The hydrological impact of geoengineering in the Geoengineering Model Intercomparison Project (GeoMIP)
Bibliographic record
Abstract
The hydrological impact of enhancing Earth's albedo by solar radiation management is investigated using simulations from 12 Earth System models contributing to the Geoengineering Model Intercomparison Project (GeoMIP). We contrast an idealized experiment, G1, where the global mean radiative forcing is kept at preindustrial conditions by reducing insolation while the CO 2 concentration is quadrupled to a 4× CO 2 experiment. The reduction of evapotranspiration over land with instantaneously increasing CO 2 concentrations in both experiments largely contributes to an initial reduction in evaporation. A warming surface associated with the transient adjustment in 4× CO 2 generates an increase of global precipitation by around 6.9% with large zonal and regional changes in both directions, including a precipitation increase of 10% over Asia and a reduction of 7% for the North American summer monsoon. Reduced global evaporation persists in G1 with temperatures close to preindustrial conditions. Global precipitation is reduced by around 4.5%, and significant reductions occur over monsoonal land regions: East Asia (6%), South Africa (5%), North America (7%), and South America (6%). The general precipitation performance in models is discussed in comparison to observations. In contrast to the 4× CO 2 experiment, where the frequency of months with heavy precipitation intensity is increased by over 50% in comparison to the control, a reduction of up to 20% is simulated in G1. These changes in precipitation in both total amount and frequency of extremes point to a considerable weakening of the hydrological cycle in a geoengineered world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".