I got an email today from Barry Hearn asking me if I knew what was going on with the NCDC data set. It seems that it has started to diverge from GISS, and now is significantly warmer in April 2009.
What is interesting is that while NCDC went up in April, UAH, and GISS both went down. RSS went up slightly, but is still much lower in magnitude, about 1/3 that of NCDC. HadCRUT is not out yet.
Here is a look at the most recent NCDC data plotted against GISS data:
Here is a list of April Global Temperature Anomalies for all four major datasets:
NCDC 0.605 °C
GISS 0.440 °C
RSS 0.202 °C
UAH 0.091 °C
It is quite a spread, a whole 0.514°C difference between the highest (NCDC) and the lowest (UAH), and a 0.165°C difference now between GISS and NCDC. We don’t know where HadCRUT stands yet, but it typically comes in somewhere between GISS and RSS values.
Source data sets here:
NCDC
ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/monthly.land_and_ocean.90S.90N.df_1901-2000mean.dat
Previous NCDC version to 2007 here: ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/monthly.land_and_ocean.90S.90N.df_1961-1990mean.dat
GISS
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt
RSS
UAH
http://vortex.nsstc.uah.edu/public/msu/t2lt/tltglhmam_5.2
While it is well known that GISS has been using an outdated base period (1951-1980) for calculating the anomaly, Barry points out that they have been tracking together fairly well, which is not unexpected, since GISS uses data from NCDC’s USHCN and COOP weather station network, along with GHCN data.
NCDC made the decision last year to update to a century long base period, this is what Barry Hearn’s junkscience.com page said about it then:
IMPORTANT NOTE May 16, 2008: It has been brought to our attention that NCDC have switched mean base periods from 1961-90 to 1901-2000. This has no effect on absolute temperature time series with the exception of land based temperatures. The new mean temperature base is unchanged other than land based mean temperatures for December, January and February (the northern hemisphere winter), with each of these months having their historical mean raise 0.1 K.
At this time raising northern winter land based temperatures has not
altered published combined annual means but we anticipate this will
change and the world will get warmer again (at least on paper, which
appears to be about the only place that is true).
So even with this switch a year ago, the data still tracked until recently. Yet all of the sudden in the past couple of months, NCDC and GISS have started to diverge, and now NCDC is the “warm outlier”.
Maybe Barry’s concern in the second paragraph is coming true.
So what could explain this? At the moment I don’t know. I had initially thought perhaps the switch to USHCN2 might have something to do with this, but that now seems unlikely, since the entire data set would be adjusted, not just a couple of months.
The other possibility is a conversion error or failure somewhere. Being a USA government entity, NCDC works in Fahrenheit on input data, while the other data sets work in Centigrade. Converting NCDC’s April value of of 0.605(assuming it may be degrees °F) to Centigrade results in 0.336°C, which seems more reasonable.
Unfortunately, since NCDC makes no notes whatsoever on the data they provide on the FTP site, nor even a readme file about it with anything relevant, it is hard to know what units we are dealing with. They have plenty of different datasets here: ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/
But the readme file is rather weak.
What is clear though is that there has been a divergence in the last couple of months, and NCDC’s data went up when other datasets went down.
So, I’m posting this to give our readers a chance to analyze and help solve this puzzle. In the meantime I have an email into NCDC to inquire.
What REALLY needs to happen is that our global temperature data providers need to get on the same base period so that these data sets presented to the public don’t have such significant differences in anomaly.
Standardized reporting of global temperature anomaly data sets would be good for climate science, IMHO.
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[snip OT]
Anthony
Thanks for explaining the server issues and cost constraints.
A note to everybody. The noise floor on this thread is getting too large. There were almost as many off-topic posts as on-topic.
All of the off-topic posts have been deleted.
I appreciate the interest in WUWT, but threads where I’m asking for data analysis help should not be used as a “hey look what I found!” soapbox.
Richard Wright (08:16:18) :
Often, a small change from a reference value is often easier to measure than an absolute value. I can estimate how much water is in this lake, but I can tell you with better precision how much water there is compared to last year using a set of marks I made around the perimeter.
The technical jargon used in geophysical sciences terms this kind of measurement an “anomaly.” Value judgments about what is more desirable or ideal are due to the interpreter, not due to the label on the measurements.
It’s true that anomalies can’t be compared without specifying respective reference values, but that’s akin to saying a measurement is useless without the units.
George E. Smith (09:05:16) :
Reminder: formal error and numerical error are two different things.
George E. Smith (09:22:41) :
Well, one way to find out more about something is to measure it.
Climate is usually described in terms of the mean and variability of temperature, precipitation and wind over a period of time, ranging from months to millions of years (the classical period is 30 years).
Surface and lower troposphere temperature time series seem as good a place to start as any.
Anthony asks “The real question is, which of these is the correct global temperature anomaly for April ?”
Well, the real real question is which of these (on a common baseline, this time using the 1979-1998 standard for UAH/RSS) is the correct global temperature anomaly for April?
NCDC 0.3498 °C
GISS 0.2519 °C
RSS 0.202 °C
UAH 0.091 °C
Though we have to remember that, despite considerably variability for single months, the long-term trends of NCDC, RSS, GISS, and HadCRUT are nearly identicle (though UAH trends a bit lower):
http://i81.photobucket.com/albums/j237/hausfath/Picture22.png
A quick followup, while April temps vary a bit across series, March was surprisingly uniform. Using the same 1979-2008 baseline, we have:
GISS 0.202 °C
HadCRUT 0.207 °C
RSS 0.194 °C
UAH 0.208 °C
NCDC 0.284 °C
Is there any detail on the average anomalies? The differences would be a lot more obvious if they weren’t averaged. Certainly, the data for where most people live is a lot more important to us than sea surface temperatures and empty deserts.
I presume that all the averages are weighted by area, but certain sets must have different interpolations. The unexpected differences will lead us to a lot more insight than meaningless precision.
Zeke Hausfather (12:17:58) :
A quick followup, while April temps vary a bit across series, March was surprisingly uniform. Using the same 1979-2008 baseline, we have:
I’ve been posting on WUWT about the use of common base periods for months. No-one takes a blind bit of notice. I hope you have more luck than me.
George Smith’s lesson on “all of a sudden” is, perhaps, not entirely complete.
The following entry comes from entymology.com:
SUDDEN: c.1290 (implied in suddenly), perhaps via Anglo-Fr. sodein, from O.Fr. subdain “immediate, sudden,” from V.L. subitanus, variant of L. subitaneus “sudden,” from subitus “come or go up stealthily,” from sub “up to” + ire “come, go.” Phrase all of a sudden first attested 1681, earlier of a sudayn (1596), upon the soden (1558). Sudden death, tie-breakers in sports, first recorded 1927; earlier in ref. to coin tosses (1834).
—————————————–
So, first documented use of the expression comes from 1681. Qualifies as the King’s English to me … but of course I’m even soft on Shakespeare and don’t correct his dozen-plus different spellings of his own name when I encounter it, either.
But that’s not what is happening in temperature measurements. They are, in fact, measuring temperature, not temperature variations. (To use your analogy, they are indeed measuring the amount of water in the lake, not the level compared to last year.) They measure temperature but report “anomalies”. The use of the term is misleading (anomaly: “something that deviates from what is standard, normal or expected”). The presumption in these graphs is that the baseline is standard and anything that deviates from it is anomalous. It makes the word “anomalous” meaningless in a scientific sense, but it is useful in the hands of propagandists.
Sorry, source should be:
http://www.etymonline.com/index.php?search=sudden&searchmode=none
and should also be “when I encounter them”
I don’t get this whole argument about the importance of the baseline, in anomaly measurments. I would think the baseline is totally irrelevent; particulkarly since we apparently don’t know anything more precise about the baseline than we do avout the current measurments.
Surely the choice of baseline simply sets the value of zero anomaly for that data set.
If you simply look at changes in anomaly rather than the anomaly itslef; then any baseline ought to suffice.
I prefer the basleine that takes zero as -273.15 Celsius degrees below the freezing point of water. Well I’d even accept that freezing point of water as a suitable baseline.
When I look at RSS/UAH/GISS/HAD, the only thing that registers in my mind, is how they change relative to themsleves; not how they compare with each other.
Richard, if the analysis method contains biases, then this will be reflected in the “global temperature index.” In the limit of “everything else remains the same,” the “anomaly” (!) will be more precise.
To continue with the lake analogy, the real measurement might be akin to a series of soundings which are then mapped to a volume “model.” If you are concerned with something slightly more complicated than density, say the mass of the water, then you will have to use even more modeling.
“Anomaly” might be a funny word, but so are the words “normalized,” “mean,” “standard deviation,” and even “normal distribution.” They are all words with precise meanings within the sciences and not chosen for any nefarious purposes.
snip away if needed!
my take is this…. lies upon lies, compounding lies, and more lies.
why any of these releases are even near the same is amusing!
TX boss
P.S. u get my E-mail on solar grafts?
NOAA is increasing the difference with Hadcrut3 and ERSST (Land+ocean), it’s not an isolated difference, it’s a different trend in the last few years.
NOAA-Hadley:
http://globalwarming.blog.meteogiornale.it/files/noaa-hadley-70-18-5-09.JPG
NOAA-ERSST:
http://globalwarming.blog.meteogiornale.it/files/noaa-ersst-70-18-5-09.JPG
The post is in italian language, you can traslate using google translate:
http://globalwarming.blog.meteogiornale.it/2009/05/18/qualcosa-non-va-con-i-dati-gw-del-noaa/
Someone says the trend are similar and this is true if we look at the long range, but this is not true if we look at the medium range, these are the trends in the last 30 years (NOAA, Hadley, ERSST): http://globalwarming.blog.meteogiornale.it/files/surf99-09-11-05-09.JPG
NOAA: +0,132°/decade
Hadley: +0,06°
ERSST: +0,096°.
some body recently posted hat ALL land based systems are outliers, because they should show significantly lower trends than the satellite based systems.
I think this statement is based on the model expectation, that higher altitudes in most regions and particularly should warm faster than sea-level.
http://www.realclimate.org/images/2xCO2_tropical_enhance.gif
integrating above data for 1000 mbar sea-level and UAH 600 mbar level would require an approx. 1.5 times steeper trend for satellite data compared to land based measurement.
We know that instead the slope for GISS over land isn’t 1.5 times smaller than UAH, it even higher, what allows only 3 conclusions:
– GISS temeprature trend over land is way too high
– models are wrong
– both
I came across this on Accuweathers GW blog.It’s from the NASA Earth observatory home page.
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
There is a huge positive temp increase that begins around 1980 and lasts up to winter 2009. WUWT Anthony?
Mutual grooming and caressing, when gratified through tipping produce a great variety of “scientific truths”..:-)
“I don’t get this whole argument about the importance of the baseline, in anomaly measurments.”
Maybe not, but to compare measurements, it would at least be helpful if they all used the same baseline.
“To continue with the lake analogy, the real measurement might be akin to a series of soundings which are then mapped to a volume “model.” If you are concerned with something slightly more complicated than density, say the mass of the water, then you will have to use even more modeling.”
Lake levels are normally reported as feet above sea level for the pool. I don’t care how deep the pool is, but, you don’t report the anomoly, you report the level. Same with rivers, etc. The reason I beleive that they use anomolies in Climate reporting is because they can use a really, really small scale that makes changes of .1degree C look positively huge, where if you showed a graph of daily min, max, and average, it wouldn’t really look all that scary.
I agree with the poster who isn’t bothered by the reference period. Readers at CA occasionally raise this issue and I’m unsympathetic to these concerns. Whatever is causing the divergence, it will be in the data, not in the reference period.
I’ve discussed the NOAA-GISS divergence at CA on a few occasions – NOAA runs hotter than GISS. GISS makes its own attempt to adjust US data for UHI – I do not regard the GISS ROW algorithm as a UHI adjustment attempt: it’s more of a random permutation of data. The difference between GISS and NOAA in the US is as high as 0.5 deg C since the 1930s.
If a new divergence has developed, it might be to do with the new changepoint adjustment.
Sounds like trying to measure Global Mean Temperature is meaningless. So why bother replying?
It seems to be that using the term “anomaly” to describe the difference between a temperature reading and some reference point is incorrect. The term infers that change is anomalous, whereas we know that the climate is always changing. How about using “deviation” instead?
I noticed the NCDC anomaly seemed unexpectedly high compared with the GISS figure when it came out the other day. Since I knew the NCDC anomaly is based on the 1901-2000 period while the GISS anomaly is based on the 1951-1980 period I compared the anomalies of the two datasets in the overlapping period and found that the difference between the two is only about .01 degrees Celsius. So the difference is due to something other than base period. NCDC does frequently make significant adjustments to their figures in the weeks and even months following the initial release so it may be best to see where their final figure ends up.
Regarding the NCDC base period, I have been following this time series fairly closely over the past two years and recall that its base period has been 1901-2000 throughout this time. The time series was significantly adjusted in December of 2007, an adjustment which affected the values throughout the entire time series, but this adjustment had nothing to do with a change in the base period.
John W. (06:48:04) :
“I’d be interested in seeing the actual temperatures”
Then it would be harder to deceive us.
Pofarmer (14:24:00) :
That’s interesting, the lake level is reported as a difference from sea level (a reference level which needs to be specified to be completely meaningful).
David (15:29:56) :
Well, since there is already a “standard” deviation, I suppose this deviation would be… anomalous? (Sorry, I couldn’t resist).
Anyhow, I’m with George E. Smith and Steve McIntyre on this: the cause of the divergence should be examined in the data. Disagreements about reference data sets or what you call the difference from the reference level are interesting but somewhat beside the point.