MODELLING DRY AND WET EXTREMES OVER THE CANADIAN PRAIRIE PROVINCES BASED ON THE DYNAMICAL DOWNSCALING AND MULTIVARIATE FREQUENCY ANALYSIS APPROACHES
Bibliographic record
Abstract
The primary tools to assess climate change are the Atmosphere-Ocean General Circulation Model (AOGCM) or Regional Climate Model (RCM) transient climate change simulations.Currently, RCMs offer higher spatial resolution than AOGCMs and therefore are preferred for assessing impact of climate change on different components of the hydrological cycle at regional domains of interest.The overall purpose of this research was to evaluate the impact of climate change on dry and wet climate extremes over the Canadian Prairie Provinces of Alberta, Saskatchewan and Manitoba using a multi-RCM ensemble from the North American Regional Climate Change Assessment Program (NARCCAP).This region of Canada is characterized by highly variable hydroclimate, with recurrent droughts and floods and localized summer convective storm activity often resulting in heavy precipitation events and thus poses many challenges for water managers.At first, the Saskatchewan River Basin, the largest river in the study area, was evaluated and drought vulnerable parts of the basin were identified based on historical data and multivariate frequency analysis approaches.For the development of projected changes to drought characteristics, the research effort was extended over the entire study area and changes to various return levels of drought severity, duration and maximum severity were developed based on NARCCAP RCM simulations and multivariate frequency analysis approaches.It was found that the southern and south-western parts of the study area will experience increased drought severity in the future.Based on the projected bi-and trivariate joint occurrence probabilities of drought characteristics, southern parts along with the central parts of the study area were found to be highly drought vulnerable, whereas the southwestern and southeastern parts were found less vulnerable.Though producing reliable estimates of changes in precipitation extremes remains an important challenge under climate change, this study attempted to develop projected changes to April-October short-and long-duration precipitation extremes based on the NARCCAP RCM simulations and regional frequency analysis approach.Projected changes to selected regional return levels of precipitation extremes were found mostly statistically significant, with relatively larger changes noted for the southeastern regions and smaller for the southwestern and western regions of the study area.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".