Narrowing uncertainty on past and future human-induced warming using Kriging
Bibliographic record
Abstract
Using historical observations to constrain climate projections is an old idea. A variety of approaches, time periods and scales have been used to this purpose. Simultaneously, detection and attribution (D&A) methods have been developed to assess the contribution of subsets of forcings to historical changes, and have also been used to constrain projections. Here, we describe a unified statistical method to constrain the entire forced response pathway of global mean temperature using an adaptation of Kriging. We start by introducing this new statistical approach. Then, we derive consistent observationally-constrained estimates of attributable warming to date for various forcings, attributable warming rate, the response to various scenarios, Transient Climate Response (TCR), and Equilibrium Climate Sensitivity (ECS). Using revised observations of near-surface atmospheric temperature, we estimate a total forced warming of 1.19+/-0.15°C in 2019, with respect to the 1850-1900 baseline. Based on the newly available CMIP6 ensemble, we find that historical observations narrow uncertainty on past and future warming by about 50%, while evidence suggests that the proposed technique is not over-confident. Remarkably, both sides of uncertainty ranges are affected, leading to a 5–95% range for TCR of 1.44–2.35°C. We also compare and discuss the differences between the CMIP5 and CMIP6 ensembles. The proposed method is easily transposable, thus opening the possibility to monitor climate change and narrow uncertainty at the regional scale and/or for different climate variables.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".