December 2, 2020 | David F. Coppedge

Rings Around Climate Theories

Tree ring data should provide accurate checks on climate models, right? It’s complicated.

By common assumption, a tree produces one ring per year, as a reflection of the changing seasons (heavy growth in summer, slow growth in winter). Scientists can use that cycle to count back over centuries. A tree, however, is not obligated to follow the calendar; it is responding to a bevy of conditions that allow for growth. One of the most obvious factors is climate; rings should be more densely concentrated in dry years, more widely spaced in wet years. That’s the theory. By comparing ring patterns from multiple trees, scientists should be able to use the data as a proxy for changing climate.

Scientists from around the world contribute tree ring data into a database called the International Tree-Ring Data Bank (ITRDB). How easy is it to determine climate trends from this collection of observations? Qi-Bin Zhang and Ouya Fang, writing in the journal Science, point out multiple factors that make the data noisy (i.e., hard to discern climate signals).

Tree rings have long been recognized as a useful proxy for past climate variations because of their special characteristics, such as precise dating, annual resolution, long time series, and climate sensitivity. The International Tree-Ring Data Bank (ITRDB) archives a wealth of tree-ring records contributed by scientists around the world. However, the use of these data for reconstruction of a specific climate variable over a large geographical region is not a straightforward procedure. Because a variety of environmental characteristics (such as macroclimate, local habitats, environmental disturbances, and tree physiology) influence tree growth, the signals from the macroclimate must be extracted while removing the nonclimate noise embedded in the rings.

Sequoia log exposing rings going back almost 2000 years (DFC)

Assuming that noise is random, scientists believe they can factor it out. Nevertheless, gleaning a climate signal blurs the line between art and science.

Sustained growth reduction in trees at a sampling site should be interpreted carefully, because it might be the result of either prolonged adverse climate or damage of tree health after site disturbances or climate extremes. Also, trees do not merely respond passively to adverse climate but develop ecophysiological resilience to resist and recover from the influence; this makes the macroclimate signals more difficult to extract. In addition, sites where tree growth is not strongly sensitive to macroclimate should be excluded from regional climate reconstruction.

Zhang and Fang go on to describe methods for interpreting tree ring data. Climate scientists would like to look for a ‘tipping point’ in which a major climate change could be discerned from the rings. The authors warn, however, that these scientists often focus on just one climate variable at a time, which can be misleading.

Real-world climate systems involve many variables whose complex interactions could either cause negative feedbacks that reduce the probability that any single variable crosses its tipping point or cause positive feedbacks that increase the probability that multiple variables cross their tipping point.

Several factors complicate the art of detecting signals in the noise. Among them are:

  • Local changes in water availability, such as stream redirection
  • Local vs regional microclimates
  • Regional vs global macroclimates
  • Environmental disturbances (e.g., logging in an area, forest fire)
  • Change to the health of the sampled tree (e.g., by insect attack)
  • Differences in sampled species’ ability to respond to climate changes
  • Interaction of nonclimate influences that create positive feedback, mimicking climate
  • Other unknown influences

The authors are not convinced that a recent paper in the same issue of Science makes an airtight case for an abrupt change to a hotter and drier climate in East Asia over the past 20 years – much less that the conclusions can be extrapolated to argue for “a dangerous trend” illustrating global warming. In particular, “other researchers have reported a hotter-wetter climate in the Tibetan Plateau” that contradicts the East Asia trend.

Given that climate systems involve feedback among multiple variables, it is crucial to be able to predict when each variable will exceed its tipping point and when changes in these variables will lead to a domino effect, which has more severe deleterious consequences for ecosystems and society than do individual variable changes. Development of trustworthy long-term climate records is a critical prerequisite for accurate detection of temporal changes in the interactions of climate variables, for discovering past climate regime shifts, and for predicting thresholds of potential future shifts.

The other paper seems overconfident of its interpretation. “Extreme episodes of hotter and drier climate over the past 20 years, which are unprecedented in the earlier records, are caused by a positive feedback loop between soil moisture deficits and surface warming and potentially represent the start of an irreversible trend,” they say. Maybe, though, their conclusions are more art than science. Correlation is not the same as causation.

Jeffrey pine forest in northern California (DFC). Is every individual tree responding in the same way to local climate?

These papers illustrate the tension between data and interpretation in science. Trees cannot talk. They cannot tell us exactly what they experienced. To scientists trained in simplistic scientific methods, it may seem straightforward to collect data from a tree and discern patterns of past climate. Comparing one tree with other trees, they think, corroborate past conditions, especially if the pattern can be traced over a wide area. Maybe they can even discern a similar pattern in cave stalactites. But since they were not there 24 x 7 x 365 for 20 years, let alone 2,000 years, they cannot say for sure what was going on. Even if they claim to be able to account for the known unknowns, what about the unknown unknowns?

By illustration, say that a diligent scientist collects ring data by boring holes into a test tree, one borehole per year. That way, he thinks, he is comparing the tree’s response to climate that he himself experienced by being there. But what if the tree is responding not just to the temperature, but by the injury of being drilled into repeatedly? If he changed his technique by boring into a nearby tree instead of the same tree, how can he be sure the nearby tree responds the same way? Ring widths also differ by the radius sampled. You can’t cut through the tree to look at the whole circumference, then glue it back and do it again the following year.

Scientists must respect data, but science is done by humans, not by data. Conclusions do not just jump out of the measurements. When someone claims that tree rings confirm that global warming is real, remember this example of complicating factors that affect conclusions.

Exercise: Think of possible “unknown unknowns” that could affect the interpretation of tree ring data.



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