Comparing variability and trends in observed and modelled global‐mean surface temperature
Bibliographic record
Abstract
The observed evolution of the global‐mean surface temperature over the twentieth century reflects the combined influences of natural variations and anthropogenic forcing, and it is a primary goal of climate models to represent both. In this study we isolate, compare, and remove the following natural signals in observations and in climate models: dynamically induced atmospheric variability, the El Niño‐Southern Oscillation, and explosive volcanic eruptions. We make clear the significant model‐to‐model variability in estimates of the variance in global‐mean temperature associated with these natural signals, especially associated with the El Niño‐Southern Oscillation and explosive volcanic eruptions. When these natural signals are removed from time series of global‐mean temperature, the statistical uncertainty in linear trends from 1950 to 2000 drops on average by about half. Hence, the results make much clearer than before where some model estimates of global warming significantly deviate from observations and where others do not.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".