Abstract
Contributions of the insolation variations together with different natural and anthropogenic factors to the trends of the surface air temperatures at different latitudes of the Northern and Southern Hemispheres on various temporal horizons are estimated from climate data since the nineteenth century with the use of empirical autoregressive models. As the natural climate variability modes, we take into account Atlantic Multidecadal Oscillation, El-Nino/Southern Oscillation, Interdecadal Pacific Oscillation, Pacific Decadal Oscillation, and Antarctic Oscillation. According to the obtained results, the contributions of the insolation variations to the trends of the surface air temperature are statistically insignificant on the time intervals under study, i.e. from a decade and longer. Taking into account the insolation variations in the autoregressive models weakly alters the estimates of the contributions of the greenhouse gases and natural variability modes to the temperature trends: the changes are not more than several per cent. Numerically, the estimated contributions of the insolation variations can considerably exceed the respective contributions of the natural variability modes both on short (less than two decades) and long (longer than a century) time intervals.
Similar content being viewed by others
References
Allen, M. R., Gillett, N. P., Kettleborough, J. A., Hegerl, G., Schnur, R., Stott, P. A., Boer, G., Covey, C., Delworth, T. L., Jones, G. S., & Mitchell, J. F. (2006). Quantifying anthropogenic influence on recent near-surface temperature change. Surveys in Geophysics, 27, 491–544.
Allen, M. R., & Stott, P. A. (2003). Estimating signal amplitudes in optimal fingerprinting, part I: Theory. Climate Dynamics, 21, 477–491.
Allen, M. R., & Tett, S. F. B. (1999). Checking for model consistency in optimal fingerprinting. Climate Dynamics, 15, 419–434.
Anet, J. G., Rozanov, E. V., Muthers, S., Peter, T., Brönnimann, S., Arfeuille, F., Beer, J., Shapiro, A. I., Raible, C. C., Steinhilber, F., & Schmutz, W. K. (2013). Impact of a potential 21st century “grand solar minimum” on surface temperatures and stratospheric ozone. Geophysical Research Letters, 40, 4420–4425.
Arsenovic, P., Rozanov, E., Anet, J., Stenke, A., Schmutz, W., & Peter, T. (2018). Implications of potential future grand solar minimum for ozone layer and climate. Atmospheric and Chemical Physics, 18, 3469–3483.
Attanasio, A., & Triacca, U. (2011). Detecting human influence on climate using neural networks based Granger causality. Theoretical and Applied Climatology, 103(1–2), 103–107.
Bindoff, N. L., Stott, P. A., AchutaRao, K. M., Allen, M. R., Gillett, N., Gutzler, D., Hansingo, K., Hegerl, G., Hu, Y., Jain, S., II., Overland, M. J., Perlwitz, J., Sebbari, R., & Zhang, X. (2013). In: Climate Change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change (pp. 867–952). Cambridge University Press.
Enfield, D. B., Mestas-Nunez, A. M., & Trimble, P. J. (2001). The Atlantic Multidecadal Oscillation and its relationship to rainfall and river flows in the continental U.S. Geophysical Research Letters, 28, 2077–2080.
Feulner, G., & Rahmstorf, S. (2010). On the effect of a new grand minimum of solar activity on the future climate on Earth. Geophysical Research Letters, 37(5), L05707.
Foster, G., & Rahmstorf, S. (2011). Global temperature evolution 1979–2010. Environmental Research Letters, 6, 044022.
GISS, (2018). Forcings in climate models. national aeronautics and space administration, goddard institute for space studies. (https://data.giss.nasa.gov/modelforce/Miller_et_2014/Fi_Miller_et_al14_upd.txt).
Gong, D., & Wang, S. (1999). Definition of Antarctic oscillation index. Geophysical Research Letters, 26(4), 459–462.
Granger, C. W. J. (1963). Economic processes involving feedback. Information and Control, 6, 28.
Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.
Gruza, G. V., & Rankova, E. Y. A. (2012). Observed and expected climate changes over Russia: Surface and temperature. RIHMI-WDC. (in Russian).
Hasselmann, K. (1993). Optimal fingerprints for the detection of time-dependent climate change. Journal of Climate, 6, 1957–1971.
Hasselmann, K. (1997). Multi-pattern fingerprint method for detection and attribution of climate change. Climate Dynamics, 13, 601–611.
Hegerl, G. C., von Storch, H., Hasselmann, K., Santer, B. D., Cubasch, U., & Jones, P. D. (1996). Detecting greenhouse-gas-induced climate change with an optimal fingerprint method. Journal of Climate, 9, 2281–2306.
Hegerl, G. C., & Zwiers, F. (2011). Use of models in detection and attribution of climate change. Wires Climate Change, 2, 570–591.
Henley, B. J., Gergis, J., Karoly, D. J., Power, S. B., Kennedy, J., & Folland, C. K. (2015). A tripole index for the interdecadal pacific oscillation. Climate Dynamics, 45(11–12), 3077–3090.
Huang, B., Banzon, V. F., Freeman, E., Lawrimore, J., Liu, W., Peterson, T. C., Smith, T. M., Thorne, P. W., Woodruff, S. D., & Zhang, H.-M. (2014). Extended reconstructed sea surface temperature version 4 (ERSST.v4): Part I. Upgrades and intercomparisons. Journal of Climate, 28, 911–930.
Huang, B., Banzon, V.F., Freeman, E., Lawrimore, J., Liu, W., Peterson, T.C., Smith, T.M., Thorne, P.W., Woodruff, S.D., and Zhang, H.-M. (2015). Extended reconstructed sea surface temperature (ERSST), Version 4. NOAA National Centers for Environmental Information. https://doi.org/10.7289/V5KD1VVF (ftp://ftp.ncdc.noaa.gov/pub/data/noaaglobaltemp/operational/timeseries/).
Huntingford, C., Stott, P. A., Allen, M. R., & Lambert, F. H. (2006). Incorporating model uncertainty into attribution of observed temperature change. Geophysical Research Letters, 33, L05710.
Imbers, J., Lopez, A., Huntingford, C., & Allen, M. R. (2013). Testing the robustness of the anthropogenic climate change detection statements using different empirical models. Journal of Geophysical Research Atmospheres, 118, 3192–3199.
Imbers, J., Lopez, A., Huntingford, C., & Allen, M. R. (2014). Sensitivity of climate change detection and attribution to the characterization of internal climate variability. Journal of Climate, 27, 3477–3491.
Jia, L., & DelSole, T. (2012). Optimal determination of time-varying climate change signals. Journal of Climate, 25, 7122–7137.
Jones, G. S., Lockwood, M., & Stott, P. A. (2012). What influence will future solar activity changes over the 21st century have on projected global near-surface temperature changes? Journal of Geophysical Research, 117, D05103. https://doi.org/10.1029/2011JD017013
Kajtar, J. B., Collins, M., Frankcombe, L. M., England, M. H., Osborn, T. J., & Juniper, M. (2019). Global mean surface temperature response to large-scale patterns of variability in observations and CMIP5. Geophysical Research Letters, 46, 2232–2241.
Kaufmann, R., Kauppi, H., Mann, M., & Stock, J. (2011). Reconciling anthropogenic climate change with observed temperature 1998–2008. Proceedings of the National Academy of Sciences, 108, 11790–11793.
Kaufmann, R., Kauppi, H., & Stock, J. (2006). Emissions, concentrations, & temperature: A time series analysis. Climatic Change, 77, 249–278.
Kaufmann, R. K., & Stern, D. I. (1997). Evidence for human influence on climate from hemispheric temperature relations. Nature, 388, 39–44.
Kodra, E., Chatterjee, S., & Ganguly, A. R. (2011). Exploring Granger causality between global average observed time series of carbon dioxide and temperature. Theoretical and Applied Climatology, 104(3–4), 325–335.
Kopp, G., & Lean, J. (2011). A new, lower value of total solar irradiance: Evidence and climate significance. Geophysical Research Letters, 38, L01706.
Lean, J. L., & Rind, D. H. (2008). How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006. Geophysical Research Letters, 35, L18701.
Lean, J. L., & Rind, D. H. (2009). How will Earth’s surface temperature change in future decades? Geophysical Research Letters, 36, L15708.
Liu, W., Huang, B., Thorne, P. W., Banzon, V. F., Zhang, H. M., Freeman, E., Lawrimore, J., Peterson, T. C., Smith, T. M., & Woodruff, S. D. (2014). Extended reconstructed Sea surface temperature version 4 (ERSST.v4): Part II. Parametric and structural uncertainty estimations. Journal of Climate, 28, 931–951.
Lockwood, M. (2008). Recent changes in solar outputs and the global mean surface temperature. III. Analysis of contributions to global mean air surface temperature rise. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, 464(2094), 1387–1404.
Loehle, C., & Scafetta, N. (2011). Climate change attribution using empirical decomposition of climatic data. Open Atmospheric Science Journal, 5, 74–86.
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., & Zhou B. (eds.) (2021). Climate Change 2021: the physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. Cambridge University Press.
Maycock, A. C., Ineson, S., Gray, L. J., Scaife, A. A., Anstey, J. A., Lockwood, M., Butchart, N., Hardiman, S. C., Mitchell, D. M., & Osprey, S. M. (2015). Possible impacts of a future grand solar minimum on climate: Stratospheric and global circulation changes. Journal of Geophysical Research Atmospheres. https://doi.org/10.1002/2014JD022022
McBride, L. A., Hope, A. P., Canty, T. P., Bennett, B. F., Tribett, W. R., & Salawitch, R. J. (2021). Comparison of CMIP6 historical climate simulations and future projected warming to an empirical model of global climate. Earth System Dynamics, 12, 545–579.
Meehl, G. A., Arblaster, J. M., & Marsh, D. R. (2013). Could a future “grand Solar Minimum” like Maunder Minimum stop global warming? Geophysical Research Letters, 40(1789), 1793.
Miller, R. L., Schmidt, G. A., Nazarenko, L. S., Tausnev, N., Bauer, S. E., DelGenio, A. D., Kelley, M., Lo, K. K., Ruedy, R., Shindell, D. T., & Aleinov, I. (2014). CMIP5 historical simulations (1850–2012) with GISS ModelE2. Journal of Advances in Modeling Earth Systems, 6(2), 441–477.
Mokhov, I. I., Bezverkhnii, V. A., Eliseev, A. V., & Karpenko, A. A. (2006). Model estimations of global climate changes in the 21st century with account for different variation scenarios of solar activity. Doklady Earth Sciences, 411(8), 1327–1330.
Mokhov, I. I., Bezverkhnii, V. A., Eliseev, A. V., & Karpenko, A. A. (2008). Model estimations of possible climatic changes in 21st century at different scenarios of solar and volcanic activities and anthropogenic impact. Cosmic Research, 46(4), 354–357.
Mokhov, I. I., & Smirnov, D. A. (2008). Diagnostics of a cause–effect relation between solar activity and the Earth’s global surface temperature. Izvestiya, Atmospheric and Oceanic Physics, 44(3), 263–272.
Mokhov, I. I., & Smirnov, D. A. (2009). Empirical estimates of the influence of natural and anthropogenic factors on the global surface temperature. Doklady Earth Sciences, 427(1), 798–803.
Mokhov, I. I., & Smirnov, D. A. (2016). Relation between the variations in the global surface temperature, El Nino/La Nina phenomena, and the Atlantic Multidecadal Oscillation. Doklady Earth Sciences, 467(2), 384–388.
Mokhov, I. I., & Smirnov, D. A. (2016). The Trivariate Seasonal Analysis of Couplings between El Nino, North Atlantic Oscillation, and Indian Monsoon. Russian Meteorology and Hydrology, 41(11–12), 798–807.
Mokhov, I. I., & Smirnov, D. A. (2017). Estimates of mutual influences between sea surface temperature variations in tropical Pacific, Atlantic, and Indian oceans from long-period data series. Izvestiya, Atmospheric and Oceanic Physics, 53(6), 613–623.
Mokhov, I. I., & Smirnov, D. A. (2018). Estimating the contributions of the Atlantic Multidecadal Oscillation and variations in the atmospheric concentration of greenhouse gases to surface air temperature trends from observations. Doklady Earth Sciences, 480(1), 602–606.
Mokhov, I. I., & Smirnov, D. A. (2018). Contribution of greenhouse gas radiative forcingand Atlantic Multidecadal Oscillation to surface air temperature trends. Russian Meteorology and Hydrology, 43(9), 557–564.
Mokhov, I. I., & Smirnov, D. A. (2022). Contributions to surface air temperature trends estimated from climate time series: Medium-term causalities. Chaos, 32, 063128. https://doi.org/10.1063/5.0088042
Mokhov, I. I., Smirnov, D. A., & Karpenko, A. A. (2012). Assessments of the relationship of changes of the global surface air temperature with different natural and anthropogenic factors based on observations. Doklady Earth Sciences, 443(1), 381–387.
Mokhov, I. I., Smirnov, D. A., Nakonechny, P. I., Kozlenko, S. S., Seleznev, E. P., & Kurths, J. (2011). Alternating mutual influence of El-Nino/Southern Oscillation and Indian monsoon. Geophysical Research Letters. https://doi.org/10.1029/2010GL045932
Mukhin, D., Gavrilov, A., Seleznev, A., & Buyanova, M. (2021). An atmospheric signal lowering the spring predictability barrier in statistical ENSO forecasts. Geophysical Research Letters, 48(6), e2020GL091287. https://doi.org/10.1029/2020GL091287
NCEI. (2022). National Oceanic and Atmospheric Administration, National Centers for Environmental Information. (https://www.ncei.noaa.gov/pub/data/cmb/ersst/v5/index/ersst.v5.pdo.dat).
PSL, (2022). National Oceanic and Atmospheric Administration, Physical Sciences Laboratory. (AMO:http://www.esrl.noaa.gov/psd/data/correlation//amon.us.long.data; ENSO:https://psl.noaa.gov/gcos_wgsp/Timeseries/Data/nino34.long.anom.data; IPO:https://psl.noaa.gov/data/timeseries/IPOTPI/tpi.timeseries.hadisst1.1.data; AAO:https://psl.noaa.gov/data/20thC_Rean/timeseries/monthly/SAM/sam.20crv2.long.data).
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., & Kaplan, A. (2003). Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research, 108(D14), 4407.
Ribes, A., Azais, J.-M., & Planton, S. (2009). Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate. Climate Dynamics, 33, 707–722.
Ribes, A., & Terray, L. (2013). Application of regularised optimal fingerprinting to attribution. Part II: Application to global near-surface temperature. Climate Dynamics, 41, 2837–2853.
Santer, B. D., Wigley, T. M., Doutriaux, C., Boyle, J. S., Hansen, J. E., Jones, P. D., Meehl, G. A., Roeckner, E., Sengupta, S., & Taylor, K. E. (2001). Accounting for the effects of volcanoes and ENSO in comparisons of modeled and observed temperature trends. Journal of Geophysical Research, 106(D22), 28033–28059.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464.
Seleznev, A., Mukhin, D., Gavrilov, A., Loskutov, E., & Feigin, A. (2019). Bayesian framework for simulation of dynamical systems from multidimensional data using recurrent neural network. Chaos, 29(12), 123115.
Smirnov, D. A. (2014). Quantifying causal couplings via dynamical effects: A unifying perspective. Physical Review E, 90(6), 062921.
Smirnov, D. A. (2022). Generative formalism of causality quantifiers for processes. Physical Review E, 105(3), 034209.
Smirnov, D. A., & Mokhov, I. I. (2009). From Granger causality to “long-term causality”: Application to climatic data. Physical Review E, 80(1), 016208.
Smirnov, D. A., & Mokhov, I. I. (2015). Relating Granger causality to long-term causal effects. Physical Review E, 92(4), 042138.
Song, X., Lubin, D., & Zhang, G. J. (2010). Increased greenhouse gases enhance regional climate response to a Maunder Minimum. Geophysical Research Letters, 37, L01703. https://doi.org/10.1029/2009GL041290
Stern, D. I., & Kaufmann, R. K. (2014). Anthropogenic and natural causes of climate change. Climatic Change, 122, 257–269.
Stips, A., Macias, D., Coughlan, C., Garcia-Gorriz, E., & San Liang, X. (2016). On the causal structure between CO2 and global temperature. Scientific Reports, 6, 21691.
Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., & Midgley, P. M. (Eds.) (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. Cambridge University Press.
Stolpe, M. B., Medhaug, I., & Knutti, R. (2017). Contribution of Atlantic and Pacific multidecadal variability to twentieth-century temperature changes. Journal of Climate, 30, 6279–6295.
Tol, R. S. J., & de Vos, A. F. (1993). Greenhouse statistics–time series analysis. Theoretical and Applied Climatology, 48, 63–74.
Triacca, U., Attanasio, A., & Pasini, A. (2013). Anthropogenic global warming hypothesis: Testing its robustness by Granger causality analysis. Environmetrics, 24, 260–268.
Tung, K. K., & Camp, C. D. (2008). Solar cycle warming at the Earth’s surface in NCEP and ERA-40 data: A linear discriminant analysis. Journal of Geophysical Research, 113, D05114.
Verdes, P. F. (2007). Global warming is driven by anthropogenic emissions: A time series analysis approach. Physical Review Letters, 99, 048501.
Zhou, J., & Tung, K. K. (2013). Deducing multidecadal anthropogenic global warming trends using multiple regression analysis. Journal of the Atmospheric Sciences, 70, 3–8.
Funding
This study was supported by the Russian Science Foundation project No. 19–17-00240. The results obtained within the framework of the RSF-NSFC project 23-47-00104 were also used
Author information
Authors and Affiliations
Contributions
Both authors contributed to the study design and data analysis. Both authors wrote the main manuscript text, reviewed the manuscript and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare they have no financial interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mokhov, I.I., Smirnov, D.A. Contribution of Solar Irradiance Variations to Surface Air Temperature Trends at Different Latitudes Estimated from Long-term Data. Pure Appl. Geophys. 180, 3053–3070 (2023). https://doi.org/10.1007/s00024-023-03317-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00024-023-03317-8