Towards better understanding the urban environment and its interactions with regional climate change - The WCRP CORDEX Flagship Pilot Study URB-RCC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. • There is a need to better simulate the interactions between urban areas and regional climate change. • FPS URB-RCC aims to investigate the effect of urban areas on the regional climate & vice versa. • Using coordinated regional climate modelling experiments with urban schemes. • Improved urban climate change information supports adaptation and the transition towards climate resilient cities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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 it