The recent IPCC AR5 includes a discussion on the sources of uncertainty in climate projections (Fig. 11.8, section 220.127.116.11), which updates previous analyses using CMIP3 (temperature, precipitation) to the latest CMIP5 simulations. The dominant source of uncertainty depends on lead time, variable and spatial scale.
There are three main sources of uncertainty in projections of climate: that due to future emissions (scenario uncertainty, green), due to internal climate variability (orange), and due to inter-model differences (blue). Internal variability is roughly constant through time, and the other uncertainties grow with time, but at different rates. Although there is no perfect way to cleanly separate these uncertainties, different methods have given similar results.
Overall, the conclusions from CMIP5 are not much changed from CMIP3. For global temperature, the spread between RCP scenarios is the dominant source of uncertainty at the end of the century, but internal variability and inter-model uncertainty are more important for the near-term. For the next decade or so, internal variability is the dominant source of uncertainty. A small caveat to this is the role of anthropogenic aerosols, which are assumed to decline quite rapidly in all RCPs in the next 20 years, and so this scenario uncertainty may be smaller than it should be.
For global temperature, the figures below show two different representations of this information, either as a ‘plume’ (Fig. 1) or as a fraction of the total variance (Fig. 2).
For other variables and on regional spatial scales, the picture can be very different. For example, for European winter temperatures, the internal variability component is more important (Fig. 2). And, for European winter precipitation, scenario uncertainty is almost irrelevant because the internal variability and inter-model differences are relatively much larger (Fig. 3). In fact, for precipitation in all regions, the RCP scenario uncertainty is relatively small when compared to the other sources of uncertainty.
The key messages are that resolving inter-model differences could reduce uncertainty significantly, but there is still a large irreducible uncertainty due to climate variability in the near-term and, particularly for temperature, future emissions scenarios in the long-term.