Uncertainty in climate projections and time of emergence of climate signals in the western Canadian Prairies
Bibliographic record
Abstract
This paper has examined the relative significance of uncertainty in future climate projections from a subset of the coupled model intercomparison project phase 5 (CMIP5) global climate models for the Prairie Provinces of western Canada. This was undertaken by determining: (a) the contribution of model and scenario uncertainty and natural variability to the total variance of these future projections, and (b) the timing of climate signal emergence from the background noise of natural climate variability. We examined future projections of mean temperature, precipitation and summer climate moisture index (CMI). In this region, natural climate variability plays an important role in future uncertainty until the end of this century, particularly for precipitation and to a lesser extent, summer CMI. Model uncertainty also contributes to total uncertainty for these variables throughout this century, while scenario uncertainty becomes more important towards the end of the century. For the region as a whole, significant climate change (i.e., signal/noise >2) occurs earliest for summer mean temperature, with median time of emergence around 2035 for the RCP8.5 radiative forcing scenario. Although the median precipitation signal emerges from the noise (i.e., signal/noise >1) around the 2070s in winter and the 2080s in spring, significant values do not occur in any season for this variable before 2100. For summer CMI, the median time of emergence for significant change is around 2085. At the grid scale, signal‐to‐noise ratios are significant for all seasons for mean surface air temperature, with earliest times of emergence occurring in summer. In contrast, the summer precipitation signal is not significant this century; for summer CMI, significant values are obtained in the eastern half of the region, occurring from about 2065 onwards. Median times of emergence are towards the end of the century for summer CMI in western Saskatchewan and in Alberta, although some areas of Alberta do not exhibit significant signals this century.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".