Statistical Prediction of Climate Change Signal on Northeast Asia

摘要

The response of the global climate to external forcing mechanisms is inherently complex. The previous studies which attributed global-mean surface temperature increases over the last hundred years to anthropogenic forcings (e.g. Mitchell et al., 1995; Santer et al., 1996) relied on a few integrations of complex coupled atmosphere-ocean general circulation models (AOGCMs) and considered only limited number of forcings. In reality, over the last hundred years the Earth's energy balance has been altered by many natural and anthropogenic forcings (Shine and Forster, 1999). Although some forcings, such as changes in carbon dioxide (CO_2), can be reasonably well quantified, the magnitude and spatial pattern of some other possible forcings are poorly known, such as the indirect effect of sulphate aerosol (SO_4) on clouds. There are also large uncertainties in the various feedback mechanisms and in the internal variability of the climate system. Due to the complex nature of the various feedbacks operating in the climate system, the simulation of the global surface temperature response with the AOGCMs is both computationally demanding and uncertain. The most recent improvement in the climate signal detection problem is the inclusion of several possible sources of climate forcing. It has become apparent that when anthropogenic climate forcing is considered, one must include the forcings of greenhouse gases and sulfate aerosols (North and Stevens, 1998; Berliner et al., 2000; Lee et al, 2001). In this study, we applied a detection technique, called Bayesian fingerprints, and showed that decadal changes in the patterns of surface temperature in Northeast Asia can be explained partly by anthropogenic factors. Furthermore, we show that for regionally averaged surface temperature, internal noise in the AOGCM is small enough that a signal emerges from the data even on interannual time scales. Finally, although anthropogenic forcings have had a significant impact on global mean surface temperature, it is shown that their influence on the pattern of local deviations about this mean is hardly detectable so its signal is weak.