skierinvermont

I have no idea why you changed your mind. But first you mention that RATPAC has gridding. Then the next post you say "My mistake... RATPAC has no gridding technique" It's all right there in black and white. Everybody else can see it so why bother lying? Furthermore, I don't need to make arguments for RATPAC's viability. It's a peer-reviewed widely used data source found in the AR5. You have presented no valid criticisms (which coincidentally is why you have changed nobody's mind).

Also to answer soc's question why he would say there is gridding and then say there is no gridding is he changed his mind. "Actually i made a mistake... Ratpac does no gridding." See below. He was actually correct before he changed his mind (although the critique of coverage was still incorrect). It appears he is taking his own post out of context without acknowledging he later changed his mind, in order to deceive you mallow. I'm sorry but I really can't stomache this kind of deception without calling it out. Don't be confused mallow. i understand it can be tedious for those not following. But it is impossible for the thread to function when you have this level of deception going on. I can't say or respond to anything without more deception and weasling in response. I appreciate nzucker putting his foot down for what is right.

The real story regardless of the semantics is that the criticism of ratpac is unfounded. He made up something not true to criticize a fairly solid data source. He made it sound like there was no area weighting but there is. And the coverage is more than sufficient for long term climate monitoring. There are peer reviewed estimates of the uncertainties related to coverage and gridding techniques for those interested ( hint: they are not "awful" as soc said) I have yet to see a peer reviewed critique of ratpac suggesting the uncertainty is greater than published. Until then I suggest we drop this argument and continue using ratpac as a decent source of data with uncertainty on par or perhaps a bit better than msu data such as rss. I welcome don's posting of the data and will continue to defend it vigorously from baseless attacks.

Likewise, the surface data sets have huge holes which they extrapolate over. The RATPAC procedure is similar and more than sufficient. Plus your post was a blatant lie so you don't have any credibility anyways. You're obviously just out to undermine a well accepted peer-reviewed (albeit imprecise) data set in favor of a controversial non peer-reviewed source. RATPAC is not 'a simple average of the 85 stations' and the data is gridded. Your post is factually incorrect.

Plus the point of your post was to criticize RATPAC. The criticism is completely unjustified. The data is area weighted and has sufficient global coverage. Your post is factually incorrect on the details (it is gridded, it is not 'merely an average') and it is incorrect in the big picture as well. The area coverage and spatial homogenization is perfectly acceptable.

So you said it's an average of the 85 stations. It's not. You said there is no gridding OR spatial homogenization. It is gridded, and it is spatially homogenized.

Wait, I thought you said there was 'nothing in the way of gridding...'

Because the statement is wrong no matter how you slice it. The global coverage is more than sufficient. It is not a simple average. It is an area weighted average which accomplishes the exact same thing as extrapolation. Your statement is wrong both on its face and in its intent (to criticize). The criticism is unjustified (really just a hack move on your part).

Lies!

First of all, the way you're defining homogenization doesn't even fit the way you're using it. You're saying it's only homogenization if you're removing faulty data. But then you say that they aren't doing homogenization because there is no extrapolation between grid cells (which there is). So you're contradicting yourself. Second, your definition of homogenization is wrong. Homogenization is the removal of non-climactic signals. Processing the data, by example area weighting, fits such a definition. Third, you said specifically that they simply average the 85 stations. That is blatantly false. You have not addressed this falsity at all.

I wouldn't want to lose Don's posts because of some bad posters here and in the economics thread in the politics forum. He makes great contributions to both.

Right here. You said they "merely take the data from the 85 stations and average it out." You also said they "do nothing in the way of gridding." Both statements are blatantly false. They use the data to define trends for 36 grids and then do an area-weighted average of those 36 grids.

False. Homogenizing is any processing step that attempts to improve the data by removing a non-climactic signal. For example, if the data is too heavily weighted to north America, area weighting the data would remove that non-climactic signal.

You said that they simply average all 85 stations and there is no area weighting. That is false. Why won't you acknowledge this? Second of all, it is homogenizing. Homogenizing is, broadly, the removal of non-climactic signals. Any attempt to area weight the data is the removal of a non-climactic signal. I don't really care about the terminology either. But when you use big words you should know what they mean. Most of your posts are an abuse of the english language. It's actually not "normalizing" either. Look up the definition of normalizing. You can call it spatial homogenization, or an area weighted average. But the term 'normalizing' is not descriptive at all in this case and appears to be an unnecessary use of a big word.

In other words, your original statement that they average the 85 stations was false. They spatially homogenize the data by creating regional gridboxes and averaging the gridboxes. The method of spatial homogenization is different than GISS, but is simply another way of removing some of the spatial inhomogeneities.

Maybe you should look up the definition of homogenization. Homogenization simply means, very broadly, the removal of non-climactic signals. It does not mean in any way anything specific such as infilling the data. Any attempt to accurately spatially weight the data would be a spatial homogenization. What RATPAC does is spatial homogenization. Moreover, you said specifically that they simply average the 85 stations. That is false.

Wrong. This is a spatial homogenization technique. It's a simple one and it ensures that no region of the globe is weighted too heavily due to having a higher number of stations. You said specifically that they simply average the 85 stations together. This is a blatantly false lie. Everybody here can see that, so who are you trying to impress? They broke the globe into 36 gridboxes and averaged the 36 boxes and calculated an area weighted average of the gridboxes. This is false. There are several studies using MSU data that provide results different (warmer) than UAH or RSS. There is STAR (I said tropospheric not lower tropospheric). There is RATPAC. There is RICH, RAOBCORE, IUK and several others that also use sonde data. Some of them use entirely different data than RATPAC (wind data instead of temperature data) and are thus entirely independent. This is easily 6+ probably 10+ sources using independent methods and/or independent data. RSS and UAH show the least warming out of all of them. This is the definition of outlier. We're not talking about 15-20 years here. We're talking about 36 years. Over 36 years it is extremely unlikely the lower troposphere would warm slower than the surface. You haven't provided a shred of evidence to the contrary. All you're doing is trying to muddy the waters.

First of all, 85 stations over such a long period is more than sufficient and the areal coverage looks reasonable. Accurate global averages have been constructed with far fewer stations. Second, you are incorrect in your assertion that they do not do gridding or spatial homogenization. A very quick read of the RATPAC paper reveals there spatial homogenization procedure: In an effort to obtain spatially unbiased large-scale means, we compensate for uneven longitudinal distribution of stations by creating regional means before averaging data into zonal bands. Each 30° zonal band was divided into three longitudinal regions of 120° each: 30°W to 90°E, 90°E to 150°W and 150°W to 30°W. Hemispheric (0–90°), tropical (30°S–30°N) and extratropical (30–90°) means were calculated from these zonal means, areally weighted using the cosine of the latitude of the midpoint of the zone, and the global mean was the average of the hemispheric means. http://onlinelibrary.wiley.com/doi/10.1029/2005JD006169/full All you are succeeding at is an obviously biased attempt to make false accusations and cheap shots at an otherwise reputable peer-reviewed source. The spatial homogenization technique had its own heading in the RATPAC paper! You didn't even bother to skim the paper before making this false accusation! Where is your credibility? Clearly you are on a tirade and don't care what the truth is at all or you would have actually read the paper you are critiquing. The AR5 assesses the uncertainty in RATPAC as similar to that of UAH and RSS (+/- .1C/decade). There are also a number of other independent radiosonde LT sources that use very different data and/or methods and conclude even greater warming than RATPAC finds. UAH and RSS show the least warming out of at least 6+ different independent tropospheric sources, some of which are MSU based and some of which are radiosonde based. They are also fairly inconsistent with surface data, which is generally considered more reliable, and theoretical expectations of how the troposphere warms in relation to the surface over 35+ years. RSS and UAH have assessed uncertainies of +/- .1C/decade. This is the definition of outlier.

I believe the 'new' UAH trend is the same as the RSS trend. .11C/decade is correct for an implied surface trend. The TLT should be warming faster if the water vapor feedback is correct. You could say the difference between surface and satellite is statistical noise, but that would be true even if the UAH trend was .07C/decade because the error bars for MSU data are so large (+/- .1C/decade). It's still of scientific interest.

If the TLT trend is .13C/decade, it would imply one of either two things 1) The surface trend is .11C/decade (following the predicted surface-TLT relationship) or 2) The predicted surface-TLT relationship is wrong, the water feedback is non-existent or negative instead of very positive as expected, and climate sensitivity is much less than expected. In both cases, climate sensitivity is much less than expected. A TLT trend of .13C/decade implies low climate sensitivity no matter how you look at it. This is false. There are many sources that use MSU data to measure TLT. UAH and RSS both require huge amounts of quality control and adjustment so I am not sure how you can say they require quantitatively less than RATPAC. Nor would I say that having more or less quality control is necessarily a bad thing.

Wow!

The long-term trend between RSS/UAH vs other satellite (STAR and several other peer-reviewed criticisms of UAH/RSS), RATPAC, and surface data is more than noise and is scientifically significant. The RSS trend since 1979 is .13C/decade. This would imply a surface trend of around .11C/decade. The measured surface trend is .17C/decade. This is 50% more than implied by RSS. This is very significant scientifically and has serious implications for climate sensitivity. The balance of evidence suggests UAH/RSS is in the minority, and is more prone to error given how susceptible the results are to arbitrary choices in methodology.

Is this post for real?

Funny you had to explain that one to him. So much bias he will say just about anything to 'win.'

Because the satellites have long-term biases. Resolution is not a bias, it is simply a lack of precision which over time averages out to zero.