Skip to main content
Log in

Comparison of methods for extracting annual cycle with changing amplitude in climate series

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

Extracting annual cycle properly from climate series is important in the study of annual cycle and anomaly series. However, the extracting approaches are various and may lead to inconsistent results. Since the real annual cycle is unknown in observed records, the reliability and applicability of them are hard to estimate. In this study, five popular decomposition methods used to extract annual cycle in climate series are evaluated through idealized numerical experiments for the first time; i.e., fitting sinusoids, complex demodulation, ensemble empirical mode decomposition (EEMD), nonlinear mode decomposition (NMD) and seasonal trend decomposition procedure based on loess (STL). Their performances are examined by comparing the extracted annual cycles and its amplitude with the preset one. The annual cycles are set with three different changing amplitudes: constant, linear increasing and nonlinearly varying; superposed with fluctuations of different long-term persistence (LTP) strength. Results indicate that (1) NMD performs best in depicting annual cycle and obtaining its amplitude change; (2) fitting sinusoids, complex demodulation and EEMD methods are more sensitive to LTP strength of superimposed fluctuations, which leads to over-fitted annual cycles and noisy amplitude changes, oppositely, the STL are less responsive to the variation of annual cycle; (3) when overall long-time trend of annual cycle change is the main concern, all of these methods performed well. However, over short time scales, the errors on account of noise and LTP are common in the first three methods and STL is too rough to give the details of amplitude change. Those results are also verified by applying them to observed records and the case with both amplitude and phase change.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Barbosa SM (2009) Changing seasonality in Europe’s air temperature. Eur Phys J Spec Top 174(1):81–89

    Article  Google Scholar 

  • Bloomfield P (2004) Fourier analysis of time series: an introduction. Wiley, Mississauga, p 97

    Book  Google Scholar 

  • Cleveland RB, Cleveland WS, Terpenning I (1990) STL: a seasonal-trend decomposition procedure based on loess. J Off Stat 6(1):3

    Google Scholar 

  • Cornes R, Jones P, Qian C (2017) Twentieth-century trends in the annual cycle of temperature across the Northern Hemisphere. J Clim 30(15):5755–5773

    Article  Google Scholar 

  • Daubechies I, Lu J, Wu HT (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal 30(2):243–261

    Article  Google Scholar 

  • Deng Q, Nian D, Fu Z (2018) The impact of inter-annual variability of annual cycle on long-term persistence of surface air temperature in long historical records. Clim Dyn 50(3–4):1091–1100

    Article  Google Scholar 

  • Dwyer JG, Biasutti M, Sobel AH (2012) Projected changes in the seasonal cycle of surface temperature. J Clim 25(18):6359–6374

    Article  Google Scholar 

  • Eliseev AV, Mokhov II (2003) Amplitude-phase characteristics of the annual cycle of surface air temperature in the Northern Hemisphere. Adv Atmos Sci 20(1):1–16

    Article  Google Scholar 

  • Graves T, Gramacy RB, Franzke CLE, Watkins NW (2015) Efficient Bayesian inference for natural time series using ARFIMA processes. Nonlinear Process Geophys 22(6):679

    Article  Google Scholar 

  • Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc R Soc A Math Phys 454:903–995

    Article  Google Scholar 

  • Iatsenko D, McClintock PV, Stefanovska A (2015) Nonlinear mode decomposition: a noise-robust, adaptive decomposition method. Phys Rev E 92(3):032916

    Article  Google Scholar 

  • Koscielny-Bunde E, Bunde A, Havlin S, Roman HE, Goldreich Y, Schellnhuber HJ (1998a) Indication of a universal persistence law governing atmospheric variability. Phys Rev Lett 81(3):729

    Article  Google Scholar 

  • Koscielny-Bunde E, Eduardo Roman H, Bunde A, Havlin S, Schellnhuber HJ (1998b) Long-range power-law correlations in local daily temperature fluctuations. Philos Mag B 77(5):1331–1340

    Article  Google Scholar 

  • Makse HA, Havlin S, Schwartz M, Stanley HE (1996) Method for generating long-range correlations for large systems. Phys Rev E 53(5):5445

    Article  Google Scholar 

  • Mann ME, Park J (1996) Greenhouse warming and changes in the seasonal cycle of temperature: model versus observations. Geophys Res Lett 23(10):1111–1114

    Article  Google Scholar 

  • Paluš M, Novotná D, Tichavský P (2005) Shifts of seasons at the European mid-latitudes: natural fluctuations correlated with the North Atlantic Oscillation. Geophys Res Lett 32(12):161–179

    Article  Google Scholar 

  • Qian C, Zhang X (2015) Human influences on changes in the temperature seasonality in mid- to high-latitude land areas. J Clim 28(15):5908–5921

    Article  Google Scholar 

  • Qian C, Wu Z, Fu C, Zhou T (2010) On multi-timescale variability of temperature in China in modulated annual cycle reference frame. Adv Atmos Sci 27(5):1169–1182. https://doi.org/10.1007/s00376-009-9121-4

    Article  Google Scholar 

  • Qian C, Fu C, Wu Z (2011a) Changes in the amplitude of the temperature annual cycle in China and their implication for climate change research. J Clim 24(20):5292–5302

    Article  Google Scholar 

  • Qian C, Fu C, Wu Z, Yan Z (2011b) The role of changes in the annual cycle in earlier onset of climatic spring in northern China. Adv Atmos Sci 28(2):284–296

    Article  Google Scholar 

  • Regonda SK, Rajagopalan B, Clark M, Pitlick J (2005) Seasonal cycle shifts in hydroclimatology over the western United States. J Clim 18(2):372–384

    Article  Google Scholar 

  • Stine AR, Huybers P (2012) Changes in the seasonal cycle of temperature and atmospheric circulation. J Clim 25(21):7362–7380

    Article  Google Scholar 

  • Stine AR, Huybers P, Fung IY (2009) Changes in the phase of the annual cycle of surface temperature. Nature 457(7228):435–440

    Article  Google Scholar 

  • Thomson DJ (1995) The seasons, global temperature and precession. Science 268(5207):59

    Article  Google Scholar 

  • Vecchio A, Carbone V (2010) Amplitude-frequency fluctuations of the seasonal cycle, temperature anomalies, and long-range persistence of climate records. Phys Rev E 82(6):066101

    Article  Google Scholar 

  • Vecchio A, Capparelli V, Carbone V (2010) The complex dynamics of the seasonal component of USA’s surface temperature. Atmos Chem Phys 10(19):9657–9665

    Article  Google Scholar 

  • Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(01):1–41

    Article  Google Scholar 

  • Wu Z, Huang NE, Long SR, Peng CK (2007) On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc Natl Acad Sci 104(38):14889–14894

    Article  Google Scholar 

  • Wu Z, Schneider EK, Kirtman BP, Sarachik ES, Huang NE, Tucker CJ (2008) The modulated annual cycle: an alternative reference frame for climate anomalies. Clim Dyn 31:823–841

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge support from National Natural Science Foundation of China through Grant no. 41675049. We thank Dr. Cheng Qian for his suggestions on the operation of EEMD method and Prof. Christian Franzke’s reminder of a practical method STL. The valuable comments and suggestions from the anonymous reviewers are appreciated and helpful in further improving the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zuntao Fu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, Q., Fu, Z. Comparison of methods for extracting annual cycle with changing amplitude in climate series. Clim Dyn 52, 5059–5070 (2019). https://doi.org/10.1007/s00382-018-4432-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-018-4432-8

Keywords

Navigation