Estimating the likelihood of GHG concentration scenarios from probabilistic IAM simulations
Bibliographic record
Abstract
Climate change adaptation under resource constraints and future climate uncertainties would benefit from fully probabilistic climate risks assessments. Conducting such risk analyses requires assigning probabilities to the future greenhouse gases (GHG) and land-use scenarios used by global climate models. This paper proposes an approach to estimate the relative likelihood of carbon dioxide (CO2) concentration scenarios used in key climate change modeling experiments. The approach relies on the comparison of CO2 emissions from probabilistic simulations of Integrated Assessment Models (IAM) with compatible CO2 emissions diagnosed by global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6). The approach is demonstrated with five emission simulations from four IAMs, leading to independent estimates of the relative likelihood of CMIP5 Representation Concentration Pathways and CMIP6’ Shared Socioeconomic Pathways (SSP) up to 2100. Results suggest that SSP5-8.5 is an unlikely scenario for the second half of the century, but there is no clear consensus on the most likely scenario. Scenario likelihood is affected by a number of potential errors, including sampling errors, differences in emission sources simulated by the IAMs, and the lack of a common experimental framework for IAM simulations. These errors, along with the small IAM ensemble size, limit the applicability of the results. The delivery of fully probabilistic climate risk assessments would benefit from a coordinated probabilistic IAM experiment jointly designed with a coordinated climate modeling experiment where Earth System Model are driven by representative emission pathways.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".