Extreme precipitation under climate change conditions: Validation of a regional climate model over a small watershed using a spatial disaggregation model.
Bibliographic record
Abstract
Regional Climate Models (RCMs) are valuable tools to evaluate impacts of \nclimate change (CC) at regional scale. However, as the size of the area of \ninterest decreases, the ability of a RCM to simulate extreme precipitation events \ndecreases due to the RCM spatial resolution. Thus, it is difficult to: (i) evaluate \nwhether a RCM bias on localized extreme precipitation is caused by the spatial \nresolution or by a misrepresentation of the physical processes by the model and \n(ii) consequently assess projections of CC impacts for localized extreme \nprecipitation. Spatial statistical disaggregation models can bring the RCM \nprecipitation data at a finer scale and reduce the bias caused by the spatial \nresolution. In addition, disaggregation models can generate an ensemble of \noutputs, producing an estimate by interval instead of a unique punctual estimate. The objective of this work is to illustrate how a spatial statistical disaggregation \nmodel applied on extreme daily precipitations can provide a framework to assess \na RCM for a period of reference and help to evaluate the impacts of CC over a \nsmall area. Three simulations of the Canadian RCM (CRCM) covering the period \n1961-2099 are used over a small watershed (130 sq km) located in southern \nQuebec, Canada. The disaggregation model applied is based on Gibbs sampling \nand accounts for physical properties of the events (wind speed, wind direction, \nand convective available potential energy (CAPE)), leading to realistic spatial \ndistributions of precipitation. The use of the disaggregation model reduces \nsignificantly the impact of the RCM spatial resolution and enables the estimation \nof the level of significance for the difference between observed and simulated \nextremes for the reference period. The results indicate that the three simulations \ntend to overestimate precipitation, but with different levels of significance. When \ncomparing to the RCM raw data, the disaggregation does not affect the CC \nsignal, and does indicate that the impact is statistically significant for each \nsimulation tested.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".