Problems with data

• Missing components or errors in the data
• “Noise” in the data associated with biased or incomplete observations
• Random sampling error and biases (non-representativeness) in a sample

Problems with models

• Known processes but unknown functional relationships or errors in the structure of the model
• Known structure but unknown or erroneous values of some important parameters
• Known historical data and model structure, but reasons to believe parameters or model structure will change over time
• Uncertainty regarding the predictability (e.g., chaotic or stochastic behavior) of the system or effect
• Uncertainties introduced by approximation techniques used to solve a set of equations that characterize the model.

Other sources of uncertainty

• Ambiguously defined concepts and terminology
• Inappropriate spatial/temporal units
• Inappropriateness of/lack of confidence in underlying assumptions
• Uncertainty due to projections of human behavior (e.g., future consumption patterns, or technological change), which is distinct from uncertainty due to “natural” sources (e.g., climate sensitivity, chaos)