Global mean surface temperature response to large‐scale patterns of variability in observations and CMIP5

Abstract

Global mean surface temperature (GMST) fluctuates over decadal to multidecadal time‐scales. Patterns of internal variability are partly responsible, but the relationships can be conflated by anthropogenically‐forced signals. Here we adopt a physically‐based method of separating internal variability from forced responses to examine how trends in large‐scale patterns, specifically the Interdecadal Pacific Oscillation (IPO) and Atlantic Multidecadal Variability (AMV), influence GMST. After removing the forced responses, observed variability of GMST is close to the central estimates of Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, but models tend to underestimate IPO variability at time‐scales >10 years, and AMV at time‐scales >20 years. Correlations between GMST trends and these patterns are also underrepresented, most strongly at 10‐ and 35‐year time‐scales, for IPO and AMV respectively. Strikingly, models that simulate stronger variability of IPO and AMV also exhibit stronger relationships between these patterns and GMST, predominately at the 10‐ and 35‐year time‐scales, respectively.