Patterns of change: whose fingerprint is seen in global warming?
Bibliographic record
Abstract
Abstract Attributing observed climate change to causes is challenging. This letter communicates the physical arguments used in attribution, and the statistical methods applied to explore to what extent different possible causes can be used to explain the recent climate records. The methods use fingerprints of climate change that are identified on the basis of the physics governing our climate system, and through the use of climate model experiments. These fingerprints characterize the geographical and vertical pattern of the expected changes caused by external influences, for example, greenhouse gas increases and changes in solar radiation, taking also into account how these forcings and their effects vary over time. These time–space fingerprints can be used to discriminate between observed climate changes caused by different external factors. Attribution assessments necessarily take the natural variability of the climate system into account as well, evaluating whether an observed change can be explained in terms of this internal variability alone, and estimating the contribution of this source of variability to the observed change. Hence the assessment that a large part of the observed recent warming is anthropogenic is based on a rigorous quantitative analysis of these joint drivers and their effects, and proceeds through a much more comprehensive and layered analysis than a comparison at face value of model simulations with observations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".