GISS Temp for Jan 2012 - and TempLS

2012-02-15T08:57:00.001+11:00

TempLS showed a decline in global mean anomaly in January, 2012, from 0.35 °C to 0.219 °C. GISS showed a similar fall, from 0.45 °C to 0.36 °C. Time series graphs are shown here

As usual, I compared the previously posted TempLS distribution to the GISS plot. Unusually, there was a significant discrepancy, which turned out to be a kind of Y2012 issue in my code.
Update - on further checking the error was simple - I had plotted Jan 2011.

Here is GISS:

It's broadly similar to the one I posted here, but it gets the USA and N Africa quite wrong. So I went back to check. I was aware of the date issue, because my plot was dated Jan 2011, as I noted. I thought that was the only effect, but I was wrong (in fact, the whole plot was Jan 2011). When I re-ran, ,aking sure my script now detected the year as well as the month, I got:

This now matches well. The white blob far NW is where the cold runs off the color scale. The date error did not affect the monthly average number, only the plot.

Previous Months

December
November
October
September
August

More data and plots

January 2012 TempLS down 0.14°C

2012-02-12T08:34:00.002+11:00

The TempLS analysis, based on GHCNV3 land temperatures and the ERSST sea temps, showed a monthly average of 0.219°C, down from 0.35 °C in December. This echoes a greater drop in the satellite averages. There are more details at the latest temperature data page.

Below is the graph (lat/lon) of temperature distribution for January (It's Jan 2012, despite the title).
Update - despite my confidence here, it is actually Jan 2011 - a sort of Y2012 error (I had the year hard-coded). The correct plot, with GISS comparison, is here.

This is done with the GISS colors and temperature intervals, and as usual I'll post a comparison when GISS comes out. There was a cold region mid-Asia whic h goes outside the standard color range.

And here, from the data page, is the plot of the major indices for the last four months:

GHCN V3 homogeneity adjustments - revised data.

2012-02-11T17:14:00.004+11:00

The previous post was A study of GHCN V3 homogeneity adjustments, Commenter PaulM noted that GHCN had notified a change, with an apparent error in the data version I used. He suggested a recalc, and I think that needs to be done. So here it is. It uses almost the same code as before. There is an issue with a station called COLOSO which seems to be duplicated in the inventory. In the previous version, the unadjusted data file has data for both entries, the adjusted only for one. That required a fix. Now the adjusted file refers to both, so I had to unfix. It's not an issue for here, because the station has little data and falls below the 30-yr threshhold.

Below are the revised histograms and the map. PaulM noted that some of the larger adjustments were moderated - however, the mean adjustment to the trend increased significantly.

Update: The R code to download and generate a CSV file of trend diffs is here

Distribution of trend changes

Again there are three subgroups - stations that have histories with actual reports for at least

360 months (30 years)
540 months
720 months

Mean 0.031 °C/Decade

Mean 0.0302 °C/Decade

Mean 0.0283 °C/Decade

The histograms seem similar, but the means are about 50% higher.

The Google maps app

Details as previously. I didn't revise the discussion of extreme cases - CORONA NM comes down from about 8 C/Cen to about 6 C/Cen.

A study of GHCN V3 homogeneity adjustments.

2012-02-07T23:38:00.002+11:00

From time to time, bloggers discover that GHCN produces an adjusted temperature file, and are shocked to find that in the process, temperatures are altered. A noted example occurred in late 2009, when Willis Eschenbach became excited about GHCN V2 adjustments to the temperature at Darwin. He intoned sternly:

Those, dear friends, are the clumsy fingerprints of someone messing with the data Egyptian style ... they are indisputable evidence that the "homogenized" data has been changed to fit someone’s preconceptions about whether the earth is warming.

This created quite a stir, and drew a response from no less than the Economist, among others. It also drew a response from me - in fact, it was the stimulus to start this blog. I showed (following Giorgio Gilestro) that the V2 adjustments could be quite large, but were fairly balanced overall in trend effect. Darwin was an outlier, and I showed one case in particular which went in the opposite direction, to a greater extent.

So now an adjusted version of V3 is out, and there was a similar discovery at WUWT. This time it was Iceland, particularly Reykjavik, and I wrote about that particular case here. There were similar thunderings about rewriting of history etc (and calls for legal sanctions etc). What these protesters are reluctant to acknowledge is that GHCN has always produced two files - one unadjusted, one adjusted. The unadjusted file is not altered, and has at least until recently been the prime reference source. The adjusted file is derived fromn it using what seems at the time to be the best available algorithm. This changes.

What is also not understood is that honogenization is not done to correct the record. It is done to reduce bias prior to calculation of an index. Many irregularities occur in a large temperature record - instruments change, stations move. Records are patchy. Some of these artefacts will cancel, but there is a possibility of bias. So an algorithm is used to try to identify and correct them. In the process, there will inevitably be false positives. The process is valuable as long as the introduced errors have smaller bias than the errors removed.

In this post, I want to update (for V3) the statistical analysis of the effect on trend. I'll also produce a "cherry pickers guide" so that the stations which have their trends changed markedly up or down can be readily identified. I'll do this using the Google Maps application that I developed earlier for GHCN stations.

Distribution of trend changes

For GHCN V2, we showed a histogram of trend changes that was fairly symmetric with a slight upward bias - the mean trend increase from adjustment was 0.0175°C/decade. So I'll start with the corresponding plots for V3. This time I've selected three subgroups - stations that have histories with actual reports for at least

360 months (30 years)
540 months
720 months

Mean 0.0271 °C/Decade

Mean 0.0227 °C/Decade

Mean 0.0196 °C/Decade

The distribution is not quite as balanced as in V2, but the means are still small relative to the warming trend. The spread is larger, so that the Iceland adjustments were to extreme, but came just within the top decile.

The Google maps app

I've included all the previous facilities, which are described in detail here. The Google Maps facilities are available, and you can select and color various groups of stations. The pattern is that you make choices, press the radio button to prime them, and then press one of the top color buttons - your chosen subgroup will be recolored according to the button you press. They combine with or logic, so it is better at first to make sure only one radio button is pressed at a time (they toggle). I've marked with gold fringes the buttons relevant to this topic. You can choose a range of large pos or neg trends and color them. You'll probably find it clearer if you first set everything to invisible. Anyway, I'll describe some detailed cases later. The trends are for stations with at least 60 years data; others will show -999 (for NA). Here's the app:

An extreme case of trend raised

To find some extreme cases (mainly to check my code) I did the following sequence

I selected All and clicked Invisible, to start with a blank plot.
I unset All, and set the Trend Adjustment to 3°C/Century, and the inequality to >. Set the Trend radio button, then clicked Yellow.
Then I changed the trend to -3°C/Century, and the inequality to <, then clicked Pink.
I clicked on the stations to show the balloons with data. The US has 4 out of about 9 of these extremes, so I'll show it. The US generally has bigger adjustments, possibly due to USHCN. Corona, NM, was the whopper, at 8°C/Century (a world record, I think):

So I plotted the adjustment differences (adj-unadj):

and indeed, the adjustments, reaching 5°C create a strong positive trend. Why?

Well, here are the plots before and after adjustment:

These are annual means after subtracting unadjusted monthly means (to minimise the effect of missing values). There does indeed seem to be a sudden drop of about 3°C at about 1953, which may well be due to a station change. The further drop at about 1925 seems a bit more doubtful, and the big changes in the 70's have unclear effect, but it's not surprising that the algorithm reacted. In any case, the adjusted version is indeed calmer, and has lost the rather extreme downward trend.

An extreme case of trend lowered

Though not as much. The one pink station in the picture is St George, and the adjustment brought the trend down by -3.2°C.

Here are the adjustments, reaching 4°C to make the trend more negative.

And here again are the plots before and after adjustment:

Again, there was an uptrend before adjustment, which turned it into a slight downtrend. The unadjusted is volatile, and although by eye I can't see obvious step changes, there is plenty for the algorithm to react to. Oddly, though, the homogenized result still seems to have substantial irregularities.

Population matters

These were extremes. I though it was worth trying to see how such outliers could arise. But remember, the purpose is to remove bias prior to averaging. So the proper tests are statistical, not by studying extremes. The histogram above is a start.

The reason for the change in V3 is the availability of a new pairwise comparison algorithm from Menne and Williams. A lot of work has gone into homogenization, back to the early days of GHCN V1 (mid '90s). I'm not on top of it, and I think determined critics of the process have a lot of reading to do.

References

Added: I should give some more references here. There is a NOAA intro to GHCN V3. That has a download link. The GHCN data is updated several times a month as new data comes in. Each file is named according to date and other metadata.

The Intro links to their homogeneity adjustment page here.

As of Dec 15th, GISS is using the GHCN adjusted data. They seem to be currently adding their own adjustment (they used this previously with unadjusted data). I presume this will be rationalized.

The Menne&Williams paper on pairwise matvhing is linked above. The following paper would be of interest, but is paywalled:
Lawrimore, J. H., 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.
The older overview of Peterson is still worth reading.

moyhu

GISS Temp for Jan 2012 - and TempLS

Previous Months

January 2012 TempLS down 0.14°C

GHCN V3 homogeneity adjustments - revised data.

Distribution of trend changes

The Google maps app

A study of GHCN V3 homogeneity adjustments.

Distribution of trend changes

The Google maps app

An extreme case of trend raised

An extreme case of trend lowered

Population matters

References