AMO’s structure and climate footprint in observations and IPCC AR5 climate simulations
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This study aims to characterize the spatiotemporal features of the low frequency Atlantic Multidecadal Oscillation (AMO), its oceanic and atmospheric footprint and its associated hydroclimate impact. To accomplish this, we compare and evaluate the representation of AMO-related features both in observations and in historical simulations of the twentieth century climate from models participating in the IPCC’s CMIP5 project. Climate models from international leading research institutions are chosen: CCSM4, GFDL-CM3, UKMO-HadCM3 and ECHAM6/MPI-ESM-LR. Each model employed includes at least three and as many as nine ensemble members. Our analysis suggests that the four models underestimate the characteristic period of the AMO, as well as its temporal variability; this is associated with an underestimation/overestimation of spectral peaks in the 70–80 year/10–20 year range. The four models manifest the mid-latitude focus of the AMO-related SST anomalies, as well as certain features of its subsurface heat content signal. However, they are limited when it comes to simulating some of the key oceanic and atmospheric footprints of the phenomenon, such as its signature on subsurface salinity, oceanic heat content and geopotential height anomalies. Thus, it is not surprising that the models are unable to capture the majority of the associated hydroclimate impact on the neighboring continents, including underestimation of the surface warming that is linked to the positive phase of the AMO and is critical for the models to be trusted on projections of future climate and decadal predictions.
KeywordsAMO CMIP5 Climate models Historical simulations Timescale of variability Hydroclimate impact Salinity Ocean heat content
The authors wish to acknowledge support from the NOAA grant NA10OAR4310158. They also wish to thank Dr. Edwin K. Schneider, Executive Editor at Climate Dynamics and two anonymous reviewers for their constructive comments and insightful references that helped improve the paper, as well as Jose Caceres, Assistant System Administrator at University of Maryland, for providing help with respect to data access from the Earth System Grid (ESG) website. Finally, they wish to acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and wish to thank the climate modeling groups used in this paper for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
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