Thermal Stress Dynamics Across Local Climate Zones: A High-Resolution Analysis of Ireland's Future Climate
Bibliographic record
Abstract
Climate change is driving a global rise in temperatures. In Ireland, increasing temperatures pose a significant challenge, as residents are not naturally acclimated to extreme heat. The escalation of thermal risks is attributed to the accelerating pace of urbanization. Additionally, variations in land cover and urban environments lead to differing levels of thermal risk across regions.This study investigates the relationship between local climate zones (LCZs) and thermal stress (measured via the Universal Thermal Climate Index, UTCI) to inform heat risk mitigation in Ireland’s future development. High-resolution (~4 km) regional climate projections were generated by dynamically downscaling CMIP6 data using atmosphere-only and coupled atmosphere-ocean-wave regional climate models (RCMs). Outer domains (12 km for COSMO-CLM and 20 km for WRF) aligned with the Euro-CORDEX framework were nested to refine Ireland-specific projections. Data from a 30-year reference period (1981–2010) and three future 30-year periods (2021–2050, 2041–2070 and 2071–2100) were used for the analysis of the Irish climate for each of the four RCP-SSP scenarios. The estimated UTCI is subsequently analysed in tandem with LCZs.By evaluating the correlation between heat risk and LCZs, this research aims to inform climate-resilient planning and development in Ireland. The findings provide valuable insights for thermal risk mitigation strategies across different urban morphologies, particularly crucial as cities adapt to increasing thermal stress under climate change scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".