Updated attribution of GSAT changes and implications
Bibliographic record
Abstract
The observed increase of global air surface temperature (GSAT) has long been attributed to human activities. However, updating estimates of human-induced changes, and changes induced by specific subsets of forcings (e.g., green-house gases) remains of high interest to better understand recent changes and also produce refined projections. Here, we use the newest climate model ensemble (CMIP6), improved observations, and a new statistical method to narrow uncertainty on the response to historical forcings. In addition, we focus on estimating the total warming since the pre-industrial (using 1850-1900 as a reference baseline), while most previous studies considered shorter periods. Results suggest that most of the observed warming since the pre-industrial (+1.22°C +/-0.15°C in 2020) is human-induced (+1.15°C +/-0.15°C) and that a substantial fraction of GHG-induced warming (+1.54°C +/-0.33°C) has been offset by other anthropogenic factors (-0.39°C +/-0.28°C). We also quantify the contribution of specific forcings to the 2010-2019 warming rate, suggesting that the current rate of human-induced warming is +0.22°C/decade (+/-0.05°C/decade). We then derive implications of these findings in terms of future climate change, i.e., the response to a range of scenarios. Our results suggest that historical observations and historical climate change are already very informative about future changes and the property of the Earth System in general.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".