Global- and regional-scale hydrological response to early Eocene warmth
Bibliographic record
Abstract
Earth's hydrological cycle is expected to intensify in response to global warming, with a 'wet-gets-wetter, dry-gets-drier' response anticipated.The subtropics (˜15-30°N/S) are predicted to become drier, yet proxy evidence from past warm climates suggests these regions may be characterised by wetter conditions.Here we use an integrated data-modelling approach to reconstruct global-and regional-scale rainfall patterns during the early Eocene (˜48-56 million years ago), with an emphasis on the subtropics.Model-derived precipitation-evaporation (P -E ) estimates in the tropics (0-15°N/S) and high latitudes (>60°N/S) are positive and increase in response to higher temperatures, whereas model-derived P -E estimates in the subtropics (15-30°N /S) are negative and decrease in response to higher temperatures.This is consistent with a 'wet-gets-wetter, dry-gets-drier' response.However, some DeepMIP model simulations predict increasing -rather than decreasing -subtropical precipitation at higher temperatures (e.g., CESM, GFDL).Using moisture budget diagnostics we find that the models with higher subtropical precipitation are characterised by a reduction in the strength of subtropical moisture circulation due to weaker meridional temperature gradients.These model simulations (e.g., CESM, GFDL) agree more closely with various proxy-derived climate metrics and imply a reduction in the strength of subtropical moisture circulation during the early Eocene.Although this was Posted on 23 Nov 2022 -CC-BY 4.0 -https://doi.org/10.1002/essoar.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".