Temperature averages of continuously reporting stations from the GISS dataset
Guest post by Michael Palmer, University of Waterloo, Canada
Abstract
The GISS dataset includes more than 600 stations within the U.S. that have been
in operation continuously throughout the 20th century. This brief report looks at
the average temperatures reported by those stations. The unadjusted data of both
rural and non-rural stations show a virtually flat trend across the century.
The Goddard Institute for Space Studies provides a surface temperature data set that
covers the entire globe, but for long periods of time contains mostly U.S. stations. For
each station, monthly temperature averages are tabulated, in both raw and adjusted
versions.
One problem with the calculation of long term averages from such data is the occurrence of discontinuities; most station records contain one or more gaps of one or more months. Such gaps could be due to anything from the clerk in charge being a quarter drunkard to instrument failure and replacement or relocation. At least in some examples, such discontinuities have given rise to “adjustments” that introduced spurious trends into the time series where none existed before.
1 Method: Calculation of yearly average temperatures
In this report, I used a very simple procedure to calculate yearly averages from raw
GISS monthly averages that deals with gaps without making any assumptions or adjustments.
Suppose we have 4 stations, A, B, C and D. Each station covers 4 time points, without
gaps:
In this case, we can obviously calculate the average temperatures as:
A more roundabout, but equivalent scheme for the calculation of T1 would be:
With a complete time series, this scheme offers no advantage over the first one. However, it can be applied quite naturally in the case of missing data points. Suppose now we have an incomplete data series, such as:
…where a dash denotes a missing data point. In this case, we can estimate the average temperatures as follows:
The upshot of this is that missing monthly Δtemperature values are simply dropped and replaced by the average (Δtemperature) from the other stations.
One advantage that may not be immediately obvious is that this scheme also removes
systematic errors due to change of instrument or instrument siting that may have occurred concomitantly with a data gap.
Suppose, for example, that data point B1 went missing because the instrument in station B broke down and was replaced, and that the calibration of the new instrument was offset by 1 degree relative to the old one. Since B2 is never compared to B0, this offset will not affect the calculation of the average temperature. Of course, spurious jumps not associated with gaps in the time series will not be eliminated.
In all following graphs, the temperature anomaly was calculated from unadjusted
GISS monthly averages according to the scheme just described. The code is written in
Python and is available upon request.
2 Temperature trends for all stations in GISS
The temperature trends for rural and non-rural US stations in GISS are shown in Figure
1.
This figure resembles other renderings of the same raw dataset. The most notable
feature in this graph is not in the temperature but in the station count. Both to the
left of 1900 and to the right of 2000 there is a steep drop in the number of available
stations. While this seems quite understandable before 1900, the even steeper drop
after 2000 seems peculiar.
If we simply lop off these two time periods, we obtain the trends shown in Figure
2.
The upward slope of the average temperature is reduced; this reduction is more
pronounced with non-rural stations, and the remaining difference between rural and
non-rural stations is negligible.
3 Continuously reporting stations
There are several examples of long-running temperature records that fail to show any
substantial long-term warming signal; examples are the Central England Temperature record and the one from Hohenpeissenberg, Bavaria. It therefore seemed of interest to look for long-running US stations in the GISS dataset. Here, I selected for stations that had continuously reported at least one monthly average value (but usually many more) for each year between 1900 and 2000. This criterion yielded 335 rural stations and 278 non-rural ones.
The temperature trends of these stations are shown in Figure 3.
While the sequence and the amplitudes of upward and downward peaks are closely similar to those seen in Figure 2, the trends for both rural and non-rural stations are virtually zero. Therefore, the average temperature anomaly reported by long-running stations in the GISS dataset does not show any evidence of long-term warming.
Figure 3 also shows the average monthly data point coverage, which is above 90%
for all but the first few years. The less than 10% of all raw data points that are missing
are unlikely to have a major impact on the calculated temperature trend.
4 Discussion
The number of US stations in the GISS dataset is high and reasonably stable during the 20th century. In the 21st century, the number of stations has dropped precipitously. In particular, rural stations have almost entirely been weeded out, to the point that the GISS dataset no longer seems to offer a valid basis for comparison of the present to the past. If we confine the calculation of average temperatures to the 20th century, there remains an upward trend of approximately 0.35 degrees.
Interestingly, this trend is virtually the same with rural and non-rural stations.
The slight upward temperature trend observed in the average temperature of all
stations disappears entirely if the input data is restricted to long-running stations only, that is those stations that have reported monthly averages for at least one month in every year from 1900 to 2000. This discrepancy remains to be explained.
While the long-running stations represent a minority of all stations, they would
seem most likely to have been looked after with consistent quality. The fact that their
average temperature trend runs lower than the overall average and shows no net warming in the 20th century should therefore not be dismissed out of hand.
Disclaimer
I am not a climate scientist and claim no expertise relevant to this subject other than
basic arithmetics. In case I have overlooked equivalent previous work, this is due to my ignorance of the field, is not deliberate and will be amended upon request.
Garrett Curley (@ga2re2t) (October 24, 2011 at 3:13 am)
What’s your pleasure?
A world which is warming, cooling or static?
Just tell me your preference and I’ll give you a time frame to match your desires.
But, as for human contribution…
See my previous comment.
The RUTI vs BEST graph is interesting. I was born in San Diego in 1934 and lived there until 1973. I think it was winter on 1972 that I saw snow in our neighborhood (Clairmont) for the very first time. That experience fits with the RUTI graph.
Dave Springer says:
CO2 has very little effect over the oceans which are 71% of the planet’s surface because the ocean only gives up 20% of its solar heating through radiation and CO2 only slows down radiative cooling. CO2 DOES NOT slow down evaporative, convective, or conductive cooling. Over land surfaces, especially dry surfaces, the dominant mode of cooling is radiative. CO2 will have its maximum theoretical effect there and there-only.
Henry@Dave
This theory of yours does seem to confirm my findings, which showed little or no warming over the SH, even in the antarctic. But how did you get to this value of 20%?
As you know, when I carefully look at what happens more inland on the SH, like on the South American continent, I find cooling which indeed could be linked to de-forestation. Here in Pretoria, or Brisbane there was no warming but these are countries that have large deserts with dry climates and little greening. Most of the greening is happening in the NH and here I find the heat being trapped.
What I have discovered so far from my (silly?) carefully chosen sample of 15 weather stations is that the overall increase of maxima, means and minima was 0.036, 0.012 and 0.004 degrees C respectively per annum over the past 35 years. So the ratio is 9:3:1. Assuming that my sample is reasonably representative of earth (70/30 balanced & + – latitude balanced) , I have to conclude that it was the maximum temps (that occur during the day) that pushed up the average temps. and the minima. So either the sun shone more brightly or there were less clouds. Or, even, perhaps the air just simply became cleaner (less dust? Are there records on that?).. Amazingly, taking the NH separate, I find that that the increase in maxima, means and minima was at a ratio 1:1:1.So, somehow, more of that extra heat coming to earth naturally, – I don’t know if you agree with that?> – is trapped in the NH. The question is why?
My point has been that if it were an increase in CO2 or GHG’s that is doing the warming, you would expect to see the exactly the same results for NH and SH because these gases should be distributed evenly in the whole of the NH and SH hemisphere. So I must conclude that it never was the increase in CO2 that is doing it. The only logical explanation I can think of is the difference in the rate by which the earth is greening. In South America we still had massive de-forestation over this period whereas Australia and Southern Africa have large deserts. Obviously, the NH has most of the landmass and here everyone seems to be planting trees and gardens. A recent investigation by the Helsinki university found that 45 countries were more green then previously out of a sample of 70.
Paradoxically, the increase in greenery is partly due to human intervention, partly due to more heat coming available (increase in maxima!) and partly due to the extra CO2 that we put in the air which appears to be acting as a fertilizer/ accelerator for growth.
For my data, see:
http://www.letterdash.com/HenryP/henrys-pool-table-on-global-warming
(make a copy for yourself of the tables)
I am not convinced by your theoryDave because if it were true, a place like Honolulu should show little or no warming. I think you are speculating by seeing little or no warming in the SH and making the link that the SH is mostly covered by oceans. But the individual trends that I picked up seems to support my theory: if it greens more, it becomes warmer.
Lucy is right. Let is guard the gates, and stick to the truth.
.
malagaview says:
October 24, 2011 at 9:57 am
I admire your faith in satellite data that is not calibrated against earth based thermometers… satellite data that cannot be independently verified / processed / c
;—————————————————————————————————————-
It’s my understanding there are 3 ways to measure the temperature, namely
(1) satellites
(2) balloons
(3) thermometers on the Earth’s surface (which is usually land primarily in the North Hemisphere.)
The first two agree – but the land based thermometers don’t agree with either (1) or (2).
The different land bases indices come from essentially the same set of thermometers – the difference in the indexes is primarily a result of cherry picking the thermometers and then cooking the data.
I admire your faith.
I think the problem is when new shorter series are added to the long ones, I mean anomalies. Optimally, you need several years of comparing the new ones to the old ones to find out the difference and to set what real anomaly of the new series is. Lets say you measure temperature since 1900 somewhere, you have absolute numbers for 1961-1990 period and you can easily explain the record in form of anomalies. But then you add another site starting in 1985 – 300 m different altitude and 500 km nearby – so which anomaly will be the first year? If you manage to make it higher from the beginning and mix it with the long record, the whole post-1980 mix will be steeper. This is exactly what we can see, when comparing long-term US stations and all of them.
I would rather use several hundreds of long-term records for global record than thousands of who knows how calculated anomalies for the shorter versions.
barry says: October 24, 2011 at 7:30 pm
Thanks Barry for all that work, all refs of which I checked. The graph was the telling item: “rural” warming rising faster than “urban” warming” – as I expected. So I quote my earlier words
Bob Kutz
“I think it a bit much to lump all of us into one group with one set of beliefs.”
You’re right and I don’t mean to. It’s just that I sometimes get the feeling that those amongst you who say you believe the Earth is warming are still comforted by data showing otherwise. But maybe I’m misinterpreting.
“But we don’t ostracize those with dissenting ideas here. We talk about those ideas.”
Talking about dissenting ideas is great. But you’re not doing your movement (if I may call it that) any favor by continuously regurgitating the doubt as regards GW. Old ideas sometimes need to be laid to rest. Geophysicists rightly do not pay attention to those who bring up pre-plate-tectonic theories.
“That is how science usually begins; a hypothesis is developed and a means of testing it is devised.”
Sure. I just find that WUWT is not a great place for putting forward a hypothesis. This site is to the fore in climate skepticism and I’ve come here to see things from another perspective. A basic, probably highly flawed, hypothesis made by a microbiologist is a turn-off for people like me.
phlogiston
“Who is it who should be expected to have coherent data on global temperatures? Is it the climate research community, … Or an amateur group of scientists and concerned citizens, including some climate scientists sacked for heretical views?”
The climate research community is coherent with regards their conclusion on trends in the Earth’s temperature: it’s warming. They consider their data to be coherent. You disagree, but as you said, you’re a group of amateur scientists and concerned citizens, so maybe your definition of coherent is flawed because you lack the expertise? Having said that, this site does have a big responsibility: when the quasi-totality of climate experts, along with the large majority of professional scientists, believe that AGW is real and a likely danger to human-kind, then any group that wishes to disagree and slow down or stop actions being taken to mitigate AGW needs to make sure that their argument is coherent and based on fact, not amateurish hypotheses.
“And now a straightforward demonstration by Michael Palmer that, sorting for the best quality continuous data records in the USA annihilates at a stroke any sign of warming on that continent” You say “best quality” without justification. The records were “continuous”, but not necessarily best-quality. You say it “annihilates” before even waiting to see a critical analysis of his demonstration. That’s quite a leap of faith, if I may say so. Are you sure that you’re in no way biased? A non-biased statement, in my opinion, would say something more like: “A simple mathematical analysis by Michael Palmer, using continuous raw data records, shows no net warming on the US continent over the past one hundred years”. Again, ask yourself whether you are biased or not.
Cheers.
formatting test
[NOTE: Site Policy requires a valid e-mail address. Further comments wikll not be posted without one. -REP]
Garrett Curley says:
“I just find that WUWT is not a great place for putting forward a hypothesis.”
We then, let’s fix that. Here is my testable hypothesis:
The global increase in CO2 is harmless, and beneficial to the biosphere. Up to the maximum UN/IPCC’s projected levels, CO2 is a net benefit to the environment. More CO2 is better.
Falsify that hypothesis, if you can.
Garret Curley says: The climate research community is coherent with regards their conclusion on trends in the Earth’s temperature: it’s warming. They consider their data to be coherent…..
Henry@Garret
Most sceptics do not doubt that it is warming. The question remains: what is causing it. Understand that it is alleged that due to increased green house gases in the atmosphere, heat is trapped that cannot escape from earth. So if an increase in green house gases were to be blamed for any warming, it should be minimum temperatures (that occur during the night) that must show the increase (of modern warming). In that case, the observed trend should be that minimum temperatures should be rising faster than maxima and mean temperatures, pushing up the avergae temperature.
So don’t you think that any set of data displaying the increase in average temperatures is pretty useless unless it shown TOGETHER with the development of minima and maxima?
So far on my sample the trend is opposite – it is maxima (happening during the day) pushing up the average temps.
http://www.letterdash.com/HenryP/henrys-pool-table-on-global-warming
Michael Palmer says: “peetee, this IS the FINAL release, and the paper is peer-reviewed right here. That means you and your peers!
“But wait”, I hear you saying, “you can’t be serious! My troll posts count for peer review?” To which I reply: “Why yes, for sure! You have no idea what real academic peer review can be like.”
Glad we could clear that up.”
Mr. Palmer, respectively, as vaunted as the reach of WUWT is, surely you should seek a legitimate scientific peer-review route… no? Particularly in regards to Anthony’s expressed reservations concerning the BEST (lack of) peer-review, why should one give your findings any more consideration in that they constitute nothing more than ‘blog science’. As you appear to be well published in your non-climate related discipline, that would suggest you would equally seek a legitimate peer review for this “WUWT paper”. That you consider and label it as a FINAL release seems contradictory… yes?
[SNIP: You were told to use a legitimate e-mail. Please do so. -REP]
peetee says:
October 25, 2011 at 6:06 am
“surely you should seek a legitimate scientific peer-review route… no?”
—
No. It is not worth my time. Anyone who finds it worth their time is welcome to the data and the code.
****
steven mosher says:
October 24, 2011 at 1:22 pm
TOBS is an empirically derived adjustment. you can read karls paper or the subsequent verification of it.
****
Yes, as I said, TOBS is legit, when done properly.
*****
Arguing about TOBS is a waste of time. It needs to be applied in ANY analysis that does simple averages. Otherwise you will get the wrong answer. demonstrably wrong.
*****
Yes — read my first comment. But the “averaging method” you mention is a waste of time. The only method that will convince me is when the metadata is used for each individual station & the proper correction applied to that station. Yeah, I know the amount of data (and some is certainly missing) makes this tedious/difficult, but nothing less will suffice IMO, especially for such a large correction compared to the changes we are trying to detect.
Henry@Garret
Garret your comment got wiped, this does not happen a lot here,
You must give them a valid e-mail address.
Anyway, I read it on my BBerry and I understand from it that you don’t know how the greenhouse effect works.
Quote from Wikipedia (on the interpretation of the greenhouse effect);
“The Earth’s surface and the clouds absorb visible and invisible radiation from the sun and re-emit much of the energy as infrared back to the atmosphere. Certain substances in the atmosphere, chiefly cloud droplets and water vapor, but also carbon dioxide, methane, nitrous oxide, sulfur hexafluoride, and chlorofluorocarbons, absorb this infrared, and re-radiate it in all directions including back to Earth.”
If you want a bit more understanding of the problem, you can go here:
http://www.letterdash.com/HenryP/the-greenhouse-effect-and-the-principle-of-re-radiation-11-Aug-2011
(I have tried to keep it as simple as possible, and have asked for comments from my peers here if there is anything wrong with this explanation)
Obviously, this being the case, it follows that, if you say that the extra warming we experience is not natural, we must assume that the flow of warmth from the sun is or was constant – in which case maxima should not be rising. If you say that the warming is due to an increased greenhouse effect it should be minimum temperatures that are rising.
My findings thusfar show exactly the opposite, as also explained here:
http://wattsupwiththat.com/2011/10/24/unadjusted-data-of-long-period-stations-in-giss-show-a-virtually-flat-century-scale-trend/#comment-777081
I am still hoping that Dave Springer will give me a reply on that, because I don’t know where or how he got to that 20% number.
Great work Michael. What you have done here answers some questions that I have always had. I’ve always wondered about what rural, long term, unadjusted records would look like. I didn’t expect to see a negative trend. But I did expect to see a trend that was less than what our major suface temperature sources were showing.
I do have a question. In figure 1, the absolute rural anomalys look to be significantly lower than the non rural. In figure 2 they appear to be about the same. Is there a difference in baselining?
Lucy @ur momisugly <a href=http://wattsupwiththat.com/2011/10/24/unadjusted-data-of-long-period-stations-in-giss-show-a-virtually-flat-century-scale-trend/#comment-777203</here
There is a much simpler way of doing this. UAH satellite record has a trend for global land of 0.18C/decade from 1979. The trend is very similar for the surface records (land only). The satellite records are not impacted by UHI. Urbanisation rates have increased over time to present, in line with population. If UHI is significantly impacting surface temp trends, they should diverge significantly from the satellite temp trends. They do not.
UAH also have a trend for the US lower 48, of 0.2C per decade since 1979. This record is unaffected by UHI. I’ll leave it to you to plot the linear trend for US stations from 1979.
Don’t know if you noticed, but I posted a link to an analysis of 60 global rural stations with 90 year records (similar to Michael’s effort, but global in extent).
http://moyhu.blogspot.com/2010/05/just-60-stations.html
Also, the links I posted discussed their methods for determining rural/urban stations. It’s not as if they didn’t make the effort – they all outline their methods! – so I’m not sure why you quote yourself saying that they are making “unstated assumptions.” Are you sure you actually read them?
barry says:
“Yes, it would appear the good doctor [Miskolczi] does not believe we’ve experienced global ice ages over the past million years.”
OK, barry, quote verbatim where Dr Miskolczi states that.
barry continues:
“I’d rather lick my way to the centre of the earth than attempt to make points through your endlessly shifting goal posts.”
As wayne points out [“Smokey: one thing you are is consistent (with a capital ‘C’)”], I am entirely consistent in my comments. So start licking, barry.
And I note that the only non-response to my hypothesis that CO2 is harmless and beneficial is the sound of crickets. As the saying goes, put up or shut up.
@Smokey et al
Even if you reject the 97%, other polls have surveyed scientists more generally and still found the overwhelming majority of scientists accept AGW. You’re right, science is not by consensus and they could be wrong, but the question is, what is a layperson justified in believing? It’s possible those (let’s say) 80% of scientists could be wrong, but it’s clearly more likely that the 20% is wrong. I would like an example in modern science for when the 20% was right and the 80% was wrong after decades of research.
A common argument seems to be that government money introduces a bias. Really? More of a bias than the fossil fuel lobbies which are overtly funding “skeptics”?
Sure models aren’t as good as experimentation when that’s an option (which it isn’t in this case), but we run nuclear reactors using models, built an atomic bomb that worked on the first try, we calculate plume dispersion from pollution, we determine how proteins fold, how fluids flow etc. All of these models seem to work for the most part, why is this different?
When I say skeptics are “crazy” I don’t mean schizo, I mean so ruled by cognitive biases that the conclusions they reach, with the certainty they reach them is demonstrably illogical. As you move from GW to AGW to CAGW the certainty of the science decreases as does the irrationality of disbelief.
Scientists think AGW will probably be bad, partially because we’ve built society around how the climate is right now. Is it likely to be catastrophic? No. The Gore/Greenpeace “alarmists” as you call them are out of step with the science on this. The point is that if AGW is “probably” bad and even “possibly” terrible, it makes sense to start taking modest steps to address it, using best-estimate cost-benefit analyses. If 10 years from now we realize it’s not warming as fast as we thought, it’s a lot harder to cut back on GHG emissions than we thought and mitigation is likely to be cheaper than avoidance, fine. Our policies can evolve. Scientific knowledge evolves and our opinions and actions should be based on the best available knowledge at the time.
barry sez:
“UAH satellite record has a trend for global land of 0.18C/decade from 1979. The trend is very similar for the surface records (land only).”
1) Peer reviewed science begs to differ. Quoting Dr. Roger Pielke Sr.:
“Our [peer reviewed] paper… has clearly documented an estimated warm bias of about 30% in the IPCC reported surface temperature trends.”
http://sbvor.blogspot.com/2009/09/warm-bias-of-about-30-in-ipcc-reported.html
2) In the USA, simply insist that any trend analysis date range start and stop at similar points in the AMO cycle and the warming signal essentially disappears:
http://sbvor.blogspot.com/2011/10/amo-as-driver-of-climate-change.html
Mosher: “Arguing about TOBS is a waste of time. It needs to be applied in ANY analysis that does simple averages. Otherwise you will get the wrong answer. demonstrably wrong.”
Frankly, Mosher, I don’t think that you can produce any physical evidence that it would produce the wrong answer. Here is what I get from Karl’s paper as the physical explanation for why we need TOBS.
Karl shows statistics that many people moved their station reading time from the late afternoon to the early morning. Why does this matter. The stations, when read, contain a maximum temperature and a minimum temperature. Maximum temperatures are likely to happen some time between noon and four. So when you read the thermometer at 7 AM you are highly likely to get the high from the previous day. When taking a monthly average, then, that means that your fist day of the month actually has the data from the previous month. Every day that follows will have that one day offset for it’s high reading. Now let’s say that you are taking readings in April. That means that your first sample was actually from March. And each day will have one sample that represents an earlier day. In April, temperatures are increasing rapidly. And since all your days readings are offset by half a day in the past, it means that you will have average temperatures for April that are biased cool. So, in order to get rid of that bias, you have to apply a warming correction.
But here’s the catch. That offset continues month after month. When you get to fall, you are taking your first sample of the month from the previous month and that sample is too warm. So you get a too cool offset in the spring and a too warm offset in the fall. This means that while you have months that are biased in themselves, over the course of a year, there should be no bias. The warm biased months and the cold biased months should cancel each other out.
Bottom line is that it appears to me that there should be no TOBS over the long term. This means that your individual months will be off a little, but there should be no bias on a yearly basis.
So if anyone out there can explain to me, physically, why a long term TOBS bias should exist, please do so; because the one that Karl uses for his temperature record is huge. And it seems to me that it should be zero.
In my previous comment, the more complete (and more to the point) quote from Dr. Roger Pielke Sr. would have been:
“Our [peer reviewed] paper… has clearly documented an estimated warm bias of about 30% in the IPCC reported surface temperature trends… Moreover, despite the claim in the IPCC (2007) report, the tropospheric and surface temperature trends have not NOT reconciled”
crosspatch says:
October 24, 2011 at 12:14 am
In the 21st century, the number of stations has dropped precipitously. In particular, rural stations have almost entirely been weeded out
At some point they are going to run out of tricks to use to create a warming signal.
________________________________________
The Hoaxsters do not care.
By then all us old foggies will be dead and the youngsters will be too badly educated to think their way out of a paper bag. More important the youngsters will all be too busy working three jobs trying to put food on the table and heat their cold water flats to care.
Frank Lansner says:
October 24, 2011 at 1:05 am
I just wanted to say thanks for your post at Joanne Nova’s (and all the work) http://joannenova.com.au/2011/10/messages-from-the-global-raw-rural-data-warnings-gotchas-and-tree-ring-divergence-explained/#comment-625436
It is a real eye opener ant the type of work I would EXPECT from a real scientist.
Brian says:
“Even if you reject the 97%, other polls have surveyed scientists more generally and still found the overwhelming majority of scientists accept AGW.”
Brian’s 97% number is completely bogus. It was based on a questionable survey of 77 respondents to a vaguely worded poll. Contrast that truly puny number with the more than thirty thousand co-signers of the OISM Petition, all of whom reject the alarmist position. And all the alarmist counter-OISM petition signers put together don’t total nearly the number of OISM petition co-signers. The large majority of engineers and scientists reject – in writing – the false belief that CO2 is a problem.
Brian continues:
“A common argument seems to be that government money introduces a bias. Really? More of a bias than the fossil fuel lobbies which are overtly funding ‘skeptics’?”
Government money absolutely introduces a bias. And companies give much more money to watermelons than to scientific skeptics. Companies are simply buying plausible deniability and protection from eco-extortionists, the same way that Jesse Jackson extorts loot from companies so they won’t be labeled “racist.” In addition to the lopsided amounts of payola that companies give to alarmists compared to skeptics, the U.S. government has handed over $99 billion in payola to “study climate change”, almost all of it to climate alarmists and their organizations. Per a report from the Congressional Budget Office:
http://www.cbo.gov/ftpdocs/112xx/doc11224/03-26-ClimateChange.pdf
So far, Brian has been wrong about everything. That’s what happens when someone gets their “facts” from propaganda blogs like Skeptical Pseudo-Science.
By the way, here is Karl’s paper on TOB:
ftp://ftp.ncdc.noaa.gov/pub/data/ushcn/v2/monthly/karl-etal1986.pdf
I can understand from this paper why there are monthly biases. But those biases should be cool if someone changes their reading time in the spring, and hot if they change it in the fall. I can see no reason for biases that should persist for longer than a year. If anyone can find a physical explanation in this paper for why biases should persist for more than a year, please explain it to me.