Climate Dynamics

, Volume 46, Issue 3–4, pp 1257–1276 | Cite as

NAO and PNA influences on winter temperature and precipitation over the eastern United States in CMIP5 GCMs

  • Liang NingEmail author
  • Raymond S. Bradley


The historical and future relationships between two major patterns of large-scale climate variability, the North Atlantic Oscillation (NAO) and the Pacific/North America pattern (PNA), and the regional winter temperature and precipitation over the eastern United States were systemically evaluated by using 17 general circulation models (GCMs) from the Coupled Model Intercomparison Project phase 5. Empirical orthogonal function analysis was used to define the NAO and PNA. The observed spatial patterns of NAO and PNA can be reproduced by all the GCMs with slight differences in locations of the centers of action and their average magnitudes. For the correlations with regional winter temperature and precipitation over the eastern US, GCMs perform best in capturing the relationships between the NAO and winter temperature, and between the PNA and winter temperature and precipitation. The differences between the observed and simulated relationships are mainly due to displacements of the simulated NAO and PNA centers of action and differences in their magnitudes. In simulations of the future, both NAO and PNA magnitudes increase, with uncertainties related to the model response and emission scenarios. When assessing the influences of future NAO/PNA changes on regional winter temperature, it is found that the main factors are related to changes in the magnitude of the NAO Azores center and total NAO magnitude, and the longitude of the PNA center over northwestern North America, total PNA magnitude, and the magnitude of the PNA center over the southeastern US.


NAO PNA Teleconnection CMIP5 Regional climate 



This research is jointly supported by the US Department of the Interior’s Northeast Climate Science Center, under USGS funding G12AC00001, the Strategic and Special Frontier Project of Science and Technology of the Chinese Academy of Sciences (Grant No. XDA05080800), the National Natural Science Foundation of China (Grant No. 41420104002) and The Priority Academic Development Program of Jiangsu Higher Education Institutions (Grant No. 164320H116). The high-resolution observation data were obtained from the Climatic Research Unit at University of East Anglia. The NCEP reanalysis data were obtained from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL)—Physical Science Division (PSD). The WCRP CMIP5 multi-model dataset is made available by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modeling (WGCM). James Hurrell kindly provided the observed NAO time series. The observed PNA time series were obtained from National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC). We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation. We thank referees for their valuable comments, which greatly improved the manuscript.

Supplementary material

382_2015_2643_MOESM1_ESM.docx (11 mb)
Supplementary material 1 (DOCX 11215 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Key Laboratory of Virtual Geographic Environment of Ministry of Education, School of Geography Science, Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex System, School of Mathematical ScienceNanjing Normal UniversityNanjingChina
  2. 2.Department of Geosciences, Northeast Climate Science Center, Climate System Research CenterUniversity of MassachusettsAmherstUSA
  3. 3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina

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