Decadal oscillations of the Northern Hemisphere average temperature within current global warming

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

The average temperatures of the Northern Hemisphere for surface air, the lower troposphere and the upper layer of the ocean from 0 to 100 meters are considered. It turned out that all these time-series are similar to each other in that they consist of two components: a warming trend and fluctuations on an approximately ten-year scale superimposed on this trend. It is hypothesized that this quasi-decadal temperature variability is associated with the El Niño–Southern Oscillation. After removing trends from the series under study, their autocorrelation functions demonstrate an exponential decrease and subsequent fluctuations near zero with shifts of approximately 5 years or more, which theoretically makes it possible to predict their changes with a lead-time of 1–4 years. An analysis of the results of the “Historical” experiment of 58 CMIP6 models confirmed the conclusions drawn and showed that the quasi-decadal variability of the average surface air temperature of the Northern Hemisphere is significantly influenced by large volcanic eruptions. Results from the “piControl” experiment of 50 CMIP6 models demonstrated the ability to predict changes in average Northern Hemisphere temperatures several years into the future based on natural interannual climate variability, the main component of which is the El Niño–Southern Oscillation.

Full Text

Restricted Access

About the authors

N. V. Vakulenko

Shirshov Institute of Oceanology, Russian Academy of Sciences

Email: iserykh@ocean.ru
Russian Federation, Moscow

I. V. Serykh

Shirshov Institute of Oceanology, Russian Academy of Sciences

Author for correspondence.
Email: iserykh@ocean.ru
Russian Federation, Moscow

D. M. Sonechkin

Shirshov Institute of Oceanology, Russian Academy of Sciences

Email: iserykh@ocean.ru
Russian Federation, Moscow

References

  1. Арушанов М.Л. Причины изменения климата Земли как результат космического воздействия, развеивающее миф об антропогенном глобальном потеплении // German International J. Modern Sci. 2023. № 53. С. 4–14. https://doi.org/ 10.5281/zenodo.7795979
  2. Бышев В.И., Нейман В.Г., Романов Ю.А. и др. О статистической значимости и климатической роли Глобальной атмосферной осцилляции // Океанология. 2016. Т. 56. № 2. С. 179–185. https://doi.org/10.7868/S0030157416020039
  3. Вакуленко Н.В., Котляков В.М., Монин А.С., Сонечкин Д.М. Особенности календаря ледниковых циклов позднего плейстоцена // Изв. РАН. Физика атмосферы и океана. 2007. Т. 43. № 6. С. 773–782.
  4. Вакуленко Н.В., Нигматулин Р.И., Сонечкин Д.М. К вопросу о глобальном изменении климата // Метеорология и гидрология. 2015. № 9. С. 89–97.
  5. Вакуленко Н.В., Сонечкин Д.М. Свидетельство скорого окончания современного межледниковья // Докл. АН. 2013. Т. 452. № 1. С. 92–95. https://doi.org/10.7868/S0869565213260198
  6. Володин Е.М. О механизме колебания климата в Арктике с периодом около 15 лет по данным модели климата ИВМ РАН // Изв. РАН. Физика атмосферы и океана. 2020. Т. 56. № 2. С. 139–149. https://doi.org/10.31857/S0002351520020145
  7. Логинов В.Ф. Космические факторы климатических изменений // ГНУ “Институт природопользования НАН Беларуси”. Минск: 2020. 168 с.
  8. Нигматулин Р.И., Вакуленко Н.В., Сонечкин Д.М. Глобальное потепление в реальности и в климатических моделях // Турбулентность, динамика атмосферы и климата. Труды междунар. конф. памяти А.М. Обухова. 13–16 мая 2013 / Ред. Голицын Г.С. и др., М.: ГЕОС, 2014. С. 255–263.
  9. Полонский А.Б. Изменения климата: мифы и реальность // Министерство науки и высшего образования РФ РАН. Севастополь: Институт природно-технических систем, 2020. 229 c. https://doi.org/10.33075/978-5-6044196-5-6
  10. Семенов В.А. Колебания современного климата, вызванные обратными связями в системе атмосфера – арктические льды – океан // Фундаментальная и прикладная климатология. 2015. Т. 1. С. 232–248.
  11. Семенов В.А. Современные исследования климата Арктики: прогресс, смена концепций, актуальные задачи // Изв. РАН. Физика атмосферы и океана. 2021. Т. 57. № 1. С. 21–33. https://doi.org/10.31857/S0002351521010119
  12. Сонечкин Д.М., Даценко Н.М., Иващенко Н.Н. Оценка тренда глобального потепления с помощью вейвлетного анализа // Изв. РАН. Физика атмосферы и океана. 1997. Т. 33. № 2. С. 184–194.
  13. Шерстюков Б.Г. Асинхронные связи колебаний климата атмосферы и океана с солнечной активностью // Геомагнетизм и аэрономия. 2022. Т. 62. № 5. С. 671–680. https://doi.org/10.31857/S0016794022050121
  14. Шерстюков Б.Г. Глобальное потепление и его возможные причины // Гидрометеорология и экология. 2023. № 70. С. 7–37. https://doi.org/10.33933/2713-3001-2023-70-7-37
  15. Bast J.L. Seven Theories of Climate Change // SPPI Reprint Series. Chicago: The Heartland Institute, 2010. 29 p.
  16. Enfield D.B., Mestas-Nuñez A.M. Multiscale variabilities in global sea surface temperatures and their relationships with tropospheric // J. Climate. 1999. V. 12. P. 2719–2733. https://doi.org/10.1175/1520-0442(1999)012<2719: MVIGSS>2.0.CO;2
  17. Eyring V., Bony S., Meehl G.A. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization // Geosci. Model Dev. 2016. V. 9. P. 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
  18. Gregory J.M., Andrews T., Ceppi P. et al. How accurately can the climate sensitivity to CO2 be estimated from historical climate change? // Clim. Dyn. 2020. V. 54. P. 129–157. https://doi.org/10.1007/s00382-019-04991-y
  19. Gregory J.M., Andrews T., Good P. et al. Small global-mean cooling due to volcanic radiative forcing // Clim. Dyn. 2016. V. 47. P. 3979–3991. https://doi.org/10.1007/s00382-016-3055-1
  20. Intergovernmental Panel on Climate Change (IPCC). Climate change 2013: The Physical Science Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change / T.F. Stocker, et al. (eds.). Cambridge Univ. Press. 2013. 1535 p.
  21. Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change / Masson-Delmotte et al. (eds.) Cambridge Univ. Press. 2021. In press. https://doi.org/10.1017/9781009157896
  22. Kalicinsky C., Koppmann R. Multi-decadal oscillations of surface temperatures and the impact on temperature increases. // Sci. Rep. 2022. V. 12. 19895. https://doi.org/10.1038/s41598-022-24448-3
  23. Latif M. Chapter 25 – The Ocean's role in modeling and predicting decadal climate variations // International Geophysics. 2013. V. 103. P. 645–665. https://doi.org/10.1016/B978-0-12-391851-2.00025-8
  24. Locarnini R.A., Mishonov A.V, Baranova O.K. et al. World Ocean Atlas 2018, V. 1: Temperature // A. Mishonov Technical Ed.; NOAA Atlas NESDIS81. 2018. 52 p.
  25. Mazza D., Canuto E. Evidence of solar 11-year cycle from Sea Surface Temperature (SST) // Academia Letters. 2021. 3023. https://doi.org/10.20935/AL3023.
  26. Mears C.A., Wentz F.J. A satellite-derived lower tropospheric atmospheric temperature dataset using an optimized adjustment for diurnal effects // J. Clim. 2017. V. 30. № 19. P. 7695–7718. https://doi.org/10.1175/jcli-d-16–0768.1
  27. Meehl G.A., Goddard L., Murphy J. Decadal prediction: Can it be skillful? // Bull. Am. Meteorol. Soc. 2009. V. 90. № 10. Р. 1467–1485. https://doi.org/10.1175/2009BAMS2778.1
  28. Morice C.P., Kennedy J.J., Rayner N.A. et al. An updated assessment of near-surface temperature change from 1850: the HadCRUT5 dataset // J. Geophys. Res.: Atmospheres. 2020. https://doi.org/10.1029/2019JD032361
  29. Nnamchi H.C., Farneti R., Keenlyside N.S. et al. Pan-Atlantic decadal climate oscillation linked to ocean circulation // Commun. Earth Environ. 2023. V. 4(121). https://doi.org/10.1038/s43247-023-00781-x
  30. Ogurtsov M. Decadal and bi-decadal periodicities in temperature of Southern Scandinavia: Manifestations of natural variability or climatic response to solar cycles? // Atmosphere. 2021. V. 12. P. 676. https://doi.org/10.3390/atmos12060676
  31. Scafetta N. Empirical analysis of the solar contribution to global mean air surface temperature change // J. Atmos. Sol. Terr. Phys. 2009. V. 71. P. 1916–1923. https://doi.org/10.1016/j.jastp.2009.07.007
  32. Scafetta. N. Climate change and its causes: A discussion about some key issues // La Chimica e l’Industria 1. 2010. P. 70–75.
  33. Serykh I.V., Sonechkin D.M. Nonchaotic and globally synchronized short-term climatic variations and their origin // Theoretical and Applied Climatology. 2019. V. 137. № 3–4. P. 2639–2656. https://doi.org/10.1007/s00704-018-02761-0
  34. Serykh I.V., Sonechkin D.M. El Niño forecasting based on the global atmospheric oscillation // Int. J. Climatol. 2021. V. 41. P. 3781–3792. https://doi.org/10.1002/joc.6488
  35. Serykh I.V., Sonechkin D.M. Global El Niño–Southern Oscillation Teleconnections in CMIP6 Models // Atmosphere. 2024. V. 15 № 4 (500). Р. 500. https://doi.org/10.3390/atmos15040500
  36. Tyrrell N.L., Dommenget D., Frauen C. et al. The influence of global sea surface temperature variability on the large-scale land surface temperature // Clim. Dyn. 2015. V. 44. P. 2159–2176. https://doi.org/10.1007/s00382-014-2332-0
  37. Welch P.D. The use of Fast Fourier Transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms // IEEE Transactions on Audio and Electroacoustics. 1967. AU-15 (2). P. 70–73.
  38. Zhoua W., Li J., Yan Z. Progress and future prospects of decadal prediction and data assimilation: A review // Atmospheric and Oceanic Sci. Lett. 2024. V. 17 (100441). https://doi.org/10.1016/j.aosl.2023.100441

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. a) Variations of anomalies of the average annual temperature values of the Northern hemisphere with components of the general trend: 1 – for the near-surface air layer for 1955-2023. (quadratic trend), 2 for the lower troposphere in 1979-2023 (linear trend), 3 for the upper 100–meter ocean layer in 1955-2023. (quadratic trend). b) The same after three years of smoothing and removing trends for all three series.

Download (264KB)
Indexing metadata
3. 2. Autocorrelation functions based on three–year moving average annual temperatures of the Northern hemisphere after trend deduction: 1 – for the near–surface air layer for 1955-2023, 2 - for the lower troposphere for 1979-2023, 3 - for the upper 100-meter ocean layer for 1955-2023.

Download (144KB)
Indexing metadata
4. Fig. 3. Changes in the average annual air temperature anomalies near the surface of the Northern Hemisphere in 1851-2014 according to the results of the Historical experiment for 58 CMIP6 models: 1 – average, 2 – maximum, 3 – minimum values.

Download (526KB)
Indexing metadata
5. 4. The average values for 58 CMIP6 models, modulo the autocorrelation coefficients with shifts from 0 to 30 years, of the average annual air temperature anomalies near the surface of the Northern Hemisphere according to the results of the Historical experiment for 1851-2014: 1 – initial values, 2 – with the removal of the linear trend, 3 – with the removal of the quadratic trend.

Download (132KB)
Indexing metadata
6. 5. Estimates of energy spectra with maximum resolution of normalized time series of average annual air temperature anomalies near the surface of the Northern Hemisphere based on the results of the Historical experiment for 58 CMIP6 models for 1851-2014: 1 – average, 2 – maximum, 3 – minimum values.

Download (745KB)
Indexing metadata
7. Fig. 6. The average for 58 CMIP6 models is a wavelet diagram of normalized time series of average annual air temperature anomalies near the surface of the Northern Hemisphere based on the results of the Historical experiment for 1851-2014.

Download (441KB)
Indexing metadata
8. Fig. 7. Mutual correlation functions of the annual values of the ONI ONI index and average air temperature anomalies near the surface of the Northern Hemisphere with shifts from -10 to + 10 years according to the results of the piControl experiment for 50 CMIP6 models for the number of model years indicated in Table 2: 1 – average, 2 – maximum, 3 – minimum values.

Download (178KB)
Indexing metadata

Copyright (c) 2025 Russian Academy of Sciences