Phil Jones and the Great Leap Forward

The other key network in the seminal Jones et al 1990 on urbanization (relied upon in AR4) is their Chinese network. The idea that China between 1954 and 1983 – the age of Chairman Mao and the Great Leap Forward – could have achieved consistency in temperature measurement that eluded the U.S. observing system (with changing times of observation, instruments etc) is a conceit that seems absurd on its face. However Peterson 2003 in a recent literature review held the Jones Chinese network as one of only a few “homogeneous” networks. Jones et al 1990 described their QC procedures as follows:

The stations were selected on the basis of station history; we selected those with few, if any changes in instrumentation, location or observation times.

In this case, I have been able to track down third-party documentation on stations used in Jones’ China network and it is impossible that Jones et al could have carried out the claimed QC procedures. NDP039 states the following:

Few station records included in the PRC data sets can be considered truly homogeneous. Even the best stations were subject to minor relocations or changes in observing times, and many have undoubtedly experienced large increases in urbanization. Fortunately, for 59 of the stations in the 65-station network, station histories (see Table 1) are available to assist in proper interpretation of trends or jumps in the data; however, station histories for the 205-station network are not available. In addition, examination of the data from the 65-station data set has uncovered evidence of several undocumented station moves (Sects. 6 and 10).

I have plotted locations of the 65-station network and, while the stations are in the same general area as the 205-station network, in a first look, few seem to match. Thus, it appears that many, if not most, of the stations in the Jones network are from stations for which there are no station histories and that lack of homogeneity is characteristic of the stations for which there is a station history. Here are details.

Jones et al 1990 describe their eastern China network as follows:

We assembled a network of 42 station pairs of rural and urban sites in the eastern half of China (Fig 1c). The data cover the period 1954-83. The 84 stations were selected form a 260-station temperature set recently compiled under the US Department of Energy and PRC Academy od Sciences Joint Project on the Greenhouse Effect. [15 – Koomanoff, et al. 1988, BAMS 69, 1301-1308]. The stations were selected on the basis of station history; we selected those with few, if any changes in instrumentation, location or observation times. All 84 records were complete for the 30-year period. The urban stations were in regions with populations of over 0.5 million, whereas for the rural stations populations mostly less than 0.1 million (according to 1984 population statisitcs). From the 42-station rural network. we formed an average RCHI for eastern China using the iunverse-weighting scheme. The 42-station urban network UCHI was averaged in the same way. Using the gridded data from [1], a regional time series JCHI was developed for the region encompassing 15 grid points. An average series VCHI for the region making use of 32 stations was developed form the data in [4]

The reference Koomanoff et al 1988 is available online here , but really wasn’t much help.

I located the Chinese data partly by chance. While I was browsing information from GHCN v1 (NDP041) in connection with Russia, I noticed the following mention on page 41 to a Chinese data set that looked like it might be connected to Jones.

I googled “60-station China temperature” and starting hitting paydirt thruugh two related references – TR055 and data set NDP039. NDP039 contains two Chinese networks – one with 60 sites (updated in 1997 to 65 sites) and one with 205 sites. Is it possible that Jones used a different 260-series Chinese network obtained at the same time. Impossible. This has to be the source of the Jones data. A 1997 report obtained by googling stated:

The first data product, Two Long-Term Instrumental Climatic Data Bases of the PRC (Tao et al, 1991, 1997) was published initially in 1991 and updated in 1997. This numeric data package (NDP) now includes data through 1993. The Institute of Atmospheric Physics(IAP) of the CAS provided records from 367 stations, partitioned into two networks of 65 and 205 stations….

NDP039 is online here and contains some extremely important statements that are relevant for evaluation of the QC. Recall that Jones et al 1990 stated:

The stations were selected on the basis of station history; we selected those with few, if any changes in instrumentation, location or observation times.

NDP039 says of the 205-station network:

Unfortunately, station histories are not currently available for any of the stations in the 205-station network; therefore, details regarding instrumentation, collection methods, changes in station location or observing times, and official data sources are not known.

NDP039 to its credit provides access to individual station histories for 59 stations in the 65-site network. Links from this page go to photocopies of the original station history (Excellent!). Gaps for each station in the 65-station network (and they are not inconsiderable) are listed here.

NDP039 stated:

Few station records included in the PRC data sets can be considered truly homogeneous. Even the best stations were subject to minor relocations or changes in observing times, and many have undoubtedly experienced large increases in urbanization. Fortunately, for 59 of the stations in the 65-station network, station histories (see Table 1) are available to assist in proper interpretation of trends or jumps in the data; however, station histories for the 205-station network are not available. In addition, examination of the data from the 65-station data set has uncovered evidence of several undocumented station moves (Sects. 6 and 10).

They provide the following caveat (in red bold letters)

Only a portion of the questionable values found in the data have been thoroughly researched and edited via communication with CAS. Most questionable values have been left intact and flagged (Sects. 6 and 9) so that the user may determine how to treat them. Users are strongly advised to check data values for associated flags that may have been assigned. Not doing so may to lead to spurious analytical results.

Station listings are here . Station data is in the same directory.

65 Site Network

Jones et al 1990 contains the following chicken scratch map of site locations:jones_40.gif
Locations of Jones China network.

I plotted up the locations of the 65-site network (for which there are station histories) obtaining the following.
A listing of sites collated from the CDIAC information together with 1 km by 1 km populations (kindly provided by D Linton) is in china2.xls Warwick Hughes will probably be able to identify these locations with WMO site numbers. I’ve looked in detail at many of the locations and have been unable to locate Jones sites in the 65-site network with station histories and thus come from the 205-station network lacking histories. The conclusion that many of the sites lack station histories is unavoidable.
Locations of 65-site network.


  1. D. F. Linton
    Posted Feb 27, 2007 at 10:03 AM | Permalink

    We are licensees of the LandScan 2005 database ( This gives the world’s population on a 30-arcsecond (roughly 1 km by 1 km) grid. I extracted the population of the grid cells containing your 270 sites.

    Here are the station ids and population per grid cell:

    id Population
    46 826
    70 2533
    15 13
    52 2
    56 6986
    10 1134
    137 864
    51 6284
    32 35
    63 3810
    24 288
    68 57
    22 965
    142 2060
    20 2735
    173 16449
    3 1
    197 2164
    90 1907
    29 1
    18 27783
    154 2118
    44 384
    80 236
    85 21898
    31 24227
    168 18701
    76 4009
    43 815
    37 3
    125 126
    23 6011
    89 1
    204 269
    49 1017
    51 405
    33 3
    191 6
    91 400
    180 38
    11 876
    174 4324
    40 3
    74 1174
    31 340
    75 1671
    29 543
    146 3060
    45 2018
    188 1164
    9 352
    77 6605
    172 4573
    56 41364
    87 201
    30 1
    169 5364
    110 46
    100 541
    39 1382
    42 1358
    189 208
    192 6
    61 2724
    167 193
    198 161
    46 3823
    5 3473
    203 1245
    65 6523
    1 3962
    7 79
    8 13422
    20 12
    52 28216
    40 113
    170 10388
    177 4246
    131 125
    163 31341
    199 16669
    96 2763
    17 1637
    6 20
    27 387
    50 41
    179 1234
    185 1074
    13 13606
    123 188
    92 709
    2 1179
    1 0
    176 4809
    67 46
    160 24006
    187 6217
    62 390
    25 25279
    18 94
    128 3025
    147 1848
    81 6550
    9 2528
    181 1855
    119 9
    14 8723
    98 2598
    32 4
    23 22920
    5 49
    88 1889
    36 34240
    22 4806
    95 5666
    12 5532
    139 11872
    27 3
    120 148
    34 2
    118 2384
    103 1
    184 13890
    193 1352
    122 2702
    127 8
    162 2257
    83 2
    71 2474
    43 14539
    99 11150
    115 5
    58 20
    200 57
    166 366
    133 15666
    132 1672
    28 0
    106 19
    156 1036
    195 1061
    130 4987
    114 1915
    36 499
    107 323
    17 13756
    101 170
    53 820
    145 27028
    49 38306
    63 3859
    190 13168
    175 4258
    134 574
    148 7590
    2 2612
    117 20
    53 18
    126 58
    149 155
    64 5848
    113 12
    186 11515
    129 4
    109 674
    12 1016
    28 0
    35 1
    26 5921
    48 5128
    65 6501
    205 510
    4 57
    19 2505
    104 0
    54 1200
    93 159
    25 12
    26 730
    50 6851
    62 3868
    194 4129
    158 26091
    183 1740
    19 12432
    202 89636
    171 6254
    157 383
    73 1923
    112 2
    47 5
    164 252
    69 2354
    144 5474
    4 94
    16 1194
    16 508
    35 13
    21 18658
    37 1261
    38 29
    13 5448
    82 7
    72 7332
    44 81
    151 1
    182 3382
    21 89
    48 2218
    41 18
    136 7899
    79 6823
    94 5941
    124 1774
    55 2838
    55 42003
    108 3081
    161 642
    42 2676
    178 8269
    7 8903
    54 56
    60 16642
    59 1855
    38 15615
    111 7
    34 9084
    102 80
    135 8145
    86 430
    165 1929
    60 12603
    57 54
    11 18501
    141 5393
    3 635
    47 9120
    116 5896
    61 1709
    59 2385
    78 3862
    24 9955
    97 729
    30 0
    6 75
    121 741
    41 1279
    8 3756
    159 2694
    14 92
    57 23516
    152 5455
    153 137
    150 9872
    15 29
    201 18924
    33 621
    66 453
    138 118
    105 4
    84 15317
    10 67
    196 10277
    64 3850
    39 2472
    45 498
    58 6981
    143 1213
    140 1264
    155 1289

  2. John Hekman
    Posted Feb 27, 2007 at 10:38 AM | Permalink

    Steve: very interesting work.\
    D.F. Linton: very cool.

    Steve, with the testimony you have given in Washington, is there anyone you are talking to about the need to have some research funded that is not inside the Hockey Team Lockbox? I am thinking mainly of a new look at surface temps. The only thing that keeps us from being in a total fantasyland is that the satellite temps are not in the hands of the Hockey Team. As the satellite and surface measures continue to diverge, we will be separated into two camps, each relying on different measures of the “truth.” Given the insanity of this whole issue, that is the best we can hope for at present–that there are two sides.

    But the non-HT side needs to have a better measure of surface temps. One that uses data that are available for examination and has been researched for surface instument reliability.

    Do you know if this is even being talked about in Washington?

  3. Steve McIntyre
    Posted Feb 27, 2007 at 10:43 AM | Permalink

    #1. Thanks. I’ve input that data into my spread sheet and re-uploaded it. There are definitely some small population sites in the list, which is good.

  4. Jean S
    Posted Feb 27, 2007 at 11:09 AM | Permalink

    Steve, I suppose you noticed the paper reprinted in the Appendix D of the linked document NDP-039:

    Wang et al: Urban heat islands in China, Geophysical Research Letters, Volume 17, Issue 13, p. 2377-2380, 1990.

    Amazing… tried to google it with only few results … I suppose it does not exist in the Team World.
    Also notice that Karl is the last author of both Wang et al (1990) and Jones et al (1990) 🙂

  5. Brooks Hurd
    Posted Feb 27, 2007 at 11:18 AM | Permalink


    These stations are located in eastern China where the world’s highest rate of urban growth has occured over the past century. The rate has accellerated since Mao’s death. People say that if you only go to China once a year, that you will not recognize it. This is not far from the truth.

    When I was in Shanghai in September and October last year, it was my first trip since 1998. The city had expanded in 3 dimensions to such an extent that it was mind boggling. In 1998 the population estimate was 12 million people. Today, I heard estimates ranging from 15 to 19 million inhabitants. I believe that the actual population is closer to the high end than to the low end of these estimates. Any census badly underestimates populations by the time it is published.

    By selecting stations in eastern China, I am certain that Jones has built into his data a large amount of urban growth. Jones’ claim that the station data is homogeneous flies in the face of the reality of growth in eastern China.

  6. Brooks Hurd
    Posted Feb 27, 2007 at 11:31 AM | Permalink

    Re: 1
    D. F. Linton,
    Is it possible to increase the area included around each station? The reason that I am asking is that some stations may appear to be rural at a 30 arc second resolution but may in fact be in a rice field adjacent to a city and thus be within the UHI.

  7. John Hekman
    Posted Feb 27, 2007 at 11:55 AM | Permalink

    I think what is needed is to take a statistically valid sample of temp stations and do an in-depth and site-visit analysis of them to see if they can be relied on to measure 100 years of temp history. Roy Spenser did something like this in his article about the California Central Valley and the effects of land use on temp measurements.

  8. MarkR
    Posted Feb 27, 2007 at 12:08 PM | Permalink

    #6 Brooks Hurd. May I suggest that if all else fails, google earth will show the nature of the surrounding area for a given set of co-ordinates?

  9. nanny_govt_sucks
    Posted Feb 27, 2007 at 12:19 PM | Permalink

    Selecting Russia and China as areas with homogenous temperature records just repeats a theme of a kind of distain for all things American or at least Western. Jones appears to be unable to resist slapping the West in the face with another veiled bogus charge. It’s the horrid emissions, or the depraved record keeping – take your pick – that makes the West “evil”, in Jones’ eyes anyway. Only the Communists can do this stuff right.

  10. MarkW
    Posted Feb 27, 2007 at 12:27 PM | Permalink

    I think I have it figured out.
    Jones is defining lack of evidence that a station moved, as evidence that it did not.
    Therefore, the lack of a station history, is considered positive evidence that no changes occurred at that station.

  11. Steve Sadlov
    Posted Feb 27, 2007 at 12:39 PM | Permalink

    In general the sites are within the most heavily populated part of China with very few out in the desolate Western areas. Even in “rural” parts of the East, development patterns and population density would qualify as low income suburban by Western standards. In that part of the country, you are never far away from other people. Even in farming areas, the properties are 10s of an acre or less, sometimes much less, with a home (at least one home) on each plot. Very small scale farming is the norm in Eastern China. And industry is scattered throughout.

  12. JerryB
    Posted Feb 27, 2007 at 12:41 PM | Permalink

    Re #7,


    I am guessing that you might have meant Christy et al, rather
    than Roy Spencer, doing the California study.

    See abstract

  13. Steve Sadlov
    Posted Feb 27, 2007 at 12:41 PM | Permalink

    RE: #5 – And nearby Shanghai, in a so called “rural” area, look at what has happened with Kun Shan since the early 1990s! That is but one specific example.

  14. Brooks Hurd
    Posted Feb 27, 2007 at 12:53 PM | Permalink

    Re 8, Mark R

    I eyeballed the locations with MapBar, the Chinese map web site. Goggle Maps does not have much detail in for most of east Asia.

  15. pj
    Posted Feb 27, 2007 at 1:27 PM | Permalink


    You are doing great work on this site. The surface record has bothered me for a number of years, though I haven’t had the wherewithal
    to look into it in great depth. This kind of work has been sorely needed within the climate science community. It’s really a shame
    that it has to come from without. Keep it up.

  16. jae
    Posted Feb 27, 2007 at 4:06 PM | Permalink

    Good article over at Climate Science on problems with modeling even seasonal variations. There is a link to one of Pielke’s papers that discusses temperature measurement problems.

  17. MarkW
    Posted Feb 27, 2007 at 4:57 PM | Permalink

    In other words, unless someone can prove that the data is bad, Jones accepts it as good.

  18. Shoes
    Posted Feb 27, 2007 at 8:38 PM | Permalink


    A comment and a question. First, it is well past time that surface temperature record receive the scrutiny that the rather more transparent ballon and MSU satellite data have received. Thank you for your efforts in this area despite the difficulties involved in recovering the raw data.

    Question. In this series of blogs about the Jones constuctions of surface temperature networks, has the point been that these results of rather dubious homogeneity been used to rejigger the US surface record? Or have I missed the point entirely. The US record has been highly problematic in that it has stubbornly refused to show a strong warming signal in the late 20th century vs that seen in the 1910-1950 time frame. Having it look more like the global records would be highly useful to some. This is beginning to look like malfeasance or willful delusion.

  19. Steve McIntyre
    Posted Feb 27, 2007 at 9:45 PM | Permalink

    #18. There’s a big difference between malfeasance and bias. Authors proposing that past temperatures be reduced justify this in terms of time-of-observation bias and they can support these arguments. The difficulty arises because the amount of the adjustment seems to be about the same size as the effect being measured. I get the impression that the authors are much more zealous in finding reasons to adjust past temperatures than the opposite and are too quick to accept very slight and weakly argued papers arguing against (say) UHI. I don’t think that it’s helpful to worry about malfeasance. This doesn’t appear to be a case where authors are withholding adverse verification statistics.

  20. Louis Hissink
    Posted Feb 28, 2007 at 1:28 AM | Permalink


    That means that adjusting the data for whatever reason is the same as the effect being measured to me means that adjustment for level makes a geochemical disappear explicitly means the geochemical anomalies are spurious. Real anomalies don’t disappear if some minor ajustments to levels is done.

    Thaaat means that the temperature anomalies (-.4K to +.4K) are simply random noise which will vary if “adjustments” are made.

    But the historical climate changes are real but the cause may not be temperature but the latitude of the anomaly varying over time.

    Whooops I just wrote a heresy. 🙂


    Louis Hissink

  21. Louis Hissink
    Posted Feb 28, 2007 at 4:53 AM | Permalink

    #20 – bit of an internet wizzle – the first sentence is missing anomaly ” ..that adjustment for level makes a geochemical ANOMALY disappear…”.


  22. Posted Feb 28, 2007 at 4:56 AM | Permalink

    #19 There’s a big difference between malfeasance and bias.

    How about using bristle cone pines after they were found to be un-reliable in an earlier paper?



  23. JP
    Posted Feb 28, 2007 at 7:09 AM | Permalink

    Means and standard deviations of each station’s respective monthly values were calculated. Values lying 3.5 or more standard deviations away from their respective means were flagged if not corroborated by temperatures at neighboring stations

    I understand perfectly why the temp, precip, cloud cover, and other quantifiable data would under go standard deviation
    checks, but weather conditions unlike some other types of data can have fluccuations due to geography. If they applied this procedures in Central Europe they could have big problems. Cloud cover and or fog due can keep one station in a “cold regime” relative to its adjacent stations for days on end. I’ve seen in Germany where a small drainage wind can clear stratiform clouds from one valley, while the next valley 3km away remains in 0/0 conditions. Even in the Great Plains, where the terrain in fairly even, snow cover can affect surface temperatures and cloud cover. I wonder if researchers appply thier QA checks with a intimate understanding of the local geography and all of its attendent quirks. By smoothing the instrument readings to this degree, the researchers are creating a climate picture that in the long run doesn’t exist.

    On the other hand, the data validation subroutines built into the short range forecast models for NOAA and the Air Force concentrate on obvious errors (wind gusts to 500mph, hour to hour temp increases over over 15 deg F, etc..) the observations are kicked back to the observer for correction or validation; if the station is automated and had preceived errors, the readings in question were given a reality check.

  24. Jeff Norman
    Posted Feb 28, 2007 at 7:30 AM | Permalink

    Re: #23,

    That reminds me of a story about the airport at Halifax. When they were looking to relocate the airport (to its current location) they tried to find a location with the clearest weather. Apparently fog was a problem at the old airport. After searching the area they selected a location 35 km from Halifax and began construction. After they cut down the trees the fog rolled in.

  25. Posted Feb 28, 2007 at 3:09 PM | Permalink

    I found this on Michael Crichton’s website from his 2005 testimony to the US Senate.

    For a person with a medical background, accustomed to this degree of rigor in research, the protocols of climate science appear considerably more relaxed. In climate science, it’s permissible for raw data to be “touched,” or modified, by many hands. Gaps in temperature and proxy records are filled in. Suspect values are deleted because a scientist deems them erroneous. A researcher may elect to use parts of existing records, ignoring other parts. But the fact that the data has been modified in so many ways inevitably raises the question of whether the results of a given study are wholly or partially caused by the modifications themselves.

    The “relaxed” approach to data handling seems to be in evidence here.

  26. Willis Eschenbach
    Posted Mar 1, 2007 at 5:21 AM | Permalink

    JP, I was taken aback by this one as well:

    Means and standard deviations of each station’s respective monthly values were calculated. Values lying 3.5 or more standard deviations away from their respective means were flagged if not corroborated by temperatures at neighboring stations.

    Given the cavalier handling of statistics by Jones and Mann in the Svalbard affair, this makes me nervous.

    First, and most important, standard estimators of standard deviation can gravely underestimate the standard deviation of a time series with either short or long term autocorrelation. This is particularly a problem with series with high Hurst coefficients, such as many climate measurement series. It is not unheard of for temperature series to have a true standard deviation which is three times as large as the estimate in the usual manner. Which, of course, means that if the SD is three times as large, the 3.5 standard deviation cutoff used by Jones is actually only a bit more than one standard deviation of variance when measured correctly.

    Second, in addition to assuming lack of autocorrelation, normal statistics assume a trendless dataset, which is certainly not true for climate data. This also increases the standard deviation, and must be accounted for separately.

    Third, many datasets contain bpth gaps and station changes. Estimates of means and standard deviations must be done carefully, and will certainly be less accurate.

    Fourth, I don’t know about this paper, but in the past Jones has used the standard deviation of a 30-year reference period (say 1951-1980) as his measuring stick. Depending on the characteristics of the 30 year period chosen, the results can be remarkably different, both in mean and standard deviation. This has a subtle effect when you are looking for evidence of UHI, which is that the stations removed will tend to be at the opposite end of the dataset from the period chosen. And in a period of generally rising temperatures, this method will selectively remove cold temperatures from the early part of the record. This is not a good strategy to use when minor differences in the trends are important.

    Finally, consider the numbers. Odds of greater than 3.5 standard deviation are .00046. Eighty-four stations, average record length say 60 years, 12 months in a year, that’s 28 months of perfectly good data that would be thrown out even if Jones’ numbers were right … but they’re not right, it’s much worse than that. IF the standard deviation is actually 1.5 times the incorrect estimate there will be over 1,000 months thrown out. And if the standard deviation is twice as large as estimated, which is not uncommon in temperature records, the number of (mostly cold) months wrongly excluded goes up to almost 5,000.

    All in all?

    Not a pretty picture …


  27. Posted Mar 4, 2007 at 1:05 PM | Permalink

    I have just put up a page on the Jones et al 1990 Eastern China region showing what can be readily learned about the identity of their 42 urban / rural station pairs.

  28. Steve McIntyre
    Posted Mar 4, 2007 at 3:38 PM | Permalink

    #27. Warwick, that’s great. I notice that many of the identified sites do not have a companion within both 1 degree lat and 1 degree long. I wonder if we’ll ever find out what series Jones used in this article. I haven’t received any acknowledgement to my request from Jones; I’ll file a complaint with Nature fairly soon if I don’t hear anything,

  29. Steve Sadlov
    Posted Mar 5, 2007 at 10:28 AM | Permalink

    RE: #27 – A number of them outside of China, especially quite a cluster in Bangladesh. Two on Taiwan, one right at Keelung (major port which started its boom after WW2) and another just south of the high tech mecca of Hsin Chu (a post 1970s development). Quite a cluster in the part of Japan which is visible, we all know what has happened to the “rural” areas of Soutwestern Japan since the 1950s (massive growth in industry and the residential developments to accomodate all its people).

  30. Steve McIntyre
    Posted Mar 12, 2007 at 10:13 PM | Permalink

    Zhou et al, 2004. Evidence for a significant urbanization effect on climate in China PNAS is not mentioned by Jones in AR4.

  31. Posted Mar 13, 2007 at 1:23 AM | Permalink


    Brohan et al:

    The studies finding a large urbanisation effect [Kalnay & Cai, 2003, Zhou et al., 2004] are based on comparison of observations with reanalyses, and assume that any difference is entirely due to biases in the observations. A comparison of HadCRUT data with the ERA- 40 reanalysis [Simmons et al., 2004] demonstrated that there were sizable biases in the reanalysis, so this assumption cannot be made, and the most reliable way to investigate possible urbanisation biases is to compare rural and urban station series.

    Zhou et al:

    It is impossible to find a corresponding rural station for most of the urban ones, especially in eastern and southern China. Consequently, if using the rural’€”urban difference to estimate the UHI, one possibly is comparing temperature between two different urban stations at regional scales or between two different regions at large scales.

    Jones 90:

    We assembled a network of 42 station pairs of rural and urban sites in the eastern half of China.

    They should just give up and admit that ‘we know nothing’..

    Simmons 2004 : Simmons, A. J., P. D. Jones, V. da Costa Bechtold, A. C. M. Beljaars, P. W. Kà¥llberg, S. Saarinen, S. M. Uppala, P. Viterbo, and N. Wedi (2004), Comparison of trends and low-frequency variability in CRU, ERA-40, and NCEP/NCAR analyses of surface air temperature , J. Geophys. Res., 109, D24115, doi:10.1029/2004JD005306.

6 Trackbacks

  1. By The FOI Myth #2 « Climate Audit on Dec 29, 2009 at 8:36 PM

    […] in April 2007 (See cache here). The release of this data confirmed my previous surmise (see CA here, Feb 22, 2007) that the following representation in Jones et al 1990 about the Chinese network was […]

  2. […] sites at climateaudit, noting that questions about these claims were raised here (for example, here here and here . Since then, we’ve also looked at adjustments in the USHCN, GHCN and GISS […]

  3. […] in essentie ook de kracht van Climategate is. Hoe de vork werkelijk in de steel zit, dat onthulden Steve McIntyre van Climateaudit en Douglas Keenan van Infomath, maar ook Warwick […]

  4. […] actions obtained this information. We discussed Jones et al 1990 in a number of posts. We observed here that Jones et al 1990 made untrue claims on the quality control for their Chinese network (the […]

  5. […] in Feb 2007, I re-examined Jones et al 1990 at CA, with my first discussion of the Chinese network here. See tag/china for posts related to this […]

  6. […] both raised at climateaudit. The Chinese station issue was discussed at climateaudit last February here where I said: Jones et al 1990 described their QC procedures as […]

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