Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Allan AM (2013) Analyzing the present and future Pacific-North American teleconnection using global and regional climate models. Doctoral dissertation, Oregon State University, p 190

  • Allan AM, Hostetler SW, Alder JR (2014) Analysis of the present and future winter Pacific-North American teleconnection in the ECHAM5 global and RegCM3 regional climate models. Clim Dyn 42:1671–1682. doi:10.1007/s00382-013-1910-x

    Article  Google Scholar 

  • Bai X, Wang J, Sellinger C, Clites A, Assel R (2012) Interannual variability of Great Lakes ice cover and its relationship to NAO and ENSO. J Geophys Res 117:C03002. doi:10.1029/2010JC006932

    Google Scholar 

  • Barnston AG, Livezey RE (1987) Classification, seasonality, and persistence of low-frequency atmospheric circulation patterns. Mon Weather Rev 115:1083–1126

    Article  Google Scholar 

  • Bellouin N, Boucher O, Haywood J, Johnson C, Jones A, Rae J, Woodward S (2007) Improved representation of aerosols for HadGEM2. Meteorological Office Hadley Centre, Technical Note 73, March 2007

  • Bentsen M et al (2012) The Norwegian earth system model, NorESM1-M—part 1: description and basic evaluation. Geosci Model Dev Discuss 5:2843–2931

    Article  Google Scholar 

  • Bradbury JA, Keim BD, Wake CP (2003) The influence of regional storm tracking and teleconnections on winter precipitation in the Northeastern United States. Ann Assoc Am Geogr 93:544–556

    Article  Google Scholar 

  • Campbell GG, Kittel TGF, Meehl GA, Washington WM (1995) Low-frequency variability and CO2 transient climate change. Part 2: EOF analysis of CO2 and model-configuration sensitivity. Glob Planet Change 10:201–216

    Article  Google Scholar 

  • Casado MJ, Pastor MA (2012) Use of variability modes to evaluate AR4 climate models over the Euro-Atlantic region. Clim Dyn 38:225–237

    Article  Google Scholar 

  • Chylek P, Li J, Dubey MK, Wang M, Lesins G (2011) Observed and model simulated 20th century Arctic temperature variability: Canadian earth system model CanESM2. Atmos Chem Phys Discuss 11:22893–22907

    Article  Google Scholar 

  • Coleman JSM, Rogers JC (2003) Ohio River Valley winter moisture conditions associated with the Pacific-North American teleconnection pattern. J Clim 16:969–981

    Article  Google Scholar 

  • Collins WJ et al (2008) Evaluation of the HadGEM2 model. Meteorological Office Hadley Centre, Technical Note 74, Nov 2008

  • Davini P, Cagnazzo C (2014) On the misinterpretation of the North Atlantic Oscillation in CMIP5 models. Clim Dyn 43:1497–1511. doi:10.1007/s00382-013-1970-s

    Article  Google Scholar 

  • Donner LJ et al (2011) The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL Global Coupled Model CM3. J Clim 24:3484–3519

    Article  Google Scholar 

  • Ge Y, Gong G, Frei A (2009) Physical mechanisms linking the winter Pacific-North American teleconnection pattern to spring North American snow depth. J Clim 22:5135–5148

  • Gent PR et al (2011) The community climate system model version 4. J Clim 24:4973–4991

    Article  Google Scholar 

  • Handorf D, Dethloff K (2012) How well do state-of-the-art atmosphere–ocean general circulation models reproduce atmospheric teleconnection patterns? Tellus A 64:19777. doi:10.3402/tellusa.v64i0.19777

    Article  Google Scholar 

  • Hannachi A (2007) Pattern hunting in climate: a new method for finding trends in gridded climate data. Int J Climatol 27:1–15

    Article  Google Scholar 

  • Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int J Climatol 34:623–642

    Article  Google Scholar 

  • Hartley S, Keables MJ (1998) Synoptic associations of winter climate and snowfall variability in New England, USA, 1950–1992. Int J Climatol 18:281–298

  • Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Brönnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM (2013) Observations: atmosphere and surface. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

  • Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095–1107

    Article  Google Scholar 

  • Hewitson BC, Crane RG (1996) Climate downscaling: techniques and application. Clim Res 7:85–95

    Article  Google Scholar 

  • Hewitson BC, Crane RG (2006) Consensus between GCM climate change projections with empirical downscaling: precipitation downscaling over South Africa. Int J Climatol 26:1315–1337

    Article  Google Scholar 

  • Hollingsworth A, Arpe K, Tiedtke M, Capaldo M, Savuärvi H (1980) The performance of a medium-range forecast model in winter—impact of physical parameterizations. Mon Weather Rev 108:1736–1773

    Article  Google Scholar 

  • Horton RG, Eastering W, Kates R, Ruth M, Sussman E, Whelchel A, Wolfe D, Lipschultz F (2014) Ch. 16: Northeast. Climate change impacts in the United States: the third national climate assessment. Melillo JM, Terese (T.C.) Richmond, Yohe GW (eds) US Global Change Research Program, 371–395. doi:10.7930/JOSF2T3P

  • Hu Z-Z, Wu Z (2004) The intensification and shift of the annual North Atlantic oscillation in a global warming scenario simulation. Tellus 56A:112–124

    Article  Google Scholar 

  • Hurrell JW (1995) Decadal trends in the North Atlantic oscillation: regional temperature and precipitation. Science 269:676–679

    Article  Google Scholar 

  • Hurrell JW, Deser C (2009) North Atlantic climate variability: the role of the North Atlantic oscillation. J Mar Syst 78:28–41

    Article  Google Scholar 

  • Hurrell JW, National Center for Atmospheric Research Staff (eds) (2013) The climate data guide: Hurrell North Atlantic oscillation (NAO) Index (station-based). Last modified 08 Oct 2013. Retrieved from https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-station-based

  • Huth R (1997) Continental-scale circulation in the UKHI GCM. J Clim 10:1545–1561

    Article  Google Scholar 

  • IPCC (2007) Climate Change: the physical science basis. Cambridge University Press, Cambridge, UK and New York, NY, USA

  • Johns TC et al (2006) The new Hadley Centre climate model (HadGEM1): evaluation of coupled simulations. J Clim 19:1327–1353

    Article  Google Scholar 

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471

    Article  Google Scholar 

  • Kunkel KE et al. (2013) Monitoring and understanding trends in extreme storms: state of knowledge. Bull Am Meteorol Soc 94:499–514

    Article  Google Scholar 

  • Kunkel KE, Angel JR (1999) Relationship of ENSO to snowfall and related cyclone activity in the contiguous United States. J Geophys Res 104:19425–19434. doi:10.1029/1999JD900010

    Article  Google Scholar 

  • Leathers DJ, Yarnal B, Palecki MA (1991) The Pacific/North Amercian teleconnection pattern and United States climate. Part I: regional temperature and precipitation associations. J Clim 4:517–528

    Article  Google Scholar 

  • Lee Y-Y, Black RX (2013) Boreal winter low-frequency variability in CMIP5 models. J Geophys Res Atmos 118:6891–6904. doi:10.1002/jgrd.50493

    Article  Google Scholar 

  • Lian T, Chen D (2012) An evaluation of rotated EOF analysis and its application to tropical Pacific SST variability. J Clim 25:5361–5373

    Article  Google Scholar 

  • Liu H-L et al (2010) Thermosphere extension of the whole atmosphere community climate model. J Geophys Res. doi:10.1029/2010JA015586

    Google Scholar 

  • Marsland SJ, Haak H, Jungclaus JH, Latif M, Röske F (2003) The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Model 5:91–127

    Article  Google Scholar 

  • Meehl GA, Teng H, Branstator G (2006) Future changes of El Niño in two global coupled climate models. Clim Dyn 26:549–566. doi:10.1007/s00382-005-0098-0

    Article  Google Scholar 

  • Meehl GA et al (2007) Global climate projections. In: Solomon S et al (eds) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, pp 747–845

    Google Scholar 

  • Mignot J, Bony S (2013) Presentation and analysis of the IPSL and CNRM climate models used in CMIP5. Clim Dyn. doi:10.1007/s00382-01301720-1

    Google Scholar 

  • Moss RH et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756

    Article  Google Scholar 

  • Neale RB, Richter J, Park S, Lauritzen PH, Vavrus SJ, Rasch PJ, Zhang M (2013) The mean climate of the Community Atmosphere Model (CAM4) in forced SST and fully coupled experiments. J Clim 26:5150–5168

    Article  Google Scholar 

  • Ning L, Bradley RS (2014) Winter precipitation variability and corresponding teleconnections over the northeastern United States. J Geophys Res Atmos. doi:10.1002/2014JD021591

    Google Scholar 

  • Ning L, Bradley RS (2015) Winter climate extremes over the northeastern United States and southeastern Canada and teleconnections with large-scale modes of climate variability. J Clim 28:2475–2493

  • Ning L, Qian Y (2009) Interdecadal change of extreme precipitation over South China and its mechanism. Adv Atmos Sci 26:109–118

    Article  Google Scholar 

  • Ning L, Mann ME, Crane R, Wagener T (2012a) Probabilistic projections of climate change for the mid-Atlantic region of the United States—validation of precipitation downscaling during the historical era. J Clim 25:509–526

    Article  Google Scholar 

  • Ning L, Mann ME, Crane R, Wagener T, Najjar RG, Singh R (2012b) Probabilistic projections of anthropogenic climate change impacts on precipitation for the mid-Atlantic region of the United States. J Clim 25:5273–5291

    Article  Google Scholar 

  • Notaro M, Wang W-C, Gong W (2006) Model and observational analysis of the northeast US regional climate and its relationship to the PNA and NAO patterns during early winter. Mon Weather Rev 134:3479–3505

    Article  Google Scholar 

  • Raddatz TJ, Reick CH, Knorr W, Kattge J, Roeckner E, Schnur R, Schnitzler KG, Wetzel P, Jungclaus J (2007) Will the tropical land biosphere dominate the climate–carbon cycle feedback during the twenty-first century? Clim Dyn 29:565–574

    Article  Google Scholar 

  • Raible CC, Stocker TF, Yoshimori M, Renold M, Beyerle U, Casty C, Luterbacher J (2005) Northern hemispheric trends of pressure indices and atmospheric circulation patterns in observation, resconstructions, and coupled GCM simulations. J Clim 18:3968–3982

    Article  Google Scholar 

  • Randall DA et al (2007) Climate models and their evaluation. In: Solomon S et al (eds) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, pp 589–662

    Google Scholar 

  • Rotsayn L, Collier MA, Dix MR, Feng Y, Gordon HB, O’Farrell SP, Smith IN, Syktus J (2010) Improved simulation of Australian climate and ENSO-related climate variability in a global climate model with an interactive aerosol treatment. Int J Climatol 30(7):1067–1088

    Google Scholar 

  • Schmidt GA et al (2014) Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J Adv Model Earth Syst 6:141–184. doi:10.1002/2013MS000265

    Article  Google Scholar 

  • Stephenson DB, Pavan V, Collins M, Junge MM, Quadrelli R (2006) North Atlantic Oscillation response to transient greenhouse gas forcing and the impact on European winter climate: a CMIP2 multi-model assessment. Clim Dyn 27:401–420

    Article  Google Scholar 

  • Stoner AM, Hayhoe K, Wuebbles DJ (2009) Assessing general circulation model simulations of atmospheric teleconnection patterns. J Clim 22:4348–4372

    Article  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498

    Article  Google Scholar 

  • Trenberth KE et al (2007) Observations: surface and atmospheric climate change. In: Solomon S et al (eds) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, pp 235–336

    Google Scholar 

  • Van Vuuren DP et al (2011) The representative concentration pathways: an overview. Clim Change 109:5–31

    Article  Google Scholar 

  • Vavrus S, Notaro M, Liu Z (2006) A mechanism for abrupt climate change associated with tropical Pacific SST. J Clim 19:242–256

    Article  Google Scholar 

  • Voldoire A et al (2012) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn 40(9):2091–2121. doi:10.1107/s00382-01101259-y

    Google Scholar 

  • Wallace JM, Gutzler DS (1981) Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon Weather Rev 109:784–812

    Article  Google Scholar 

  • Watanabe M et al (2010) Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J Clim 23:6312–6335

    Article  Google Scholar 

  • Wilks DS (2006) Statistical methods in atmospheric sciences, 2nd edn. Academic Press, Burlington, p 648

    Google Scholar 

  • Yukimoto S et al (2011) Meteorological research institute-earth system model version 1 (MRI-ESM1): model description. Technical Reports of the Meteorological Research Institute No. 64, 2011, p 83

  • Zhou T, Song F, Chen X (2013) Historical evolution of global and regional surface air temperature simulated by FGOALS-s2 and FGOALs-g2: How reliable are the model results? Adv Atmos Sci 30:638–657

    Article  Google Scholar 

  • Zhou Z-Q, Xie S-P, Zheng X-T, Liu Q, Wang H (2014) Global warming-induced change in El Niño teleconnections over the north Pacific and North America. J Clim 27:9050–9064

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  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 Science, Nanjing Normal University, Nanjing, 210023, China

    Liang Ning

  2. Department of Geosciences, Northeast Climate Science Center, Climate System Research Center, University of Massachusetts, Amherst, MA, 01003, USA

    Liang Ning & Raymond S. Bradley

  3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China

    Liang Ning

Authors
  1. Liang Ning
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raymond S. Bradley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liang Ning.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 11215 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ning, L., Bradley, R.S. NAO and PNA influences on winter temperature and precipitation over the eastern United States in CMIP5 GCMs. Clim Dyn 46, 1257–1276 (2016). https://doi.org/10.1007/s00382-015-2643-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-015-2643-9

Keywords

Search

Navigation