Detecting the spatio-temporal propagation of heat waves in a regional single-model large ensemble
Bibliographic record
Abstract
Heat waves are among the most hazardous climate extremes in Europe, commonly affecting large regions for a considerable amount of time. Especially in the recent past heat waves account for substantial economic, social and ecologic impacts and loss. Projections suggest that their number, duration and intensity increase under changing climate conditions, stressing the importance of quantifying their characteristics. Yet, apart from the analysis of single historical events, little research is dedicated to the general propagation of heat waves in space and time. Heat waves are rare in their occurrence and limited observational data provide little means for robust analyses and the understanding of dynamical spatio-temporal patterns. Therefore, we seek to increase the number of analyzable events by using a single-model initial condition large ensemble of a regional climate model (Canadian Regional Climate Model Version 5, CRCM5-LE). This provides 50 model members of comparable climate statistics to robustly assess various spatial patterns and pathways of European heat waves in a data set of high spatial resolution. Using the CRCM5-LE allows us to explore a novel data-driven approach to infer cause-and-effect relationships, in this case the spatio-temporal propagation of spatially distributed phenomena. Our aim is to investigate specifically the transitions and inter-dependencies among heat wave core regions in Europe to better understand their evolution during the recent past. We define heat waves as a minimum of three consecutive hot days with temperatures above the 95th JJA (1981-2010) percentile. If a reasonable fraction of the domain land area exhibits a hot day, this time step is used for clustering in order to derive core regions. Each core region is represented by a spatially aggregated time series of the cluster footprint. The approach further includes the derivation of directed links between these core regions using causal discovery and the analysis of associated atmospheric conditions. Results indicate that directed links among core regions of heat wave occurrence over Europe reproduce parts of observed movements. This helps to group and characterize heat waves according to, e.g. seasonality. Examples of these heat wave cluster transitions show an associated shift of high pressure patterns, suggesting that the approach allows capturing the spatial dislocation of heat wave centers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".