Direct Answer
Climate scenarios require 3 distinct layers: CMIP6, SSPs, and downscaling. CMIP6 provides the coordinated climate-model framework; SSPs describe plausible socioeconomic and climate pathways; and downscaling translates broader climate signals into a more localized decision context. A model is not a scenario, and downscaling adds local relevance without eliminating uncertainty or model spread (Eyring et al., 2016; O’Neill et al., 2016; IPCC, 2021).
How It Works
Scenario analysis becomes more credible when each layer is kept separate. The 3 layers are: 1. CMIP6, which organizes comparable climate-model experiments across modeling centers. 2. SSPs, which describe plausible future pathways and climate-forcing assumptions. 3. Downscaling, which makes broad climate signals more relevant to regions, cities, infrastructure, and assets. For asset owners, the problem is not simply choosing a map. It is about selecting sources, variables, hazards, time horizons, and scenario pathways that align with a specific decision. ClimaTwin’s Climate Business Intelligence™ approach keeps the model, scenario, and downscaling layers visible so users can understand what is being compared, what is uncertain, and what the output can support. That transparency matters for resilience planning, insurance preparation, capital allocation, public-sector planning, and disclosure.
Limitations
Scenario analysis is not forecasting. SSPs are structured pathways. Models are imperfect representations of the climate system, and downscaled results can inherit uncertainties from source models, observations, and bias treatment. Local-looking outputs should not be interpreted as exact future site conditions unless the method supports that interpretation
Frequently Asked Questions (FAQs)
- What are the 3 layers of scenario analysis? CMIP6 models, SSP scenario pathways, and downscaled local or regional climate information.
- Is CMIP6 a scenario? No. CMIP6 is a coordinated framework for climate-model experiments. SSPs are scenario pathways used with model simulations.
- Does downscaling remove uncertainty? No. Downscaling improves local relevance but can preserve or introduce uncertainty from models, observations, and methods.
- Why keep models and scenarios separate? Separating them helps users understand whether differences come from model behavior, scenario assumptions, or downscaling choices.
- How does ClimaTwin support scenario transparency? ClimaTwin documents source models, scenario pathways, downscaling methods, uncertainty ranges, and decision context.
Sources
- Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9, 1937-1958. https://doi.org/10.5194/gmd-9-1937-2016.
- Intergovernmental Panel on Climate Change. (2021). Chapter 10: Linking global to regional climate change. In Climate change 2021: The physical science basis. Cambridge University Press.
- O’Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., & Sanderson, B. M. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9, 3461-3482. https://doi.org/10.5194/gmd-9-3461-2016.
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