Interaction of impacts of doubling CO<sub>2</sub> and changing regional land‐cover on evaporation, precipitation, and runoff at global and regional scales
Bibliographic record
Abstract
Abstract The Community Climate System Model version 2.0.1 is running for 40 years under 355 ppm CO2 conditions, without and with natural and anthropogenic land‐cover changes that are assumed in the inner core of four hydrothermally different, but similar‐sized (≈3.27x106 km2) regions (Yukon, Ob, St. Lawrence, Colorado, and lands adjacent to them). A further set of simulations assumes 710 ppm CO2 conditions without and with these land‐cover changes. Impacts of (1) doubled CO2, (2) changed land‐cover, and (3) the interaction between doubled CO2 and changed land‐cover on the four regional water cycles are elucidated using analysis of variance plus multiple testing. For the Yukon, Ob, and St. Lawrence regions, doubling CO2 significantly increases precipitation, evapotranspiration, and residence time nearly year‐round; the opposite is true for precipitation and evapotranspiration in Colorado. In general, doubling CO2 slows down water cycles regardless of land‐cover changes. Since land‐cover changes occur locally, they more strongly affect regional than global water cycling. Sometimes land‐cover changes alone reduce regional‐scale precipitation and evapotranspiration. Water‐cycle changes of comparable absolute magnitude can occur in response to either changed land‐cover or doubled CO2. Significant interactions between the two treatments indicate that local land‐cover changes, even if they have little impact under reference climate conditions, may have substantial regional impact in a warmer climate. Increased residence time after doubling CO2 indicates a generally increased influence of upwind regions on downwind regions. If land‐cover changes occur concurrently with CO2 changes, they will have farther‐reaching impact than under reference CO2 conditions. Thus, due to atmospheric transport the interaction between impact of land‐cover changes and CO2 doubling on water‐cycle‐relevant quantities may occur even in regions with unchanged land‐cover. A sensitivity study for tripled CO2 showed similar results, but more pronounced slowed‐down regional water cycles and interaction of the two treatments. Copyright © 2008 Royal Meteorological Society
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".