Mechanisms of the Extreme Wind Speed Response to Climate Change in Variable-Resolution Climate Simulations of Western, Central, and Atlantic Canada
Bibliographic record
Abstract
Abstract Confidence in climate change projections of midlatitude wind extremes is limited by poor model skill at representing near-surface winds and incomplete understanding of the physical mechanisms that may cause extreme winds to respond to climate change. This study addresses these issues by analyzing climate change projections of extreme winds with regional refinement at 7 km over western, central, and Atlantic Canada, using the variable-resolution version of the National Center for Atmospheric Research’s Community Earth System Model (NCAR VR-CESM). This study extends previously reported results for Central Canada’s Southern Ontario region. VR-CESM consistently represents conditions linked with extreme winds in all regions more credibly than the global uniform (100 km) resolution version of CESM, which lends confidence to the projected climate response of the refined-resolution simulations. VR-CESM also consistently projects a strengthening of extreme wind speeds over land in all regions, albeit with mixed statistical significance, while uniform-resolution CESM projections exhibit weakening. The increased extreme wind speeds in VR-CESM are associated with downward mixing of high-momentum air in the boundary layer, which is present in all regions. This association is poorly represented with coarse resolution. However, additional factors, including increasing regional extratropical cyclone intensity, also contribute to the extreme wind response in the western and Atlantic regions. The changes to extratropical cyclone intensity exhibit resolution dependence that is harder to explain. Our results highlight the need for regionally focused dynamical downscaling of global climate projections for climate change impact assessment.
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 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".