Low-Frequency Variations of Surface Temperature in Observations and Simulations

Abstract

This paper documents the low-frequency (i.e., decadal) variations of surface temperature for the period 1899?1998 in observations, and in simulations conducted as part of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The space?time structure of low-frequency variations is extracted using optimal persistence analysis, which is a technique that linearly decomposes a vector time series into a set of uncorrelated components, ordered such that the first component maximizes the decorrelation time, the second maximizes the decorrelation time subject to being uncorrelated with the first, and so on. The results suggests that only the first two optimal persistence patterns (OPPs) in the observation-based record are statistically distinguishable from white noise. These two components can reproduce the spatial structure of local linear trends over various multidecadal periods, indicating that they give an efficient representation of the observed change in surface temperature. In contrast, most simulations suggest the existence of a single physically significant OPP, all with qualitatively similar time series but each with somewhat different spatial structure. The leading OPP computed from the full model grid is surprisingly consistent with the leading OPP computed from the observation-based grid with missing data masked out, suggesting that the observation-based grid does not pose a serious barrier to extracting the dominant low-frequency variations in the global climate system. The regions in which the leading optimal persistence patterns agree in their predictions of warming coincides with the regions in which warming has in fact been observed to occur.