Through Giant Green Goggles – the An Taisce edition

Green goggles are somewhat akin to beer goggles, but the wearer sees what he or she wants to see rather than reality. A frequent synptom of the green goggles wearer is a willingness to accept at face value and redistribute misinformation without a thought as to whether the “information” in question defies common sense. The “timeline” below, circulated widely, illustrates the phenomenon with misinformation at almost every timestep. This blog post dissects it for you by allowing you to compare the “predictions” in this timeline with the claimed original sources.

Update (November 22, 2016): You may notice that the link below for the Copenhagen Diagnosis report is to an Australian university rather than to the Copenhagen Diagnosis website. This is the link provided at http://www.copenhagendiagnosis.org/press.html , this page having now replaced the old Copenhagen Diagnosis homepage.

This post is a reworking of an earlier post, with some added material, drawing attention in particular to the use of a very misleading article by An Taisce. Not, unfortunately, an isolated aberration on the part of An Taisce.

ATezineA

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GHCN-M Raw Data from Ireland

(now subtitled) The Little Known Tropical Rain-forest of Ireland

Updated:

From the latest two GHCN-M v3 status.txt files:

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

03/11/2017

User feedback indicated a problem with some mean temperature data for select stations in Ireland.  The problems were traced to a particular data source (MCDW), and for the time being until that source is corrected, the data are now being sourced to the UK Met Office “Climat” data (“K” source flag”), which are believed to be the correct values.  The data changeover to the UK Met Office has occurred, but the source flag (“K”) for the corrected values was inadvertently left out.  Those source flags should be added within the next production cycle.

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“select” stations in Ireland meaning those stations for which GHCN-M v3 has continued to include data in recent years. Although how NOAA can be confident that the problem is confined to stations in Ireland without discovering the cause of these errors in MCDW (one of their own products) is something which escapes me.

Normally you might expect that some care would be taken to get the correction right. Not however here. They have botched it again, leaving many rogue values unchanged. Some, but not all of these are flagged as probably erroneous and to be omitted from further analysis. Others however pass their quality control. And they have not reverted to the (correct) values which they had earlier shown as received from the UK Met Office.

One absurd record caught my eye. Cork Airport in 2013 has been corrected, but in 2014 still has six identical rogue values including July, August and December. Five of these are flagged, but August still slips through as the rogue value of 10.4° slips through as not sufficiently outlying to be caught by their quality control. I asked myself how probable six identical monthly means would be. Not impossible for a tropical rain-forest climate zone I thought. Singapore for example has monthly means of daily mean temperatures lying throughout the year within a range of less than 2°C, but could not match the record six identical monthly means of Cork Airport. Examining the complete GHCN-M v3 raw data file I found 24 stations with  six or more identical monthly means, all well within the tropics. So book your next holiday in the tropical rain-forest climatic zone of Cork. Just beware of the crocodiles, and be aware that no guarantee is given that Cork will match the temperatures of the other 24 stations.

Their quality control procedures allow for manual flagging of erroneous values not caught by their automated procedure. Somehow I think it would have been prudent to take more care having admitted their own MCDW values were wrong, and if necessary resort to manual flagging until the cause of this MCDW problem had been determined and corrected.

END OF UPDATE

corkcomposite

Errors are not confined to 2013 to 2016, and not confined to this one station.

You can easily verify this. The Met Eireann most recent four year monthly data can be found at Monthly Data

The longer (not necessarily full length record however) data can be found at Historical Data  (I’ll return to add navigation advice here when I have completed other sections of this post. Navigation on this section of the Met Eireann site may not be intuitively obvious)

The GHCN-M version used above was ghcnm.tavg.v3.3.0.20161230.qcu.dat (which of course had not had a December 2016 value added, whereas Met Eireann calculates and shows a month-to-date mean, 7.4°C up to December 30th)

Station history

As shown below, the correct 2013 values were shown by GHCN-M for a time in 2013, corrupted for a time later in 2013, and briefly reappeared again in 2014, before settling down again as corrupted values.

Now follow the history of the April 2013 value (7.4°C according to Met Eireann). In the first GHCN-M file below (dated May 19th 2013) it is correctly recorded, and attributed to a CLIMAT report as source (740  P). This value is the most recent value to reach GHCN-M, and in this case the CLIMAT report has been correctly decoded. I will return to this question of correct or incorrect decoding of CLIMAT reports below).

By July 9th the still correct value has as data source “received by the UK Met Office” (740  K). This has been the usual change of data source, first CLIMAT report, then the UK Met Office. As seen on May 19th the March 2013 value had already been processed in this way (430  K).

On (or before) the 8th November the data source changed to “Monthly Climatic Data of the World (MCDW) QC completed but value is not yet published” (1040 WC). The value had now become the rogue value 10.4°C. the “W” quality flag indicates “monthly value is duplicated from the previous month, based upon regional and spatial criteria”. My experience of my region would suggest that duplicating the mean temperature of the previous month would very rarely produce a correct estimate for the following month. What “regional and spatial criteria” have required the replacement of a recorded monthly mean my a rogue value?

After that this rogue value has been retained, except for a brief return to the correct value and UK Met Office as source on (and possibly around) June 28th 2014. In mid 2015 the data source changed to “Final (Published) Monthly Climatic Data of the World (MCDW)”.

corkcomposite2013

 

Each monthly value above is followed by either one or two letters. A single letter, or the second of two letters, gives the data source:

C = Monthly Climatic Data of the World (MCDW) QC completed but value is not yet published
K = received by the UK Met Office
M = Final (Published) Monthly Climatic Data of the World (MCDW)
P = CLIMAT (Data transmitted over the GTS, not yet fully processed for the MCDW)
W = World Weather Records (WWR), 9th series 1991 through 2000

The first letter of two letters is a quality control flag:

S = monthly value has failed spatial consistency check. Any value found to be between 2.5 and 5.0 bi-weight standard deviations from the bi-weight mean, is more closely scrutinized by examining the 5 closest neighbors (not to exceed 500.0 km) and determine their associated distribution of respective z-scores.  At least one of the neighbor stations must have a z score with the same sign as the target and its z-score must be greater than or equal to the z-score listed in column B, where column B is expressed as a function of the target z-score ranges (column A). See GHCN-M README for table.
W = monthly value is duplicated from the previous month, based upon regional and spatial criteria and is only applied from the year 2000 to the present.

CLIMAT reports

to be added

As noted above, the April 2013 value for Cork Airport was initially decoded correctly from a CLIMAT report. But this correct decoding has not always been the case.

valentia2012a

 

 

When Valentia Observatory (62103953000) became an AWS station in April 2012 the first April values entering GHCN-M from decoded CLIMAT reports were rogue values

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An Taisce’s Dahr Jamail timeline : source comparison

This is a short comparison of the “timeline” published by An Taisce in its January 2014 ezine with the sources claimed for these “timeline” items. For a more exhaustive (and exhausting!) treatment of this “timeline” see Through Giant Green Goggles

Update (November 22, 2016): You may notice that the link below for the Copenhagen Diagnosis report is to an Australian university rather than to the Copenhagen Diagnosis website. This is the link provided at http://www.copenhagendiagnosis.org/press.html , this page having now replaced the old Copenhagen Diagnosis homepage.

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GHCN and Gistemp Data Quality

Post to be completed when the post Self-adjusting the Adjustments is complete. Posted now as placeholder to allow linkage from previous post.

Location metadata

 

Temperature data

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Self-adjusting the Adjustments

This post is unfinished. I’m posting it now in order to refer to it in a comment on another blog, and intend to finish it in the near future.

April 14: finishing delayed while recoding data extraction to speed it up and enable further exploration of the effects of large GHCN adjusted value ranges.

If the title of this post seems familiar you may be recalling a Climate Audit post from 2010 NASA GISS – Adjusting the Adjustments which suggested that:

It is entirely possible that the change in GISS US since August 2007 is primarily due to the replacement of USHCN v1 methodology (TOBS and that sort of thing that we discussed in the past) with Menne’s changepoint methodology used in USHCN v2

This present post examines the volatility of the adjustments due to that changepoint methodology in the Global Historical Climatology Network-Monthly (GHCN-M v3) and the U.S. Historical Climatology Network (USHCN v2), and in NASA Gistemp, which uses the adjusted GHCN-M data as input.

The Climate Audit post considered changes resulting from a major change in methodology. Here I examine the changes resulting from the “less major” (I hesitate to use the word “minor” in the absence of disclosure of the updated code) changes in methodology from subversion to subversion, and the day-to-day changes in adjusted station records using the same subversion – hence the change in title to “Self-adjusting the Adjustments”. And while the focus of the Climate Audit post was primarily on the US, my focus is primarily global, with only peripheral discussion of USHCN. Continue reading

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Urban corrections in Gistemp (and GHCN-M), “then and now”

Further content coming soon. This placeholder post published now to allow a link to the coming post to be included in an online comment.

Hansen et al. 1999 (Hansen, J., R. Ruedy, J. Glascoe, and M. Sato, 1999: GISS analysis of surface temperature change. J. Geophys. Res., 104, 30997-31022, doi:10.1029/1999JD900835) illustrates the Gistemp urban adjustment with two examples, Tokyo and Phoenix.

Hansen1999Fig3

Hansen1999Fig3caption

This post first of all updates these two examples. First consider recent data, Gistemp up to September 2015 (the “now”). In all plots below the values in the key show the temperature rise, in degrees C per decade for the corresponding data set. Data series based on GHCN-M v2 (the “then”) are shown as dotted lines:

TokyoGISS

TokyoAdjustments

Most of the adjustment comes from the adjusted GHCN-M data now used by Gistemp as input. Gistemp itself makes a smaller contribution to the overall adjustment.

PhoenixGISS

PhoenixAdjustments

In this case GHCN-M adjusted and Gistemp contribute roughly equally to the overall adjustment.

The overall adjustment for Tokyo, 16 years on, is somewhat less that Hansen et al. 1999 had shown, although they suggested that “The true nonclimatic warming in Tokyo may be even somewhat larger than suggested by Figure 3”, while that for Phoenix is almost the same 16 years on.

Now consider an older Gistemp data set, with data up to March 2009 (date chosen as the archived data is conveniently to hand on disk). In this case Gistemp was still using the unadjusted GHCN-M data as input.

TokyoGISS200903

TokyoAdjustments200903

PhoenixGISS200903

PhoenixAdjustments200903

The broken-line Gistemp adjustments are seen more clearly here, since the inclusion of the GHCN-M adjustments on the earlier plot tends to obscure the broken-line nature of that Gistemp adjustment.

Finally consider both dates plotted together:

TokyoGISSboth

TokyoAdjustmentsboth

PhoenixGISSboth

PhoenixAdjustmentsboth

We see

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GHCN-M adjustment samples: (7xx) Antarctica

For details of what is illustrated here, start from Wanderings of a Marseille January 1978 temperature, according to GHCN-M, continue with GHCN-M: Stations similar to Marseille/Marignane

Further discussion to come later once images have been generated and added for remaining regions.

700 Antarctica (34)

As data for some of these stations comes from both GHCN-M and SCAR, Gistemp can have two different values in the same month. Values derived from SCAR data are distinguished by use of a filled black triangle symbol. Stations which have no adjustments and SCAR stations which do not appear in the GHCN-M inventory are not shown here.

SANAE
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