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Contrasting stratospheric–tropospheric multi-fractal behaviors in NAM variability

  • Da Nian
  • Zuntao FuEmail author
Article

Abstract

As a harbinger of anomalous weather regimes in the troposphere, the Northern Annular Mode (NAM) propagates from the stratosphere to the troposphere. This fact makes understanding and predicting NAM variability of great importance. In this study, the multi-fractal behaviors of NAM variability are investigated using extended self-similarity based, multi-fractal detrended fluctuation analysis (ESS-MF-DFA) and the NAM indices from 1000 to 10 hPa. The results show that there are contrasting multi-fractal behaviors between the stratosphere and the troposphere that have a transition band near 200 hPa. The stratospheric NAM variability is more complicated and has multiple multi-fractal regimes over different scales with marked contrasting warm–cold season features. To understand these contrasting stratospheric–tropospheric multi-fractal behaviors, three surrogate methods are adopted to show how temporal ordinal patterns over an annual scale contribute to these behaviors, whereas the distribution of NAM variability only plays a minor role. Further studies show that contrasting warm–cold variability may lead to these contrasting behaviors. Among them, warm–cold seasonal variations, power spectral density (PSD), and autocorrelation provide a similar conclusion. Results indicate that although predictions of the NAM index over the stratosphere are required and necessary, the complicated multi-fractal behaviors make linear prediction strategy difficult to obtain high realizable predictability of NAM variations over the stratosphere.

Keywords

Northern Annular Mode (NAM) Multiple multi-fractal behavior Stratospheric–tropospheric variability Warm–cold season variations 

Notes

Acknowledgements

The authors thank the anonymous reviewers for their helpful suggestions for improving the readability of this paper. The authors acknowledge the supports from National Natural Science Foundation of China (nos. 41675049, 41475048).

References

  1. Ambaum MH, Hoskins BJ (2002) The NAO troposphere–stratosphere connection. J Clim 15(14):1969–1978.  https://doi.org/10.1175/1520-0442(2002)015%3c1969:TNTSC%3e2.0.CO;2 CrossRefGoogle Scholar
  2. Badin G, Domeisen DIV (2014a) A search for chaotic behavior in Northern Hemisphere stratospheric variability. J Atmos Sci 71(4):1494–1507.  https://doi.org/10.1175/JAS-D-13-0225.1 CrossRefGoogle Scholar
  3. Badin G, Domeisen DIV (2014b) A search for chaotic behavior in stratospheric variability: comparison between the northern and southern hemispheres. J Atmos Sci 71(12):4611–4620.  https://doi.org/10.1175/JAS-D-14-0049.1 CrossRefGoogle Scholar
  4. Badin G, Domeisen DIV (2016) Nonlinear stratospheric variability: multifractal de-trended fluctuation analysis and singularity spectra. Proc R Soc A Math Phys 472(2191):20150864.  https://doi.org/10.1098/rspa.2015.0864 CrossRefGoogle Scholar
  5. Baldwin MP (2001) Annular modes in global daily surface pressure. Geophys Res Lett 28(21):4115–4118.  https://doi.org/10.1029/2001GL013564 CrossRefGoogle Scholar
  6. Baldwin MP, Dunkerton TJ (1999) Propagation of the Arctic Oscillation from the stratosphere to the troposphere. J Geophys Res 104(D24):30937–30946.  https://doi.org/10.1029/1999JD900445 CrossRefGoogle Scholar
  7. Baldwin MP, Dunkerton TJ (2001) Stratospheric harbingers of anomalous weather regimes. Science 294(5542):581–584.  https://doi.org/10.1126/science.1063315 CrossRefGoogle Scholar
  8. Baldwin MP, Thompson DW (2009) A critical comparison of stratosphere–troposphere coupling indices. Q J R Meteorol Soc 135(644):1661–1672.  https://doi.org/10.1002/qj.479 CrossRefGoogle Scholar
  9. Baldwin MP, Stephenson DB, Thompson DW, Dunkerton TJ, Charlton AJ, O’Neill A (2003) Stratospheric memory and skill of extended-range weather forecasts. Science 301(5633):636–640.  https://doi.org/10.1126/science.1087143 CrossRefGoogle Scholar
  10. Bamzai AS (2003) Relationship between snow cover variability and Arctic Oscillation index on a hierarchy of time scales. Int J Climatol 23(2):131–142.  https://doi.org/10.1002/joc.854 CrossRefGoogle Scholar
  11. Cai M, Ren RC (2007) Meridional and downward propagation of atmospheric circulation anomalies. Part I: Northern Hemisphere cold season variability. J Atmos Sci 64(6):1880–1901.  https://doi.org/10.1175/JAS3922.1 CrossRefGoogle Scholar
  12. Cohen J, Foster J, Barlow M, Saito K, Jones J (2010) Winter 2009–2010: a case study of an extreme Arctic Oscillation event. Geophys Res Lett 37(17):L17707.  https://doi.org/10.1029/2010GL044256 CrossRefGoogle Scholar
  13. Domeisen DIV, Badin G, Koszalka IM (2018) How predictable are the Arctic and North Atlantic oscillations? Exploring the variability and predictability of the Northern Hemisphere. J Clim 31(3):997–1014.  https://doi.org/10.1175/JCLI-D-17-0226.1 CrossRefGoogle Scholar
  14. Feldstein B, Franzke CL (2017) Atmospheric teleconnection patterns. In: Franzke CL, O’Kane TJ (eds) Nonlinear and stochastic climate dynamics. Cambridge University Press, Cambridge, pp 54–104CrossRefGoogle Scholar
  15. Fu ZT, Shi L, Xie FH, Piao L (2016) Nonlinear features of northern annular mode variability. Physica A 449:390–394CrossRefGoogle Scholar
  16. Fujiwara M, Wright JS, Manney GL, Gray LJ, Anstey J, Birner T et al (2017) Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems. Atmos Chem Phys 17(2):1417–1452.  https://doi.org/10.5194/acp-17-1417-2017 CrossRefGoogle Scholar
  17. Gerber EP, Martineau P (2018) Quantifying the variability of the annular modes: reanalysis uncertainty vs. sampling uncertainty. Atmos Chem Phys 18(23):17099–17117.  https://doi.org/10.5194/acp-18-17099-2018 CrossRefGoogle Scholar
  18. Gerber EP, Baldwin MP, Akiyoshi H, Austin J, Bekki S, Braesicke P et al (2010) Stratosphere–troposphere coupling and annular mode variability in chemistry-climate models. J Geophys Res.  https://doi.org/10.1029/2009JD013770 Google Scholar
  19. Gillett NP, Graf HF, Osborn TJ (2003) Climate change and the North Atlantic oscillation. Geophys Monogr Am Geophys Union 134:193–210Google Scholar
  20. Gong DY, Wang SW, Zhu JH (2001) East Asian winter monsoon and Arctic oscillation. Geophys Res Lett 28(10):2073–2076.  https://doi.org/10.1029/2000GL012311 CrossRefGoogle Scholar
  21. Gong DY, Kim SJ, Ho CH (2007) Arctic Oscillation and ice severity in the Bohai Sea, east Asia. Int J Climatol 27(10):1287–1302.  https://doi.org/10.1002/joc.1470 CrossRefGoogle Scholar
  22. Hirata Y, Shimo Y, Tanaka HL, Aihara K (2011) Chaotic properties of the Arctic oscillation index. SOLA 7:33–36.  https://doi.org/10.2151/sola.2011-009 CrossRefGoogle Scholar
  23. Hitchcock P (2019) On the value of reanalyses prior to 1979 for dynamical studies of stratosphere–troposphere coupling. Atmos Chem Phys 19(5):2749–2764.  https://doi.org/10.5194/acp-19-2749-2019 CrossRefGoogle Scholar
  24. Hoerling MP, Hurrell JW, Xu T (2001) Tropical origins for recent North Atlantic climate change. Science 292(5514):90–92.  https://doi.org/10.1126/science.1058582 CrossRefGoogle Scholar
  25. Huang Y, Fu Z (2019) Enhanced time series predictability with well-defined structures. Theor Appl Climatol.  https://doi.org/10.1007/s00704-019-02836-6 Google Scholar
  26. Kalnay E, Kanamitsu M, Kistler R, Collins W et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–472.  https://doi.org/10.1175/1520-0477(1996)077%3c0437:TNYRP%3e2.0.CO;2 CrossRefGoogle Scholar
  27. Kantelhardt JW, Koscielny-Bunde E, Rego HH, Havlin S, Bunde A (2001) Detecting long-range correlations with detrended fluctuation analysis. Phys A 295(3–4):441–454.  https://doi.org/10.1016/S0378-4371(01)00144-3 CrossRefGoogle Scholar
  28. Kantelhardt JW, Zschiegner SA, Koscielny-Bunde E, Havlin S, Bunde A, Stanley HE (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Phys A 316(1–4):87–114.  https://doi.org/10.1016/S0378-4371(02)01383-3 CrossRefGoogle Scholar
  29. Keeley SPE, Sutton RT, Shaffrey LC (2009) Does the North Atlantic oscillation show unusual persistence on intraseasonal timescales? Geophys Res Lett 36(22):L22706.  https://doi.org/10.1029/2009GL040367 CrossRefGoogle Scholar
  30. Kerr RA (1999) A new force in high-latitude climate. Science 284:241–242.  https://doi.org/10.1126/science.284.5412.241 CrossRefGoogle Scholar
  31. Kerr RA (2001) Getting a handle on the North’s El Nino. Science 294:494–495.  https://doi.org/10.1126/science.294.5542.494b CrossRefGoogle Scholar
  32. Kobayashi S, Ota Y, Harada Y, Ebita A, Moriya M, Onoda H et al (2015) The JRA-55 reanalysis: general specifications and basic characteristics. J Meteorol Soc Jpn Ser II 93(1):5–48.  https://doi.org/10.2151/jmsj.2015-001 CrossRefGoogle Scholar
  33. Limpasuvan V, Hartmann DL (1999) Eddies and the annular modes of climate variability. Geophys Res Lett 26(20):3133–3136.  https://doi.org/10.1029/1999GL010478 CrossRefGoogle Scholar
  34. Limpasuvan V, Hartmann DL (2000) Wave-maintained annular modes of climate variability. J Clim 13(24):4414–4429.  https://doi.org/10.1175/1520-0442(2000)013%3c4414:WMAMOC%3e2.0.CO;2 CrossRefGoogle Scholar
  35. Movahed MS, Jafari GR, Ghasemi F, Rahvar S, Tabar MRR (2006) Multifractal detrended fluctuation analysis of sunspot time series. J Stat Mech 02:P02003.  https://doi.org/10.1088/1742-5468/2006/02/P02003 Google Scholar
  36. Nian D, Fu Z (2019) Extended self-similarity based multi-fractal detrended fluctuation analysis: a novel multi-fractal quantifying method. Commun Nonlinear Sci Numer Simul 67:568–576.  https://doi.org/10.1016/j.cnsns.2018.07.034 CrossRefGoogle Scholar
  37. Osprey SM, Ambaum MH (2011) Evidence for the chaotic origin of Northern Annular Mode variability. Geophys Res Lett 38(15):L15702.  https://doi.org/10.1029/2011GL048181 CrossRefGoogle Scholar
  38. Riddle EE, Butler AH, Furtado JC, Cohen JL et al (2013) CFSv2 ensemble prediction of the wintertime Arctic Oscillation. Clim Dyn 41:1099–1116.  https://doi.org/10.1007/s00382-013-1850-5 CrossRefGoogle Scholar
  39. Sigeti DE (1995) Exponential decay of power spectra at high frequency and positive Lyapunov exponents. Phys D 82:136–153.  https://doi.org/10.1016/0167-2789(94)00225-F CrossRefGoogle Scholar
  40. Sigeti D, Horsthemke W (1987) High-frequency power spectra for systems subject to noise. Phys Rev A 35(5):2276.  https://doi.org/10.1103/PhysRevA.35.2276 CrossRefGoogle Scholar
  41. Simpson IR, Hitchcock P, Shepherd TG, Scinocca JF (2011) Stratospheric variability and tropospheric annular-mode timescales. Geophys Res Lett.  https://doi.org/10.1029/2011GL049304 Google Scholar
  42. Stockdale TN, Molteni F, Ferranti L (2015) Atmospheric initial conditions and the predictability of the Arctic Oscillation. Geophys Res Lett 42(4):1173–1179.  https://doi.org/10.1002/2014GL062681 CrossRefGoogle Scholar
  43. Sun J, Ahn JB (2015) Dynamical seasonal predictability of the Arctic oscillation using a CGCM. Int J Climatol 35(7):1342–1353.  https://doi.org/10.1002/joc.4060 CrossRefGoogle Scholar
  44. Thompson DW, Wallace JM (1998) The Arctic oscillation signature in the wintertime geopotential height and temperature fields. Geophys Res Lett 25(9):1297–1300.  https://doi.org/10.1029/98GL00950 CrossRefGoogle Scholar
  45. Thompson DW, Wallace JM (2001) Regional climate impacts of the Northern Hemisphere annular mode. Science 293(5527):85–89.  https://doi.org/10.1126/science.1058958 CrossRefGoogle Scholar
  46. Thompson DW, Baldwin MP, Wallace JM (2002) Stratospheric connection to Northern Hemisphere wintertime weather: implications for prediction. J Clim 15(12):1421–1428.  https://doi.org/10.1175/1520-0442(2002)015%3c1421:SCTNHW%3e2.0.CO;2 CrossRefGoogle Scholar
  47. Wang J, Ikeda M (2000) Arctic oscillation and Arctic sea-ice oscillation. Geophys Res Lett 27(9):1287–1290.  https://doi.org/10.1029/1999GL002389 CrossRefGoogle Scholar
  48. Wang L, Ting M, Kushner PJ (2017) A robust empirical seasonal prediction of winter NAO and surface climate. Sci Rep 7(1):279.  https://doi.org/10.1038/s41598-017-00353-y CrossRefGoogle Scholar
  49. Ye Z, Hsieh WW (2008) Enhancing predictability by increasing nonlinearity in ENSO and Lorenz systems. Nonlinear Processes Geophys 15:793–801.  https://doi.org/10.5194/npg-15-793-2008 CrossRefGoogle Scholar
  50. Yuan N, Fu Z, Mao J (2013) Different multi-fractal behaviors of diurnal temperature range over the north and the south of China. Theor Appl Climatol 112:673–682.  https://doi.org/10.1007/s00704-012-0762-3 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Lab for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of PhysicsPeking UniversityBeijingChina

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