This simulation models how a tree lays down one growth ring per year, with each ring's width responding to that summer's climate. The ring width index follows a simple linear model, w_t = b + c1 · T_summer + c2 · P_summer + ε, where a baseline growth b is modulated by temperature and precipitation signals plus random noise. A 120-year series is generated for two trees so their patterns can be compared.
The sliders set temperature sensitivity c1 and moisture sensitivity c2, the noise level, and the year a drought strikes (which sharply narrows that ring). Toggles overlay a second tree for cross-dating and colour rings from cool blue to warm amber. Cross-dating like this lets dendrochronologists match patterns between samples to assign exact calendar years and reconstruct past climate from wood.
What is dendrochronology?
Dendrochronology is the science of dating events and reconstructing past environments by analysing the annual growth rings of trees. Because most temperate trees add exactly one ring per year, counting and measuring rings yields a year-by-year record. This simulator reproduces that record and lets you study how climate shapes ring width.
How is each ring's width calculated here?
Each ring uses the linear model w_t = b + c1 · T_summer + c2 · P_summer + ε, with a baseline b of 1.0. Temperature T and precipitation P for each year come from smooth oscillations plus random variation, then are scaled by the sensitivity sliders and a noise term. The width is floored at 0.05 so it never goes negative.
What do the temperature and moisture sliders do?
The c1 slider controls how strongly summer temperature drives growth, and c2 controls how strongly summer precipitation does. Both range from 0 to 1.0. Raising a value makes the rings respond more sharply to that climate variable, mimicking trees from temperature-limited versus moisture-limited sites.
Cross-dating compares the ring-width pattern of one sample against another to confirm that rings line up to the same calendar years. The simulator shows a second tree and reports a Pearson correlation r between the two series; a high r indicates the trees experienced the same climate and can be matched.
The drought slider marks a stress year where growth collapses. In the model, rings within three years of the chosen offset are multiplied by 0.25, producing a conspicuous narrow band. Such marker years are invaluable in real dendrochronology because they appear across many trees and anchor the dating.
When the colour-code toggle is on, ring colour interpolates from cool blue for narrow rings to warm amber for wide ones, while drought rings are highlighted in red. This makes climate signals visually obvious: bands of warm colour mark favourable summers and blue bands mark cold or dry spells.
Pearson's correlation coefficient r measures how linearly two ring-width series move together, ranging from -1 to +1. Here it compares Tree 1 and Tree 2 across all 120 years. Values near +1 mean the trees share a strong common climate signal, which is exactly what makes cross-dating reliable.
It is a simplified educational model, not a calibrated ecological one. The core relationships are realistic: rings respond to temperature and moisture, droughts cause narrow rings, and shared climate yields correlated patterns. Real growth also depends on age trends, soil, competition and prior-year carbohydrate stores, which are not modelled here.
In seasonal climates, cambium cells produce large, thin-walled earlywood in spring and dense latewood in late summer, then growth pauses in winter. The earlywood-to-latewood boundary marks one year. This annual rhythm is what lets each ring be tied to a specific calendar year.
By calibrating ring widths against instrumental temperature or rainfall records over recent decades, researchers derive a statistical relationship, then invert it to estimate climate for centuries before measurements existed. Overlapping samples from living and ancient wood extend these chronologies thousands of years, supporting paleoclimate research and archaeological dating.