Impacts of different<scp>ENSO</scp>flavors and tropical Pacific convection variability (<scp>ITCZ</scp>,<scp>SPCZ</scp>) on austral summer rainfall in South America, with a focus on Peru
Bibliographic record
Abstract
ABSTRACT El Niño in the eastern and central Pacific has different impacts on the rainfall of South America, and the atmospheric pathways through the South Pacific Convergence Zone (SPCZ) and Inter‐Tropical Convergence Zone (ITCZ) are poorly understood. To address this, we performed linear regression analysis of E (eastern Pacific) and C (central Pacific) indices of sea surface temperature (SST), as well as precipitation indices for theSPCZandITCZ, with gridded precipitation and reanalysis data sets during the austral summer (December–February) for the 1980–2016 period. Positive C induces dry anomalies along the tropical Andes and northern South America (NSA), while wet anomalies prevail over southeastern South America (SESA). Moreover, it produces wet conditions in the northwestern Peruvian Amazon. In contrast, positive E enhances wet conditions along the coasts of Ecuador and northern Peru associated with the southward displacement of the eastern PacificITCZand induces dry conditions in Altiplano, Amazon basin, and northeastern Brazil (NEB). Both El Niño Southern Oscillation (ENSO) indices are associated with weakened upper‐level easterly flow over Peru, but it is more restricted to the central and southern Peruvian Andes with positive E. BothSPCZindices, the zonal position of theSPCZand its latitudinal displacement, suppress rainfall along western Peruvian Andes when are positive, but the latter also inhibits rainfall over the Bolivian Altiplano. They are also linked to upper‐level westerly wind anomalies overall of Peru, but these anomalies do not extend as far south in the first. The southward displacement of the eastern PacificITCZalso induces wet anomalies inSESAwhile dry anomalies prevail overNEB, the western Amazon basin, and Bolivia. Oppositely, the southward displacement of the central PacificITCZinduces dry anomalies inNEBand along the northern coast of Peru; while wet anomalies occur mainly in eastern Brazil, Paraguay, and Bolivia through an enhancement of the low level jet.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".