Arctic isolated versus "urban" stations show differing trends

It really is worse than I thought! Corrupted data wherever you look. Thanks for bringing this to our attention!

Excellent. This is important data. If places like CRU were not so distracted by having to make the case for AGW, they could be doing this sort of basic, decent work to add to our knowledge.

the R^2 value (the statistical significance for the trend) is very low, 0.023

This betrays a fundamental misunderstanding of statistics. The R^2 value is a measure of linear dependence between two (random) variables, and nothing more. A trend with extremely high significance may still have a low R^2 if the trend is non-linear or if the observations are noisy.
Can you clarify how you classified sites as “urban” and “rural”? The inclusion of Eureka and Nord Ads in “urban” seems problematic, as you mention that each site has a winter population of less than ten. If these sites are switched to “rural”, then the “rural” sites are on average at a higher latitude than the “urban” ones (Wilcoxon rank sum test, p = 0.014) and that alone may explain away the whole pattern.

Interesting. Here is what I do not like about his work, at least as I understand it — I would greatly prefer to see this work done on some sort of double-blind system. One group, without any knowledge of station temperature numbers, sorts the stations (e.g. urban vs. rural) while another works on the temperature trends. This way there is no danger of the sorting decisions being pre-biased by knowledge of their characteristics (something that arguably happens all the time in dendro-climatology).

Maud Kipz wrote: “If these sites are switched to “rural”, then the “rural” sites are on average at a higher latitude than the “urban” ones and that alone may explain away the whole pattern.”
Can you volunteer any hypothesis as to why that may be? AFAIAA, the arctic is postulated to be one place where the warming effect of CO2 will be most visible ie high warming trends. Why then, as you travel further north, would the warming trend seem to completely disappear? These results need careful examination.

“double-blind system”?
A bit hard to do when there is just me!

Ice doesn’t melt by suggestion or supposition and the overall Arctic trend is undoubtedly downward. Have you looked at the most recent ice extent data (“ruh-roh!”)?

While publishing their article in English, authors should use correct English transliterations of Russian geographical names, rather than mixing German- and English-style transliterations (for example, should be “Verkhoyansk”, not “Verhojansk” — the latter is how Germans write it).

Maud Kipz says: at 9:08 am
“This betrays a fundamental misunderstanding of statistics.”
That caught my attention also. Regarding “r” or the Pearson product-moment correlation coefficient:
“It is widely used in the sciences as a measure of the strength of linear dependence between two variables.”
http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
The graphic with the above article might be helpful for some folks. The following is more directly related to the issue raised by Maud Kipz’s comment:
Regarding r^2 or the “Coefficient of Determination”
“is useful because it gives the proportion of the variance (fluctuation) of one variable that is predictable from the other variable.”
http://mathbits.com/mathbits/tisection/statistics2/correlation.htm
Note the word “proportion” in the quote.

Tim, see the Hinkel study cited for Barrow UHI. Basically as you describe.
Murray, State archives show Barrow airport construction in the 60s. I suspect that there was an earlier crude strip in support of NPR-A and DEW line work. Latest temp trend jump takes place as pipeline worked ramped up. The town has doubled in size and its housing and other facilities have been constructed or steadily improved, enlarged, etc., over the last 30 years.

I co-authored a paper on just this subject several years back:
http://climate.gi.alaska.edu/ResearchProjects/pages/AKpaper2.html
The Urban Heat Island Effect at Fairbanks, Alaska
N. Magee1, J. Curtis2, and G. Wendler2
(1) The Pennsylvania State University, University Park, PA 16802
(2) Geophysical Institute, University of Alaska Fairbanks, Fairbanks AK 99775
Abstract
Using surface observation comparisons between Fairbanks and rurally situated Eielson Air Force Base in Interior Alaska, the growth of the Fairbanks heat island was studied for the time period 1949-1997. The climate records were examined to distinguish between a general warming trend and the changes due to an increasing heat island effect. Over the 49-year period, the population of Fairbanks grew by more than 500%, while the population of Eielson remained relatively constant. The mean annual heat island observed at the Fairbanks International Airport grew by 0.4°C, with the winter months experiencing a more significant value of 1.0°C. Primary focus was directed toward long-term heat island characterization based on season, wind speed, cloud cover, and time of day. In all cases, minimum temperatures were affected more than maxima and periods of calm or low wind speeds, winter clear sky conditions, and nighttime exhibited the largest heat island effects.
You can download a pdf version of the paper at the above link.

I have a reservation that “UHI effect” can impose only uptrend. There could be also a downtrend. All would depend on location of a particular sensor relative to the center of heat island. If the sensor is situated on outskirt of the heat spot, convective pattern of airflow will draft surrounding air masses and bias the main wind pattern, and likely cause some cooling trend if the heat island grows over historical time. So, without actual measurements from oversampled grid of temperature sensors all these “UHI corrections” are likely a BS.

Probably one of the best/more relevant posts on WUWT. Time for the people responsible for GISStemp to be replaced….

Slightly O/T …
Pollution not to blame for rapid ocean cooling, says Phil Jones paper.

AndyW says:
September 22, 2010 at 11:07 am
“Circumnavigation of the globe this year in the Northern Hemisphere for the first time ever it seems.”
We used to say paper doesn’t refuse ink. An update might be that the internet doesn’t refuse electrons.
Modern ships and technology allow folks to do many things now that they would not have thought of nor been able to do not many years ago. It took Lewis and Clark a couple of years to cross to the Pacific and back while today it is done in a day. What has “climate disruption” to do with any of this?

Ratus,
“self refuting”? Read the article again.
Yes, the high arctic has warmed in the satellite era. That only extends back to 1979, the beginning of the latest AMO upswing. In a few years that map will show cooling as the AMO swings back to cooling.

Al, Ada and Pauls Valley are in Oklahoma, not Texas.

You chart of the average of all isolated stations ( the analysis segregating the stations was very weak) is done in absolute temperature. You cant do that unless all stations have data for the entire period. there has to be perfect overlap. Otherwise, if a cooler or warmer station drops out you get spurious results.
Also, your regression done from the start of the record is misleading. check out your hockey stick at the end. That of course is partially due to the “purple station”
see its the coldest. see its ONLY in the average for a short time in the middle.
THAT depresses your average. This is why we use anomalies.
There’s more: the segregation of urban and rural must be done OBJECTIVEY, with a criteria that others can apply. So nighlights: pixel on/pixel off. You can check my results. I looked at your pictures ( some were not as described) and I could not tell what criteria you used. not repeatable. subjective.
Finally, AMO. the last years look ominous for any kind of correlation analysis.
So, what you need to do with this nice start is the following:
1. fix the math errors. You cannot compare images with different base periods.
use anomalies. Do not fit regression lines to time series where there is KNOWN
autocorrellation without correcting for that. Estimate the uncertainty of slopes.
Dont ignore regime changes ( around the mid 70s)
2. establish an objective criteria ( like the CRN criteria, or nightlights, or building height, or population) Before you do your categorization. That criteria has to be
objective. Someone using only your criteria has to be able to duplicate your categorization.
3. when you argue that two time series are correlated, actually do the correlation study, don’t eyeball.

Hmm looks like my post on your mistake comparring GISS 1933-1963 with the satilite got eaten.
Basically, you cannot compare two charts with different anomaly periods. You will get the wrong answer.
the anomaly process sets the base period to ZERO. so by choosing 1933-1963 ( 31 years?) you set that period to ZERO. then your current figures are the increase since that period. the satillite record has a different base.
If you set 1933-1963 to 0, then look at today, suppose you found today was +1.
Thats +1 OVER the average of 1933-63. In the satilite chart the base period is the current period. So, OF COURSE it will be lower. by definition.
Too many mistakes in this.

“Juho says:
September 22, 2010 at 11:38 am
Anthony, you have a station
Gmo Im.E. K. F (77.7 N, 104.3 E), Tamyr Peninsula, Russia
listed on both, urban and rural. Which it is?”
No, the urban one is Gmo I’m. E.T. A different station.

Fortunately, incandescent light bulbs will be a federal crime in the U.S. starting in 2012, thanks to the 2007 energy bill. So cozy warm Stevenson screen interiors – at least in Alaska – will soon be a thing of the past. Let’s hope less progressive Arctic nations will follow in our reduced-carbon footprints. Who says there’s nothing we can do about global warming?

Well well well
looki what I found at Noaa
ftp://ftp.ncdc.noaa.gov/pub/download/reliability-of-us-temp-record-central-region-meeting.ppt
hmm. its got todays date. I though I saw this before.

Rattus Norvegicus says:
September 22, 2010 at 10:58 am
A self refuting post! All you have to do look at the sat map to see that there is substantial high latitude warming.
A self refuting comment!
All you have to do is look at the sat map to see that it is for 2009 only.

Satellites do not go back to 1933. Ed didn’t use the GISS 1950-1980 GISS baseline because that was a cool AMO phase. So he took the 1933-63 (warm AMO) period as a rough comparison to show that GISS probably is too warm.
Of course the 1979 -present satellite shows its warmer – right in line with the AMO trend. Who is disputing that?
Then he compared a set of rural stations with a set of “urban” stations and found there’s a difference. The methodology of selection is very important at later stages, but not so much when one is only in the process of forming a hypothesis – rough selection is where one starts out.
I don’t know anyone who forms a preliminary hypothesis using exact methodology. The first step is simple observation, which is what Ed has done. Are we not free to observe, and develop a hypothesis? And then look more closely, (which would be the next step)?

P Gosselin says:
September 22, 2010 at 1:33 pm
Hey Mosh,
It isn’t like he was going to submit this to Nature. The essay is exploratory and is meant as to shine light on the issues of Arctic stations, and it brings up a host of legitimate points and observations that need to be looked into. It’s a launching pad.
Perhaps the professional way of doing this would be to send an e-mail, and not barge and shoot off as Mr. Peer Reviewer.
The real analysis is still a ways down the road.
##############
P.
where possible I always choose to make my comments in public. All knowledge is a work in progress, and I dont hold prior mistakes against myself or against others. a mistake is just a mistake. everybody makes them. It’s only when we pass into the realm of politics and religion and love affairs where people seem to want to hold onto them. I prefer things open.
In any case, I think the approach of looking at sites has merit.
HOWEVER, the method must be sound. There is a little tidbit I got from an Oke paper that might change the way people look at urban/rural.
basically, UHI is modulated by the surrounding rural environment.
Like so: city A has rural surroundings B. City X has rural surrounding ~B.
the UHI you see in A will differ from the UHI you see in X because the UHI effect
is driven by the urban landscape AND specific characteristics is the rural landscape
surrounding it.
Interested folks probably know the Oke paper I am talking about.

Ed Caryl says:
September 22, 2010 at 2:01 pm
Steven Mosher
Yes, the base periods are different. No argument. But they are analogous. There is no satellite data before 1979, as you know. So I looked for an earlier period where the surface temperatures were similar. Note that the cooling spots on both maps are quite similar.
###############
the point is if a climate scientist tried to argue that way ( “analogous”) we would be all over them for being imprecise. Point being, you have to be more rigorous than those you criticize. Anyways, now you would have to show that they are in fact “similar” how similar? what is the rms? etc etc.
“The average for the isolated stations was done by first normalizing each data set to the average temperature for all. This makes the “ends” where not all stations have data, a bit “noisy”, but it allows averaging without distortion.”
to prove there is no distortion
you should take the anomaly approach. further you regressed a line through points
with different variances. I’m assuming that your annual regression was done on the average for each year, you dont say. But if you have “noisy” ends and you supress that noise by doing an average and then regress on the average, you are not exactly showing the trend in the data, you are fitting a line to an average yearly figure.

Steve Mosher 2.15
Yes, of course you’re right. That’s the next step. Ed’s work should be viewed as a first step in the process. So admit it. His work merits a lot of credit, and that it’s a good start.
Of course, the next step is to do it scientifically. Can you fund him?

Ed Caryl,
Appreciate your post. It was readable and logically constructed.
Thanks for helping my longish continuing education.
John

P>
“I don’t know anyone who forms a preliminary hypothesis using exact methodology. The first step is simple observation, which is what Ed has done. Are we not free to observe, and develop a hypothesis? And then look more closely, (which would be the next step)?”
the problem is its misleading. if you do the analysis the correct way you would put Giss on the SAME base period as the satillite. 1979-2009. Then you would look at the two maps for 2009.
When you do this, as I just posted, you see there is no problem to explain in 2009.
Now, the UHI analysis has issues of its own, mathematical and methodological.
So does the AMO analysis.
I dont buy everything that comes out of climate science unless I can check it myself and unless the analysis makes sense and is done in a clear replicable way. why would I suddenly change that ethic just because i happen to agree with the conclusions.
is UHI a problem? yup. is this the best way to show it? Nope. will we get better understandings by critcicizing each others work. yep, unless people insist I wear kid gloves. which I wont, so dont bother asking. for me this is just about numbers. no hard feelings and no pulling of punches even for friends or people I agree with. Sorry.

Steven mosher
If you look at the Isfjord Radio discussion in the article you will see an anomaly map using the base period 1989 – 2008. I could do the one you suggest, but I can’t post it here.

evanmjones wrote: “So we would need over 120,000 stations? That’s a lot of stations.”
Sure it is. But I am afraid you might need more. The example of stations 50km apart having opposite long-term trends means that we don’t know what trend is in between, and what is around in the same proximity. It means that the sampling grid must be less than half of 50km, or 25km at most. Given the Earth surface as 5.1E+8 km2, the 25x25km grid coverage would need about 800,000 equally-spaced stations to determine the average trend. Of course, you can try this oversampling on smaller regional areas, and assume that oceans are more uniform (although we don’t know this either). But for lands alone you need about 220,000 stations to begin with.
More, we still have no idea if the 25x25km is enough to capture complexities of local micro-climates, so be prepared to another half-scale, which would quadruple the number of necessary stations. Without this uniform sampling grid of data it is not serious to discuss any mathematics of subsets or else. This is what physical science says. Sorry.

Steven mosher, Ed Caryl, Rattus Norvegicus, Billy Liar, P Gosselin: Here’s a gif animation with four north polar stereographic maps. You can download them through the KNMI Climate Explorer. The datasets are GISS LOTI (1200km smoothing), GISS with 250km smoothing, RSS TLT, and UAH TLT. The base years for all are 1979-80, the beginning of the satellite period. And the anomalies are for calendar year 2009. In effect, the four maps are showing the rises in surface temperature and TLT anomalies since 1979-80.
Do these maps confirm or disagree with your earlier comments?
http://i55.tinypic.com/2eq7sy1.jpg

Ed Caryl,
Nice post.
It clearly shows, as you note:
“The temperature as measured at stations isolated from any UHI is simply tracking the AMO.
Looks like an awfully good fit. There is very little, if any, global warming. We need to wait until the bottom of the next AMO cycle to get a decent reading of global temperature change. That will be in about 2050 if the AMO cycles as it has since 1850.”
========
Now, let’s figure out the larger and longer term cycles, this cycle is embedded in.
Or, let’s figure out the shorter term cycles, affecting this cycle.
Just some suggestions, to add to your fun 🙂
I’ll say it again, nice post.

Navy Bob says:
September 22, 2010 at 1:05 pm
“Fortunately, incandescent light bulbs will be a federal crime in the U.S. starting in 2012, thanks to the 2007 energy bill. So cozy warm Stevenson screen interiors – at least in Alaska – will soon be a thing of the past. Let’s hope less progressive Arctic nations will follow in our reduced-carbon footprints. Who says there’s nothing we can do about global warming?”
Nah, incandescents for Stevenson screens are exempt. Scientific use and all, you understand.

“Looks like an awfully good fit. There is very little, if any, global warming. ”
Thanks for the chuckle. “Awfully good fit” isn’t very precise, perhaps you could supply us with something more meaningful (you seem to preferentially report R^2 values). And just because the mean annual temperature of your selected Arctic stations “fits” the – wait for it – the AMO means no global warming? Wow. I bet the “urban” arctic stations (if there truly is such a thing) also fit the AMO. But you didn’t show that.
The sad part is this – amplified Arctic warming is a completely predictable phenomena (check the models!) associated with increased greenhouse gas concentrations, and its a function of decreasing sea-ice extents (less sea ice means warmer air temperatures). Yet nobody around here apparently trusts either the observations OR the models, which leads me to believe that it is close to hopeless trying to convince anyone otherwise. There is no UHI in the Arctic, and its warming, and its because of us.

Smokey: Way to change the topic. But since you asked, the simple answer is “its complicated”. The longer answer is: (1) stronger circumpolar circulation opens up more polynas, leading to more ice growth (paper here) , (2) warmer air temperatures produce warmer waters, increased precipitation, and a freshening (i.e. decreased salinity) of the surface waters. This freshening causes increased stratification in the southern oceans, and a corresponding decrease in the amount of heat transport as convection is suppressed. Less heat transport results in increased ice. (paper here) .

Roger Sowell says:
September 22, 2010 at 9:11 pm
Mr. Mosher, you are highly critical of Mr. Caryl’s work in this post, and I take exception to that.
##################
did you take exception to the fact when I was highly critical of Mann or Jones?
did you take exception when a bunch of us spent a couple weeks on CA taking apart Parkers paper? or how about spending nearly 6 months now working through all the warts in GHCN? do you take exception to the last 2 weeks I’ve spent trying to put together a package so that people can look at ICOADS dat in its raw form so they can rip that apart? How about the last 8 hours tying in 185 data descriptions so that other people can work on data to find errors.
“What you fail to realize is that a plot of temperatures over time tells the story. Mr. Caryl’s graph labeled “Urban Stations” shows a definite upward trend in temperatures over the time period. In stark contrast, the graph labeled “Isolated Stations” shows zero or very little upward trend.”
What you fail to realize is that there is no objective criteria for separating or categorizing those two “classes” frankly when I looked at the pictures I thought he got the categaories backwards in some cases. In some cases there was NO EVIDENCE that the site fit he catagory. So, I question the very premise namely
“here are a set of isolated stations” and here are a set of “urban stations”
Look The biggest problem in UHI studies, the biggest FLAW in studies that say there is NO UHI, is lousy metadata. I’ve been saying that for 2-3 years. So, when I see sloppy non repeatable categorization I am going to say it. So, when I found the CRN guide on NOAA’s site and pointed ANthony at it, the idea was this:
here is an objective standard. something you can say ” 10 feet away” or no trees within X feet. Ed’s criteria is….. well nobody knows. If I gave you the pile of pictures and asked you to sort them, chances are you cant. To be accurate, the criteria mist be stated in advance. they must be based in the physics of UHI, and they must be objective. Not ” look at how the town blends into the airport” Not, “look at the trucks close to stevenson screen”. THAT is not the kind of evidence that impresses me, or is applicable to other sites. further, the mathematical methods were not robust or even correct. Sorry, they are not.
“Mr. Caryl’s point is valid. UHI in the Arctic stations caused the upward temperature trends. No amount of fiddling with anomalies or gridding or other statistical methodology will invalidate that basic fact.”
point number 1. Ed said that he subtracted the entire average from each series. that is a form of doing an anomaly. 2. he averaged stations together. that is a form of gridding. Its called unweighted gridding.
So, I guess you reject his work now. please. You are out of your depth. Its ok that you dont understand. keep quiet and we will not be the wiser.
“For those of us who work with vast amounts of data from manufacturing, often from different facilities around the world, this is nothing new. I would never perform the statistics you claim must be performed, simply to achieve an anomaly. ”
Sorry bud. My experience with manufacturing data is way vaster than yours and more important. See how silly that sounds. but I suggest you not try the game of who has worked with more data from more places for a longer period of time. The facts that you dont realize that that an aggregate average is a form of gridding and that Ed actually defended his work by saying that he did standardize by subtracting the global mean, shows me that I should not trust what you say about your skills.
“If one temperature trend has a different average value than another, one can simply overlay the trend lines. My previous work was with data from oil refineries, usually several hundred refineries world-wide, each with thousands of data points for one year. The multi-refinery studies were conducted annually for several years, then every other year thereafter.”
That probably WHY you missed the point I made about the noisy end points he discussed in his methods. and probably WHY you missed the point about autocorrelation and the point about changing variance. I’ll do a little toy problem to show you the problem.
Pipe A 10 10 10 10 10 10 10 10 10 10 10
Pipe B 5 5 5 5 5
Pipe C 1 1 1 1 1
Now take the average and draw the regression of all averaged. See the problem?
now its not this obvious in Eds case, but if you have sparse data, missing data,
you have to take care. its not at ALL like the the problems of hundreds of pipes with thousands of data points. AS ED NOTED the series are noisy at the ends. that is what you get with sparse data. And guess what happens to a regression line when end points are noisy.
now go ahead and apply anomaly methods to the problem above. See how you get the right answer. of course you do. Do you know why you use anomaly methods? well if you have 100s of pipes and thousands of data points you dont have to. temperature series aint like oil in pipes. But nice try.
Kudos to you, Mr. Caryl. It will be quite interesting to watch the so-called experts try to manipulate the obvious in a manner that defends their status quo. This is devastating evidence that CO2 is innocent.

Thoughts on Anomaly Temperatures
Temperature is a PROXY for Energy.
The Energy content and the Energy transfer is what you really need to track. Calculating an Anomaly of High Energy air averaged with Low Energy air is only a rough approximation, even if the statistics are pristine. It takes more energy to change the temperature of Humid air.
Three examples where temperature anomaly is not telling the full story
* humidity
* surface
* radiant energy transfer
Humidity
You have probably seen the example (my excerpt from Max Hugoson post at WUWT)
http://wattsupwiththat.com/2010/06/07/some-people-claim-that-theres-a-human-to-blame/#more-20260
Go to any online psychometric calculator.
*Put in 105 F and 15% R.H. That’s Phoenix on a typical June day.
*Then put in 85 F and 70% RH. That’s MN on many spring/summer days.
What’s the ENERGY CONTENT per cubic foot of air? 33 BTU for the PHX sample and 38 BTU for the MN sample. So the LOWER TEMPERATURE has the higher amount of energy. …..Thus, without knowledge of HUMIDITY we have NO CLUE as to atmospheric energy balances.
———————– (end of excerpt)
So might a better anomaly track temperature in similar humidity areas? Tracking Phoenix with itself might be OK, but maybe we shouldn’t track Minneapolis even with itself, since summer vs winter humidity is significantly different. It has been suggested that Dew Point might be a better indicator than Tmin averaged with Tmax.
Surface
Surface temperatures on land are actually ‘near surface’ air temperatures 1 to 2 meters above ground. The energy flow has already started its trek toward space. Ocean temperatures, especially the ARGO floats, are more truly surface or sub surface measures (before the energy moves to the air). Heat Capacity (used to determine energy content) of liquid water does not change much with temperature, so ‘averaging’ warm and cold water is a smaller error than for dry vs humid air. Which is why OHC, Ocean Heat Content, has been suggested to be a better measure of the Earth’s warming or cooling. And finally, because liquid water has a much higher Heat Capacity than air, when energy moves from the ocean to the air (as in an El Nino) a temperature change in the liquid, gives rise to a larger temperature change in the air. So again, an Anomaly that averages land surface with ocean temps is another ‘apples to bananas’ comparison – both fruit, but different texture.
Radiant Energy Transfer
Energy transfer from Earth toward space begins at the true surface, the dirt, grass, pavement, etc. On a clear sunny day, the surface temperature of an asphalt parking lot can be much higher than the air above it (the measured air temperature is then another proxy of the surface). Radiant energy transfer from the surface toward space is proportional to the absolute temperature to the 4th power (T^4). As the average anomaly temperature changes linearly, the energy transfer changes to the 4th power. An anomaly that averages a 5 degC change in the Sahara with a winter time change in Siberia isn’t telling the full energy story.
I have toyed with the idea of an ‘anomaly correction’ for radiant affect, but have not actually worked it past the concept stage. The idea would be to take each location, adjust for Radiant Potential (temp to the 4th power), then compute a Radiant Anomaly on the transformed temperatures. The Radiant Anomaly might then let us compare the Sahara to Siberia in terms of the surface ability to shed heat. Sort of like the ACE energy metric for hurricanes, but applied to surface temperature.

“”” Dave in Delaware says:
September 23, 2010 at 5:43 am
Thoughts on Anomaly Temperatures
Temperature is a PROXY for Energy. “””
Dave, I have for some considerable time pointed out, that even if it was possible to measure the true average global (surface) Temperature; (which it isn’t) , that we still would know nothing about the energy transfers; and the roughly black body Stefan-Boltzmann like fourth power relationship, is one part of that problem.
It is a trivial problem in calculus and trigonometry to prove that if the Temperature goes through any arbitrary, single valued continuous function (of time) cycle, whose average value is Tzero, that the average value of the instantaneous fourth power of that temperature function, is ALWAYS Tzero + deltaT.
Now a lot of folks love to point out that the earth surface is NOT a “Black Body”, so they argue that the fourth power thing is not valid.
Well the black body assumption does set a maximum for the amount of radiant cooling that can occur; and many surfaces have a sufficiently constant spectral Radiant emissivity over the range of LWIR wavelengths that can be present in the thermal radiation from that surface at prevailing Temperatures; that simply applying some average emissivity to the BB calculated value from the S-B formula is a respectable value for the actual surface radiant emittance.
Actually the deep oceans behave like a fairly good black body absorber; well a grey body to be pedantic, since the surface Fresnel reflectance is about 2% (normal) over a fairly wide specral range; and certainly over the solar spectrum range; and perhaps 3% over the full range of incidence angles. So the deep oceans would be fairly well characterized as a Grey body with 0.97 total emissivity.
Actual LWIR reflectances at typical ocean surface temperatures; are not quite so easy to figure but I would expect the BB (with emissivity of 0.97 would be quite close to reality for the oceans; which after all are 70 % of the total surface.
Employing (with caution) BB radiation theory to the problem also gives us some other inputs to the Green House Gas absorptin of surface emitted LWIR thermal radiation.
If the surface emissions are in fact roughly black body like, then it is known that the spectral radiant emittance at the spectral peak of that emission varies as the FIFTH power of the Temperature, and NOT the FOURTH; and then the Wien Displacement law moves that peak to shorter wavelengths (~3000/T microns), so the higher the surface Temperature, the further down the thermal radiation tail the CO2 absorption band (15 micron) is. The total captured energy still goes up with Temperature; but the fraction of the emission spectrum energy that is capoured goes down; and more of it escapes the atmosphere. The spectral peak which is about 10.1 microns for the global average Temperature of 288K (they claim) will move further into the atmospheric window also.
On the other hand for colder regions the Wien Displacement moves the thermal radiation peak closer to the CO2 15 micron band; but the surface Total radiant emittance goes down severely for the colder regions.
All of which supports my contention that it is the hottest driest mid day tropical desert regions, that do most of the real radiant cooling (land) . The polar snow and ice regions are quite ineffective in cooling the planet; but if the arctic ocean should become ice free, then the north polar region would become a better cooler for that part of the planet.
And of course although this note is all about radiant cooling; we never lose sight of the fact that the ocean regions do a heck of a lot of cooling via the evaporation/convection mechanism, transporting latent heat into the upper atmosphere.

“”” Al Tekhasski says:
September 22, 2010 at 4:45 pm
evanmjones wrote: “So we would need over 120,000 stations? That’s a lot of stations.”
Sure it is. But I am afraid you might need more. “””
Well Al you must be new around here. If you had been visiting here more often, you would know that the general theory of sampled data systems is apparently quite unknown in “Climate Science” Institutions.
So your Nyquist Sampling Theorem is trumped by their Statistical Analysis, and probably the Central Limit Theorem as well. So long as they get the right r^2 value and proper trend line (with a slope error no more than +/-50%; or a 3:1 range) they don’t have to worry about undersampling.
But they are very good at what they call oversampling; which is creating a whole raft of phony values that nobody measured, on their computer. They can make as many grid points as they like; limited only by the size of the supercomputer that the tax payers bought for them. Well they don’t actually measure anything real at all those oversampled grid points. For some reason their computers are not able to go back and predict; excuse me that’s project, the actual values that would have been read at the handful of real actual global measuring stations. but they can interpolate somethign feirce.
So in climate science it is legitimate to core bore a single tree; and from those small sectors of that one dimansional sample ofr the three dimensional tree, in an even bigger forest; you can describe the complete climate history as to Temperature, wind, moisture, sunlight, humidity (maybe I alreadys aid that) and anything else you want to know; well but only for the age of the tree. And you can determine the age of the tree by doing a radio-carbon 14 C assay on some of those pieces of the extracted core. There might be other ways to tell the age of the tree and date the climate conditions; but they probably aren’t as reliable as 14 c assays.
And due to the coherence of anomalies, it is ok to measure the temperature in downtown San Jose California; and apply that Temperature value to the small town of Loreto about 1/2 way down the Baja, on the Sea of Cortez.
So they used to monitor the weather and climate of the entire arctic (north of +60 degrees) with just 12 total weather stations; now they have some totally huge number like 70-80 .
So I doubt that anybody is going to heed your request for 100,000 sampling locations.
And by the way; just in case you haven’t noticed these climate reporting stations get their daily Temperature from a min-max temperature reading; which gives you two samples during each 24 hour cycle; but since the diurnal temperature variation is not pure sinusoidal; there must be at least a second harmonic 12 hour periodic component presnt, so they fail they Nyquist criterion for the Time variable by at least a factor of two which means that the aliassing noise makes even the daily Temperature average value unrecoverable. So the spatial aliassing noise is just superfluous; which is why they don’t care about it.
But it is good to see somebody else with some understanding of sampled data systems.

Thanks for bringing back one of my favorite pictures ever, the Stevenson screeen at Verhojansk. I just love the light bulb inside. No spurious warming from that…

“”” Briney Eye says:
September 23, 2010 at 12:15 pm
Re: “the warm glow inside the Stevenson Screen is just the color temperature of an incandescent light bulb”
I am a skeptic because I see lots of problems with the the data used by the “climate disruption” camp, but in the interest of complete honesty I feel compelled to point out an alternative source for the illumination.
The gentleman on the step appears to be taking a picture of the inside of the enclosure, and the light could very well be coming from the focus-assist LED. They can be quite bright. I took a very charming picture of my granddaughter last Christmas in which she is squinting because it, and well illuminated by that characteristic “warm glow”. “””
Well I have never seen a digital camera on which the “Focus assist” LED was anything but a RED LED; and if you look more closely you will see there isn’t any light on the open door outside the Owl box. That certainly isn’t a Whie LED or even a Warm white LED, and it is definitely NOT the color of an AlINGaP yellow/Amber LED; which although extremely bright, is visually very close to a Sodium yellow (589.0/6 nm).
And hitting that exact yellow color is extremely difficult, since the human eye sees only about a 5 nm range of wavelength to be yellow. Outside of that it would be gold on the long wavelength end, and grellow on the short wavelength end; so making yellow LEDS with a standard colr is quite a chore so manufacturers aren’t going to mess with the color; unless somebody wants to pay for several tons of LEDs.
Long and the short is, it isn’t a camera LED.

Ed,
I am a northern resident who has spent recent time in all of the Canadian arctic communities which you mention in this post. As such, your categorization of Inuvik, Cambridge Bay and Eureka as ‘Urban Stations’ beggars belief.
Inuvik’s weather station is located at the airport, more than 10 km from the town itself, and more than 250 m from the nearest heated building. There is No significant heat effect from the town on that weather station.
Cambridge Bay’s airport and weather station are more than 2 km from the Town itself, with the weather station located more than 80 m from the nearest heated building.
Eureka is a site established as a weather station, and primarily manned for that purpose. As you state with virtually never more than 20 people there, and normally less than 10. To argue that it is ‘urban’ is so ludicrous that it casts doubt on anything else you say, however much merit it may have.
Please don’t ‘sex up’ your point by using such poor examples. Your analysis will stand or fall on it’s own merits, and referring to these stations as urban does your argument a disservice.

E.M.Smith says:
“Measuring a fractal gives different answers based on the ‘size ruler’ you use. And we’re measuring ‘climate change’ with a ruler who’s size constantly changes over time.”
Yep, exactly. Except it is not a ruler, it’s more likely a “wet finger”. Measuring fractals in practice is a non-trivial task, it is called “Hausdorff Dimension” in certain scientific circles far from climatology. Been there, tried that. Given substantial level of unrelated noise from instruments, it is nearly impossible. At least an attempt would require many “halving” of the ruler scale, a thing that is remotely acceptable in climatology.
It is well known that models with progressively finer grid scale could approach the level of details of realities, while crude sampling schemes are totally off. Precipitation patterns is one example. Here is another good example, about “disappearing glaciers”:
http://www.nichols.edu/departments/Glacier/impact_of_sampling_density_on_gl.htm
The initial impression was that the glacier’s annual balance was decreasing on the scale of decade, when they used their normal sampling density 12 sticks/km2. But when they re-calculated the balance using sampling density of 388 points/km2, the deficit in ice balance disappeared!!!. What if the sampling density would be 1200 points? Maybe the glacier was advancing, not retreating after all? This heretic thought was apparently rejected at the stage of peer review…
Also, do they monitor all 350 glaciers in Northern Cascade with 388pt/km2 sampling density? Hell no. If anything, only a handful of glaciers are monitored:
http://www.nichols.edu/departments/Glacier/quick_map_edited.jpg
Yet they arrived to unconditionally certain conclusion that global glaciosphere is shrinking like never before, all thanks to men.

Maud Kipz wrote:
“Temporal sampling is where Shannon-Nyquist most naturally applies.”
Duh. The Shannon-Nyquist-Kotelnikov-Whittaker Theorem, a.k.a. the Cardinal Theorem of Interpolation Theory, or simply the Sampling Theorem naturally applies to any attempted interpolation of any smooth bandwidth-limited function regardless of particular physical meaning of its domain, and regardless of its dimensionality. It is a mathematical fact. Even if no lines or differentiable manifolds exist in Nature, Mathematics is a precise language that allows us to express relationships between objects and derive conclusions with absolute certainty.
“it turns out that the lowest and highest order statistics taken together are complete sufficient statistics. In other words, they’re all you need to know if you want to know a and b”
Sufficient for what? Given that (a+b)/2 has no physical meaning and therefore no physical law could be used nor applied to “explain” its behavior, it is sufficient for nothing. That’s why here we are.

“”” Maud Kipz says:
September 23, 2010 at 8:52 pm
@george E. Smith:
My apologies. Reading your response I see I confused Al Tekhasski’s comments about spatial sampling with yours about temporal sampling. Temporal sampling is where Shannon-Nyquist most naturally applies. But consider that reporting the minimum and maximum is not equivalent to twice-a-day sampling. “”””
Well Maud perhaps you should unconfuse yourself; because I made NO RESTRICTION of my comments to just Temporal Sampling; I’m in total agreement with with Al Tekhasski that the Nyquist sampling theorem applies to any multi-variable sytem of mathematics; whether it it is connected to any rreal physical ssytem or not. It is a property of mathematics; not of Physics.
In the case of Climate or weather science the Temperature (of the earth) can be considered to be a map diagramming in space and time what the Temperature is (everywhere) and at any time; well we typically limit it to surface or near surface temperature. That Temperature is a single valued continuous function of time and space; giving a unique value for any point on the surface for any instant of time. Now we can’t practically keep track of such a double infinity of information; so of necessity we have to sample it; and we have to sample it both in time and in space; and mathematically it doesn’t matter in what order we treat the two (in this case) variables.
In principle we can take a snapshot of the entire surface at any instant, and get a Temperature for any point. Now it is a simple integration problem to multiply each sampled Temperature by the elemental area for which it is cvalid (say one square km, or metre or even mm) and then add them all up, and divide by the total area of the surface; and Voilla !!, we have the average Temperature for the surface for that instant of time. So we repeat that one second later and get a new average, and so on, and then add those all up and divide by the total eleapsed time.
Well because of known quite cyclic variations over time; diurnally and over the course of a year; we would likely want to average a whole year of observations; to take into consideration expected variations due to orbital position and daily rotation and the like.
And of course it doesn’t matter the order. We choose to average at least daily for each and every sampling station; so we actually do the time averaging first. That doesn’t change the result if we do it properly; but we don’t because a min-max average temperature is only valid (in the Nyquist sense) for certain kinds of diurnal cyclic variations; and Nyquist itself says only for a single frequency sinusoidal cycle of Temperature since twice a day sampling is the minimum required for equidistant samples of a single frequency. Now generally a fixed regimen of 12 hourly sampling is still inadequate to reconstruct the complete cycle; that is a degenerate case; so we can’t recover the amplitude of the cycle; just the average. Min-max sampling of a sinusoid has the added benefit that it does recover the amplitude as well; even though we just want the average.
But perish the thought that it should warm faster in the morning, than it cools after sundown; which will introduce at least a second harmonic component; and don’t even talk about clouds passing by.
But I am on exactly the same page as Al here. NO ! we don’t need to be able to reconstruct the original continuous function; although in signal processing which is the most common application we would do that in effect just to get the average.
But you see in the case of the global temeprature; other than the diurnal cycle; we do in fact reconstrudct the continuous function.
That is the samples measured at each weather station; for daily average; when plotted on a map and smoothly interpolated; whcih is exactly what happens in any real communication network where sampled signals are transmitted and processed for recovery; they basically do an interpolation that conforms to rules well understood by signal processing engineers.
But the problem for climatology is that the violation of the sampling theorem with global temperature sampled data; makes even the global average unrecoverable;.
So we don’t have any idea what the global mean surface temperature is; and that is a failing of the sampling stratagem; it is not a limitation of our instrumentation or of mathematics (including statistics).
And note Al’s remonstration that the rules for sampled data processing are purely mathematical and involve no need for any connection to any real world phenomenon; it is strictly a (fictional) mathematical discipline, that has practical applicability to many real world systems.
And your Statistics likewise is a purely (fictional) mathematical discipline; that can be applied to any sets of data even totally fictitious made up sets of data; or real world numbers from some physical system (like the climjate).
And that is why I threw out the Telphone directory example.
I can make up a totally arbitrary set of data (numbers) and give it to you to do your Statistical wizardy on; and the results you get; are no less valid (ormeaningful) than a set of experimenta measured data values that might be obtained in the most well behaved well understood Physical experiment. And that is so BECAUSE your statistics is a property of a set of mathematical axioms on which statistical theory is built. Statistics is NOT a property of the Physical universe; but we can use it usefully to describe certain properties of that physical universe.
But always remember; is it Godel’s conjecture, that no formal mathematical system is complete in the sense that there will always be problems which can be meaningfully stated within the axioms of that mathematics; but which cannot be answered rigorously within those same axiomatic constraints. I believe it was his conjecture on “undecideability.”
The mathematics called “Projective Geometry; which is a plane Geometry has three very simple axioms.
#1 Two Points define a line (joining them)
#2 Two lines define a point (their intersection)
#3 There are at least 4 points.
That’s it. The four points are usually drawn at the corners of a kite, or the southern cross.
Using #1 you can construct the lines joining those four points; and because of #2, you will find that the three pairs of lines so drawn, will locate three more points. In fact that is the first theorem of Projective Geometry; that there are at least seven points.
Constrained by those axioms and the definitions of some procedures; it is impossible to prove there are any more points. There may be; probably are; but you can’t prove it within projective geometry.
Some will immediately object that parallel lines do not intersect at a point. Axiom #2 says they do; and adds that parallel lines do meet at a point on “The Line at Infinity.” Which conveniently one can always draw on the page.. Is it not true that two railway tracks merge to a single point on the horizon. The line at infinity is important in other ways, in that parabolas touch the line at infinity at two coincident points but ellipses never reach the line at infinity. Hyperbolas on the other hand intersect the line at infinity at two non co-incident points.
Therea re two special points on the line at infinity; and they are called the “Circular Points at Infinity”. So you can consider the line at infinity as defined to be that unique line that joins the two circular points at infinity. All circles (every one) pass through the circular points at infinity; so that means that circles are hyperbolas; not ellipses.
Within projective geometry; it doesn’t introduce any inconsistencies. But Godel still says there are undecideable problems within that confinement.

George, “So I’ll drop my objection to yellow focus lights but notice they were observed on Digicams. I guess I will have to look at the focus light on my Panasonic digicam; never noticed it.”
I just checked my Casio. The light is more like “orange”, although it is hard to conduct full spectral analysis by eye and assign a single moniker to a continuous function. I guess we can settle for “orange”. 🙂

jose says:
September 22, 2010 at 10:22 pm
“Smokey: Way to change the topic. But since you asked, the simple answer is “its complicated”. […]”
Beautiful! Pure Gavin Schmidt!