Dendroclimatology

Dendroclimatology is the science of determining past climate conditions from trees, primarily tree rings. In general, tree rings are wider when conditions favor growth and narrower when times are hard. Using tree rings, scientists have estimated many local climate conditions for hundreds to thousands of years before modern times. By combining multiple tree-ring studies (sometimes with other climate proxy records), scientists have estimated past regional and global climates.

Dendroclimatology is a young science, and because a large number of factors affect the growth of tree rings, there are many limitations in terms of interpreting the data accurately. Nonetheless, methods are being improved to squeeze out more insights from tree ring evidence. It has been argued that current inferences from tree ring patterns, albeit imperfect, are better than knowing nothing about earlier climate conditions.

Advantages

Tree rings are especially useful as climate proxies in that they can be well-dated via "wiggle-matching" of the rings from sample to sample (an approach known as dendrochronology). This allows extension backwards in time using deceased tree samples, even using samples from buildings or from archeological digs. Another advantage of tree rings is that they give abundant data, clearly demarked in year increments, as opposed to other proxy methods such as boreholes. Furthermore, tree rings respond to multiple climatic effects (temperature, moisture, cloudiness), so that various aspects of climate (not just temperature) can be studied. However, this can be a double-edged sword as discussed below (see "Climate factors").

Limitations

Some limitations of dendroclimatology are: Confounding factors, geographic coverage, annular resolution, and collection difficulties. The field has developed various methods to partially correct for these challenges.

Confounding factors

There are multiple climate and non-climate factors as well as nonlinear effects that impact tree ring width. Methods to isolate single factors (of interest) include botanical studies to calibrate growth influences and sampling of "limiting stands" (those expected to respond mostly to the variable of interest).

Climate factors

Climate factors that affect trees include temperature, precipitation, sunlight, and wind. To differentiate among these factors, scientists collect information from "limiting stands." An example of a limiting stand is the upper elevation treeline: Here, trees are expected to be more affected by temperature variation (which is "limited") than precipitation variation (which is in excess). Conversely, lower elevation treelines are expected to be more affected by precipitation changes than temperature variation. This is not a perfect work-around, as multiple factors still impact trees even at the "limiting stand," but it helps. In theory, collection of samples from nearby limiting stands of different types (for example, upper and lower treelines on the same mountain) should allow mathematical solution for multiple climate factors. However, this method is rarely used.

Non-climate factors

Non-climate factors include soil, tree age, fire, tree-to-tree competition, genetic differences, logging or other human disturbance, herbivore impact (particularly sheep grazing), pest outbreaks, disease, and CO₂ concentration. For factors which vary randomly over space (tree to tree or stand to stand), the best solution is to collect sufficient data (more samples) to compensate for confounding noise. Tree age is corrected for with various statistical methods: Either fitting spline curves to the overall tree record or using similar aged trees for comparison over different periods. Careful examination and site selection helps to limit some confounding effects, for example picking sites undisturbed by modern man.

Non-linear effects

In general, climatologists assume a linear dependence of ring width on the variable of interest (for example, moisture). However, if the variable changes enough, response may level off or even turn opposite. The home gardener knows that one can underwater or overwater a house plant. In addition, it is possible that interaction effects may occur (for example "temperature times precipitation" may affect growth as well as temperature and precipitation on their own). Here, also, the "limiting stand" helps somewhat to isolate the variable of interest. For instance, at the upper treeline, where the tree is "cold limited," it's unlikely that nonlinear effects of high temperature ("inverted quadratic") will have numerically significant impact on ring width over the course of a growing season.

Botanical inferences to correct for confounding factors

Botanical studies can help to estimate the impact of confounding variables and in some cases guide corrections for them. These experiments may be either ones where growth variables are all controlled (for example, in a greenhouse), partially controlled (such as in FACE [Free Airborne Concentration Enhancement] experiments), or where conditions in nature are monitored. In any case, the important thing is that multiple growth factors are carefully recorded to determine what impacts growth. With this information, ring width response can be more accurately understood and inferences from historic (unmonitored) tree rings become more certain. In concept, this is like the limiting stand principle, but it is more quantitative—like a calibration.

Geographic coverage

Trees do not cover the Earth. Polar and oceanic climates cannot be estimated from tree rings. In tropical regions, the trees grow all year round and don't show clear annual rings. In some forested areas, the tree growth is highly influenced by multiple factors (no "limiting stand") to allow clear climate reconstruction. The coverage difficulty is dealt with by acknowledging it and by using other proxies (for example, ice cores, corals) in difficult areas. In some cases it can be shown that the parameter of interest (temperature, precipitation, and so on) varies similarly from area to area, for example by looking at patterns in the instrumental record. Then one may be justified in extending the dendroclimatology inferences to areas where no suitable tree ring samples are obtainable.

Annular resolution

Tree rings show the impact on growth over an entire growing season. Climate changes deep in the dormant season (winter) will not be recorded. In addition, different times of the growing season may be more important than others (such as, May versus September) for ring width. However, in general the ring width is used to infer the overall climate change during the corresponding year (an approximation). Another problem is "memory" or autocorrelation. A stressed tree may take a year or two to recover from a hard season. This problem can be dealt with by more complex modeling (a "lag" term in the regression) or by reducing the skill estimates of chronologies.

Collection difficulties

Tree rings must be obtained from nature, frequently from remote regions. This means that special efforts are needed to map sites properly. In addition, samples must be collected in difficult (often sloping terrain) conditions. Generally, tree rings are collected using a hand-held borer device, that requires skill to get a good sample. The best samples come from felling a tree and sectioning it. However, this requires more danger and does damage to the forest. It may not be allowed in certain areas, particularly with the oldest trees in undisturbed sites (which are the most interesting scientifically). As with all experimentalists, dendroclimatologists must, at times, decide to make the best of imperfect data, rather than resample. This trade off is made more difficult, because sample collection (in the field) and analysis (in the lab) may be separated significantly in time and space. These collection challenges mean that data gathering is not as simple or cheap as conventional laboratory science. However, they also give the field's practitioners much enjoyment, working out of doors, with hands on trees and tools.

New measurements

Initial work focused on measuring the tree ring width—this is simple to measure and can be related to climate parameters. But the annual growth of the tree leaves other traces. In particular maximum latewood density is another metric used for estimating temperature. It is, however, harder to measure. Other properties (for example, isotope or chemical trace analysis) have also been tried. In theory, multiple measurements on the same ring will allow differentiation of confounding factors (for example, precipitation and temperature). However, most studies are still based on ring width at limiting stands.

Relationship to global warming studies

Tree rings hold the promise of telling us whether twentieth-century warming is or is not precedented over last 1000 years or so. The importance of understanding posited global warming from man-made CO₂ has moved dendroclimatology from a sleepy science to a high profile field. However, the field has also been impacted by the acrimony of popular debates on the issue of global warming.

See also

• Climate
• Dendrochronology
• Paleoclimatology
• Tree

References

ISBN links support NWE through referral fees

• Briffa, K., and E. Cook. Methods of response function analysis. In Methods of Dendrochronology. Edited by E.R. Cook and L.A. Kairiukstis. Dordrecht, Netherlands: Kluwer, 1990. ISBN 0792305868
• Fritts, Harold C. Tree Rings and Climate. London: Academic Press, 1976. ISBN 0122684508
• Luckman, B.H. Dendroclimatology. In Encyclopedia of Quaternary Science, edited by Scott A. Elias, 1: 465-475. Amsterdam, Netherlands: Elsevier, 2007. ISBN 978-0444519191
• Schweingruber, Fritz Hans. Tree Rings and Environment Dendroecology. Berne: Paul Haupt, 1996. ISBN 3258054584

External links

All links retrieved July 26, 2022.

• International Tree-Ring Data Bank. NOAA. (Paleoclimatology Program and World Data Center for Paleoclimatology.)

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