Six hundred years of South American tree rings reveal an increase in severe hydroclimatic events since mid-20th century
Bibliographic record
Abstract
South American (SA) societies are highly vulnerable to droughts and pluvials, but lack of long-term climate observations severely limits our understanding of the global processes driving climatic variability in the region. The number and quality of SA climate-sensitive tree ring chronologies have significantly increased in recent decades, now providing a robust network of 286 records for characterizing hydroclimate variability since 1400 CE. We combine this network with a self-calibrated Palmer Drought Severity Index (scPDSI) dataset to derive the South American Drought Atlas (SADA) over the continent south of 12°S. The gridded annual reconstruction of austral summer scPDSI is the most spatially complete estimate of SA hydroclimate to date, and well matches past historical dry/wet events. Relating the SADA to the Australia-New Zealand Drought Atlas, sea surface temperatures and atmospheric pressure fields, we determine that the El Niño-Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) are strongly associated with spatially extended droughts and pluvials over the SADA domain during the past several centuries. SADA also exhibits more extended severe droughts and extreme pluvials since the mid-20th century. Extensive droughts are consistent with the observed 20th-century trend toward positive SAM anomalies concomitant with the weakening of midlatitude Westerlies, while low-level moisture transport intensified by global warming has favored extreme rainfall across the subtropics. The SADA thus provides a long-term context for observed hydroclimatic changes and for 21st-century Intergovernmental Panel on Climate Change (IPCC) projections that suggest SA will experience more frequent/severe droughts and rainfall events as a consequence of increasing greenhouse gas emissions.
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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 | 0.001 |
| 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) | 0.002 | 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".