Climate Change 2001:
Working Group I: The Scientific Basis
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8.10 Sources of Uncertainty and Levels of Confidence in Coupled Models

8.10.1 Uncertainties in Evaluating Coupled Models

Our attempts to evaluate coupled models have been limited by the lack of a more comprehensive and systematic approach to the collection and analysis of model output from well co-ordinated and well designed experiments. Important gaps still remain in our ability to evaluate the natural variability of models over the last several centuries. There are gaps in the specification of the radiative forcing (especially the vertical profile) as well as gaps in proxy palaeo-data necessary for the production of long time series of important variables such as surface air temperature and precipitation.

In order to assist future coupled model evaluation exercises, we would strongly encourage substantially expanded international programmes of systematic evaluation and intercomparison of coupled models under standardised experimental conditions. Such programmes should include a much more comprehensive and systematic system of model analysis and diagnosis, and a Monte Carlo approach to model uncertainties associated with parametrizations and initial conditions. The computing power now available to most major modelling centres is such that an ambitious programme that explores the differing direct responses of parametrizations (as well as some indirect effects) is now quite feasible.

Further systematic and co-ordinated intercomparison of the impact of physical parametrizations both on the ability to simulate the present climate (and its variability) and on the transient climate response (and its variability) is urgently needed.

The systematic analysis of extremes in coupled models remains considerably underdeveloped. Use of systematic analysis techniques would greatly assist future assessments.

It is important that in future model intercomparison projects the experimental design and data management takes heed of the detailed requirements of diagnosticians and the impacts community to ensure the widest possible participation in analysing the performance of coupled models.

8.10.2 Levels of Confidence

We have chosen to use the following process in assigning confidence to our assessment statements; the level of confidence we place in a particular finding reflects both the degree of consensus amongst modellers and the quantity of evidence that is available to support the finding. We prefer to use a qualitative three-level classification system following a proposal by Moss and Schneider (1999), where a finding can be considered:

"well established" - nearly all models behave the same way; observations are consistent with nearly all models; systematic experiments conducted with many models support the finding;
"evolving" - some models support the finding; different models account for different aspects of the observations; different aspects of key processes can be invoked to support the finding; limited experiments with some models support the finding; parametrizations supporting the finding are incompletely tested;
"speculative" - conceptually plausible idea that has only been tried in one model or has very large uncertainties associated with it.

8.10.3 Assessment

In this chapter, we have evaluated a number of climate models of the types used in Chapter 9. The information we have collected gives an indication of the capability of coupled models in general and some details of how individual coupled models have performed.

We regard the following as "well established":

We regard the following as "evolving":

We regard the following as "speculative":

Our overall assessment
Coupled models have evolved and improved significantly since the SAR. In general, they provide credible simulations of climate, at least down to sub-continental scales and over temporal scales from seasonal to decadal. The varying sets of strengths and weaknesses that models display lead us to conclude that no single model can be considered "best" and it is important to utilise results from a range of coupled models. We consider coupled models, as a class, to be suitable tools to provide useful projections of future climates.



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