By Blake Snow – FOXNews.com
Image: NASA / Goddard Institute for Space Studies – Maps from NASA’s GISS reveal temperatures where no data exist, thanks to mathematical extrapolation of data.
NASA was able to put a man on the moon, but the space agency can’t tell you what the temperature was when it did. By its own admission, NASA’s temperature records are in even worse shape than the besmirched Climate-gate data.
E-mail messages obtained by a Freedom of Information Act request reveal that NASA concluded that its own climate findings were inferior to those maintained by both the University of East Anglia’s Climatic Research Unit (CRU) — the scandalized source of the leaked Climate-gate e-mails — and the National Oceanic and Atmospheric Administration’s National Climatic Data Center.
The e-mails from 2007 reveal that when a USA Today reporter asked if NASA’s data “was more accurate” than other climate-change data sets, NASA’s Dr. Reto A. Ruedy replied with an unequivocal no. He said “the National Climatic Data Center’s procedure of only using the best stations is more accurate,” admitting that some of his own procedures led to less accurate readings.
“My recommendation to you is to continue using NCDC’s data for the U.S. means and [East Anglia] data for the global means,” Ruedy told the reporter.
“NASA’s temperature data is worse than the Climate-gate temperature data. According to NASA,” wrote Christopher Horner, a senior fellow at the Competitive Enterprise Institute who uncovered the e-mails. Horner is skeptical of NCDC’s data as well, stating plainly: “Three out of the four temperature data sets stink.”
…
Global warming critics call this a crucial blow to advocates’ arguments that minor flaws in the “Climate-gate” data are unimportant, since all the major data sets arrive at the same conclusion — that the Earth is getting warmer. But there’s a good reason for that, the skeptics say: They all use the same data.
…
Neither NASA nor NOAA responded to requests for comment. But Dr. Jeff Masters, director of meteorology at Weather Underground, still believes the validity of data from NASA, NOAA and East Anglia would be in jeopardy only if the comparative analysis didn’t match. “I see no reason to question the integrity of the raw data,” he says. “Since the three organizations are all using mostly the same raw data, collected by the official weather agency of each individual country, the only issue here is whether the corrections done to the raw data were done correctly by CRU.”
Corrections are needed, Masters says, “since there are only a few thousand surface temperature recording sites with records going back 100+ years.” As such, climate agencies estimate temperatures in various ways for areas where there aren’t any thermometers, to account for the overall incomplete global picture.
“It would be nice if we had more global stations to enable the groups to do independent estimates using completely different raw data, but we don’t have that luxury,” Masters adds. “All three groups came up with very similar global temperature trends using mostly the same raw data but independent corrections. This should give us confidence that the three groups are probably doing reasonable corrections, given that the three final data sets match pretty well.”
But NASA is somewhat less confident, having quietly decided to tweak its corrections to the climate data earlier this month.
In an updated analysis of the surface temperature data released on March 19, NASA adjusted the raw temperature station data to account for inaccurate readings caused by heat-absorbing paved surfaces and buildings in a slightly different way. NASA determines which stations are urban with nighttime satellite photos, looking for stations near light sources as seen from space.
Of course, this doesn’t solve problems with NASA’s data, as the newest paper admits: “Much higher resolution would be needed to check for local problems with the placement of thermometers relative to possible building obstructions,” a problem repeatedly underscored by meteorologist Anthony Watts on his SurfaceStations.org Web site. Last month, Watts told FoxNews.com that “90 percent of them don’t meet [the government’s] old, simple rule called the ‘100-foot rule’ for keeping thermometers 100 feet or more from biasing influence. Ninety percent of them failed that, and we’ve got documentation.”
Read the entire story at Fox News.com
“DirkH (12:19:27) :
[…]
When there is a lot of wind, the electricity exchange marketplace in Germany offers negative prices for electricity.”
Clarification: BULK prices on the sport market for people that are capable of accepting some MegaWatts NOW! Unfortunately, not end consumer prices.
Maybe it will some day be possible to profit from fluctuating prices by installing batteries in my home and a smart meter, we’ll see. But at the moment, there are no end consumer tariffs that exploit these possibilities.
Trevor (11:33:09) : And I suggest that YOU read the article you referenced. You got it all wrong, bud. Your hero Mears didn’t use that term.,/i>
Aliasing? Technically true I supposed but he did use the term ALIAS…in the SECOND PARAGRAPH OF THE ARTICLE NO LESS. To quote:
“An important component of this analysis is to account for long term drifts in the measurements that arise from drifts in local measurement time that can ALIAS the local diurnal cycle into the long term record”
http://www.remss.com/papers/Correcting_MSU_Channel_2_Temperature_for_Diurnal_Drifts_IGARSS.pdf
Trevor (11:33:09):Perhaps that’s because the term “aliasing” doesn’t really apply to the situation with satellite drift.
I suggest you read the above quote again.
Tom,
I don’t mean to pile on here but one additional thought spurred by your quote regarding Vienna and London… In the US at least, there does appear to be a difference in temperature trends that correlate with population. On a km2 basis, surface stations that are in higher populated areas exhibit higher temperature trends. Or at least that’s what Dr. Spencer seems to show for the here:
http://wattsupwiththat.com/2010/03/16/spencer-direct-evidence-that-most-u-s-warming-since-1973-could-be-spurious/
I don’t pretend to be able to explain that with paving and structures. At some point, and to your point about Vienna and London, – think also NYC, Tokyo, Hong Kong, etc – from a footprint standpoint you would expect the effect to reach saturation point. Past that point, the surface is effectively (with the exception of usually long-established green areas like parks) structure changes tend to consist of smaller buildings getting replaced with bigger/taller buildings.
That said, there is a consumption (as I call it) aspect of UHI to consider as well. Compare London in 1900 with London 2010 – there were no cars (losing 80% of their fuel consumption directly to heat), Central Air and Heat, electrical service, etc.
By my admittedly rough calculations, power use would be around 10% of the magnitude of the changes due to paving and structures. Does that explain the entirety odd correlation demonstrated by Dr. Spencer – probably not. Like I said, I don’t pretend to be able to explain all this stuff, just have a gut feel that it really needs to be studied and accounted for, and that as of now, it has not been.
In case it doesn’t continue, this has been an interesting conversation. I think being challenged as to what I think I know is a good thing. If I’ve learned anything in life, it’s that the more I know… the more I know I don’t know : )
Regards
Correction:
I don’t pretend to be able to explain that with paving and structures. At some point, and to your point about Vienna and London, – think also NYC, Tokyo, Hong Kong, etc – from a footprint standpoint you would expect the effect to reach saturation point. Past that point, the surface is effectively covered with structures and pavement (with the exception of usually long-established green areas like parks) structure changes tend to consist of smaller buildings getting replaced with bigger/taller buildings.
Sorry, was trying to multitask with my single threaded processor 😛
Not really. Since most of the Earth’s surface is water….a relatively small fraction of the data is so ‘contaminated’, The effect of this ‘contamination’ which can be examined directly by comparing analyses with and without such stations, is found to be small.
There are not a lot of ocean sensors (unless you count ARGO, which shows slight cooling).
There are a number of larger errors (both warming and cooling) with surface stations that may be canceling each other out. More on that in future, though, as research is currently in progress.
There is a large direct offset effect (demonstrated and currently undisputed). But there is also an effect on trend, particularly Tmin. (Menne failed to take certain factors into consideration.)
One can also use the statistical correlations between contaminated and non-contaminated stations to remove any bias.
One can, but one doesn’t. According to what we know about NOAA and GISS procedures, no microsite adjustment is applied. NOAA believes it unncessary (as I said, Menne missed a few “tricks”). GISS applies a (small and controversial) UHI adjustment, while NOAA USHCN2 applies none at all.
I agree that the road will be warmer than the surrounding natural surface. But warmER does not imply warmING. Warming involves a temperature trend in TIME whereas you seem to be talking about temperature variations in SPACE. Not the same thing.
Yes. That is important not to forget. That is what is currently being looked at: effect on trend as opposed to offset.
Consider also that if proximity to concrete increases warming effects in a warming phase, it should also increase cooling effects during a cooling phase (as the effect “undoes” itself). Provided always that the microenvironment remains constant during the study period . . . if concrete is added (or removed) meanwhile, all bets are off.
Evan,
An equal and opposite cooling effect does not fit the observations. When it snows in the winter, even on residential streets that do not get much traffic (which in fairness should be considered as a possible bias) it sticks on natural surfaces (trees, grass) before it “sticks” (i.e. falls and doesn’t melt) on roads, and melts on roads before it melts on natural surfaces.
I’m not trying to be argumentative here, but everything points to manmade surfaces exhibiting a higher average equlibrium temperatures than natural surfaces. Transpiration and, I think, Stephan-Boltzmann (sp? – the folks who talk about energy transfer as a function of emissivity, temperature and surface area) explain this quite readily. An object with less surface area (again, compared to fins on a radiator for leaves or grass) with all other things (like albedo) being equal will tend to exhibit higher equilibrium temperatures than an equivalent object with less surface area.
Explain a situation where, for extended periods, paving is cooler than the surrounding natural surfaces? I’ve never seen it but I would be happy to slap down my current ideas on things with good evidence. Neither the observations, or the physics seem to back up that idea.
Come to think of it, I could have sworn I saw a documentary about an attempted road build in Alaska (maybe to support the building of the Alaska pipeline but not sure) where they put asphalt directly over the tundra (and underlying permafrost) and the road sank into the ground. They had to go back and build a roadbed up on top of the natural surface to keep it from melting the underlying earth and sinking.
If a net cooling effect cannot be demonstrated at that latitude, I’m not sure it would ever show up in areas with higher levels of insolation.
“Explain a situation where, for extended periods, paving is cooler than the surrounding natural surfaces?”
Here are comments from the Newfoundland Department of Transportation and Works who have simultaneous measurements of pavement and atmospheric temperatures. These indicate that the pavement can be hotter or colder than the air
The Importance of knowing pavement temperatures verses air temperatures
Air temperatures are not usually good indicators of what the roadway surface temperature actually is. During the fall the pavement is often kept warmer than the surrounding air because of the warm soil. During the spring the reverse may be true. The pavement temperatures can be colder than the air because the soil is still frozen from the low winter temperatures. The sun also has a strong influence on the pavement temperatures. It can help heat the pavement and speed the melting process. Air and pavement temperatures can often differ by many degrees. On a bitterly cold early winter day when the air temperature is well below freezing, the pavement or surface temperature may be somewhat warmer, primarily because the subsurface temperature has not yet cooled.
Here’s the link
http://www.roads.gov.nl.ca/cameras/temperaturedetails.stm
NickB. (13:57:57) posted a link to Spencer who says
My last few posts have described a new method for quantifying the average Urban Heat Island (UHI) warming effect as a function of population density, using thousands of pairs of temperature measuring stations within 150 km of each other. The results supported previous work which had shown that UHI warming increases logarithmically with population, with the greatest rate of warming occurring at the lowest population densities as population density increases.
There is a confusion here associated with the fact that Spenser flips between population and population density. These are not the same. Did he mean population rather than population density? If not it would imply he has TWO relations…one relating warming to population and one relating it to population density. I doubt this is the case. So for the purposes of discussion I’ll assume he just means population.
A simple model of a city follows on assuming a CONSTANT population density and growth due to horizontal spread. For a circular city it’s size is given by its radius and its growth is characterized by a changing radius.
Suppose the radius of the city increases by Δr every year due to construction at its edges. Then the city area increases by 2πrΔr. Suppose the new area experiences a warming ΔT say and the rest of the city is unaffected. Then average warming over the entire city is 2(Δr/r)ΔT. For those familiar with the calculus this implies logarithmic growth with time in the limit of small Δr.
“For those familiar with the calculus this implies logarithmic growth with time in the limit of small Δr.”
Under the assumption that r grows linearly in time
evanmjones (21:41:49) : Yes, quite. The effect of pavement on climate would appear to be negligible. The effect of pavement on climate stations, however, is profound.
More generally the claim that the combined effects of instrument placement, the urban heat island and the growth of urban areas contribute to the warming in the temperature record was debunked some time ago…
http://news.bbc.co.uk/2/hi/uk_news/4021197.stm
Tom,
Regarding the Newfoundland study – no disagreements there. The atmosphere (air) temps can fluctuate wildly and quickly, no doubt, and the surface temps of pavement *or* natural surfaces change much more slowly.
The point here is the difference between pavement and the natural surfaces it replaces. I don’t see anyrhing in that study that seems to disagree with what I see as commonly accepted observations on surface behavior – and that is that pavement tends to cool more slowly than the natural surfaces it replaces, and warm up more quickly (i.e. it exhibits higher average temperatures in general).
Maybe I have confused this some – the surface temps I’m describing are the equivalent of a FLIR image description or, said another way, the “real” surface temps – what this means from 2m off the ground (surface temp station height) is a whole different ball of wax. One would expect there to be some effect on average… but the spatial relationship, direction and strength of wind speeds in the area, and maybe many other variables I’m not thinking about (always a possibility), will all effect how this would affect a given station.
Tom,
Regarding Dr. Spencer, I’m not sure exactly what he was referring to with the reference to “population” – it is possible that it could be work by others that derived similar relationships by using population, instead of population density. The question would then be, which is the better fit?
Spencer’s analysis (the one linked at least) was strictly centered on population densities. You should check out the population tool from Columbia he used – the link is in there somewhere – very cool stuff.
Tom W (13:27:34) :
“Aliasing? Technically true I supposed but he did use the term ALIAS…in the SECOND PARAGRAPH OF THE ARTICLE NO LESS. To quote:”
‘An important component of this analysis is to account for long term drifts in the measurements that arise from drifts in local measurement time that can ALIAS the local diurnal cycle into the long term record'”
http://www.remss.com/papers/Correcting_MSU_Channel_2_Temperature_for_Diurnal_Drifts_IGARSS.pdf”
Ah, there’s the problem. I was talking about Mears et al (2005). You’re talking about Mears et al (2002). (For the record, I mentioned Mears et al (2005) in my very first comment, and never mentioned the 2002 paper; and the only time you attached a date to any paper that Mears was an author of, you said “2005”, so if you want to blame someone for the misunderstanding, it’s clearly YOUR fault. No one ever said anything about a 2002 paper, until now. And you’re only bringing it up now because I proved you wrong on your arrogant and condescending correction of my assumption that you were talking about “bias” rather than “alias”, when the more general term “bias” was, clearly, the more appropriate term.)
You are correct, Mears did use the term “alias” in his 2002 paper. Of course, I maintain that his use of the term is incorrect, as “alias” refers specifically to a problem of an inadequate rate of sampling, not inconsistent sampling. And I think it’s safe to say Mears now realizes this as well, since he dropped the term from his 2005 paper, which was dealing with exactly the same issue. And so it is you, Tom who needs to “learn the basics of signal processing”, as Mears apparently did sometime between 2002 and 2005.
But, since you brought up the 2002 paper, let’s talk about that for a minute. Mears estimates that “this diurnal correction increases the resulting global decadal trends by 0.02 to 0.05 K/decade, with the largest effect on trend over land”. Using the upper and lower limits of this range as fixed values for water and land, respectively, and taking a weighted average (.7 for water and .3 for land) results in 0.029 K/decade, which I’ll round to 0.03. Now, Mears didn’t specify what data set this increase was in relation to, but there’s really only two things it can be. It’s either 1) the UNadjusted RSS tropospheric temperature trend (0.090 K/decade) or 2) the adjusted trend estimated by Christy et al (2000) (0.060 K/decade). Adding the weighted average trend-increase of 0.03 K/decade yields an adjusted trend of 0.12 K/decade when added to the unadjusted RSS trend and 0.09 K/decade when added to Christy’s adjusted trend. I’ll give Mears and you the benefit of the doubt and go with the larger (RSS + wtd-avg Mears adjustment) figure of 0.12 K/decade. This is still less than the surface temperature trend (0.16 K/decade) and less than 2/3 of what the models predict for the global tropospheric temperature trend (0.16 x 1.2 = 0.192)
But even if we forget about weighted averages, give you and Mears the benefit of the doubt, and take the upper limit (0.05) of Mears’s range of trend increases, and apply it only to the unadjusted RSS trend, you come up with a tropospheric temperature trend of just 0.14 K/decade. (Note: this is the largest possible adjusted value of the tropospheric temperature trend that you could get from any interpretation of this paper, and this is a very unlikely interpretation.) Even that figure is still less than the surface temperature trend, and 27% below what the models say it should be. Are you sure this is the Mears et al paper you want to hang your hat on, Tom?
Also, you haven’t answered my question about whether the models included stratospheric cooling in their calculations of how much the troposphere should warm relative to the surface. So I’ll ask it again. Do the models that predict that the troposphere should warm 1.2 times as much as the surface include the effect of stratospheric warming in their calcultions (in which case, any adjustment to remove stratospheric warming from the tropospheric temperature trend is entirely inappropriate), OR do they NOT include that effect (in which case, the models are significantly over-predicting surface warming)?
Regards,
Trevor
Trevor (06:22:55) : Ah, there’s the problem. I was talking about Mears et al (2005). You’re talking about Mears et al (2002).
Nice try but in fact both papers speak of aliasing, 2002 explicitly and 2005 implicitly. According to the 2005 paper
If the temperature being measured changes with the time of day (e.g., the diurnal cycle of daytime heating and nighttime cooling), slow changes in observation time can cause spurious long-term trends, which must be removed from each satellite’s data record before attempting to merge the data together into a single data
In other words they are concerned with misinterpreting the diurnal oscillation as a long term trend.That is aliasing demonstrating that you claim.
Perhaps that’s because the term “aliasing” doesn’t really apply to the situation with satellite drift.
is hogwash.
If that is not enough we have a 2007 paper containing the phrase
“So which channels and satellites are most affected by the changes in sampling due to diurnal aliasing?”
http://journals.ametsoc.org/doi/abs/10.1175/BAMS-86-10-1437
It seems that everybody is wrong but you…who, strangely, didn’t even know what aliasing was 48 hours ago.
“Stratospheric cooling”
This phrase still bothers me. Technically speaking, there’s no such thing as “cool” or “cooling”. What we call “cool” is really just a lack of warmth, and what we call “cooling” is really the removal of warmth. So when we say, for instance, that “an ice cube dropped in a glass of tea cools the tea”, what we mean is that the tea loses heat to the ice, which causes the tea to become “cooler” and for the ice to become warmer (and then to melt, and then for the resultant slightly-above-freezing liquid water to warm further, approaching a temperature equal to the tea). Similarly. when we say that “the stratosphere cools the troposphere”, what that really means is that heat energy flows out of the troposphere into the stratosphere. However you look at it, heat doesn’t just disappear, as we know from the Law of Conservation of Energy. It has to go somewhere, and if the stratosphere is what is causing the heat to leave the troposphere, then the stratosphere is where that heat is going.
So, what we have, essentially, is a difference in temperature between the stratosphere and the troposphere, with the stratosphere being cooler. We know that heat tends to diffuse from areas where there’s a lot of it to areas where there is less of it. So it makes sense that heat would flow from the troposphere to the stratosphere.
Now, the stratosphere is (about) 4 times thicker than the troposphere (I say “about” because the thicknesses of both vary widely, but the ratio of the means is near 4 to 1). Now because the atmosphere is a sphere and not a rectangular prism, the four-fold thickness of the stratosphere actually results in slightly more than 4 times the VOLUME of the troposphere. But because the thickness of the stratosphere is so small compared to its radius, “4 times as much volume” as the troposphere is “close enough for government work”.
Let us say, for the sake of argument (and because no one has answered my question on this yet), that the CGM models that predict that the troposphere will warm 1.2 times as much as the surface, IGNORE the effect of global cooling.
Now, if we really believe that the troposphere should (without stratospheric cooling) warm about 1.2 times as much as the surface, then during the past 30-odd years, when surface temperatures warmed by 0.16 K/decade, we would expect to see 0.192 K/decade of warming in the troposphere. What we ACTUALLY see, however, is, at most, 0.09 K/decade (RSS unadjusted trend). That means that, if we really believe stratospheric cooling ameliorates tropospheric warming, then over half (0.102 K/decade) of the 0.192 K/decade expected tropospheric warming is being sucked off into the stratosphere.
But if the troposphere is losing heat to the stratosphere, then that drastically reduces (by over half, in fact) the amount of heat that it can lose to the surface. The heat lost from the troposphere to the surface is what we call the “greenhouse effect”. But that greenhouse effect is cut in half by stratospheric cooling of the troposphere! And the CGM models are completely ignoring this. The CGM models, therefore, are drastically over-predicting the surface warming trend.
Unless, of course, contrary to our initial assumption, the CGMs do include the effect of stratospheric cooling in their predictions that the troposphere will warm 1.2 times as much as the surface. If that is the case, then, as I have noted several times already, it is inappropriate to “adjust” tropospheric temperature trends for stratospheric cooling.
So, any warmists out there? Any CGM apologists? Any tropospheric-temperature-trend deniers? Tom W, where are you, buddy? Show me where I’m wrong! And please, be as arrogant and condescending as you can be. That will make my eventual victory all the sweeter.
Regards,
Trevor
Tom:
Speaking of “nice tries”…
As I’ve already told you (twice now, in fact), “alias” or “aliasing” refers to a inaccurate signal caused by an inadequate rate of sampling, not by inconstistent sampling (which is what Mears, in both 2002 and 2005, was looking at). That comes directly from the Wikipedia article you linked to There is nothing in the word “spurious” (or any other word in your quote for that matter) that implies an “aliasing” type of error.
Maybe I didn’t know what “alias” meant 48 hours ago. But you STILL don’t, Tom.
However, thanks for the quote from the 2007 paper. Is that Mears as well? If so, it confirms that, like you, he still doesn’t understand what “alias” means.
Regards,
Trevor
Trevor (12:11:30) : aliasing” refers to a inaccurate signal caused by an inadequate rate of sampling
Much too vague. Aliasing is more than just an “inaccurate signal”, it’s inaccurate in a very particular way. To recognize that the Mears 2005 quote I gave implies he is studying aliasing this has to be appreciated.
All I can do is offer another link. Try it and then go back and read the Mears 2005 quote I gave.
http://mathworld.wolfram.com/Aliasing.html
However, thanks for the quote from the 2007 paper. Is that Mears as well?
The third paper whose authors you claim don’t know what they are talking about? No Mears is not an author.
I also offer a THIRD Mears paper from 2006 which states once again that the issue is aliasing…
The NOAA satellites that fly the MSU
instruments are flown in sun-synchronous polar
orbits. The local equator crossing time for these
orbits drifts slowly over the lifetime of the satellite,
leading to slow changes of the local measurement
time that if uncorrected, would alias the diurnal
cycle into the long-term data record. This effect
can be removed if sufficient details about the
diurnal cycle as measured by the MSU channel in
question can be obtained.
ams.confex.com/ams/pdfpapers/104116.pdf
Although I doubt you will believe your lying eyes.
Finally you seem to think that ‘inconsistent sampling’ is a technical term, it’s not…it’s merely another way of saying ‘bad sampling’, sampling that gives rise to significant error. Aliasing is therefore a form of ‘inconsistent sampling’.
Why can’t we send these people before a grand jury for them to return a true bill and send the matter on for trial?
All it needs is some money (I don’t have it).
The universities sector has blown its credibility. It sems to me truth (as I was taught as an undergraduate) has gone out of the window. It is more like calypso song:-
“The whole idea of calypso song
Is that you make it up as you go along”
Go for it!!
Yes, I understand all that, Tom. And you’re still wrong. I know my explanation of “aliasing” was vague – I didn’t want to spend 3,000 words explaining it, when a half dozen sufficed for establishing that you are wrong. Getting more specific most certainly does not help your argument. The new reference you provided still doesn’t describe aliasing in a way that comes close to including the effects of satellite drift.
And yes, I know that “inconsistent sampling” is not a technical term. It was just the first term that came to mind that the general public might understand. Doesn’t change a thing. Though both satellite drift and “aliasing” can be called “bad sampling” (that’s not a technical term either, by the way), aliasing is a very specific type of bad sampling (which I referred to as inadequate sampling, which isn’t a technical term either) which satellite drift simply does not fall under.
Look, Tom, I understand that you made a mistake, and now you feel the only way you can save face is to keep arguing your point even after everyone knows you’re wrong. You’re just making yourself look like a fool. And I would love to continue helping you to do so. But there’s a much more important discussion that I would very much like to have with you, or any other tropospheric-temperature-trend denier. So, for the sake of discussing this more important topic, I’m going to let you continue to believe your incorrect definition of “aliasing”, and stop belittling you for it, on the condition that you stop belittling me for my CORRECT definition, as summarized from BOTH of the references you provided links for.
The more important topic, as I have mentioned several times now, is whether the CGMs that predict tropospheric warming include the effect of “stratospheric cooling” in their calculations. This is very important because, if they DO include that effect, then it is entirely inappropriate to perform any kind of adjustment to tropospheric temperatures before comparing them to the models’ predictions. On the other hand, if the models do NOT include this effect, then they are missing a huge source of negative feedback, which will reduce the ultimate effect on surface temperatures by more than half of what the models predict. Either way, the models are clearly wrong.
I know you’ve seen this Tom, because you’re still reading my posts about “aliasing”. And from your lack of comment on it, I can only assume you have no answer for it. So why don’t you go ask Gavin (RealClimate) how to respond?
Regards,
Trevor
Corrected post…
Tom W (05:57:56) : Your comment is awaiting moderation
“Look, Tom, I understand that you made a mistake..”
Let’s see. Trevor (06:22:55) claims that Mears came to ‘appreciate’ by 2005 that he was wrong for claiming in his 2002 paper that the satellite error he was studying was to aliasing of the diurnal cycle. How did Trevor come to that conclusion? The word aliasing didn’t appear in a 2005 Mears paper on the subject. Hardly a convincing argument but apparently good enough for Trevor.
Trevor is wrong of course since Mears describes the problem as one of diurnal aliasing once again in a 2007 paper (see Tom W (09:56:15)), a reference Trevor apparently want to continue to pretend doesn’t exist.
I’m done with this silly ‘exchange’.
Tom:
You got me there, Tom. I made a mistake. Apparently, Mears, like you, still hasn’t learned the definition of “aliasing”. Mea culpa, buddy. There! You finally proved me wrong on something. But, for the record, I hedged a little on that. I didn’t exactly say that Mears had figured it out, I said that I thought it was a safe bet that he had figured it out. Moreover, it’s not just that Mears didn’t use the term in “a 2005 paper”. The 2005 paper was about exactly the same thing (error in tropospheric temperature measurement due to satellite drift), yet he did not use the term “alias” in describing this error. But yeah, I was guilty of assuming that Mears was capable of discovering his own error. My bad.
Of course you’re done with this “silly exchange”. You’ve lost and you know it. The one argument of mine that you can find to nitpick (my use of the term “bias” over your “alias”), turns out you don’t even know what you’re talking about (and, no, neither does your hero Mears). And you won’t even touch the much more important issue I’ve brought up, regarding whether adjustments for stratospheric cooling are even appropriate.
Regards,
Trevor