Exploring the role of evaporation in atmospheric heat transport and seasonal low-latitude precipitation biases in CMIP
Bibliographic record
Abstract
Tropical and subtropical precipitation impact millions of people via agriculture and rainfall driven disasters. However, a wide spread remains in future regional projections of low-latitude precipitation, dominated by uncertain shifts in rainfall, and models continue to show a variety of biases in the location and intensity of rain.The Energy Flux Equator framework has emerged as a powerful tool in interpreting the location of low-latitude rainfall via atmospheric heat transport (AHT), which in turn can be understood through the top of atmosphere and surface energy fluxes. Recent work using a novel decomposition of the zonal-mean AHT suggests that its spatial structure is dominated by the meridional structure of the latent heat flux. Here, we apply this decomposition to investigate intermodel differences in AHT on the seasonal timescale.We find that throughout the year, intermodel differences in total AHT and the latitude of maximum zonal mean precipitation both correlate strongly with the heat transport contribution attributed to evaporation. Curiously, spatial regressions appear to suggest that evaporation over land provides a key contribution to this spread, despite the net surface heat flux over land being close to balanced. To interrogate the causality underlying this correlation with land evaporation, we make use of the 1pctCO2-bgc simulations, in which only the carbon cycle responds to increasing carbon dioxide, with one consequence being altered evapotranspiration.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".