The Cold Ocean–Warm Land Pattern: Model Simulation and Relevance to Climate Change Detection

Abstract

Surface air temperatures from a 1000-yr integration of a coupled atmosphere–ocean model with constant forcing are analyzed by using a method that decomposes temperature variations into a component associated with a characteristic spatial structure and a residual. The structure function obtained from the coupled model output is almost identical to the so-called cold ocean–warm land (COWL) pattern based on observations, in which above-average spatial mean temperature is associated with anomalously cold oceans and anomalously warm land. This pattern features maxima over the high-latitude interiors of Eurasia and North America. The temperature fluctuations at the two continental centers exhibit almost no temporal correlation with each other. The temperature variations at the individual centers are related to teleconnection patterns in sea level pressure and 500-mb height that are similar to those identified in previous observational and modeling studies. As in observations, variations in the polarity and amplitude of this structure function are an important source of spatially averaged surface air temperature variability. Results from parallel integrations of models with more simplified treatments of the ocean confirm that the contrast in thermal inertia between land and ocean is the primary factor for the existence of the COWL pattern, whereas dynamical air–sea interactions do not play a significant role. The internally generated variability in structure function amplitude in the coupled model integration is used to assess the importance of the upward trend in the amplitude of the observed structure function over the last 25 yr. This trend, which has contributed to the accelerated warming of Northern Hemisphere temperature over recent decades, is unusually large compared with the trends generated internally by the coupled model. If the coupled model adequately estimates the internal variability of the real climate system, this would imply that the recent upturn in the observed structure function may not be purely a manifestation of unforced variability. A similar monotonic trend occurs when the same methodology is applied to a model integration with time-varying radiative forcing based on past and future CO₂ and sulfate aerosol increases. This finding illustrates that this decomposition methodology yields ambiguous results when two distinct spatial patterns, the “natural” COWL pattern (i.e., that associated with internally generated variability) and the anthropogenic fingerprint, are present in the simulated climate record.