ORIGINAL PAPER Natural and forced air temperature variability in the Labrador region of Canada during the past century
Bibliographic record
Abstract
past century (1881–2011) shows multi-scale climate variabil-ity and strong linkages with ocean-atmospheric modes of variability and external forcings. The Arctic Oscillation, Atlantic Multidecadal Oscillation, and El Nino Southern Oscillation are shown to be the dominant seasonal and inter-annual drivers of regional air temperature variability for most of the past century. Several global climate models show dis-agreement with observations on the rate of recent warming which suggests that models are currently unable to reproduce regional climate variability in Labrador air temperature. Using a combination of empirical statistical modeling and global climate models, we show that 33 % of the variability in annual Labrador air temperatures over the period 1881–2011 can be explained by natural factors alone; however, the inclusion of anthropogenic forcing increases the explained variance to 65 %. Rapid warming over the past 17 years is shown to be linked to both natural and anthropogenic factors with several anomalously warm years being primarily linked to recent anomalies in the Arctic Oscillation and North Atlantic sea surface temperatures. Evidence is also presented that both empirical statistical models and global climate models under-estimate the regional air temperature response to ocean salin-ity anomalies and volcanic eruptions. These results provide important insight into the predictability of future regional climate impacts for the Labrador region. 1
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".