How much of the historical global mean surface temperature record is needed to well constrain projections of future warming?
Bibliographic record
Abstract
Observational constraints are widely used to reduce uncertainty in multi-model projections and have been proven to be effective. Implementations of constraints vary widely, ranging from using temperature trends over different time periods to incorporate the full evolution of the historical climate time series and even a range of covariates. In this study we consider two Bayesian approaches to developing a constraint on future global warming, using the historical time evolution of global mean surface temperature (GMST) in one case, and historical GMST trends during recent decades in another. We also consider which period in the historical GMST record provides the most effective constraint on future projections. We conduct our studying using large ensemble simulations from climate models with different sensitivities. When using a time series of annual GMST values, we find an effective constraint only becomes possible when data from the recent period of rapid transient climate change are included in the analysis. Furthermore, incorporating the full transition from a quasi-equilibrium pre-industrial state to the recent strong transient response results in a better constrain. Using a simple linear warming trend from recent decades does improve upon unconstrained projections but to a lesser extent than using the full time series for the same period. Accounting for the intercept obtained in linear trend estimation, which provides information about the warming that occurred before the trend estimation period and thus how represents the Earth system transitioned from a quasi-stationary state to its current state of rapid transient response, improves the skill of trend based constraints. Nevertheless, a constraint based on both the trend (the recent rate of warming) and intercept (the accumulated warming prior to the trend period) does not perform as well as a constraint that uses the entire historical GMST record from 1850 to present.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".