Trajectories of the L84-S61 model: ocean variables (latitudinal temperature (T) and salinity (S) differences) on the main axes, atmospheric variables (X,Y,Z) on insets
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Figure 1. Trajectories of the L84-S61 model: ocean variables (latitudinal temperature (T) and salinity (S) differences) on the main axes, atmospheric variables (X,Y,Z) on insets. Ocean variables show 10 000 year simulations; atmospheric variables a single year. (a) and (b) show trajectories for Fm = 7 while (c) and (d) show trajectories for Fm = 8. Red points show the IC locations of 10 000 member ensembles. For single ocean variable time series see supplementary figure S3 (available at stacks.iop.org/ERL/8/034021/mmedia) (Fm = 7) and figure S4 (available at stacks.iop.org/ERL/8/034021/mmedia) (Fm = 8).
Can today's global climate model ensembles characterize the 21st century climate in their own 'model-worlds'? This question is at the heart of how we design and interpret climate model experiments for both science and policy support. Using a low-dimensional nonlinear system that exhibits behaviour similar to that of the atmosphere and ocean, we explore the implications of ensemble size and two methods of constructing climatic distributions, for the quantification of a model's climate. Small ensembles are shown to be misleading in non-stationary conditions analogous to externally forced climate change, and sometimes also in stationary conditions which reflect the case of an unforced climate. These results show that ensembles of several hundred members may be required to characterize a model's climate and inform robust statements about the relative roles of different sources of climate prediction uncertainty.