Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations

Abstract

Low-frequency internal climate variability (ICV) plays an important role in modulating global surface temperature, regional climate, and climate extremes. However, it has not been completely characterized in the instrumental record and in the Coupled Model Intercomparison Project phase 5 (CMIP5) model ensemble. In this study, the surface temperature ICV of the North Pacific (NP), North Atlantic (NA), and Northern Hemisphere (NH) in the instrumental record and historical CMIP5 all-forcing simulations is isolated using a semiempirical method wherein the CMIP5 ensemble mean is applied as the external forcing signal and removed from each time series. Comparison of ICV signals derived from this semiempirical method as well as from analysis of ICV in CMIP5 preindustrial control runs reveals disagreement in the spatial pattern and amplitude between models and instrumental data on multidecadal time scales (.20 yr). Analysis of the amplitude of total variability and the ICV in the models and instrumental data indicates that the models underestimate ICV amplitude on low-frequency time scales (.20 yr in the NA; .40 yr in the NP), while agreement is found in the NH variability. A multiple linear regression analysis of ICV in the instrumental record shows that variability in the NP drives decadal-to-interdecadal variability in the NH, whereas the NA drives multidecadal variability in the NH. Analysis of the CMIP5 historical simulations does not reveal such a relationship, indicating model limitations in simulating ICV. These findings demonstrate the need to better characterize low-frequency ICV, which may help improve attribution and decadal prediction.

Do CMIP5 Models Reproduce Observed Low‐Frequency North Atlantic Jet Variability?

“…The reanalysis/CMIP5 differences in jet strength variability are most prominent for periods of approximately 70 years (Figure ), which is consistent with a link between multidecadal variability in jet strength and AMV that was identified previously by Woollings et al (, who found a correlation of −0.48 between AMV and multidecadal jet strength variability using a similar jet strength metric). Studies show that the observed AMV is stronger than that in most CMIP5 models (Cheung et al, ; Zhang & Wang, ). The comparatively prominent observed low‐frequency strength variability compared to CMIP5 is consistent with previous work suggesting a too weak feedback between AMV and atmospheric variability (Kim et al, ; Peings et al, ; Wang et al, ).…”

Pronounced differences between observed and CMIP5‐simulated multidecadal climate variability in the twentieth century

“…Our results are also broadly consistent with recent analyses of Cheung et al [], who documented substantial mismatches between their estimated internal components of the observed and CMIP5‐simulated AMO, PMO, and NMO variability. However, these authors used subtraction of the scaled CMIP5 MMEM signal to deduce the internal variability in historical simulations of individual CMIP5 models.…”

“…Our present methodology alleviates these problems and provides consistent estimates of the CMIP5‐simulated internal variability in the historical and preindustrial control runs. In particular, we find that Cheung et al [] underestimate the true differences between the observed (semiempirical) internal variability and the internal variability in historical CMIP5 simulations.…”

A Review of the Role of the Atlantic Meridional Overturning Circulation in Atlantic Multidecadal Variability and Associated Climate Impacts

“…The AMV imprint on global and Northern Hemisphere mean surface temperature has also been identified in many studies with different statistical analysis methods applied to both observations and modeling results (Barcikowska et al, ; Chen et al, ; Chen & Tung, ; Cheung et al, ; Chylek, Klett, et al, ; Chylek et al, ; DelSole et al, ; Kravtsov et al, ; Kravtsov & Spannagle, ; Steinman et al, ; Stolpe et al, ; Tung & Zhou, ; Z. Wu, Huang, et al, ; Wyatt et al, ; Zhou & Tung, ). These statistical studies are consistent with the above‐mentioned AGCM/hybrid CGCM experiments (Semenov et al, ; Zhang et al, ), illustrating that the enhanced surface heat flux released from the ocean to the atmosphere during a positive AMV and AMOC phase leads to the hemispheric‐scale anomalous surface warming and vice versa.…”