File : D2.9
Author : Ingrid Super (TNO) et al.
This report describes a statistically coherent methodology to establish uncertainties in gridded emission inventories/models. Uncertainties in the underlying parameters (activity data, emission factors, spatial proxy maps and time profiles) are established and used in a Monte Carlo simulation to determine how they affect the uncertainty in the total emissions. This can be done at different levels of detail, e.g. per country or source sector, but also at different spatiotemporal scales. The results are shown to be useful for inverse modelers for several reasons:
- It gives insight in which model parameters are most important and should (at least) be optimized
- It provides a covariance matrix of uncertainties at the required level of detail
- It can be used to characterize the temporal/spatial correlation lengths of the uncertainties in the emissions
Figure 1 : Normalized spread in total CO2 emissions caused by uncertainty in individual parameters. Show here are emission factors (EF) and time profiles (T) per (sub)sector
