Climate Dynamics

, Volume 50, Issue 7–8, pp 2845–2865 | Cite as

Modes of interannual variability in northern hemisphere winter atmospheric circulation in CMIP5 models: evaluation, projection and role of external forcing

Article

Abstract

Models from the coupled model intercomparison project phase 5 (CMIP5) dataset are evaluated for their ability to simulate the dominant slow modes of interannual variability in the Northern Hemisphere atmospheric circulation 500 hPa geopotential height in the twentieth century. A multi-model ensemble of the best 13 models has then been used to identify the leading modes of interannual variability in components related to (1) intraseasonal processes; (2) slowly-varying internal dynamics; and (3) the slowly-varying response to external changes in radiative forcing. Modes in the intraseasonal component are related to intraseasonal variability in the North Atlantic, North Pacific and North American, and Eurasian regions and are little affected by the larger radiative forcing of the Representative Concentration Pathways 8.5 (RCP8.5) scenario. The leading modes in the slow-internal component are related to the El Niño-Southern Oscillation, Pacific North American or Tropical Northern Hemisphere teleconnection, the North Atlantic Oscillation, and the Western Pacific teleconnection pattern. While the structure of these slow-internal modes is little affected by the larger radiative forcing of the RCP8.5 scenario, their explained variance increases in the warmer climate. The leading mode in the slow-external component has a significant trend and is shown to be related predominantly to the climate change trend in the well mixed greenhouse gas concentration during the historical period. This mode is associated with increasing height in the 500 hPa pressure level. A secondary influence on this mode is the radiative forcing due to stratospheric aerosols associated with volcanic eruptions. The second slow-external mode is shown to be also related to radiative forcing due to stratospheric aerosols. Under RCP8.5 there is only one slow-external mode related to greenhouse gas forcing with a trend over four times the historical trend.

Keywords

Modes of interannual variability Atmospheric circulation Northern hemisphere CMIP5 models Climate change 

Notes

Acknowledgements

CMIP5 data is available from http://pcmdi9.llnl.gov. The WMGHG time series are available from http://data.giss.nasa.gov/modelforce. We acknowledge the World Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups 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. We acknowledge the resources and support of the National Computational Infrastructure at the Australian National University for maintaining the CMIP5 data at the Australian Earth Systems Grid node. J. Sisson provided invaluable assistance in pre-processing the CMIP5 data. KY was supported by the National Key Research and Development Progarm (2016YFA0600402) and National Natural Science Foundation of China (41405090). KY would also like to thank the School of Earth, Atmosphere and Environment, Monash University for allowing her to visit and conduct this research with CSF.

References

  1. Barnston AG, Livezey RE (1987) Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon Weather Rev 115:1083–1126CrossRefGoogle Scholar
  2. Biagio VD, Calmanti S, Dell’Aquila A, Ruti PM (2014) Northern Hemisphere winter midlatitude atmospheric variability in CMIP5 models. Geophys Res Lett 41:1277–1282. doi: 10.1002/2013GL058928 CrossRefGoogle Scholar
  3. Casado MJ, Pastor MA (2012) Use of variability modes to evaluate AR4 climate models over the Euro-Atlantic region. Clim Dyn 38:225–237. doi: 10.1007/s00382-001-1077-2 CrossRefGoogle Scholar
  4. Cassou C (2008) Intraseaonal interaction between the Madden–Julian oscillation and the North Atlantic oscillation. Nature 455(523):527. doi: 10.1038/nature07286 Google Scholar
  5. Christiansen B (2008) Volcanic eruptions, large-scale modes in the northern hemisphere, and the El Niño-Southern oscillation. J Clim 21:910–922CrossRefGoogle Scholar
  6. Compo GP et al (2011) The Twentieth century reanalysis project. Q J R Meteorol Soc 137:1–28. doi: 10.1002/qj.776 CrossRefGoogle Scholar
  7. DelSole T, Chang P (2003) Predictable component analysis, canonical correlation analysis, and autoregressive models. J Atmos Sci 60:409–416CrossRefGoogle Scholar
  8. Driscoll S, Bozzo A, Gray LJ, Robock A, Stenchikov G (2012) Coupled model intercomparison project 5 (CMIP5) simulations of climate following volcanic eruptions. J Geophys Res 117:D17105. doi: 10.1029/2012JD017607 CrossRefGoogle Scholar
  9. Flato G et al (2013) Evaluation of climate models. In: Stocker TF et al (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, pp 741–866Google Scholar
  10. Frederiksen CS, Frederiksen JS (1992) Northern Hemisphere storm tracks and teleconnection patterns in primitive equation and quasi-geostrophic models. J Atmos Sci 49:1443–1458CrossRefGoogle Scholar
  11. Frederiksen JS, Frederiksen CS (1993) Monsoon disturbances, intraseasonal oscillations, teleconnection patterns, blocking and storm tracks of the global atmosphere during January 1979: linear theory. J Atmos Sci 50:1349–1372. doi: 10.1175/1520-0469(1993)050<1349:MDIOTP>2.0.CO;2 CrossRefGoogle Scholar
  12. Frederiksen JS, Frederiksen CS (1997) Mechanisms of the formation of intraseasonal oscillations and Australian monsoon disturbances: the roles of latent heat, barotropic and baroclinic instability. Contrib Atmos Phys 70:39–56Google Scholar
  13. Frederiksen CS, Grainger S (2015) The role of external forcing in prolonged trends in Australian rainfall. Clim Dyn 45:2455–2468. doi: 10.1007/s00382-015-2482-8 CrossRefGoogle Scholar
  14. Frederiksen CS, Zheng X (2004) Variability of seasonal-mean fields arising from intraseasonal variability. Part 2, application to nh winter circulations. Clim Dyn 23:193–206. doi: 10.1007/s00382-004-0429-6 CrossRefGoogle Scholar
  15. Frederiksen CS, Zheng X (2007a) Variability of seasonal-mean fields arising from intraseasonal variability. Part 3: application to SH winter and summer circulations. Clim Dyn 28:849–866. doi: 10.1007/s00382-006-0214-9 CrossRefGoogle Scholar
  16. Frederiksen CS, Zheng X (2007b) Coherent patterns of interannual variability of the atmospheric circulation: the role of intraseasonal variability. In: Denier J, Frederiksen JS (eds) Frontiers in turbulence and coherent structures. World scientific lecture notes in complex systems, vol 6. World Scientific Publications, Singapore, pp 87–120. doi: 10.1142/6320 CrossRefGoogle Scholar
  17. Frederiksen CS, Zheng X (2007c) A method for constructing skillful seasonal forecasts using slow modes of climate variability. ANZIAM J 48:C89–C103. http://anziamj.austms.org.au/ojs/index.php/ANZIAMJ/article/view/114. Accessed 3 May 2007
  18. Friedman AR, Hwang Y-T, Chiang JCH, Frierson DMW (2013) Interhemispheric temperature asymmetry over the twentieth century and in future projections. J Clim 26:5419–5433. doi: 10.1175/JCLI-D-12-00525.1 CrossRefGoogle Scholar
  19. Grainger S, Frederiksen CS, Zheng X (2013) Modes of interannual variability of Southern Hemisphere atmospheric circulation in CMIP3 models: assessment and projections. Clim Dyn 41:479–500. doi: 10.1007/s00382-012-1659-7 CrossRefGoogle Scholar
  20. Grainger S, Frederiksen CS, Zheng X (2014) Assessment of modes of interannual variability of southern hemisphere atmospheric circulation in CMIP5 models. J Clim 27:8107–8125. doi: 10.1175/JCLI-D-14-00251.1 CrossRefGoogle Scholar
  21. Grainger S, Frederiksen CS, Zheng X (2017) Projections of southern hemisphere atmospheric circulation interannual variability. Clim Dyn 48:1187–1211. doi: 10.1007/s00382-016-3135-2 CrossRefGoogle Scholar
  22. Haarsma RJ, Selten F (2012) Anthropogenic changes in the Walker circulation and their impact on the extra-tropical planetary wave structure in the northern hemisphere. Clim Dyn 39:1781–1799. doi: 10.1007/s00382-012-1308-1 CrossRefGoogle Scholar
  23. Higham NJ (2002) Computing the nearest correlation matrix—a problem from finance. IMA Numer Anal J 22:329–343. doi: 10.1093/imanum/22.3.329 CrossRefGoogle Scholar
  24. Kalnay E et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471. doi: 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2 CrossRefGoogle Scholar
  25. Kuzmina SI, Bengtsson L, Johannessen OM, Drange H, Bobylev LP, Miles MW (2005) The North Atlantic oscillation and greenhouse-gas forcing. Geophys Res Lett 32:L04703. doi: 10.1029/2004GL021064 CrossRefGoogle Scholar
  26. Lee Y-Y, Black RX (2013) Boreal winter low-frequency variability in CMIP5 models. J Geophys Res 118:6891–6904. doi: 10.1002/jgrd.50493 Google Scholar
  27. Linkin ME, Nigam S (2008) The North Pacific oscillation–west pacific teleconnection pattern: mature-phase structure and winter impacts. J Clim 21:1979–1997. doi: 10.1175/2007JCLI2048.1 CrossRefGoogle Scholar
  28. Miller RL et al (2014) CMIP5 historical simulations (1850–2012) with GISS ModelE2. J Adv Model Earth Syst 6:441–478. doi: 10.1002/2013MS000266 CrossRefGoogle Scholar
  29. Monahan AH, Fyfe JC, Ambaum MHP, Stephenson DB, North GR (2009) Empirical orthogonal functions: the medium is the message. J Clim 22:6501–6514. doi: 10.1175/2009JCLI3062.1 CrossRefGoogle Scholar
  30. Rayner NA (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res. doi: 10.1029/2002JD002670
  31. Renwick JA, Wallace JM (1995) Predictable anomaly patterns and the forecast skill of northern hemisphere wintertime 500-mb height fields. Mon Weather Rev 123:2114–2131. doi: 10.1175/1520-0493(1995)123<2114:PAPATF>2.0.CO;2 CrossRefGoogle Scholar
  32. Rowell DP, Folland CK, Maskell K, Ward MN (1995) Variability of summer rainfall over tropical north Africa (1906–92): Observations and modelling. Q J R Meteorol Soc 121:669–704. doi: 10.1002/qj.49712152311 Google Scholar
  33. Schneider T, Griffies SM (1999) A conceptual framework for predictability studies. J Clim 12:3133–3155. doi: 10.1175/1520-0442(1999)012<3133:ACFFPS>2.0.CO;2 CrossRefGoogle Scholar
  34. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. doi: 10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  35. van Vuuren DP et al (2011) The representative concentration pathways: an overview. Clim Change 109:5–31. doi: 10.1007/s10584-011-0148-z CrossRefGoogle Scholar
  36. Venzke S, Allen MR, Sutton RT, Rowell DP (1999) The atmospheric response over the north atlantic to decadal changes in sea surface temperature. J Clim 12:2562–2584. doi: 10.1175/1520-0442(1999)012<2562:TAROTN>2.0.CO;2 CrossRefGoogle Scholar
  37. Wang W, Anderson BT, Kaufmann RK, Myneni RB (2004) The relation between the North Atlantic oscillation and SSTs in the North Atlantic Basin. J Clim 17:4752–4759CrossRefGoogle Scholar
  38. Zhang L (2016) The roles of external forcing and natural variability in global warming hiatuses. Clim Dyn 47:3157–3169. doi: 10.1007/s00382-016-3018-6.CrossRefGoogle Scholar
  39. Zheng X, Frederiksen CS (2004) Variability of seasonal-mean fields arising from intraseasonal variability: part 1, methodology. Clim Dyn 23:177–191. doi: 10.1007/s00382-004-0428-7 CrossRefGoogle Scholar
  40. Zheng X, Frederiksen CS (2006) A study of predictable patterns for seasonal forecasting of New Zealand rainfall. J Clim 19:3320–3333. doi: 10.1175/JCLI3798.1 CrossRefGoogle Scholar
  41. Zheng X, Frederiksen CS (2007) Statistical prediction of seasonal mean southern hemisphere 500-hPa geopotential heights. J Clim 20:2719–2809. doi: 10.1175/JCLI4180.1 CrossRefGoogle Scholar
  42. Zheng X, Sugi M, Frederiksen CS (2004) Interannual variability and predictability in an ensemble of climate simulations with the MRI-JMA AGCM. J Meteorol Soc Jpn 82:1–18. doi: 10.2151/jmsj.82.1 CrossRefGoogle Scholar
  43. Zheng X, Straus DM, Frederiksen CS (2008) Variance decomposition approach to the prediction of the seasonal mean circulation: comparison with dynamical ensemble prediction using NCEP’s CFS. Q J R Meteorol Soc 134:1997–2009. doi: 10.1002/qj.330 CrossRefGoogle Scholar
  44. Zheng X, Straus DM, Frederiksen CS, Grainger S (2009) Potentially predictable patterns of extratropical tropospheric circulation in an ensemble of climate simulations with the COLA AGCM. Q J R Meteorol Soc 135:1816–1829. doi: 10.1002/qj.492 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.The Bureau of MeteorologyMelbourneAustralia
  2. 2.The School of Earth, Atmosphere and EnvironmentMonash UniversityClaytonAustralia
  3. 3.CAS Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

Personalised recommendations