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.
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
Bloomfield P (2004) Fourier analysis of time series: an introduction. Wiley, Mississauga, p 97
Cleveland RB, Cleveland WS, Terpenning I (1990) STL: a seasonal-trend decomposition procedure based on loess. J Off Stat 6(1):3
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
Daubechies I, Lu J, Wu HT (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal 30(2):243–261
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
Dwyer JG, Biasutti M, Sobel AH (2012) Projected changes in the seasonal cycle of surface temperature. J Clim 25(18):6359–6374
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
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
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
Iatsenko D, McClintock PV, Stefanovska A (2015) Nonlinear mode decomposition: a noise-robust, adaptive decomposition method. Phys Rev E 92(3):032916
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
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
Makse HA, Havlin S, Schwartz M, Stanley HE (1996) Method for generating long-range correlations for large systems. Phys Rev E 53(5):5445
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
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
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
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
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
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
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
Stine AR, Huybers P (2012) Changes in the seasonal cycle of temperature and atmospheric circulation. J Clim 25(21):7362–7380
Stine AR, Huybers P, Fung IY (2009) Changes in the phase of the annual cycle of surface temperature. Nature 457(7228):435–440
Thomson DJ (1995) The seasons, global temperature and precession. Science 268(5207):59
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
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
Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(01):1–41
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
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
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
Corresponding author
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-018-4432-8