November 2013 Russian “Hotspot” – Alarmists Are Overlooking Something

UPDATE: Corrected the typo in Figure 3. 1988 now correctly reads 1989.

# # #

There’s lots of blogosphere chatter about the warm temperatures in Russia in November 2013. In their global State of the Climate Report this month, NOAA stated:

According to Roshydromet, Russia observed its warmest November since national records began in 1891. Some areas of the Urals, Siberia, south of the Far East region, and on the Arctic islands in the Kara Sea had temperatures that were more than 8°C (14°F) higher than the monthly average.

NOAA even discussed the record warm temperatures on their global map here.

It might be true that Russian land surface air temperatures were at record levels for the month of November, but NOAA failed to present something that’s blatantly obvious in the data. In 1988, surface air temperature anomalies for much of Russia shifted upwards by more than 1 deg C.

The Russian “hotspot” stands out very clearly in the NOAA map presented in Figure 1. Based on it, I’ve used the coordinates of 50N-70N, 30E-140E for the NOAA NCDC data, and the climate model outputs, presented in the following graphs. That region covers a major portion of Russia.

Figure 1

Figure 1

Figure 2 presents the NCDC land surface air temperature anomalies for the Russian “hotspot”, for the period of January 1920 to November 2013. I’ve highlighted about when the shift occurred. Before that shift, surface temperatures there warmed very little, if at all. And after it, surface temperatures appear to have warmed, but not at an excessing rate. We’ll confirm that later.

Figure 2

Figure 2

The shift is much easier to see if we smooth the data with a 13-month filter, minimizing the visual impact of the monthly variations. In fact, with the aid of period average temperatures (the horizontal lines) and with some color-coding, the shift in 1988 becomes obvious. See Figure 3. Based on the period-average temperatures before and after 1988, that climate shift raised Russian “hotspot” surface temperatures by about 1.1 deg C.

Figure 3c

Figure 3


Figure 4 is a model-data comparison graph for the surface air temperature anomalies of the Russian “hotspot” for the period of January 1920 through December 1987. Both the NCDC surface temperature data and the climate model outputs have been smoothed w/ 13-month running average filters. The climate models are the multi-model ensemble mean of the models stored in the CMIP5 archive, using the historic and RCP6.0 scenarios. The CMIP5 archive, as you’ll recall, was used by the IPCC for their 5th Assessment Report. And we discussed why we use the model mean in the post here.

Figure 4

Figure 4

NOTE: The trends in Figures 4 and 5 are based on the “raw” data and model outputs, not the smoothed versions.

The models did a reasonable job of simulating the warming rate from 1920 to 1987. In more than 65 years, they only overestimated the warming by about 0.23 Deg C. But the models perform quite poorly for the period from January 1989 to November 2013. See Figure 5. During this much-shorter 25-year period, the models overestimated the warming by more than 1.1 deg C.

Figure 5

Figure 5

Let’s state that again: the models overestimated the warming by more than 1.1 deg C over the most recent 25-year period.

Climate model failings at the regional levels are not unusual. We discussed those failings in numerous posts over the past year and in my book Climate Models Fail.


The timing of the shift in the Russian surface temperatures is similar to the shift in Scandinavian surface air temperatures. See the post here. There we discussed that the shift in surface temperature was possibly a response to a shift in the sea level pressure and interrelated wind patterns associated with the Arctic Oscillation.

Additionally, see de Laat and Crok (2013) A Late 20th Century European Climate Shift: Fingerprint of Regional Brightening? The authors argue that a shift in the North Atlantic Oscillation (similar to the Arctic Oscillation) in the late 1980s caused more sunlight to warm European surface temperatures in an apparent shift. I would suspect that something similar occurred over Russia at that time as well.


Like other regions, a climate shift, not the long-term effects of manmade greenhouse gases, is responsible for a major portion of the warming that occurred over much of Russia.

And, of course, climate models performed poorly when attempting to simulate the warming that occurred there since the 1988 shift, overestimating the warming by a large amount. So what else is new?


The NCDC surface temperature data and the CMIP5-archived climate model outputs are available through the KNMI Climate Explorer.

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Robert Brooke
December 20, 2013 5:29 am

Isn’t 1988/89 also when the ‘great dying of thermometers’ took place? Large numbers of Soviet military bases with weather stations, many in cold remote locations, closing post Glasnost.

December 20, 2013 5:36 am

But let’s not forget the governmental change at that time. How many stations were reporting pre- and post-1988? Does it make a difference? Given the well-documented fudging of other data by the former USSR, people have speculated that temperatures were under-reported for reasons of convenience (do you want to trudge out to the thermometer station in the depth of winter?) and to support pleas for more heating fuel from central control. This timing coincidence is precise enough to warrant verifying that the measurements are accurate before saying the step increase is real.

December 20, 2013 5:40 am

The climate shift seen in Russia coincides with the rapid rate of ice loss in the Arctic that started in the late eighties as well as warming in Scandinavia.
It is most likely a response to the polar amplification of the AGW which was predicted, with warmer air moving further North. It is not possible to find a previous episode of similar magnitude of warming that correlates with changes in the Atlantic Osscilation. The fact that models do not duplicate this local detail is a red herring. Models do not simulate the local climate and regional shifts with this level of detail.
Ascribing the observed warming to an unforced variation when there is no historical precedent, but a forced climate change does account for the observations might be mistaken.

December 20, 2013 5:41 am

Well, frankly, I rather suspect a sensing issue or data-processing issue, combined with the collapse of the soviet heavy industry after the collapse of the Soviet Union during the late 1980ies to be the cause of the shift in Russia.
The shift described here has taken place exactly during the times of the collapse of the Soviet Union. After the fall of the Soviet Union, thousands of meteorological sites had been abandoned, due to further lack of funding. Therefore, a failure of a station still in use would weigh in much heavier to the combined result of all stations, because the size of the sample was substantially reduced.
Also, after the fall of the Soviet Union, the Soviet Union’s heavy industry had taken a serious blow, which MUST have had a substancial effect on the cleanliness of the air over Russia and, hence, on the resulting land temperatures.

December 20, 2013 5:41 am

Yes , Bob, you are right about the AO index being a factor . There was a major shift of the AO index from negative to mostly positive in 1989, allowing more warm air north . The AO index has has again been strongly positive the last 2 months.

December 20, 2013 5:42 am

@- Gary
The claim it might be connected with the change of political control and the closure of observation sites is refuted by the observations being confirmed by satellite data.

Green Sand
December 20, 2013 5:46 am

“….and on the Arctic islands in the Kara Sea had temperatures that were more than 8°C (14°F) higher than the monthly average.”
The Kara Sea has approx 50% more ice area than this time last year. I wonder how quickly it would have frozen if it hadn’t been subjected the Nov “heat wave”?

jai mitchell
December 20, 2013 5:55 am

The temperatures went up in Siberia when Perestroika led to the collapse of the soviet union. This caused a collapse in their production of sulfur dioxide emissions which, up to that point, had provided a significant and localized cooling event.

December 20, 2013 5:57 am

Let’s ask the Russians in west Siberia — are you suffering from the November “heat”?

December 20, 2013 6:09 am

Nothing new, happen before.
Need to ‘persuade’ citizenry that working in Siberia ain’t that bad, a convenient temperature chart helps a bit

December 20, 2013 6:11 am

We have family in Russia and it was one of the coldest Autumns in recent history. As usual with the warm-tards, eco-fascists, the devil is in the data details. I would not bet Sheryl Crow’s square piece of toilet paper, that the Russian data has anything to do with reality or is reliable.

December 20, 2013 6:25 am

There was a large drop in the number of weather stations worldwide following the collapse of the USSR. These stations were mostly lost from Siberia. At the same time global average temperatures were reported to be increasing. Clearly the problem was introduced sample bias due to loss of stations from a colder than average area of the globe.

December 20, 2013 6:29 am

I question whether new remote electronic equipment started being used. Electronics providing heat inside the temperature sensor box.

December 20, 2013 6:36 am

As fast as it came , the hot spot is gone and North Asia is back to the cold weather a

Joe Born
December 20, 2013 6:45 am

Forgive the tangent, but here’s something that seems to be apparent to everyone but me: Why the 13-month filter? I know that’s what everyone does, but if a 12-month filter were used instead it would essentially eliminate the 1/ year component, and the resultant values could be placed at the month boundaries.
I’m sure I’m going to be embarrassed by how obvious the answer is, but I’m too curious to hold back. Can someone help me out?

December 20, 2013 6:48 am

I can’t remember where I read it, but at some point in the past local russian authorities had an interest in showing their temperatures as low as possible, because that meant that they were entitled to a bigger ammount of gas for heating. I don’t recall the dates, though. But it would not surprise me if this practice of fabricating lower temperatures had ended around 1988. Anyone here remembers about it?

Steve from Rockwood
December 20, 2013 6:49 am

Seems hard to believe you could have a one-time shift in almost 100 years of temperature data caused by an effect that is defined as an “oscillation”. A shift up should be followed by and preceded by a shift down. Where is the shift down?

December 20, 2013 6:50 am

Gary says: “But let’s not forget the governmental change at that time. How many stations were reporting pre-and post-1988?”.
Under the old order when almost everyones paycheck was issued by the state , the people who lived in the northern Siberia usually got some extra cold weather incentives inversly proportional to the locally recorded temperatures added to the standard fare, and I have been told that this practice did have some effect on the low temperatures recorded there, and that when this custom was abandoned or changed under the new political system , it may have had something to do with the big step change in the temperture history that occured after the demise of the USSR. I have never personally seen any concrete proofs that it is true though, so it could just be an urban myth.

Steve from Rockwood
December 20, 2013 6:59 am

From Wikipedia…”In June 1988, at the CPSU’s Nineteenth Party Conference, Gorbachev launched radical reforms meant to reduce party control of the government apparatus. On 1 December 1988, the Supreme Soviet amended the Soviet constitution to allow for the establishment of a Congress of People’s Deputies as the Soviet Union’s new supreme legislative body.”
Was there a loss of some weather station reporting during the collapse of the Soviet Union? I visited Russia several times a decade ago and recall the stories of rapid infrastructure change. One day the geologists are working in their offices, the next day the offices are condominiums with a new owner and the geologists are out on the streets with their maps and rock samples.

December 20, 2013 7:01 am

My previous post did not show the current weather in NORTH ASIA properly

Steve from Rockwood
December 20, 2013 7:03 am

Nice catch on the shift Bob Tisdale. From your Figure 3 it looks “obvious”. The minimums after 1988 barely reach the average prior to the shift. Has anyone else ever reported on this and if not, did you just recently find the shift? If the latter it makes me wonder what to think of world temperature data.

December 20, 2013 7:17 am

Yes it was indeed very warm this autumn in Russia. Let’s compare snow cover with 2008 another year of rapid refreeze in the arctic:
Anyway the relative warmth in Russia is more then compensated by the cool in the US, north Africa, Middle East, Southern Asia, Caraïbs and Northern South America.

December 20, 2013 7:22 am

Regarding the model temperature graph, are you saying they overestimated global temperatures by 1C, or temperatures in Russia?

December 20, 2013 7:25 am

Bob or Others:
SHORT VERSION: I have seen this 13 month averaging a number of times now and cannot see the justification for that time period. It would retain a sinusoidal element in the calculated data as periods are over-represented by the first month.
EXTENDED VERSION: For instance, a 13 month average, centred on January 1, 2012 will have July 2011 and July 2012 included. In my neck of the woods, July is the hottest month. This average would, all other things being equal, be hotter than the average centred on August 1, 2012 which will include February 2012 to February 2013. I realize that an odd number is preferred for centred moving averages as this allows the averaged data point to be located and associated with a raw data point. This would seem to be outweighed by the implications of retaining a sinusoidal element that I assume we are trying to smooth out. This element would be inverted relative to the original data, with the maximum during the coldest month and the minimum during the hottest month. Try it out in excell.
Data and method follow.
Use this data:
10 01-Jul-11
9 01-Aug-11
8 01-Sep-11
7 01-Oct-11
6 01-Nov-11 December, January and February
5 01-Dec-11 are the same temp. to effect
5 01-Jan-12 a mirror like data set with January
5 01-Feb-12 as the mirror line. Note that this
6 01-Mar-12 is necessary with an even number of
7 01-Apr-12 months in a year.
8 01-May-12
9 01-Jun-12
Copy and paste the numbers on the left as the “temperature”. Increase the months for a few years (3 will do)
The centred 13 month average for 01-Jan-12 will include the data from 01-Jul-11 to 01-Jul-12 (a 10 not shown). This gives a value of 7.308 (for all Januaries).
The centred 13 month average for 1-Jun-12 (and July and August) is 6.923.
Note that the correct point for a centred moving average is at the centre of the data used, hence the preference for an odd number of data points.
We have indeed smoothed the data, somewhat, but we now have a new sinusoid that reflects the periodic change in data, but is shifted 6 months. If we use a 12 month moving average each average is 7.08 (the true average of the periodic data) and we have smoothed the variance associated with the 1 year period. I won’t get into the implications of months with fewer days, etc.
Just curious.
Cheers and Merry Christmas

Gail Combs
December 20, 2013 7:40 am

Robert Brooke says: @ December 20, 2013 at 5:29 am
Isn’t 1988/89 also when the ‘great dying of thermometers’ took place? Large numbers of Soviet military bases with weather stations, many in cold remote locations, closing post Glasnost.
EM Smith looked into that a while ago.
Thermometer Years by Latitude Warm Globe: As the Thermometers March South, We Find Warmth
Then there is Verity Jones (Digging in the clay) who has also done a lot of work and a series of posts on The ‘Station drop out’ problem
She says of her graphs of Asia

The key obsevation from Figures 7 and 8 is that Chinese stations are the dominant contribution in Asia. It is also clear that are the primary cause of the sudden increase in the number of reporting stations after 1950. Note that the increase in the numbers for Japan and the Russain Federations is much more gradual. All three countries show the ‘precipitous drop out’ of reporting stations around 1989/1990. Why? The Chinese stations in particular drop from a high number of 361 in 1990 to only 14 in 1991. Very odd? In addition to upsetting Environment Canada, has NOAA also broken off diplomatic relations with the Chinese? It looks like diplomatic relations with Mongolia were broken off a little earlier than they were with China as the Mongolian station ‘drop out’ occurs after 1982/83. Meanwhile, having enjoyed good relations with South Korea from 1973 when the number of reporting stations increased to over 60, sadly after 1993, the South Koreans appear to have also fallen out with NOAA with the numbers dropping to only 10 in the subsequent years.

Blow up of the graphs:
Frank Lansner over at Jo Nova’s also addressed the Russian station issue.

Oh there is many more than 5-8 temperature stations in Siberia, and they hold really many long series, so its really good to work with.
This article, “RUTI: Russia” is nof finished, but for each square i show I use around 5-8 long series from unadjusted GHCN:
Its even possible to get hands on more series.
But at the time I was making this article Appinsys was down, so I used Crutem3 via KNMI.
Is the area I show just 5-8 stations in CRUTEM3, is that what you say?
Anyways, “Russia” has complained to CRU about their warm trended choice of stations in Russia, but since i work with detrended data, this issue is not present too much. The specific peaks and dives show a remarkable connection with nino3,4 and unless this is a “coincidence” i think that Crutem3 is ok for this use. But perhaps GHCN would be slightly better.

“Russia” has complained to CRU refers to this 2009 report:

Russia affected by Climategate
….Climategate has already affected Russia. On Tuesday, the Moscow-based Institute of Economic Analysis (IEA) issued a report claiming that the Hadley Center for Climate Change based at the headquarters of the British Meteorological Office in Exeter (Devon, England) had probably tampered with Russian-climate data.
The IEA believes that Russian meteorological-station data did not substantiate the anthropogenic global-warming theory.
Analysts say Russian meteorological stations cover most of the country’s territory, and that the Hadley Center had used data submitted by only 25% of such stations in its reports.
Over 40% of Russian territory was not included in global-temperature calculations for some other reasons, rather than the lack of meteorological stations and observations.
The data of stations located in areas not listed in the Hadley Climate Research Unit Temperature UK (HadCRUT) survey often does not show any substantial warming in the late 20th century and the early 21st century.
The HadCRUT database includes specific stations providing incomplete data and highlighting the global-warming process, rather than stations facilitating uninterrupted observations.
On the whole, climatologists use the incomplete findings of meteorological stations far more often than those providing complete observations

And finally WUWT:

Gail Combs
December 20, 2013 7:41 am
December 20, 2013 7:45 am

@- Steve from Rockwood
“Seems hard to believe you could have a one-time shift in almost 100 years of temperature data caused by an effect that is defined as an “oscillation”.”
Good point.
There is no historical data that shows any similar temperature shifts from this “oscillation”.
@-” A shift up should be followed by and preceded by a shift down. Where is the shift down?”
The AO shifted back down to the pre-eighties level some years ago, which makes attributing this November’s warmth to the same cause as the shift in 1988 very unconvincing.

December 20, 2013 7:56 am

Joe Born says:
December 20, 2013 at 6:45 am
Forgive the tangent, but here’s something that seems to be apparent to everyone but me: Why the 13-month filter?

An odd numbered filter is used because it includes the “current month” and an integer number of months on both sides so one winds up with smoothed data centered around a point common to the unfiltered data. If an even numbered filter is applied one would have smoothed data referenced to midway between two of the original data points. If one were plotting such data in Excel or some such they’d have to adjust the indices appropriately which is slightly less convenient.

December 20, 2013 8:03 am

Off topic but interesting news … especially, if you know where Ellesmere Island is!
A biologist has discovered 400-year-old moss in Nunavut. The moss was buried under a glacier on Ellesmere Island where it survived under the ice. Catherine Lafarge (sp) is a biologist with the University of Alberta. She was able to grow the moss in the lab. Lafarge says this discovery could help in space travel.
“Looking at is there any life on Mars or whatever. I do think something like Arctic organisms would be one of the first group of organisms that you would try to see whether they could survive in extraterrestrial systems.”
Lafarge plans to look at ice patches for ancient plant species on Baffin Island and in the Yukon next year. Some of the ice is up to 120,000 years old.

December 20, 2013 8:04 am

Well lets not forget Climategate:

Climategate has already affected Russia. On Tuesday, the Moscow-based Institute of Economic Analysis (IEA) issued a report claiming that the Hadley Center for Climate Change based at the headquarters of the British Meteorological Office in Exeter (Devon, England) had probably tampered with Russian-climate data.

December 20, 2013 8:05 am

Joe Born: On the 13-month filter, you need an odd number of samples to make the filter symmetrical about the month in question. 13 months gives you six months before and six months after the month.
Joe and John: The 13-month smoothing averaging is done on the anomalies, effectively the residuals that remain after averages for each month over many years are taken. So the idea that, say a January 13-month average is raised by including two July readings is not valid. It is common analytic practice to look for patterns in residuals of fits.

Pippen Kool
December 20, 2013 8:06 am

If you go to Fig 3 (Monthly November ice extent for 1978 to 2013) at you will see an “obvious” break in the curve at 1988 that would correspond to your 1 degree jump. The reason no one has commented on it before is that it looks like noise.

December 20, 2013 8:07 am

Bob: What do the satellite measurements for the Siberia area report? Do they see a similar step up at the end of the 1980s?

Steve from Rockwood
December 20, 2013 8:09 am

A 13-month filter is the shortest possible filter for eliminating seasonal effects while remaining symmetric about a given month. The next available filter would be 25 points long. A 3 point filter would show the average seasonal effect, which should reveal itself as a sinusoid (all this assumes continuous monthly data – unshifted).

December 20, 2013 8:14 am

Thanks Bob, this is very interesting. I can see a scientific paper here.

December 20, 2013 8:24 am

The a strongly positive ARCTIC OSCILLATION does seem to correlate with warm tempertaures in Russia . For example , the AO INDEX was 3.106 and 3.279 in January and Februray 1989. . The winter temperature in Moscow during January and Februray of 1989 was only -2.3C and -.0.7C when the tyicals are vastly colder [ january/february average in the 1980’s was closer to -8C and as high as -17.7 in January 1987 and -14.1 in February 1987]. So like in November 2013 , there was a spike in the warm AO and the temperatures went up.[ daily high for AO in NOVEMBER 2013 was over 4]. What caused this short term spike in the positive AO? . During November 2013, there was a major spike also in solar activity when the solar flux shot up to 174 and the sunspot number close to 290. I don’t know if there is a connection but the extra solar activity could account for the sudden higher positive AO].

December 20, 2013 8:35 am

Thanks Bob, Very good post.
Again to show that ENSO causes permanent surface temperature shifts, not just oscillations.

December 20, 2013 8:42 am

For what it’s worth, if one splits this temperature record into a pair of before/after linear trends without the (hokey IMHO) jump discontinuity, the natural break point is circa 1970 and the CMIP5 model comparison would look a lot better than what Tisdale has shown.

December 20, 2013 8:46 am

@- Curt
” What do the satellite measurements for the Siberia area report? Do they see a similar step up at the end of the 1980s?”
Yes, both the 1988 ‘shift’ and the present extreme are validated and confirmed by satellite data and other readings of temperature and ice extent from non-Russian sources.
The changes in weather monitoring stations in the USSR from the political collapse are a red herring.
Especially as the big collapse happened AFTER November 1988.

December 20, 2013 8:57 am

What Shift?
please note. The NCDC chart is based on Monthly data and of course when you use a subset of all the data you’ll increase the odds of finding odd stuff. A good analyst looks at all datasources. Plus, defining russia by lat lon is a really stupid approach when you can actually use the exact border to pull out the data.
note also.
REPLY: I see the shift in your Tavg chart. If you had presented graphs that were at useful time scales to the discussion at hand, you’d like see it too. Remember from our conversation at AGU I see the “specks” that you do not. I have tool to test for this, I’ll run it tonight and do a new post. – Anthony

Bernie Hutchins
December 20, 2013 8:58 am

I’m not at all sure using a 13 month filter rather than 12 months is a good idea. True you can get linear phase (or zero phase with respect to the center) if you use 13, but at the expense of imperfect cancellation of the full-year periodicity. If you must use 13, re-weight the first and last months by 1/2.

December 20, 2013 9:12 am

That’s right folks: global warming caused the Soviet Union to fall.

December 20, 2013 9:13 am

Back in the day I was told that co2 positive feedback crazy warming would be gradual not in big shifts.

lurker, passing through laughing
December 20, 2013 9:16 am

Whatever the source of the change or the motive for the change, it is once again evidence that the manmade part of AGW has a lot to do with how data is ‘handled’ and very little to do with actual temperatures.

Werner Brozek
December 20, 2013 9:18 am

herkimer says:
December 20, 2013 at 7:01 am
My previous post did not show the current weather in NORTH ASIA properly

What I find interesting is the two cold areas. If those two cold areas were warm in November, and no data was in the middle, GISS and HadCRUT4 would have very different anomalies. Unfortunately we are forced to have this discussion without knowing the HadCRUT4 November anomaly. However the satellite data, whatever their differences with GISS, do not give grounds for confidence here by ranking November 2013 as 9th for UAH and 16th for RSS.

December 20, 2013 9:19 am

I can`t recall when it was…but wasn’t there a year when the headlines all screamed record warm October and it was later realized that Russia used their September data for October?
Just a thought.

Duke C.
December 20, 2013 9:26 am

Slightly OT-
Wind patterns over Russia for Nov. 15, 2013. Cool app.,35.26,279

Steve from Rockwood
December 20, 2013 9:45 am

John Eggert says:
December 20, 2013 at 7:25 am
John, It doesn’t make enough of a difference because you are talking about only one extra month divided by 13. While July is much warmer than January (where I live) it isn’t much different than adjacent months June and August. I tried your idea of weighting the first and last months by 0.5 (e.g. 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5 divided by 12) compared to equal weights and the one graph is hidden below the other virtually indistinguishable (I used the HadCrut 3 data from 1850 onward).

Matt G
December 20, 2013 9:58 am

The shift in 1988 doesn’t seem to be sea ice related around Russia, where from 1987 to 1988, in the North of the country there was very little sea ice change in just one year.
Most sea ice extent in March 1987
Most sea ice extent in March 1988

Mike Maguire
December 20, 2013 9:59 am

We often refer to and use the AO(Arctic Oscillation) and there are probably some that don’t know exactly what that is. This site does a great job explaining it:

December 20, 2013 10:07 am

You can find a chart of the great dying of Russian thermometers here:

December 20, 2013 10:10 am

The irony is that Mosher’s BEST product is the one data set designed to simply remove such data jumps, algorithmically, so if it’s still there in their final plot, they must have had to tweak the overall parameter knobs and gizmos to obtain their global hockey stick, Russia be damned.

Ulric Lyons
December 20, 2013 10:23 am
December 20, 2013 10:25 am

could this have something to do with “The Great Thermometer Cull” from the 90’s

Gunga Din
December 20, 2013 10:36 am

I was born and raised during the Cold War. (Not the Warm War today’s kids are in.) Of course the Cold War was political. The USSR was very secretive.
I’ve read that the US Midwest was once an inland sea.
Perhaps Russia has a secret inland ocean and that’s where the heat has been hiding?
(Long way around to make a joke.)

Ulric Lyons
December 20, 2013 10:37 am

izen says:
“The climate shift seen in Russia coincides with the rapid rate of ice loss in the Arctic that started in the late eighties as well as warming in Scandinavia.”
The accelerated ice loss is from increasingly *negative* AO/NAO episodes from 1996 onwards:

Joe Born
December 20, 2013 10:39 am

MarkB, Curt, and Steve from Rockwood,
Thanks a lot for the responses.
I actually was aware of the symmetry issue. And, if anomaly use suppressed all of the 1 / year component, there would be no preference for 13 months over any other period. So I wouldn’t have thought those reasons explain the high popularity.
But your answers suggest to me that there’s likely no better justification, so they probably do.

December 20, 2013 11:02 am

Bob, Concerning the model/data comparison (figure 4 & 5) and associated discussion, you haven’t accounted for the presumed “shift” in your accounting of the measured data trend.
To wit, Figure 4 shows 88 years of 0.005 degree/decade or about 0.044 degrees over the interval. Figure 5 shows 25 years of 0.079 degree/decade or about 0.198 degrees, so a total of 0.242 degrees over your data set. In contrast, Figure 3 suggests something more like 1 degree over your data set so you’ve lost something on the order of 0.75 degrees in your accounting.

Dell from Michigan
December 20, 2013 11:03 am

Why is it that when a region is warmer than normal its supposed proof of Global Warming (i.e. Climate Change), but when a region is cooler than normal it is discounted as “just weather”????

December 20, 2013 11:05 am

So the take away I am getting is: Using a 13 month moving average is OK because the results are only a little wrong because we are using residuals, it is only 1/13 of the result, etc. Whatever your excuse for this practice, the fact remains it re-introduces a periodicity and hence defeats the purpose of a moving average which is to smooth periodicities. Yes, Yes, it isn’t huge. About 7% in the data set I showed which varies seasonally from 5 to 10. If those seasonal variations are in the residual rather than the raw, it is still a 7% difference. For a time series with a periodicity over an even number of time units, there is no justification for using an odd number of time units except you can’t shift your graph 1/2 unit. There is no mathematical justification for using 13 other than it makes the point on the graph “fall on the line” rather than “between the lines”. Aesthetically pleasing is not a mathematical argument I’m familiar with.

David L. Hagen
December 20, 2013 11:23 am

Following on from fredberple’s notes, McKitric & Michael 2004 show an order of magnitude increase in empty temperature data cells in the USSR after 1990 than before.
A test of corrections for extraneous signals in gridded surface temperature data

ABSTRACT: Monthly surface temperature records from 1979 to 2000 were obtained from 218 indi-vidual stations in 93 countries and a linear trend coefficient determined for each site. This vector of trends was regressed on measures of local climate, as well as indicators of local economic activity (income, gross domestic product [GDP] growth rates, coal use) and data quality. The spatial pattern of trends is shown to be significantly correlated with non-climatic factors, including economic activity and sociopolitical characteristics of the region. The analysis is then repeated on the corresponding Intergovernmental Panel on Climate Change (IPCC) gridded data, and very similar correlations appear, despite previous attempts to remove non-climatic effects. The socioeconomic effects in the data are shown to add up to a net warming bias, although more precise estimation of its magnitude will require further research.

Bernie Hutchins
December 20, 2013 11:24 am

In considering averaging over 12 months or 13 months, I have suggested that a much better length 13 filter is:
h13modified=[ 1/2 1 1 1 1 1 1 1 1 1 1 1 1/2 ] / 12
as compared to
h13=[1 1 1 1 1 1 1 1 1 1 1 1 1] / 13
We can also compare this to length 12:
h12 = [1 1 1 1 1 1 1 1 1 1 1 1] / 12
Indeed, h13modified, like h12, completely rejects a frequency of 1 (one year) where the “sampling frequency” is 12/year. The h13 filter, in contrasts, lets through about 7.7% at the frequency 1.
This is just simple digital filtering theory.
It may not show on graphs as much difference, but h13 is clearly subject to question, as Joe Born originally suspected.

Gunga Din
December 20, 2013 11:27 am

Dell from Michigan says:
December 20, 2013 at 11:03 am
Why is it that when a region is warmer than normal its supposed proof of Global Warming (i.e. Climate Change), but when a region is cooler than normal it is discounted as “just weather”????

Without the myth that what Man does is controlling the weather then there is no excuse for the measures implemented for controlling Man.

Bernie Hutchins
December 20, 2013 12:11 pm

To John Eggert at 11:05 AM Dec. 20, 2013:
Indeed. I agree that having the output shifted 1/2 sample (half a month), zero phase with respect to the center of symmetry, is far preferable to non-zero magnitude error. I calculated that error using freqz in Matlab and got 0.07692307692308. And you then just say it’s 1/13! Sure is. Nice – very nice.
Folks who have studied the 1/2-sample shift associated with the four cases of even/odd symmetry and even/odd lengths of linear-phase FIR digital filters won’t bat an eye at this. But if someone has not done this, and found it natural, perhaps it makes some uneasy.

December 20, 2013 12:26 pm

Why do we expect every day, every month, every year, everywhere to be average?
Russia did experience a very mild November in 2013. See:
They had October temps in November. This is was a regional not global event…that happens somewhere every year. Thus the phrases: “early spring” “late spring” “Indian summer” ….. We are supposed to freak out because a small region of the planet had 1 month of pleasant weather?

December 20, 2013 12:31 pm

Obviously, HPs of 1045hPa in November over Siberia must be warm air according to the surface temperature grid… LOL
Once again, without an accompanying analysis of lower tropospheric circulation, these monthly statistics are meaningless on a climatological level. For instance, during the first 10 days of that month, very cold air 1045hPa over central Siberia advected warm, moist air over western Russia and just as it is happening today on the West Coast, in a clockwise vortex in the wake of the cold air pushing southward.

Bill Illis
December 20, 2013 1:07 pm

NikFromNYC says:
December 20, 2013 at 10:10 am
The irony is that Mosher’s BEST product is the one data set designed to simply remove such data jumps, algorithmically, so if it’s still there in their final plot, they must have had to tweak the overall parameter knobs and gizmos to obtain their global hockey stick, Russia be damned.
On the contrary Nik. The BEST algorithm favors “up” jumps and will then add that to any nearby stations that don’t have the “up” jump through the “regional expectations” filter. Its the “down” jumps that are filtered out.
Its not that anyone has a copy of the actual algorithm showing this but it is inevitable in the math on how the raw data turns into such a large increase in the temperature trends.

December 20, 2013 1:09 pm

I just ran two statistical tests, one on GHCN Tavg data bounded by lat/lon that Tisdale identified, another on BEST Russian Tavg data.
Both show a shift upwards about 1 degree in 1989. BEST is particularly troublesome since their scalpel method is supposed to fix such things (assuming it isn’t a natural event). The stats test I used has a peer reviewed provenance, so it isn’t just some Mannian made up methodology.
I will have a detailed post on this coming up this weekend.

Steve from Rockwood
December 20, 2013 1:37 pm

John Eggert says:
December 20, 2013 at 11:05 am
Bernie Hutchins says:
December 20, 2013 at 12:11 pm
Look at the raw data. Hadcrut3 from 1850 to present shows monthly changes of up to +/-0.9 while the difference between a 13 point smoothed time series using equal coefficients and one with half end points shows a peak variation of +/- 0.025 degrees. Is this something you really want to quibble over?
Try looking at the unfiltered time series of monthly temperature data (e.g. Hadcrut3). The monthly variation is up to +/- 0.9 degrees. Now filter the data with a 13 point equal weighted box car filter. Monthly variation drops to +/- 0.07. Now filter the original data with your half-end-weighted (“quibble”) filter and the variation drops to +/-0.05. Is this difference meaningful given that the accuracy of the original data is likely +/- 0.5 degrees (25 times greater than the difference in the filtering methods).

Steve from Rockwood
December 20, 2013 1:44 pm

@Bernie. 7% may sound like a lot but it isn’t. Different filters often show different results relative to each other, but when you compare the filtered data to the raw original data the variation in filter response is well below the noise level of the original time series. You are left defending signal variations due to filtering differences that are not even measureable.

Brian H
December 20, 2013 2:05 pm

Green Sand says:
December 20, 2013 at 5:46 am
“….and on the Arctic islands in the Kara Sea had temperatures that were more than 8°C (14°F) higher than the monthly average.”
The Kara Sea has approx 50% more ice area than this time last year. I wonder how quickly it would have frozen if it hadn’t been subjected the Nov “heat wave”?

One physics phact to keep in mind is that condensation and freezing of water involve a massive “swap” of heat energy as it changes state. That is, the air must absorb all the latent heat that is removed from the water as it goes “downscale” from vapour to liquid to solid.

Brian H
December 20, 2013 2:27 pm

Bill Illis says:
December 20, 2013 at 1:07 pm

Its not that anyone has a copy of the actual algorithm showing this but it is inevitable in the math on how the raw data turns into such a large increase in the temperature trends.

AFAIK, Anth.y discovered that the pristine rural cool stations were rejected as “outliers” and homogenized with the nice warm urban ones, systematically.

Brian H
December 20, 2013 2:32 pm

Same “outlier” trick that was pulled with ARGO, coincidentally. Cold buoys’ data stripped from the “raw” records. Same with the 1990 “Great Dying of the Thermometers”. Inconvenient Andes stations’ data replaced with the average of equidistant coastal and jungle ones. Duh!

Bernie Hutchins
December 20, 2013 2:43 pm

Steve from Rockwood on December 20, 2013 at 1:44 pm said in part:
“@Bernie. 7% may sound like a lot but it isn’t.”
Really? 7% may not be a lot compared to 100%, but here we are saying that it is 7% compared to a desired 0%. We are supposed to have, and can get, a null there – that was the purpose. Are you happy to let 7% of what is exactly the (yearly) component through? That’s why our first priority is to place zeros (nulls) on components to be rejected.
And I showed you how to get a good null AND avoid the shift of 1/2 a month.
Is there a reason for intentionally doing it in a way you know is not the best?

Steve from Rockwood
December 20, 2013 3:01 pm

@Bernie. You are technically correct. The filter coefficients should be balanced to completely eliminate the one month variation.
Applying the standard 13 point filter reduces inter-monthly variation by 92.2% while using your filter reduces it by 94.4%.
But in this post there is a 1.0 degree shift in the data. That is a far greater issue than 2.2% improvement in filtering. But you are technically correct.
Also note that the 2.2% reduction is the absolute greatest difference (the worst variation). The average variation (of the difference between the two filters for all values is 0.0%).

Bernie Hutchins
December 20, 2013 3:38 pm

Steve from Rockwood on December 20, 2013 at 3:01 pm said in part:
” @Bernie. You are technically correct. ”
Thank you Steve. And I basically agree with you too.
But I think it CAN be very important to be factually correct, especially about something well known to the general public – that there are 12 and not 13 months in a year. When Al Gore said (factually incorrect) that the rocks below our feet were millions of degrees, he basically got away with it because not that many people had any idea about it.
It seems to be a lot easier (and more satisfying) to just do things exactly right rather than to have to explain (possibly to contentious listeners) why it does not really matter. Credibility is too often fragile at best.

December 20, 2013 3:50 pm

Björn was the first to explain correctly the instant +1C leap.
Under reporting your local temperatures increased your coal allowance off the state. When the system was declared bankrupt the remaining stations started to report the real temperatures.
Isn’t it about time Hadley corrected the past?
Moreover, seems the same Hadley have also falsely reported Russian data too. A time will come to pay the piper.

Arno Arrak
December 20, 2013 3:59 pm

The authors contradict themselves. First they claim the shift shows up in satellite and ground-based temperature records. Then they argue is spurious. And then again they want it considered as “a fingerprint of European brightening during the last few decades.”
I checked global satellite and ground-based records and there is no trace of any shift in 1988. That year just happens to be the peak year of the 1988 El Nino, the one that Hansen told the senate was proof of global warming. Between 1979 and 1997 there are five such El Nino peaks, with La Nina valleys in between. That 1988 El Nino is the middle one of these five. They are an unremarkable part of the ENSO oscillation in the eighties and nineties.

Steve from Rockwood
December 20, 2013 4:13 pm

bones says:
December 20, 2013 at 10:07 am
You can find a chart of the great dying of Russian thermometers here:
It should be possible to backward recalculate temperature anomalies prior to 1989 using just the temperature stations used after 1989. If the shift remains, it isn’t the great dying of Russian thermometers.

December 20, 2013 4:15 pm

Perhaps the warm Russian November was influenced by the brief sharp drop in neutrons.

December 20, 2013 4:36 pm

I find this argument weak.
Sorry but I can really only see the shift you talk about in the graphs if I squint and turn my head sideways while standing on one leg wearing welding goggles and drinking whiskey through a straw. The colorized versions with distraction lines do help a bit to see what you are talking about. But I wouldn’t have picked a shoft there without that assistance. Your conclusion that this is a “climate shift” and not warming seems like an exercise in semantics. I see what you are saying (I think) but I’m unconvinced.
The bottom line for me is that recorded land temperatures in November in Russia are higher than usual. Why? Is this a real effect or a consequence of lousy Russian weather measurement. Russian scientists are capable of doing very good science. But in Russia government run programs like temperature measurement are often poorly managed. In fact the Russians “have form” for messing up their climate data. Back in 2008 they actually got the month wrong and created a whopping temperature anomaly out of nothing.
The satellite measurements show slightly warm in Russia in November but not usually so. Until the data is thoroughly checked by someone I trust, I’ll stick with the satellite measurements. December in Russia is definitely not shaping up to be warm, if the news reports can be believed.

Mike McMIllan
December 20, 2013 7:03 pm

Maybe they accidentally repeated the October data.

John F. Hultquist
December 20, 2013 7:24 pm

izen says:
December 20, 2013 at 5:40 am
“The climate shift seen in Russia coincides with the rapid rate of ice loss in the Arctic …

Darn. I was just getting used to the idea that ice loss in the Arctic made the NH cold. Sure wish they’d get this science settled.

Werner Brozek
December 20, 2013 7:27 pm

The November anomaly for HadCRUT4 just came in. It was 0.596 which puts November 2013 as its third warmest November behind 2005 at 0.620 and 2001 at 0.604.

December 20, 2013 7:31 pm

As far as I can tell, from examination of the satellite data-bearing in mind the caveat that I am using version 5.5 although the latest data from UAH is 5.6, sadly KNMI does not have 5.6 yet-I estimate that:
Over the region in question, a change in the LT temperature of one degree in November corresponds to a change of ~1.62 at the surface. Using this factor, the LT anomaly for November (+2.24229) should correspond to an anomaly of +3.64945 at the surface. Setting the NCDC data to the same baseline: +5.16835. It appears, to me at least, that *this* November was not as warm in the region in question as NCDC estimates.
My methodology for finding that factor was to take the November anomalies from 1981-2010 climatology for UAH data and NCDC data in the region in question, then detrend both, then do a simple linear regression with the UAH detrended November anomalies as the independent variable and the NCDC detrended november anomalies as the dependent variable. I then take the slope of the regression, multiply the UAH anomalies (*non* detrended, now) by that factor, and compare them to the NCDC non-detrended anomalies. That’s good, I think many people here would “like” that answer. On the other hand if I take all the months, calculate their trends, and take the average of 11 and 13 month centered averages for each (which makes a nice annual smoothing technique, a little less attenuation than just the 13 month centered average) and remove the trends from those, and do a regression like I described (detrended smooth UAH as the predictor, same NCDC as the predictee) I get a factor of about 1.58, and if I then use this factor together with the UAH anomalies to “predict” the surface anomalies, the predicted annually smoothed anomalies warm at a rate of 0.047 K per year versus 0.028 K per year for the actual NCDC anomalies in this region. Many among the alarmed will like this: it suggests NCDC has *underestimated* warming in this region, at least since 1979, although it can’t speak to prior to that.
I will also note that I have done the analysis for USHCN: again there is a *cooling* bias if any, but this time it is tiny to the point that it is almost certainly not statistically significant-it’s certainly not a significant fraction of the trend. This good agreement over the US ought to convince anyone who thinks the USHCN algorithm is good that my technique is probably pretty good. Of course, I’m sure many here, and many skeptics, will decide my technique can’t be right, USHCN has to be warm biased. Has to be.
Now that I have all skeptics ringing their hands angry at me and all the alarmed cheering me, I can drop the real bombshell. I have done this same kind of analysis with global data from various sources, and whether I use GISS or HADCRUT4 or even Cowtan and Way (and I would bet good money NCDC, too, though I can’t recall doing the analysis for that) and it generally shows that the factor for UAH as a predictor for surface temp anomalies globally should be ~.77 (in one case with GISS I got an even smaller factor!): using such an analysis I generally find a *warming bias* of the global surface temperature datasets on the order of ~.1 K per decade. That is a *huge* warming trend bias. Keep in mind the surface warming trend is something on the order of-what, .15 per decade, give or take a couple of hundredths? Wow. That’s two thirds of the warming in the last 30 years that is just a warming bias.
Incredible turn around! I can already hear the hissing and booing as the alarmed recoil in pain! “He has trickeds us! Tricked we says! He is tricksy!” For they thought, here’s something wonderful, the satellite data showing that warming is understated! And when I pull back the curtain to reveal the rest of the iceburg, it is like Dracula finding he has been made to touch a cross.
Wow. My most beautiful mixed metaphor yet.

Chris Schoneveld
December 21, 2013 12:01 am

Bob, I cringe every time I read on WUWT that temperatures warm. Also you abuse the English language when you write “surface temperatures there warmed very little”. Is it so difficult to write: “surface temperatures there increased very little”? A temperature does not warm. A temperature is not a physical entity (like air or water) that can be warmed.

Bill Illis
December 21, 2013 1:07 am

If anyone wants to take this one on, the Carbon Dioxide Information Analysis Centre (CDIAC) has been archiving a lot of the old temperature records.
They have an archive of 518 Russian temperature stations and another of 223 former USSR stations from 1881 to fairly recent. Looks like a lot of work however.
And for serious users, there are a lot of old records in the Archive, this http:/FTP directory. Have a look around. The description of what’s in each NDP__/whatever subdirectory is at the bottom of the page. Some are interesting, some not so, some requiring a fortran program.

Ron Stewart
December 21, 2013 1:17 am

Burt Ruttan interview explained most of this.

Pamela Gray
December 21, 2013 12:21 pm

Large hotspots on the globe look like jet stream sourced blocking highs to me. The shape and location of the Russian hotspot is correct for such an occurrence. My hunch is that there are correlating weather pattern variation events that would predict such a temperature trend. Certainly the anthropogenic portion of CO2 greenhouse effects would not be able to produce such a large scale and fairly well defined and oval shaped event. Not enough energy available to produce the regional large scale atmospheric conditions necessary for such a temperature rise. Anthropogenic CO2 can be easily and readily dismissed in this case.

Matt G
December 21, 2013 1:30 pm

Having a hot spot with a significant positive NAO period is quite normal for Russia when mild air from the Atlantic reaches Siberia. The issue is how much the hot spot shows compared with more reliable alternative data sets. It has just spread the worse anomaly over a massive area that is only located in much smaller regions than shown.

Matt G
December 21, 2013 1:41 pm

Taking the update into account,
The ice extent in 1988
does decline a little to the North of Russia in 1989
and declines further in 1993.
Don’t see how a further change has no immediate affect in 1993, yet did in 1989.

Matt G
December 21, 2013 1:56 pm

Therefore this spread of data over Russia since 1989 with the change in lack of stations used is likely the cause of the 1c+ sudden rise in the graph.

December 21, 2013 6:44 pm

Figure 1 shows Tasmania as above average for November. In reality, this has been the coldest and wettest spring for ~30 years. Usually I sow pumpkins in November. Soil temperature required for germinating pumpkins (15C) wasn’t attained until mid-December, far too late to sow. Pumpkin vines need to be a metre long by Christmas to produce a ripe crop. I’d say Figure 1 is totally bogus so any argument over other parts of the chart could well be on a par with debating whether Earth is about to be invaded by a gigantic swarm of twelve foot piranha bees or eaten by a gigantic mutant space goat.

Pamela Gray
December 22, 2013 7:32 pm

Some of our hottest days in the late Summer in NE Oregon are preceded by a long cold Spring, and sometimes with a Spring Wheat killing freeze. So just because an area on the globe is warm, does not mean that we are not entering a regional and possibly global cooling period that could become dangerous.

Brian H
December 26, 2013 11:32 pm

As the GIT implies above, the first assumption when examining an NOAA figure, or graph, or data set is that it is tweaked and manipulated up the kazoo, and utterly unreliable. It’s come to that. Look around for a curtain to yank back.

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