List of CMIP5 members.

The upward trend of observed global surface air temperature anomalies exhibits a well-known multidecadal undulation, largely muted in the state-of-the-art climate models. We provide a comprehensive spatiotemporal description of these differences in the estimated unforced residuals representing the internal climate variability between a multi-model ensemble of historical simulations and two different reanalysis data sets, using optimal filtering. The signal identification was carried out within a limited set of observed and model simulated Northern Hemisphere’s climate indices, but full two-dimensional spatial patterns of this signal, in the global gridded surface air temperature (SAT) and sea-level pressure (SLP) fields were also obtained. We then compared the magnitudes, spatial patterns, and characteristic time scales of the observed and simulated dominant low-frequency variability so defined. The observed variability is characterized by a hemispheric-to-global scale multidecadal signal exhibiting coherent anomalies over the North Atlantic, North Pacific and Southern Oceans. The simulated signals have time scales similar to observed, but different spatial patterns, and are weaker, with substantial sampling variability between different models and between simulations of each individual model. Few outlier models produce multidecadal signals with magnitudes comparable or exceeding those observed yet with vastly different spatial patterns dominated by the North Atlantic SAT variations. In the ensemble average, the simulated SLP pattern is negatively correlated with the observed pattern, which hints at root cause of the observed vs. simulated multidecadal-signal differences, with the former likely reflecting internal ocean dynamics and the latter largely consistent with the ultra-low-frequency atmospheric noise.