Skip to main content

Is long-term climate memory important in temperature/precipitation predictions over China?

A Correction to this article was published on 24 September 2018

This article has been updated

Abstract

Long-term climate memory is ubiquitous in climate systems, but its contribution to climate prediction has not been assessed systematically. We used an integral fractional statistical model (FISM) to quantify climate memories in different variables over China. Their contributions to climate prediction were estimated using explained variances. We found different climate memory effects for different variables in different regions. The contribution of climate memory to climate variability is stronger in temperature than in precipitation records. For temperatures (including both air temperature and land temperature), the average variance explained by climate memory is around 3∼4%. For precipitation, the average explained variance was 0.6%. The low values for explained variances indicate that, on average, the contributions of climate memory to temperature and precipitation predictions are small. But in specific regions, higher climate memory effects may occur. For precipitation, climate memory can contribute 3% of the variance in southeast China. For temperature, climate memory can explain ≥ 10% of the variance in northeast and southwest China, which is not low and should be considered in prediction. Therefore, for more accurate climate prediction, we suggest first determining the contribution of climate memory. For variables or regions with strong climate memory effects, a scheme considering climate memory effects may help improve future climate predictions.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Change history

  • 24 September 2018

    The authors note that: “Fig. 5 in the published paper appeared incorrectly. The correct figure and the figure caption are provided below. The main message and the interpretation of our paper remain unaffected by this correction.”

References

  1. Abry P, Veitch D (1998) Wavelet analysis of long-range-dependent traffic. IEEE Trans Inf Theory 44 (1):2–15. https://doi.org/10.1109/18.650984

    Article  Google Scholar 

  2. Anderson BT, Gianotti JS, Guido S, Jason F (2016) Dominant time scales of potentially predictable precipitation variations across the continental United States. J Clim 29(24):8881–8897. https://doi.org/10.1175/JCLI-D-15-0635.1

    Article  Google Scholar 

  3. Arneodo A, Bacry E, Graves PV, Muzy JF (1995) Characterizing long-range correlations in DNA sequences from wavelet analysis. Phys Rev Lett 74(16):3293–3296. https://doi.org/10.1103/PhysRevLett.74.3293

    Article  Google Scholar 

  4. Blender R, Fraedrich K (2006) Long-term memory of the hydrological cycle and river runoffs in China in a high-resolution climate model. Int J Climatol 26(12):1547–1565. https://doi.org/10.1002/joc.1325

    Article  Google Scholar 

  5. Boer GJ (2000) A study of atmosphere-ocean predictability on long time scales. Climate Dyn 16(6):469–477. https://doi.org/10.1007/s003820050340

    Article  Google Scholar 

  6. Boer GJ (2004) Long time-scale potential predictability in an ensemble of coupled climate models. Climate Dyn 23(1):29–44. https://doi.org/10.1007/s00382-004-0419-8

    Article  Google Scholar 

  7. Bogachev MI, Bunde A (2011) On the predictability of extreme events in records with linear and nonlinear long-range memory: Efficiency and noise robustness. Physica A 390(12):2240–2250. https://doi.org/10.1016/j.physa.2011.02.024

    Article  Google Scholar 

  8. Bunde A, Eichner JF, Kantelhardt JW, Havlin S (2005) Long-term memory: a natural mechanism for the clustering of extreme events and anomalous residual times in climate records. Phys Rev Lett 94(4):048701. https://doi.org/10.1103/PhysRevLett.94.048701

    Article  Google Scholar 

  9. Chen X, lin GX, Fu Z (2007) Long-range correlations in daily relative humidity fluctuations: A new index to characterize the climate regions over China. Geophys Res Lett 34(7):L07804. https://doi.org/10.1029/2006GL027755

    Article  Google Scholar 

  10. Doblas-Reyes FJ, García-serrano J, Lienert F, Biescas AP, Rodrigues LRL (2013) Seasonal climate predictability and forecasting: status and prospects. WIREs Clim Change 4(4):245–268. https://doi.org/10.1002/wcc.217

    Article  Google Scholar 

  11. Feng T, Fu Z, Deng X, Mao J (2009) A brief description to different multi-fractal behaviors of daily wind speed records over China. Phys Lett A 373(45):4134–4141. https://doi.org/10.1016/j.physleta.2009.09.032

    Article  Google Scholar 

  12. Fraedrich K, Blender R (2003) Scaling of atmosphere and ocean temperature correlations in observations and climate models. Phys Rev Lett 90(10):108501. https://doi.org/10.1103/PhysRevLett.90.108501

    Article  Google Scholar 

  13. Franzke C, Woollings T (2011) On the persistence and predictability properties of North Atlantic climate variability. J Climate 24(2):466–472. https://doi.org/10.1175/2010JCLI3739.1

    Article  Google Scholar 

  14. Franzke C, Osprey S, Davini P, Watkins N (2015) A dynamical systems explanation of the Hurst effect and atmospheric low-frequency variability. Sci Rep 5:9068. https://doi.org/10.1038/srep09068

    Article  Google Scholar 

  15. Govindan RB, Vyushin D, Bunde A, Brenner S, Havlin S, Schellnhuber HJ (2002) Global climate models violate scaling of the observed atmospheric variability. Phys Rev Lett 89(2):028501. https://doi.org/10.1103/PhysRevLett.89.028501

    Article  Google Scholar 

  16. Hasselmann K (1976) Stochastic climate models Part I Theory. Tellus 28(6):473–485. https://doi.org/10.1111/j.2153-3490.1976.tb00696.x

    Article  Google Scholar 

  17. He W, Zhao S (2018) Assessment of the quality of NCEP-2 and CFSR reanalysis daily temperature in China based on long-range correlation. Climate Dyn 50(1-2):493–505. https://doi.org/10.1007/s00382-017-3622-0

    Article  Google Scholar 

  18. Hurst HE (1951) Long-term storage capacity of reservoirs, trans. Am Soc Civil Eng 116:770–808

    Google Scholar 

  19. Jiang L, Li N, Fu Z, Zhang J (2015) Long-range correlation behaviors for the 0-cm average ground surface temperature and average air temperature over China. Theor Appl Climatol 119(1-2):25–31. https://doi.org/10.1007/s00704-013-1080-0

    Article  Google Scholar 

  20. Jiang L, Li N, Zhao X (2017) Scaling behaviors of precipitation over China. Theor Appl Climatol 128 (1-2):63–70. https://doi.org/10.1007/s00704-015-1689-2

    Article  Google Scholar 

  21. Kantelhardt JW, Koscielny-Bunde E, Rego HHA, Havlin S, Bunde A (2001) Detecting long-range correlations with detrended fluctuation analysis. Physica A 295(3-4):441–454. https://doi.org/10.1016/S0378-4371(01)00144-3

    Article  Google Scholar 

  22. Kantelhardt JW, Koscielny-Bunde E, Rybski D, Braun P, Bunde A, Havlin S (2006) Long-term persistence and multifractality of precipitation and river runoff records. J Geophys Res 111(01):D01106. https://doi.org/10.1029/2005JD005881

    Article  Google Scholar 

  23. Király A, Bartos I, Jánosi IM (2006) Correlation properties of daily temperature anomalies over land. Tellus A 58(5):593–600. https://doi.org/10.1111/j.1600-0870.2006.00195.x

    Article  Google Scholar 

  24. Koscielny-Bunde E, Bunde A, Havlin S, Roman HE, Goldreich Y, Schellnhuber HJ (1998) Indication of a universal persistence law governing atmospheric variability. Phys Rev Lett 81(3):729–732. https://doi.org/10.1103/PhysRevLett.81.729

    Article  Google Scholar 

  25. Kurnaz ML (2004) Application of detrended fluctuation analysis to monthly average of the maximum daily temperatures to resolve different climates. Fractals 12(04):365–373. https://doi.org/10.1142/s0218348x04002665

    Article  Google Scholar 

  26. Lennartz S, Bunde A (2009) Trend evaluation in records with long-term memory: application to global warming. Geophys Res Lett 36(16):L16706. https://doi.org/10.1029/2009GL039516

    Article  Google Scholar 

  27. Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–141. https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2

  28. Lovejoy S, Schertzer D (2012). In: Sharma AS, Bunde A, Baker D, Dimri VP (eds) Extreme events and natural hazards: the complexity perspective, low frequency weather and the emergence of the climate. AGU monographs, Washington, pp 231–254. https://doi.org/10.1029/2011GM001087

  29. Ludescher J, Bunde A, Franzke CL, Schellnhuber HJ (2016) Long-term persistence enhances uncertainty about anthropogenic warming of Antarctica. Climate Dyn 46(1-2):263–271. https://doi.org/10.1007/s00382-015-2582-5

    Article  Google Scholar 

  30. Malamud BD, Turcottr DL (1999). In: Dmowska R, Saltzman B (eds) Advances in geophysics: long range persistence in geophysical time series, self-affine time series: I. generation and analyses. Academic Press, San Diego, pp 1–87. https://doi.org/10.1016/S0065-2687(08)60293-9

  31. Mandelbrot BB, Van Ness JW (1968) Fractional Brownian motions, fractional noises and applications. SIAM Review 10(4):422–437. https://doi.org/10.1137/1010093

    Article  Google Scholar 

  32. Massh M, Kantz H (2016) Confidence intervals for time averages in the presence of long-range correlations, a case study on Earth surface temperature anomalies. Geophys Res Lett 43(17):9243–9249. https://doi.org/10.1002/2016GL069555

    Article  Google Scholar 

  33. Monselesan DP, O’Kane TJ, Risbey JS, Church J (2015) Internal climate memory in observations and models. Geophys Res Lett 42(4):1232–1242. https://doi.org/10.1002/2014GL062765

    Article  Google Scholar 

  34. Pattantyús-Ábrahám M, Király A, Jánosi IM (2004) Nonuniversal atmospheric persistence: different scaling of daily minimum and maximum temperatures. Phys Rev E 69(2):021110. https://doi.org/10.1103/physreve.69.021110

    Article  Google Scholar 

  35. Peng C-K, Buldyrev SV, Havlin S, Simon M, Stanley HE, Ary LG (1994) Mosaic organization of DNA nucleotides. Phys Rev E 49(2):1685–1689. https://doi.org/10.1103/PhysRevE.49.1685

    Article  Google Scholar 

  36. Rybski D, Bunde A, von Storch H (2008) Long-term memory in 1000-year simulated temperature records. J Geophys Res 113(2):D02106. https://doi.org/10.1029/2007JD008568

    Article  Google Scholar 

  37. Talkner P, Weber RO (2000) Power spectrum and detrended fluctuation analysis: application to daily temperatures. Phys Rev E 62(1):150–160. https://doi.org/10.1103/PhysRevE.62.150

    Article  Google Scholar 

  38. Turcotte D (1997) Fractals and chaos in geology and geophysics, 2nd edn. Cambridge University Press, Cambridge

    Book  Google Scholar 

  39. Varotsos C, Kirk-Davidoff D (2006) Long-memory processes in ozone and temperature variations at the region 60os-60on. Atmos Chem Phys 6(12):4093–4100. https://doi.org/10.5194/acp-6-4093-2006

    Article  Google Scholar 

  40. Vyushin D, Zhidkov I, Havlin S, Bunde A, Brenner S (2004) Volcanic forcing improves atmosphere-ocean coupled general circulation model scaling performance. Geophys Res Lett 31(10):L10206. https://doi.org/10.1029/2004GL019499

    Article  Google Scholar 

  41. Vyushin DI, Fioletov VE, Shepherd TG (2007) Impact of long-range correlations on trend detection in total ozone. J Geophys Res 112(D14):D14307. https://doi.org/10.1029/2006JD008168

    Article  Google Scholar 

  42. Vyushin DI, Kushner PJ (2009) Power-law and long-memory characteristics of the atmospheric general circulation. J Clim 22(11):2890–2904. https://doi.org/10.1175/2008jcli2528.1

    Article  Google Scholar 

  43. Weber RO, Talkner P (2001) Spectra and correlations of climate data from days to decades. J Geophys Res 106(D17):20131–20144. https://doi.org/10.1029/2001jd000548

    Article  Google Scholar 

  44. Yuan N, Fu Z, Mao J (2010) Different scaling behaviors in daily temperature records over China. Physica A 389(19):4087–4095. https://doi.org/10.1016/j.physa.2010.05.026

    Article  Google Scholar 

  45. Yuan N, Fu Z, Liu S (2013) Long-term memory in climate variability: a new look based on fractional integral techniques. J Geophys Res 118(23):12962–12969. https://doi.org/10.1002/2013JD020776

    Article  Google Scholar 

  46. Yuan N, Fu Z, Liu S (2014) Extracting climate memory using fractional integrated statistical model: a new perspective on climate prediction. Sci Rep 4:6577. https://doi.org/10.1038/srep06577

    Article  Google Scholar 

  47. Yuan N, Fu Z (2014) Century-scale intensity modulation of large-scale variability in long historical temperature records. J Clim 27(4):1742–1750. https://doi.org/10.1175/JCLI-D-13-00349.1

    Article  Google Scholar 

  48. Yuan N, Ding M, Huang Y, Fu Z, Xoplaki E, Luterbacher J (2015) On the Long-term climate memory in the surface air temperature records over Antarctica: a nonnegligible factor for trend evaluation. J Clim 28(15):5922–5934. https://doi.org/10.1175/JCLI-D-14-00733.1

    Article  Google Scholar 

  49. Zhu X, Fraedrich K, Liu Z, Blender R (2010) A demonstration of long-term memory and climate predictability. J Clim 23(18):5021–5029. https://doi.org/10.1175/2010jcli3370.1

    Article  Google Scholar 

Download references

Acknowledgements

The homogenized air temperature and precipitation data used in this research are provided by the Information Center of China Meteorological Administration. The land temperature data are obtained from the China Meteorological Data Sharing Service System (http://data.cma.cn). We thank LetPub for its linguistic assistance during the preparation of this manuscript.

Funding

This study is supported by the National Key R&D Program of China (2016YFA0600404 and 2016YFA0601504), the National Natural Science Foundation of China (No. 41675088, No. 41705041, and No. 41675068), and the CAS Pioneer Hundred Talents Program.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Naiming Yuan.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(DOCX 2.62 MB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xie, F., Yuan, N., Qi, Y. et al. Is long-term climate memory important in temperature/precipitation predictions over China?. Theor Appl Climatol 137, 459–466 (2019). https://doi.org/10.1007/s00704-018-2608-0

Download citation

Keywords

  • Long-term memory
  • Climate memory effects
  • Integral fractional statistical model
  • Climate prediction