Izvestiya of Saratov University.

Earth Sciences

ISSN 1819-7663 (Print)
ISSN 2542-1921 (Online)


Full text:
(downloads: 33)
Language: 
Russian
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Article type: 
Article
UDC: 
551.5
EDN: 
LPKOEH

Application of time series models for forecasting the global temperature anomalies

Autors: 
Bogdanov Mikhail Borisovich, Saratov State University
Morozova Svetlana Vladimirovna, Saratov State University
Alimpieva Mariya A., Saratov State University
Abstract: 

Spectral analysis of the time series for average annual values of the globally averaged surface temperature anomaly shows the presence of harmonics of the lunar nodal cycle with a period of 18.6 years, which can be used to predict the values of the series. Three models of the series were considered: autoregression AR(p), combined model of autoregression – integrated moving average ARIMA(p,d,q) and artificial neural network. It is shown that the ARIMA(4,1,4) model gives the best results for predicting the global temperature anomaly for three years.

Reference: 
  1. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change / eds. V. P. Masson-Delmotte [et al.]. URL: https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/ (дата обращения: 25.07.22).
  2. Дымников В. П., Лыкосов В. Н., Володин Е. М. Моделирование климата и его изменений : современные проблемы // Вестник РАН. 2012. Т. 82, № 3. С. 227–336.
  3. Переведенцев Ю. П., Вильфанд Р. М., Шанталинский К. М., Гурьянов В. В., Николаев А. А., Исмагилов Н. В. Мониторинг и прогнозирование климатической изменчивости на территории Приволжского федерального округа // Гидрометеорологические исследования и прогнозы. 2019. № 1 (371). С. 67–94.
  4. Шерстюков Б. Г. Колебательная система климата, резонансы, дальние связи, прогнозы. Обнинск : ФГБУ «ВНИИГМИ-МЦД», 2021. 222 с.
  5. Morice C. P., Kennedy J. J., Rayner N. A., Jones P. D. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset // Journal of Geophysical Research. 2012. Vol. 117. D08101. https://doi.org/10.1029/2011JD017187
  6. Ghil M., Allen M. R., Dettinger M. D., Ide K., Kondrashov D., Mann M.E., Robertson A. W., Saunders A., Tian Y., Varadi F., Yiou P. Advanced spectral methods for climatic time series // Rev. Geophys. 2002. Vol. 40. 3.1–3.41. https://doi.org/10.1029/2000RG000092
  7. Keeling C. D., Whorf T. P. Possible forcing of global temperature by the oceanic tides // Proc. National Acad. Sci. USA. 1997. Vol. 94. P. 8321–8328.
  8. Scafetta N. Reconstruction of the Interannual to Millennial Scale Patterns of the Global Surface Temperature // Atmosphere. 2021. Vol. 12. P. 147.
  9. Munk W. H., Wunsch C. Abyssal recipes II: Energetics of tidal and wind mixing // Deep-Sea Res. 1998. Vol. 45. P. 1977–2010.
  10. Ray R. D. Decadal Climate Variability: Is There a Tidal Connection? // Journal of Climate. 2007. Vol. 20. P. 3542– 3560.
  11. Press W. H., Teukolsky S. A., Vetterling W. T., Flannery B. P. Numerical Recipes in Fortran 77. The Art of Scientific Computing. Cambridge : Press Syndicate of the University of Cambridge, 1997. 1004 p.
  12. Бокс Д., Дженкинс Г. Анализ временных рядов. Прогноз и управление : в 2 выпусках. Вып. 1. Москва : Мир, 1974. 406 с.
  13. Shumway R. H., Stoffer D. S. Time Series Analysis and Its Applications. New York : Springer Verlag, 2000. 549 p.
  14. Аггарвал Ч. Нейронные сети и глубокое обучение : учебный курс. Санкт-Петербург : ООО «Диалектика», 2020. 752 с.
  15. Tran T. T. K., Bateni S. M., Ki S. J., Vosoughifar H. A Review of Neural Networks for Air Temperature Forecasting // Water. 2021. Vol. 13. 1294 p. https://doi.org/10.3390/w13091294
  16. Lai Y., Dzombak D. A. Use of the Autoregressive Integrated Moving Average (ARIMA) Model to Forecast Near-Term Regional Temperature and Precipitation // Weather and Forecasting. 2020. Vol. 35, iss. 3. P. 959–976.
Received: 
15.08.2022
Accepted: 
01.09.2022
Published: 
23.12.2022