Continental configuration controls the base-state water vapor greenhouse effect: lessons from half-land, half-water planets
Bibliographic record
Abstract
The distribution of land and ocean on Earth’s surface shapes the global atmospheric circulation and climate by modulating fluxes of water and energy between the surface and the atmosphere. Here we rearranged land in an idealized climate model to explore the effect of eight simplified continental configurations on global climate, finding several counterintuitive results. The limited capacity of land to hold water and the smaller heat capacity of land compared to ocean---rather than surface albedo differences---are the primary drivers of continental control on global mean temperature. Specifically, the presence of land in certain locations can enhance tropospheric water vapor content, increasing the greenhouse effect and clear-sky shortwave absorption; these effects can warm the planet more than the cooling effect of higher land surface albedos. For example, continental configurations with land in polar regions and large tropical oceans have the warmest, wettest global climates. Configurations with large tropical land masses are not hot desert planets, but have the coolest global climates due to reduced evaporation and thus reduced atmospheric water vapor compared to configurations without land in the tropics. Interactions between the small heat capacity of land and the seasonal cycle can lead to certain continental configurations having even warmer, wetter atmospheres than an aquaplanet. Our results demonstrate that different configurations of land, such as those obtained through past tectonic movement or on rocky exoplanets, set planetary climate through mechanisms beyond those involving surface albedo or orographic effects.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".