Terrestrial evaporation and global climate: lessons from Northland, a planet with a hemispheric continent
Bibliographic record
Abstract
From a climate perspective, land differs from the ocean in several fundamental physical ways, including albedo, heat capacity, amount of water storage, and differences in resistance to evaporation. These differences alter the surface energy and water budgets over land compared to ocean, with implications for both surface climate and atmospheric circulation. In this study, we use an idealized general circulation model (Isca) to explore the climate state of Northland, a planet with a northern land hemisphere and a southern ocean hemisphere. These idealized simulations are motivated by the asymmetry of continental distribution on the globe, with a greater concentration of landmasses in the northern hemisphere and a larger area of ocean in the southern hemisphere, and further illuminate the basic role that land-sea contrasts play in global atmospheric dynamics. We find a much larger seasonal cycle of temperature over land compared to ocean, as expected. The continent is seasonally wet in the tropics, has a subtropical desert, and a moist high-latitude ``swamp, where moisture transported from the tropics accumulates. Decreasing the land albedo leads to warming. In contrast to past studies, suppressing evaporation from the land surface cools the climate, resulting from decreased atmospheric water vapor and reduced trapping of longwave radiation, which dominates over the warming associated with reduced evaporative cooling at the surface. The ITCZ in the Northland simulations extends farther polewards over both the land and ocean hemispheres than the ITCZ in an aquaplanet. Our results demonstrate the potential for land and hemispheric asymmetries in controlling the large-scale axisymmetric atmospheric circulation.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".