Skillful predictions of decadal trends in global mean surface temperature
Bibliographic record
Abstract
[1] We compare observed decadal trends in global mean surface temperature with those predicted using a modelling system that encompasses observed initial condition information, externally forced response (due to anthropogenic greenhouse gases and aerosol precursors), and internally generated variability. We consider retrospective decadal forecasts for nine cases, initiated at five year intervals, with the first beginning in 1961 and the last in 2001. Forecast ensembles of size thirty are generated from differing but similar initial conditions. We concentrate on the trends that remain after removing the following natural signals in observations and hindcasts: dynamically induced atmospheric variability, El Niño-Southern Oscillation (ENSO), and the effects of explosive volcanic eruptions. We show that ensemble mean errors in the decadal trend hindcasts are smaller than in a parallel set of uninitialized free running climate simulations. The ENSO signal, which is skillfully predicted out to a year or so, has little impact on our decadal trend predictions, and our modelling system possesses skill, independent of ENSO, in predicting decadal trends in global mean surface temperature.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".