Snowpack variations since AD 1150 in the Andes of Chile and Argentina (30°–37°S) inferred from rainfall, tree‐ring and documentary records
Bibliographic record
Abstract
The Andean snowpack is the main source of freshwater and arguably the single most important natural resource for the populated, semi‐arid regions of central Chile and central‐western Argentina. However, apart from recent analyses of instrumental snowpack data, very little is known about the long term variability of this key natural resource. Here we present two complementary, annually‐resolved reconstructions of winter snow accumulation in the southern Andes between 30°–37°S. The reconstructions cover the past 850 years and were developed using simple regression models based on snowpack proxies with different inherent limitations. Rainfall data from central Chile (very strongly correlated with snow accumulation values in the adjacent mountains) were used to extend a regional 1951–2010 snowpack record back to AD 1866. Subsequently, snow accumulation variations since AD 1150 were inferred from precipitation‐sensitive tree‐ring width series. The reconstructed snowpack values were validated with independent historical and instrumental information. An innovative time series analysis approach allowed the identification of the onset, duration and statistical significance of the main intra‐ to multi‐decadal patterns in the reconstructions and indicates that variations observed in the last 60 years are not particularly anomalous when assessed in a multi‐century context. In addition to providing new information on past variations for a highly relevant hydroclimatic variable in the southern Andes, the snowpack reconstructions can also be used to improve the understanding and modeling of related, larger‐scale atmospheric features such as ENSO and the PDO.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".