Revealing past climate variability using tree rings and stable isotopes from tropical and boreal regions in the Americas
Bibliographic record
Abstract
High-resolution records of centennial climate variability are crucial considering the scarcity and overall short length of instrumental meteorological data in many regions of the world. The application of stable isotopic analysis in tree rings has emerged as a robust methodological tool for elucidating the intricate complexities of environmental history. This presentation will travel from high latitudes in North America to the Tropical Andes in South America to show how tree-ring stable isotopes can be used to reconstruct climate variability and atmospheric patterns across the Americas, as well as changes in Sea Surface Temperatures (SST). Stable oxygen isotopes (δ18O) measured in tree rings from white spruce trees from the Northwest Territories of Canada record similar large-scale climate patterns as modelled precipitation δ18O from a general circulation model (NASA GISS ModelE2 isotopically-equipped). Trees from the species Polylepis tarapacana growing at high elevation (~5,000 m a.s.l) at the South American Altiplano were used to reconstruct annual precipitation variability, which is driven by the South American Summer Monsoon, over the last 300 years. This newly developed tree-ring δ18O chronology revealed a robust hydroclimatic teleconnection showing interannual (2–5 years) and decadal (~11 years) periodicities consistent with records of Altiplano precipitation, central tropical Pacific SST, Andean ice core δ18O and tropical Pacific coral δ18O. Furthermore, new tree species of the genus Polyelpis growing in the inner tropics were discovered and found to have significant sensitivity to local and regional hydroclimate variability, showing a close link to tropical Pacific SST and El Niño–Southern Oscillation. Overall, our findings point out the importance of developing longer stable isotopes tree-ring records to overcome the inherent difficulties to reconstruct global hydroclimate variability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".