Projecting the evolution of the urban climate of metropolitan French cities using a convection-permitting model
Bibliographic record
Abstract
Urban areas will not be spared from the effect of climate change and will face more and more extreme weather events (e.g., heatwaves and floods). Such changes already cause severe environmental issues, economics damages, and many casualties, especially in cities where most activities and populations are concentrated. Evaluating the impacts of future extreme events on urban areas is a major challenge to prepare the adaptation.Urban areas are very complex systems that require specific modeling tools. The latest advances in regional climate modeling allow simulations to be performed over longer time periods with finer horizontal resolutions of up to few kilometers. The scientific community emphasizes the considerable improvements in using Convection-Permitting Models (CPM), especially for the representation of small-scale phenomena. Also, CPMs offer an interesting modeling framework for studying the interaction between regional climate and urban effects (especially urban heat island) and city-scale impacts through the explicit coupling of the atmospheric climate model with a dedicated urban surface model.In this study, the CNRM-AROME CPM, coupled with the Town Energy Balance (TEB) urban-canopy model, is used at 2.5-km horizontal resolution. Climate simulations were performed on an extended France domain (northwestern Europe) as part of the EUropean Climate Prediction system (EUCP) project over an historical period (1986-2005) and two future periods (mid-term, 2041-2050 and long-term, 2080-2099) using the RCP8.5 emission scenario. Here, scientific objectives are (1) to evaluate the urban heat island evolution for some metropolitan French cities, and (2) to quantify the evolution of specific meteorological hazards on cities and population. With this aim, we selected indicators related to heatwaves (based on Ouzeau et al. (2016) methodology) and heavy precipitation event, considered as some of the most relevant extreme meteorological events. These indicators were tested and evaluated on the evaluation simulation, and then analyzed in a changing climate to quantify the impacts on the cities. The multi-city approach makes it possible to investigate the variability that can exist between cities depending on their geographical and climatic contexts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".