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This is the science that reconstructs historic aspects of climate using tree ring records, particularly for times before instrumental records. Dendroclimatology is the study of tree rings to determine the relationship between tree rings and environmental variations, including climate.
Dendrochronology is different, not to ne confused with dendroclimatology. Dendrochronology is the comparison of tree ring series back through history for dating purposes. It is a very reliable dating mechanism. A series is a number of tree rings on a trunk cross-section, perhaps fifty rings.
Some available series go back two thousand years, and there are series that go back several thousand years, with some parts of the series floating. Floating means that there are gaps in the series. With floating series, radio-carbon dating is used to check the ages of the tree rings.
With continuous series, the dating is so accurate that it is used to calibrate the radio-carbon dating. An anchored bristle-cone pine series in south-west USA go back eight thousand years. Fully anchored chronologies extending back ten thousand years exist for oak trees in Germany.
There is much work proceeding to try and line up dendrochronologic series with older series such as stalagtite, coral, sedimentary deposits, ice cores and so on.
Dendroclimatology is a far more difficult and unreliable science that attempts to estimate climate variables, such as temperature and rainfall, from differences in the width and density of year by year tree-ring variations.
When you cut through a tree trunk, as you see, most tree cross-sections are not symmetrical. At present, I can find little authoritative data on the causes of these asymmetries. A large number of growing conditions are suspected, and they may well be compound. Cores are taken from trees by drilling with an auger [5 millimetres diameter] but, of course, the people who are analysing the tree rings are not usually the people doing the sampling. A height of 1.3 metres above the ground is recommended. Without cutting down the tree there is no way to know with certainty the degree and directions of asymmetries.
Tree ring analysis master lists are also developed from archeological samples. These samples are often in poor condition, dragged out of swamps, or difficult to access, as in the roof of a cathedral. In such circumstances, the wood may be waterlogged and the sapwood will be friable. This makes the use of an auger often impracticable. More on this subject can be read in Timber: dendrochronology of roof timbers in Lincoln Cathedral.
Also you can see in the pictures above, the thickness of the rings varies considerably according to the symmetries. The ring thickness also rambles randomly round the circumference of the trunk. These various factors should introduce some degree of caution in trusting these measurements. The best that can be hoped for is that these problems can be somewhat mitigated by averaging out processes. It is common to strive for a precision of one one-hundredth of a millimetre; to my judgment, this suggests ‘delusions of accuracy’ [a belief that the measurements are more precise than reality allows].
To give you some idea of how difficult is the measuring and matching in the real world, here is a quote from Timber: Dating of the Roof Timbers at Lincoln Cathedral, p.7.
For some further background, the photograph just above makes clear some other details of a cross-section through a tree trunk. This trunk has been left in the open for a while after felling, and you can see very clearly the distinction between the heartwood (‘dead wood’) and the sapwood. In the sapwood, the capillaries are still open and, thus, water can ingress. You can see the signs of damp and mould (the bluish darkening). Naturally, the sapwood is more vulnerable to deterioration from rotting and attack, especially over long time periods.
You will also see the remains of old branches, commonly known as knots - the pointed ovals or boat shapes. In the management of forestry, knots are mostly considered as a flaw in the wood (artists and other woodworkers may sometimes take a different view), and so knotted wood fetches lower prices in the market. Hence, lower branches are usually sheared off as the tree grows, in order to improve wood quality and price.
Thus, in the photo above, you may note that some of the knots finish within the heartwood and do not extend to the surface of the trunk. The most important branches, from the point of view of the tree, are those that access the light. Because of this, many trees shed lower branches as they fight for height in the canopy in dense forest and plantations.
I was once amazed when I asked a very capable botanist what a particular flower was. It looked to me like a typical garden variety. I was told it was a common meadow flower (weed!?). I said it was nothing like the flowers I’d seen under that name - the one in the garden was at least twice as high and the flowers at least two or three times the size
I was informed that the difference was solely down to better soil and fertilisers and attention.
Soil varies over a few feet, the flower had been taken from a nearby flood plain field. Trees a few metres apart can be in very different conditions, let alone on different continents.
Two main variables are currently under examination as temperature proxies/surrogates:
However, a great number of variables effect the growth of trees: microclimates, soil conditions, shelter, root depth, crowding, but above all rainfall and temperature. When trying to obtain useful samples as temperature proxies, trees at the edge of their range are sought out, because that is the sort of area where the trees are most likely to be stressed. Even then, the proxies are not very reliable for any particular year, even conditions in previous years can have effects of later years of growth.
Sometimes there are false rings, and sometimes the year’s rings may be close to invisible, especially in individual tree samples. Thus, it is usual to take many samples to establish averages for a particular growing area.
In order to try to discern whether the growing conditions have changed over long periods, say hundreds of years, sample averages and variations from groups of years are compared one with another to check consistencies.
During the life of a tree, in the first year or seven, the rings grow more quickly and are, therefore, wider. So ring sequences are standardised, according to the age of the ring, to a notional width for say first-year or fifth-year growth. As different tree species have different growing patterns and other characteristics, for dendroclimatology single species are used. However, it is not unusual to see series from different species used in an attempt to establish regional conditions.
Notice that there is a lot of averaging and adjusting going on to set up a ring or density series, at which point you are no longer dealing with raw data or individual trees, but with synthesised data.
This becomes more dubious. We only have thermometer temperatures going back one or two hundred years. Many of those temperature series are far from reliable, and are very often nowhere near the most useful trees that may reflect the variable such as temperature that you are attempting to model. And remember each sample represents only a small, local area, so attempts are made to combine averages from many areas. To make things still more difficult, the critical temperature differences usually lie in only one, or a small number of summer months. Thus the proxy temperatures are also similarly limited. The degree of difference between seasonal temperatures in different areas is, therefore, a factor.
Even when all this done, correlation between the measurement, such as temperature, and the tree measurement may correlate reasonably well, but are still well away from 100% correlation.
Thus you will hope for too much, if you expect to use tree ring data for more than having an idea of temperature trends over a period of time. Do not expect to be able to pin down accurate temperatures in any particular year. Remember that even with the trends, there are error factors that should be shown, as can be seen in examples given on understanding graphs and charts. 
This is one of several graphs of the guesses for climate over the last 1000-plus years.
wild variations of the plots.
It is from data of this type that the recent temperature advance is calculated, and this trend is calculated against a long-term downward trend.
Note the dark black line which shows you the combined average of all the original series. It is from summary plots of this sort of data that squeals about ‘the hockey stick’ come from the denialist industry ☺. As you will see, the data does suggest a recent (industrial age) advance, as exhibited by the recent turn-up in the first graph .
As for ‘the hockey stick’, that depends on just how you select the scales for your graphs. I have seen suggestions that only a 95% significance level is ‘scientifically acceptable’. This is nonsense. The interpretation of statistical tests of ‘significance’ are always a matter of judgment.
Consider your best beloved is apparently dying of some dread disease. The best medicine can do is to guess that the disease is one of two possibilities. A treatment is known for both diseases, but you know that disease A is four times as common/likely as disease B. You also know that if you give the wrong treatment, the person will die, and if you do not treat, they will die within the year. In this situation, you have only an 80% chance of being correct if you apply treatment for A.
80% becomes highly significant in making your choices. In fact, well-presented work should not use the word ‘significant’ loosely, but should use phrases such as ‘statistically significant at the 95% level, or the 80% level’, or whatever. And very likely, the presenter should give other details such as the statistical test being used, and the sample size.
It is only in context
that you can decide whether a trend is ‘significant’
or not. It is only in context that
Each person must decide whether a statistical pattern reflects real world phenomenon, or whether it is just a statistical/numerical artifact.
The general consensus is that there is global warming, and that it is mainly human caused. The simple reason for this consensus is because the data convinces most people who bother to dig deeply enough, that anthropogenic global warming is far the most likely explanation of the data.
Whether it is a correct interpretation does not depend on whether you want it to be so, or on whether you have interests in filthy fossil fuels, or on whether your tenure at university depends on studying climate science, or whether you just want to keep on running that 4x4 and don’t want your fun curtailed. Old men often prefer to ignore the mess they are leaving for the next generation. Witness the hugh build-up of debt by irresponsible politicians. The young are likely to be less sanguine.
The tree ring data is ‘wrung out’ of the original raw data by typical/various statistical techniques in which I do not place great trust.
For more problems with statistical analysis, see ‘intelligence’: misuse and abuse of statistics.
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© abelard, 2009,20 december
the address for this document is http://www.abelard.org/briefings/dendroclimatogy.php