Temperature change signals in northern Canada: convergence of statistical downscaling results using two driving GCMs
Bibliographic record
Abstract
Abstract Coarse resolution global climate models (GCMs) have inherent difficulty simulating a reliable climate regime in coastal areas, as in northern Canada, where sea ice and snow cover are highly sensitive to fine‐scale climate forcings. As a result, strong biases are present in GCM temperature regimes in this region, and the direct use of raw‐GCM climate change signals at the local scale is problematic. However, fine resolution climate change information for use in impact studies can be obtained via statistical downscaling (SD) methods. This study investigates the regression‐based SDSM model with respect to its potential to simulate reliable and plausible changes in mean values as well as probabilities of extreme temperatures, in some specific locations in northern Canada. Four sets of independent climate predictors, from the outputs of two GCMs (i.e. CGCM2 and HadCM3) and using two SRES emission scenarios (i.e. A2 and B2), are used by the SDSM model to construct climate scenario information for this region over the period 2070–2099. The results demonstrate that the SD model is able to capture the major part of the temperature change signal, with a plausible climatic regime for higher warming in winter than in summer and in A2 than in B2 runs. The combination of relevant atmospheric predictors in the SD process is able to take into account most key factors of the temperature change signal, with strong convergence in the magnitude and the timing of the changes in all results. The downscaling signals are more consensual and physically‐plausible in comparison with the raw GCM anomalies, with relatively better skill using HadCM3 predictors than those from CGCM2. The study also confirms that scrupulous analysis of the climate change regime and its temporal and spatial distribution at the scale of interest is essential for it to be useful in impact studies. Copyright © 2007 Crown in the right of Canada. Published by John Wiley & Sons, Ltd
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".