One day later: Mauna Loa CO2 graph changes

It seems that almost every single data point in that diagram have been adjusted, not by much but still. What is interesting is that it happens within 24h after they release a result that shows a new record low for first 7 months.
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. 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?

I think it is important to remember that NOAA did put a disclaimer that the data is provisional for up to one year. The tells me that the correction for the July value was justified and had nothing to do with the direction of the error. However, the changes to data over one year old are quite odd and require further explaination although I would assume that there is a good reason and people should not assume nefarious motives.
It also worth remembering that Ilinois Cryrosphere has had numerous data problems this year where they ended up dramtically reducing the amount of ice melt reported after the corrections.
The bottom line is we want these agencies to report their data in a timely fashion but they might stop doing that if they get beat up in the blogosphere every time their instrument has a hiccup.
REPLY: I give them the benefit of the doubt, which is why I sent a message asking for an explanation. At the same time, there doesn’t appear to be any valid reason to adjust data from 2004 and further back. If they publish data publicly, then change it with no notice (as they did today), then a public change log should exist, otherwise I think they are in violation of DQA.
It seems haphazard to publish preliminary data on a weekend, and then change it on a Monday, all with no public notice. While nefarious motives may not be there, its just damn sloppy IMHO, and given this is the crown jewel for CO2 data I expect far better. -Anthony

After looking at the data yesterday, I was a little surprised by the old July value (384.93) – 3.55 ppm less than May’s number (388.48).
I couldn’t find such a big 2-month drop anywhere in the records.
So maybe the new number is the correct one.

It was a bright cold day in April, and the clocks were striking thirteen.

D. Quist,
1984
How far that seemed in the future.
How far it seems in the past.
How precious are our moments.
How quickly do they pass.
And what from a gov’ment poet
but some convoluted gas.

There’s something odd at nsidc as well, where their graph suddenly bends downwards – it was updated just a couple of days ago. You can see it at:
http://nsidc.org/data/seaice_index/images/daily_images/N_timeseries.png
This seems completely at odds with the marginal increase in arctic sea ice shown at:
http://arctic.atmos.uiuc.edu/cryosphere/IMAGES/sea.ice.anomaly.timeseries.jpg

Anthony,
My comment was directed at other posters – not you.
I agree on your comments about undocumented changes and DQA. Even if there is a good reason for changing the historical data it should have been declared clearly in the header of the file.

Can I ask what is the point of putting out a new datapoint that is unadjusted and then putting out an adjusted one a few days later? Why not wait and just put out the adjusted one?
And why adjust the entire graph’s datapoints in retrospect? Very weird.

jeez – isn’t anomaly just extent minus the 1979-2000 average? In which case extent and anomaly should be perfectly correlated.
Or, to put it another way, why doesn’t the nsidc data correlate with the cyrosphere data for sea ice area at the following link.
http://arctic.atmos.uiuc.edu/cryosphere/IMAGES/current.365.jpg
This doesn’t show the downward kink that the nsidc data shows. Or am I missing something? Is is area not the same as extent? Or is the anomaly calculated from a daily mean rather than the overall mean? If so, that is not obvious from the data presentation in the graphs.

If there is data manipulation, it would not be a new phenomenon in the “scare” dynamic. In my studies of the first prominent UK scare – the “salmonella in eggs” scare in 1988-9, I caught out a leading government epidemiologist “reinterpreting” figures in a food poisoning outbreak to turn it from “unknown origin” into a definite egg case. Unfortunately for the man, he had already released the “uncorrected” data, to which we had access.
In my review of 60 official “egg” outbreaks – peer reviewed for my PhD – only four could be reliably attributed to eggs. In three of the “egg” outbreaks, there had been episodes of illness originating from the focal premises before the egg-based food attributed as the vehicle of infection had been consumed and, in one of these, the outbreak (in a hotel) had started before the eggs attributed to the outbreak had been delivered to the premises.
We also had enormous problems with determining sources of infection in poultry flocks, the official explanation being infected animal feed – despite multiple test results showing no such infection. When we reliably demonstrated airbone infection of poultry sheds from contaminated manure spread on adjoining fields, this was officially rejected because there was no provision for such category on the official epidemiology form and thus no mechanism for reporting it.
Yet, on the basis of wholly flawed data, the UK suffered a “scare” which cost many millions and put an estimated 9,000 poulty keepers out of business. The “scare dynamic”, it seems, is alive and well.
Of course, sometimes the data manipulation works the other way.