Evaluation of GCM Simulated Climate over the Canadian Prairie Provinces
Bibliographic record
Abstract
The objective of this study was to evaluate eleven Global Climate Model (GCM) simulations based on capabiliy to replicate 1961–1990 mean surface air temperature and total precipitation for the Canadian Prairie Provinces. The seasonal and annual magnitudes and spatial patterns of the GCM climates were compared to those of three observed data sets. The results demonstrated that most of the GCMs simulated the observed mean temperature magnitudes and spatial patterns reasonably well. The spatial correlation coefficients were high (> 0.8) and the pattern root mean square errors (PRMSE) were low (< 2°C) for most models. However, all GCMs over-predicted the total annual precipitation by 8 to 66%. The degree of over-prediction varied seasonally; winter and spring precipitation amounts were highly overestimated, autumn values were moderately over-predicted while summer amounts were only slightly overestimated, or even underestimated in some cases. The GCMs varied considerably in capability to represent precipitation spatial patterns. For example, the spatial correlation coefficients and PRMSEs for annual total precipitation ranged between –0.1 and 0.8 and 35 and 158 mm, respectively. In general, ECHAM4, HadCM3 and NCAR-PCM demonstrated the best simulated mean temperature and total precipitation over the provinces. Seven GCM runs were also used for an intercomparison of modelled future temperature and precipitation scenarios for the 30-year periods centred on 2050 and 2080. The models demonstrated a very high amount of variability in predicted future changes for both mean temperature and total precipitation. These results will contribute to an improved understanding of both present day and future GCM-simulated climate in the Prairie Provinces.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".