Terrestrial Evaporation and Global Climate: Lessons from Northland, a Planet with a Hemispheric Continent
Bibliographic record
Abstract
Abstract Motivated by the hemispheric asymmetry of land distribution on Earth, we explore the climate of Northland, a highly idealized planet with a Northern Hemisphere continent and a Southern Hemisphere ocean. The climate of Northland can be separated into four distinct regions: the Southern Hemisphere ocean, the seasonally wet tropics, the midlatitude desert, and the Great Northern Swamp. We evaluate how modifying land surface properties on Northland drives changes in temperatures, precipitation patterns, the global energy budget, and atmospheric dynamics. We observe a surprising response to changes in land surface evaporation, where suppressing terrestrial evaporation in Northland cools both land and ocean. In previous studies, suppressing terrestrial evaporation has been found to lead to local warming by reducing latent cooling of the land surface. However, reduced evaporation can also decrease atmospheric water vapor, reducing the strength of the greenhouse effect and leading to large-scale cooling. We use a set of idealized climate model simulations to show that suppressing terrestrial evaporation over Northern Hemisphere continents of varying size can lead to either warming or cooling of the land surface, depending on which of these competing effects dominates. We find that a combination of total land area and contiguous continent size controls the balance between local warming from reduced latent heat flux and large-scale cooling from reduced atmospheric water vapor. Finally, we demonstrate how terrestrial heat capacity, albedo, and evaporation all modulate the location of the ITCZ both over the continent and over the ocean.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".