Global warming--the Data, and serious debate

[thaumaturgy]

Does anyone want to defend that 16 deg jump in temperature? Does anyone want to actually defend the 10 deg jump in Susanville California in 1990? Thaumaturgy, you seem to like defending things that are problematical. Want to do this one? Or are you willing to acknowledge that this data is crap?

This data looks messy. I will admit that. But I live in California and the variability from location to location can be extreme. Here we call it the "microclimate effect".

What I'd like to know about the Electra station is: was it "moved"? Obviously as has been pointed out in the various articles so far sited some stations have been moved historically, or otherwise altered.

Indeed great pains are taken to factor those systematic changes into the data upon interpretation. I am reminded of the Peterson paper cited earlier.

This is the real danger of anecdotal data. We have no way to know what the history of this single station is or how its data is treated in the overall assessment of continental and global temperature changes.

Again, I will not defend messy data that I have no background on.

Oh, btw, could you tell me where you're getting your data from? (Like a link or something). When I download data fro the Electra, CA station (#42728 from THIS site) this is what I get for monthly averages from 1905 to 1994:

Here's the "Electra Anomaly" graph (versus its own 1912-1922 average temp mean).

So I can see the same general shape up until the 1990's. The data I can get from Oak Ridge National Labs site appears to stop at 1994, but I should think by 1994 according to your graph I would see these massive spikes. Could you post a link to your data sources?

 

[Chalnoth]

The data set provided has no error associated with each data point, presumably because it is simply a single measurement (month and year). But I cannot say for sure.

I suspect this is because the statistical error on these temperature measurements is effectively zero. Basically all of the error is systematic, and systematic errors are notoriously difficult to estimate.

 

[thaumaturgy]

Glenn really needs to tell us where he got his USHCN data for Electra, CA station from. I simply don't see those spikes at the high end. It will help immensely if he provides a link. The USHCN site I'm using is the one I posted earlier (the ORNL site)

 

[grmorton]

The problem isn't near zero. You keep saying at zero. Yes, a trend results in strong low-frequency components. But the zero mode itself has nothing to do with the trend at all: the zero mode is just the average value of the data taken, since the zero-frequency mode is just a constant.

Also, it's worth noting that the amplitude of the frequency spectrum alone doesn't say whether or not there is a trend. The phase is also important, so it's best to look at the data in the time domain to see whether or not there is a trend.

That is pretty much what I told him when he asked me. I said that one would have to look at the phase diagram, and I posted one. No one I know knows how to intuitively understand the effect of the phase diagram on the final shape. I hope he listens to you more than he did to me.

Of the satellite data, the trend is decidedly much much less than the surface station data. And that is why I brought it up. If one were to have measured the satellite data from Dec 1979 to May 2008, the rise in temperature was 0.007 deg C--That is not much of a secular trend!!!!!

Meanwhile the land temperature record, with its air conditioner exhaust fans, its roof top thermometers, its road side thermometers etc, supposedly has gone up .65 deg in about the same time frame..That is the chart that Thaumaturgy posted. Clearly the two methodologies are not measuring anything near the same temperature rise.

As of August, the temperature has rebounded a bit and now the rise is .18 deg C.

 

[Chalnoth]

I've already posted on this. When you take the properly-calibrated satellite data, the temperature trend almost exactly matches that from station data. It's a bit lower, but that can easily be explained by the lack of weather stations in places like Antarctica.

 

[grmorton]

OK, just so we know you are unable to address the professional time series experts on this topic. That way I know where you stand.
Mr. Glenn Morton takes exception to the SAS Institute and a PhD statistician and he can't explain why they are wrong and he is right.
Got it!

Sigh, I have addressed it several times, but you are too thick to understand it. In the note that you are replying to, I addressed it.

You then claimed that the low frequency component in the satellite data means there is a secular trend. You are in the process of changing by now claiming that it can mean a secular trend. It can, but your logic is highly flawed. While secular trends will have some low frequency, you can’t do what you did and look at an FFT and conclude that there is a secular trend. It is a one way gate. Most secular trends require low frequency (unless it is very steep) but not all low frequencies indicate a secular trend. Below is a power spectra from seismic data. It has no secular trend, yet it has a huge amp at 1 hz. Thus, you can't look at the satellite data and claim that the FFT proves there is a secular trend any more than you can say one exists in a box function or on seismic data. Flawed logic leads to flawed conclusions.

As you said earlier, you have never done time series analysis before and now you are an add-water-become-instant-expert. You got over your head in a debate about GW, an area you thought would be an easy slam-dunk and now you are just looking for anything with which to salvage something. But you continue to make mistake after mistake. You don’t even recognize responses to the things you are demanding a response for.
Your logic is turned about. All crows are black, but not all black things are crows. Can you comprehend that???
Trends CAN cause low frequency spikes, but as Chalnoth and I have repeatedly told you, you can’t reverse the logic (all black things are crows) and be successful. Not all low frequency peaks are due to trends. If you are incapable of understanding this, then this really is a waste of time.
All babies have bottoms, but not all bottoms are on babies! Some are on adults, some bottoms are on jars, some bottoms are on ships etc ad nauseum.
You are trying to look at a power spectra and determine that the peak represents a trend. YOU CAN”T DO THAT anymore than you can claim that every bottom you see belongs to a baby.
The above fits what the SAS says and the statistician said. Trends can cause low frequencies, But as I showed with the boxcar function, not every low frequency peak is due to a trend. How hard is that, mr. New to time series analysis and showing it??
I think I could come up with some time series with trends that didn’t have such a spike. Add a low slope with 100 hz data that has 5000 times the amplitude as the trend. That wouldn’t come out with a peak at 1 hz but would peak at 100 hz. I haven’t done this but that is what I would expect.

Now, look Glenn, you are clearly an amateur in statistics and I hate to have to keep correcting your freshman mistakes, but a p-value like that on a fit means there's a 99.99% chance that it is non-zero (ie a trend).

How rich. Given all the mistakes you have made (and you did this on the yearly data) I am amazed that you are still claiming what you are claiming.

But from what I can tell, Fisher's Kappa indicates no such "cyclicity" among the noise to a 99% level of assurity.

. . .

I think I was mistaken about the Kappa function. It does show a statistical significance for cyclcity when it is low on the p-value.
No problem. We see from the graph that, as Glenn has pointed out, there is, indeed, cyclicity. AND it has a multi-year period. The residuals bear this out

Do you want me to cite more of your errors? you are the one who averaged the yearly values, you are the one who can’t seem to understand that temperature gauges on air conditioner exhausts would bias the results, and you are the one who cant seem to understand the simple fact that when the measurement error is larger than the presumed signal, it is a meaningless measurement. You continually claim that I know nothing, but it is you that keeps making the mistakes.
Now, notice, T, I have no doubt that the regression will show a trend. Shoot, taking the regression on a sine function from 0 to pi/2 will show a regression trend. That doesn’t make the sine function a linear function. The reality is, if you look at the 80s on the Satellite data, you will see it is highly periodic. The ups and downs are clearly NOT random noise. This is clearly a cyclical function with a wide range of frequencies. It isn’t a linear change with random noise. Random noise in a Fourier transform is a bias in the frequency domain. Random noise has a constant energy across all frequencies. The transform doesn’t show that. But of course, you won’t understand this any more than you understood

Whatever the big picture really is, there is no doubt there is some kind of trend within the time span of your data.

I haven’t denied that from start to finish, over 30 years, one can see a small rise in tropospheric temperature. What you have never addressed that I have seen is the fact that it is far smaller than what you posted about land, which, as far as I recall, you didn’t explain why it exaggerated the global warming rise. The IPCC says .74 deg C over the past 100 years (I finally found it in the 2004 volume. But you have well over a degree on your chart.

No, what I said was there is a measurable, statistically significant linear trend, and that it was up to you to prove the data was dominated by a cyclic function. That was before I educated myself on time-series analysis.

LOL. I see you are still trying to say that a trend lies in the low frequencies. It may, but it doesn’t have to as you are claiming.

THEN ADDRESS THE POINTS BY THE SAS INSTITUTE AND A PhD STATISTICIAN.

I did. See above. See my previous posts on this.
In the note to which you are responding I also replied to it. You ignored it. From the post you are replying to and now demanding that I respond to the SAS institute and the Ph. D. Statistician, I wrote:

You then claimed that the low frequency component in the satellite data means there is a secular trend. You are in the process of changing by now claiming that it can mean a secular trend. It can, but your logic is highly flawed. While secular trends will have some low frequency, you can’t do what you did and look at an FFT and conclude that there is a secular trend. It is a one way gate. Most secular trends require low frequency (unless it is very steep) but not all low frequencies indicate a secular trend. Below is a power spectra from seismic data. It has no secular trend, yet it has a huge amp at 1 hz. Thus, you can't look at the satellite data and claim that the FFT proves there is a secular trend any more than you can say one exists in a box function or on seismic data. Flawed logic leads to flawed conclusions.

I think I am in agreement with what those two sources of yours are saying.

I said, that a trend could show up as a low frequency peak but that your logic was backwards. Obviously you ignored it and now demand that I state it again.

1. Do you or do you not think that low frequency wavelengths will show up as a low frequency spike on a periodogram

What in the heck is this? Of course a single frequency, low frequency wave, of one, will appear as a spike on the power spectra. So will a high frequency wave appear as a spike. Your terminology here is not very clear as to what you are asking

2. Do you or do you not think an extreeeeemely low frequency cyclic trend in the data can show up as a steady increase when sampled over a shorter time frame (ie much, much, much shorter than its wavelength)

For about the 3rd (or 4th) time, yes, they CAN, but you can’t turn the logic around and say that just because you have low frequency energy, that you have a trend. The box car function has a low frequency peak. I showed this. You were too thick to follow it. So, I will post a picture from the Robert Sheriff, Encyclopedic Dictionary of Applied Geophysics. 2002 P. 149
Now, on the box car function, show me the secular trend? I dare you. It starts at precisely zero and ends at precisely zero. The peak in the spectrum is at zero Hz. Why Zero hertz? Because this function does have some DC shift, or as Chalnoth put it, it has an average value. Zero frequency is about as low frequency as it gets. And if you try to claim that the low frequency represents a secular trend, Mr. Instant-time-series-analyst, please show that trend in the data which starts at zero at infinity, rises to some value at -1/2, and then returns to zero at +1/2 remaining at zero to positive infinity.
Where is the trend. Until you understand what the statistician is saying and what the SAS book is saying, you will wander in the darkness.

3. Do you or do you not believe that such a trend, if sampled at a small enough fraction of its wavelength could appear to be a linear trend.

Of course but irrelevant. We started this with the satellite data, which for the life of me is the weaker data for your position, which you now go to the mat over. If you are saying that the warming of the past century is part of a 500 year long cycle, then of course, it would appear as a linear trend, but, and this is important, it wouldn’t be a trend, it would be a cycle. No matter how much you proclaimed it as a trend, it wouldn’t be a trend because eventually it goes back to the value it started at—which is what the satellite data did from Dec 1978 to May 2005—it returned to its value---a cycle, not a trend! Gosh, you make things hard.
I suggest you answer these questions.

Then we'll get back to your "understanding" of the difference between confidence intervals and standard deviations.

First off you have to understand something. I am not writing here for a scientific article. I am writing for the people who are reading us. I want them to understand what is said. Jargon laced language is bad for that. I am not even really writing to you any more, given all the errors you have had to acknowledge in this discourse, yet refuse to actually discuss the temperature record. You are the one who over and over is forced to admit errors. Now explain either where the secular trend is in the boxcar function or admit that a big peak in the low frequency range doesn’t necessarily mean a secular trend.
Here is some more data on California (for those who might have interest, as Thaumaturgy doesn’t seem to). The maximum yearly temperature change is plotted for each station. Note that Electra had one year in which the temperature changed by 18 degrees F. Now anyone who knows the heating and energy industry knows that degree days are an important number. They tell the power companies what to expect in the way of demand. Usually it is degree days above or below some reference. Well in Electra CA, one year got an additional 6570 degree days.
I looked up Electra’s degree days, If you cool your house when the temperature is above 65 deg F, you have 602 degree days annually. But the temperature record says that in this maximum change, Electra suddenly got 6570 more degree days. And people believe the temperature record. Data from
http://www.nahbrc.org/evha/HDD.pdf

btw, being a blackbelt is not a very important criteria for addressing Fourier transforms.

 

[grmorton]

This data looks messy. I will admit that. But I live in California and the variability from location to location can be extreme. Here we call it the "microclimate effect".

What I'd like to know about the Electra station is: was it "moved"? Obviously as has been pointed out in the various articles so far sited some stations have been moved historically, or otherwise altered.

Indeed great pains are taken to factor those systematic changes into the data upon interpretation. I am reminded of the Peterson paper cited earlier.

This is the real danger of anecdotal data. We have no way to know what the history of this single station is or how its data is treated in the overall assessment of continental and global temperature changes.

Again, I will not defend messy data that I have no background on.

Oh, btw, could you tell me where you're getting your data from? (Like a link or something). When I download data fro the Electra, CA station (#42728 from THIS site) this is what I get for monthly averages from 1905 to 1994:

Here's the "Electra Anomaly" graph (versus its own 1912-1922 average temp mean).

So I can see the same general shape up until the 1990's. The data I can get from Oak Ridge National Labs site appears to stop at 1994, but I should think by 1994 according to your graph I would see these massive spikes. Could you post a link to your data sources?

This is an interesting criticism, and finally you have one that has some merit. So, let's look at what they say about the data you are plotting.

The full U.S. HCN data base contains the original data; data adjusted for time- of-observation, station moves, and instrument changes; and data adjusted for urbanization effects.To save space on this server, only the data adjusted for urbanization effects are available here.

http://cdiac.esd.ornl.gov/r3d/ushcn/ushcn_r3.html

Thaumaturgy, you have gotten a sanatized dataset. Clearly you didn't read what it was that you were getting. I have come to expect that of you. This is clearly a mistake on your part.

I don't want a sanatized data set. I want the raw data because that is what is important. The sanatized data is what I get after someone who believes the earth is warming massages the data to make it look like it is warming! I get my data from http://www.co2science.org/index.php.

The data I am showing you is the raw data, which is of interest to me because, they have to take the raw data and put it into the sanitized shape. That way believers like you will not suspect how bad the data is.

Given the spurious warming trend identified by Balling and Idso (2002) that exists in the "corrected" versions of the USHCN database, the Center has opted to utilize the database's RAW version throughout the U.S. Climate Data section of its website.

This site was founded by people who believed global warming, but after looking at the temperature records, changed their mind.Their original site, as I understand it was in favor of global warming. This site has easy access to the original data.

I lived in China where the government spoon fed data to its people, data that it wanted them to believe. YOu have fallen for this by not realzing that you are being fed a bunch of garbage.

Below is a plot of Electra Raw (blue) vs Electra Sanitized for public consumption (red). I don't know what you would call this, being the expert on statistics that you are, but I would call it cheatin'. They have no idea what the temperature in Electra was in 1986.

I laugh when I see the little details they put in the Electra record in 1986. the raw data goes up, but they put a little dip into the data as if it is real. What a real laugh!!!! They are making up the data when they don't like it. And one thing I know about science, making up data is or should be verboten.

attached are the pictures of both Electra and Susanville stations. If you think this is good science, then we have a difference of opinion. And I would also note that you lack any scepticism about this because you are a beleiver in GW. Believers never doubt. They accept anything that supports their position without a single question. YOu have just demonstrated that.

Why didn't you tell us that this was sanatized data?????????

answer: you don't have an ounce of scientific skepticism.

I do want to thank you for adding to my arsenal of reasons not to believe your government, regardless of what they do.

 

[grmorton]

I've already posted on this. When you take the properly-calibrated satellite data, the temperature trend almost exactly matches that from station data. It's a bit lower, but that can easily be explained by the lack of weather stations in places like Antarctica.

Are we talking 'properly-calibrated' data like what is done with the surface stations as I posted above? Is that what you mean?

 

[grmorton]

Glenn really needs to tell us where he got his USHCN data for Electra, CA station from. I simply don't see those spikes at the high end. It will help immensely if he provides a link. The USHCN site I'm using is the one I posted earlier (the ORNL site)

That is because you are not actually examining what they say about the data you got. Why is it, T, that you constantly ignore things people write? I have addressed the issues of SAS and your statistician 3 times and yet you demand me answer them. And now you present us with sanatized data and act as if it is the raw data, when the site you got it from clearly says it is sanatized?????

You need to be more observant.

See above for the answers to your questions.

 

[grmorton]

Glenn is under no obligation to reply to this, but I'd like to hear Chalnoth's response:

I would dearly love some of our local experts to explain the following:

I ginned up some data on Excel to prove a point to myself. Then I ran a time-series analysis on JMP. If you can't explain this I will respect, but I am going the extra mile to check my own thinking as well as yours.

DATASET:
A = arbitrary amplitude (100)
f = arbitrary frequency (0.85)
X = 1 to 256

Y = A*SIN(X*f) {NO TREND CYCLIC DATA SET)
Here's the data and the spectral density plot:

Y= A*SIN(X*f)+(X+mean(X)-mean(A*sin(X*f)+(X+mean(X)) {To ensure the y-mean = 0 --no offset)

Y with a Box Function (generated by taking X-values 65-192 and adding in an arbitrary offset = 200) Then I took this data and removed any offset such that the y-value mean = 0

What this says:

• A single pulse offset in the data will result in a spike on the spectral analysis at or near zero.
• A monotonic secular trend in the data will also result in a spike on the spectral analysis at or near zero

If you have any comments on this, you now have the formulae I used and the graphics. The y-values (except for the Y=A*sin(X*f)) have a mean value of ZERO.

This means further:

• You (Glenn and Chalnoth) are correct
• I am correct

What have I done wrong in my reasoning here? If you like I can send you a tab delimited copy of the data so you can run your various analyses on it to prove that I am somehow in error.

You have not convinced me. Nor have you proven that SAS and a PhD statistician are somehow equally in error.

NOTE: I am NOT saying you are incorrect in attributing a spike at or near zero to some arbitrary data offset, however, I AM saying that a monotonic secular trend will equally show up as a spike at or near zero in the spectral analysis of the data.

If you wish to declare me wrong, prove it to me. Use this data set. Show me the error. Show me the offset. Show me why the spike at or near zero DISAPPEARS when the secular trend is removed.

That is all you have to do.

T, you still haven't taken into your thinking the power spectra of the box car function, which has a big peak at low frequencies (indeed at zero because it has a DC bias) but it has no trend.

Remember all cats have whiskers, but not all things that have whiskers are cats! Your logic is highly flawed when you claim that anything with a big low frequency peak must have a secular trend.

 

[grmorton]

It is extremely difficult to find an intelligent discussion of the facts concerning global warming. Usually the 'discussions' consist of one side or the other throwing postures and spin, with a final altar call of "therefore thou shalt believe this."

I'm definitely in the undecided camp in this particular election. For instance, I found the Al Gore show (along with many other supposed 'proofs' for GW) to be long on scare and short on facts. Which is actually disturbing, considering he and others are asking (nay, demanding) us to change our lives to conform to their fears. Such a potentially important topic needs more open discussion of the observable facts and less hype.

Likewise I find the "cons" to be typically dismissive and parochial. For instance, I've grown wary of oil industry wanks running another game on us. I'm not referring specifically to Mr. Morton, but I'm sure that he's seen it.

With that in mind, I'd like to applaud both Morton and Thaumaturgy for about the best discussion I've ever seen on the facts involved. (or at least some of them) I have a faint idea of what this match has cost both of them in terms of time and especially, emotional energy. I know how long it took me just to wade thru the arcana of these 20 pages. But I'd like to think that it's for my benefit, as just another American looking to make informed decisions.

So by all means guys, do keep sparring. Try to limit the ad hominem, and keep making the main thing, the main thing: IS GLOBAL WARMING ACTUALLY HAPPENING? And it's corrolary: IS IT ANTHROPOGENIC? Inquiring people do want to know why you believe as you do. A lot might depend on it.

I love to debate. It helps me learn. Tonight, after I got back from the ranch, Thaumatugy gave me a great idea to compare the sanatized data with the raw data, which is what I have been using. The comparison of the data makes it pretty clear that there is some serious editing going on.

As to your last questions. No doubt there is some warming due to CO2. CO2 is a greenhouse gas. Is it a big deal? No. 55 million years ago the earth had 1000 ppm of CO2 in its atmosphere. Today it has 385 and a hundred years ago it had 280 ppm. The earth survived the 1000. It also survived the nearly 3000 ppm of CO2 during the Cretaceous and Jurassic. People fear change and global warming hysteriacs fear the modest change of CO2 as if it is going to be a disaster. It won't.

125,000 years ago, Greenland was nearly devoid of ice. Beijing was underwater 80,000 years ago because sea levels were higher. There is a raised beach which was the ocean level within the past 200 million years.

"The Suffolk strandline, at 20-30 ft altitude, extends discontinuously from New Jersey to the eastern Gulf coast, with a mapped extent, including gaps, of at least 800 mi. The plain extednig eastward from it is covered with sediments (Cap May formation, Pamilico formation) containing a marine fauna recording temperatures slightly higher than those of today. At four localities these sediments overlie a zone of rooted tree stumps (cypress and cedar), showing that the Suffolk sea was preceded by a sealevel lower than the Suffolk and possibly lower than that of today. Radiocarbon dates on wood from two of these localities imply that the pre-Suffolk low sealevel antedates the last major glacial maximum."

ABSTRACT
Examination of published data reveals that a marine bed in Beijing can be dated as 80 ka or younger on the basis of abundant nannofossils. This age is 30 times younger than that published previously on the basis of magnetostratigraphic and biostratigraphic interpretations. The abundant nannofossils and foraminifers suggest that Beijing was inundated by the sea within the past 80 k.y. The very recent nature of this marine transgression has profound societal and geological implications and thus calls for new studies and thorough evaluation of all relevant data sets.

Global warming not unprecedented
"The Pleistocene was characterized by long periods of extensive Northern Hemisphere glaciation, interrupted by relatively brief inter-glacials during which continental ice retreated to a few strongholds in the Canadian Arctic and Greenland. During the last inter-glacial-referred to as marine isotope stage (MIS) 5e-global mean sea level was 4 to 6 m higher than during the current Holocene period. A substantial fraction of this sea-level rise can be attributed to a smaller Greenland ice sheet."

"The last interglacial is an interesting analog for the future, because the Arctic was several degrees Celsius warmer than during the 20th century, within the. scope of projections for the coming decades. However, the analogy only goes so far, because melting of the Greenland ice sheet during MIS 5e was driven mainly by greater summer insolation, not by increased levels of greenhouse gases. During MIS 11 (three inter-glacials before MIS 5e), summer insolation was not very different from that during the Holocene. MIS 11, however, lasted from 425,000 to 375,000 years ago, twice the duration of MIS 5e. This interglacial thus provides a different analog for the future, allowing us to examine what happens to the ice sheet and surrounding land mass when subjected to protracted warmth. MIS 11 cannot easily be studied by looking at ice cores: Any ice this old has long since melted away or has been subject to irreparable thinning and distortion at the base of the ice sheet. On the other hand, a continuous record exists offshore."

"de Vernal and Hillaire-Marcel analyzed a marine sediment core from the Ocean Drilling Program (ODP) site 646, raised from a depth of 3460 m. At this site, sediment has been deposited continuously since at least MIS 17 (7). The core contains a rich terrestrial pollen record, because the core is located on the south Greenland continental rise, which captures runoff from the adjacent land mass. Taxa currently extant in southern Greenland are well represented, including spores from mosses and club mosses and pollen from shrub birch and alder. During inter-glacials, the record is punctuated by marked increases in total pollen concentrations and additional contributions from boreal coniferous trees, namely spruce and pine, neither of which survives in Greenland today. The pollen assemblages differ tremendously between inter-glacials, with direct implications for the past development of ecosystems in south Greenland. For example, spruce pollen concentrations were three times as high during MIS 13 and 5e, and more than 20 times as high during MIS 11, as during the Holocene. On the other hand, MIS 9 and 7, have unspectacular conifer pollen signatures similar to those in the Holocene"
"How can we be sure that spruce grew in southern Greenland during MIS 13, 11, and 5e, and thus that the ice sheet was sufficiently reduced to allow for regional development of boreal forests? The spruce pollen in these interglacial sediments cannot be attributed to enhanced long-distance transport from North America or Europe. Because spruce pollen is far less easily dispersed than pine pollen, long-distance transport would lead to reduced spruce/pine ratios."

The earth has been through warm times even warmer than we will be at the end of this century. There is no need to fear things. When oil runs out, one will wish for a bit of warmth.

I would also point out that the sun gives off about 5 watts per meter squared more when there are lots and lots of sunspots. Think about this when you read the following.

"The reconstruction shows that the current episode of high sunspot number, which has lasted for the past 70 years, has been the most intense and has had the longest duration of any in the past 8,000 years. Based on the length of previous episodes of high activity, the probability that the current event will continue until the end of the t wenty-first century is quite low (1%)."

but of course, GW beleivers don't want to hear that the heating may be largely due to the sun.

In reality the question of how much effect CO2 will have on the earth is still a question. The IPCC has used several different values for the climate sensitivity to CO2 in their reports over the years. This too is ignored by global warming advocates.

 

[grmorton]

I thought I would show Waterville, WA raw minus sanatized. these are t he 'corrections' to the data. Does anyone really believe that they can make corrections like this and that they be scientifically rigourous???? I for one don't. It seems all too convenient.

 

[thaumaturgy]

Thaumaturgy, you have gotten a sanatized dataset. Clearly you didn't read what it was that you were getting. I have come to expect that of you. This is clearly a mistake on your part.

You are an alarmingly priapic individual. But I expect nothing less of you.

So your thing is to look at unfiltered, unprocessed data. So any efforts to normalize for urban heat effects are removed.

Fair enough. At least I know now where you are getting your data from.

Do you do the same thing with the seismic data you recieve? Do you perhaps do any signal processing on it to make it make sense?

If so, how do you possibly justify such "shenanigans"?

I don't want a sanatized data set. I want the raw data because that is what is important.

Fair enough. But then when one looks at raw data one must realize that raw data has messy offsets and induced signals. Many of those induced signals can be dealt with using perfectly reasonable assumptions, similar to how one processes signals in any data set. In real science those are laid out and open for all to see. (As is noted in the ORNL data set).

Now, of course when you look at the raw data form you may be seeing issues from station movement or some known factor that caused the jump. To then draw a conclusion from that data you are further making an assumption that the scientists who use this data have made a similar faulty conclusion that you were drawing. You assume there is a significant increase caused by a leveraging problem. (This is what one deals with in data sets when outlying data points near the ends of the set pull the data too far off).

So you are concerned about seeing a huge jump in average temperature! Yikes! But if the people who keep track of this can in some way correct for the offset then it would appear from the ORNL mirror site data set that they have done this very thing.

So again you are stuck with a strawman of sorts.

I agree filtered data doesn't show the whole story but do be clear your preference for the unfiltered data without explanation of how the filtered data came from the unfiltered raw data provides just as little insight.

The ORNL data is cleaned up to account for some of the known effects similar to those discussed in the Peterson paper (here) or at NASA's site (here)

The sanatized data is what I get after someone who believes the earth is warming massages the data to make it look like it is warming!

No, you are wrong. You are completely and wholly wrong. I find that amazing for someone who deals with seismic and geophysical data where signal processing is the name of the game.

I'm not going to say "processed data" is superior by definition, but I will say that unprocessed data presented withou explanation or assessment of possible corrections (as you did) is merely presenting your own confirmation bias.

You simply want to see confusing and messy data. It helps you make specious claims.

Did NOAA, NASA, Hansen, University of East Anglia, or anyone else claim that the Electra data set shows global warming???? Did they?

If they did, where did they claim it? In fact, the processed data shows no such thing.

So what you have done is deliberately avoid the proper data set in order to confirm your claim that the data is somehow "crap".

But the important point to make here is that the data has been treated not to provide some false premise in support of global warming but rather to make sure the data is as "consistent" as is humanly achievable.

Otherwise there would be no way to ever produce a century's worth of useful data because the minute a station was moved or encroached upon by human civilization you'd have to cut off the measurement.

Below is a plot of Electra Raw (blue) vs Electra Sanitized for public consumption (red). I don't know what you would call this, being the expert on statistics that you are, but I would call it cheatin'. They have no idea what the temperature in Electra was in 1986.

So you honestly think that this is cheating? If it is it's cheating in favor of the folks who don't believe in global warming.

In point of fact the "cheating" as you call it is explained in a number of different places (cited earlier)

I laugh when I see the little details they put in the Electra record in 1986. the raw data goes up, but they put a little dip into the data as if it is real. What a real laugh!!!! They are making up the data when they don't like it. And one thing I know about science, making up data is or should be verboten.

No one is making data up. They are processing a noisy signal.

Again, if people believed in global warming simply because the Electra California RAW DATA SHOWED THE UPTICK then you'd have a valid criticism.

The raw data is obviously available, and just like in all science, signal processing can and is applied every day to a number of different things.

In chemistry I subtract background spectra from FTIR's all the time. Am I cheating?

Richard Feynman even renormalized equations in QED which is one of the most well verified theories in science. Is all of physics "cheating"?

Sorry, but you are showing your confirmation bias. It is good to see the raw data but your "conspiracy theory" crap doesn't hold water. ESPECIALLY WHEN THE "SANITIZED" DATA MAKES YOUR POINT BETTER THAN YOUR RAW DATA DOES!



(This is why you conspiracy theory nuts sometimes trip up...you forget to check to see who is confirming your bias and who isn't!)

I do want to thank you for adding to my arsenal of reasons not to believe your government, regardless of what they do.

Quod Erat Demonstrandum!

 

[thaumaturgy]

...

 

[thaumaturgy]

Hey Glenn, I can't help but note that when statistics beyond just means and standard deviations comes up you are still strangely silent. Do you remember you wanted me to talk about the data from New York? Well, I did address your point back in Post #176. Did you care to respond?

(Because you seemed sloppy and failed to explain where you got your data or what you are plotting I had to guess what you were plotting, but I was able to make point that might help you learn the difference between standard deviation and confidence interval.

Here's my post again:

You took temperature data and averaged it for an entire year for sites in New York state? And you only found a standard deviation of +4 degrees on the worst year? That surprises me because I've lived in NY and I thought the temperature was more extreme than that.

Now, you focused on standard deviation. In the worst year, ~1966 you found a standard deviation of 4deg. I will assume this about 300 measurements (1 a day or so) which means that the MEAN TEMPERATURE in that year, is likely within 95% confidence equal to the mean +0.45

Do you see how I got that number?

95%CI = 1.96*s/sqrt(N)

s=standard deviation
1.96=z-score (95% of the population of a normal distribution is within 1.96 standard deviations of the mean, it would probably be slightly higher with t-score in place, so I rounded to 2 for this calculation)

N=number of observations.

The standard deviation you like to point out is actually about 68% of the population of the measurements collected. The CONFIDENCE INTERVAL is the likelihood that the "true" mean is somewhere in a much narrower band.

 

[thaumaturgy]

...

 

[thaumaturgy]

...

 

[thaumaturgy]

You then claimed that the low frequency component in the satellite data means there is a secular trend. You are in the process of changing by now claiming that it can mean a secular trend. It can,

Indeed, but then, I didn't rely solely on the peak at or near zero. I noted originally the secular trend was visible in the F-Test of the linear least-squares fit.

I merely proved the link with the near zero peak in the fourier transform!

Most secular trends require low frequency (unless it is very steep) but not all low frequencies indicate a secular trend.

I agreed with that already.

Below is a power spectra from seismic data. It has no secular trend, yet it has a huge amp at 1 hz. Thus, you can't look at the satellite data and claim that the FFT proves there is a secular trend any more than you can say one exists in a box function or on seismic data. Flawed logic leads to flawed conclusions.

Well, be fair to me, the fact that the linear least squares regression data showed a STATISTICALLY SIGNIFICANT NON-FLAT TREND would lend credence to the interpretation of the peak at or near zero as, at least being in part due to a secular trend.

That's all I'm asking. Is that you take the totality of the data interpretation I provided!

You are just responding to the stuff you feel comfortable with and have stuidously avoided the f-test data of the linear least squares fit.

(As I stated earlier I showed this data to a PhD statistician and he confirmed that there is, indeed, an increase in the overall data set)

Since our original disagreement which got us into the FFT was about the cyclicity of the satellite data and you now agree with me, I see no reason to spend any further time educating the unwilling on this issue.

Then will you address the statistical issues? If we are leaving FFT then why won't you address substantively the statistical significance of the non-zero trend in the data?

Do you wish to show how the F-test was flawed for the linear least squares fit?

(In the past week I've read more stuff on time-series analyses than I ever thought I would and in just about every text book and reference I look at I see examples where initially data is fit to a line to show secular trends but then later modeled with time-series statistics which is, essentially, fourier analysis of the data. So I've seen exactly what I did initially to this data done over and over and over again.

Want an example? Then check out Page 824 of the book Statistics for Business and Economics by James T. McClave and P. George Benson,
Dellen Publishing Co. San Francisco, 1988)

Believe what you want about the secular trend. What you currently believe is wrong

p<0.0001 means I am 99.999% likely to not be in error by rejecting the null hypothesis of a flat "non-trend".

It isn't what I believe. It is your confirmation bias fed by your failure to deal with what statistical analysis says.

Got a problem with statistics?

I won&#8217;t discuss it further with you.

Probably because you can't handle the statistics. You can be nasty and bullying on fourier transform but you are a mouse who only occasionally squeaks "standard deviation!" when statistics comes up.

Man-up and deal with the logic behind the F-test. Show me how the F-test on the original data set was in error.

(And I won't just take your word for it that one of your buds said it doesn't have any meaning. You can run the Mean Squares and Sum of Squares for yourself. I highly recommend you do that).

(this time It is over really since you clearly agree that the satellite data is periodic, which is contra your original claim)

It is periodic with a secular trend likely in the data as proven by the f-test.

(I have already indicated it might be some gigantic long cycle but neither you nor I can say one way or the other. The data clearly shows a secular trend.)

When you have said that I should deal in things I am familiar with, I am doing that&#8212;by your own admission in post 72 you were new to time series analysis and the kappa function and you have screwed both up.

Again, my sincerest apologies if honesty offends you.

And you even had to withdraw your statement that there was no cyclicity based on the p-test (see above). And you say I don&#8217;t know what I am talking about or that I don't understand statistics. LOL.

It was ME WHO FIGURED OUT I WAS MISTAKEN ON THE KAPPA FUNCTION! It was MY HONESTY that revealed that bit of info!

You had nothing to do with that!

(Did you even know what Fisher's Kappa was???)

In fact, in just about all the cases you sited in your little list of my "errors", It was ME who revealed that I was mistaken!!!!! And I further provided the PROOF I was mistaken!

If you could even hope to be half the man I am you'd know what it takes to actually get a PhD!

You've shown your skills with statistics are limited to "standard deviation" and "mean". Freshman statistics. (Oh but you are co-author on a paper that has statistics but so far the only thing you've actually talked about here is standard deviation).

Oh well. By your fruits we know ye.

 

[Chalnoth]

Are we talking 'properly-calibrated' data like what is done with the surface stations as I posted above? Is that what you mean?

No. The satellite data is quite different, and has very different systematics. The problem with the satellite data early-on was that they didn't take into account the slow orbital decay of the satellite, which led to both different times at which the data was read, and less atmosphere through which to read it. Overall this slowly, over time, biased the results to the cold side, and resulted in a temperature trend that was nearly flat. When they went back and corrected for this effect, the satellite data almost exactly matched the station data.

 

[thaumaturgy]

The Nefarious Cabal at the National Oceanographic and Atmospheric Administration!

Hey Glenn, I know this might harsh your "Conspiracy Mellow" but when people post publicly how they process the data it kind of makes the "conspiracy" angle harder to justify.

Let's look at the extremes of how the USHCN process their data

From NOAA (LINK):

(I will bold and add emphasis in the following):

Quality Control, Homogeneity Testing, and Adjustment Procedures


The data for each station in the USHCN are subjected to the following quality control and homogeneity testing and adjustment procedures.

1. A quality control procedure is performed that uses trimmed means and standard deviations in comparison with surrounding stations to identify suspects (> 3.5 standard deviations away from the mean) and outliers (> 5.0 standard deviations). Until recently these suspects and outliers were hand-verified with the original records. However, with the development at the NCDC of more sophisticated QC procedures this has been found to be unnecessary.
2. Next, the temperature data are adjusted for the time-of-observation bias (Karl, et al. 1986) which occurs when observing times are changed from midnight to some time earlier in the day. The TOB is the first of several adjustments. The ending time of the 24 hour climatological day varies from station to station and/or over a period of years at a given station. The TOB introduces a non climatic bias into the monthly means. The TOB software is an empirical model used to estimate the time of observation biases associated with different observation schedules and the routine computes the TOB with respect to daily readings taken at midnight. Details on the procedure are given in, "A Model to Estimate the Time of Observation Bias Associated with Monthly Mean Maximum, Minimum, and Mean Temperatures." by Karl, Williams, et al.1986, Journal of Climate and Applied Meteorology 15: 145-160.
3. Temperature data at stations that have the Maximum/Minimum Temperature System (MMTS) are adjusted for the bias introduced when the liquid-in-glass thermometers were replaced with the MMTS (Quayle, et al. 1991). The TOB debiased data are input into the MMTS program and is the second adjustment. The MMTS program debiases the data obtained from stations with MMTS sensors. The NWS has replaced a majority of the liquid-in-glass thermometers in wooden Cotton-Region shelters with thermistor based maximum-minimum temperature systems (MMTS) housed in smaller plastic shelters. This adjustment removes the MMTS bias for stations so equipped with this type of sensor. The adjustment factors are most appropriate for use when time series of states or larger areas are required. Specific details on the procedures used are given in, "Effects of Recent Thermometer Changes in the Cooperative Network" by Quayle, Easterling, et al. 1991, Bulletin of the American Meteorological Society 72:1718-1724.
4. The homogeneity adjustment scheme described in Karl and Williams (1987) is performed using the station history metadata file to account for time series discontinuities due to random station moves and other station changes. The debiased data from the second adjustment are then entered into the Station History Adjustment Program or SHAP. The SHAP allows a climatological time series of temperature and precipitation adjustment for station inhomogeneities using station history information and is the third adjustment. The adjusted data retains its original scale and is not an anomaly series. The methodology uses the concepts of relative homogeneity and standard parametric (temperature) and non parametric (precipitation) statistics to adjust the data. In addition, this technique provides an estimate of the confidence interval associated with each adjustment. The SHAP program debiases the data with respect to changes other than the MMTS conversion to produced the "adjusted data". Specific details on the procedures used are given in, "An Approach to Adjusting Climatological Time Series for Discontinuous Inhomogeneities" by Karl, and Williams, Jr. 1987, Journal of Climate and Applied Meteorology 26:1744-1763.
5. Estimates for missing data are provided using a procedure similar to that used in the homogeneity adjustment scheme in step three. This fourth adjustment uses the debiased data from the third adjustment (SHAP) and fills in missing original data when needed (i.e. calculates estimated data) based on a "network" of the best correlated nearby stations. The FILNET program also completed the data adjustment process for stations that moved too often for the SHAP program to estimate the adjustments needed to debias the data.
   Each of the above adjustments is done is a sequential manner. The areal edits are preformed first and then the data are passed through the following programs (TOBS, MMTS, SHAP and FILNET). At the end of each program, a dataset is produced and the graphs below show the annual temperature departures for each of the adjusted values.
6. The final adjustment is for an urban warming bias which uses the regression approach outlined in Karl, et al. (1988). The result of this adjustment is the "final" version of the data. Details on the urban warming adjustment are available in "Urbanization: Its Detection and Effect in the United States Climate Record" by Karl. T.R., et al., 1988, Journal of Climate 1:1099-1123.

Currently all data adjustments in the USHCN are based on the use of metadata. However station histories are often incomplete or changes that can cause a time series discontinuity, such as replacing a broken thermometer with one that is calibrated differently, are not routinely entered into station history files. Because of this we are developing another step in the processing that will apply a time series discontinuity adjustment scheme described in Peterson and Easterling (1994) and Easterling and Peterson (1995). This methodology does not use station histories and identifies discontinuities in a station's time series using a homogeneous reference series developed from surrounding stations.

So you see the importance of knowing how the data has been processed is probably equally important to the raw data.

You conveniently forgot to explain this "processing" and hence lead the readers to believe it was some sort of "mystery" or, in your fun southernism "cheatin'".

Too bad you were wrong. Cheaters usually don't reveal exactly what they are doing to the data.

But don't let that get in the way of your confirmation bias.

You talk big about how no one can intuitively understand certain FT diagrams, but now you want to "understand" raw, unprocessed data?

Gimme a break.

(Oh and in case you wish to learn more about how the data is processed and why, here's an interesting link:

National Temperature Trends: The Science Behind the Calculations
(SOURCE)