Abstract
We propose using multiple regression to create a statistical model for describing climate change under the influence of specified climate-forming factors. This model provides not only estimates of the temporal evolution of global temperature, but also a set of corresponding confidence intervals with a high level of statistical significance (probability). Eliminating the linear trend of climatic temperature series (CRUTEM) and atmospheric CO2 concentration allows an objective quantitative assessment of the impact of natural factors on climate change. The global CRUTEM temperature responds quasi-synchronously to the fluctuations in the average surface temperature of the North Atlantic (AMO index); however, to changes in solar activity (Wolf numbers), it does so with a delay of approximately 15 years. The linear trend of increasing CO2 concentration in the atmosphere explains almost all the interannual variability and reflects the linear trend of global temperature, but covers only part of its interannual variability.
Similar content being viewed by others
REFERENCES
Broecker, W.S., Peteet, D.M., and Rind, D., Does the ocean–atmosphere system have more than one stable mode of operation?, Nature, 1985, vol. 315, pp. 21–26.
Brohan, P., Kennedy, J.J., Harris, I., Tett, S.F.B., and Jones, P.D., Uncertainty estimates in regional and global observed temperature changes: A new dataset from 1850, J. Geophys. Res., 2006, vol. 111, D12106. https://doi.org/10.1029/2005JD006548
Christy, J.R., Spencer, R.W., and Braswell, W.D., MSU tropospheric temperatures: Dataset construction and radiosonde comparisons, J. Atmos. Oceanic Technol., 1999, vol. 17, no. 9, pp. 1153–1170. https://doi.org/10.1175/1520-0426
Christy, J.R., Herman, B., and Pielke, R., Sr., et al., What do observational datasets say about modeled tropospheric temperature trends since 1979?, Remote Sens., 2010, vol. 2, no. 9, pp. 2148–2169.
Courtillot, V., Le Mouël, J.-L., Kossobokov, V., et al., Multi-decadal trends of global surface temperature: A broken line with alternating ~30 yr linear segments?, Atmos. Clim. Sci., 2013, vol. 3, no. 3, pp. 364–371.
Dickey, D.A. and Fuller, W.A., Distribution of the estimators for autoregressive time series with a unit root, J. Am. Stat. Assoc., 1979, vol. 74, pp. 427–431.
Douglass, D.H., Christy, J.R., Pearson, B.D., and Singer, S.F., A comparison of tropical temperature trends with model predictions, Int. J. Climatol., 2008, vol. 28, no. 13, pp. 1693–1701. https://doi.org/10.1002/joc.1651
Enfield, D.B., Mestas-Nunez, A.M., and Trimble, P.J., The Atlantic multidecadal oscillation and it’s relation to rainfall and river flows in the continental U.S., Geophys. Res. Lett., 2001, vol. 28, pp. 2077–2080.
Estrada, F., Perron, P., Gay-Garcia, C., and Martinez-Lopez, B., A time-series analysis of the 20th century climate simulations produced for the IPCC’s fourth assessment report, PLoS One, 2013, vol. 8, no. 3, p. e60017. https://doi.org/10.1371/journal.pone.0060017
Global Average Temperature Anomaly (HadCRUT4 dataset; monthly time coverage from 1856 to 2013. http:// www.cru.uea.ac.uk/cru/data/temperature/.
Förster, E. and Rönz, B., Methoden der Korrelations- und Regressionsanalyse, Berlin: Verlag die Wirtschaft, 1979; Moscow: Finansy i statistika, 1983.
Frolov, I.E., Gudkovich, Z.M., Karklin, V.P., and Smolyanitskii, V.M., Changes in the Arctic and Antarctic climate as a result from the action of natural causes, Probl. Arkt. Antarkt., 2010, no. 2, pp. 52–61.
Idso, S.B., CO2 and climate: Where is the water vapor feedback?, Theor. Appl. Climatol., 1982, vol. 31, no. 4, pp. 325–329.
IPCC,2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley, Eds., Cambridge: Cambridge University Press. http://www.ipcc.ch/report/ar5/index.shtml.
IPCC,2018: Intergovernmental Panel on Climate Change. http://www.ipcc.ch/report/sr15/.
Lean, J., Beer, J., and Bradley, R., Reconstruction of solar irradiance since 1610: Implications for climate change, Geophys. Res. Lett., 1995, vol. 22, no. 23, pp. 3195–3198.
Lean, J. and Rind, D., Climate forcing by changing solar radiation, J. Clim., 1998, vol. 11, pp. 3069–3094.
Lean, J., Evolution of the Sun’s spectral irradiance since the maunder minimum, Geophys. Res. Lett., 2000, vol. 27, no. 16, pp. 2425–2428.
Lorenz, E.N., The predictability of a flow which possesses many scales of motion, Tellus, 1969, vol. 21, p. 19.
Palmer, M.D., Harris, G.R., and Gregory, J.M., Extending CMIP5 projections of global mean temperature change and sea level rise due to thermal expansion using a physically-based emulator, Environ. Res. Lett., 2018, vol. 13, p. 084003.
Parker, A., Why global warming went missing since the year 2000, Nonlinear Eng., 2013, vol. 2, nos. 3–4, pp. 129–135.
Pokrovsky, O.M., Determination of monthly average precipitation in Russia from remote sensing data, Issled. Zemli Kosmosa, 2004, no. 3, pp. 22–31.
Pokrovsky, O.M., Ocean surface temperature as an indicator of natural climate variations at moderate and high latitudes of the Northern Hemisphere, Issled. Zemli Kosmosa, 2005a, no. 6, pp. 3–13.
Pokrovsky, O.M., Changes in ocean surface temperature in the North Atlantic and variations in European climate, Issled. Zemli Kosmosa, 2005b, no. 4, pp. 24–34.
Pokrovsky, O.M., Using remote sensing data on ocean surface temperature, ice cover and atmosphere in the Arctic to study climate change trends in Russia, Issled. Zemli Kosmosa, 2007, no. 3, pp. 20–33.
Pokrovsky, O.M., A coherency between the North Atlantic temperature nonlinear trend, the eastern Arctic ice extent drift and change in the atmospheric circulation regimes over the northern Eurasia, Influence of Climate Change and Sub-Arctic Conditions on the Changing Arctic, Nihoul, J.C.J. and Kostianoy, A.G., Eds., Springer, 2009, pp. 25–36. http://www.springerlink.com/index/ n7111278w0866276.pdf.
Pokrovsky, O.M., Analysis of climate change factors according to remote sensing and contact measurement data], Issled. Zemli Kosmosa, 2010a, no. 5, pp. 11–24.
Pokrovsky, O.M., Relationship between the Atlantic Multidecadal Oscillation and the ice extent in Kara Sea, CLIVAR Exchanges, 2010b, vol. 46, pp. 8–9. http:// eprints.soton.ac.uk/67029/01/Exch_46.pdf.
Rayner, N.A., Brohan, P., Parker, D.E., Folland, C.K., Kennedy, J.J., Vanicek, M., Ansell, T., and Tett, S.F.B., Improved analyses of changes and uncertainties in marine temperature measured in situ since the mid-nineteenth century: The HadSSt2 dataset, J. Clim., 2006, vol. 19, pp. 446–469.
Spencer, R.W. and Christy, J.R., Precise monitoring of global temperature trends from satellites, Science, 1990, vol. 247, pp. 1558–1562. https://doi.org/10.1126/science.247.4950.1558
Sunspot Index. Monthly sunspot numbers. Time Interval: Monthly, Time Coverage: 1749 to May 2018. SIDC, RWC Belgium, World Data Center for the Sunspot Index, Royal Observatory of Belgium, 2018. http://sidc. oma.be/html/SIDCpub.html.
Tett, S.F.B., Stott, P.A., Allen, M.R., Ingram, W.J., and Mitchell, J.F.B., Causes of twentieth century temperature change near the Earth’s surface, Nature, 1999, vol. 399, pp. 569–572.
Wallace, J.M., Zhang, J., and Bajuk, L., Interpretation of inter decadal trends in northern hemisphere surface air temperature, J. Clim., 1996, vol. 9, no. 2, pp. 249–259.
Author information
Authors and Affiliations
Corresponding author
Additional information
Translated by M. Chubarova
Rights and permissions
About this article
Cite this article
Pokrovsky, O.M. Quantitative Estimates of the Impact of the Most Important Factors on Global Climate Change over the Past 150 Years. Izv. Atmos. Ocean. Phys. 55, 1182–1188 (2019). https://doi.org/10.1134/S0001433819090354
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0001433819090354