 5 “clean” pieces.
They are glued together… HOW? Eliminate the saw-tooth and make a ramp? If “the trend is the only thing you can trust” how can they do anything else?
I say the use of the scalpel and suture has made a bad data situation much worse.
Stephen, could not agree more with what you have said. Well put. That is pure common sense, not even needing deep statistics, and you know this is happening, the trends are being manufactured to some degree, intentionally or not from such manipulation of the data.
So many people have misread what the UHI effect actually is. They think it about energy USED in the cities warming the Earth causing a global trend and that is totally wrong.
UHI has to do with trends of growth from tiny cooler undeveloped towns to warmer cities over many decades and the trends the temperature stations create out of solar heat absorbed by manmade structures that heats the thermometers locally more than when the structure didn’t even exist years before, not warming the Earth itself.
BEST is right that cities only account for a tiny fraction of the world’s area and the energy they use is insignificant but BEST is also so wrong for it is each individual local thermometer that is being affected giving the illusion of widespread warming when it is just a warmer thermometer caused by UHI growth over the decades.
The splicing you speak of above just magnifies this illusion and you explained how this happens so clear.

Stephen Rasey: Have I made the point clear enough that the splices cannot contain the low frequencies?
Your point was clear, but I think you are mistaken for the most part. That is, there may be some records for which the technique masks low frequency variation and introduces spurious low frequency variation. Your example shows that it is possible. It is always possible after the fact to create a single time series, or a few time series, that defeat a particular analysis technique. That’s one of the reasons that so many analysis techniques have been invented. So what you wrote may apply to some of the records. It’s something that the authors might be able to check on.
More likely, in these r records, there is some variability related to natural oscillations, some to trend ( which may include UHI). For example, a small airport near a tiny town that grew from 1950 – 2010, may have had it’s runway first paved and two new buildings added in about 1957, and the thermometer reconditioned. the resultant (presumptive) jump in temperature would pervert the trend and any low frequency signal, but cutting and splicing would restore the trend and the low frequency signal to something more like their true values.
So the question works out to a question about the preponderance of cases as you describe (sawtooths) vs. the preponderance of cases such as I describe (trend line plus sinusoid, with a few random jumps.) When you think of the sources of the low frequency signal (solar, AMO, PDO,) and the otherwise flat or monotonic trend in most temperature records, and reflect that there are 39,000 station records, I think that the cases like I describe predominate. Eventually I’ll ask the authors about this, in a professional meeting of some sort.
More points: The ‘discontinuities” are not noise.
to the degree that they pervert estimating the trend, they are noise.
The discontinuities are themselves the removal of a great deal of UHI from the record.
That’s desirable in this case, like “partialling out” the effect of a covariate.
The suture, the glue, is no guarantee the original signal is preserved.
There are no guarantees, so this method is one more method that is no guarantee.
I think that your sawtooth example is interesting. It is the first I have read of the possibility that a station may have been repeatedly located away from a growing heat source. I think there is no way of estimating UHI or eliminating its effect without some kind of systematic attempt to study the “cooling” and “warming” thermometers. Anthony Watts has tried to lead an effort, but there are too many to be examined in sufficient detail, and the sampling is biased. So a random sample instead of a census might be required.

diogenes: my view from a number of blogs is that Steven Mosher is about 14 years old
That is most inaccurate and unfair. Steven Mosher very knowledgeable about the topics that posts on, and his posts are always worth reading and considering. If he is sometimes mistaken (who isn’t?) he’s never written a post as stupid as that one from you. (n.b. that is a criticism of the post, not of the person who wrote it. For all I know you are a great and knowledgeable person who just goofed.)

A significant issue is how this method will handle a volcanic event. A rapid cooling followed by decade long recovery. All this emphasises the need for BEST to study what their method is actually doing .
Their paper simply states the effect of splicing “should be trend neutral”. This seems to be a clear and honest declaration that they have not even looked.
It must be remembered that this is UNREVIEWED and UNPUBLISHED at this stage, so I would expect this issue to be addressed during the review period.