More accurate quantification of model-to-model agreement

They quantified the relative roles of model-to-model differences and internal variability in causing uncertainty in climate model projections. They find that uncertainty across the globe in long-term projections of temperature and precipitation is dominated by model-to-model differences, but that this is not true for temporal temperature and precipitation variability. When assessing model-to-model agreement they can identify differences in the forced response to external forcing across the land surface, and find that although all models show warming under a strong forcing scenario, the magnitude of that warming varies by 4^oC over Europe, Australia and Asia and 10^oC over the Arctic.

Individual simulations of climate models differ when exposed to external forcing, such as that from increasing greenhouse gases, due to both structural differences between the models and their phase of chaotic internal climate variability. Which of the two causes is at work matters a great deal – if it is the structural differences, model-to-model agreement might increase in the future causing the uncertainty in the response to external forcing to decrease. If, however, it is the internal variability, the spread in projections will not decrease. Determining why model simulations differ is limited in the traditional use of multi-model ensembles of climate models due to small numbers of realisations. This is further complicated because the models are not independent, sharing both components and code. This can lead to overconfidence in projections. 

In this study, Maher and her co-authors use a new set of six largely independent single model initial-condition large ensembles (SMILEs) to separate the uncertainties due to internal variability and model differences for temperature, precipitation, and their temporal variability. By using SMILEs they can easily quantify both the externally forced response in each model (ensemble mean) and its internal variability (the spread of ensemble members). They additionally implement a new method for a multi-model ensemble (CMIP5), where they create small sub-ensembles of models that share an atmospheric component, which are hence not independent. They find that the uncertainty in temperature and precipitation projections is dominated by model-to-model differences (Figure 1; left columns). However, for the temporal variability of both quantities, the uncertainty due to internal variability is generally larger than model differences in the extra-tropics (Figure 1; right columns). This has an important implication; that increasing model-to-model agreement in this region for these quantities may not improve the spread of projections.