Association of parameter, software, and hardware variation with large-scale behavior across 57,000 climate models

• DOI: 10.1073/pnas.0608144104
• Publication Date: 2007-07-19T00:55:06Z
• Authors: Christopher G. Kn...; Sylvia H. E. Knight; Neil Massey; Tolu Aina; Carl Christensen; Dave J. Frame; Jamie A. Kettlebo...; Andrew Martin; Stephen Pascoe; Ben Sanderson; David A. Stainforth; Myles R. Allen
• Keywords: Computers; Physics; Climate; General circulation models; Computing; Temperature; Atmospheric,Oceanic,and Planetary physics; Models, Theoretical; Sensitivity and Specificity; 7. Clean energy; 01 natural sciences; Distributed computing; 13. Climate action; Classification and regression trees; Climate change; Sensitivity analysis; Software; 0105 earth and related environmental sciences

In complex spatial models, as used to predict the climate response to greenhouse gas emissions, parameter variation within plausible bounds has major effects on model behavior of interest. Here, we present an unprecedentedly large ensemble of >57,000 climate model runs in which 10 parameters, initial conditions, hardware, and software used to run the model all have been varied. We relate information about the model runs to large-scale model behavior (equilibrium sensitivity of global mean temperature to a doubling of carbon dioxide). We demonstrate that effects of parameter, hardware, and software variation are detectable, complex, and interacting. However, we find most of the effects of parameter variation are caused by a small subset of parameters. Notably, the entrainment coefficient in clouds is associated with 30% of the variation seen in climate sensitivity, although both low and high values can give high climate sensitivity. We demonstrate that the effect of hardware and software is small relative to the effect of parameter variation and, over the wide range of systems tested, may be treated as equivalent to that caused by changes in initial conditions. We discuss the significance of these results in relation to the design and interpretation of climate modeling experiments and large-scale modeling more generally.