GHCN V4 warming (updated 2/8)

In this post I investigate what has changed in global temperatures moving from GHCN-V3 to GHCN-V4, and in particular why V4 gives higher temperatures than V3  after 2000.

Significant increase in V4C global temperatures relative to V3C after 2000

Whenever a new temperature series is released it inevitably shows an increase in recent warming, forever edging closer to  CMIP5 models.   The Hiatus in warming as reported in AR5,  has now completely vanished following regular “updates” to the HadCRUT4 temperatures since 2012.  Simultaneously model predictions have been edging downwards through a process of “blending” them to better fit the data. Ocean surface temperature data are now joining in to play their part in this warming process. The new HADSST4 corrections produces ~0.1C more warming than HADSST3, and the main reason for this is simply a change in the definition of the measurement depth of floating buoys. No doubt a new HadCRUT5 is now in the pipeline to complete the job. Of course nature itself doesn’t care less about how we measure the global temperature,  and the climate remains what it is. It is just the ‘interpretation’ of measurement data that is changing with time and this process seems to always increase recent temperatures. The world is warming by 10ths of a degree overnight as each new iteration is published. Now I have discovered that the latest GHCN V4  station data is continuing this trend as identified in the previous post. I have looked more deeply into why.

GHCNV4 has far more stations (27410) than V3 (7280) but turns out to be a completely new independent dataset. It is not an evolution of V3 even though it is called V4. GHCNV4 is 85% based on GHCN-Daily which is an NCDC archive of daily weather station records from around the world. V4 has no direct ancestry to V3 at all. Even the station ID numbering has been radically changed from that used in V3, making it almost impossible to track down any changes in station measurement data between V3 and V4. Despite that, I decided  to dig down a bit further.

About a year ago I actually studied GHCN-Daily using a 3D icosahedral grid to integrate the daily anomalies into annual anomalies.  In the end I got almost exactly the same result as CRUTEM4 for recent years after 1950, which  also agreed with the then GCHN V3. That implies that the data were then aligned with the results of both CRU and V3C. So something else has changed since then when moving data from GHCN-Daily to GHCN-V4.

GHCN-Daily Annual temperatures compared to CRUTEM4 and major volcanic eruptions

So how is it possible that now V4 shows significantly more warming than V3 after 2002, when a year ago GHCN-Daily did not? Have the underlying station data been “corrected” yet again since V3C? To investigate this I used a convoluted method to identify only the V3 stations buried inside the V4 inventory by using their WMO IDs mapped through the GHCN-Daily directory. This procedure identified about half of  the 7280 versions of V3 stations, bearing in mind that V4 contains 24710 stations! The other half are not primary WMO stations. I then used my standard Spherical Triangulation algorithm to calculate annual global temperatures based only on these 3500 V4 versions of  V3 stations. If the underlying station temperatures were  the same as those in V3C then they should produce the same results as those from V3C.  Do they?

The results  are shown below.

Annual temperature anomalies for the full V4C, V3C and a restricted version of V4C using the same stations as those in V3C

Perhaps even more striking is the monthly agreement between the full V4C result and the V4 result restricted to 3500 V3 stations. The agreement is remarkably good. It should be compared to the V4 versus V3 comparison in the previous post.

Comparison of GHCN-V4C full result (27k stations) and V3 limited result (3500 stations).

So the answer to the question is no they do not agree with V3.  This must mean that the V4 versions of V3 station data are indeed different to those in the original V3 station data. So it is these changes that have caused the apparent increase in warming since 2004. The graphs above  show  that they are almost identical to the full  station results from V4C. It is also not true that somehow V4  has greater coverage in the Arctic and this can explain the increased warming over V3. The reason is simply that the underlying data have somehow been changed.

You get a different results from V4 and V3 even using the same station data.

Update 2/8

Nick Stokes has been looking into the same thing. He used V4 but then interpolated the V4 mesh nodes onto the V3 mesh for each  month, and then used the V3 mesh to calculate the global average. This can then compared to the standard V3. So he is using the same geometry as V3 but using all V4 stations, his idea being that it is the spatial distribution that matters not the underlying station data. He then found that V3 and V4 were indeed similar for the raw data (QCU) , but differed for the adjusted data (QCA). Since I had always used the adjusted data, I repeated the same analysis as described above using the raw (unadjusted) data for both V3 and V4 with identical stations.

Comparison of unadjusted annual anomalies from V3U and V4U limited to overlapping WMO stations.

So Nick is correct. The raw V4 data gives more similar values to the V3 raw data when restricted to the same stations. It is the adjusted data that show V4 warmer than V3, and since the mesh geometry is the same in both cases, it must be the V4 homogenisation that produces more warming after 2002.

The raw data for identical stations in V3 and V4 are essentially the same, but become warmer in V4 than in V3 once “adjusted”.

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15 Responses to GHCN V4 warming (updated 2/8)

  1. Nick Stokes says:

    Snap! My post is here. My conclusion is that it works like this. There are a few places where V4 has coverage and V3 had very little. Generally, at least in our methods, that means that with V3 those areas wre represented by sea values to a large extent. With V4 the land values prevail, and they are generally hotter.

    So it isn’t that V3 and V4 are saying different things. V3 wasn’t saying anything at all.

  2. John McLean says:

    Clive, I’ve just posted a comment to your 29 June 2019 blog piece about H3-H4 temperature differences. What I say there might be applicable here too.

  3. Nick Stokes says:

    You may have an equal number 3500 in each inventory. But were there an equal number of stations reporting? Corresponding ones? I don’t think over those years that anywhere near 3500 stations reported in GHCN V3 in any month.

  4. John Kennedy says:

    “The new HADSST4 corrections produces ~0.1C more warming than HADSST3, and the main reason for this is simply a change in the definition of the measurement depth of floating buoys.”

    Hi Clive,

    That’s not correct. The major differences between HadSST3 and 4 is that we have improved estimates of biases from ships making engine room measurements and that we have a better estimate of how many ships were using which methods and when.



    • Clive Best says:

      Hi John,

      I did read your paper. However ship measurement biases should affect the early era not the post 2000 era which still shows ~0.1C warming relative to HadSST3. How do you explain that?

      I was interpreting this statement probably erroneously:

      “In this paper, rather than specify that SSTs are estimated for a particular depth, we will instead use SST measurements from drifting buoys as our reference. This is conventionally reckoned equivalent to an SST measurement at an approximate depth of 20cm”

      whereas in HadSST3:

      “Movement of the (drifting) buoy and the action of waves mean that the measurement is representative of the upper 1m of the water column”



      • John Kennedy says:

        Hi Clive,

        Because the data set is an anomaly data set, changes to the ship data in the climatology period (1961-1990) affect anomalies calculated for drifters and other buoys.


        • Clive Best says:

          Thanks – that explains it.

          So your normals have reduced resulting in current anomalies based on buoys to increase. But of course the ocean temperature itself hasn’t increased at all. Perhaps someone should explain that to Greta Thunberg !

  5. Pingback: GHCN V4 warming – Climate Collections

  6. Olof R says:

    I think the main reason for the difference between GHCN v4 and v3 is that v4 has solved the arctic cooling bias.
    In v3 the PHA found the rapidly warming arctic stations suspicous, because they were so few, and adjusted them down ( from Svalbard eastward to Alaska).
    V4 has more Arctic stations supporting the rapid warming, which convinces the PHA that the warming is real, hence they are not adjusted..

    I believe the GHCN arctic cooling bias was first described here:

  7. climanrecon says:

    I’ve just noticed that GHCNV4 for Buenos Aires Observatory “unadjusted” data is significantly different to the same data for V3, with more recent warmth in V4 relative to V3, giving a difference in trends. The only legitimate explanation I can think of is that V3 is simply monthly averages given to it, but V4 has been derived from scratch from daily data. I’m now checking this hypothesis.

    • Clive Best says:

      Yes, it is very confusing. I am pretty sure that V3, CRUTEM4 etc have no direct connection to V4. V4 seems to be 75% based on GHCN-Daily monthly averages, but maybe not. When I made monthly averages of GHCN-Daily I got almost exactly the same result as HADCRUT4/V3

      green is HADCRUT4. So something has changed in the meantime.

      • climanrecon says:

        I’ve checked GHCNV4 for Buenos Aires Observatory, it matches the monthly average of (Tmax + Tmin)/2 from GHCN-Daily, at least for March 1954, and there is other evidence that that is the origin, such as that there is no difference between v3 and v4 before 1908, the start date of daily data.

        But that sudden change in v4 data, when daily data begins, is a mistake, because it introduces a distortion. For whatever reason, the v3 monthly averages were mostly underestimated by around 0.1 to 0.5C, so suddenly correcting them only from 1908 results in an overestimation of modern temperatures relative to times before 1908, for this particular station.

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