One day later: Mauna Loa CO2 graph changes

Hmm, this could run and run over the next few months.
(given probable trends)
Or should that be change and change again.
“Jim where are you, we need you NOW..” – LOL …
GREAT SPOT.

ROFFFFL!!! It’s not like they adjusted this months data, they did it for years. And based on that data Anthony suggests some things to consider. [snip]
Perhaps [Tamino] can explain all of that ever changing data. Is it a mistake that was caught years later? Is it like the photo of a flooded house (let’s make the CO2 rise before some one gets the idea that residence time isn’t really hundreds of years)?
Damn the CO2 uptake of a cooling ocean! So is the annual downward slide of the sawtooth on the CO2 graph really all due to that northern hemisphere vegitation sucking CO2 out of the atmosphere? Or maybe it also has something to do with the annual cooling of them big ol’ oceans in the southern hemisphere cooling every southern winter?
Maybe someday this science will get settled, but one thing’s for sure; it sure as hell aint settled right now.

I looked at random at the data of 2000 (outside the graph). I find similar changes, now in the 2. digit, for July aund August. Maybe you can run a difference program on all the data of the original files.

The changes go all the way back to 1974 (for those who haven’t seen the other thread).
I sometimes theorize that NOAA & GISS must have time machines that allow them to get new readings 34+ years after the fact. I can imagine no other reason that policy makers base decisions worth hundreds of billions on an ever-shifting chimera of data.

Looking now at the blink comparator, it appears all the data has been adjusted.
That’s weird.

We’ve all become accustomed to the lies put out every month on inflation and other economic measures.
See http://www.tampabay.com/news/article473596.ece
Now it appears we can’t even get straight answers on temperatures and CO2 levels.
Lying is a lot like eating peanuts; it’s hard to stop with just one.

Thanks for the reminder, D. Quist. Oh well, down the memory hole.

Right. They change the data the same day as the extent of the anomaly is pointed out. Have they _ever_ made such extensive changes in the past on the Mauna Loa data? And the change just _happens_ to make June-july 2008 now rank 3rd in greatest drop after 2004 by a hundredth of a ppm. How convenient. Unless there is a darn good explanation for these changes, the coincidence is just too close, especially with the changes happening right after Anthony Watts posts data. Pretty darn brazen. And scary. I always poo pooed the idea that true scientists would fudge data to match their preconceptions. Now I’m beginning to suspect I was too naive. Not only that, but these latest changes are being done between 12 am and 5 am east coast time. Why? Isn’t this the least bit suspicious for anyone?
I think a serious investigation needs to be done. Maybe even a special prosecutor. Anyone have access to a congresscritter?

Call it what you want. Bend over backwards to give them the benefit of a doubt which they have never extended the other way – even when the consequence of their lack of doubt is the condemnation of the whole humanity.
You can repudiate me if you want, but I’ll call it as I see it.
What they did there is the OJ Simpson defence. As the comedian Wanda Sykes would say ‘you’ve got to stick with your lie’ .

Anthony,
My last comment is inaccurate in terms of when the new data was posted. Turns out since I had accessed that ftp page earlier in the day, my computer was looking at a cached copy, not the new data set. Once I hit my refresh button, the new data file popped up.

Now here is something odd that perhaps a statistician can address. Why should the difference between the old and new data sets be a specific pattern?
I compared the old and new data sets between July 2006 and July 2008. I looked at only the seasonal correction numbers. Here are the differences between the old, August 3rd dataset and the new, August 4th dataset in hundredths of a ppm.
Isn’t it pretty convenient that prior to 7/08 the pluses virtually cancel out the minuses? And what a nice pattern. Every plus is followed by a minus. Why?
This looks like the data was fudged, quite frankly.
comparing old versus new dataset
7/06 plus 3
8/06 minus 1
9/06 plus 6
10/06 minus 2
11/06 plus 3
12/06 minus 1
1/07 plus 1
2/07 minus 5
3/07 plus 11
4/07 minus 11
5/07 plus 14
6/07 minus 11
7/07 plus 3
8/07 minus 3
9/07 plus 6
10/07 minus 6
11/07 plus 3
12/07 minus 6
1/08 plus 7
2/08 minus 8
3/08 plus 12
4/08 minus 7
5/08 plus 13
6/08 minus 25
7/08 plus 71

While we (the world) deserve a clear explanation/understanding of what these adjustments are and why they were made, this is one dataset where the adjustments don’t yet bother me too much as long as the past trend line does not alter appreciably as a result. The reason it doesn’t bother me is that if we are, indeed, beginning to see a downtrend in the CO2 level measured as Mauna Loa, then they won’t be able to mask it for very long, since CO2, unlike temperature has been claimed to be monotonically increasing. Any sustained downtrend over the next year or so would break that cycle and change the whole ballgame. On the other hand, if the overall trend resumes an upward direction, then the downtick would have appeared to be an outlier and wouldn’t have survived as the biggest nail in the coffin of the AGW hysteria. So, as usual, we’ll have to wait and see. In the meantime, I look forward to reading whatever you hear back for an explanation.

An answer might come out of this:
http://www.esrl.noaa.gov/gmd/co2conference/pdfs/changingcarbon.pdf
Note the summary. It seems the winds are strong enough to blow the words away 😉

Looking forward to the log which will explain changes in Mauna Loa monthly means back to May 1974. I wonder if there is a separate explanation for each month’s adjustment. Also, how many historic data points HAVE been adjusted?
Thanks,
Mike Bryant

Anthony,
I know this is off topic and may have already been posted in another area (I have a hard time keeping up with all the comments some days). If you don’t want this one posted go ahead and delete, but I thought it was interesting, maybe some truth will come about from it. Seems alaska is sueing Feds about the decision to make Polar Bears endagered.
http://hosted.ap.org/dynamic/stories/P/POLAR_BEAR_LAWSUIT?SITE=VANOV&SECTION=HOME&TEMPLATE=DEFAULT

Combine this with a blinking graph on the hockey stick too! You may go down in history with Charles Johnson of LGF with the ‘throbbing memo’ of Dan Rather fame…er, infamy. Good catch! Keep em honest out there.

I’ve always been a “show me the data” kind of guy when it comes to this global warming caused by man hype. It’s why I love this site. What really concerns me is that the government is massaging the data to support some obscure agenda.
That’s huge if you think about it. I’m anxious to hear their response.

Small adjustments go back at least to the 70’s.

There is also a suspicious graph change at NSIDC: http://www.nsidc.com/arcticseaicenews/
It seems some of last months data has been adjusted downward. Also there is a very suspicious almot linear drop up to the 4th August from about 28th July. A blink comparison with the same graph further down the page (although that graph is only up to 1st August). This would show a definite data adjustment for late July.

I am not a conspiracy theory supporter. Hopefully, the changes documented above will be shown to be necessary and done for good reason.
However (although this is a little off-thread), lest anyone think that science will easily carry the day in overturning the AGW meme, I offer this piece from the Boston Globe by Dr. John P. Holdren of Harvard and Woods Hole. It is a reality check that AGW won’t go quietly into the dark night.
http://www.boston.com/bostonglobe/editorial_opinion/oped/articles/2008/08/04/convincing_the_climate_change_skeptics/
Excerpts:
“THE FEW climate-change “skeptics” with any sort of scientific credentials continue to receive attention in the media out of all proportion to their numbers, their qualifications, or the merit of their arguments. And this muddying of the waters of public discourse is being magnified by the parroting of these arguments by a larger population of amateur skeptics with no scientific credentials at all…”
Dr. Holden concludes:
“The extent of unfounded skepticism about the disruption of global climate by human-produced greenhouse gases is not just regrettable, it is dangerous…Those who still think this is all a mistake or a hoax need to think again.”

Unknown and unsubstantiated data adjustments are the way of the future.
Once government started on the “pay more in taxes so government could pretend to control the weather” path, we were cooked. Government as an honest broker no more, the day increasing taxes and controlling behavior becomes the goal of science. Want proof, all you have to do is read the NASA site, AGW tripe and BS they pass off as PROVEN FACT is just eye popping.

We all try not to believe in any “conspiration theory”.
But lets be honest, ok? Ill say it as it is, maybe say what many thinks, but many dare not to say because we all want to appear serious and well balanced:
Take the last hundred “adjustment” we have had in the last years.
I mean adjustments that are making a difference in the debate pro/contra AGW beliefs.
How many of such adjustments happends to favour IPCC/AGW/Alarmists ideas?
I have NEVER seen a single big adjustment done by the alarmists that does not end up being a support for their opinions. NEVER.
But lets say i have missed some, and its only 90 % of their corrections that support themselves.
But if they where honest, adjustment should in 50% of the cases support them and in 50% support the sceptics.
Adjustments are result of bad equipment etc, should not have a tendency to allways support one viewpoint.
How big statistically is the chance that if you through a dice 100 times, that 90% of the times you get 4,5 or 6? Statistically its vertually impossible.
Thats why i believe its fair to finaly conclude: “So many adjustments that just happend to allways support the ones who makes them? Its not statistically possible and thus i find it hard to believe”.
This does not mean that you are a conspirasy freak. It just means that you have the ability to think.

Any bets on how soon data from satellite surface temperature measurements are put through the Hansenizer?

Ed Reid,
What is possible is to collect duplicate or triplicate samples. Alternatively they could do something similar to what they will be doing in Bejing pretty soon which is to divide the single urine sample into two or more subsamples and then if a spurious result is returned by the analysis the the instruments can be rechecked and the duplicate or sub sample can be tested to see if the result can be duplicated or if it is substaintially different suggesting that a calibration or some other type of error occurred.

It’s interesting if you use the annual average mauna loa data to compare the annual ppm change in CO2 concentrations to estimates of global population and fossil fuel CO2 emissions in 1977 and 2007. With all three data sets indexed to 1.0 in 1977, you get values of 1.7 for population growth, 1.9 for carbon emissions and 0.8 for the annual PPM increase in CO2. Therefore, carbon energy use almost doubles with population , yet the rate of increase in CO2 decelerates to less than it was in 1977.

Just an observation:
Where the data points meet between the trend line and the seasonal line, the adjustment is far less than between where the lines don’t touch.

Anthony – perhaps I’m missing something but the latest date’s numbers weren’t the only ones to change based on your blink comparison… watch how many of the spikes appear to suddenly level off – the excuse that the equipment broke for a few days doesn’t hold up across the long term differences in that blink comparison.
REPLY: I spotted that immediately when I made the blink compare, but I’m waiting to hear additional explanations before making my own comments on it.

The explanation has been posted elsewhere, and is simple and proper.
They don’t get daily readings from Mauna Loa, for various reasons. This has been true throughout the entire record. Mauna Loa is designed to measure well-mixed air arriving on the trade winds, and on days when the wind isn’t blowing, they don’t use the measurement to avoid measuring local air, for example. Some days are missing from nearly every month.
For monthly averages, they have been simply averaging the daily readings they get for that month. In July 2008, they only got readings for the last 10 days of the month. Since CO2 levels fall during July, the last 10 days have a lower average than the entire month would have. Apparently, this was the first time they had such a skewed set of daily averages to apply for the month, it made a substantial difference to the monthly average, so they (quite properly) decided to correct it – the average of the last 10 days of July, when there are falling values for all of July, is NOT the appropriate number to use for the average for all of July.
They applied an algorithm to adjust the monthly average to account for which days in the month are missing, and they (again, quite properly) applied it to their entire record. This would make almost no difference in months where the slope of the record is near zero, or where there are missing values near the middle of the month. It will make a bit of difference when missing values are near the ends of the month. And it leads to more accurate values for every month.
Thus the changes you see throughout the entire record.
REPLY: Another new name Lee? What is that, six now?

Bill Illis (07:35:48)
Thank you sir!
That time series graph that is part of the Aug 1 report of NSIDC (Fig 2) is actually showing the data as of 31 July and it clearly shows that as of the end of July the melt rate had slowed.
It’s good to know I wasn’t imagining it.
I missed saving the graph for 1 August (posted 2 Aug), but I have saved images for 2,3, & 4 August, which I’ve saved as (July 31 values saved as August 00) August_00, 02, 03, & 04.
Now, when I flip between Aug 00 & 02, I can clearly see how changes were made several days back into July.

“Too bad for the self-described “skeptics”.”
Pointless condescension towards people interested in what you’re working on. How classy.

The Watergate rule is now in effect.
It is not the crime that is the big problem, it is the cover-up.
Let the “explanations” begin.
This should be most entertaining.

Thank you Dee!
a) “Too bad for the self-described “skeptics”.”
b) EGAD! I hate to sound snippy… but I guess we know how Dr. Tans feels personally about what the data “should” say.
c) As asked before… why did these adjustments need to start right now? I’m certain they’ve had intrument failures/issues in the past, right?
HINT: see a) above.

Tom Barney: The Mauna Loa data (as we have seen) is strongly affected by ENSO (which affects uptake by the Pacific ocean), so the difference in ppm increase between 1977 and 2007 may be becuase of and El Nino instead of a LaNina?

Jerker Andersson (00:09:47) :
“What is interesting is that it happens within 24h after they release a result that shows a new record low for first 7 months.”
I agree. That means is a big risk for quality mistakes in the change.
“If there is an error and they notice it, it must be corrected. In the process it lookes like they found errors on every month at least for the last 4 years.
They say the runned the adjusted program for all days all years, where it has put in “some data” (could be running mean values) these days when there was none. But a problem, I think, is if they have changed data according to new code which hasn’t going throuth a planned quality validation process of at least a weekor so. That’s not acceptable in any organisation which has a quality reputation to defend.
What I’ve read about the Mauna Loa record is that there data from the days when the wind is blowing from the volcano is omitted. I hope they have not included any of these data now! A fast change like this by some engineer is always very risky.
It also doesn’t seem likely that the first July data presented was only the 10 last days of the month, since the change when the first 20 days of data was included is only about 20 percent or so… It should be at least a 50 percent change if the 20 first days of data was actually first missed and includeed later. That’s because the continous change where the lowest values are at the end of the month.
Another thing: I also find it surpricing that Lucia suggested this July value was a calibration error which gonna be corrected. Isn’t callibration something which is done only once, and that re-callibration creates discontinuation in a record?
Finally: If Henry´s law is real (and it is) no one shall be surpriced if the CO2 concentration has to rise slower or to stop ries during a cooling phase in the coming years. No matter we yearly emitts about 3 percent of the naturally emitted CO2. (Only about 5 percent of the CO2 in the atmosphere is measured to be from fossile fuel.)
did they start that process yesterday also, reverifying those 4 years?
Did they actually manage to recalibrate, make new meassuremnents of all those samples from the last years in one day or is it a mathematical adjustment of the results?
It looks like that allmost all datapoints that sticks out a bit have been smoothed up AND down in order to create a straight line showing that there are less variability.
The adjustment does not seem to affect the overall CO2 trendline in any significant way though, just making it smoother.
So what errors did they find that gave both too high values when the CO2 level was above trendline and too low when it was below trendline?

Ooops!
I missed to remove text (by Jerker) in the last 4 paragraphs. From “did they start that process…” all lines shall be removed!
I’m really sorry.

What I took from what Pieter Tans said was that there were missing days in the record. That causes them to have to calculate a “fill” value. That “fill” value is probably a function of the average over time. When something is done to change that average over time, it changes those fill values and when those fill values change, the monthly average values change.
If you were looking at daily data instead of monthly data, you would only see certain days jump around with the change. But those few days do impact the average.
What bothers me most is that errors seem to get found and corrected that make the CO2 level “too low”. But I wonder how many errors there might be that make “too high” and aren’t found because rising CO2 is the expected result. Only when something is lower than it should be does anyone notice anything. How many times in the past were readings higher than they should have been? We will never know.

And were the missing days in previous months and years always in the second half of the month?
I have not studied the changes carefully, but it does seem odd if the alterations only seem to go one way (in the main).

“the earlier monthly average consisted of only the last 10 days.”
It took them 30 years to figure out that monthly averages should involve division by the number of samples, not the number of days? Where’s their raw data?

Xavier, at the risk of sounding nasty, we ARE talking about government bureaucrats here, PHD or not.

The explanation sounds as vague as can be. The had “…problems with the equipment” which wiped out the first 20 days of data? And they didn’t know this until yesterday?
And then: “This change also affected the entire
record because there are missing days here and there.”
Why would this change “affect the entire record” years into the past? What does this “equipment problem,” which occured “in the first half of July,” have to do with “missing days here and there” in previous years? How did the discovery of this problem, supposedly yesterday, lead them to the discovery of the “missing days here and there” in previous years? If there was no connection between the two problems, did they really discover them coincidentally on the very same day, which coincidentally happened to be the day Anthony noticed the unusual drop in July?
???

Just a little heads up … the July data are now in NOAA’s NCDC Climate At A Glance page. The page says June still but the July data are there. It looks like this July was cooler than last year. Last year’s North American July average (according to NOAA’s adjusted USHCN v2 numbers) was 75.56 F and this year is 74.93 for a 0.63 drop from last year. Preceeding 12 month temperatures from 1998 to this year show a negative trend of 0.4 degrees/decade.

It’s perfect! It’s beautiful! It is an excellent example of data modeling. The numbers for July were lower than they expected. So they used a COMPUTER PROGRAM to extrapolate the numbers on the ASSUMPTION that the missing numbers (first half of July) would have been higher. Then they ran the COMPUTER PROGRAM for a few years back to IMPROVE those calculations as well – to APPLY THE MODEL.
THIS, my friends, is how computer models work. This is an excellent example of how the single most important part of the global warming hysteria – the computer models – change by ASSUMPTION and EXTRAPOLATION.
(my creds – 28 years in computing including work on several models)

Beaker: current ENSO view neutral-
•ENSO-neutral conditions are present in the equatorial Pacific Ocean.

Dee Norris:
“I think we are too conditioned to data getting Hansenized and may be jumping to conclusions. So far, unlike Hansen, Dr Tans has been forthright with communicating his approach.”
I agree. Lets not go making mountains out of molehills. The overall impact of the change is trivial and I am satisfied with Dr. Tans’ explanation. We should be applauding his transparent approach to what happened and what he has done and move along. Personally, I am satisfied with the explanation he has given.
I am still bothered, though, that only data that goes unexpectedly in one direction gets attention but that is more of an overall issue and not something directly aimed at Dr. Tans’ work.

Magnus: Weather data from the Kona airport (FAA) should show wind direction and velocity daily for all 24 hours. Whenever the wind direction from the south (Kona wind), the VOG (outgasing) will corrupt data collection on Mauna Loa. I should not be too difficult to compare NOAA data that is discarded with FAA actual wind data to determine if NOAA is cooking the books rather than accumulating data honestly.

Dr Tans replied:
“You are good.
When I was at it, I made another adjustment to the program. I used to fit 4 harmonics (sine, cosine with frequencies 1/year through 4/year) to describe the average seasonal cycle. I changed that to 6 harmonics.
Therefore, there will be small systematic differences as a function of time-of-year in the de-seasonalized trend. That will be on top of adjustments caused by months in the past during which there were a number of missing days not symmetrically distributed during that month.”
So, they can just do this? A guy can sit down to fix a problem from three weeks ago that has supposedly just been discovered, and while he is “at it,” go ahead and make another adjustment to the historical record just because he feels like it at the moment?
Is that how it works? Are these changes logged somewhere? Is the rationale for these changes the product of previous discussions, of which a record is kept, or can it be just a spur of the moment thing that someone happens to think of while doing something else? Can the changes he describes be replicated?
In short, the July fix, plus the harmonics change, plus the previous “missing days” fix had to be done all together, and they had to be done precisely last night?

Beaker:
Thanks for the info. I mistakenly identified the base index year as 1977, it was 1973-which was an El Nino year.

Like Dee, I also don’t smell a rat. I know to those who suspect all adjustments it seems odd that the big negative number caught Dr. Tans eye. But bear in mind, this data point was a major outlier relative to the linear fit I show above. Any experimentalists would notice the big excursion and investigate what might have happened.
The answer is straight forward enough.
That other changes were made suggests that this measurement program is a bit informal. But that happens sometimes. The degree of formality associated with measurements varies from program to program, and will often depend on the use of the data. As interested as climatologists and bloggers are in this data, they are not direct inputs to weather prediction, health or safety programs or anything else.
Remember: No one was physically injured as a result of the August CO2 blog kerfuffle.

Ed Ried,
I think you understand the point I was making. Furthermore duplicate samples tested at an independent laboratory would certainly lend credibiliy to their data set and also avoid having lengthy periods with no data recorded. With regard to the subsequent adjustments to the graph I couldn’t agree with you more. As for why it took twenty days to get their instruments fixed well maybe the pack mule they use to carry the equipment up and down Mauna Loa was lame.

I would agree that the change is for the better during any kind of month that shows rapid variability. I don’t see anything statistically wrong with doing that. It clears the noise so that trends can be seen.
However, sometimes change in variability is an important statistic. Months that fly all over the graph may show trends in raw data variability that may be much more sensitive to climate change than steady as she goes months.
I would sure lurve to have the raw data for every moment of measure.

Dee:
Thanks for the leg work and the level-headed interpretation. If you can ask a follow up question of Dr. Tan, perhaps you can ask him (?) what prompted the move form 4 to 6 harmonics that would at least help understand what issues they are trying to handle.
Thanks

Given the speed and complexity of the adjustments made to so much data the idea of a log of the changes made and how and why seems at the very least reasonable (prerequisite more like).
The importance of the Mauna Loa data to everyone makes the fact this is not already the case almost unbelievably “sloppy”….
As far as I’m aware the FULL Mauna Loa data is not avaliable, maybe, just maybe this episode, if it gets the attention it so rightly deserves, might just make this data set full release become a more realistic proposition.
Nirvana you might say, or should that be “contaminated with Vostock”…
Ahh well, I can dream.
Whatever happens in the longer term, (if the present trends continue) further changes are a racing certainty,
so I’d suggest this WILL run and run…..
“Hockey sticks”..

Minor nit but a pet peeve of mine … “dampening” … it should be “damping”. To dampen is to make something wet. To damp something (like a chimney damper) is to smooth out change.
“Damping:
Damping is dissipation of energy in an oscillating system. Limits maximum amplitude at isolator natural frequency.”

Jesus, what a madness around. The GW skepticism now also turned fanatic religion ?

Isn’t it more likely that the correction is to compensate for the changing wind patterns rather than a calibration of the equipment?

Well I hope to wake-up someday and just find out I have been dreaming.
Read this link It’s from a great site, and it put’s this whole issue I.E.
the state of science and research in the world today, were doomed!
I think I am just going to drink the kool-aid and jump on the bandwagon.
http://nzclimatescience.net/index.php?option=com_content&task=view&id=321&Itemid=1

You don’t need conspiracies or malicious intent to explain adjustments favouring one hypothesis. The phenomenon is called confirmation bias and is well known in the social sciences.
Also, scientists often claim to be engaged in “value-free” dispassionate enquiry, giving their prognostications special status as knowledge. Yet, you can see Dr Tan’s values sticking out like a, er, sore thumb.
The only solution to value-laded research and confirmation bias is to have competing hypotheses and falsifiable empirical tests. This is my big concern with climate science – the growing number of post-hoc explanations as to why the predictions were wrong. Just what is the predictive ability of the AGW hypothesis? What scientific puzzles has it solved?
If your theory is immune to data, it is not science.

I agree with Frank L. It’s like a “mistake” in a restaurant bill — 90% of the time the mistake favors the restaurant.

I do not see how a conspiracy, but as a stupid move.
Until yesterday I thought that a large part of the increased CO2 concentration was anthropogenic.
Today I have doubts. And the only way to eliminate this would know the concentration of oxygen in the atmosphere Mauna Loa (with the same equipment and two days of work, I have the problem). You will get the following result of the increased concentration of CO2 and increase the concentration of O2.
C + O2 > CO2 ( Imagine O2 up and CO2 up)
Ecotretas- um abraço em português.
best site = http://www.surfacestations.org

lucia:
//That other changes were made suggests that this measurement program is a bit informal. But that happens sometimes. The degree of formality associated with measurements varies from program to program, and will often depend on the use of the data.//
This ends up being one of the central issues in the whole climate change discussion for me. The problem is that the degree of formality around the data is dependent largely on the originally intended use of the data, which in many cases now differs greatly from the actual use of the data.
The CO2 data, the historical temperature records, and the majority of the current temperature tracking data are being treated with a level of precision that is simply not supported by the actual data collection methods. And the resulting analysis is driving trillion dollar policy decisions that should not be based on data that was intended for far more pedestrian uses.
I certainly don’t see anything nefarious about this current data point correction, but it does seem illustrative of the larger problem.

As usual, these corrections appear when the values seem lower than normal, not when they appear to be higher than normal. Higher than normal is “evidence for AGW”, not an indication that a correction might be needed.

Mr IceFree (15:19:29) refers to a New Zealand website. The article carried there is one of many pieces of fine analysis by Prof John Brignell which are collected on his own website. It’s one of my faves and well worth a read:
http://www.numberwatch.co.uk
On the topic of this thread, I must again expose my ignorance and naivety for your delectation. It comes as a great surprise to me that two sets of results are not habitutally released, one being the raw data and the other being the data as adjusted/smoothed. I don’t doubt Dr Tans’ sincerity in the adjustments he makes but we all learn by exposing our analyses to scrutiny and inviting others to comment. Others might well learn from what we have done and we might well learn from suggestions made. What I do not understand is why there is no standard practice of releasing raw data as well as adjusted data in all fields where public policy might be affected by the results (subject, where appropriate, to issues of intellectual property rights and commercial sensitivity).

This guy Will Alexander is for real. Here is a poor country’s view of climate catastrophism.
http://www.fcpp.org/pdf/FB051%20Will%20Alexander%20Climate%20Change%20and%20Africa260706%20with%20picsdraft%20edited%20-%20erin.pdf

I was employed by a chemical plant for 20 years. If we had pulled the stuff that I see posted on this site regarding testing, we would have been sent to prison by the EPA. Correct me if i am wrong, but are not world decisons bases on this CO2 data? Gee Wizz, I have zero faith in the temp data, chemical anazlysis and every other graph that is out there.

Now we know why the earth is not warming as much as the models predict. Could it be the effect of water vapor and clouds? Negative feedback? The latest program on PBS, Nova series was just aired. The main subject was “solar dimming”. The link to the transcript: http://www.pbs.org/wgbh/nova/transcripts/3310_sun.html
But, the real subject was AGW and James Hansen was the major interviewee. (What a surprise!)

Lucia,
It would be interesting to know how many readings required adjustment because they didn’t fall within the bound of what is expected. If these are commons maybe the adjustment aren’t required after all.

Even if Dr. Tans’ adjustment is reasonable and defensible on its face, it is still a bit troubling. Would a similar adjustment have been made if the CO2 numbers were higher than expected? Somehow I doubt it. And if not, it has the potential to introduce a bias into the numbers.
One of the cardinal rules of statistical analysis is that you choose your criteria BEFORE you see the data. Otherwise it’s very easy to fool yourself (and others) into thinking your results are significant.
By analogy, Dr. Tans should have carefully chosen an averaging method IN ADVANCE and then stuck with it.
In climate science, this principle seems to have been thrown out the window.

No one has taken a shot at these questions yet.
How many times in the past have these adjustment algorithms been run?
How long has Dr. Pieter Tan been responsible for adjustments?
Did his predecessor instruct in the necessity of these wholesale adjustments?
Are there any records of previous adjustments?
Did Dr. Keeling initiate a protocol that required regular adjustments?
Would it be worthwhile to use the way way back machine to set up more blink comparators?
This may be slightly paranoiac, but still would be good to know the truth.
Thanks all have enjoyed every comment,
Mike Bryant

Dee,
Just before the correction was made, I suspected that the Mauna Loa Data was too clean.
Could you ask the good Dr.
“What is the fixed standard in CO2 measurement?
What is the material of the CO2 sensor?
How often is the CO2 sensor calibrated against the standard?
What is the drift coefficient of the CO2 sensor?
What is the linearity of the CO2 sensor with electronics?
What is the drift of the linearity of the CO2 sensor with electronics – is this calibrated as well?
How old is the electronics used to measurement the CO2 sensor?
How often is the electronics calibrated against known electronic standards?
How much random noise is in their measurement?
How specific is the sensor to only CO2?
Does the sensor react with any other gas?
Is the sensor calibrated over temperature?
Is the data adjusted for temperature?
What is the sensor accuracy vs. temperature?
How long does the sensor temperature soak before measurement?
Is the sensor calibrated over atmospheric pressure?
What is the sensor accuracy vs. atmospheric pressure?”
And what specifically broke on the instrument, why down for 20 days, etc.

Time for a Press Release
“Dr. Pieter Tans of NOAA caught altering Manua Loa CO2 data by AGW skeptics”
Altering data with no peer review, public comment, no before and after, no root cause identification, no plan to prevent this problem again, changing when the data is against your bias is very very wrong and Dee and Anthony are wrong to give this guy a pass. In life if a guy like Dr. Tans is comfortable enough to make a change like this in 24 hours, any detective will tell you it was not his first time.
[snip – potentially libelous label]

“The reason was simply that we had a problem with the equipment for the
first half of July, with the result that the earlier monthly average
consisted of only the last 10 days. Since CO2 always goes down fast
during July the monthly average came out low. I have now changed the
program to take this effect into account, and adjusting back to the
middle of the month using the multi-year average seasonal cycle. This
change also affected the entire record because there are missing days
here and there. The other adjustments were minor, typically less than
0.1 ppm. Too bad for the self-described “skeptics”.”
Am I wrong or does this sound like a self-fullfilling model instead of representation of a data set? Have missing days? Just smooth them all out, since CO2 is “well mixed”. Have data that doesn’t match the algorythm patterned to match expected seasonal swings? Just adjust data.
Missing days…why not just take a measurement a few times a year, and plot the increase as a straight line that is the expectation of the rate of increase. An algorythm could be made to model that. Then if the data from the few measurements don’t match the expected rate increase, just fudge that data.

Personally, I would just like to know how many days the data has been thrown out based on the parameters or malfunctioning equipment. After a certain point you are not logging enough data to be meaningful.
As far as adjusting the current July using Deltas from previous years, it SOUNDS just ducky. There is a slight issue though. The previous years did NOT have a sun as quiet, ocean temps and air temps flat for as long or dropping… In other words, it may be the best they can do for their data analysis, BUT, it guarantees the data will look more like the past rather than whatever the heck is happening now!!!!
What is the point of monitoring something if you end up trying to make the data fit into your preconceptions??? Their quality control sounds more like they are guaranteeing their expectations!!

Ferdinand,
they need 6 hours of data within their guidelines before it is acceptable. We now know they need more than 10 days data to compute a month without infilling AND ARE INFILLING 20 DAYS DATA!!!!!! THIS IS JUNK!!!!!
Apparently you are wrong about the data not being changed as it HAS. See the previous posts by others where the data was downloaded.
Another technical issue is that the diurnal variation in CO2 is fairly large also. If the data they get is not from the same time of day due to QC rejections they are massaging the daily data to get normalised readings also. If they are actually rejecting the data because they can not get it during the right time period I can see a high rejection rate!!
Now, be serious. Why was this change necessary. This process has been ongoing for over 50 YEARS!!!!!!! They just realised that they did not have the correct procedure to deal with this situation?? This was the FIRST TIME IN OVER 50 YEARS THIS HAPPENED?!?!?!?!?!?
Are you seriously telling us that this has never happened before and they had to scramble to fix it?? For at least a WEEK no one paid any attention to the measurements and didn’t notice a thing until the incorrect results were posted?? How did they know they were incorrect?? When did someone actually look at them?? They rejected 2/3 of the data for the month and they didn’t realize they had an issue until someone e-mailed them about the large drop?!?!?!?!
At the least you have a group that is underperforming on a highly paid Hawaiian vacation and should be fired for their negligence!!! It is more likely that their is intentional fraud. I simply can’t believe they are THAT INCOMPETENT!!!
If I sound incoherent I AM!!!

These are no adjustments of the raw data, this only smoothes the presentation of the curve
I don’t understand this comment. By this standard, there is never an adjustment to any raw data, no?
and doesn’t change the trend, which is now about +60 ppmv over the past 50 years. If you compare the Mauna Loa data with the SH stations like the South Pole, you see exactly the same trend, but with some delay and far less seasonal variability (2 ppmv vs. 6-10 ppmv for MLO).
It seems to me that this is essentially the same argument which has been used to defend the hockey stick. “Ok, so Mann cut a few corners but his results were essentially correct.”
In this case, it’s very likely that the results are essentially correct but the incident is still troubling.

Niteowl,
Amazing graphs! If I’m interpreting them correctly it looks like very clear evidence that CO2 change is an effect of temperature change and not the other way around. Good stuff, and I hope they don’t get overlooked down here at the tail end of the Mauna Loa discussion, they deserve more scrutiny.

@Dee Noris
you write:
“Suppose the equipment failed on July 20th and the failure caused the loss of the monthly data to date the issue becomes one of sloppy data safeguarding.”
Yes it is a possibility.
So the failure made the machine delete all previous july data and thus we did not have a Mauno Loa “doing nothing” in 20 days of no data. It was only a sinlge incidence the 20´th july that erased 20days of july data.
Lucky that the machine did not erase june data? Or all other data?
That the machine should exactly erase july data seems a little hypothetical to me.
But anyway, i work at NovoNordic, medicine. i imagine if we had a machine that could possibly either
1) break down without anyone able to fix it faster than 20 days..
or
2) a machine that when breaking down erases all data in present month AND having no backup. Even my personal PC has a backup system..
In either case if this where NovoNordic we would have been closed down by the FDA in no time.
And yes I find it strange that an obvious problem of missing data has never been corrected for before alle the way back to 1974?
These things are not impossible, and to me it means a lot that Anthony Watts impression is that “they´re ok”.
But of course one cannot neclect, that this is one of 1000 corrections done by Alarmists that much much too often ends up supporting their views. Its just not reliable. Everytime theres “something with the equipoment” it ends up with a change who supports alarmists. Its simply not looking good even though we want to be understanding sensible and tollerant.

I am still trying to understand why the original data set was published if the problems with the data collection were known.
Is it common practice to publish information when there have been problems with equipment then simply change it later?
Why publish findings in this way – which are inconvenient to those who claim we are forcing up CO2 levels – then revise them to a more convenient narrative one day later?
And some people wonder why I have doubts about what we are told about CO2 and AGW…

niteowl,
Wonderful graphics and nice reasoning.
What software do you use to generate those graphs?
Jack

Hello, I’m slightly off topic here but I’m seeking guidance from you good people who’ve been looking at this longer than I.
I need some help please in understanding more about several areas regarding Anthropogenic CO2 vs global warming… the very basics I’ve got.
I need to learn a lot more about c12 and c13. This is often cited as a means of detecting the anthropogenic portion of the co2 part of the greenhouse effect, but I know almost nothing about this and haven’t found anything useful by ‘googleing’ it (I’ve no doubt it will be a word soon!). Can you give me some direction please?
I’m also very interested in anything you can direct me to regarding oceanic outgassing and the longer oceanic cycles.
Lastly, and closer to this particular thread, I hope you able to help me with this, which I can’t quite get straight.
I’ve just graphed the Scripps Mauna Loa CO2 and Alaska CO2 together. What I see is that the two go in lock step together seasonally but about a month apart. Loosely speaking end of summer gives a CO2 low, and end of winter gives a co2 high. That’s not unexpected. The bit I don‘t understand is why the co2 low in Alaska always precedes the co2 low in Mauna Loa. If the ‘leaf on, leaf off’ argument were true wouldn’t I see the opposite? The tropics should be sinking the CO2 in the summer and this deficit should show up in the more polar regions later? The opposite appears to be the case. Mauna Loa always follows Alaska by about a month and also the annual range for Alaska is much larger than Mauna Loa, which I don’t understand. I’m sure I’ve missed something here that you’re all very aware of and hope for a bit of guidance.

KuhnKat,
The raw hourly averages are available up to 2006 at:
ftp://ftp.cmdl.noaa.gov/ccg/co2/in-situ/
where MLO is Mauna Loa, SPO the South Pole, BRW is Barrow (North Alaska) and SMO is Samoa (island in the SH Pacific).
All raw data, except for instrument malfunction, are available. With instrument malfunction, of course the data are blank values. Extreme values, or values with known local influence are flagged and not used for daily and monthly averages. I have looked at the 2004 raw data: over 10% were missing due to instrument malfunction (including several weeks in June), over 10% were rejected due to upslope winds. Instrument malfunction can need extensive repair and/or replacement of equipment, which may need several weeks for inflight of equipment and calibration. Despite the high rate of rejections, there was (for 2004) no difference in trend by including or excluding the flagged data…
The problem they encountered was obvious now and specific for the NH, because July is typical a month with a sharp reduction in CO2. If they had this in October, it would have been the other way out. Something similar (a 20 days malfunction, 10 days good data at the end of the month) at the South Pole wouldn’t have had much impact, simply because there is far less seasonal variation in the SH (and seasonal opposite to the NH). I suppose that the 2007 raw data will be available soon, but for the 2008 data you will have to wait another year…
Thus we are talking now about a change in smoothing algorithm, which does affect the seasonal variation around a trend, but doesn’t influence the trend itself at all. If they had the July monthly average as first calculated, this would only represent the last 10 days, which are certainly lower than the “normal” average for July, even for the cold July 2008, and next month with full data would recover back to (near) normal… I don’t know if this type of problem ever occured before. It is possible not, or this would have been solved long ago.
Thus all together, a lot of fuss for nothing. The smoothing is not necessary at all, but gives a nicer graph. The only interest of the real variation around the trend may be for people interested in the seasonal uptake/release of CO2 by vegetation, which has changed in recent decades.

The most concerning part about this sordid affair is the lack of a quality process.
A quality process is NOT a single researcher changing the presentation of data, discovering “broken equipment” data one day after the data set is shown to not support AGW. Every major corporation (medical, engineering, industrial) worldwide understands the ISO9000 process and how to make engineering changes (PCNs). It would be nice to see Manua Loa at least make a small attempt to follow a quality process. Dr. Tans data is being used to support a massive attempt to regulate our lives and potentially cost us all a huge drop in our standard of living – so his data manipulation with no peer review, within 24 hours, no documentation, no impact study, etc. tells me they are NOT a serious science organization. As one poster said, after 50 years of doing it one way, within 24 hours they discover the need to change in 24 hours LOL. What was the rush, ask him that Dee!
While some of you may say “it is not a big change, did not change the slope”, this change goes to the credibility of that location, the whole data set. I, for example, believed the Mauna Loa data was raw and was unassailable only 48 hours ago. I never challenged it in any post, any discussion, etc. Now, I wonder.
Now we know, they have problems with their data despite “back up systems”, that the nearby volcano causes issues, that data outside of certain ranges is flagged and not included. Now we know why their data does not conform to the initial AIRS data, Now we know why Mauna Loa data looks way to smooth. That’s the inconvenient truth.

Joy,
On my explanation page I have the two graphs of raw data for 2004: with and without outliers. Most outliers were to the low side (due to upslope winds, some 4 ppmv below average). That was a year with few days with volcanic outgassing. 1984 e.g. was a year with many days of high outliers due to volcanic outgassing (peaks of +20 ppmv). Both outliers are not used for daily, monthly and yearly averages, but still are available in the raw hourly averages data file.
I suppose that they simply have an automatic update of the monthly data, with as only algorithm that there must be at least 10 days of valid data. What they probably never had encountered is that if these 10 days were all at the end (or the beginning) of a month, this might give a bias in months with a huge seasonal trend…

Perhaps I’m naive, but they could have avoided this whole kerfluffle by simply posting an explanatory statement along with the altered graph. Any looming questions would then have been answered up front.
Oh well, live and learn….

John McDonald has nailed it . . .
“Dr. Tans data is being used to support a massive attempt to regulate our lives and potentially cost us all a huge drop in our standard of living – so his data manipulation with no peer review, within 24 hours, no documentation, no impact study, etc. tells me they are NOT a serious science organization”
Indeed. And how many other changes have been made that we don’t know about ??
Scientific organization ? I think not.
Very Mickey Mouse Operation with potentially tragic economic consequences.

Been following your posts. Surprised at the number of individuals you have gathered in such a short time.
You’ve actually hit upon a subject matter that ties opinion to facts. Not to mention credible sources.

@Jack Simmons:
It’s just the MS Excel Chart function plotting data series from spreadsheet columns. Then do a “Save as Web Page”, and it generates the charts as .GIFs.
General Question:
About the issue of constantly-rising CO2 concentrations; what were global temperatures doing 800 to 1200 years ago? Wouldn’t the lag in CO2 response to temperature changes observed in ice-core samples still be happening now, or has something happened to change all that? Are we seeing minor fluctuations from current conditions against a stronger response to a background signal from long ago? I sure don’t have an answer.

Ferdinand Engelbeen:
“the mass balance: more CO2 is emitted by humans than is accumulating in the atmosphere. That means that natural sinks (over a year) are larger than natural sources.”
I’m a bit confused by this. The consensus, amongst some, seems to be that around half of anthropogenic CO2 is absorbed by natural sinks.
But what then maintained CO2 levels before mankind started burning fossil fuels? Going by the above, one would expect that the downward slope in atmospheric CO2 would have been around the same slope (except in a negative direction), and a few hundred years of that would have resulted in little or no atmospheric CO2. This would probably have resulted in a large-scale dying-off of plants, which would in turn offset the decline in CO2 by a small amount (given that the oceans are the largest CO2 sinks)
Is there any evidence of such an event?
And, even if this did happen, how did CO2 levels recover?