Calibrated language in assessments
Authors assess evidence and agreement for each key statement and express it with the IPCC confidence and likelihood terms, with a traceable account linking the term to the assessed literature.
GCRI · Reports
Uncertainty in published assessments
Uncertainty quantification and model validation decide how much a synthesis statement or a modelled result can bear. In publishing, this means calibrated language for confidence and likelihood, certainty ratings for bodies of evidence, and validation of the models behind scenario and test reports, so readers see the strength of evidence beside each claim.
01Contents
Uncertainty in published assessments in five parts.
02Application
What uncertainty quantification and model validation does here.
Uncertainty quantification and model validation characterise how uncertainty in inputs, parameters and model structure affects predictions. They establish how much confidence a model’s outputs deserve. In research and evidence, uncertainty quantification shows where risk estimates are robust and where they are fragile. It guides where better data or models would change decisions. The canonical description is on the STEM Commons.
Certainty of evidence ratings
Bodies of evidence are rated with GRADE for risk of bias, inconsistency, indirectness, imprecision and publication bias, and the rating is shown beside each summary finding.
Validation of models in method reports
Models behind scenario and test reports are checked against held-out observations or benchmark cases, with sensitivity analysis showing which assumptions drive the result.
03Monitor
Uncertainty analysis.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Uncertainty analysis
Reports · Uncertainty in published · Run R-9820
Sensitivity rankedGCRI · Reports
Total-order sensitivity
Output distribution
Inputs
| Editions in production | Uniform | editions, stated | expert elicitation |
| Values traced to sources | Uniform | of values, stated | expert elicitation |
| Review rounds | Beta | rounds, stated | expert elicitation |
| Corrections issued | Beta | corrections, stated | expert elicitation |
04Data
What the method consumes.
Datasets and records used by uncertainty quantification and model validation on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Assessed literature and traceable accounts | Basis for confidence and likelihood terms | REPORTED |
| Evidence profiles and summary of findings tables | Certainty ratings by outcome | ESTIMATED |
| Model ensembles and scenario databases | Spread and sensitivity across assumptions | MODELLED |
| Expert elicitation records | Judgement where data are absent | ESTIMATED |
05Methods and evidence
How uncertainty in published assessments is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Uncertainty bands
How far the outlook for uncertainty in published assessments could spread, shown as ranges rather than a single line.
- Central estimate
- median of the ensemble
- Ranges
- likely and very likely, in IPCC calibrated language
- Ensemble
- members, scenarios and initial conditions stated
- Read with
- the sensitivity of the result to each input
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
06Validation
How results are checked.
Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.
| Check | How it is done |
|---|---|
| Term consistency audit | Calibrated terms are checked for consistent use across chapters and between summary and full report |
| Traceability check | A sample of summary statements is traced to the literature and judgements behind them |
| Out-of-sample validation | Model performance is reported on data not used in calibration |
| Sensitivity reporting | The assumptions driving most of the output range are named in the text, not only in annexes |
Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.
07Process
How the process runs.
Process
Uncertainty in published assessments: Hazard, exposure, vulnerability
In IPCC AR6 Working Group II, climate risk results from interactions between hazards and a system’s exposure and vulnerability, and responses can add risk.
01 Drivers
- 01HazardDataset · Series editorThe potential occurrence of a natural or human-induced physical event or trend that may harm people, property, services or ecosystems.
- 02ExposureDataset · Series editorThe presence of people, livelihoods, species, ecosystems, infrastructure or assets in places and settings that could be adversely affected.
- 03SensitivityDataset · Series editorThe degree to which a system or species is affected, either adversely or beneficially, by climate variability or change.
- 04Capacity to cope and adaptDataset · Series editorThe ability of systems, institutions, humans and other organisms to adjust to damage, take opportunities or respond to consequences.
02 Assessment
- 05Risk from responsesComponent · Series editorRisks from climate responses arise when responses miss their objectives, or from trade-offs or negative side effects.
- 06VulnerabilityComponent · Series editorThe propensity or predisposition to be adversely affected. It includes sensitivity to harm and lack of capacity to cope and adapt.
- 07RiskModel · ReportsRisk results from dynamic interactions between hazards and the system’s exposure and vulnerability. Each may be uncertain and change.
03 Response
- 08Risk management and responseStep · Series editorRisk management is plans, actions, strategies or policies that reduce the likelihood or size of adverse consequences.
- 09Residual riskRecord · Series editorThe climate impact risk that remains after adaptation and mitigation efforts. Adaptation can redistribute risk between areas and groups.
04 Decision
- 10Decision by the responsible partyDecision · Series editorThe framing names no single decision maker. Decisions on responses rest with the responsible party under its own rules.
Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval.
09Stewardship
Who maintains the method.
Systems Science and Modelling stewards uncertainty quantification and model validation for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of uncertainty quantification and model validation in this field?
Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.
How are results checked?
Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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