Extreme precipitation vulnerability in the Upper Thames River basin: uncertainty in climate model projections
Bibliographic record
Abstract
Abstract This study provides an assessment of possible future climate conditions for the Upper Thames River (UTR) basin. The interpretation of future climate from widely used global climate models has a large impact on the comparison of the simulated daily extreme precipitations. Unfortunately, literature relevant to analyzing precipitation extremes is limited and more work is necessary to develop strategies for assessing the vulnerability of extreme precipitation events on water resources and communities at both local and regional levels. The present study deals with six different atmosphere–ocean coupled general circulation models (AOGCMs) with up to three emission scenarios in order to generate a precipitation series for the 2050s. The data has been downscaled using a principal component analysis–integrated stochastic weather generator (WG‐PCA) to produce a synthetic dataset for 54 years. The variability between the AOGCMs and their emission scenarios are investigated, as well as the performance of the WG‐PCA generator in producing extreme precipitation events. The comparative analysis of 14 different scenarios shows that the AOGCM results are variable and need to be carefully screened before using them for future climate change impact assessments in the UTR basin. Future work is needed on regional studies to explore local characteristics of precipitation extremes and improve the model quality by introducing more input variables relevant to the precipitation extremes. Copyright © 2010 Royal Meteorological Society
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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 | 0.003 |
| 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 | 0.000 |
| 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".