Spatial and Temporal Variabilities of Climate Extremes over Canada in a Changing Climate
Bibliographic record
Abstract
In the past few decades, there have been more extreme climate events occurring worldwide, constituting a growing risk for our societies. For example, Canada has experienced several extreme precipitation events that resulted in billions of dollars of damage, such as the flood events of Calgary and Toronto in 2013. Meanwhile, the 2001–2002 drought in the Canadian Prairies (CP) swept almost the entire southern part of the country, which was recorded as one of the top ten worst droughts observed over the instrumental period. However, most past studies have focused on changes in the mean climate (such as precipitation and temperature). This dissertation aims to address the spatial and temporal variabilities of climate extremes in Canada under the possible impact of a changing climate, which will improve our understanding of the changing hydroclimate extremes in Canada. Therefore, the objectives of this dissertation are: (1) to characterize spatiotemporal changes of climate extremes under the possible impacts of climate change; (2) to identify how atmospheric convection, temperature, and humidity have contributed to climate extremes; (3) to detect the influences of large-scale climate patterns on climate extremes in Canada. Chapter 2 investigates the spatial and temporal changes in precipitation extremes over Canada, as well as their teleconnections to large-scale climate patterns by using trend analysis, principal component analysis, and wavelet analysis. The results show that extreme precipitation defined from 10 extreme precipitation indices of Canada has generally become more severe since the mid-twentieth century. Meanwhile, strong teleconnections are found between extreme precipitation and climate indices, but the effects of climate patterns differ from region to region. Chapter 3 analyses the relationship between Convective Available Potential Energy (CAPE) and extreme precipitation event, which indicates a positive correlation over much of the eastern US and some parts of the Canadian prairies. In addition, increasing temperature and surface specific humidity could potentially lead to an overall increase in CAPE and extreme precipitation observed over these regions. Chapter 4 studies the variations in drought characteristics in terms of duration, frequency, area, and severity in Canada using the Standardized Precipitation Evapotranspiration Index (SPEI) at seasonal and annual time scales. The results suggest that droughts in Canada have generally become less severe over 1950-2016, and the relationships between climate patterns and drought variability have changed over time. Chapter 5 evaluates the non-stationary behavior of dry and wet spells in Canada using the generalized extreme value (GEV) distribution and Bayesian quantile regression. This analysis shows that dry spells have aggravated in the southern Canadian Prairie from 1979 to 2018, and the stationarity has been compromised as more grids are found to be non-stationary under the impacts of El Niño–Southern Oscillation (ENSO) and Pacific-North American pattern (PNA). Conclusions and future research are provided in Chapter 6.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".