NASA -vs- NASA: which temperature anomaly map to believe?

Readers may recall yesterday where I posted this stunning image of cold for Europe and Russia for mid December 2009 from the NASA NEO MODIS satellite imager.

Deadly Cold Across Europe and Russia

Deadly Cold Across Europe and Russia
Color bar for Deadly Cold Across Europe and Russia

Click image above to enlarge or download large image (3 MB, JPEG) acquired December 11 – 18, 2009

In that story were links to additional images, and I’d planned to return to them for a comparison. Inspired by my posting, METSUL’s Alexandre Aguiar saved me the trouble. There’s an interesting comparison here between the surface anomaly done by weather stations (NASA GISS) and that of satellite measurement (NASA NEO MODIS) – Anthony


Guest post by Alexandre Aguiar, METSUL, Brazil

COMPARE THE TWO MAPS


NASA GISS on the left, NASA MODIS on the right

Here’s the same images but larger – click either image for full size:

South America: The vast majority of the continent is near average or below average in the NEO map, but according to GISS only the southern tip of the region is colder. The most striking difference is Northeast Brazil: colder in the NEO map and warmer at the GISS.

Africa: Most of the continent is colder than average in the NEO map, but in the GISS most of Africa is warmer than average.

Australia: The Western part of the country is colder than average in the NEO map, but the entire country is warmer in the GISS map.

Russia: Most of the country is colder than average in the NEO map, a much larger area of colder anomalies that presented in the GISS map.

India: Colder than average at NASA’s NEO website and warmer at NASA’s GISS map.

Middle East: Huge areas of the region (Israel, Jordan, Turkey, Iraq, Syria) are colder than average in the NEO map and average/warmer in the GISS map.

Europe: Near average or slightly above average in the NEO map and much above average in the GISS map.

Greenland: Entire region colder than average at NEO and much of the area warmer at GISS.

Same source (NASA), but very different maps !!!

Why:

At NEO, land surface maps show where Earth’s surface was warmer or cooler in the daytime than the average temperatures for the same week or month from 2000-2008. So, a land surface temperature anomaly map for November 2009 shows how that month’s average temperature was different from the average temperature for all Novembers between 2000 and 2008.

Conclusion

Despite being very warm compared to the long term averages (GISS, UAH, etc), November 2009 was colder in large areas of the planet if compared to this decade average.

See PDF here. December should be very interesting in the northern hemisphere.

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164 thoughts on “NASA -vs- NASA: which temperature anomaly map to believe?

  1. I understand the base periods are different but we can’t let it go at that. Look at western Australia, for example. We’re talking almost a 4C difference in anomaly between the two maps.

    Am I nuts in saying that doesn’t make sense to me?

  2. Despite being very warm compared to the long term averages (GISS, UAH, etc), November 2009 was colder in large areas of the planet if compared to this decade average.>/i>”

    Well, yes, that’s what happens with averages (they being, well, the average – so even in warm months globally some localities are cold) and when you use different baselines for comparison you get, wait for it, different results…..

    .

  3. don’t know if this has been posted yet:

    31 Dec: ScienceDaily: No Rise of Atmospheric Carbon Dioxide Fraction in Past 160 Years, New Research Finds
    To assess whether the airborne fraction is indeed increasing, Wolfgang Knorr of the Department of Earth Sciences at the University of Bristol reanalyzed available atmospheric carbon dioxide and emissions data since 1850 and considers the uncertainties in the data.
    In contradiction to some recent studies, he finds that the airborne fraction of carbon dioxide has not increased either during the past 150 years or during the most recent five decades.
    The research is published in Geophysical Research Letters

    http://www.sciencedaily.com/releases/2009/12/091230184221.htm

  4. Bah Syl! That’s nothing compared to Ellesmere Island ( just north-west of Greenland). That’s way over 4°C.

    Camp Minnesota (Antarctica) also has a huge difference.

    What strikes me as odd is in the GISS map there are a couple of places where extremely high anomalies shade very quickly into low ones. Northern Siberia, northern Alaska, west Greenland, Camp Minnesota. Those are huge differentials.

    Call me a cynic (as opposed to a skeptic) but why are all the extremely high anomolies, with the exception of South Australia) in the places where no-one can argue that they are wrong, yet have to fall off very quickly to inhabited places.

  5. It was nice of them to put up a satellite to show how poorly they’re other system does its job–has CRU seen this?

  6. What does Roy Spencer say about this? Who will call for NASA GISS to reveal all of its methods, adjustments, and modifications to the data.

  7. NASA needs to worry about real threats-like something nasty from space hitting US
    -the Russians are….
    NASA- to go back to “Boldy going”…

  8. Look at that GISS map. Who would have guessed that Hawaii’s measurements were good for, what, 1/120th of the globe?

    And wow, the Arctic regions are predominantly running 4 to 9.9 degrees high! No wonder Dr. Al Gore, the noted climate scientist, thinks the ice will be gone by 2035, it’s obvious!

  9. Al Fin
    The Nasa Giss website documents all of it’s methods, adjustments and modifications. Where you unable to find that information when you went to the NASA site?
    Thanks
    Edward

  10. Anthony,

    Have you seen the new paper by Wolfgang Knorr, supposedly in GRL, that claims their has been no rise in the “airborne fraction of anthropoenic carbon dioxide” over the past 150 years? This contradicts the claim that the existing sinks are saturating and that the rate of atmospheric CO2 will start increasing more rapidly. I haven’t had a chance to check it out yet. But I wanted to give you a heads-up.

    Merrick

  11. That pretty much confirms that it got warmer up to about ten years ago, the cause being debatable and not certain. Then it has cooled since then, again the cause is not confirmed. Where it goes from here we’ll have to wait and see.

  12. Hang on a minute

    The GISS anomaly map is for the month of November whereas the MODIS map is for ONE week (11th-18th) in December. This is not a fair comparison. Leaving aside the fact that the anomaly base periods are completely different should we not at least wait until GISS release their December figures before making any comment.

  13. Part of what we are seeing in the GISS plot is what happens when a sparse data set is used to generate a surface to plot. I’ve seen GMT plotting routines do this with ‘my’ own data set. You can get these weird wrap around areas as the program tries to fill in the empty areas. Of course NOAA/GISS has made the data set sparse by tossing large #’s of surface met stations for no good reasons so they do not get a pass on this, nor on not applying an ocean mask as was done with the Satellite data plot.

  14. Given the totally different methods, the correspondence is very good. The base periods are very different – the GISS anomaly reflects all the late 20C warming, which is why the map is so much redder.

    Remember that the differences between regional anomalies for one month are often much greater than the differences between global anomalies over a year. Even if the methods gave exactly the same temperatures, you’d still see differences based on this. The sat data probably is based on less than 20 years base data (they should say). If, say, NE Brazil was relatively warm during those years, and relatively cool 1951-80, it will show now as cool relative to GISS, no matter how accurate the measurement. And there will always be places in the world with such a mismatch.

  15. Just wondering, is any of the difference in spots where a high SST anomaly changes the colder land temps? It could be that by giving the SSTs a chance to allow the ocean to breathe heat out in colder air masses you get a false reading indicating heat. Am I mistaken?

  16. fishhead (16:52:10) :

    “Was GISS done in crayon? That could explain it.”

    No it is an overlay of their Light-Bright mapping aligned using an Etch-a-Sketch and photographed using a Barbie Fashion-Master camera.

    There are using the technology that is appropriate for the quality of the data.

  17. Say it isn’t so. James Hansen claims to be soo very busy. It is hard work to adjust numbers and tweek the data.

  18. I can’t help thinking that Hansen is cherry picking when compares temps to 1951-1980. Why those years. I have often wondered about this.
    It is quite clear that not everyone at NASA is nuts like Hansen. A number of times NASA has published studies that disagree with Hansen. The real questions why is Hansen still there?

  19. fishhead (16:52:10) :

    Was GISS done in crayon? That could explain it.

    They couldn’t keep the orange and reds inside the lines? They overlapped the blues with them?

  20. pat (16:38:22)

    Interesting. I am beginning to be a little suspicious of the almost exact linearity of the current measured increase in CO2 conc. e.g. from Mauna Kea. If – as I an I guess most others have assumed, a significant part of this is anthropogenic, one would expect to see some variation in the slope of CO2 conc. with time, reflecting for instance global economic cycles and even weather. Totally straight seems hard to reconcile with human activity.

  21. pwl (16:56:50) :

    The Baseline normals to make the Anomaly. I just set someone else straight about how when comparing Anomaly maps they both have to use the same baseline. GISS uses the years 1951 to 1980 to figure out what the “normal” average temperature is for the world then subtracts that from the real temp data. So a Normal based on temps from 51 to 80 will be much lower then the “Normal” based on 2000 to 08 the MODIS satellite is based on. Therefore GISS will show a bigger difference then MODIS. Also notice that on the GISS map the areas of “High” anomalies is much more spread out then MODIS. This is due to the fact that the Thermometers died off in alot of those areas abnd GHCN and GISS infills with airport thermometers. See Chiefio’s site for the March of the Thermometers away from cold places and towards the south and the beaches and how they seem to be only able to survive at airports.

  22. I’m in the red blob in the GISS map (+2 to 4C) in South Eastern Australia. It’s 22C here now, well below normal for this time of year.

  23. Yes, saw that article. So how does this correlate to that linearly climbing Mauna Loa curve? What to believe…..

  24. photon without a Higgs (17:33:54) :

    “They couldn’t keep the orange and reds inside the lines? They overlapped the blues with them?”

    Actually they ran out of blue pegs, they once had the whole set but the dog ate most of the blue ones :-(

  25. Someone has to speak up! This “average temperature” fraud is just that.

    It is a FRAUD. It’s a trick with NUMBERS.

    It’s MEANINGLESS.

    I’ll have more to say on this in the next 2 weeks.

    Max

  26. Just a thought…

    NASA places two very different Maps using the same data… one with Sat data incorporated showing no warming and one with land based only data showing significant warming..

    these folks need to be defunded…and defended in the court of public opinion.

    UHI the most likely cause of temp rise in land based data… Sat data blends temps and show no significant warming…

    then you publish both without finding out why there is such a discrepancy….

    Is it just me or are these guy out to lunch?

  27. DirkH (17:42:17) :

    Looks like MODIS will keep GISS from falsifying the trend from now on.

    *********************************************************

    Only if we can keep Hansen and crew from tampering with the data…

  28. Pat and Clarity2009:

    If corroborated, it simply means that man-made global warming is a lie. But don’t expect Wolfgang Knorr’s study to sway the global warmists. It takes a lot to destroy an established religion. That’s why nobody else is talking about this astonishing study except here and a handful of other skeptical sites.

  29. John Finn (17:14:53) :

    Hang on a minute

    The GISS anomaly map is for the month of November whereas the MODIS map is for ONE week (11th-18th) in December. This is not a fair comparison. Leaving aside the fact that the anomaly base periods are completely different should we not at least wait until GISS release their December figures before making any comment.
    *********************************************************

    With the data temp variation should be less pronounced in the bigger set… and its not…

    However, this goes back to publishing things when there is a problem and not correcting it first..

  30. Doesn’t Griss do that auto-kinetic, tale-transcontinental portation that tripulated anomalies between New York City and San Diego, hinged in Kansas City, and if that’s out of whack, then they auto-correlate Seattle to Miami temperature trick? (or maybe it’s Little Miami?)
    I can’t keep track.

  31. I agree that we’re comparing apples with oranges here. We know November (GISS map) was much warmer than the second half of December has been (MODIS map), certainly in the northern hemisphere. A more useful comparison would be the mid-November data from MODIS, or the December data from GISS.

  32. Does anyone know where, or how, to obtain either the grid data of the “1951-1980 mean” the anomaly grid is compared to or the single temperature these are differenced against. Also the “2000-2008 mean” would be helpful. That data might be in grid form also.

    I’m like Syl as the top post, this cannot be correct even with the different base time periods. All but a few points, being conservative, are showing greater than 2 degrees and that’s conservative.

    Am I right here, in IPCC report we are only talking of 1.7 degrees since ~1880s. So the difference between 1951-1980 mean and 2000-2008 mean should be, at most, somewhere around 0.4-0.6 degC. With this in mind there is no way they should be showing 2.0+ degC differences basically everywhere.

    I want to investigate further, to be more accurate and check if this thought is correct, but don’t know where the data exists, if it’s public at all.

    If someone is already doing that work, I don’t want to duplicate. Let me know if so.

  33. The difference to me is one looks like a real temp. Map and one looks like a lego block crayon drawing.

  34. About the “31 Dec: ScienceDaily: No Rise of Atmospheric Carbon Dioxide Fraction in Past 160 Years, New Research Finds” CO2 vs. time graph. There is a bump in CO2 starting about 1935 and ending at 1945. Coincidentally that time span straddles WWII. A lot of industry ramped up ’35 and and a lot of the product of that industry was used to blow up and burn things down in the ensuing 10 years. Any correlation? The steep 1945 negative slope may give some indication how quickly the resulting CO2 was reabsorbed.

  35. Can we substitute the paper my kids colored their Easter eggs on for the one map? probably be as accurate..

  36. Hmm funny…. Is it my old eyes or does the antartica peninsula look mostly blue ? Isn’t this the area they are claiming is warming most in antartica ?

  37. Stunning chartsmanship, as defined in the NumberWatch blog.

    As well as the different anomaly bases, which make the graphs incompatible anyway, look at the colours used to indicate temperature differences,

    The satellite graph measures from +20 to -20, and the blue really only changes hue strongly at around the -10 mark.

    In comparison, GISS use strong reds from +2 upwards, and brown from +0.5. Both these colours are strong. In comparison, pastel shades are used going down, so you really only notice the blues at -2.

    It was this kind of behaviour which made me suspicious 5 years ago. You know, if they had only kept everything above board, and responded politely to Steve McIntyre, they would probably still be terrifying us, raking in grants and have no effective opposition…

  38. >Paul Martin (18:22:48) :

    >I agree that we’re comparing apples with oranges here. We know November .
    >(GISS map) was much warmer than the second half of December has been >(MODIS map), certainly in the northern hemisphere. A more useful comparison would be the mid-November data from MODIS, or the December data from GISS.

    ————————————————————–

    Do you really think AW would make a mistake like that? Both maps are for November 2009; only the first graphic is for December. Check out Scandanavia in thwo satellite images – Nov = red, Dec = blue.

    The Dec monthly sat image is not avialble yet.

  39. Its the Han Sen effect. I guess I can understand the lack of ocean if you have to use Argo. Its not been running for 10 years, but the sat network has been running for 40. I cut my teeth on the Nimbus E microwave spectrometer.

  40. The difference is partly (maybe mostly) a resolution issue. The satellite map clearly shows many more closely spaced data points than the gridded GISS map that homogenizes and smears the station data.

  41. David Ball (19:01:34) :

    O/T (but pertinent). This article was originally removed for reasons mentioned at the beginning of the article. The CRU emails will make it very difficult to try this again. We need to watch and see if they attempt to use legal action to remove it once again. Please read and comment. http://canadafreepress.com/index.php/article/18468
    *************************************************

    Nice article…

    Oh the joy we shall have here in the US… the Blogosphere is a wonderful thing…

    Once its out its out for good….

  42. finally, it’s settled!! :)
    a belated happy new year to some of you, and happy new year to those who still have it coming!
    there is a storm coming, we’ll see whom survives.

  43. Someone asked “what does Roy Spencer think?”. I can’t answer for him, but I hope that he would be honest and point out that the GISS map is baselined on 1951 – 1980 and shows anomalies for the entire month of November. The MODIS image is baselined on 2000 – 2008 and shows one week of data in the middle of December. Basically, this comparison is useless and sows confusion.

    The comparison might be interesting if both the MODIS and GISS images were baselined on the same time period and used a similar color pallet to display their information. This post is so bad it isn’t even wrong.

  44. “Mariss Freimanis (18:38:12) :

    About the “31 Dec: ScienceDaily: No Rise of Atmospheric Carbon Dioxide Fraction in Past 160 Years, New Research Finds” CO2 vs. time graph. There is a bump in CO2 starting about 1935 and ending at 1945. Coincidentally that time span straddles WWII. A lot of industry ramped up ‘35 and and a lot of the product of that industry was used to blow up and burn things down in the ensuing 10 years. Any correlation? The steep 1945 negative slope may give some indication how quickly the resulting CO2 was reabsorbed.”

    That could be consistent with the results collected by Ernst-Georg Beck:

    http://www.biomind.de/realCO2/

    and with Segalstaad’s results about the longevity of CO2. I think the only ones who wouldn’t believe it a bit are everyone involved with the IPCC.

  45. I think it’s time for an immediate and aggressive investigation of NASA’s GISS temperature measurement, adjustment and reporting methodology. The GISS temperature data is the basis for huge proposed tax increases and government expenditures. The data is being provided by a US government entity and its veracity has been called into question. There is clear and compelling case for a Senate investigation.

    Senator James Inhoff should read this article. I sent a link and a request for Senate investigation through his Senate website:http://inhofe.senate.gov/public/index.cfm?FuseAction=Contact.ContactForm
    but we should also contact him directly. Can anyone email him a link to this article?

    Senator James Inhoff, what can we do to help you initiate a Senate investigation of NASA’s GISS temperature measurement, adjustment and reporting methodology?

  46. John Finn (17:14:53) : Hang on a minute The GISS anomaly map is for the month of November whereas the MODIS map is for ONE week (11th-18th) in December. This is not a fair comparison. Leaving aside the fact that the anomaly base periods are completely different should we not at least wait until GISS release their December figures before making any comment.

    You are right. We cannot compare the GISS anomaly of November 2009 with the MODIS anomaly for 1 week in December 2009 (11th-18th).

    However this is the MODIS anomaly for November. It still looks a lot different from the GISS anomaly for November 2009.

    http://neo.sci.gsfc.nasa.gov/Search.html?group=67

    NE Brazil, India, Russia, Central Asia, Western Australia, NW North America, North Central US, Japan

  47. At a glance, the GISS seems horribly biased towards showing warmer anomalies. Why? If you look at the two poles, where they have, by far, the least stations, it shows incredible warming. Their calculations to extrapolate the data to surrounding areas is obviously flawed.

  48. It might also point out that using the warmest decade on record for the baseline is likely to lead to some areas which show up as cooler than normal. They could still be quite warm in the scheme of things.

  49. The very strong warming shown by GISS above 80 degrees North, between 0 and East to -120 degrees, is interesting.

    There is only one Russian station I can find in this area, “Polar GMO” 20046 at 80 deg 37 min. It shows no warming from a continuous record from 1958 to 1993, then lots of discontinuities and just three slight warming years 2000, 2005, 2006.

    http://aisori.meteo.ru/climate

  50. I would like to see the MODIS map overlain with cloud cover %.
    In the far north, one could easily get a large winter anomaly between areas exposed to clear and cold conditions vs overcast and precipitating conditions.

  51. davidc (17:44:54) :

    I’m in the red blob in the GISS map (+2 to 4C) in South Eastern Australia. It’s 22C here now, well below normal for this time of year.

    I’m in the red blob too, in Melbourne.

    Max today about 22C. Cool compared to yesterdays 36C, but quite normal for this time of year. In fact, in December2009, Melbourne had twelve days where the maximum temperature did not exceed 23C. December 2008 had 19 days where the maximum did not exceed 23C.

    Average maximum for December 2009 was 25.9C compared to long term average of 24.3C. Average minimums are 14.7C and 12.9C respectively.

    http://www.bom.gov.au/climate/dwo/200912/html/IDCJDW3050.200912.shtml

    http://www.bom.gov.au/climate/averages/tables/cw_086071.shtml

    Why are you so desperate to convince yourself 22C is unusually cold for this time of year, when the data reveals that it is neither cold, nor unusual?

  52. Why are people looking at mercator maps? Primary school kids know that land surface is grossly distorted the further you move from the equator. Maybe it looks cool and scary to see all that red on the tops and bottoms. A camp fire becomes the size of a forest blaze and Ice cube looks like an iceberg. Why are people using 16th century navigation aids. A computer generated globe is easy to produce.

  53. TeresaV (17:15:58) :

    Of course NOAA/GISS has made the data set sparse by tossing large #’s of surface met stations for no good reasons

    ——————————————————————-

    No, not true. There was a very good reason.

    Always remember, James Hansen is an environmental activist with a personal agenda which he makes no efforts to hide.

  54. old construction worker (18:21:45) :
    Doesn’t Griss do that auto-kinetic, tale-transcontinental portation that tripulated anomalies between New York City and San Diego, hinged in Kansas City, and if that’s out of whack, then they auto-correlate Seattle to Miami temperature trick?

    No, that was MBH98.
    .

    And a happier new decade to all !

  55. Henry chance (17:29:21) :

    Say it isn’t so. James Hansen claims to be soo very busy. It is hard work to adjust numbers and tweek the data.

    ——————————————————————

    With how much cooling is happening in the earth “tweek” isn’t enough. It’s sore thumb territory now.

    James Hansen, the mad scientist at his computer.

  56. Mapou (17:35:51) :

    The tale of the two contradicting maps. Which one is correct? James Hansen knows.

    ———————————————————————

    I think we all know.

    [REPLY - They're both "correct". Same data, different baselines. (But, of course, one might well argue that GISS-adjusted data of any variety is suspect.) ~ Evan]

  57. NOTE: due to the inconsistency of the maps shown hereon. caution should be used in the formulation of opinions from these products.

  58. The GISS map vs the MODIS map.
    Reminds me of the early images of Mars through telescopes vs the Mariner, Pioneer and Voyager images.
    Is the resolution on the GISS map 1950 ish, or is it just dumbed down?
    NASA spaceborne is what always gets the public’s eye.
    Hot Damn, Sam, do they deliver, or what?

  59. Alex (20:54:17) :

    “Why are people looking at mercator maps? Primary school kids know that land surface is grossly distorted the further you move from the equator. Maybe it looks cool and scary to see all that red on the tops and bottoms. A camp fire becomes the size of a forest blaze and Ice cube looks like an iceberg. ”

    —…—…

    Because Greenland (with all of that nasty melting icecap) looks reeeeeeeeeeaaaaalllllyyyyy big in a Mercator projection. (Good observation, but you’ve answered your own question.)

  60. Considering a complete cycle is about 60 years, should a baseline not be identified as the period between two minima or maxima? I suggest the period from 1934 to 1998, two maxima. The period between 1951 and 1980 is known for its declining temperature. The period afterwards is the warming phase of the cycle.
    Several scientists estimate that we are 10 years into the cooling phase of a cooling cycle that will last 20 years before the start of 30 year warming cycle, jus like the two previous 60 years cycles. They are looking at the correlation of solar and oceanic cycles.

  61. Andrew30 (18:05:29) :

    Actually they ran out of blue pegs, they once had the whole set but the dog ate most of the blue ones :-(

    —————————–

    That’s right, that’s right, , I remember now, the dog again.

  62. REPLY – They’re both “correct”. Same data, different baselines. (But, of course, one might well argue that GISS-adjusted data of any variety is suspect.) ~ Evan]

    This is true, Evan, but the selection of the baseline is very arbitrary, as are map coloration schemes. I don’t object to the red/blue scheme if it is colored at every tenth.

    If I am correct, GISS uses the 1961-1990 baseline? This would show the data warmer. It may or may not be a bad selection because it wouldn’t fudge away a warming trend by moving the baseline up. Of course, you also have the issue of just how significant is a warming trend when you measure an anomaly against the baseline. Are the anomalies measured against the 1961-1990 trend also? I am unclear on what the anomaly is a deviation from.

  63. rbateman (20:26:10) :

    I would like to see the MODIS map overlain with cloud cover %.
    In the far north, one could easily get a large winter anomaly between areas exposed to clear and cold conditions vs overcast and precipitating conditions.

    *****************************************************************

    Interesting point of view..

    Anomaly or just differential?

    it would however be interesting to see the difference between those areas at night and during the day…. just to see what reflection (albedo) change it makes to temps in general.. vs heat retention..

  64. John Finn (17:14:53) wrote: “Hang on a minute. The GISS anomaly map is for the month of November whereas the MODIS map is for ONE week (11th-18th) in December. This is not a fair comparison. Leaving aside the fact that the anomaly base periods are completely different should we not at least wait until GISS release their December figures before making any comment”.

    Dear John, the map Anthony published on Europe is for ONE WEEK. He mentioned that twice here. The two global maps I refer are from the 30-days period of November in 2009 available at NEO’s website, so the base periods are the SAME (November 1 to November 30, 2009), not comparing oranges and bananas. Regarding your point that the anomaly base periods are different, please notice that I clearly mentioned that in the text: “Despite being very warm compared to the long term averages (GISS, UAH, etc), November 2009 was colder in large areas of the planet if compared to this decade average [MODIS]“.

  65. Happy New Year to All Skeptics,

    Hmmm…

    As a Canadian living in the coldest captial city in the world, I would like to make a few observations from the layman’s perspective.

    In Ottawa in the winter, it is generally much colder on clear days than on cloudy days.

    MODIS does not see through cloud. MODIS imagery aggregates data from a number of passes to put together a full image. The data that is aggregated is based on the readings on clear days when cloud is not present.

    Therefore a MODIS land temperature image for colder areas might be biased towards colder readings?

    I assume that this bias would apply to land or ice, but not to open water ocean areas?

  66. Anthony, just a small correction: Alexandre Aguiar is a Brazilian meteorologist working at MetSul, a private forecasting company located in Porto Alegre, Río Grande do Sul, Brazil.

    I wish we had him and his colleague Eugenio Hackbart working in Argentina instead of our five thumb meto bureaucrats.

    REPLY: Thanks for the note, fixed. -A

  67. OK, my bad in my comment above. They use 1951-1980. But why use one period for the baseline and another for the anomaly? If you are measuring deviation from 1951-1980, that should be your baseline if all the anomalies are calculated that way. I have all this data sitting in Excel, and the graph of the data against the baseline it is calculated from isn’t any scarier than the others, but it does have a weird double peak signal in it after the baseline period. 1934 is also strangely flat.

  68. John Finn (17:14:53) :

    Look closely, the map Anthony shows is, as you say, for part of December but the comparison maps are both for November. Notice the MODISS maps don’t look the same. I looked it up on the web site and it’s definitely the November monthly data for both.

  69. Alex (20:54:17) :

    Why are people looking at mercator maps?
    ————————————–
    One of the best points all thread… why is this primitive method used? It is an extremely poor and biased representation of the data.

  70. Max Hugoson (18:05:59) :

    Someone has to speak up! This “average temperature” fraud is just that.

    It is a FRAUD. It’s a trick with NUMBERS.

    —————————————————————

    The cooling isn’t a travesty for James Hansen. No, no, no. Because he found away to hide the decline!! Hey, when you’re the boss you can get bossy with the data!

  71. Alex (20:54:17) :

    > Why are people looking at mercator maps?

    We aren’t. The maps typically used are not projections, they have latitude on the Y axis and longitude on the X axis. I’ve been through this before, It’s too late tonight to do it again now. (Mercator has something like latitude / cos(latitude) on the Y axis.) I think it’s the projection of a sphere onto a cylinder – the poles are infinitely far away on a Mercator map. The main advantage of a Mercator map (other than making Greenland look too big) is that a compass heading, e.g. NE, is always 45° on the map. I don’t know any other map projections with that feature. Most likely given that constraint, one can only come up with the Mercator projection.

    I’d prefer a sinusoidal map, i.e. latitude on the Y axis longitude * cos(latitude) on the X axis. That’s a cheap equal area map so counting pixels and related tasks are easy and visually the poles aren’t emphasized.

    Why do so many people think those are Mercator projection maps?

  72. Alex (20:54:17) :
    “Why are people looking at mercator maps? Primary school kids know that land surface is grossly distorted the further you move from the equator.”

    I think you will find that this is not a mercator projection. Perhaps a Equirectangular projection?

    /quote/
    The projection is neither equal area nor conformal. Because of the distortions introduced by this projection, it has little use in navigation or cadastral mapping and finds its main use in thematic mapping. In particular, the plate carrée has become a de-facto standard for computer applications that process global maps, such as Celestia and NASA World Wind, because of the connection between an image pixel and its geographic position.
    /unquote/

    http://en.wikipedia.org/wiki/Equirectangular_projection

  73. Images are amongst the best brainwashing tools – especially if repeated often. A picture speaks a thousand words, or, in this case, a thousand lies.

  74. phlogiston (17:36:50) :

    Speaking of CO2 levels, have you checked out World Data Centre for Greenhouse Gases (WDCGG) at http://gaw.kishou.go.jp/cgi-bin/wdcgg/catalogue.cgi . Go to a location other than Mauna Loa with CO2 listed in “Parameters”. Look for entry listing “monthly”, not “event”. Pick “Quick Plot” then “png”. None I’ve seen look like Mauna Loa. Some even show drops in CO2. Look also at 13CO2 (CO2 isotope with extra neutron) levels. Watch the date ranges, some stoped years ago. Interesting why Mauna Loa is the wierd duck but is the only one broadcast everywhere as correct.

  75. You guys make a lot of good points, but some of you miss things.
    There are three maps the original one from the 11-18th of December,
    then the two new ones that are the same month, different value scales, different reference years for base averages, and completely different color scales.

    Some noticed the lack of Ocean masking on the GISS map and the apparent size, of the area over which the stations were extrapolated (the large dots, about 1000Km in diameter) And yes a third grader, with new crayons could have done better.

    The main problem is no standardization, the increase in resolution of the satellite data, gives many more separate pixels, and better shaded gradients for better understanding of the data.

    It makes the problems in the system easily seen, and graphics should be optimized to best convey, as much clarity of the data, as possible.

    Unless the goal is to confuse and conceal real trends. I cannot believe that with the budget, manpower, and equipment they have, that a better overall product was not produced with my tax dollars.

  76. JB Williamson (22:49:22) :
    Alex (20:54:17) :

    Wow, Wikipedia says Ptolemy claims Marinus of Tyre invented the equirectangular projection (also called the equidistant cylindrical projection) in 100 A.D. I always though mankind back then thought the world was flat!! Another first century Wikipedia revelation!

  77. ” wayne (23:07:02) :
    phlogiston (17:36:50) :
    Speaking of CO2 levels, have you checked out World Data Centre for Greenhouse Gases (WDCGG)… Mauna Loa is the wierd duck but is the only one broadcast everywhere as correct.

    Mauna Loa seems to be heading in the same direction as others I looked at. Which sites did you select ?

  78. wayne (23:33:19) :
    JB Williamson (22:49:22) :
    Alex (20:54:17) :

    “Wow, Wikipedia says Ptolemy claims Marinus of Tyre invented the equirectangular projection (also called the equidistant cylindrical projection) in 100 A.D. I always thought mankind back then thought the world was flat!!”

    Mmm – so did I until I checked this from wikipedia…

    “Various cultures have had conceptions of a flat Earth, including ancient Babylon, Ancient Egypt, pre-Classical Greece and pre-17th century China. This view contrasts with the realization first recorded around the 4th century BC by natural philosophers of Classical Greece that the Earth is spherical.
    The false belief that medieval Christianity believed in a flat earth has been referred to as The Myth of the Flat Earth.[1] In 1945, it was listed by the Historical Association (of Britain) as the second of 20 in a pamphlet on common errors in history.[2] The myth that people of the Middle Ages believed that the Earth was flat entered the popular imagination in the 19th century, thanks largely to the publication of Washington Irving’s fantasy The Life and Voyages of Christopher Columbus in 1828.[1]”

    http://en.wikipedia.org/wiki/Flat_Earth

  79. I understand that it is not a true mercator map, but it is -ish. It does distort the data, visually. We are a species that prefer visual information. I am not preaching to the scientific minded. It is the masses who are exposed to these images. They draw their conclusions from what they see. I was,merely, pointing out that this day and age we could display information better. This sort of ‘fudging’ is subtle but effective. I suggest we try to educate others to reality and present things in the way they understand, otherwise we become just as guilty as the warmers and their elitist attitude. I am not the enemy, neither a fool, but I understand human nature

  80. There will be a need for auditing investment in grid interpolation methods in die course, but first there has to be an agreed set of station temperatures.

    It would, I think, be a premature investment to start looking at gridding/interpolation/weighting algorithms while the surface temperature data are in such a ragged state. One logical step at a time.

  81. Have looked at almost all listing CO2, scrutinizing others in the South Pacific away from land. I know the slopes tend the same but most show ups, some downs, giggles, wiggles superimposed over the sine component, the way you would expect data in chaotic climate to be. You normally expect variance. Mauna Loa is surprisingly almost perfect line. Not saying it is necessary wrong, just different than most in its perfectness, that’s all.
    Why is it such a dead strait line when mankind is expanding logarithmically. Can’t answer that for myself. Do you know why?

  82. JB Williamson (00:00:10) :
    Interesting. Learn something every day. I meant that as a joke. But, seriously, if Ptolemy wrote that, I believe him. Great mind.

  83. Can anyone explain Hawaii it looks like there are two weather stations one at either side of the island chain. The picture show 3 different colors. To me it looks like they added the two weather station anomalies to get the third higher reading. I would like to know were the .5 degree data is coming from.

  84. I notice a few people talking about why this baseline and not another and some going it’s not 30 years for the satellite. Well in one of the emails released from CRU you get to find out that the reason 30 years is set as the “official” length is that it was easy to use and the WMO slected that length. As shown GISS uses 51 to 80 and CRU 61 to 90. Now here is another little fact the WMO, who along with the UNEP, are the founders of the IPCC slected the 61 to 90 baseline as their “official” baseline and passed that on to the IPCC. That means that the Satellite datasets are out and so is GISS because they use 51-90. That is why CRU is so tied to the IPCC they have the only dataset that uses the WMO baseline.

    Another little fact there was those that wanted the IPCC to switch from the 61 to 90 baseline to a 1981 to 2000 (yes thats right a 20 year baseline) to help them out. One of the objects to the switch is that the Global warming trend goes down. Phil Jones was hoping to be retired before the IPCC switches the baseline, which according to the email chain would have been after 2020 when they selected a 1991 to 2020 baseline.

    There is some discussion of going to 1981-2000 to help the modelling chapters. If we do this it will be a bit of a bodge as it will be hard to do things properly for the surface temp and precip as we’d lose loads of
    stations with long records that would then have incomplete normals. If we do we will likely achieve it by rezeroing series and maps in an ad hoc way.

    20 years (1981-2000) isn’t 30 years, but the rationale for 30 years isn’t that compelling. The original argument was for 35 years around
    1900 because Bruckner found 35 cycles in some west Russian lakes (hence periods like 1881-1915). This went to 30 as it easier to compute.
    Personally I don’t want to change the base period till after I retire !

    There is a preference in the atmospheric observations chapter of IPCC AR4 to stay with the 1961-1990 normals. This is partly because a change of normals confuses users, e.g. anomalies will seem less positive than
    before if we change to newer normals, so the impression of global warming will be muted.

    http://www.eastangliaemails.com/emails.php?eid=462&filename=1105019698.txt

  85. The data that is aggregated is based on the readings on clear days when cloud is not present.

    That would be true for the base readings too.

    If the satellite is only comparing its own readings over the decade then the colder days being clear will correct (and this is why anomolies beat absolutes, I suppose).

    So unless there is some reason why cloudy days now are different from cloudy days then, I don’t see a problem.

  86. “NASA -vs- NASA: which temperature anomaly map to believe?”

    Neither as belief has no place in science, nor does non-belief for that matter. Belief, that which is thought to be true or false without any evidence, is not science, it’s belief which is something else entirely.

    What we need is evidence. Since the evidence of the two alleged data sources potentially contradict each other it seems that in a deeper understanding of how exactly these two “map visualizations” are constructed. What is the raw data and how is it processed into these visualizations. Just like any other “manipulations” of data it must be explored and comprehended before conclusions can be drawn especially when other data contradicts it. Actually the problem prevalent in climate science seems to be “manNipulations” of the “manN” made kind. At least that needs to be ruled out… with auditing… funny that.

    It’s time to take it to the next level with Nasa’s data, manipulations, software and conclusions.

    Happy 2010!

    ps. It makes sense that Al Gore believes that it’s 2 million degrees just a few miles below, he believes in AGW so I guess some one is heating up the center of the Earth one heck of a lot! Heh heh…

  87. http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2009&month_last=11&sat=4&sst=1&type=anoms&mean_gen=11&year1=2009&year2=2009&base1=1951&base2=1980&radius=250&pol=reg

    250km smoothing is better, but it shows that the GISS algoreithm spreads warm anomalies into not covered areas. Check like empty north of Canada of central South America gets suddenly warm, few stations west from Greenland have infected the whole Arctic to be 4-10C above average and cold anomaly measured directly at Yamal peninsula (oh, did I told Yamal?) become +2C in the 1200km smoothed map.
    Bob Tislade did a great job some time ago, comparing trends in certain parts of the globe between GISS and MSUAH, finding that vast areas of Asia, Africa and Antarctic show the biggest differences and North America and Europe are quite OK.

    http://bobtisdale.blogspot.com/2009/06/part-1-of-comparison-of-gistemp-and-uah.html

  88. Spencer (00:35:52) :

    If there are actually TWO stations, then I know one way this could occur, a phantom value injected between. I am a programmer of forty years and have seen similar cases. We know via some of Hansen’s papers (goto GISS) that he has “proven” that, in the case of anomalies, he claims you can interpolate and project up to 1200 km with acceptable error bounds over missing data cells in an anomaly map. So some interpolation is occuring. In programming, the interpolation function applied on every cell in the map, the adjacent cells would be adjusted via some “unknown to us” function (more properly called a 2D filter of some size, 3×3, 4×4, so on). The gray unvalued cells would either have to be ignored (proper) or take on a zero value since zero is “no energy difference here” in an anomaly map. Depending on the particular function used, the blank cell between the two real stations can take on a larger value than either on either side due to the linear or logarithmic interpolation correction performed by the filter.

    Very close to what you do when you adjust the sharpness or smoothness of a photo in image correcting software. Looking at the pixels closely, this same affect will appear. I know, wrote image manipulation/correction and color separation software back in the late 80′s.

    If this is the actual case, it could be sloppy science depending on how the gray areas are handled. With no gray areas (missing data values) the filter could be perfectly correct. This is only one way this could feasibly occur, there are others. To me, any tinkering of the values is incorrect, especially if not stated where and why in a crystally clear way at the bottom of every image or a link to it.

  89. ShrNfr (19:01:53) :
    Its the Han Sen effect. I guess I can understand the lack of ocean if you have to use Argo. Its not been running for 10 years, but the sat network has been running for 40. I cut my teeth on the Nimbus E microwave spectrometer.
    ——————————————–
    Might you have worked with J Houghton then? Didn’t he have a hand in developing those gadgets?
    OM

  90. @ JB Williamson You forget that the Koran says the earth and sky are flat and that the sky is held up by 4 pillars.

  91. Interestingly, GISSTEMP allows you to play around with the baseline period. Changing it to 2000 – 2008 for November 2009 produces something a bit closer to the NEO map, but there are still significant variations. Africa correlates quite closely, but South America? Australia? Greenland?
    ( http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2009&month_last=11&sat=4&sst=0&type=anoms&mean_gen=11&year1=2009&year2=2009&base1=2000&base2=2008&radius=1200&pol=reg )

    I’m not a scientist by any means, so apologies if I’ve misunderstood how this is supposed to work.

    Anthony – congratulations on a superb site. Best wishes and a happy new year.

    Andy

  92. Sorry to spoil the party but this post is misleading.

    It would have been easy to use the same 2000-2008 period for the GISS map. On the NASA GISS web site you can change the base period from the default 1951-1980 to whatever period you want.

    When the same base period is used for both maps, most of the differences between the maps disappear. It is also a good exercise to compare the MODIS night time temperature map to the GISS map.

    I am actually quite surprised how well the GISS and MODIS maps compare.

  93. Bill H (22:01:48) :

    Overlay the % of the time spent cloudy.
    I can see a warm anomaly take place simply due to a location being clear at the start of Nov, then being cloudy the rest. It would bias towards warm.
    Put that same clear period at the end of Nov., and you get a colder anomaly.

  94. Alex (00:07:07) :
    “I understand that it is not a true mercator map, but it is -ish. It does distort the data, visually. We are a species that prefer visual information. I am not preaching to the scientific minded. It is the masses who are exposed to these images.”

    You make a very valid point and I agree with the thrust of your argument. However, how would you present the data in this case?

  95. I like how people commenting on this post immediately pointed out how the two maps are in some ways like comparing apples and oranges. It demonstrates scientific scrutiny and honesty which has been sadly lacking at GISS.

    I feel GISS has been far too concerned with impressing the public, and influencing public perception. That has more to do with how you present the data, and less to do with the data in and of itself.

    Simply showing these two maps demonstrates how very different perceptions can be drawn. This is a point in and of itself, separate from exacting scientific discussions of the two data sets.

  96. …The current baseline 60-90 includes half of the last 100 years ‘ice winters’ in Denmark. An ‘ice winter’ is a winter where the Danish belts and fjords freezes and normal ships need assistance.

    1995-96
    1986-87
    1985-86
    1984-85
    1981-82
    1978-79
    1969-70
    1962-63
    1955-56
    1946-47
    1941-42
    1940-41
    1939-40
    1928-29
    1923-24

    (From DMI.DK)

    In other words, the current north-west European winter baseline must be very cold.

    Very convenient – if you are a warmist.

  97. I wouldn’t normally agree with Rattus Norvegicus (19:16:45) : but for once I do. We need (a) same baseline (b) same measurement timespan (c) same colouring system.

    DirkH (19:39:02) : re Segalstad – Segalstad is important.

    Just The Facts (19:58:16) : I think it’s time for an immediate and aggressive investigation of NASA’s GISS temperature measurement, adjustment and reporting methodology.

    I hear Hansen complaints of being “overrun” with FOI requests and I’d like to see concerted action to take away that complaint option. GISS does at least make “data” available – as “raw” and “homogenized” and already these give a lot of insight if we look at them carefully. What seems most crucially missing is (a) station data right up to the present from good “rural” stations like John Daly notes (b) what if anything is done even to “raw” data (c) a realistic rule-of-thumb UHI correction factor

  98. Rattus Norvegicus (20:13:58) :

    It might also point out that using the warmest decade on record for the baseline is likely to lead to some areas which show up as cooler than normal. They could still be quite warm in the scheme of things.

    Ok- in an earlier post (John Finn (17:14:53) :)
    I might have misunderstood the source of the second MODIS image. I was assuming it was from the DEc 11-18 image that was used in the previous post. However, there is still the issue of the different base periods.

    There is a facility on the GISS webite which allows you to specify whichever base period you want. This is the GISS (land only) anomaly map for November using 2000-2008 as the base period.

    http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2009&month_last=11&sat=4&sst=0&type=anoms&mean_gen=11&year1=2009&year2=2009&base1=2000&base2=2008&radius=1200&pol=reg

    The MODIS and GISS maps now look much more in agreement. GISS still has a slight positive anomaly (0.14) but we need to remember that the UAH November anomaly was a record for that month.

    Just for completeness, here’s the GISS global map using the satellite base period (1979-1998).

    http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2009&month_last=11&sat=4&sst=1&type=anoms&mean_gen=11&year1=2009&year2=2009&base1=1979&base2=1998&radius=1200&pol=reg

    Note:
    GISS anomaly is +0.51 deg
    UAH anomaly is +0.50 deg

  99. CET figures are out.
    December 3.1: 2009 10.11
    Coldest December this decade and the coldest since 1996.
    Year is 3rd coldest of the decade (2001 and 2008 colder).
    On the 351 year record, December ranks 255= along with 1779, 1811, 1816, 1835, 1946; 2009 is 30th between 1686 and 1831.
    None of which means anything at all of course!

  100. Alexandre Aguiar – MetSul, Brazil (22:03:02) :


    John Finn (17:14:53) wrote: “Hang on a minute. The GISS anomaly map is for the month of November whereas the MODIS map is for ONE week (11th-18th) in December. This is not a fair comparison. Leaving aside the fact that the anomaly base periods are completely different should we not at least wait until GISS release their December figures before making any comment”.

    Dear John, the map Anthony published on Europe is for ONE WEEK. He mentioned that twice here. The two global maps I refer are from the 30-days period of November in 2009 available at NEO’s website, so the base periods are the SAME (November 1 to November 30, 2009), not comparing oranges and bananas. Regarding your point that the anomaly base periods are different, please notice that I clearly mentioned that in the text: “Despite being very warm compared to the long term averages (GISS, UAH, etc), November 2009 was colder in large areas of the planet if compared to this decade average [MODIS]“.

    Alexandre

    I do apologise. My only excuse is that I had been ‘celebrating’ earlier. It was the early hours of New Years day when I posted my comment.

    However, in a later (more sober) comment I have pointed out that it is possible to obtain GISS anomaly maps for whatever period you wish. I have done this for 2000-2008 and, as we would expect, there is more agreement in the MODIS and GISS maps.

    I also feel it’s useful to look at other independent data before jumping to conclusions (I don’t mean you – I mean other blog readers). The UAH temperature data, maintained by Roy Spencer and John Christy, shows that November 2009 was the wamest November on record. The relative anomalies (i.e. same base period) for UAH and GISS are in very close agreement.

    PS Thanks for your comment.

  101. I see from the above posts that analyse in depth the zits on the dead elephant, the corpse of AGW is still twitching.
    The very idea that you could measure the temperature of the whole earth to any degree of accuracy is risible.
    Referring to my recent wildfowling trip, on the way back, after evening flight, from Dornoch Firth to Findhorn, the temperature ranged between -7C at Dornoch to -1C at Findhorn, both sites being at sea level. The coastal journey by road was 65 miles but as the Pink footed goose flies, only 20 miles. There was no wind and the snow cover and depth were similar throughout. The variable was time, as, lacking a Tardis, 1 hour 20 mins elapsed whilst I travelled between points.
    Interestingly, Inverness UHI effect was noticeable as the temperature in town rose to 0C only to fall again to -2C at the airport.
    Global warming? 0.7C rise in 100 years? Hundreds of miles between data points? Six degrees difference in 20 miles? If Trolls had brains they would most certainly be dangerous!

  102. The two maps show a number of things.

    First, the Modis map is amazing – it is the best, highest resolution temperature map we have ever seen. It also correlates with the measured temperatures so we can assume it is reasonably accurate besides being better.

    Second, the GISS smoothing algorithm using 1,200 kms needs to be damped down to a much smaller distance. The Modis map shows that inconsistencies can occur – the Arctic in this month for example as well as other areas.

    Hadcrut3 and the NCDC seem to be much farther off than even GISS so who knows what they do with their data. But the smoothing algorithms need to be damped down.

    http://hadobs.metoffice.com/hadcrut3/index.html

    http://www.ncdc.noaa.gov/sotc/get-file.php?report=global&file=map-land-sfc-mntp&year=2009&month=11&ext=gif

    Third, I think the Modis data can possibly be used to develop a new temperature series. The Modis instruments have been up since 1999 and 2002 so it would give us another check and another data source. Some mentioned above keeping GISS honest. We’ve seen this before when other newer systems come along, like UAH and RSS and the Argo buoys.

  103. I am as puzzled by these results as many of you. I decided to dig in deeper to how these scientists adjust their data. Apologies if this is old news to you, but it has caused me some additional concern. I am referring to the latest document cited by NASA as affecting their temperature analysis (A closer look at United States and global surface temperature change – Hansen et al. (2001).

    In this document they make great play of adjusting for UHI effects but the major adjustment seem to be as a consequence of Time of Observation Bias and Station History Adjustment – both positive by a significant amout.

    Taking the adjustment for Time of Observation Bias – this claims to adjust for the fact that maximum and minimum temperatures are taken ,say, in the afternoon instead of at midnight, and is based on hourly records at stations in the US. Now my understanding of maximum and minimum temperatures is that one of each occurs at a random time in any 24 hour period.

    It is simple to envisage with a traditional max/min thermometer – at some time in the day the max marker is pushed up as far as it will go by the mercury and there it stays until it is pushed down by the observer). The time of the observation will not affect the magnitude of the maximum, thus any adjustment to this temperature would seem to be inappropriate.

    Assuming that the resetting of the max and min markers was carried out art consistent times then the reading should stand. The only possible effect a change in observation time could have is that may taken either before or after the maximum for the day that would have been recorderd at the old time – the consequence of this being a particular max value being assigned the the wrong day, resulting in either a double recording in effect in on day or a missed recording, depending on whether the new observation time is later or earlier than the previous.

    But this will only affect one or two days’ values. Once a new observational routine is established the need for adjustment disappears.

    In all cases an adjustment to the magnitude of a maximum would appear to me invalid, yet if I understand NASA correctly, the maximum recorded temperatures for all stations that are deemed to have changed the time of observation at some point in history are adjusted according to a positive value currently at around 0.2 degrees.

    Moving on to Station History, the Hanson et al paper shows an example of Station History adjustment, where a station is moved from an urban to a peri-urban location. This causes a discontinuity in the readings, a drop in their example – so they apply a blanket increase to all stations that have been relocated. This of course ignores the fact that relocations are often to airports (with the problems associated as discussed here on many occasions).

    The irony is that the example shows the “undisturbed temperature” ascending at a much lower rate (but still ascending – just another indication of biased thinking), but the total UHI correction they make is significantly less than the Station History adjustment introduced for moving from an urbanjust to a peri-urban location, not even a fully rural location.

    Not being a climate scientist (or scientist of any sort!) I may have misunderstood, but I think I am applying logic to this analysis, for which a scientific qualification is not required.

    For those of you interested, the graphs showing adjustments are shown in Plate 3 of the above-mentioned document. I should add that it is claimed that these adjustments are applied only to US station data. I can’t find any evidence as to what adjustments are applied to the rest of the world, but I can’t imagine they would be significantly different.

    So I think if you want to get a real comparison between the two graphics at the top of this thread, maybe you should subtract approximately 0.2 degrees (the total adjustment shown from 2000 – the latest figure given in the paper) from the GISS anomalies for starters.

  104. I regularly compare:

    GISS Temp
    CRU Surface (when it was available)
    Hadley SST
    MSU MT – RSS
    MSU MT – UAH
    MSU LT – RSS
    MSU LT – UAH

    Since 1979, the GISS is the clear high outlier.

    The 1200km smoothing radius is pretty clearly the reason.

    That smoothing exaggerates trends for areas in which
    there are few samples, notably the Arctic, and interior Africa, based on the used peripheral stations.

    For the decades of the early twentieth century,
    it’s worthy to note that
    the GISS trends were lower than CRU,
    probably because the peripheral stations to the
    ‘empty’ areas were similarly exaggerated.

    The UAH MT data set exhibits the low outlier.

    The remaining data sets exhibit relatively consistent trends.
    (With deference to all hobgoblins).

  105. Gash (22:09:44) :

    Happy New Year to All Skeptics,

    Hmmm…

    As a Canadian living in the coldest captial city in the world, I would like to make a few observations from the layman’s perspective.

    In Ottawa in the winter, it is generally much colder on clear days than on cloudy days.

    MODIS does not see through cloud. MODIS imagery aggregates data from a number of passes to put together a full image. The data that is aggregated is based on the readings on clear days when cloud is not present.

    Therefore a MODIS land temperature image for colder areas might be biased towards colder readings?

    I would believe that to be true for higher latitudes.
    But the converse is also probably true for lower latitudes,
    where clearer areas are associated with higher temperatures.

    Of course that bias is for the absolute values and not
    necessarily for the anomalies. There are probably still
    biases, but the comparison of anomlies against a baseline
    would be the comparison of this November’s clear days
    against all the November clear days of the record,
    (which could vary in many inter-related ways).

  106. Lucy Skywalker (03:44:30) :

    If you want a comprehensive look at GISTemp visit EM Smiths site. He went throught he code and has how it works step by step. Also keep in mind that GISS doesn’t really use pure “raw” data. What they get is the GHCN+USHCNv2 added. What EM Smith found was that one of the first things Gistemp trys to do is take out all the adjustments NCDC made before they got it.

    What he also found is that of the over 13,000 stations in the set only 3,000 have records over 64 years and those 3,000 long lived stations do not have the warming trend in them. It is in the 10,000 short lived station records. Also the 3,000 long lived stations supply roughly half of all the data in the set. So what you get is the a warming trend caused by adding stations in warm locations (like the tropics) and then shortly there after dropping stations in cooler areas like in the mountains. What it looks like to EM Smith was that a UN committee wanted a new network setup and NCDC dropped the cooler stations in response.

    He has indepth analysis on stations around the world as well as how GIStemp works with a good dose of humor!

    http://chiefio.wordpress.com/2009/08/05/agw-is-a-thermometer-count-artifact/

    http://chiefio.wordpress.com/2009/10/22/thermometer-langoliers-lunch-2005-vs-2008/

  107. 01.01.2010, 18:00 GMT+1 : Looks like a new arctic blast here in Germany. Snow and North wind. Creeps southward. About the only place still above freezing is Munich which is in the deep south of germany.

  108. So are the vast expanses of red at the two poles due to just one or two measurement stations (as we have recently seen in the south). The map is less alarming if the poles are not shown.

  109. If this is a duplicate post, I apologize. (That’s what WordPress is saying. But I didn’t see it get posted yet.)

    Syl (16:17:47) :

    I understand the base periods are different but we can’t let it go at that. Look at western Australia, for example. We’re talking almost a 4C difference in anomaly between the two maps.

    Am I nuts in saying that doesn’t make sense to me?

    wayne (18:32:23) :

    I’m like Syl as the top post, this cannot be correct even with the different base time periods. All but a few points, being conservative, are showing greater than 2 degrees and that’s conservative.

    The difference is not just due to a different base period. One is a monthly average, and the other is a weekly average. I suspect that it is the latter that accounts for the cognitive dissonance. Weather patterns are much more extreme week to week, than month to month. For a good idea of what I mean, just view the following:

    The first is for the week ending December 16, so will be closest in time to “non-GISS” NASA image we’re looking at in this post. Look particularly at the light blue in Western Europe. Then look at the second, which is for a week earlier; Western Europe is now “warm.” Just a week’s difference in time.

    But the GISS map is a monthly map for November. So now take a look at these images, which are weekly, going back through November, last week first:

    Just the last week of November is dramatically different than the second week of December. That is “weather” for you, folks.

    Incidentally, I believe the base period for the images linked above is a “standard” climatology of 1971-2000. I wish NASA would get with the program, and use the same 30 year climatology that most everyone else uses. (I know that the current “official” WMO climatology is 1951-1980, and only updates every 30 years, but most countries update their “normals” every decade, and following that convention, 1971-2000 is the current “standard” climatology. Of course, at the end of 2010, even the WMO climatology will update to 1981-2010.)

  110. I’ve tried to post something here a couple of times, with some odd results (and nothing showing up yet). Without duplicating everything — I had a bunch of links to demonstrate what I’m about to say — one thing I think many are missing is that the GISS map is a monthly, while the NASA image based on 2000-2008 is for a single week. This likely explains the anomaly differences that cannot be accounted for by the different baselines. The second week of December was much cooler than November. If my previous post doesn’t show up, with the links I had to illustrate, I’ll try reposting it later.

  111. I understand the base periods are different but we can’t let it go at that. Look at western Australia, for example. We’re talking almost a 4C difference in anomaly between the two maps.

    December should show a very warm anomaly in Western Australia for December, based on the averages at 32 BoM ground stations across the state:

    Albany Min Max
    100y av 13.9 21.9
    Dec 08 14.5 20.7
    Dec 09 14.9 20.8

    Balladonia
    100y av 13.2 30.2
    Dec 08 13.7 29.1
    Dec 09 14.1 32.0

    Bridgetown
    100y av 10.6 27.5
    Dec 08 10.1 26.1
    Dec 09 10.2 28.9

    Broome
    100y av 26.4 33.8
    Dec 08 26.4 33.8
    Dec 09 26.6 33.9

    Bunbury
    100y av 13.9 25.6
    Dec 08 12.5 25.8
    Dec 09 13.1 29.1

    Busselton
    100y av 12.5 26.5
    Dec 08 12.4 26.7
    Dec 09 12.4 28.3

    Cape Leeuwin
    100y av 15.7 21.8
    Dec 08 15.8 21.5
    Dec 09 16.2 22

    Cape Naturaliste
    100y av 13.9 23.5
    Dec 08 13.3 23.8
    Dec 09 13.5 25.4

    Carnarvon
    100y av 20.1 29.3
    Dec 08 20.1 29.5
    Dec 09 21.4 31.8

    Derby
    100y av 26.4 36.2
    Dec 08 26.0 36.4
    Dec 09 25.8 36.5

    Donnybrook
    100y av 12.3 28.2
    Dec 08 12.3 27.8
    Dec 09 12.4 30.2

    Esperance
    100y av 14.3 24.5
    Dec 08 14.2 23.3
    Dec 09 15.2 26.6

    Eucla
    100y av 15.0 24.7
    Dec 08 15.1 25.1
    Dec 09 15.5 27.7

    Eyre
    100y av 14.1 25.2
    Dec 08 12.9 25.1
    Dec 09 12.5 29.0

    Geraldton
    100y av 16.9 27.5
    Dec 08 15.6 28.2
    Dec 09 16.6 33.0

    Halls Creek
    100y av 24.2 37.6
    Dec 08 24.6 36.5
    Dec 09 25.7 38.5

    Kalgoorlie
    100y av 16.8 32.8
    Dec 08 17.0 31.8
    Dec 09 18.4 33.5

    Katanning
    100y av 12.1 28.4
    Dec 08 11.4 27.9
    Dec 09 10.8 29.8

    Kellerberrin
    100y av 15.0 32.0
    Dec 08 13.6 32.6
    Dec 09 14.7 34.4

    Laverton
    100y av 19.3 34.9
    Dec 08 19.8 34.1
    Dec 09 21.4 35.5

    Marble Bar
    100y av 25.5 41.6
    Dec 08 25.7 40.9
    Dec 09 26.5 42.1

    Merredin
    100y av 15.7 31.9
    Dec 08 15.0 32.5
    Dec 09 16.1 34.1

    Mt Barker
    100y av 11.4 24.2
    Dec 08 10.5 22.6
    Dec 09 9.6 23.4

    Northam
    100y av 15.3 32.1
    Dec 08 14.8 31.9
    Dec 09 15.3 34.4

    Onslow
    100y av 21.6 35.2
    Dec 08 21.8 35.0
    Dec 09 23.1 37.1

    Perth
    100y av 16.3 27.4
    Dec 08 15.6 27.8
    Dec 09 16.5 30.8

    Rottnest Island
    100y av 16.9 24.3
    Dec 08 17.2 23.3
    Dec 09 17.9 25.4

    Southern Cross
    100y av 15.5 33.0
    Dec 08 15.3 32.6
    Dec 09 15.9 34.3

    Wandering
    100y av 12.0 29.6
    Dec 08 11.5 29.6
    Dec 09 11.0 32.5

    Wiluna
    100y av 21.0 36.8
    Dec 08 20.9 37.0
    Dec 09 23.1 38.3

    Wyndham
    100y av 27.2 37.1
    Dec 08 26.3 35.6
    Dec 09 27.2 38.1

    York
    100y av 14.9 31.5
    Dec 08 13.8 31.7
    Dec 09 13.4 34.0

    The maxima in particular soared during December across Western Australia, which I suspect is due to the ongoing trend of below average rainfall in the heavily populated southern half of the state – i.e. less rain = less cloud cover = hotter days and cooler nights.

    Removal of the below average Dec 08 figures and replacement with the above average Dec 09 figures has seen the average min and max temperatures at all 32 locations combined for the 12 months to December (i.e. 2009) at .59 and 1.09 degrees C higher than the averages for the early 1900s.

    In the 12 months to Nov 09, the min was .53 and the max .92 above the early 1900s average, and in the 12 months to Oct 09 the min was .39 and .6 above the early 1900s average. i.e. the northern hemisphere seems to have been frigid but Western Australia has hit a hot spot over the past couple of months.

  112. Sam the Sceptic

    according to the monthly average temp report on my utility bill, the average Dec temp this year was 25 deg, compared to 13 deg last year. what are others recording ?

  113. This post has the details:

    Basil (09:31:22) :

    I really think anyone interested in understanding the differences between the two images needs to read it. It is not just the difference in baseline (“climatology”). There is also the difference between one being monthly, and the other weekly, and that makes all the difference in the world, so to speak.

  114. What we have here is an issue about taking issue with the old saying about enjoying “art for art’s sake” and making it a personal experience that will enlighten you, refresh your life, touch your soul, blow your mind, and give meaning and effect to color, form, contrast, etc., for the benefit of your senses. So the NASA Modis mob likes oil on black velvet and the NASA Giss guys like watercolors on cheap wet paper. The data is the same they say, so I guess the only real difference is amount they pay their inhouse art student-in-training do do the work each month/week. Looks like the budget at Modis can really be cut by quite a large sum. Oil on velvet? Really? Remember, “art for art’s sake”. Enjoy!

  115. For Africa, GISTemp, via the GHCN dataset, shows Morocco as very warm. I looked into this. It is, IMHO, an artifact of GHCN deleting the thermometers near the cool ocean currents /shore and getting more temperature readings from the Atlas Mountains on the edge of the Sahara. (They leave the cooler thermometers in the baseline, though…).

    http://chiefio.wordpress.com/2009/12/01/ncdc-ghcn-africa-by-altitude/

    For Canada, GHCN deletes (only from the recent part of the record…) all thermometers in Yukon, Norwest Territories, and all but ONE in Nunavut (and that one is in a place called “the garden spot of the Arctic” due to the unusual plants and animals that survive in it’s unusal warmth). That big blob of excess “warmth” in the middle of Canada just reflects the GIStemp “smearing” of thermometer records from warmer (more coastal and more southernly) thermometer data inland to where it (now) has none, then comparing that to a baseline that actually WAS measured in the real cold. It does this in a couple of stages (first two are 1000 km ‘smears’, the final one in the anomaly map creation is a 1200 km smear. More than enough to move Vancouver temps to Northern Alberta…)

    http://chiefio.wordpress.com/2009/11/13/ghcn-oh-canada-rockies-we-dont-need-no-rockies/

    The GIStemp anomaly map is just a fantasy. It is based on horridly “cooked” GHCN data that have had hugh deletions of cold thermometers after about 1989 and then smears the remaining warm ones into the (now empty) cold places and “Surprise” finds them warmer when compared to the older real temperatures.

    Oh, and I’m particularly fond of that South American map. Along with killing off all the thermometers in the Andies, we have that wonderful Big Red Blob over Bolivia. One small problem: GHCN has exactly NO temperature data for Boliva in recent years… From:

    http://chiefio.wordpress.com/2009/11/16/ghcn-south-america-andes-what-andes/

    We find that Bolivia ‘cuts off” in 1990:

    The GHCN “By Altitude” report for Bolivia, Country Code 302:
    
    [chiefio@tubularbells Alts]$ cat Therm.by.Alt302.Dec.ALT
        Year -MSL    20   50  100  200  300  400  500 1000 2000  Space
    DAltPct: 1919   0.0  0.0  0.0  0.0  0.0  0.0  0.0 75.0  0.0 25.0  0.0
    DAltPct: 1929   0.0  0.0  0.0  0.0  0.0  0.0  0.0 50.0  0.0 50.0  0.0
    DAltPct: 1939   0.0  0.0  0.0  0.0  0.0  0.0  0.0  9.1  0.0 90.9  0.0
    DAltPct: 1949   0.0  0.0  0.0  0.0  0.0  0.0 27.6  0.0  0.0 72.4  0.0
    DAltPct: 1959   0.0  0.0  0.0 18.5 23.9  0.0 20.8 10.0  0.0 26.6  0.0
    DAltPct: 1969   0.0  0.0  0.0 20.5 21.8  0.0 16.2 13.0  2.6 26.0  0.0
    DAltPct: 1979   0.0  0.0  0.0 23.1 15.7  0.0 14.0 12.5  6.2 28.4  0.0
    DAltPct: 1989   0.0  0.0  0.0 23.3 17.3  0.0 16.4 12.4  3.6 27.0  0.0
    DAltPct: 1990   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0100.0  0.0
    
    For COUNTRY CODE: 302
    [chiefio@tubularbells Alts]$
    
    

    The thermometers migrate lower over time, but then in 1990 drops to a single thermometer that does not survive the year. (Each decade ends with a “9′ unless the data runs out, then it ends with that year).

    Just how do you measure Bolivia with no data?

    For those who don’t know, Boliva is mostly high cold mountains. So we are comparing real historical cold snowy mountain baselines with temperatures imported from, oh, coastal Peru and the Amazon …

    I could go on, but it’s too painfull… If you want to read more, see:

    http://chiefio.wordpress.com/category/ncdc-ghcn-issues/

    for a list of articles. The “start here” page is:

    http://chiefio.wordpress.com/2009/11/03/ghcn-the-global-analysis/

  116. New NASA motto:

    ‘Making Life More Complicated With Each Passing Day”

    or

    “NASA – ASAN Spelled Backwords”

  117. sagi (16:32:21) : Look how much of the ocean the GISS graphic incorporates!

    Yup. When making the “anomaly map” GIStemp smears temperatures up to 1200 km from the nearest land station. That includes Islands. Think about it…

    Yes, all those island airports with thermometers near the tarmac can “warm” an area of ocean up to 2400 km diameter.

    http://chiefio.wordpress.com/2009/09/08/gistemp-islands-in-the-sun/

    and from:

    http://chiefio.wordpress.com/2009/12/08/ncdc-ghcn-airports-by-year-by-latitude/

    we see that the percentage of thermometers at places that are airports is rising. This table is for Australia and may help explain that warm bubble out to sea near Adelaide:

    Australia
    
    Airports percentage by lattitude band, Total on far right.
    
          Year SP -50  -45  -40  -35  -30  -25  -20  -15  -10  -NP
    [...]
    DArPct: 1929  0.0  0.0  0.0  1.6  2.4  1.2  2.0  1.3  0.4  0.0  8.9
    DArPct: 1939  0.0  0.0  0.0  1.5  2.8  1.2  1.9  1.5  0.4  0.0  9.2
    DArPct: 1949  0.0  0.0  0.3  2.6  4.4  2.4  2.4  1.8  0.9  0.0 14.9
    DArPct: 1959  0.0  0.0  0.8  3.6  6.5  4.0  3.5  2.4  1.0  0.0 21.9
    DArPct: 1969  0.0  0.0  1.2  4.4  8.3  4.4  4.1  2.8  1.9  0.0 27.0
    DArPct: 1979  0.0  0.0  1.2  4.1  8.2  3.8  3.6  3.2  2.2  0.0 26.3
    DArPct: 1989  0.0  0.0  0.9  4.8  9.1  4.2  4.0  3.8  2.7  0.0 29.4
    DArPct: 1999  0.0  0.0  1.8  5.9 11.5  5.5  5.6  4.4  3.0  0.0 37.7
    DArPct: 2009  0.0  0.0  3.8 11.0 21.9 11.4 11.4  7.6  3.8  0.0 71.0
    
    For COUNTRY CODE: 501
    From source ./vetted/v2.inv.id.withlat
    

    Notice that in the decade ending in 2009, we have 71% of total thermometers are at airports? And notice that when we distribute them by lattitude a disproportionate number end up in that 35 S up to 30 S band? So we’re basically saying that a lot of hot black tarmac is being measured around South Australia and Victoria, then used to make up fantasy temperatures out to sea to the south…

    Yes, it’s that bad.

    IMHO, the highest and best use of the deep red blobs on the GIStemp map is that it tells me exactly where to look for excess thermometer location data cooking in GHCN.

    If you want to know where NOAA / NCDC have most damaged the temperature recording system by selective cold thermometer deletions (since 1989 ) just take a look at the GIStemp map. It’s the deep red blobs…

  118. E.M.Smith (10:58:31) :
    This has me puzzled. Either your data is plumb wrong (which I don’t for one minute believe) or the GHCN figures are being massaged and stupidly massaged at that since even I know that Bolivia tends towards the chilly (!) and if you’re claiming that it is showing warm based on readings from outwith that country then you are going to get very seriously found out and very soon.
    Add it to the spate of UK stories in the last couple of weeks about the police solving burglaries by following the burglars’ footprints in the snow! I mean, it’s that stupid.
    If you’re right about the Africa and Canada figures, which again I assume you are, then the same thing applies. Is there no-one at GHCN with the balls to post on here and explain exactly why their data are worth even having let alone being used for anything meaningful?

  119. Sam the Skeptic (12:07:34) :
    E.M.Smith (10:58:31) :
    This has me puzzled. Either your data is plumb wrong (which I don’t for one minute believe) or the GHCN figures are being massaged and stupidly massaged at that since even I know that Bolivia tends towards the chilly (!)

    Well, the “good news” is that it’s not my data, it’s the base GHCN data and anyone can download a copy and take a look. (It isn’t that big a dataset – the Dec 2009 that I just downloaded is 11.7 MB compressed download, for example). Just suck it into Excel or whatever you like and take a look. It is a flat text file, so anyone can check my work (and I’ve published the code, methods, etc. that I’ve used and I’ve had several folks cross check parts of it. I’m pretty sure it’s “rock solid”. But hey, I STRONGLY encourage anyone and everyone to do this for yourselves. It’s an easy process and it will make this a very real truth to you.)

    The data set can be downloaded from:

    ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v2

    and I describe the data format here:

    http://chiefio.wordpress.com/2009/02/24/ghcn-global-historical-climate-network/

    use the v2.mean.Z file (that is what GIStemp uses) and the station detail information (like altitude, airstation flag, population, etc.) is in the v2.temperature.inv file. For some of the fancier reports (like “by altitude” or “by latitude”) you need to match these two on “country code : Station ID” but it’s easy for anyone with any programming or data analysis experience to do. For this example, you just need to search v2.mean on country code.

    Country code is the first 3 digits ( the very first digit gives continent sized regions) so for example “3″ gives you South America and “302″ gives you Bolivia. The next 5 digits are the “station ID”, then you get 3 digits for the particular sub-location (I.E. LaPaz airport, then “at the tower” vs “near the runway”. Finally their is one digit of “modification flag” for the same temperature reading, but with a different “correction” history. (Tower with TOBS of noon vs TOBS of midnight, for example).

    So to find Bolivia, just download v2.mean.Z and unzip it. Then fish out any record starting with “302″. Just after that 12 digit composite Country/Station number will be the year of the data. Look for the last year…

    In Unix / Linx it would be done with this one line command:

    
    [chiefio@tubularbells GHCN.Dec09]$ grep ^302 v2.mean | sort -k1.13,1.16 | tail
    3028526800011989  284  283  275  261-9999-9999-9999-9999-9999-9999-9999-9999
    3028528300001989  166  159  170  151-9999-9999-9999-9999-9999-9999-9999-9999
    3028528300011989  166  159  170  151-9999-9999-9999-9999-9999-9999-9999-9999
    3028528900001989  274  276  270  262-9999-9999-9999-9999-9999-9999-9999-9999
    3028531500001989  273  275  246  228-9999-9999-9999-9999-9999-9999-9999-9999
    3028531500011989  273  275  246  228-9999-9999-9999-9999-9999-9999-9999-9999
    3028536500001989-9999  290  244  215-9999-9999-9999-9999-9999-9999-9999-9999
    3028536500011989-9999  290  244  215-9999-9999-9999-9999-9999-9999-9999-9999
    3028520100011990-9999-9999-9999   75-9999-9999   58-9999-9999-9999-9999-9999
    3028522300011990-9999-9999-9999-9999-9999-9999  130-9999-9999-9999-9999-9999
    [chiefio@tubularbells GHCN.Dec09]$     
    
    

    That I just ran on the “Dec 2009″ copy I downloaded about Dec 28th.

    You can see that the command finds those lines that begin with 302, then sorts them on the first field, characters 13 to 16, then gives the last 10 lines (the “tail” command). Any unix / linux guy on the planet can repeat this test.

    Notice that the data ends in 1990 and it a bit dodgy before that (the -9999 is the ‘missing data’ flag in GHCN).

    But looking at a single theremometer record shows it was not so flaky prior to the GHCN Great Dying of Thermometers:

    3028536500011983  269  254  250  209  178  122  148  174  208  270  261  282
    3028536500011984  268-9999  241-9999  196  123  182  168  240  266  239  236
    3028536500011985  263  252  252  215  200  180  175  189  207  253  273  278
    3028536500011986  274  254  230  227  211  186  190  205  222  254  266  274
    3028536500011987  260  268  248  224  181  171  186  196  230  261  270  255
    3028536500011988-9999-9999-9999-9999-9999-9999-9999  208-9999-9999-9999-9999
    3028536500011989-9999  290  244  215-9999-9999-9999-9999-9999-9999-9999-9999  
    

    So again: Please do not believe me at all and just go look for yourself. I’m sure a “PC” guy can post the directions for how to do a text search on a PC for those folks not on Macs (which have Unix like tools under the skin in a terminal window) or on Linux / Unix.

    ANYONE can test this.

    and if you’re claiming that it is showing warm based on readings from outwith that country then you are going to get very seriously found out and very soon.

    Well, I’ve been hollering about this for the better part of a year now and so far it is met with supreme silence from NOAA / NCDC and NASA / GISS. BTW, Hansen et.al have published that “The Reference Station Method” can be used to fill in missing data up to 1200km away on anomaly maps, so they do not deny it… they endorse it.

    Still waiting to be “found out”… Frankly, I’d love to be “found out”. Any and all reporting of the cooking of the books by NCDC via GHCN deletions would be a Very Good Thing.

    Add it to the spate of UK stories in the last couple of weeks about the police solving burglaries by following the burglars’ footprints in the snow! I mean, it’s that stupid.

    Yet most folks never look upstream. They ASSUME that the data are clean and complete. Heck, I was banging my head on GIStemp for about a year before I realized that the real issue was the input data; and I’ve done forensics work before! If it took me that long, how long would it take the average researcher who just wants to look at something like “Temperature correlation with fish numbers”?

    If you’re right about the Africa and Canada figures, which again I assume you are, then the same thing applies.

    No need to assume. Just go look for yourself. It isn’t hard.

    Is there no-one at GHCN with the balls to post on here and explain exactly why their data are worth even having let alone being used for anything meaningful?

    Not as near as I can tell. A few months back I published the name and contact information for the “Dataset manager” on my web site. Nothing. (It’s a bit hard to find, but IS up on a NOAA / NCDC web page, so I was not publishing anything they did not already have published…)

    It looks like “duck and cover” to me, but might just as easily be “If we ignore it, maybe it will go away”…

    But again: DO NOT BELIEVE ME! Just go look for yourself…

    You know, the way Science is supposed to be done…

    ASSUME: It is not settled and I may have missed something and go take a whack at it. THAT is real Science.

  120. Oh, and from v2.country.codes, this excerpt:

    236 TAIWAN
    301 ARGENTINA
    302 BOLIVIA
    303 BRAZIL
    304 CHILE
    305 COLOMBIA
    306 ECUADOR

    So you can see that 302 really is Bolivia…

  121. The color choices used by NASA to indicate anomalies are used incorrectly in a way that visually over emphasizes warmth at the northern latitudes. NASA uses traditional color ordering for cooler temperatures – but reverses the standard order for warmer colors.

    In the “color wheel” concept of colors used by artists, as well as a chart of Kelvin temperature colors, yellow is the warmest color (not the dark reddish brown shown on NASA maps).

    In the NASA maps, the color choices range from “slightly warm” represented as yellow, to orange, then red, then dark red for the warmest temperatures.

    This is backwards from how colors range from cooler to warmer coolers in a Kelvin temperature color chart. Slightly warmer should be darker red, then rising to red, orange and then yellow.

    In the “color wheel” concept of colors, yellow is the warmest of hues. In the NASA maps, a little warmer is yellow, then transitions to orange, then bright red, and then dark red (almost a brown red) for the hottest areas. A simple chart showing this relationship is here:

    http://www.mediacollege.com/lighting/colour/colour-temperature.html

    An easy to read write up on the use of colors to represent “warm” or “cool” is located here

    http://www.handprint.com/HP/WCL/color12.html#warmcool

    The effect of NASA’s color choices is to visually over emphasize temperature anomalies in the higher latitude regions and to focus the eyes on the color red. As Wikipedia notes, “Studies show that red can have a physical effect, including increasing the rate of respiration, raising blood pressure and thus making the heart beat faster.” (See http://en.wikipedia.org/wiki/Color_symbolism_and_psychology#Red)

    We do not know why NASA has selected this color scheme but it is very different than traditional color selections used everywhere else. It is a curious choice in that it defies common usage and might be used to bias the viewers’ interpretation of their chart, giving the charts an appearance of a sales pitch or marketing presentation.

  122. kadaka (17:09:38) : And wow, the Arctic regions are predominantly running 4 to 9.9 degrees high! No wonder Dr. Al Gore, the noted climate scientist, thinks the ice will be gone by 2035, it’s obvious!

    GIStemp “guesses” about the Arctic. The “data” are interpolated from “satellite ice estimates” if I understand Hansen’s paper correctly (via a Hadley / CRU SST annomaly map, so we just KNOW it has to be clean and right /sarcoff> )

    That’s right. All those rosy reds in the Arctic are “optimal interpolation based on ice estimates“. No real temperatures need apply…

    Tom T (17:32:06) : I can’t help thinking that Hansen is cherry picking when compares temps to 1951-1980. Why those years. I have often wondered about this.

    I think it’s pretty easy to see that it’s a “Cherry Pick”. Look at this graph and notice that it’s right in that “blue dip”:

    http://www.smhi.se/sgn0102/n0205/upps_www.pdf

  123. E.M.Smith (12:50:56) : They simply removed cold sites!. When will you remove the removers?.
    And the same happens in almost every other field of the rightfully adjectivized (by prof.Abdussamatov) “Hollywood Science”.
    It´s the new “Nomenklatura” which you suffer there. You are brave in combating it for the sake of truth, your descendants and nobility of heart.

  124. Wonder why the guest poster didn’t use the same baseline years (2000-2008) when he pulled up the GISS map?(the map maker does let you do that) It would have been a much closer comparison. Maybe a modification to the post is in order here.

  125. wayne (18:32:23) : Does anyone know where, or how, to obtain either the grid data of the “1951-1980 mean” the anomaly grid is compared to or the single temperature these are differenced against. Also the “2000-2008 mean” would be helpful. That data might be in grid form also.

    You get to ‘roll your own’. I’m running GIStemp and the way it fabricates that baseline is a bit, er, bizantine. Unless you run a copy of GIStemp, you can’t get the same baseline values…

    “Why? Don’t ask why. Down that path lies insanity and ruin. -E.M.Smith”

    Exploring why…

    For the USA they do a ‘quasi merger’ of USHCN adjusted and GHCN unadjusted data. Which ever one you have, you keep unchanged, unless you have them both, then you do a bizzare ‘un-adjust’ on the USHCN and then a blend of it with GHCN. Sane? Why does it make sense to sometimes use one and sometimes the other?

    Then it does a ‘homgenizing’ step on the data. Missing values can be ‘made up’ based on other ‘nearby’ stations up to 1000 km away. (What does Reno have to do with San Francisco?…) and “Rural” stations can be major airports (like the largest Marine base in the world at Quantico, Virginia …) This step uses some of the data to make a comparison baseline used for some things. But it is only one of the baselines used…

    After that, an equally bizarre, and IMHO slightly buggy (but I’ve not had time to prove or disprove the bug) Urban Heat Island adjustment is done (that does things like move the past of Pisa by 1.4C in the wrong direction…)

    THEN you get to the anomaly production step that uses ANOTHER calculation of the baseline (same time period, but different input) for making the anomaly maps.

    So good luck even just figuring out what ‘the baseline’ data are…

    If you really want to persue this, hit the chiefio.worpress links above and click on “GIStemp” in one of the top tabs. You will find directions there about how to make it ‘go’…

    I’m like Syl as the top post, this cannot be correct even with the different base time periods. All but a few points, being conservative, are showing greater than 2 degrees and that’s conservative.

    IMHO it isn’t right, but most of the issue comes from the GHCN data set thermometer changes. Only after that does the bizarre GIStemp process get a shot. It does tend to make crazy changes, but some of them improve things and some of them make things worse. Sorting out the net impact would be (or, since I’ve been working on it for a while… “has been”) a bit of a nightmare…

    I want to investigate further, to be more accurate and check if this thought is correct, but don’t know where the data exists, if it’s public at all.

    The ‘base’ data ( I can’t call it ‘raw’ since it has been pre-processed) is in the links in comments above. To get the GIStemp view of the baseline, you need GIStemp running (as stated in this comment). Best approach, IMHO, is to make the baseline from the unmollested GHCN data and then reach your own conclusions.

    If someone is already doing that work, I don’t want to duplicate. Let me know if so.

    It would be very good to have it duplicated since it needs that for a cross check in any case. It’s likely to be a year before I’m done…

  126. As pointed out, this is really comparing apples and organges. One anomaly calculated froma cooler base period of 1951-1980 average, another based on a warmer base period of 2000-2008 average.

    The GISS map from NASA using an anamoly of 2000-2008 for November (December is not available) shows a similar profile as NEO.

    http://data.giss.nasa.gov/cgi-bin/gistemp/do_nmap.py?year_last=2009&month_last=11&sat=4&sst=0&type=anoms&mean_gen=11&year1=2009&year2=2009&base1=2000&base2=2008&radius=1200&pol=reg

  127. “I’m in the red blob in the GISS map (+2 to 4C) in South Eastern Australia. It’s 22C here now, well below normal for this time of year.”

    Speak for yourself. I’m in the north eastern part of that red blob, sitting in jeans & a jumper in the middle of an outback summer… Been like it for weeks now. The graph is clearly a case where one of the scientists has left the picture on a low bench and their child has taken to it with textas. My kids do it all the time.

  128. Alex (00:07:07) :

    > I understand that it is not a true mercator map, but it is -ish.

    A Mercator map the reaches the poles is infinitely tall. That’s a far, far cry from a map that uses a linear, untransformed N-S scale.

    > It does distort the data, visually.

    All 2-D representations of an almost sphere will have geometric distortions. All that are used have some benefits, all have some drawbacks.

    > I suggest we try to educate others to reality and present things in the way they understand, otherwise we become just as guilty as the warmers and their elitist attitude. I am not the enemy, neither a fool, but I understand human nature

    > A computer generated globe is easy to produce.

    What do you mean by this? Are you talking about something where you can only see no more than half the surface? (That horribly distorts the area around the horizon.) Replacing .jpgs and .gifs with Java applets or Shockwave movies? What happens if someone tries to print the globe?

    Do you propose having the map producers start providing less misleading presentations? If so, perhaps you should contact them. You could ask Anthony to boycott their products, but I suspect a lot of providers would be quite happy to be shunned by WUWT.

    We could provide a web service that takes the URL for a lat/long map and returns one for a better projection, but that’s a bit tricky because most such maps have borders with titles, scales, and other decoration, it would be a bit tricky to recognize the actual data. Probably not too bad, but I haven’t tried. I’m not going to try a globe.

  129. Spencer (00:35:52) :

    E.M.Smith (16:02:48) “Why does it make sense to sometimes use one and sometimes the other?”

    It doesn’t. Since I wrote the post above last night I have downloaded the source code generating GISTEMP and have found the area I was blindly, hypothetically speaking of above. Here is some comments from the interpolation code file:

    The software from “GISTEMP_sources.tar.gz/to.SBBXgrid.f “ Fortran source code file.

    RCRIT = 1200
    NCRIT=20
    (My note: See constants section)

    “This program interpolates the given station data or their ANOMALIES with respect to 1951-1980 to a prescribed grid.”

    “The spatial averaging is done as follows:

    Stations within RCRIT km of the grid point P contribute to the mean at P with weight 1.- d/1200, (d = distance between station and grid point in km).

    To remove the station bias, station data are shifted before combining them with the current mean.

    The shift is such that the means over the time period they have in common remains unchanged (individually for each month).

    If that common period is less than 20(NCRIT) years, the station is disregarded.

    To decrease that chance, stations are combined successively in order of the length of their time record.

    A final shift then reverses the mean shift OR (to get anomalies) causes the 1951-1980 mean to become zero for each month.

    I’m going to give Dr. Hansen and his programmers the benefit of a doubt here. See the statement above about setting the mean to zero in a certain case, this may be correct but, what should I call it, a discontinuity in the program’s logic. Whenever I have written science related code, you must write your functions so that exactly the same logic is applied to each and every point or cell without exception. These discontinuities are what can wreck havoc on your results in certain rare cases. That is the only way you can clearly and in common English language that every one can understand describe your process because it is exactly the same for each and every point or cell.

    The gist is: I used to write code in this manner years ago and through years of experience have learned never to write code in this manner, especially in science programs because math, science and mother-nature will always apply the same laws to each point or cell. You never see IF, OR, and WHEN statements in true science no matter how complex the subject is, one complex case, general relativity.

    I might totally disagree with Dr. Hansen in certain points but I’m not here to belittle, be rude, or call names of him or his staff. That has no place in science. We should all be gentlepersons and pop ourselves in the head when we are not and let our emotions overtake our logic.

    That’s all I’m going to say until I have the chance to absorb the code in this file. My last Fortran compiler was a 1984 version in MS-DOS 2.51. Yea, ancient. I can read Fortran, was my first language learned, but that was years ago. Will probably be faster for me to convert this code to C++ or C# so I can diagnose it line by line in a familiar language.

  130. FWIW, here are a couple of links to GISS global temperature records:

    The figures for the GISS Land-Ocean monthly, seasonal, and annual average global temperatures are provided in tabular form here:

    http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt

    Here’s the link for the land stations (Meteorological) Stations only: http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt

    The 12 monthly figures are on the left in each row.

    The four seasonal figures are on the right. These seasons are composed of three months, with meteorological winter considered to start in the December of the previous year. Winter is designated DJF (December / January / February); spring is MAM, etc.

    The two annual averages are in the center, under the heading Annual Mean. The leftmost column, headed J-D, is for the calendar year and is the average of the 12 monthly figures to its left. The rightmost column, headed D-N, is for the “meteorological year,” and averages the four seasonal figures to its right. (Thus it includes data from the December of the prior year.)

  131. Roger Knights (18:30:03) :

    Thanks for the links. The statement at the bottom being: “Best estimate for absolute global mean for 1951-1980 is 14.0 deg-C” leads you to believe that all of the values in those files are differenced against a singular 14.0C value, not a station-by-station or cell-by-cell base value. Will have to totally install the GISTEMP software to get the answer to that question. Thanks again.

  132. Ric Werme (17:42:35) :
    2D representations of 3D objects are going to be awkward. Its probably not possible to see all of the Earth in a single glance. Do we need to? There are links to jpegs on this site that show satellite views. Several views centred over different areas should cover the entire globe. Some temperature points may need to be shown on several images due to overlap. Whatever distortions introduced by this method would be far less than the current way of displaying this data.
    Earlier posts refer to smearing over 1000 kms from station to station, add to this the further smearing of the image and it could appear as if it is 4000 kms.
    As to boycotting the current maps-that is out of the question. We need more information, not less, even if it is inaccurate to some degree. We need to see the data so that we can question it.
    Map makers will produce what is requested, they won’t change until there is a need. I’m not suggesting that there is a conspiracy to misrepresent data on particular maps. It’s just that it has always been done that way. No-one has complained.
    Global warming /global cooling and why. I am interested but not passionate.
    Truth, lies and misrepresentation I am very passionate about.

    Love this website- its my daily addiction

  133. I can tell you precisely why the GISS differs from the MODIS…..

    James Hansen had a hand in preparing the GISS

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