2.2.2 Climate Change Uncertainty
The Uncertainty Cascade describes how uncertainties accumulate as we move from socio-economic assumptions to physical impacts. It begins with future greenhouse gas emissions, which lead to uncertain atmospheric concentrations, resulting in a range of radiative forcing values.
These forcing values are processed by climate models to produce a range of global temperature responses. Finally, these global changes are downscaled to regional impacts, where local geographical factors add further layers of uncertainty to the final projection.
Because each step in the chain introduces its own set of assumptions and errors, the 'envelope' of possible outcomes widens significantly at the end of the process. This is often visualized as an expanding funnel or cone of uncertainty.
Short-term vs. Long-term: In the near term (next 10-30 years), the 'noise' of natural variability makes it difficult to distinguish between different emissions scenarios. In the long term (50-100 years), the choice of human policy (scenario uncertainty) becomes the primary factor determining the magnitude of warming.
Global vs. Regional: Uncertainty is generally lower for global averages than for regional specifics. While we have high confidence that the planet will warm, there is much higher uncertainty regarding how specific regions will experience changes in precipitation or extreme weather events due to complex local feedbacks.
| Feature | Scenario Uncertainty | Model Uncertainty |
|---|---|---|
| Origin | Human choices, policy, and technology | Scientific gaps in physical processes |
| Dominance | Long-term (50+ years) | Mid-to-long term |
| Reducibility | Unreducible (depends on future will) | Potentially reducible through better science |
| Representation | RCPs or SSPs | Multi-model ensembles (CMIP) |
Identify the Timeframe: If a question asks about uncertainty in the next decade, focus on Internal Variability. If it asks about the end of the century, focus on Scenario Uncertainty.
The 'More Data' Fallacy: Be careful not to assume that more data always reduces uncertainty. Sometimes, discovering new complex feedbacks (like permafrost melting) actually increases the range of possible outcomes in our models.
Verification: When evaluating a projection, always check if it is based on a single model or an ensemble. Ensembles are more robust because they average out the biases of individual models and provide a better estimate of the true uncertainty range.