I’ve noticed there’s a lot of frenetic tweeting and re-tweeting of this “sound bite” sized statement from this Climate Central piece by Andrew Freedman.
July was the fourth-warmest such month on record globally, and the 329th consecutive month with a global-average surface temperature above the 20th-century average, according to an analysis released Wednesday by the National Climatic Data Center (NCDC).
It should be noted that Climate Central is funded for the sole purpose of spreading worrisome climate missives. Yes it was a hot July in the USA too, approximately as hot as July 1936 comparing within the USHCN, No debate there. It is also possibly slightly cooler if you compare to the new state of the art Climate Reference Network.
But, those comparisons aside, here’s what Climate Central’s Andrew Freedman and NOAA/NCDC won’t show you when discussing the surface temperature record:
Since I know some people (and you know who you are) won’t believe the graph above created by taking the final adjusted USHCN data used for public statements and subtracting the raw data straight from the weather station observers to show the magnitude of adjustments. So, I’ll put up the NCDC graph, that they provided here:
http://www.ncdc.noaa.gov/img/climate/research/ushcn/ts.ushcn_anom25_diffs_urb-raw_pg.gif
But they no longer update it, nor provide an equivalent for USHCN2 (as shown above), because well, it just doesn’t look so good.
As discussed in: Warming in the USHCN is mainly an artifact of adjustments on April,13th of this year, this graph shows that when you compare the US surface temperature record to an hourly dataset (ISH ) that doesn’t require a cartload of adjustments in the first place, and applies a population growth factor (as a proxy for UHI) all of the sudden, the trend doesn’t look so hot. The graph was prepared by Dr. Roy Spencer.
There’s quite an offset in 2012, about 0.7°C between Dr. Spencer’s ISH PDAT and USHCN/CRU. It should be noted that CRU uses the USHCN data in their data, so it isn’t any surprise to find no divergence between those.
Similar, but not all, of the adjustments are applied to the GHCN, used to derive the global surface temperature average. That data is also managed by NCDC.
Now of course many will argue that the adjustments are necessary to correct the data, which has all sorts of problems with inhomogenity, time of observation, siting, missing data, etc. But, none of that negates this statement: July was also the 329th consecutive month of positive upwards adjustment to the U.S. temperature record by NOAA/NCDC
In fact, since the positive adjustments clearly go back to about 1940, it would be accurate to say that: July was also the 864th consecutive month of positive upwards adjustment to the U.S. temperature record by NOAA/NCDC.
Dr Spencer concluded in his essay Warming in the USHCN is mainly an artifact of adjustments :
And I must admit that those adjustments constituting virtually all of the warming signal in the last 40 years is disconcerting. When “global warming” only shows up after the data are adjusted, one can understand why so many people are suspicious of the adjustments.
To counter all the Twitter madness out there over that “329th consecutive month of above normal temperature”, I suggest that WUWT readers tweet back to the same people that it is also the 329th or 864th consecutive month (your choice) of upwards adjustments to the U.S. temperature record.
Here’s the shortlink to make it easy for you:
@ur momisugly Walter Dnes
Question… is it possible for the general public to get ahold of raw and adjusted GHCN data for analysis?
Yes, Walter. It is all available on a station by station basis here.
http://cdiac.ornl.gov/epubs/ndp/ushcn/ushcn_map_interface.html
For more information check out
http://notalotofpeopleknowthat.wordpress.com/2012/08/14/the-myth-about-record-temperatures/
As Anthony has referred to before, although we can get individual station data, nobody outside NCDC seems to know just how the individual data is amalgamated together to provide national temperatures, and they seem determined not to release this information.
It is surely time for NCDC to publish each year a full comparison of raw and final temperatures, with a full explanation of the difference. I can’t imagine there is any other organisation, public or private, who could get away with massaging data in the way they do without full transparency and independent justification.
I would guess the public at large would be furious if they were told the truth.
KR says:
August 19, 2012 at 8:32 pm
“TOBS is clearly seen in the records when looking at individual stations (offsets when recording times change), easily replicable via Monte Carlo testing, and if ignored you will be working with data that has known errors.”
KR – could you please:
(1) State the time of observation bias (TOBS) algorithm completely so we know what you’re talking about here.
(2) Show how it is clearly seen when looking at individual stations.
(3) Show how it is easily replicable via Monte Carlo testing.
Please, no reference to papers. I want to read your explanations and see your data. Thanks in advance.
NOAA habitually (monthly) makes adjustments to their entire global temperature dataset. So far in 2012, we have downloaded 21 different versions of their dataset. They appear to be adjusting the temperatures several times per month. In fact, since we only check once every 6 to 7 days, the number ’21’ might be low since we could have missed a few over the last 7.5 months.
To give you an example of what this means, their record of the January 1880 (yes, 1880) has been adjusted 21 times – that is they have reported 21 different temperatures for January 1880 since December 31, 2011. And remember, these are only the adjustments applied in 2012, so far, and they have been doing this for years. (These are global temp adjustments, not U.S. alone.)
We do have a wide variety of fake temperature charts that may be of interest: http://www.c3headlines.com/fabricating-fake-temperatures.html
TonyM says:
“I suggest the reasons that the adjustments are positive is that many stations have been/are moved to areas with less localized influence and hence are cooler.”
NCDC say this about USHCN
The stations were chosen using a number of criteria including length of period of record, percent missing data, number of station moves and other station changes that may affect the data homogeneity,
There would therefore be no significant location changes of the type you describe, merely local, minor moves which could be warmer or cooler (e.g. a change in altitude). Where there is a major move, a new station would be created.
http://www.ncdc.noaa.gov/oa/climate/research/ushcn/ushcn.html
@DWR54
Perhaps the proof of the pudding is in the eating? For example, is the observed rate of glacier retreat in Glacier National Park, Montana over the past 30 years more suggestive of a +0.013C or a +0.23C per decade rate of temperature increase?
That’s easy. NCDC show 0.03C/decade for Montana since 1980. Much lower than your bottom figure.
http://www.ncdc.noaa.gov/oa/climate/research/cag3/mt.html
****
Frank K. says:
August 19, 2012 at 7:36 pm
I wonder what government charge code they use for blogging anyway…hmmm).
****
It goes under the catch-all category of “media”, “education”, or “communication” — word-speak for propaganda.
Anthony – “Perhaps if you had the benefit of knowing what I do, you’d understand better. For example, demonstrate that each stations actual TOBS change time data was used to adjust the temperature data for that station.”
An argument from authority? A great deal of work has been done on the TOBS issue, which has known for >150 years (http://agwobserver.wordpress.com/2012/08/01/papers-on-time-of-observation-bias/). If you have evidence of TOBS data being incorrectly applied, please share it – in particular (since I’m sure that there are the odd stations here and there where TOBS was incorrectly recorded), if it’s widespread enough to have an effect on continental US temperatures. Given the amount work that has been done, published, and publicly available; demonstrating the issue, the effects, and the corrections – the burden of proof is on you in this case.
[ Note that it isn’t even necessary to use the TOBS metadata to locate and correct for these effects: examining local groups of stations, as in the BEST methods or in Williams et al 2012 (ftp://ftp.ncdc.noaa.gov/pub/data/ushcn/v2/monthly/williams-menne-thorne-2012.pdf), losses of local correlation match instrumentation or TOBS changes which can then be corrected. And given the correlation between stations as shown in Hansen 1987 (http://pubs.giss.nasa.gov/docs/1987/1987_Hansen_Lebedeff.pdf), with US stations averaging a distance of ~85km apart, the ~0.9 correlation between neighboring stations is quite sufficient to spot these changes.
However, the USHCN uses the metadata on TOBS and instrument changes. I’ve seen no evidence supporting an assertion that they use it incorrectly. ]
REPLY: Have you looked at the data -vs- the application of it? You might be surprised. Remember we are dealing with people here. Assigned times and actual times YMMV – Anthony
I see the GISS climate charlatan didn’t like the chart I posted. Tough noogies, Perlwitz. Reality intrudes on your fantasy.☺
@Peter Rosburgh:
“There is a very clear description of the reasons behind the adjustments here:
http://www.ncdc.noaa.gov/oa/climate/research/ushcn/ushcn.html
You really do not adress the reasons for the adjustments in this post. If you want to make the point that the adjustments are not justified, or not correct, you need to give arguments for that!”
Do you find it rather strange when the entire “signal” can be accounted for by subtracting the sum total of the adjustments? NOAA knows that when it posts its monthly data, the public at large believes it to be raw information. And I cannot think of another field that relies on adjusted data to the degree the Climate field does (and that’s saying something when one considers the Department of Labor Statistics).
As I’ve said in earlier posts, Climate is nothing more than a human construct. You can create any narrative you wish if you apply to correct statistical alogorthim.
Frank K. at August 20, 2012 at 6:02 am
I see no reason whatsoever to replicate multiple man-years of effort for your sake, not when you have demonstrated literacy. Read the papers I referenced, do some searching at Google Scholar (http://scholar.google.com/) if you want more. See also the Argument by Question fallacy (http://www.don-lindsay-archive.org/skeptic/arguments.html#question).
You can lead a horse to water, but you cannot make him drink. Or, apparently, read the references…
I’m sure this has been talking about before, but what are the official explanations as to why they make this systemic upward adjustment?
Smokey says: August 19, 2012 at 8:52 pm
“Some folks didn’t like my previous chart. So they will probably hate this one.
No, we like the graph just fine. The first graph shows perfectly legitimate data and a perfectly legitimate trend line for stratospheric temperatures. And the second graph is just fine too — it shows a small, 10-year long decline in the surface temperature, together with a reasonable trendline. All data helps provide a more complete picture of what the climate is doing, and what effect, if any, people have on the climate. This sort of cooling trends force people to think about the relative effects of “internal chaotic variation”, external factors (eg solar cycles) and CO2 (but does a priori not mean any one factor can be thrown out).
What we don’t like is when you post link after link, but as often as not, the graph is wrong or misinterpreted. We know you are passionate about this issue, but inaccurate and/or misinterpreted information does nothing to move the discussion forward, but instead muddies the waters.
REPLY: I assume then you’ll take issue with Dr. James Hansen’s presentation in 1988 before the Senate, where we had just 10 years of warming from 1978 (prior to which people were talking about a new ice age). If Hansen can use a ten year warming trend from 1978-1988 to raise alarm, why can’t Smokey use a ten year cooling trend to say “hey not so fast’? Goose, gander, and all that.- Anthony
tjfolkerts says:
“What we don’t like is when you post link after link, but as often as not, the graph is wrong or misinterpreted.”
“We”? Do you presume to speak for everyone, or do you have a mouse in your pocket? I post charts because they convey information at a glance. The charts cannot be “wrong” because they are based on empirical observations and data. They are certainly not wrong “as often as not” [I post hundreds of charts, so you’re going to have to back up that claim with lots of examples, or climb down]. And if you misinterpret the charts I post, well, I don’t hear that complaint very much. Maybe you should work on your chart comprehension. Most folks have no problem understanding what they mean.
KR says:
August 20, 2012 at 7:31 am
“I see no reason whatsoever to replicate multiple man-years of effort for your sake, …”
Can’t state the TOBS algorithm eh??
[sigh] That’s OK KR…don’t worry about it. Usually when I ask these kinds of questions, the people who think it’s so simple and obvious can’t seem to answer even simple and obvious questions.
Lest people think I’m being picky in my asking questions about TOBS, here is the explanation of TOBS from NOAA (bolding is mine).
—
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.
—
Note that TOBS is: (1) an empirical model and (2) estimates the actual TOBS. Hmmm. Estimates? Really? What data were used? Error range?
Also note the referral to “TOB software”. Has any seen the listing for the TOB software? Is it generally available? If not, why not?
It has been stated that TOBS is only relevant in the United States and not the rest of the world. Why is that? Did all of the other climate monitors around the world from 1880 to the present day always log their min/max temperatures at precisely the same time?
Questions, questions…
Anthony – “Have you looked at the data -vs- the application of it? You might be surprised.”
I’m quite willing to be surprised. Please – show the data. In particular, demonstrate that there is a consistent bias or error in TOBS corrections (as opposed to random offsets from someone’s poor record keeping at a few stations, which would imply loss of accuracy but not bias) that makes the TOBS corrections erroneous.
You have asserted or implied that TOBS and instrumental corrections are being done incorrectly, with bias – I await evidence to that effect. Barring that evidence, the corrections for instrumental changes and TOBS changes are (IMO) being done properly, based on the last 150 years of study of this issue.
REPLY: I didn’t say with bias, that your words, just improperly/sloppily do. It will actually take another crowd-sourcing project to disentangle the mess (and determine the bias) they created though not paying attention to what observers actually do properly. There’s a disconnect. See Frank K.’ s comment. – Anthony
For those (unlike KR) who wish to delve more into the TOBS algorithm, the original Karl et al. paper is here.
Note that the data used to derive the TOBS equations, as described in the paper:
“seven years of hourly data (1958-64) were used at 107 first order stations in the United States…”
“Of these 107 stations, 79 were used to develop the equations, and 28 were reserved as an independent test sample.”
Note that the period 1958-64 is in Jim Hansen’s climate sweet spot (1950 – 1980), when the world was (briefly) in climate nirvana…
Question: Has anyone updated the TOBS equations/algorithm with more recent data?
And remember – TOBS only affects the continental U.S. !! For Canada, Mexico, Alaska + Hawaii (!), Puerto RIco, … rest of the world, non-TOBS data is A-OK! (Apparently).
mbw says: August 19, 2012 at 4:20 pm
It is truly amusing that these adjustments are somehow causing glaciers to melt and numerous species to shift their habits. How do they do that?
————————————————
In Northern Europe, we had the latest spring on record this year, with many trees being three or more weeks late in coming into leaf.
.
Anthony – Quite seriously, if you have evidence indicating incorrect handling of TOBS changes, by all means publish it. Not just a blog post, but a submission to a peer-reviewed journal. If such claims can be demonstrated and proven, it will refute numerous papers and methods (which ones depending on whether you are discussing the methodology or the application thereof). If you have such evidence, it would be worth looking at.
Until such time, however, I’m going to apportion belief to evidence, and consider the 150 years of considering TOBS bias and the published methodologies for correcting it to be properly executed.
TOBS and instrumental corrections remove spurious biases from the data. Unless you can demonstrate, prove that such corrections are improper, or introduce enough uncertainty to seriously degrade the data, or introduce a bias larger than they remove, the conclusions drawn from corrected data will be more accurate than those drawn from raw data.
(It’s very important to clean the telescope…)
—
Frank K. – My compliments on actually reading the Karl et al 1986 paper that I and several other commenters mentioned. I would suggest following up Vose et al 2003, “An evaluation of the time of observation bias adjustment in the U.S.
Historical Climatology Network” (ftp://ftp.ncdc.noaa.gov/pub/data/ushcn/v2/monthly/vose-etal2003.pdf).
TOBS changes have been primarily a US issue due to the number of stations in the US (mostly rural) that have changed from sunset to early morning temperature readings. The US weather network relies on a large number of volunteer weather reporters (the Cooperative Observer Program, http://www.nws.noaa.gov/om/coop/ and http://www.nws.noaa.gov/om/coop/Publications/coop_factsheet.pdf), and quite frankly morning observations are more convenient as they match hydrological reading times.
Re: richardscourtney @August 20, 2012 at 1:32 am
I respectfully disagree, and some adjustments do appear to be justifiable. However I do agree that the original data should never be adjusted. Raw data should be retained, warts and all and any suggested adjustments should accompany it, along with appropriate cautions.
There is an example of a data adjustment in the current ECMWF newsletter discussing air temperature data collected from aircraft sensors. They say:
Both adjusted and original data are available, and this is a nice example of a negative temperature adjustment improving model skill. It is also reassuring to see the ECMWF testing the model fit to the data, rather than trying to retrofit the data to the model as NOAA may be attempting.
Smokey claims: “The charts cannot be “wrong” because they are based on empirical observations and data. ”
That would be true if the charts actually were all correct. As I have pointed out before, you have posted charts where you claimed the data was centuries but it was decades. Where the charts have poor eye-ball trends passed of as mathematical fits. And here we have evidence of global warming passed off as evidence of global cooling.
If you really want to make your point, then post a graph, explain what it is, and what it means.
PS Anthony. Yes, Hansen was definitely a bit brash in going to congress with a 10 year temperature trend, especially in light of the cooling trend the decade or two before that. The 1990’s definitely fit with his predictions. The 2000’s have definitely raised some question marks. It will be fascinating to see how this plays out.
So for this supposedly so critically important problem, they used: (a) just 7 years data (1958-1964), (b) a small subset of all stations, and (c) used just 28 stations as test “samples”
And – the TOBS algorithm may well not have been updated using more recent station data.
Oh, and we can’t forget … the entire global temp record, supported by the fairly extensive set of US station data – is ALL based, not on hard data, but rather on a mishmash of multi-layered mathematical equations, algorithms and assumptions which consistently produce a strong upward bias.
Nope – nothing fishy there.
And the defenders here rarely offer technical insight and detailed answers, but rather veiled ad-hominem attacks and cites to authority/papers etc.
If its all so easy and we’re all such fools you’d think real scientist types like Perlowitz could easily explain and make us all look like the silly rubes we are. Funny thing though – they rarely if ever offer any meaningful contribution …
And this from the same type folks who insist there is no UHI effect.
It must be a figment of my imagination when I drive from my home – appx. 25 miles in to the downtown area that I can consistently watch the thermometer in my vehicle climb appx 3 deg F and as I drive home the same, an appx 3 deg F drop.
Ahhh they say – but you are talking temperature not trend – and UHI does not affect trend.
To that I simply say bullpoop.
First, in the US and in many parts of the globe there has been significant growth in and expansion of urban areas. And as a specific area continues to grow and urbanize the UHI affect increases. While it may be true to say UHI does not affect daily or short term temp “trends” – it is silly – beyond common sense – to say increasing urbanization of the world and the resultant increase in UHI areas and size of existing UHI locations, has no effect on temp trends.
Second, as to long term trends … it is equally ridiculous to claim UHI has no effect on temp trends (again vs actual temps). In 1900 there were extremely few stations affected by UHI … it largely did not exist. Since then we have seen a massive urbanization, of the US and the world.
Every one of those areas unaffected by UHI in 1900, that has become urbanized, MUST SHOW a UHI effect on the long term temp trends. A station that was rural and is now rural will show a natural temp trend. A station that was rural and became UHI affected will absolutely show a HIGHER temp trend over the same period. It is impossible not to – the increase in tempos due to UHI must be incorporated in to the stations trend.
Put another way a station that went from rural to UHI – must incorporate the UHI bias in to its trend data over time – and when you introduce a 2-3 degree F increase in temps you absolutely will increase the trend over time.
Which is why the claim there is no difference in current “trend” between UHI and non-UHI stations is so silly. A true statement on a short term basis – where an city is largely fully built out and its UHI “mass” is no longer changing. A simply and completely false statement when talking about long term trends.
IMO a paired station approach – where known UHI affected stations are compared to a group of high quality stations outside the UHI area to determine the UHI affect and bias – is the better way. This should also be applied to any lower quality station data. Then you get a true measure of the UHI affect and can compensate accordingly.
Richardscourtney says on August 20, 2012 at 5:23 am
“The effect(s) of sampling error are not known, and there is no way they can be known, so there is no known way to model them correctly.”
The corrections are for well defined changes (observation time, new housing, electronic sensors) that can be modeled well. This can be verified by looking at to the first order station records that show the “real” effects. As an example, Karl et al (1986) use an independent dataset to compare their model for TOB to the “real” bias known from the first order (hourly) station records and show convincingly that the overall quality of the measurement is drastically improved.
The fact that sampling errors are not easy to quantify does not preclude the application of a correction for some other error, as the comparison to the real bias shows. Your argument is theoretical, and trumped by emprical collected data that shows the model *is* working.