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AMO’s structure and climate footprint in observations and IPCC AR5 climate simulations

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Abstract

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.

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Notes

  1. The use of coupled, ocean–atmosphere models from the CMIP5 project allows for the ocean circulation to freely evolve, facilitating, in this way, a more accurate understanding of the AMO-related ocean state and its imprint on local as well as remote climate features. A downside of this approach, however, is that in such models, there are so many fields that are varying simultaneously, that it becomes very challenging to separate cause from effect.

  2. Note the similarity in the domain used to define the area-averaged SST anomalies by Sutton and Hodson (2003) (7.5°–75°W, 0°–60°N), as well as the difference in the way of smoothing the area-averaged SST anomalies, via the use of a 37-point Henderson filter.

  3. That is, the time it takes for the anomaly to grow from climatological conditions to reach its maximum value and then go back to climatological conditions before going in the opposite direction.

  4. To investigate this difference, we used NOAA’s Extended Reconstructed SST data set (ERSSTv3b, Smith et al. 2008) to generate a smoothed AMO index and lead/lag SST regressions (not shown); the emerging pattern agrees with the lead/lag regressions from HadISST, with SST anomalies over the Pacific being less widespread than the ones noted in the SODA lead/lag regressions.

  5. The AMO-related SST anomalies in the North Atlantic are minimum in spring, a time when the SST anomalies over the northern tropical Atlantic reach the maximum extension and have the largest impact over northeastern Brazil during the rainy season (not shown). This is all reminiscent of the so called interhemispheric mode.

  6. The influence of the AMO in central US rainfall is considerably less extensive in summer than in fall (not shown).

  7. The impact of the AMO on regional rainfall over Africa depends on the season. As noted above, the Guinean zone is affected in fall, but the Sahelian zone to its north is most affected in summer (not shown).

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Acknowledgments

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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Authors and Affiliations

  1. Department of Atmospheric and Oceanic Science, University of Maryland, 3435 Computer and Space Sciences Bldg., College Park, MD, 20742-2425, USA

    Argyro Kavvada, Alfredo Ruiz-Barradas & Sumant Nigam

  2. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, 20742-2425, USA

    Sumant Nigam

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  1. Argyro Kavvada
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Correspondence to Argyro Kavvada.

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Kavvada, A., Ruiz-Barradas, A. & Nigam, S. AMO’s structure and climate footprint in observations and IPCC AR5 climate simulations. Clim Dyn 41, 1345–1364 (2013). https://doi.org/10.1007/s00382-013-1712-1

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