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Seasonal Changes in Central England Temperatures

Author

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  • Tommaso Proietti

    (University of Rome “Tor Vergata” and Creates)

  • Eric Hillebrand

    (Aarhus University and CREATES)

Abstract

The aim of this paper is to assess how climate change is reflected in the variation of the seasonal patterns of the monthly Central England Temperature time series between 1772 and 2013. In particular, we model changes in the amplitude and phase of the seasonal cycle. Starting from the seminal work by Thomson (“The Seasons, Global Temperature and Precession”, Science, 7 April 1995, vol 268, p. 59–68), a number of studies have documented a shift in the phase of the annual cycle implying an earlier onset of the spring season at various European locations. A significant reduction in the amplitude of the seasonal cycle is also documented. The literature so far has concentrated on the measurement of this phenomenon by various methods, among which complex demodulation and wavelet decompositions are prominent. We offer new insight by considering a model that allows for seasonally varying deterministic and stochastic trends, as well as seasonally varying autocorrelation and residual variances. The model can be summarized as containing a permanent and a transitory component, where global warming is captured in the permanent component, on which the seasons load differentially. The phase of the seasonal cycle, on the other hand, seems to follow Earth’s precession in a stable manner, and the reported fluctuations are identified as transitory.

Suggested Citation

  • Tommaso Proietti & Eric Hillebrand, 2015. "Seasonal Changes in Central England Temperatures," CREATES Research Papers 2015-28, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2015-28
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    References listed on IDEAS

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    Cited by:

    1. He, Changli & Kang, Jian & Teräsvirta, Timo & Zhang, Shuhua, 2019. "The shifting seasonal mean autoregressive model and seasonality in the Central England monthly temperature series, 1772–2016," Econometrics and Statistics, Elsevier, vol. 12(C), pages 1-24.
    2. Changli He & Jian Kang & Timo Teräsvirta & Shuhua Zhang, 2019. "Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model," CREATES Research Papers 2019-18, Department of Economics and Business Economics, Aarhus University.
    3. Francesco Battaglia & Domenico Cucina & Manuel Rizzo, 2020. "Detection and estimation of additive outliers in seasonal time series," Computational Statistics, Springer, vol. 35(3), pages 1393-1409, September.
    4. Federico Maddanu & Tommaso Proietti, 2023. "Trends in atmospheric ethane," Climatic Change, Springer, vol. 176(5), pages 1-23, May.
    5. González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Gadea Rivas, María Dolores & Gonzalo, Jesús, 2020. "Trends in distributional characteristics: Existence of global warming," Journal of Econometrics, Elsevier, vol. 214(1), pages 153-174.
    7. Caterina Schiavoni & Siem Jan Koopman & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2021. "Time-varying state correlations in state space models and their estimation via indirect inference," Tinbergen Institute Discussion Papers 21-020/III, Tinbergen Institute.
    8. Bent Jesper Christensen & Nabanita Datta Gupta & Paolo Santucci de Magistris, 2021. "Measuring the impact of clean energy production on CO2 abatement in Denmark: Upper bound estimation and forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 118-149, January.
    9. Harvey, Andrew & Ito, Ryoko, 2020. "Modeling time series when some observations are zero," Journal of Econometrics, Elsevier, vol. 214(1), pages 33-45.
    10. Andrew Harvey & Ryoko Ito, 2017. "Modeling time series with zero observations," Economics Papers 2017-W01, Economics Group, Nuffield College, University of Oxford.
    11. Meng, Xiaochun & Taylor, James W., 2022. "Comparing probabilistic forecasts of the daily minimum and maximum temperature," International Journal of Forecasting, Elsevier, vol. 38(1), pages 267-281.

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    More about this item

    Keywords

    Global Warming; Seasonal Models; Structural Change; Amplitude and Phase Shifts;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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