Uncertainty budgets
Contributions from inputs, parameters, structure, numerical error and variability are estimated separately and reported together.
GCRI · Systems Science and Modelling
Uncertainty quantification
Uncertainty quantification and model validation is a method this platform stewards. It identifies the sources of uncertainty in models, propagates them to outputs, finds which matter most and tests models against independent evidence, so results can be read with the right confidence.
01Register
Uncertainty quantification on the record.
Method recordSTEM method
- MethodUncertainty quantification and model validation
- StewardSystems Science and Modelling
- Applied inSystems Science and Modelling
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
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 methods and modelling, risk management and systemic risk and 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.
Surrogate models for expensive simulations
Emulators trained on a limited number of runs stand in for expensive models, making sensitivity and uncertainty analysis feasible.
Credibility assessment
Models used in decisions are assessed against credibility criteria such as those in ASME V&V 40 and NASA-STD-7009.
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
Systems science · Uncertainty quantification · Run R-4832
Sensitivity rankedGCRI · Systems Science and Modelling
Total-order sensitivity
Output distribution
Inputs
| Models in library | Uniform | models, stated | expert elicitation |
| Models with validation record | Beta | of models, stated | expert elicitation |
| Coupled runs completed | Normal | runs, stated | expert elicitation |
| Reuse across platforms | Uniform | uses, 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 |
|---|---|---|
| Model runs | Propagation | MODELLED |
| Validation data | Testing | OBSERVED |
| Expert elicitation | Poorly known inputs | ESTIMATED |
05Methods and evidence
How uncertainty quantification 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.
Model, equations and assumptions
The model structure used for uncertainty quantification, the assumptions it makes and how each is tested.
Risk of element i
Ri = f(Hi, Ei, Vi, Ci)hazard, exposure, vulnerability and coping capacityExpected annual loss
EAL = ∑k pk · Lkover events k with annual probability pkCoupled systems
xt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Assumption | Why it is made | How it is tested |
|---|---|---|
| Hazard and exposure are independent within an event | Keeps the loss integral tractable | Compare with a copula fit on joint records |
| Couplings are linear near the operating point | Lets shocks propagate through A | Perturb beyond the linear range and compare |
| Vulnerability curves transfer between places | Local curves are rarely available | Validate against observed losses where they exist |
- Inputs
- versioned datasets with provenance
- Outputs
- distributions, never single numbers
- Code
- released with the model card and tests
- Reproduction
- same data, code, environment and seed
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 |
|---|---|
| Emulator validation | Surrogates are tested against held-out model runs |
| Interval coverage | Stated intervals are checked against outcomes |
| Structural comparison | Alternative model structures are compared |
Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.
07Process
How the process runs.
Process
Uncertainty quantification: 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 · User agencyThe potential occurrence of a natural or human-induced physical event or trend that may harm people, property, services or ecosystems.
- 02ExposureDataset · User agencyThe presence of people, livelihoods, species, ecosystems, infrastructure or assets in places and settings that could be adversely affected.
- 03SensitivityDataset · User agencyThe degree to which a system or species is affected, either adversely or beneficially, by climate variability or change.
- 04Capacity to cope and adaptDataset · User agencyThe ability of systems, institutions, humans and other organisms to adjust to damage, take opportunities or respond to consequences.
02 Assessment
- 05Risk from responsesComponent · User agencyRisks from climate responses arise when responses miss their objectives, or from trade-offs or negative side effects.
- 06VulnerabilityComponent · User agencyThe propensity or predisposition to be adversely affected. It includes sensitivity to harm and lack of capacity to cope and adapt.
- 07RiskModel · Systems Science and ModellingRisk 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 · User agencyRisk management is plans, actions, strategies or policies that reduce the likelihood or size of adverse consequences.
- 09Residual riskRecord · User agencyThe 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 · User agencyThe 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.
08Connections
Topics that rely on this method.
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.
10Questions
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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