rather than a diagnostic. For this, they require guidance from the scientific community about the appropriate range of sensitivity estimates and associated uncertainties. The kinds of questions that analysts and policy makers may pose to climate modelers include the following:
How has our understanding of the most likely value of sensitivity changed in recent decades? When and how will we have better estimates?
What do scientists think about the shape of the probability distribution for sensitivity? Can we put more credible limits on the high end of the tail of the distribution?
How good is sensitivity as a general indicator of the future severity of climate change? Does it take into account possible non-linearities in the climate system?
How does the value of sensitivity differ for different types of forcings and time scales? Are other indicators needed to express the response of the climate system to non-CO2 forcings?
Policy makers need quantitative information to better gauge the time scales and susceptibility of the climate system and to assess the potential consequences of global changes for human health, ecosystems, and social well-being. They are often interested in low-probability, high-consequence events, so it is important for the scientific community to provide information about the likelihood of such events.
Tom Wigley, from the National Center for Atmospheric Research, first provided an overview of the simplified MAGICC-SCENGEN system as an example of an integrated assessment modeling tool that produces probabilistic outputs. MAGICC (Model for the Assessment of Greenhouse-Gas Induced Climate Change) is a coupled gas cycle-energy balance climate model that can simulate the global scale behavior of comprehensive three-dimensional climate models. Users are able to choose emissions scenarios and a number of other model parameters either as single values or as probability density functions (PDFs). SCENGEN (SCENario GENerator) is a global-regional database that contains the results of a large number of climate model experiments. SCENGEN uses a scaling algorithm to provide information about spatial patterns of climate change. The primary purpose of the MAGICC-SCENGEN software is to allow nonexpert users to investigate the implications of different emission scenarios for future global mean and regional climate change and to quantify uncertainties in these changes.
Wigley discussed the general question of how climate sensitivity influences global mean temperature projections. It can be concluded from simple energy-balance climate models that both the magnitude and the timing of climate change depend on sensitivity (Figure 1, pg. 27). In a study by Wigley and Raper (2001), PDFs were created for a number of key inputs, including Seq, greenhouse gas emission rates, aerosol radiative forcing, carbon cycle feedbacks, and ocean mixing (Figure 2, pg. 28). This information was used to run more than 100,000 simulations in a simple upwelling-diffusion energy balance climate model. The resulting output is a PDF for change in global mean temperature, and one can evaluate how the results depend on the assumed input value of sensitivity.
They showed that uncertainties in the climate sensitivity, as characterized by its PDF, are a primary source of uncertainty in the projected values of global mean temperature change, especially the high-end tail of the distribution. This study illustrated the need to better quantify and define a PDF for sensitivity. The probabilistic form of the input value greatly enhances the ability to characterize uncertainties in future projections.
Other points raised by Wigley include the following:
One can utilize methods to minimize the effects of sensitivity uncertainties. For example, one can reduce the spread in output PDFs by calibrating a model against observed climate change (e.g., twentieth century warming). For given historical forcing, only a subset of the assumed range of sensitivities (and other model parameters) may be consistent with the observed warming (and its uncertainty range), so this would limit the output uncertainty range.
We must make the best possible use of available observations. Climate modelers can go beyond evaluating model calculations against the historical record of global mean temperature change; they can also try to simulate the diverse spatial and temporal characteristics of our present climate (which is essentially what is done in many current atmosphere ocean general circulation model [AOGCM] based detection studies).