Of Missing Temperatures and Filled-in Data (Part 2)

The previous post looked at the potential effect of missing months on station annual mean tempertures and anomaly values where one month of data was missing and showed in the last figure just what a high proportion of stations have at least one month missing per year in recent years.  Many stations, however, have much more than one month missing. Figure 1 shows the missing months in GHCN data from 1800.  There may have been very few stations reporting in the 19th Century, but up to 1875, those that are present in the record had more than 90% reporting rate. The record is 85-95% complete up to ~1960 then drops rapidly: currently only ~70% of station data is complete in each year. The rest? Oh dear.

Figure 1. Quantification of missing months in annual station data (analysis and graph: Andrew Chantrill).

Some 15-20% of stations now have one month missing per year; 5-8% have two months missing; 2.5% have four months missing; at least 1% have 5 months missing.  Remember that deriving an annual temperature requires at least six months of data (at least three seasonal means each composed of at least two months of data), and remember also that the number of reporting stations has dropped to around 1500 stations in recent years, so that is ~450 stations with at least one missing month in each year.  

Figure 2. Distribution of missing months by month and year (analysis and graph: Andrew Chantrill).
Fig. 2 shows how missing months are distributed across the each year.  Is is possible there is greater loss of Winter months, particularly in more recent years? Loss of cooler months in Northern latitudes – surely not? We showed previously that missing Winter months in stations with large temperature variations affect anomaly values more than missing summer months – lower accuracy of the data – again.

 

Now do I have to say it? If we are heading for cooler times, and colder winters, with lower or negative anomalies, how convenient that more Winter months are dropped.
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8 Responses to Of Missing Temperatures and Filled-in Data (Part 2)

  1. lucia says:

    But the January anomaly uses Jan. Feb uses feb etc. I don’t see how the pattern of loss would tend to bias.

  2. VJones says:

    Lucia, I can’t (and didn’t) say there is bias, only that there is greater inaccuracy. There is a possibility of bias though…

    Exploration of how the seasonal and annual means are calculated from anomalies (previous post, 25th Feb) showed that missing months had a larger effect (+ or – on the annual anomaly value) if there is a large temperature variation in the temperature data.

    Much of the warming signal in the global average data can be traced to Winter warming (lows are not as low). If we now have a series of cooler years, particularly cooler Winter months with lower lows, my concern is that missing months, particularly Winter months could lead to a warm bias. Conversely an unusually warm month missed would lead to a cool bias.

    I don’t like having missing data, no-one does I guess. My main point is that the inaccuracy of the global temperature calculation increases with missing months and this seems rarely to get a mention.

  3. Anonymous says:

    AGW is worse than we thought. Before its’ onset, people used to do their jobs. Now, even though temperatures are taken by network enabled machines, they can’t be reported. People’s jobs can no longer be done even if machines are doing them. Soon AGW will prevent you from cooking dinner. You claim there is a possibility of bias-I assure you all falls within the ensemble of th means. If useful data is missing, it will be created. Please stop this anti science nonsense.

  4. KevinS says:

    Anonymous

    Thank you for your post-normal science contribution to the debate on climate change

    “If useful data is missing, it will be created.”

  5. VJones says:

    Kevin,
    I think (hope) you’ve mis-read Anonymous’s comment. Perhaps s/he means us to stop our analysis, or perhaps it is an entreaty that we should help stop this ‘climate anti-science’ nonsense. The whole post sounds a bit tongue-in-cheek.

  6. Craig says:

    Reminds me of a typical golf scramble… the last team to turn in their scorecard seems to win a disproportionate amount of the time. If you were going to cook the books, wouldn’t you do it at the end of the year when you knew just how much cooking was needed? Also at the beginning if things were starting off too cool.

  7. VJones says:

    Craig,
    on the other hand it could be that January is started off with good intentions (don’t we all 😉 but that reporting falls by the wayside by the end of the year. Who knows? Sometimes it is just good to call what you see with the data.

  8. Anonymous says:

    Please accept my humble apologies for the above. sometimes I get really drunk and go to Tamino- with the result I lash out aimlessly. Please don’t stop-

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