Science is good fun. Lately we’ve been working with an unnamed paleoclimatologist who goes by the handle of Delayed.Oscillator. He’s been good enough to answer some questions on dendroclimatology temperature reconstructions. Recently I’ve made comments about the Yamal series data and Briffa’s corrections about not allowing for trees to grow faster as they age. Briffa uses an exponential decay for his corrections to Yamal tree growth which looks like this:

Figure 1

The data is divided by the red line in top pane in the above plots. It has been my contention that since the red line drops well below the rest of the data it amplifies the older data creating an artificial uptick at the end of the reconstruciton. DO has made the following statement at his blog:

Let me be as clear as I can be, there is no sign that I can detect that it is old trees that increase their growth at Yamal (even if identified, this phenomenon would require some hypothesis as to the cause), At Yamal, a portion of the old trees are those that were growing together during a period of climate warming. If you examine the raw ring width, there are a few fossil series that have rapid increases toward the end. If Jeff’s hypothesis were correct, we’d expect these to be the oldest, right? In fact, the seven subfossil samples I identified as having rapidly increasing growth in their later years, six had a wide range of ages from 90 to 180 years (this comes with the caveat that we don’t know the exact pith age).

Now DO beat me to an analysis suggested by Steve Mosher to separate the recent old trees from the historic old trees and verify whether the U shape in pane 1 above (the average of all old trees) is a result of recent climate or is a standard shape in older trees. There are plenty of explanations as to why trees can grow faster in older age that we can be certain DO is aware of so the parenthetic portion is a bit grumpy of him. In DO’s conclusion his plot looked like this:

Figure 2

This plot represents all trees with a last measured ring width dated before 1950. My own contention was trees greater than 200 years could show the increased growth though so it wasn’t enough to show what happens. We need to see the old trees separate from the record as Steve Mosher pointed out. So here’s a plot of the pre-1950 trees similar to that shown by DO.

There are only 19 trees to work with but here they are with an Esperesque spline fit.

Figure 3

You can see the curve in the top pane has almost the exact same shape as DO. Not bad really. In the bottom pane there is no apparent upslope in pre 1900 Yamal area trees with ages greater than 250 years. Now when this is compared to 250 year old trees with last RW after 1950 the plot looks like this.

Figure 4

The trees (first pane) show a clear upslope in later years. On the surface DO seems to be correct on this point. The trees ending before 1950 look like this.

Figure 5

Figure 5 just left more questions for me. Many of the trees seem to reach a minimum at 200 years of age and peak upward trickling back down before ending their lives. Is it possible that the difference between the post 1950 Yamal data is that they haven’t slowed down in their last 20 years before dying yet? Would that make the downslope at the end of pane 1 Figure 3. I don’t think it’s unreasonable because you can see the mid life minimums in L13371, PO9281 L21081 L11631, L00861,L01181, L01041, L13181 even though they have different climatological conditions. They started growing faster after about 200 years and then trickled off at the end. I’m still not certain that most trees don’t show an uptick as they age keep in mind that this data is the extent of my experience.

The last graph P09281(last graph above) starts increasing after 200 years and then drops off and dies. It getting warmer from 1100 to 1150 according to the last graph but not according to L09301 (third one down on the right) which was dying at that timeframe or L12641 which in 1100 AD was before its pre-200 year mimimum and still dropping (these graphs are uncorrected though so perhaps reasonable growth standardization corrections can turn it into warming).

Next is the same kind of plot for some of the Post 1950 trees. These trees haven’t shown their end of life dropoff cause most if not all were alive at the time of sampling.

Figure 6

There are a lot of trees which show increases in growth post 1900 but there are also quite a few which show less growth post 1900. Really lousy thermometers these. From Figure 4 pane 1 we would expect a steadily warming climate for the last 200 years (that’s the average) and that’s pretty well what we see in pane 3 of Figure 4.

So then I decided to calculate least squares slope fit’s to the ring widths since 1920 instead of by tree age. Since the slopes are from trees over 250 years that end post 1950, the EXP correction from Briffa or the spline corrections would have a negligible effect on slope. In a huge surprise, there are a lot of negative slopes in these trees during the greatest warming in the Yamal area. Remember these are 250 year old trees from 1920-1996 so it’s 174 years and older in pane 1 of Figure 4. – Look at pane 1 of the figure and you’ll see why negative slopes after 174 years are a surprise.

[1] 0.09525731 -1.67765546 0.08194925 -1.57939710 1.56477900 -1.08070478
[7] 0.10164856 -1.66012718 0.01617010 0.79523364 0.29315124 -4.15916120
[13] -0.15019269 -4.84568356 -0.31551278 0.02163996 -1.24181240 -1.88490060
[19] 0.10123042 0.38330849 -0.05190331 2.60531409

There are 11 negative slopes of 22 series! That was unexpected! What’s more the average slope is -.572 (negative!) with a standard deviation of 1.66 – Not exactly precision agreement. Now 1920 was picked at random with consideration of when we started producing lots of CO2 around then. Be careful and don’t overconclude form these slopes – with this noise level different years will give different results. IMO the main conclusion from this is that the slope is not strongly positive – no indication of warming!!!! and not clearly defined! Since there is no evidence that trees are thermometers, rather than conclude anything about warming lets say tree growth is apparently not unprecedentedly large in recent years. Below is a histogram of the slopes. How’s that for a bell curve!

Figure 7

So the next thing was to look at uncorrected tree ring data for trees at least 250 Years old which have a last ring after 1950. These are probably 100% living trees but we don’t know.

Figure 8 - Yamal Area Data Simple Mean

This does not look like Figure 1. The EXP corrected version (Briffa’s style) results in the plot in Figure 9.

Figure 9

The exponential hockystickization correction of Figure 9 is just enough to push the blade into unprecedented territory but not by much. This doesn’t look like Figure 1 either. The spline corrected version of the data is Figure 4. Figure 4 looks a lot like the simple mean of Figure 8, this represents reasonable standardization of old trees across the time series. This is my #1 criticism of Yamal that the original version didn’t look like the mean, indicating the blade was created by the standardization.

Ok, so let’s get into some new stuff. The total dataset in the Yamal area includes the following groups:

Yamal, Live, Jah, Por, Yad, Khad, Russ035

The russ035 is from SteveM’s sensitivity test. You learn so much more playing with the data than you can reading about it. I discovered that in several cases the ‘new’ briffa sensitivity test has the same core ID’s as the old briffa data.

The total unique series names available is 302, Yamal itself had 252, Live -17, jah – 25, por-12, yad – 10, khad – 18 (Russ035 – 35 – SteveM’s not used by Briffa). In Briffa’s sensitivity test, there were 82 series which should have been unique to prove that Yamal is not sensitive to different datasets. However, the sum of the non-Yamal series = 17+25+12+10+18 = 82 series added to Yamal for sensitivity. However there are only 302 – 252 = 50 unique new series from the original Yamal. Thirty two series were used at least twice in the Briffa sensitivity test.

JAH141 – *
JAH162 – *
M021
M202
M331
POR011 – *
POR031 – *
POR051 – *
POR081 – *
POR111 -*
X02S
X13
YAD041 – *
YAD061 – *
YAD071 – *
YAD081 – *
YAD121 – *
878031
878081

Stars are the original dozen.  You need to be following along for this but if the dirty dozen are re-included in the sensitivity test to determine the effect of the dirty dozen? Is that a fair test?

Probably the main criticism of Yamal by some dendro’s was the low core count in recent years. If we don’t double up the trees and properly sort the data, what is the core count for all the data in the region and how does that compare to the original Yamal? Core counts are shown per year in table 1. All series include SteveM’s schweingruber set.

What does the full RCS style hockeysticization reconstruction look like now with all trees included and the original Briffa style corrections.

Figure 10

Core counts in 1996 have doubled now to an anemic 9 but the blade was cut from a Figure 1 peak amplitude of 2.75 (red line) down to 2.25. A good size jump downward from the new trees but it’s still an excellent quality hockey stick. If you cut off the last 6 years the plot doesn’t look the same but that’s not the point. RCS corrections to the tree ring widths rely completely on the homogeneous nature of the data. We are applying the same correction to all the data. Differences in homogeneity will result in unexpected results. Since RCS is an ad-hoc style correction, great care needs to be used to determine if the results make sense.

Briffa’s correction used for Yamal was an exponential decay. The exponential curve plot (Figure 10 pane 1) tapers off to a flat horizontal line becoming pretty horizontal after 100 years. To be clear, there isn’t much slope in the correction after that time. This means that for the average older trees in the recent 100 years, a correct standardization would reproduce results similar to the mean of the data. There just shouldn’t be a huge difference because there isn’t a huge slope correction. My contention is that there is a big difference created entirely by RCS reacting with the inhomogeneity and has resulted in coining the phrase hockeystickization.

I must mention that Roman M is the first to point out this effect on a CA thread. Roman does a lot of things first. Anyway, this is what the mean of the data looks like. In this form the oldest data is distorted but the recent data should be reasonably similar to Figure 10.

Figure 11

Now that looks a little different wouldn’t you say?!! This means that there is nothing unique or unusual about the 20th century tree rings. There is an upslope in the last few years of the plot represented by very few trees but the ring widths don’t exceed 1930.

Conclusions:

- Briffa’s sensitivity test used the original Yamal data and got the original result!!

- RCS standardization is reacting poorly to the typical data and creating an artificial hockey stick on the end of the data!! – Homogeneity must be questioned.

- There is nothing unique or unprecedented in recent tree ring widths in Yamal area.

- Average slopes of living trees in Yamal area after 1920 for older trees are actually negative. I picked 1920 for industrialization, I did not try other years or pick the best one on purpose. Results are going to be dependent on the choice of start year.

- The standard deviation of the slope variance is huge, selection of different years will result in huge differences. – The data is noisy!!

- The math is creating most of the variance (hockeystick) in Yamal’s new version in recent years and must be thoroughly (and honestly) examined to accept any sort of corrections.

Somehow the pro’s don’t agree!

——-

Now the next step will be to redo the whole sensitivity test by Briffa without the dirty dozen and see what we get! I’m tired though and these posts take a lot of work.

Does anyone want to bet what all the data looks like with the dirty dozen removed?

]]> http://noconsensus.wordpress.com/2009/11/08/the-unstoppable-dirty-dozen/feed/ 2 Jeff Id yamalex non20c[1] Briffa trees spline pre1950 Briffa trees spline post1950 Briffa trees older than 250 pre 1950 Briffa trees older than 250 post 1950 Briffa trees older than 250 post 1950 hist Briffa trees MEAN post 1950 Briffa trees EXP post 1950 Briffa trees EXP all Briffa trees MEAN all http://noconsensus.wordpress.com/2009/11/08/american-folley/ http://noconsensus.wordpress.com/2009/11/08/american-folley/#comments Sun, 08 Nov 2009 10:15:11 +0000 Jeff Id http://noconsensus.wordpress.com/?p=6012 ]]>

Please read this at Watts Up With That. We don’t have much time left and this is a true tipping point, the suffering will be of a greater magnitude than anything predicted by AGW Advocates.  It will not be localized to America.

A Tale of Two Overkills

It’s time to stop the insanity and bring reason back to governance.

We are such fools.

This thread is in replacement of the last. You may leave comments here about Kimberly only, at least this we can agree on. The last thread was a bit wild but we’ve said our piece. Please don’t make attempts to continue the last thread here.

Kimberly Munley, the police officer who heroically charged into the enclosed room to confront the extremist Muslim terrorist and put to an end to the worst shooting rampage in US military history and the only extremist attack on US soil since 9/11. She nearly lost her life after being shot by the man multiple times.

Thank you for halting this evil tragedy when you did.

William Connolly has been gracious and not snipped any of my comments on the Tiljander debate at his blog.    I’m about ready to add him to my blogroll so this isn’t an attempt to bash his blog, however he wrote a beauty of an explanation as to why it’s ok to read thermometers upside down again.   It never ceases to amaze how far people can go to reason into almost any position. It’s like people who complain about the idiocy of government and continue to vote for more, like that will fix it.  Anyway his rationale is entertaining.

Imagine a climate proxy, accurate over the last 2kyr, that shows (for example, let us suppose) a warm period around 1000 AD and which, undisturbed, would show the recent warming. Further suppose for definiteness that this proxy is of such a nature that increases in the proxy value represent increases in temperature. Imagine this proxy is contaminated with non-climatic signal over the last 200 years, enough that the climatic signal is overwhelmed. Suppose that this contamination is of such a nature that it leads to a strong decrease in the values of the proxy over the last 200 years. Such a proxy (call it A), fed into the Mea algorithm, will be flipped over (due to its negative correlation with recent instrumental temperature) and will contribute a net cold influence around 1000 AD. How much it contributes will depend on how well it correlates to recent times.

Now there isn’t anything wrong with this paragraph that I can see.  He’s got a good handle on the multivariate nature of some of the regressions.  Consider that last sentence (which is correct) how much it contributes depends on how well it correlates.   Dead on for one of my biggest criticisms, these regressions are a form of data sorting  and are just as significant a no-no as the data elimination sorting where data is physically thrown away.  I think of the weightings of  a MV regression like modulating the data  (information) partially away rather than fully.  The fact that the modulation occurs on a correlation basis makes the complaint about the method exactly the same as the correlation based elimination methods.

Not good in my opinion but what can we do.

The next paragraph:

Now imagine a similar proxy, except the nature of the non-climatic contamination is such as to add a strong increase over the last 200 years. We’ll call it B. This time, the proxy won’t be flipped over, because its correlation to recent times will be positive. But the variance into the past will be strongly de-weighted (because we’ve just added an artificially large postiive trend). So it will imply not much change around AD 1000. But now we see this, we can see that the same problem applies to series A: unless, by bizarre co-incidence, the negative non-climate signal just happens to match the true positive instrumental signal, the variance in the past will be wrong. And since we’ve had to assume that the non-climate signal overwhelms the climate one, its likely that the recent variance will be too large, so the past will be de-weighted.

So he makes the assumption that the positive trend in the contaminated signal is larger than the result.  I’m not sure he’s looked at HadCRUT lately but I’m not certain the assumption is valid.  Let’s assume it is though.  So he’s saying that Tiljander doesn’t matter upside down because the real climate signal – which there isn’t one – is deweighted by some fraction (say 50 percent) and averaged.  So the inverted signal is deweighted to a point that William has defined it as a red – herring.

Well everything was going well until the conclusion.  There are 4 tiljander proxies in Mann 08 three of them were inverted and they all have huge blades even after the log of the varve thickness is taking one of the biggest leaps of faith I’ve witnessed to convert it to temp.

It’s rather amusing considering that the assumption is for deweighting where there is absolutely no evidence that deweighting occured.  In fact the Tiljander proxies (specifically the known bad data portion) correltated well (strongly negative) to temperature and therefore were flipped over and probably received reasonable sized weights.  Even that’s not the point though.  We already know that each tiljander series is 1 of 484 which passed correlation screening.  Even with full weighting equal to every other proxy it’s 1/484th of the reconstruction.  Nobody ever expected fixing Tiljander to fix Mann08.  Mann 08 is a disastrous attempt to kluge together a hockey stick, nothing more.  It’s worth adding that there is a good chance the series was more heavily weighted than William asserts but it still wouldn’t move the result very much.

The point is that the thing was used upside down.  It is known, thermometers read upside right (even in kindergarten) and therefore flipping them is an error. Even in kindergarten the teacher would tell you to flip it the other way round Mikey.  The physical meaning of the Tiljander proxy is warm weather melts more glacier and carries more sediment.  Flipping it upside down changes the physical interpretation to warm weather freezes more water and carries less sediment.

In case you were wondering, this does not make sense. The most Bizzarre thing about it, is the continued defense of this pile of poo for a paper.

Yesterday, in a fit of idiocy I took a stroll around Real Climate.  I found Michael Mann (who claims to be somewhat conservative) pounding on every conservative institution he could find about climate disinformation.   How many analogies are there, pot and kettle, glass house, pants on fire.  Why he didn’t just say oops, flip it, claim no change and move on is beyond me.  It could have been a huge PR win that would make the important, equally flawed yet difficult for laypeople to figure out part of his case sound reasonable to the public.  Instead we get denial, absolutely goofy answers from Mann and now an odd hand waiving half-endorsement by a well educated AGW believer of an obvious mistake.

I’ve stated here on several occasions that the ‘Recent’ Arctic ice thinning is more likely a current change issue rather than a temperature issue.  Differences in flow change the transfer of vastly more energy than a couple of degrees of air temp, however changing air temperatures are a strong indicator of differences in regional water flow.  This effect is very visible in the arctic ice videos posted here.  Recently Dr. Arnd Bernaerts asked by email that I call attention to  his paper on Arctic Warming for a period we don’t hear about enough.  He has a shorter version link which he also gave here. I really enjoy the historic discussions of climate and the paper is quite readable so I’ve put the whole paper up instead.

——————

The Circumstances of the Arctic Warming in the early 20^th Century

Author: Arnd Bernaerts

Dr. Arnd Bernaerts
Hamburg, Germany

Abstract

The Arctic has a crucial role in the world’s climatic system, and global warming may have an amplifying effect. The recently observed thinning of the sea ice has alerted scientists and policy makers alike. That was quite different when a similar warming occurred 90 years ago, which is still regarded as one of the most puzzling climatic event during the last century. That needs not to be, if the situation is being viewed from on oceanic perspective, together with the fact that the winter air temperatures in the higher Northern Hemisphere are greatly influenced by the ocean, particularly in the North Atlantic, which is partly free of sea ice up to the Fram Strait. Here also ends the West Spitsbergen Current, a current which supplies the Arctic Ocean with warm and saline Atlantic water. Already back in 1920s air temperature observation showed a strong warming at Spitsbergen during the winter season. By analyzing the winter temperature profile of five coastal stations it can be demonstrated that the climatic shift at the end of the 1910s had been closest to Spitsbergen, allowing the conclusion that circumstances related to the West Spitsbergen Current have caused the early Arctic warming almost a century ago.

Introduction

The Arctic is an ocean. By the Fram Strait at about 80° North, between Greenland and Spitsbergen, it is connected with the Atlantic, which serves as a major gate for the supply of warm and saline water to the Arctic Ocean, which is coming with the West Spitsbergen Current. The subject of the paper is whether the warming in the early 20^th Century has been caused here.

The term “circumstances” implies the observation or influence of “existing conditions”. Here the use of the term shall mean the presentation of circumstances which rectify to draw certain conclusions concerning the period of Arctic warming in the early 20^th Century on how it started and how it shaped up. The paper will cover both aspects, but with a clear priority for the circumstances around the year 1919 when the warming started. The method of investigation will be explained in the second section of the paper, based on a general picture that will be presented first of all. The third section is about the “circumstances” which will analyze temperature records in the Northern North Atlantic realm that may provide clues concerning the location, the timing, and the likely source of the warming event, and thereon discuss the information.

Arctic warming now and than

Overview

Nowadays the polar region is often mentioned, because the Arctic is currently experiencing a rapid warming with a dramatic loss of Arctic sea ice, which could be due to a combination of a global warming signal and fortuitous phasing of intrinsic climate patterns (Overland, 2008:289). The rapid warming is likely to be anthropogenic (IPCC, 2007b:83, 86). According to Serreze et al.: “Rises in surface air temperature (SAT) in response to increasing concentrations of greenhouse gases are expected to be amplified in northern high latitudes, with warming most pronounced over the Arctic Ocean owing to the loss of sea ice“, (Serreze, 2004). A recent U.S. government report concluded that the “temperature change in the Arctic is happening at a greater rate than at other places in the Northern Hemisphere and that the ice cover in the Arctic began to diminish in the late 19^th century and this shrinkage has accelerated during the last several decades (US Geological Survey, 2009:486, 6). But the recent warming is the second during the last 100 years. The last IPCC report refers to a warm period from 1925 to 1945 (IPCC, 2007a:7), while an earlier report mentions a warming around the years 1920-1940 (IPCC, 1990:215). More to the point is the finding by Polyakov et al. that concludes: the warming during the 1920s –1930s was very fast in spring, autumn and winter, but much weaker and slower in the summer, while the period between 1918 and 1922 displays exceptional rapid winter warming (Polyakov, 2003:2072). Nevertheless the authors assume: that the complicated nature of the Arctic temperature and pressure variations is making the understanding of possible causes of the variability, and evaluation of the anthropogenic warming effect most difficult” (Polyakov, 2003:2076).

The general situation from the late 1910s to the 1940s is well illustrated by the annual temperature records from Spitsbergen, Angmagssalik, and Andenes (Fig. 4 to 6). They indicate a shift of the mean temperature level of approximately 1.5 to 3 degrees between the decade prior and after 1920, and that the period with an increased mean lasted until about 1940.

How is the EAW explained today

Although 90 years have passed since the earlier Arctic Warming (EAW) commenced, it seems that the issue is still one of the most puzzling climatic anomalies of the 20^th century (Bengtsson, 2004:4055), and there are many questions not answered yet. Instead the matter is often sidelined by regarding it as:

• Natural variability is the most likely cause (Bengtsson, 2004:4045);
• We theorize that the Arctic warming in the 1920s/1930s was due to natural fluctuations internal to the climate system (Johannessen, 2004:341)
• The temperature anomalies are due primarily to natural variability in the weather system (Overland, 2008:81).

Such notions explain too little. The matter becomes even more critical if claimed without sufficient prove, for example: That the past 100 years are significant for the changeover of a climate system dominated by natural forcing to a climate system dominated by anthropogenic influences, as done recently by S. Brönnimann et al., while admitting that “Our understanding of the climate mechanisms operating in the Arctic on different timescales is still limited”. (Brönnimann, 2008: 3, 20) Is it helpful to dramatize the shrinking sea ice during a recent time period, if one is not fully aware of what happened in the early years of the last century?

Actually, few but sufficient air temperature data are available since long time. The paper will use them to show that the early warming was initiated and sustained over two decades by the West Spitsbergen Current, a branch of the Gulf Current in the North Atlantic. Naming the sea as the cause of the Arctic warming from 1919-1939 could help to understand better the current situation in the Arctic.

How was the EAW primarily recognized

On the 2^nd of November 1922, The Washington Post published the following story: “Arctic Ocean Getting Warm; Seals Vanish, and Icebergs Melt”. The corresponding report in the Monthly Weather Review of November 1922 (Ifft, 1922:589) had also stated that the ice conditions in the Northern North Atlantic were exceptional; in fact, so little ice has never before been noted. Few years later the Spitsbergen data were published with the accompanying text saying that it is “Probably the greatest yet known temperature rise on earth (Birkeland, 1930:236). One of the first to investigate the EAW was R. Scherhag with two papers in 1936. The first he called “A remarkable climatic change across Northern Europe” (Eine bemerkenswerte Klimaänderung über Nordeuropa”) (Scherhag, 1936a:96), while the second draws the attention to the change of temperatures and atmospheric circulation during the previous 15 years since 1920, (Scherhag, 1936b:397).

From thereon the subject received considerable attention until the breakout of World War II. For example, after R. Scherhag had attributed the warming to a change in atmospheric circulation, his American colleague C.E.P. Brooks objected by saying:

while explaining the general situation as follows:

The sea was practically only mentioned once by a Russian scientist informing the Royal Scottish Geographical Society in Edinburgh, on the 30^th of January 1935:

The WWII ended the lively discussion. As at the same time not only the exceptional warming in the Arctic ended, but actually the Northern Hemisphere went through a global cooling for three decades, the EAW was a non-issue for more than 50 years, only returning back on the agenda with the debate on global warming, and when the Arctic summer sea ice started to diminish significantly in the 1990s.

Scope and method of investing “Circumstances”

The EAW offers a unique opportunity to confine the analysis to two aspects concerning the warming from 1919-1939, namely with regard to the annual season and geographical localization.

Winter air temperature

Due to the lack of any sufficient sea surface temperature data (SST), the investigation will assess the warming conditions only by the use of surface air temperatures (SAT). It will furthermore confine the investigation to the winter season for two reasons.

• Only the winter seasons from 1919 to 1939 show a strong warming, while the increase during the summer periods is only modest.
• The direct influence of the sun can be neglected during the winter season. The sea, as far as not covered by sea ice is getting a dominating role during the sunless winter season.

The reduced influence of the sun during the winter season is the pre-eminent factor. Instead, in the northern part, north of Iceland and the Shetland Islands, the ocean is providing the bulk of heat to the atmosphere. After all, higher temperatures must have been generated by something; either the rise had been observed in the location, or at another place, which could be identified, from where the heat had been transported to the place of measurement. For this purpose some coastal stations, in close reach to the sea, will be analyzed with regard to the timing and values of changes observed. Significant difference diversions, delays may rectify drawing not only the conclusion that the EAW generated in the North Atlantic, but also that it was generated by the West Spitsbergen Current.

The core period for the investigation are the years around 1918/1919 when the warming started. The suddenness of the shift could be one of the key indicators for the mechanism of the change. However as the number of stations eligible for such investigation is very limited, it seems advisable to present at first the EAW period from 1919 to 1939, so that the analysis of individual stations is seen in the wider context of the period.

Showing that the cause of warming had been due to the sea could possibly even answer the question concerning the “change in air circulation” (see above: Scherhag & Brooks), or the question a Russian scientist raised ten years ago: Why are the maximum climate fluctuations confined to the Atlantic sector of the Arctic?” (Zakharov, 1997:71). A higher heat release from the sea will inevitably influence the winter temperature and the atmospheric circulation, locally and in a wider region.

Geographical localization

It is furthermore reasonable to choose the region where the Arctic Ocean and the Atlantic meet, as it is acknowledged since the 1930s that the center of the warming had been here, as the material by Scherhag shows (Fig. 7a-c). Even more recent research confirm that the warming occurred during winter close to the Fram Strait and the pick was in the wider region of Spitsbergen, with a varying intensity from south-west Greenland to the Russian Island, Severnaya Zemlya, at 80°N and 100° East; see Figure 8 for the 1920s, and Figure 9 for two decades from 1920 to 1939.

Furthermore, due to the fact that the Arctic Ocean used to be covered by sea ice one hundred years ago (Fig.2), a substantial increase of the air temperature during the winter season could not have been generated in ice covered areas, which makes it virtually impossible to transfer a great amount of heat from sea to the atmosphere within a short period of time. This is practically only available off Spitsbergen, with the most northern sea ice free area during the winter season. (Fig.11) On the other hand the lack of sufficient temperature data from the North Pole restricts the scope as well. Practically, north of app. 76°N only the Spitsbergen record since the 1910s is available, which start in 1912. (Fig.4)

The Circumstances of the EAW

The selection of stations

In the Northern North Atlantic there are about one dozen stations which have a record covering the period prior the 1910s, some since 1880, provided online by NASA/GISS (Goddard Institute for Space Studies) by annual record plots, as reproduced in Figure 4-6, and “monthly data as text” (Nasa/Giss,2009) As it would not necessarily enhance the analysis if all available data records are presented, five representative stations have been selected for the Northern North Atlantic region, namely two from the western part (Greenland and Iceland ), two the eastern part (Norway), and the Isfjord Radio station on Spitsbergen. See: Figure 10

Although all stations are close to the sea, they are not necessarily close to open water, due to very different sea ice conditions. This is particularly the case with regard to the eastern coast of Greenland where a current is moving cold water from the Arctic southwards. The maximum sea ice extend usually appears in April and may vary considerably from year to year. Insofar the sea ice conditions during winter 1917/18 (Fig. 11a-c) reflect an average situation.

The annual temperature trend from 1919 to 1939

On first view the annual record of the stations presented (Fig. 4 to Fig. 6) seem to be quite identical. Prior to the year 1920 all stations have a lower level of temperatures over the years shown than thereafter. At a second glance one can observe that prior to 1920 the lowest annual value differs between west and east of the North Atlantic. In the former it is 1918 and in the latter it is 1917. After these years all stations, except Angmagssalik, observe an increase of the values, which is very pronounced at the eastern stations, with a first maximum in 1920. At Grimsey the increase is modest. At Angmagssalik the value for 1920 is actually lower than in 1919. However, the increase of the annual values for two decades at all stations is not very impressive.

The temperature during the winter 1912 – 1923

The following Figures 12 to 16 show the combined figure for the winter months January, February and the previous December (D-J-F).

Which are the general observations that can be made (without Spitsbergen)?

• At the Norwegian stations, there was, prior 1919, a longer period of cooling since about 1914.
• At the stations Angmagssalik and Grimsey the period from 1912 to 1917 remained in a moderate band, with only one big exception, in 1918. (see below).

Table 1: Winter temperatures from 1912 to 1923 at five stations (see. Fig.9)

What is particularly significant regarding the Spitsbergen data?

• A much higher range of temperature variations (10°C between 1918 and 1919) towards the other stations (e.g. Vardø ca. 2°C).
• In January 1919 the mean was as low as –5.7°C, while the pervious years had been well below –20°C (except 1915, -12.5°; and 1913, -14°C). From thereon the January mean got warmer (e.g. in 1922, -3.8°; and 1933, -2.3°). The following table shows that the other stations are quite distinct from Spitsbergen in this respect.

Table 2: A comparison of the January 1919 with the years 1916 to 1918 at five stations (Fig. 9).

The low D-J-F 1918 month figure at Angmagssalik and Grimsey

While the time period from 1912 to 1917 corresponds largely with the data from 1920 to 1923, the 1918 value with –13.8°C is surprising and unique in the data record since 1895. The previous coldest winter 1896 was –11°C. The enormous temperature variation at these stations in 1918 is confirmed by three other Iceland stations. For example, with regard to Reykjavik, the D-J-F figure is for the years: 1917 (-0.3°); 1918 (-3.5°); 1919 (-0.8°), and 1920 (-2.5°), showing the next negative figure only ten years later in 1931 (-0.3°). However, any correlation with the start of the arctic warming seems remote. Here we can only guess that the low temperature had been supported by a temporary and regional change of sea surface temperatures, which might have been enhanced by colder water in the Northern North Atlantic that had also bought colder conditions to the Norwegian stations and Spitsbergen over the brief period of time from about 1914 to 1918.

Spitsbergen compared with Andenes and Vardø

On first glance, the temperature trend at the three stations looks as if there is little to comment. The general trend is quite comparable, although it is noticeable that the cooling phase prior 1920 had ended at Spitsbergen in 1918, and one year later at Andenes and Vardø, in 1919.

Furthermore the figure value of changes is remarkable different. Although it seems to be a very modest aspect, with regard to the prevailing sea current system in the region, presumably it is of considerable relevance. Actually, all three locations are directly connected to the warm North Atlantic Gulf Currents. Along the coast of Andenes is the Norwegian Current, which separates here into the West Spitsbergen Current carrying warm water northwards to the Arctic Ocean, and the North Cape Current moving eastwards with warm water for the Barents Sea. As all three stations are in a very close distance to the warm water currents mentioned, the huge difference in the change of temperature is remarkable. Furthermore, the distance between Spitsbergen and the two continental stations is with less than 1,000 kilometers, not only quite modest, but due to the considerable speed of the currents, the water needs from Andenes to Vardø, and Spitsbergen only a few weeks. Insofar one could have expected a much less difference between the north and the south, a matter which will be raised in the next section again.

The situation at the West Coast of Greenland

One Scherhag figure (Fig. 7) indicated that there was a warming at the South-West coast of Greenland from 1920 to 1930 as well. The station Godthab Nuuk, which is representative for few other West Coast stations, shows a temperature shift starting with the year 1923 (Table 3). The succeeding two years, 1921 and 1922, had been below the previous average.

Table 3: Godthab Nuuk, West-Greenland (64°N, 52°W),
Winter temperature (D-J-F) 1912-1941; Source: NASA/Giss.

The warming period since 1923 is modest and rather short, lasting only until the early 1930s. Two further figures published by Scherhag, here redrawn (Figure 17 and 18); do not indicate a significant warming in south-west Greenland since the mid 1930s.

The situation in the east of Vardø

Concerning the full time period in question, from the late 1910s to 1940, it is not easy to assess the situation during the initial years of the period, while a substantial warming during the 1930s is well established for many decades. With reference to the Scherhag figure (Fig.7) the 1920s indicate only a modest rise of the winter temperature, which can be attributed to the increase at Spitsbergen and Greenland and carried from there with the Jet-Stream eastwards to Severnaya Zemlya Island and beyond. This explanation is not sufficient for the strong warming in the east of Spitsbergen during the 1930s, which is not only confirmed by the Scherhag figure (Fig. 17 & 18), but also by Figure 8 which is based on an image published by H.H. Lamp in 1982. The strong warming since the mid 1920s was presumably enhanced by Atlantic water that had entered the Arctic Ocean, and moved eastwards along the continental shelf in the Nansen Basin. Due to observation by Polyakov et al. that a sea water anomaly would need app. 4 to 5 years to reach the Laptev slope (Polyakov, 2005:1), which is immediately east of Severnaya Zemlya Island. However, it can not be excluded, that an impulse came also by the North Cape Current via Barents Sea and Kara Sea. As there is no indication what so ever, that any heating potentials had been in this region prior the warming at Spitsbergen, the issue is here not further scrutinized.

Discussion

The basis for discussing the matter is the widely acknowledge fact that the center of the Arctic warming in the early 20^th Century was Spitsbergen, covering a region from south-west Greenland to the Russian Island Severnaya Zemlya. This was at least the situation in the initial phase. It is therefore possible to concentrate fully on the circumstances of the warming, with regards to when and why.

There is no information available about a region or location with a substantial or noticeable heating potential from where the rise of winter temperature at Spitsbergen could have been evoked at the end of the 1910s. That applies for the Northern Hemisphere in general as well as for the North Atlantic in particular. Although all investigated stations have registered a warming after 1919, but in no case, earlier than at Spitsbergen. At the Greenland/Iceland stations the change in winter temperature was modest and manifested only after 1920. With regard to the station in the very north of Norway, the increase from 1918 to 1919 by 1.5° to 2.5° hardly permit to call this a heating potential, when at the same time the rise at Spitsbergen was 10°C. That may already serve as a first indication that a change in air circulation, as claimed by Scherhag (see above) is a rather weak argument. Furthermore, after 1919, the temperatures at Andenes and Vardø, between 1920 and 1923, (Fig. 14 & 15 and Fig. 6) leave little room for the anticipation that heat could have been transferred from here to the north. At Andenes the rise lasts only until 1921 and the figures are lower in the next two years. At Vardø the figures are neutral, while Spitsbergen records his highest figure in 1923. However not the trend during the initial years, but the fact that the trend at Spitsbergen did not limped behind Andenes and Vardø is remarkable, and that the heating potential and the continental stations was in no way excessive. The higher temperatures have also not been generated in Europe; see Figures: 8, 9, 17 and 18. This can be a clear indication that the volume of warm Atlantic water traveling northwards with the Norwegian Current did not increased during the time period in question. Actually some time ago the IPCC (IPCC, 1990: 228) noted:

As the source that caused the Arctic warming in the early 20^th Century must be placed in the north of Iceland and Scandinavia, only the sea between Andenes and Spitsbergen remain. As Norwegian Sea could not have generated extra heat during the winter season without heat supply from elsewhere, the Arctic warming since 1919 is the result of changes in the system of the West Spitsbergen Current. But how did it come about? As the matter had never been investigated, finding a complete answer looked bleak only few years ago. But could that change in the near future if one takes notice of what Dmitrenko et al. said about their assessment of the current situation in the Arctic last year, May 2008, of which we reproduce an excerpt here without any further comment:

Despite the great shortcomings in knowledge about the way the Spitsbergen Current changed during the first decades of the last century; one aspect may provide a hint, namely the suddenness. All presented temperature plots record a fairly modest variation, before it came to a very brief cooling around 1916 to 1918, after which a very brisk temperature rise occurred at all stations. The rise between 1918 and 1923 actually resulted in a shift of level. The annual temperature figure for three stations (Fig. 4 to 6) demonstrate very clearly that the level before 1920 was significant lower than after 1920. This sudden shift gives the observation by Schokalsky (see above) special weight, whereby the thickness of the cold sea surface layer of 200m in Nansen’s time (about 1893-1896) has been found reduced to less than 100 meters in thickness about 40 years later. This would release more heat to the atmosphere. But as mentioned already, the warming prior 1920 was very modest, which could mean that the status of the surface layer had been fairly constant. This consequently allows the conclusion that the thinning of the cold surface layer came very suddenly and during a brief time frame, from 1918 to 1923. Already in 1922 The Washington Post could report: “Arctic Ocean Getting Warm; Seals Vanish and Icebergs Melt”.

Under the prevailing circumstances as elaborated here it seems rectified to account the change in circulation as source for the warming as claimed by R. Scherhag (see above), to the same source that generated significant warmer air temperatures at Andenes, Vardø, and Spitsbergen. It would be the account Brooks had asked for (see above).

Conclusion

The sudden warming of the Arctic in the early 20^th Century is presumably not as puzzling as elsewhere assumed if investigated on three parameters, namely winter temperature observation in the region, the prevailing sea ice conditions, and the impact the sea has on air temperatures at high latitudes during the sunless winter season. The observed temperature profile at several representative coastal stations in the Northern North Atlantic, indicate that the most northern station on Spitsbergen observed the change into a warmer stage first and most pronounced. As this change came with an unknown suddenness, and the temperature increase had evidently not been carried from outside the Arctic Ocean and Northern North Atlantic into the Spitsbergen region, the source of the warming is the West Spitsbergen Current, which must have seen a substantial system shift within a very short period of time. Particularly the explosion of the temperatures and showing up in the subsequent winters until 1940 leave little room for any other option. The circumstances of the Arctic warming since the winter 1918/19 are ocean related in general, and the West Spitsbergen Current in particular. How the shift came about so suddenly is not to be answered here, but the observation published by J. Schokalsky in 1936, that “the surface layer of cold water which was 200 meters thick in Nansen’s time, has now been reduced to less than 100 meters in thickness“, could have happened over a time period of 2-3 dozen years, but also within a period of several months. The sudden thinning of the sea surface layer in the high North could be an interesting starting point to bring more light in the early Arctic warming.

More about the EAW at: http://www.arctic-heats-up.com

References

Bengtsson, L., Semenov, V.A., and Johannessen, O.M. (2004), “The Early Twentieth-Century Warming in the Arctic—A Possible Mechanism”, Journal of Climate, Vol. 17, pp. 4045-4057.

Birkeland, B.J. (1930), „Temperaturvariationen auf Spitzbergen“, Meteorologische Zeitschrift, Vol.47, pp. 234-236.

Brooks, C.E.P., (1938); “The Warming Arctic”, The Meteorological Magazine, Vol.73, pp.29-32.

Brönnimann, S., Ewen, T., Luterbacher, J., Diaz, H. F., Stolarski, R. S. and Neu, U. (2008) “A Focus on the Climate During the Past 100 Years”; Springer, Dordrecht.

Dmitrenko, I. A., Polyakov, I. V., Kirillov, S. A., Timokhov, L. A., Frolov, I. E., Sokolov, V. T., Simmons, H. L., Ivanov, V. V. and Walsh, D. (2008); „Toward a warmer Arctic Ocean: Spreading of the early 21st century Atlantic Water warm anomaly along the Eurasian Basin margins“, Journal of Geophysical Research, 113, C05023, doi:10.1029/2007JC004158. Abstract, viewed 26 May 2009, http://www.agu.org/pubs/crossref/2008/2007JC004158.shtml

Ifft, G.N. (1922), “The Changing Arctic”, Monthly Weather Review, Vol.50, p.589.

IPCC (1990); Houghton, J.T., Jenkins, G.J., Ephraums, J.J. (ed); „Climate Change –The IPCC Scientific Assessment“, Cambridge University Press, Cambridge.

IPPC (2007a); „A report of Working Group I of the Intergovernmental Panel on Climate Change, Summary for Policymakers“, pp. 1-18, viewed 25 May 2009, http://www.ipcc.ch/ .

IPPC (2007b); „A report of Working Group I of the Intergovernmental Panel on Climate Change, Technical Summary“, pp. 21-91, viewed 20 May 2009, http://www.ipcc.ch/ .

Johannessen, O.M., Bengtsson, L., Miles, M.W., Kuzmina, S.I., Semenov, V.A., Alekseev, G.V., Nagurnyi, A,P., Zakharov, V.F., Bobylev, L., Pettersson, L.H., Hasselmann, K., and Cattle, H.P. (2004); „Arctic climate change – Observed and modeled temperature and sea ice variability“; Tellus, 56A, pp. 328 –341, Corr., pp. 559-560.

Lamp, H.H. (1982 ); „The Climate Environment of the Arctic Ocean“, in: Louis Rey (ed), ‚The Arctic Ocean’, Macmillian Press, London, pp. 135 – 161 (Fig. 7.10a).

Nasa/Giss, (2009); GISS (Goddard Institute for Space Studies) Surface Temperature Analysis; GISS Website Curator: Robert B. Schmunk ; Responsible NASA Official: James E. Hansen; at: http://data.giss.nasa.gov/gistemp/station_data/ ; viewed April/May 2009

NSIDC (2009) The National Snow and Ice Data Center; University of Colorado Boulder, Monthly sea ice maps since 1900, viewed 15 May 2009, http://polar.ncep.noaa.gov/seaice/climatology/months.html

Overland, J.E. (2008); „Arctic change: multiple observations and recent understanding“, Weather, Vol.61, pp.78-83.

Polyakov, I.V., Bekryaev, R.V.; Alekseev, G.V., Bhatt, U.S., Colony, R.L., Johnson, M.A., Makshtas, A.P. and Walsh, D. (2003); „Variability and trends of air temperature and pressure in the maritime Arctic, 1875 – 2000“; Journal of Climate, Vol.16, pp. 2067-2077.

Polyakov, I.V., Beszczynska, A., Carmack, E.C, Dmitrenko, I.A., Fahrbach, E., Frolov, I.E., Gerdes, R., Hansen, E., Holfort, J., Ivanov, V.V., Johnson, M.A., Karcher, M., Kauker, F., Morison, J., Orvik, K.A., Schauer, U., Simmons, H.L., Skagseth, Ø., Sokolov, V.T., Steele, M., Timokhov, L.A., Walsh, D. and Walsh, J.E..; (2005), „One more step toward a warmer Arctic“; Geophysical Research Letters, 32, L17605, doi:10.1029/2005GL023740.

Scherhag, R. (1936a) ‘Eine bemerkenswerte Klimaveränderung über Nordeuropa’; Annalen der Hydrographie und Maritimen Meteorologie, Vol.64, pp. 96-100.

Scherhag, R. (1936b); “Die Zunahme der atmosphärischen Zirkulation in den letzten 25 Jahren”; Annalen der Hydrographie und Maritimen Meteorologie, Vol.64, p. 397-407 ( Fig. 7)

Scherhag, R (1939) „Die Erwärmung des Polargebiets“; Annalen der Hydrographie und Maritimen Meteorologie, Vol.67, pp. 57-67.

Schokalsky, J. (1936); “Recent Russian researches in the Arctic Sea and the in mountains of Central Asia”, The Scottish Geographical Magazine, Vol. 52, pp. 73-84.

Serreze, M.C. and Francis, J.A., (2006), “The Arctic Amplification Debate” , Climatic Change, Vol.76, pp. 241-264.

U.S. Geological Survey (2009), „Past Climate Variability and Change in the Arctic and at High Latitudes“, 533 pages, viewed 15 May 2006, http://downloads.climatescience.gov/sap/sap1-2/sap1-2-final-report-all.pdf

Zakharov, V.F. (1997), “Sea ice in the climate system”, World Climate Research Programme/Arctic Climate System Study WMO/TD-No. 782, 80 pp.

Professor Ben Herman has done a guest post at Roger Pielke Sr’s blog on the differences we’ve seen in UAH and RSS.  The land sea effect was noted in blogland by Chad at treesfortheforest (link on right) but the experts in this case have been paying very close attention to this substantial issue.  Today I will add to this post a video showing the differences between UAH and RSS by month for 3 atmospheric levels.  The video is still uploading.   In the meantime check out Dr. Herman’s post below.

Update: Video added.

—————

Guest Post By Ben Herman Of The University of Arizona

Guest Weblog By Professor Ben Herman of the University of Arizona

In a recent post at the website The Air Vent titled

Land/Sea Bias In Satellite Temperature Metrics

the problem of correcting MSU (Micro Wave Sounding Unit) brightness temperature data for differences in the diurnal temperature variations between land and ocean was brought up. This is indeed an issue of considerable importance if the resulting data is to be used for determining temperature trends over time for climate change purposes. The reason is quite obvious. In a stable, non-changing thermal regime, due to orbital drift, the satellite will pass over any given latitude at different times of the day, thus causing a trend in the resulting temperatures, positive for crossovers starting at, say 7:00A:M to about 3:00P:M, and negative thereafter till the following morning.  Thus, to obtain the true trend (zero trend in the previous illustration) it is important to correct for these diurnal variations.

Read the rest at Roger Pielke’s blog HERE.

===========================

Abstract from Ben Herman’s paper:

Using limited time period trends as a means to determine attribution of discrepancies in microwave sounding unit–derived tropospheric temperature time series

Robb M. Randall

Department of Atmospheric Sciences, University of Arizona, Tucson, Arizona, USA

Institute of Atmospheric Physics, University of Arizona, Tucson, Arizona, USA

Benjamin M. Herman

Department of Atmospheric Sciences, University of Arizona, Tucson, Arizona, USA

Institute of Atmospheric Physics, University of Arizona, Tucson, Arizona, USA

Limited time period running trends are created from various microwave sounding unit (MSU) difference time series between the University of Alabama in Huntsville and Remote Sensing System (RSS) group’s lower troposphere (LT) and mid troposphere to lower stratosphere channels. This is accomplished in an effort to determine the causes of the greatest discrepancies between the two data sets. Results indicate the greatest discrepancies were over time periods where NOAA 11 through NOAA 15 adjustments were applied to the raw LT data over land. Discrepancies in the LT channel are shown to be dominated by differences in diurnal correction methods due to orbital drift; however, discrepancies from target parameter differences are also present. Comparison of MSU data with the reduced Radiosonde Atmospheric Temperature Products for Assessing Climate radiosonde data set indicates that RSS’s method (use of climate model) of determining diurnal effects is likely overestimating the correction in the LT channel. Diurnal correction signatures still exist in the RSS LT time series and are likely affecting the long-term trend with a warm bias. Our findings enhance the importance of understanding temporal changes in the atmospheric temperature trend profile and their implications on current climate studies.

============================

The video is of the differences between RSS and UAH over the complete record.  It gets interesting where in 2002 UAH switched from the NOAA satellites which don’t keep stable orbits to AQUA which has station keeping thrusters.  It’s important to note that in the past RSS and UAH had an opposite decay satellite to help compensate for inacuracies in diurnal correction.  Currently RSS only has NOAA15 but this is indicative of only half of the signal in the past.  Post 2002 the single satellite is the whole signal and the inacuracies in RSS dirunal corrections are highly visible.  Watch the lower  stratosphere oscillate wildly in recent years, you can then replay it and see some of the variance in the lower troposphere as well.  Finally, Chad at treesfortheforest noted that the difference in trend between the two was land/ocean based and in the still image below you can see Australia and South America are blue while being surrounded by oceans of red.

 

Click to play

 

The following are video representations of the published RSS and UAH data.  This time the videos include lower troposphere, mid troposphere and lower stratosphere versions.  As before there are a number of interesting features to the data including oscillations between poles, contrasting anomalies between layers and strong variance in the polar regions compared to the equator.   I find the lack of variance at the equator very interesting as it demonstrates the quality of the signal used to determine whether the IPCC fingerprint exists is actually decent.  Another interesting point is that RSS doesn’t do the spatial filtering  of UAH and has several areas of missing data which show as dark purple where UAH presumably does some infilling.  The size of the polar UAH infilling is also apparent.  My favorite difference is the heating and cooling in the lower troposphere are often opposed by the stratosphere.  If you freeze the frames at different points you can see big lower troposphere hotspots and dark blues in the stratosphere.  In other cases the hot/cold effect switches to cold/hot.

R unfortunately decided to start making a scribble across Russia and Canada even though I’ve set the flags not to do that.  I’ll have to get my money back.

RSS Video YouTube Click to Play

The corresponding UAH video:

UAH Video YouTube - Click to play

Back in my college days on a whim, a friend and I made an electronic scale from a quad strain gauge taken from a small yet very high precision printing press. The guage was attached to a 0.20 thick piece of spring stainless. Everything except the electronics was taken from junk of one kind or another including a gorgeous precision analog meter from a device I don’t remember. Anyway, during calibration experiments it could weigh a 0.062″ diameter piece of writing paper with a 20% scale deflection so it was very sensitive. Small breezes in the room caused expected problems on the signal by deflecting the beam so the whole thing was intended to go in a breeze tight box.

By the way, the Quad strain guage configuration is good for thermal and side strain corrections so it was ideal and my friend and I spent a week on it. However after it was finished we found an imperceptibly small gust of wind on the room temperature very low power electronics could cause a 5% deflection. As planned, the whole project went into a wind protecting box to guard from any breeze yet because of room temperature drift, the intended accuracy was impossible. We had balanced the electronics plus and minus to account for temp but you know, even our best guess wasn’t enough. – wrong again.

There is no substitute for confirmation of theory.

Today my venting elicited a response from my post on Briffa’s Yamal. DO, is apparently a climatologist of some kind (he’s not saying but if you google DO you can understand) so his knowledge of the detail of the subject of climatology is far better than my own. He’s been quite honest in his email and other replies which is more than I can say for most of the AGW blogs so I’m happy to link.

DO does not understand how a non-climatologist could be frustrated with the state of dendroclimatology and he reacts strongly against the tone of my posts here. I’m not going to apologize for my tone yet am always happy to learn. To explain our (mine and your) collective angst to DO further, I’m forced again to quote Ryan O – “Guage R & R” every dendro should read up on it.

I don’t want to be overly kind so there are several points he claims were made which are misunderstandings of tAV own points. The temptation would be to call them straw men but I think they are honest misunderstandings and reaction to the Air Vents IMO appropriate tone. For instance -

Finally, what of the claim that combining the mean-detrended series demonstrates that the RCS method is invalid?

The careful readers here know that while my comments are often extreme sounding to those who don’t follow, they are constrained slightly better than that. I don’t recall claiming RCS is invalid, nor has anyone else I recall. What’ Ive claimed is that exponential decay RCS does not allow for increased growth in later years as demonstrated by many series and that process is invalid. There are a few other points made which I’ll work on later.

Beyond that, there isn’t much to do except put my own vents and work Link 1 & 2 for reference and Delayed’s new criticisms in link 3 below. Enjoy.

Here’s tAV which you’ve read before:

Hockeystickization Revisited

Fixing Briffa’s Latest

Below is DO’s latest. Check it out, leave comments and questions. Unlike RC, Delayed Oscillator has been quite open to questions and minor criticisms. Let me know if you’re not getting through.

Yamal Reply DO

Those of us who aren’t dendro’s may not know trees to the same degree but we know signal processing pretty well. We know it from a confirmation of experience – of which I have MANY. Our job’s depend on proper metrology and the eyes closed methods of dendro’s would get us driven into downtown by our coworkers and dropped off in a dark alley.

The reply is appreciated but like so many have said in different ways– Confirmation rules physics. We’ll see if I can replicate and explain the difference between his post and mine.

More experience makes better videos.  The following are two UAH TLT (lower troposphere) temperature anomaly videos.  There is a ton of detail in them, you can see the 1998 temperature spike – some have had questions as to where this spike came from.  You can also see the recent cooling wave, Ryan O noticed the oscillation back and forth between the poles.

The first video is high speed and came out quite good.  I’ve worked out some of the video compression difficulties and am producing higher quality videos now.  The 10 fps is just for viewing bulk properties and is way too fast to see small details.  The 5 fps video is much better for watching detail.

UAH TLT Temperature Anomaly 10 Frames per Second - Click to play

The next video is the same thing at a lower frame rate which caused youtube to hesitate every few frames.

UAH TLT Temperature Anomaly 5 Frames per Second - Click to play.

]]> http://noconsensus.wordpress.com/2009/11/02/uah-temperature-anomaly-video/feed/ 15 Jeff Id UAH temperature019 UAH temperature019 http://noconsensus.wordpress.com/2009/11/01/fixing-briffas-latest/ http://noconsensus.wordpress.com/2009/11/01/fixing-briffas-latest/#comments Sun, 01 Nov 2009 04:20:10 +0000 Jeff Id http://noconsensus.wordpress.com/?p=5887 ]]>

This is a very difficult post for me because I don’t believe any of this has anything to do with temperature, however some well paid scientists disagree. This is therefore in response to a Keith Briffa pre-paper replying to SteveM’s post on Yamal where he replaced some of the data using a Schweingruber dataset. Briffa presented what he referred to as a sensitivity test of Yamal by adding different datasets in. While he didn’t provide his code, he did provide the data and plots of the RCS corrections applied to the various datasets.

RCS is a generic method of fitting a curve to a set of tree ring data. In the original Yamal an exponential decay was used to represent tree ring widths. I’m critical of the exponential function method because it doesn’t recognize the potential for trees to increase in growth rate as they age. Since nothing I’ve read biologically defines that this is impossible it makes no sense to ignore the possibility unless you want older trees to curve upward after standardization. It’s odd to see it being ignored by the pros but it happens to result in spurious hockey sticks through a complex and surprisingly common mathematical phenomena referred to here as hockestickization.

Yamal is a driver amongst hockey sticks – I mean a good old fashion 1 wood. Straight shaft, sharp blade, wide sweet spot. Since Steve’s discovery the climatoknowledgists have been scrambling to explain how so few trees could define such strong warming around the globe. In Briffa’s response he showed several methods using new data to also create hockey sticks. However, Dr. Briffa again used a method which is functionally identical to the exponential decay method for correcting tree ring widths. It seems he simply cannot recognize that trees may increase in ring width.

Figure D from Briffa's response. Click to expand. This is a series of chronologies included with Yamal to show that you can always get a similar hockey stick.

You can see that the bottom pane has a variety of curves which all have an uptrend similar to the original Yamal. Briffa did one nice thing in that he didn’t repeat Dr. Tom P’s mistake of including Yamal only years in the data. If the sensitivity data ends early, Briffa correctly ended the comparison curves early. When this story first broke, Tom P argued that Steve McIntyre had failed to do his sensitivity analysis correctly but in fact Tom had mistakenly extended the Yamal data past the sensitivity data. My reply was here. I never did read an admission of error from him on that one.

Anyway, note the curves used for correcting the datasets. They are very similar to the exponential curves used to create the original Yamal emulated in this next figure. Before I continue, much of this post was created from code written and published by Steve McIntyre although I’ve made many changes so mistakes are my own.

Fig 2

The last pane of the above figure shows the Yamal hockey stick, the middle pane shows that not many trees were used in recent years to make it. My problem with it is addressed here and that is the exponential curve in the top pane of the above 3 graphs. The curve doesn’t follow the average tree age which trends upward after about 200 years. Since the middle of the series has both young and old trees overlapping, standardization problems average out. This means that errors in standardization will only have a real effect on either end. The problem for RCS is therefore summarized like this:

1 – Standardization affects only ends of chronologies

2 – Trees grow at a variable average rate even in the same climate conditions. This rate is manifested in variable ring widths.

3 – Trees often grow faster after a certain age when competition is beaten and resources are monopolized.

4 – Old trees are easier to find when alive, fossil and subfossil records will tend toward a younger age.

5 – Exponential decay does not correct for increasing growth in later years.

The result then is an uptick at the end of the RCS chronology created by the math. So this post attempts to fix this one aspect of the Yamal series. There are a bunch of graphs to show but the point is pretty simple. I used a spline correction similar to the Esper method, fitting the data to the mean ring width for the various new trees from Briffa.

Fig 3

Fig 4

Fig 5

Fig 6

Fig 7

OK, now the RUSS series is the Schweingruber series that SteveM used in the Yamal data. Instead of playing around with a boring sensitivity test I combined all of the data into full chronologies a couple of different ways using the maximum amount of data. Before we move on, look at the age of the trees in the first pane of each chronology. Several follow a nice exponential drop until about 200 years followed by a rise later on. There are a couple who don’t follow a nice curve like that, I’m not sure why but the point is that RCS needs the ability to fit the rise and a pure exponential decay doesn’t allow for that. A second issue you’ll notice is that older trees have a lot of variation in ring width. The signal goes wild as trees age. This could be due to less old trees in the series but it is a noticeable effect.
First, I looked at an average of the above series after correction by spline.

Fig 8

The red line is the new chronology. As I explained above the only real differences created by different RCS methods occur at the ends of the chronology.The huge spike at the end is reduced from the original Yamal but is still positive. The next graph is a zoomed in version which if it were temperature, the red line would probably be a better match to measured data than the original black. However, IMO it isn’t temperature.

Fig 9

The next graph is the same data as the above with a 5 year filter instead of a 21 year. It’s almost always better to look at endpoints with less filtering because the ends are affected by the assumptions. So, for those who see temperature in these things, what about this?

Fig 10

We have just removed any resemblance to the hockey sticks from from Yamal by using all the data. This isn’t any less valid than Briffa’s versions.

But wait, I’ve got another version. The above reconstruction uses a different RCS spline fit for each region, there certainly is an argument for that method but the resulting average isn’t weighted according to the number of trees so a series with 10 trees has the same weight as one with 25. I took all the data together and did a single RCS spline fit.

Fig 11

The reconstruction is the bottom pane. Great Jeff you made your own hockey stick, nice work right? You can see the huge core count in recent years right before the blade happens. The blade in this case though deserves a closer look.

This is the unfiltered version.

Fig 12

You can see the blade is very thin in reality. The whole series ends in 1996 so I chopped one year (ONE SINGLE VALUE ON THE END) to see the difference.

Fig 13

Now visually that’s a significant difference from the previous pane yet it’s only chopping a single value from 1996. There were 19 cores in 1996 but in 1994 there were 42. I’m certain that the lack of new and young trees has a lot to do with the unprecedented nature of the spike. You can just look at the lack of variance in the early portion of the curve fit’s (pane 1) in the above chronologies (fig 3, 4,5,6 …) and it becomes very apparent that it would be almost 100% impossible for a group of young trees to make a 3 sigma deviation from the mean of the curve.

I hope that makes sense, it’s what drives me nuts about this treemometer stuff. It just ain’t science.

It aint!!

Anyway, our eyes tend to focus on the big bladey looking bit at the end without realizing just how narrow it really is. Here’s a filtered versioin truncated only 5 years to 1990. I picked 1990 because that’s the year that the data availability jumped up to over 80 cores.

Fig 14

The entire 19th century using all the data is now one of the coolest on record!!

My conclusion is that there is no REAL uptick in Yamal. There are methods which show an uptick but the number of cores is still low. For the uptick to be continuous for 100ish years as corrected HadCRUT temperature is you need to have an RCS which creates a continuous upslope like exponential RCS. What bugs me so much about that is that this continuous slope is just about all that is required for correlation to temp.

No matter how you see the last 3 graphs, they sure as heck don’t look like temp. It’s my contention that they are of equal quality to Briffa’s original Yamal. This post took a long time because there were hundreds of other plots I could have shown and have done, but there are enough plots here for the serious to figure out what’s going on.

I scaled all the GISS thermometer records to the same temperature scale, and ran them all from 1880 to 2020 at the same time scale (GISS graphs do not do this). I overlaid them as transparent lines along their approximate mean temperatures for comparison. Mean temperatures (visually judged) vary from around -2ºC (Pecora) to -13ºC (Selagoncy, Olenek, Hatanga, and Ostrov Uedine) and even -15ºC (“Gmo Im E.K. F”). The calibrations are degrees Centigrade anomaly, and decades.

Ha! Straightway we see clear patterns emerging. Let’s agree them:

Thermometer records: (1) time-wise, thermometers show temperatures rising from 1880 to 1940 or so; (2) temperatures fall a little from 1940 to 1970; (3) temperatures then rise a little but do not quite regain the heights of the 1940’s; (4) despite mean temperatures ranging from -2ºC to -15ºC (total means range 13ºC), and a range of temperature anomalies from each mean of only 9ºC from warmest year to coldest year, when mean temperatures are aligned, clear correlations emerge; (5) there are high variations between adjacent years. We shall investigate all this more closely in a minute.

Treering records: I’ve shown here the full records given for the 10 trees that runs from 1800 to 2000; but below, I use the same timescale as the thermometer records (1880-2020) for comparison. It is useful to see a few things here already: (6) treering sizes are increasing from 1830; (7) before that they show a decrease; (8) they do show correlation from 1880 on (this is NOT proof that the correlation is due to temperature).

Yamal area: (9) The 7 stations around Salehard seem to go in lock step with each other pretty well. (10) The five Yamal treering records (YAD) also correlate with each other, showing spikes around 1910, 1925, 1940, 1955, 1965, and 1980-1990. (11) But the treerings fall out with each other 1990-2000; and (12) these treering spikes do NOT correspond to the thermometer temperature spikes; but (13) there is a slight correlation with the longterm temperature; however, (14) crucially, there is no hockeystick blade in the thermometer record (15) nor is there one in the treering record if we remove the red YAD061 which is clearly an outlier – only a plateau’d elevation of the peaks throughout the 20th century starting before the real CO2/temp rise (and this is actually matched by pre-1800 values at times).

Excuse me for wondering if treerings beat to a different drum than temperature – it is certainly curious that there appears to be something causing correlations in the treerings. Wind? Sunspots? The moon? But let’s check by zooming in a little closer…

Salehard close-up: (16) all the nearby thermometer records mirror Salehard closely, although stations are up to 500 miles apart, the range of mean temperatures is -2ºC to -9ºC, and the range of annual temperatures at each station is up to about 9ºC – altogether a remarkable consistency. Click to see animated version of these records. (17) The close fit of Mys Kamennij (pale sea-blue) is particularly significant, since it is maritime and rural, and the same distance as Salehard from the treering site (some 120 miles), but in the opposite direction; (18) Ostrov Waigatz (Vaigach Island) shows the same pattern but with greater extremes; (19) in comparison with all this, the treering records show virtually no correlation at all – yet since treering differences between summer and winter exist at all, one would expect to see some correlation with warmer and colder years. (20) Perhaps if a far larger sample were used, a correlation might be detected, but clearly (21) we have trees here that are far too individual – especially YAD061.

Polar Urals: Here are seven station records around the Polar Urals site, compared with the five Taimyr (POR) treering records. (22) Mean temperatures are lower here – further North but also more continental, so perhaps the summers are as warm as Yamal, with similar near-treeline environment. (23) more noise in the temperature record, but the overall pattern is still the same; (24) 1943, 1967, 1983 are warm in common with the Salehard records, and 1940 is cold; other years are harder to compare. (25) The early fragmentary records for Dudinka and Turuhansk still fit together and overlay the Salehard records well, showing clear temperature rise between 1880 and 1940. (26) The treering records are fairly coherent, more so than the Yamal ones, and (27) they fit the Yamal records’ spikes in 1910, 1925, 1940, 1955, 1965, and 1980 on, but (28) again, this does not fit the temperature record.

The best of both record series: Really rural thermometer records from the maritime Arctic: (29) show the strongest pattern yet which (30) fits the other two sets of thermometer records but (31) does not fit the treering records even though (32) the treerings show coherent patterns within themselves, despite the two sites being some 800 miles apart.

Briffa’s full chronology: The Yamal chronology Briffa used (black) is compared with Polar Urals (green) and shows recent temperatures exceeding the Medieval Warm Period but (33) this is highly questionable, as is the recent final uptick. No MWP supports the alarmist “Unprecedented!” yet Polar Urals generally have been shown to fit local thermometer records better than Yamal for the period of overlap.

More GISS Arctic graphs: There are many serious problems with GISS but we can only take the evidence here. (34) GISS 64ºN+ shows a misleading trend line – temperatures rise to 1940, fall to 1970, rise to 2000 but not higher than 1940, then level off after 2000; (35) I don’t know what stations went into this composite – the final uptick alerts my suspicions to some UHI or other station problems; (36) Tamino takes the biscuit for cherrypicked trends in the GISS 80ºN+ North Polar winter record (sea green) – it clearly opposes the general worldwide fall in temperatures 1940-1970. However, it’s interesting to see such extremes.

GISS’ homogeneity adjustments: Thankfully, only a few of these Russian records are “adjusted”. But the alterations are telling. Surgut spikes upwards over Salehard from about 1960 on – but (36) the adjustment (probably UHI) is perversely done by truncating and moving earlier records upwards, instead of adjusting later records downwards. And (37) why were Salehard’s and Ostrov Uedine’s earlier “raw” records omitted in the adjusted records? I think every correction here will tend to amplify global warming trends.

GISS world temperatures, 2008: This map was shown in Tingley & Huybers’ latest Hockey Stick presentation at PAGES conference. GISS’ own station records around Yamal and Polar Urals appear to show (38) this map is misleading, since according to GISS’ own records, above, averages local to Yamal / Polar Urals after 2000 are at the most 1.5ºC anomaly (above local mean).

CRU Arctic temperatures, seasonal anomalies: (graph by romanm) Since this is from uncheckable individual station records, (39) the figures could be contaminated by various “correction” factors, (41) UHI is especially likely in the winter. But note that (42) the difference in character between months, and between summers and winters, is striking – summers have hardly changed – and (43) still no definitive Hockey Stick as per illustrations and per Briffa’s Yamal treering record, nothing beyond the range of natural patterns clearly evidenced here. Even the known slight overall increase during the twentieth century takes place mainly earlier in the century.

Conclusions: There is no sign whasoever of a Hockey Stick shape with serious uptick in the twentieth century, in the thermometer records. Yet these records are clearly very consistent with each other, no matter how long the record or how cold, high, or maritime the locality, with a distance span of over a thousand miles. Neither does the Hockey Stick consistently show in the treerings except in the case of a single tree. Even with thermometer records that are incomplete and suffering other problems, the “robust” conclusion is -
“Warmist” treering proxy temperature evidence is falsified directly by local thermometer records.

I’ve got plenty of posts right now to work on but today Steve McIntyre called our attention to a couple of acerbic replies from Keith Briffa to Steve’s discovery that the Briffa Yamal temperature data which has a HUGE hockey stick blade was actually the EXACT same data as the orignal H&S Yamal which shows NONE. The difference is in the RCS “standardization” method (AKA – hockeysickization) used to correct for tree ring widths. If I don’t miss my guess, we’ll be seeing more of RCS with improper exponentialesque curve standardizations as in my opinion it’s a near guarantee of a hockey stick blade and after Mann and now Briffa I don’t trust these paloclimate ’scientists’ any farther than I can throw them.

I’ve got to say again Briffa’s original Yamal is a disgusting piece of garbage work and the sooner paleo’s drop the P.O.S. the better. It’s got an unreasonable blade created from RCS with NO science or verification to prefer the ‘accidentally’ chosen exponential curve that is ENTIRELY RESPONSIBLE for the big evil bullcrap blade. See one of my posts on this HERE.

Well I’m not happy with Briffa or anyone who chooses to defend this garbage work so I’ve decided to dig through his reply to Steve a bit and make a little trouble. In particular, I want to focus on this little gem by Briffa himself.

We would never select or manipulate data in order to arrive at some preconceived or regionally unrepresentative result. However, as we will show here, the fact that we did not incorporate the KHAD data has no serious implications for the general validity of our published work.

My emphasis. But those of us who follow climate know that is not the case. So I went looking for a few examples from his own PUBLISHED work. Here is an article from Briffa. although Osbourne was the lead author. I assure you this is standard in paleo science where proxy’s are often assumed to be temperature without any verification other than mathematical correlation. There are many papers which have similar statements, Mann08 is no exception. I could write a whole post on the differences between clear and obvious selection or the ‘partial selection’ created by multivariate methods where non-correlating proxies are deweighted rather than deleted resulting in a less transparent selection of data based on preconceived conclusions.

The 387 density chronologies were screened according to their correlation with the “local” grid-box April-to-September temperature, already computed by Briffa et al. (2002a; their Figure 5a) over the period 1881–1994. The actual period over which these correlations were computed is often shorter than 1881–1994, because either some temperature observations are missing (see section 2.2) or the trees were sampled before 1994 (Figure 2a). Almost half of the chronologies exhibit a correlation of 0.5 or above, and almost 90% exhibit a correlation of 0.22 or above (the latter threshold indicates that we can be 95% confident of a true association between MXD and April-to-September temperature, given a sample of 81 years, which is the average overlap between chronology and temperature data). The 46 chronologies that do not meet this criterion were excluded from further use;

Oh yeah, Steve McIntyre is the bad guy. They would NEVER select data to arrive at some preconceived result. No chance. IT’S RIGHT THERE IN PRINT!!

A reasoned mind might ask the obvious question which dendroclimatology has never answered clearly. – What about the fact that trees might not ACTUALLY be good linear thermometers?

Briffa et al. (2002a) have already identified many of the climate signals recorded in the treering data, and concluded that the tree-ring density variability is dominated by the temperature during the growing season.

Well that about settles it then. Briffa has determined that MXD (latewood density) data IS temperature so we don’t have to WORRY OUR PRETTY LITTLE HEADS about that anymore. But wait, in the same PEERREVIEWEDLITERACHURE there’s this.

A number of factors were taken into account when selecting the most appropriate periods for calibration and verification of the gridded density data against observed temperatures. The most important factor is the identification by Briffa et al. (1998a) of a recent downward trend in the highlatitude tree-ring density data, relative to (and apparently unrelated to) warm-season temperature. This density decline becomes large enough to impair the calibration after about 1960. For this reason, both Briffa et al. (2001) and Briffa et al. (2002a) used only pre-1961 data for calibration of their subcontinental, regional temperature reconstructions. This is a reasonable choice, provided that it is explicitly stated that this approach assumes the apparent recent density decline is due to some anthropogenic factor and that similar behaviour is assumed, therefore, not to have occurred earlier in the reconstruction period – which would otherwise introduce bias in the reconstructed temperatures. At present, no satisfactory explanation of the relative MXD decline has been identified, and further work must dictate whether this assumption will be supported or rejected (Briffa et al., 1998a, 2003, and Vaganov et al., 1999, discuss and investigate possible causes).

MY GOD, it’s time for dendros to stop the charade. I know they’ve spent years trying to extract temperature from noisy data but scrapping data for PRECONCEIVED RESULTS is REGULARLY done!! Equally invalid are reweighting methods that reduce the importance of lower correlation data- its the same damn thing in a more confusing approach.

But wait, why is Jeff so wound up today?!! Because Briffa responded specifically to the criticisms of too few proxies ignoring the criticisms of the method and repeated the same MESS with RCS and more trees. See the new improved YAMALINATOR at the link in the quote below!

We have now undertaken a more extensive sensitivity test, using the RCS approach, to examine the relative growth rates of trees at each of the 3 original locations removed by McIntyre, as well as the KHAD site. We have also taken the opportunity to acquire and incorporate additional data from the 3 original sites, in this analysis.

Figure C This Figure shows the mean RCS indices plotted by site over the period 1801 to the end of each chronology (1996 for YAD, 1994 for POR, 1991 for JAH, and 1990 for KHAD). In each case the chronology was constructed using a RCS growth/age model based only on the tree-ring data for that site (shown in the upper panel). The "signal-free" implementation of RCS is employed so as to reduce bias in the RCS arising from co-incident climate signal in any near-equal-aged trees when aligned by life cycle. The upper panel reveals the differences in the magnitude of expected ring-width as a function of tree age at different sites. The lower panel shows the differences in the temporal trends of chronology indices between sites. The comparatively low growth at the KHAD site after 1970 is clear.

We don’t have the actual math used for this yet but if you see the RCS curves they came up with, they are functionally no different than the original exponential curves of Briffa Yamal the original.

Next are these new datasets with Yamal included.

Figure D This Figure is equivalent to Figure C except that at each site the RCS curve, and resulting site indices, are calculated including the Yamal_SF data as well as the measurement data from living trees. The dominance of the common sub-fossil measurements produces a very similar RCS curve in each case (upper panel). The indices produced exhibit a similar picture of recent growth trends varying between sites, as that seen in Figure C, with mean tree-ring index trends higher for the POR and YAD sites, lowest (even negative after 1970 with respect to the long-term mean) at KHAD, and at an intermediate level at JAH. The black line in the lower panel represents the chronology (from 1750) produced using all of the data, Yamal_All, standardised with a Yamal_All RCS curve.

For some reason EVERY RCS CORRECTION Briffa can conceive of refuses to turn upward to fit the ACTUAL data. This lack of flexibility in the RCS curves is what creates the HS. We ARE going to see more of this, mark my words! Below is what the Yamal original data looked like when averaged per year.

Original Yamal Tree Ring Width Data Averaged Per Year

Old trees grow faster in their end years, the RCS curves used by Briffa in the new improved Yamal don’t match the data again!! It’s the same exact thing as the original Briffa Yamal.

I’m tired again. The insanity of paleoclimatology is getting to be too much. Mann’s doubletalk on flipping proxies upside down, RCS creating hockey sticks out of nothing. People creating one obviously bad result after another. Comments by people on the internet which are confused by this ignorant crap. There is just too much bad work for my head to absorb. WTF, none of this is any better than a simple mean. NONE!! Data IS thrown away for predetermined conclusions I’m SICK TO DEATH of the lying and posturing. NONE of this is reasonable.

I have email conversations with Dr. Christy which do nothing but make sense and then am assaulted with this IDOCY.

Understanding Keith Briffa will make you dumber!!! It will suck the brains right out of your ears – don’t read it…. ever!!

In my opinion, this is not honest science.

This came to me by an email just to remind people how far we’ve come.  I can’t tell people how much tax is right but I can say we have too much.

A Tax Poem

At first I thought this was funny…..
then I realized the awful truth of it.

Be sure to read all the way to the end!…

Tax his land,
Tax his bed,
Tax the table
At which he’s fed.

Tax his tractor,
Tax his mule,
Teach him taxes
Are the rule.

Tax his work,
Tax his pay,
He works for peanuts
Anyway!

Tax his cow,
Tax his goat,
Tax his pants,
Tax his coat.

Tax his ties,
Tax his shirt,
Tax his work,
Tax his dirt.

Tax his tobacco,
Tax his drink,
Tax him if he
Tries to think.

Tax his cigars,
Tax his beers.
If he cries
Tax his tears.

Tax his car,
Tax his gas,
Find other ways
To tax his ass.
Tax all he has
Then let him know
That you won’t be done
Till he has no dough.

When he screams and hollers,
Then tax him some more,
Tax him till
He’s good and sore.

Then tax his coffin,
Tax his grave,
Tax the sod in
Which he’s laid.

Put these words
upon his tomb,
“Taxes drove me to my doom…”

When he’s gone,
Do not relax,
Its time to apply
The inheritance tax.

Accounts Receivable Tax
Building Permit Tax
CDL license Tax
Cigarette Tax
Corporate Income Tax
Dog License Tax
Excise Taxes
Federal Income Tax
Federal Unemployment Tax (FUTA)
Fishing License Tax
Food License Tax
Fuel Permit Tax
Gasoline Tax (42 cents per gallon or more)
Gross Receipts Tax
Hunting License Tax
Inheritance Tax
Inventory Tax
IRS Interest Charges IRS Penalties (tax on top of tax)
Liquor Tax=0
Luxury Taxes
Marriage License Tax
Medicare Tax
Personal Property Tax
Property Tax
Real Estate Tax
Service Charge Tax
Social Security Tax
Road Usage Tax
Sales Tax
Recreational Vehicle Tax
School Tax
State Income Tax
State Unemployment Tax (SUTA)
Telephone Federal Excise Tax
Telephone Federal Universal Service Fee Tax
Telephone Federal, State and Local Surcharge Taxes
Telephone Minimum Usage Surcharge Tax
Telephone Recurring and Non-recurring Charges Tax
Telephone State and Local Tax
Telephone Usage Charge Tax
Utility Taxes
Vehicle License Registration Tax
Vehicle Sales Tax
Watercraft Registration Tax
Well Permit Tax
Workers Compensation Tax
California Redemption Tax
Recycling Tax

STILL THINK THIS IS FUNNY?

Not one of these taxes existed 100 years ago, and our nation
was the most prosperous in the world.

We had absolutely no national debt, had the largest middle
class in the world, and Mom stayed home to raise the kids.

Dr. Christy offered some explanations for the divergence in RSS vs UAH which is visually related to land vs sea, originally discovered in blogland by Chad at treesfortheforest – link on the right.  Chad is doing some very interesting work beyond this minor issue.  His plots lately could take a dozen posts up.

First, Dr. Christy gave an explanation of the types of corrections for RSS and UAH which was new to me.

RSS computes diurnal corrections based on climate model diurnal signals for each grid. UAH calculates the effect empirically (from 3 AMSUs which observed the earth simultaneously for about 13 months but at different times of the day – so sampling a specific grid 6 times per day from which we could reconstruct a diurnal cycle for land or ocean for each month of the year.)

UAH focuses on large-scale precision, so at each latitude a single land diurnal correction is applied to all land grids in that band, and similarly for oceans. It turns out that the noise generated in trying to calculate the diurnal correction of a single grid is so great, that more harm than good comes of it. Our goal is to make the zonal and large scale averages as precise as possible, so we deal with large scale corrections since the noise is beaten down that way. In a pleasant outcome, our gridpoint anomalies more precisely match those of radiosondes at those grids than either RSS or ZOU-STAR

So for the pre-2002 years RSS using models to correct for diurnal cycle and UAH using a 3 satellite calulation from actual data came up with what visually seem to be the same answer. The next figure shows that the main difference between the two sets seems to be latitude based prior to 2002. Two completely different methods got what appears to be almost the same result.

Unfortunately for those of us who like things simple, that’s not the end of the story. I asked for more clarification of how the trends could be 2 C different suddenly in the record over Africa and South America.

The RSS v3.2 relies on NOAA-15 for most of the post 2002 period. NOAA-15 is backing up in time which assumes a warming over land (the earlier temps from 6 p.m. dominate the earlier temps from 6 a.m.). So to correct this over land, RSS “adds” a lot of cooling. The effect is different over the oceans for which RSS “adds” a little warming to compensate for assumed cooling. Note that the backing up of NOAA-15 is the first time a morning satellite has been used through such a long-term drift, so this is the first time such a difference could be identified – hence we wouldn’t have seen it with NOAA-10, and 12 for example. And, previously, the data from drifting by morning satellites, NOAA-10 or NOAA-12, were averaged with afternoon satellites. RSS is using only NOAA-15 post 2004 – so we are seeing the impact of (1) the largest adjustments for longest drift of a morning satellite and (2) a morning satellite that is NOT being averaged with an afternoon satellite, so its errors will be exposed more clearly.

This explains a lot of my questions, look again at the equatorial crossing time plot.

Basically he’s saying that the large drift of NOAA 15 is currently not being balanced by another satellite. I don’t know why NOAA16 isn’t being used but as you can imagine by the short lives of the individual satellites, the individual instruments often go out over time, also the correction factor is too strong for land/ocean relationship. Basically Dr. Christy claims it has been that way all along and it’s only now showing up because there’s no opposing saellite data to balance it. It’s good evidence that the RSS satellite temp curve needs some work.

What I’m not confident has been resolved entirely is the pre-AQUA UAH, the recent AQUA data has exposed a real issue with the datasets. Dr. Christy also said the following.

Our contention is that RSS’s adjustments are actually over-corrections which is best seen when the satellites need the largest adjustments. So, as NOAA-14 drifted to diurnally-cooler temps through 2002 or so, RSS over-adjusted the temps to make them too warm. Similarly, with NOAA-15 now drifting into warmer temps (diurnally), the RSS adjustment over-cools the temps – seen especially over land (i.e. since 2002 RSS would show cooling relative to UAH). There is a paper in press which compares RSS, UAH and the new ERA-Interim Reanalysis (since 1989 so as to correct a flaw in the old ERA-40 related to poor handling of HIRS 11 during Pinatubo). The new ERA-I matches UAH exceedingly closely and demonstrates that RSS possesses relative warming through 2002 then a relative cooling thereafter – just as I had described above. So, here we have independent evidence for our contention.

The NOAA 11 is he discusses is likely the step transition seen when taking a difference of the two datasets. Christy has written on this particular step in the dataset where he used radiosonde data to determine that RSS was the series which was out of alignment. I did a simpler analysis using GISS data and got similar results HERE. I wasn’t totally happy with the result b/c of some sensitivity in result to time windows chosen but it turned out that giss based trend corrections preferred UAH results over RSS. – See Christy et al, Tropospheric temperature change since 1979 from tropical radiosonde and satellite measurements Published in JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112, 2007 – It’s a truly nice paper, there’s a lot of information which is relatively easy to grasp.

With the variety of corrections and this new explanation, I feel we’re getting closer to an accurate and trustworthy lower troposphere trend. Personally I don’t trust the land metrics at all, the corrections are too large and too arbitrarily quantified. However, there is still a difference in annual signal in RSS vs UAH which can be shown through a (fast) fourier transform that shows a stronger annual signal for UAH in recent years. This that means the point is not entirely settled. I almost accidentally nailed the timeframe for the AQUA satellite transition to after 2003 in this post here. See Figure 1, where the 1 year spike is much greater than the 1 year spike in RSS.  This means UAH has a different annual max/min average curve from before the AQUA switch and therefore means UAH likely wasn’t correcting for diurnal trend perfectly either. I say almost accidentally nailed,  because the 1 year cycle  was visible in the anomaly but I didn’t know why.  In other words UAH probably ain’t perfect either.

Anyway, here’s a plot of the difference in trend between RSS and UAH global data. I took the global trend as averaged by the source scientists and re-anomalized them for post June 2002 data. The global UAH-RSS difference on average doesn’t reach significance compared to series noise, there isn’t much question that the land average alone or sea average alone don’t though.

I’ll leave this for now with another quote.

Our contention is the same as yours, that the diurnal adjustments regionally, and globally are overdone in RSS. RSS is working on a new version and may have some of these problems solved soon.

Thanks again to John Christy for giving some insight into what’s happening behind the scenes.  It should be interesting to see how these issues are resolved in the near future.  The  nice thing about the satellite data is that many of these issues while complex, are quantifiable.  Science minded people who are skeptical of AGW claims are said to prefer satellite records over ground measurements simply because of the lower trends.  In my opinion the preference is due to the cleaner and more sensible data.  After all, it’s not like satellites don’t show statistically significant warming.

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