Towards better understanding the urban environment and its interactions with regional climate change - The WCRP CORDEX Flagship Pilot Study URB-RCC
Bibliographic record
Abstract
High-quality climate information tailored to cities' needs assists decision makers to prepare for and adapt to climate change impacts, as well as to support the targeted transition towards climate resilient cities. During the last decades, two main modelling approaches emerged to understand and analyse the urban climate and to generate information. Firstly, meso- and microscale urban climate models commonly resolve the street to city scale climate (1 m to 1 km) through simulating short “weather” type episodes, possibly under climate change conditions. Secondly, regional climate models (RCMs) are currently approaching the kilometer scale grid resolutions (1–4 km) and becoming increasingly relevant to understand the interactions of cities with the regional climate on timescales from decades up to a century. Therefore, the WCRP CORDEX Flagship Pilot Study “ URBan environments and Regional Climate Change (FPS URB-RCC)” brings together the urban climate modelling community and the RCM community and focuses on understanding the interactions between urban areas and regional climate change, with the help of coordinated experiments with an RCM ensemble having refined urban representations. This paper presents the FPS URB-RCC, its main aims, as well as the initial steps taken. The FPS URB-RCC advances urban climate projections and information to support evidence-based climate action towards climate resilient cities.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".