Wanderings of a Marseille January 1978 temperature, according to GHCN-M

This post will be updated from time to time as further GHCN-M data is archived.

Last updated December 17th 2015 (plots brought up to date to December 10th)

Temperatures quoted by NOAA and others are “what would have been measured in the past by systems in use today”, so it is not unreasonable for the numbers to change frequently.

It does however seem unreasonable that these past numbers should change quite drastically overnight, then in a short time again change quite drastically overnight, then in another short time …


Click the image (and later images) to view full-size

That green box shown early in 2015 corresponds to a sudden jump in the GHCN-M adjusted value for January 1978, from 5.78°C on January 5th 2015 to 6.41°C on January 6th 2015, followed by a similar drop, from 6.49°C on February 9th 2015 to 5.77°C on February 10th 2015. Note how the January 1978 temperature changes frequently in GHCN-M v3, even overnight, and that the changes may include correction for both “urban heating” and “urban cooling”, seen when the adjusted value become greater than or less than the unadjusted value [My understanding is that Pairwise Homogeneity Adjustment may also include adjustment for urban heating/cooling as well as instrument changes, relocation, TOBS correction, etc]. Anyone willing to suggest station relocations take place this frequently? Problems with Pairwise Homogeneity Adjustment seem more likely.

As adding values for the recently introduced GHCN-M v4 beta has made recent data points more difficult to distinguish above, here is a similar plot, but showing data only from the introduction of the current GHCN-M v3 version, v3.3.0 onward:


While v3.3.0 had fairly stable adjustments, these become more volatile with v4 beta. The unadjusted data is also slightly lowered in value.

This behaviour is not a peculiarity related to the January 1978 temperature. The other 1978 monthly temperatures behave similarly, as seen below (each month is shifted slightly down, in a different colour, for visibility – without this offset all would coincide)


This post examines the behaviour of GHCN-M adjustments for past temperatures at Marseille and nearby (in the climate data sense) stations, using saved GHCN-M data sets. The choice of Marseille arises from a blog post LE GISS ET LES SÉRIES LONGUES DE TEMPÉRATURES. I have seen similar behaviour closer to home with past data for Irish stations, but illustration using an Irish station would have restricted the choice of nearby stations to a generally easterly direction, while choice of Marseille provides nearby stations around the compass in France, Spain, Italy and Switzerland. A supplementary post with examples of stations in other countries with similar “volatile” adjustments can be found at GHCN-M: Stations similar to Marseille/Marignane.

It may help to visualise the changing adjustments by looking at the actual adjusted temperature records for a few dates:


and focusing on the recent years:


The dates chosen are January 5th and 9th 2015 (see green box above), and the two dates exhibiting the extremes of the range of adjustments under GHCN-M v3.x.x (or at least the extremes of the data I have archived). The unadjusted GHCN-M v3 record is also shown, as well as the trends (°C per decade) from 1980 and from 1966. I have also marked the range of adjusted values for 1978 and 1880.

There is no special reason for choosing the past temperature values for January 1978 – I simply took a year from the plotted temperature records for Marseille in that blog post, and used that year for other nearby stations as well. I chose January as the first month in each record, along with December easiest to locate when performing manual checks. In retrospect, a later month in the year might have been a better choice – working through stations I noticed some without January and/or December values. I’ll look at these again later more carefully. My guess now is that these may be stations not manned in winter. I have pointed out to the owners of that blog (the post itself did not provide an opportunity to add comments) that GISS are using the adjusted GHCN-M data as input, and that this, rather than the Gistemp processing, may be the source of the variations discussed in that blog post. Whether this choice of input is wise is another question. MarseilleJan78inJan15 MarseilleJan78inFeb15 Recent values do not display this instability. January 2015 for example remains 7.60°C in GHCN-M adjusted files from February 10th, the date the value first became available, until now. It will be interesting to see whether version 3.0.0 has reduced this instability in past adjusted values or not. As these jumps seem to occur in the first part of the month, I will update here around mid-July. In both cases, the second day is the day an additional item of raw data becomes available. These additional items are not outliers for Marseille. On January 6th the value for December 2014, 8.90°C, arrives; on February 12th, the value for January 2015, 7.60°C. 8.90°C is 0.86 standard deviation units above the mean of December values, 7.60°C is 0.57 standard deviation units above the mean of January values. Relatively few values for other stations are added or changed  on either of these dates. (discussion of these added or changed values coming) One noticeable feature of the adjusted data is that values are missing from May 1970 to March 1971, although values for these months are present in the unadjusted data. With GHCN-M v3.0.0 these values have quality control flag X (= pairwise algorithm removed the value because of too many inhomogeneities). This information was not shown for previous GHCN-M versions. The temperature values in the GHCN-M data sets are shown and discussed here, rather than anomalies. The changing base means which would be subtracted to calculate anomalies are tabulated below for the four dates discussed and for four base periods.

ghcnm.tavg.v3.2.2. 1951-1980 1961-1990 1971-2000 1981-2010
20150105.qca.dat 14.3 14.4 14.8 15.2
20150106.qca.dat 14.9 15.1 15.3 15.4
20150209.qca.dat 15.0 15.1 15.3 15.4
20150210.qca.dat 14.2 14.4 14.8 15.2

Abrupt overnight changes are not simply an artifact of one station, Marseille. (Gistemp values will be added to the images below. Only the first, Marseille, image shows these for now. The data for v3.0.0 which I discovered I had retained will also be added to all images) MarseilleNeighbours The period 1971-1980 is missing from the Salon record, which is not shown in the GISS list above. Toulon has stable adjusted values. Mont Ventoux has data from 1949 to 1968 only. Nimes/Courbes has stable adjusted values, Montpellier is missing data from 1898 to 2000. Nice has stable adjusted values. Mont Aigoual is the closest station displaying jumps in the Pairwise Homogeneity adjusted values for January 1978. (More comments coming here and below between station images) Aigoual
Mont Aigoual shows different behaviour for v4 beta when compared with v3.3.0 − v3.3.0 remained unadjusted. v4 beta has a lower unadjusted value, is now adjusted, and these adjustments are moderately volatile.
Torino/Bric does not appear in the GISS list above as it is dropped during Gistemp processing. I have included it here as it shows a station which is generally unadjusted, but with two dates where an adjusted value departs from the unadjusted value. This would not seem unreasonable behaviour for an automated adjustment process – two “glitches” among more than 500 data sets examined here. There are also more than 160 dates on which an adjusted value for January 1978 is missing. TorinoBLyonMontseny
In v4 beta the January unadjusted temperature for January 1978 appears to have been adjusted down. Later months show smaller changes.
StBernardCh GenovaS
Genova Sestri appears to have been dropped in v4 beta, replaced by Genova UniversityAjaccio
Ajaccio also appears to have been dropped in v4 beta
ToulouseB GeneveC
Milano/Malpensa was unadjusted in v3.x.x, but is now adjusted in v4 beta.

Alghero is missing January 1978 values, so December 1978 values are shown below instead. v4 beta values are shown, but these are for Alghero Fertilia, which may not be the same station as in v3.x.x, although nearby.
Alghero MilanoL
Milano/Linate appears to have been dropped in v4 beta

Pisa has missing January values for 1978 for some 2014 and 2015 datasets, but these values are not missing in later 1978 months. PisaJan PisaFebPisaMar


and going further than the GISS list above, since Pairwise Homogenization uses more stations:  GHCN-M: Stations near Marseille/Marignane but beyond the GISS list

For anyone wondering about the possible effects of metadata location errors, this post is concerned with GHCN Pairwise Homogeneity adjustments rather than Gistemp adjustments. There are location errors in the metadata for some of the stations shown above. A commenter called Harry at another blog wrote:

I found the computer code for the Pairwise Homogeneity Adjustment (PHA) algorithm they use. It is on the NOAA website

My response to that was:

The code on the NOAA website appears to be v3.0.0, not the code currently used. I was tempted to download and run this code to try to determine the cause of these erratic adjustments, but thought better of it in the absence of current code. Having downloaded and recoded the Gistemp code with additional diagnostic output, I am aware of the scale of such an undertaking. It may come as a surprise to Harry to find that some of us have “had the energy” to do this, and have contributed by notifying GISS of bugs found in their code – another good reason for making the code available. You can verify that I have done so by looking for my name at http://data.giss.nasa.gov/gistemp/updates/

So I cannot comment on any possible effect of location errors on PHA.  Here are two of the location errors illustrated (green pushpin corrected, yellow pushpin GHCN-M metadata). In this case these two errors have minimal effect on Gistemp. Even though Marseille/Marignane by its very name confirms that it cannot previously have been located at the GHCN-M location, in the 4th or 5th arondissement of Marseille, the nightime luminance at both locations is clearly urban. Similarly, although it would literally require a mountain to be moved to relocate the Puy de Dôme station at the GHCN-M location, both locations are clearly rural (and for anyone still in doubt I can confirm that I have stood beside the  Puy de Dôme station, GPS in hand, and found no evidence of such a move!)MarseilleLocation


Note that this post is based on GHCN-M v3.x.x, not the recent Karl et al paper. For anyone who may want to know who is involved with both, I’ve indicated common authorship below. Karl et al uses the same Pairwise Homogenization for land temperatures, but with many additional station records. GHCNM (version 3): J. H. Lawrimore, M. J. Menne, B. E. Gleason, C. N. Williams, D. B. Wuertz, R. S. Vose, and J. Rennie (2011), An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3, J. Geophys. Res., 116, D19121, doi:10.1029/2011JD016187. Possible artifacts of data biases in the recent global surface warming hiatus Thomas R. Karl, Anthony Arguez, Boyin Huang, Jay H. Lawrimore, James R. McMahon, Matthew J. Menne, Thomas C. Peterson, Russell S. Vose, Huai-Min Zhang

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13 Responses to Wanderings of a Marseille January 1978 temperature, according to GHCN-M

  1. Pingback: GHCN-M: Stations similar to Marseille/Marignane | Peter O'Neill's Blog

  2. Pingback: GHCN-M: Stations near Marseille/Marignane but beyond the GISS list | Peter O'Neill's Blog

  3. Pingback: GHCN-M: Stations similar to Marseille/Marignane: (2xx) Asia | Peter O'Neill's Blog

  4. Pingback: GHCN-M: Stations similar to Marseille/Marignane: (3xx) South America | Peter O'Neill's Blog

  5. Pingback: GHCN-M: Stations similar to Marseille/Marignane: (4xx) Central and North America | Peter O'Neill's Blog

  6. Pingback: GHCN-M: Stations similar to Marseille/Marignane: (5xx) Pacific and Australasia | Peter O'Neill's Blog

  7. Pingback: GHCN-M: Stations similar to Marseille/Marignane: (6xx) Europe and (7xx) Antarctica | Peter O'Neill's Blog

  8. Pingback: GHCN-M adjustment samples: (7xx) Antarctica | Peter O'Neill's Blog

  9. Pingback: Instability of GHCN adjustment algorithm | Climate Scepticism

  10. Pingback: More on the Bombshell David Rose Article: Instability in the Global Historical Climate Network | Watts Up With That?

  11. Pingback: Mehr zum ein­schlägigen Artikel von David Rose: Insta­bilität im Global Historical Climate Network GHCN – EIKE – Europäisches Institut für Klima & Energie

  12. Nick Stokes says:

    I have done a spatial analysis comparing two times about 20 months apart, in a post here. There is a histogram by station (of RMS difference) and a sphere map with color according to RMS. There is a very strong association of flutter with remore location, althoug the US also has quite a lot. About half stations have none or very little.

  13. Steven Mosher says:

    “[My understanding is that Pairwise Homogeneity Adjustment may also include adjustment for urban heating/cooling as well as instrument changes, relocation, TOBS correction, etc]. Anyone willing to suggest station relocations take place this frequently? Problems with Pairwise Homogeneity Adjustment seem more likely.


    Pha is purely STATISTICAL

    In the past NOAA used to do adjustments in a discrete manner

    1. An adjustment for station moves
    2. an adjustment for TOBS
    3. And adjustment for instruments

    No adjustment for UHI

    Pha takes a different approach to attack the problem of missing or innacurate metadata.

    So. TOBS is still applied ( but tests show that this isnt necessary ) and then all stations are compared to their neighbors. What you are looking for is a solution that minimizes the divergence of stations one to another. basically you dont need any metadata ( but it performs better with metadata.

    You could think of it as annealing where stations are continually adjusted as new data comes in.

    As Nick Stokes notes Remote stations will be most fickle ( flutter ) . Thats because they have the most distance neighbors.

    Remember, this approach to adjustment is not Intended to get individual stations “more correct”
    It is intended to reduce the Overall or global impact of bias.

    If you want to get each and every individual more correct, then you work bottoms up, with as much local data and history as you can get. But then you have Human hands and human eyes and human choice involved.

    Many NWS take this approach where they painstakenly adjust individual stations to try to get each one correct. take CET as an example. The issue here is consistency accross countries with one country doing its adjustments one way and another doing it another way,

    CRU takes the position that these NWS products are superior because of local knowledge.
    Skeptics complain that the humans involved have their thumbs on the scale. So CRU uses these NWS created series.

    NOAA ( and BEST ) take an entirely diffrent approach. Its a top down data driven approach that lets an algorithm decide which data is out of wack when compared to its neighbors. Since the input data changes month to month and day to day, you will see some station adjustments that Never change and some station adjustments that “flutter”. The goal of a top down approach is NOT to get every station correct, but rather to bring the entire collection to a more consistent (less bias) estimation of the past.

    Unless you fully understand the difference between the bottom up discrete approach ( make a change for TOBS, make a change for station moves, make a change for instrument switches ) and a top down approach— Adjust to minimize overall bias, you really cant understand what you are seeing,

    Pha code has been posted for years. Along with the tests showing how it reduces bias.

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