The Global Centre for Risk and Innovation (GCRI)
The methods Systems Science and Modelling relies on, the assumptions they carry and the limits of what they can show.
Systems science · Methods and uncertainty
unit · cadence · label
| Models in library | models | monthly | OFFICIAL | SP-9504 |
| Models with validation record | of models | monthly | DERIVED | SP-6894 |
| Coupled runs completed | runs | weekly | OBSERVED | SP-0370 |
| Reuse across platforms | uses | quarterly | DERIVED | SP-3045 |
Systems Science and Modelling · Evidence registerEvidence register, in outline: domain structure with placeholder names and illustrative values, not a record.
The Systems Science and Modelling methods page sets out the methods the platform relies on, among them systems engineering, probability and statistics, and uncertainty quantification and model validation, with their assumptions, validation and limits. Each analysis lists its assumptions and data, reports ranges and confidence beside central estimates, is checked against observations or other models, and states what it cannot show.
Fig.How a finding is reviewed before it is published. Design, not a record.
For methods and uncertainty, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery and land cover with relief in 3D and true colour from NASA GIBS, framed on North America; every other NASA layer, live feed and forecast waits in the layer list.
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For methods and uncertainty, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery and land cover with relief in 3D and true colour from NASA GIBS, framed on North America; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Layers and dates
| Source | Why it matters here | How it is reported |
|---|---|---|
| Coupling | Interfaces between models introduce inconsistencies | Interface assumptions documented |
| Structural | Different models disagree | Intercomparison |
| Parameters | Many are poorly constrained | Sensitivity analysis |
| Scenarios | Deep uncertainty | Scenario discovery |
Each analysis lists its assumptions and the data it depends on.
Ranges and confidence are reported alongside central estimates.
Models are checked against observations and, where possible, against other models.
What an analysis cannot show is stated beside what it can.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Uncertainty quantification | Characterising and propagating the uncertainties in a model to state confidence in its outputs. |
| Sensitivity analysis | Repeating an analysis under different assumptions to show how much the result depends on them. |
| Model validation | Comparing model predictions with independent observations to judge fitness for a stated use. |
| Systems engineering | Systems engineering is an interdisciplinary approach to designing and managing complex systems over their life cycle. |
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.