Reconstructing the annual mass balance of the Echaurren Norte glacier (Central Andes, 33.5° S) using local and regional hydroclimatic data
Bibliographic record
Abstract
Abstract. Despite the great number and variety of glaciers in southern South America, in situ glacier mass-balance records are extremely scarce and glacier–climate relationships are still poorly understood in this region. Here we use the longest (> 35 years) and most complete in situ mass-balance record, available for the Echaurren Norte glacier (ECH) in the Andes at ∼ 33.5° S, to develop a minimal glacier surface mass-balance model that relies on nearby monthly precipitation and air temperature data as forcing. This basic model is able to explain 78 % of the variance in the annual glacier mass-balance record over the 1978–2013 calibration period. An attribution assessment identified precipitation variability as the dominant forcing modulating annual mass balances at ECH, with temperature variations likely playing a secondary role. A regionally averaged series of mean annual streamflow records from both sides of the Andes between ∼ 30 and 37° S is then used to estimate, through simple linear regression, this glacier's annual mass-balance variations since 1909. The reconstruction model captures 68 % of the observed glacier mass-balance variability and shows three periods of sustained positive mass balances embedded in an overall negative trend over the past 105 years. The three periods of sustained positive mass balances (centered in the 1920s–1930s, in the 1980s and in the first decade of the 21st century) coincide with several documented glacier advances in this region. Similar trends observed in other shorter glacier mass-balance series suggest that the Echaurren Norte glacier reconstruction is representative of larger-scale conditions and could be useful for more detailed glaciological, hydrological and climatological assessments in this portion of the Andes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".