Precipitation response to climate change and urban development over the continental United States
Bibliographic record
Abstract
Abstract Appropriately characterizing future changes in regional-scale precipitation requires assessment of the interactive effect owing to greenhouse gas-induced climate change and the physical growth of the built environment. Here we use a suite of medium resolution (20 km grid spacing) decadal scale simulations conducted with the Weather Research and Forecasting model coupled to an urban canopy parameterization to examine the interplay between end-of-century long-lived greenhouse gas (LLGHG) forcing and urban expansion on continental US (CONUS) precipitation. Our results show that projected changes in extreme precipitation are at least one order of magnitude greater than projected changes in mean precipitation; this finding is geographically consistent over the seven CONUS National Climate Assessment (NCA) regions and between the pair of dynamically downscaled global climate model (GCM) forcings. We show that dynamical downscaling of the Geophysical Fluid Dynamics Laboratory GCM leads to projected end-of-century changes in extreme precipitation that are consistently greater compared to dynamical downscaling of the Community Earth System Model GCM for all regions except the Southeast NCA region. Our results demonstrate that the physical growth of the built environment can either enhance or suppress extreme precipitation across CONUS metropolitan regions. Incorporation of LLGHGs indicates compensating effects between urban environments and greenhouse gases, shifting the probability spectrum toward broad enhancement of extreme precipitation across future CONUS metropolitan areas. Our results emphasize the need for development of management policies that address flooding challenges exacerbated by the twin forcing agents of urban- and greenhouse gas-induced climate change.
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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 | 0.000 |
| 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) | 0.001 | 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".