Multimodel Multisignal Climate Change Detection at Regional Scale
Bibliographic record
Abstract
Abstract Using an optimal detection technique and climate change simulations produced with two versions of two GCMs, we have assessed the causes of twentieth-century temperature changes from global to regional scales. Our analysis is conducted in nine spatial domains: 1) the globe; 2) the Northern Hemisphere; four large regions in the Northern Hemispheric midlatitudes covering 30°–70°N including 3) Eurasia, 4) North America, 5) Northern Hemispheric land only, 6) the entire 30°–70°N belt; and three smaller regions over 7) southern Canada, 8) southern Europe, and 9) China. We find that the effect of anthropogenic forcing on climate is clearly detectable at global through regional scales. The effect of combined greenhouse gases and sulfate aerosol forcing is detectable in all nine domains in annual and seasonal mean temperatures observed during the second half of the twentieth century. The effect of greenhouse gases can also be separated from that of sulfate aerosols over this period at continental and regional scales. Uncertainty in these results is larger in the smaller spatial domains. Detection is improved when an ensemble of models is used to estimate the response to anthropogenic forcing and the underlying internal variability of the climate system. Our detection results hold after removal of North Atlantic Oscillation (NAO)-related variability in temperature observations—variability that may or may not be associated with anthropogenic forcing. They also continue to hold when our estimates of natural internal climate variability are doubled.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".