Large isotope signals in tropical precipitation require large-scale changes in rainout
Bibliographic record
Abstract
In the tropics, oxygen isotope signals of past climate change range from less than 1‰ to upwards of 7‰ or more. Regardless of the amplitude, these signals are often interpreted to reflect changes in local rainout. However, stable isotopes in precipitation can carry information about rainout across thousands of kilometers, making it hard to parse the local and non-local effects. Here, we present a framework that links the amplitude of tropical isotope signals to spatial patterns of climate change that cause them. Using three models of varying complexity, we show that the largest signals require coherent hydrologic change across ~1,000 to 8,000 kilometers. This pattern can be explained by the balance of vapor being rained out versus replenished as it moves over space. Within ~1,000 kilometers, upwind changes in rainout are too localized for a large isotope shift to emerge. Beyond ~8,000 kilometers, the rainout signal is overwhelmed by more locally-sourced vapor. We find that rainout in this ~1,000-8,000 km upwind window causes the largest isotope shifts in tropical paleoclimate, even when the isotope composition is strongly correlated with local precipitation amount. Our results indicate that large amplitude isotope signals are reliable tracers of large scale hydrologic change, and their link to local precipitation amount is tenuous.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".