• Andrews, M., J. Knight, and L. Gray, 2015: A simulated lagged response of the North Atlantic Oscillation to the solar cycle over the period 1960–2009. Environ. Res. Lett., 10, 054022, https://doi.org/10.1088/1748-9326/10/5/054022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arfeuille, F. X., D. Weisenstein, H. Mack, E. Rozanov, T. Peter, and S. Brönnimann, 2014: Volcanic forcing for climate modeling: A new microphysics-based data set covering years 1600–present. Climate Past, 10, 359375, https://doi.org/10.5194/cp-10-359-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benestad, R., and G. Schmidt, 2009: Solar trends and global warming. J. Geophys. Res., 114, D14101, https://doi.org/10.1029/2008JD011639.

  • Bengtsson, L., S. Hagemann, and K. I. Hodges, 2004: Can climate trends be calculated from reanalysis data? J. Geophys. Res., 109, D11111, https://doi.org/10.1029/2004JD004536.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, M., and K.-K. Tung, 2012: Robustness of dynamical feedbacks from radiative forcing: 2% solar versus 2×CO2 experiments in an idealized GCM. J. Atmos. Sci., 69, 22562271, https://doi.org/10.1175/JAS-D-11-0117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camp, C. D., and K. K. Tung, 2007: Surface warming by the solar cycle as revealed by the composite mean difference projection. Geophys. Res. Lett., 34, L14703, https://doi.org/10.1029/2007GL030207.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, D., E. C. Kent, D. I. Berry, and P. Huybers, 2019: Correcting datasets leads to more homogeneous early-twentieth-century sea surface warming. Nature, 571, 393397, https://doi.org/10.1038/s41586-019-1349-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiodo, G., J. Oehrlein, L. M. Polvani, J. C. Fyfe, and A. K. Smith, 2019: Insignificant influence of the 11-year solar cycle on the North Atlantic Oscillation. Nat. Geosci., 12, 9499, https://doi.org/10.1038/s41561-018-0293-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coddington, O., J. Lean, P. Pilewskie, M. Snow, and D. Lindholm, 2016: A solar irradiance climate data record. Bull. Amer. Meteor. Soc., 97, 12651282, https://doi.org/10.1175/BAMS-D-14-00265.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Douglass, D. H., and B. D. Clader, 2002: Climate sensitivity of the Earth to solar irradiance. Geophys. Res. Lett., 29, 33-133-4, https://doi.org/10.1029/2002GL015345.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dudok de Wit, T., G. Kopp, C. Fröhlich, and M. Schöll, 2017: Methodology to create a new total solar irradiance record: Making a composite out of multiple data records. Geophys. Res. Lett., 44, 11961203, https://doi.org/10.1002/2016GL071866.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Folland, C. K., O. Boucher, A. Colman, and D. E. Parker, 2018: Causes of irregularities in trends of global mean surface temperature since the late 19th century. Sci. Adv., 4, eaao5297, https://doi.org/10.1126/sciadv.aao5297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fröhlich, C., and J. Lean, 1998: The sun’s total irradiance: Cycles, trends, and related climate change uncertainties since 1976. Geophys. Res. Lett., 25, 43774380, https://doi.org/10.1029/1998GL900157.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gray, L. J., and Coauthors, 2010: Solar influences on climate. Rev. Geophys., 48, RG4001, https://doi.org/10.1029/2009RG000282.

  • Hegerl, G. C., K. Hasselmann, U. Cubasch, J. F. Mitchell, E. Roeckner, R. Voss, and J. Waszkewitz, 1997: Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change. Climate Dyn., 13, 613634, https://doi.org/10.1007/s003820050186.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2017: Extended reconstructed sea surface temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 81798205, https://doi.org/10.1175/JCLI-D-16-0836.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaplan, A., M. A. Cane, Y. Kushnir, A. C. Clement, M. B. Blumenthal, and B. Rajagopalan, 1998: Analyses of global sea surface temperature 1856–1991. J. Geophys. Res., 103, 18 56718 589, https://doi.org/10.1029/97JC01736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lean, J., 2000: Evolution of the sun’s spectral irradiance since the Maunder Minimum. Geophys. Res. Lett., 27, 24252428, https://doi.org/10.1029/2000GL000043.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lean, J., and D. H. Rind, 2008: How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006. Geophys. Res. Lett., 35, L18701, https://doi.org/10.1029/2008GL034864.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lean, J., J. Beer, and R. Bradley, 1995: Reconstruction of solar irradiance since 1610: Implications for climate change. Geophys. Res. Lett., 22, 31953198, https://doi.org/10.1029/95GL03093.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MacMynowski, D. G., H.-J. Shin, and K. Caldeira, 2011: The frequency response of temperature and precipitation in a climate model. Geophys. Res. Lett., 38, L16711, https://doi.org/10.1029/2011GL048623.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matthes, K., and Coauthors, 2017: Solar forcing for CMIP6 (v3.2). Geosci. Model Dev., 10, 22472302, https://doi.org/10.5194/gmd-10-2247-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., J. M. Arblaster, K. Matthes, F. Sassi, and H. van Loon, 2009: Amplifying the Pacific climate system response to a small 11-year solar cycle forcing. Science, 325, 11141118, https://doi.org/10.1126/science.1172872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meinshausen, M., and Coauthors, 2011: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109, 213241, https://doi.org/10.1007/s10584-011-0156-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, R. L., and Coauthors, 2014: CMIP5 historical simulations (1850–2012) with GISS ModelE2. J. Adv. Model. Earth Syst., 6, 441478, https://doi.org/10.1002/2013MS000266.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Misios, S., and Coauthors, 2016: Solar signals in CMIP-5 simulations: Effects of atmosphere–ocean coupling. Quart. J. Roy. Meteor. Soc., 142, 928941, https://doi.org/10.1002/qj.2695.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J. Geophys. Res., 117, D08101, https://doi.org/10.1029/2011JD017187.

    • Search Google Scholar
    • Export Citation
  • Pittock, A. B., 1978: A critical look at long-term sun–weather relationships. Rev. Geophys., 16, 400420, https://doi.org/10.1029/RG016i003p00400.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Proistosescu, C., and P. J. Huybers, 2017: Slow climate mode reconciles historical and model-based estimates of climate sensitivity. Sci. Adv., 3, e1602821, https://doi.org/10.1126/sciadv.1602821.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sato, M., J. E. Hansen, M. P. McCormick, and J. B. Pollack, 1993: Stratospheric aerosol optical depths, 1850–1990. J. Geophys. Res., 98, 22 98722 994, https://doi.org/10.1029/93JD02553.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scafetta, N., and B. J. West, 2005: Estimated solar contribution to the global surface warming using the ACRIM TSI satellite composite. Geophys. Res. Lett., 32, L18713, https://doi.org/10.1029/2005GL023849.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, D. M., and Coauthors, 2020: North Atlantic climate far more predictable than models imply. Nature, 583, 796800, https://doi.org/10.1038/s41586-020-2525-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, M. J., and G. R. North, 1996: Detection of the climate response to the solar cycle. J. Atmos. Sci., 53, 25942608, https://doi.org/10.1175/1520-0469(1996)053<2594:DOTCRT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Svensmark, H., M. Enghoff, N. Shaviv, and J. Svensmark, 2017: Increased ionization supports growth of aerosols into cloud condensation nuclei. Nat. Commun., 8, 2199, https://doi.org/10.1038/s41467-017-02082-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Theiler, J., S. Eubank, A. Longtin, B. Galdrikian, and J. D. Farmer, 1992: Testing for nonlinearity in time series: The method of surrogate data. Physica D, 58, 7794, https://doi.org/10.1016/0167-2789(92)90102-S.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thomason, L. W., and Coauthors, 2018: A global space-based stratospheric aerosol climatology: 1979–2016. Earth Syst. Sci. Data, 10, 469492, https://doi.org/10.5194/essd-10-469-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tung, K. K., and C. D. Camp, 2008: Solar cycle warming at the Earth’s surface in NCEP and ERA-40 data: A linear discriminant analysis. J. Geophys. Res., 113, D05114, https://doi.org/10.1029/2007JD009164.

    • Search Google Scholar
    • Export Citation
  • Tung, K. K., J. Zhou, and C. D. Camp, 2008: Constraining model transient climate response using independent observations of solar-cycle forcing and response. Geophys. Res. Lett., 35, L17707, https://doi.org/10.1029/2008GL034240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y.-M., J. Lean, and N. Sheeley Jr., 2005: Modeling the sun’s magnetic field and irradiance since 1713. Astrophys. J., 625, 522538, https://doi.org/10.1086/429689.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. Academic Press, 704 pp.

  • Wolter, K., and M. S. Timlin, 1998: Measuring the strength of ENSO events: How does 1997/98 rank? Weather, 53, 315324, https://doi.org/10.1002/j.1477-8696.1998.tb06408.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 224 224 139
Full Text Views 45 45 24
PDF Downloads 58 58 26

Global Surface Temperature Response to 11-Yr Solar Cycle Forcing Consistent with General Circulation Model Results

View More View Less
  • 1 Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts
  • 2 Department of Earth and Climate Sciences, San Francisco State University, San Francisco, California
© Get Permissions
Restricted access

ABSTRACT

The 11-yr solar cycle is associated with a roughly 1 W m−2 trough-to-peak variation in total solar irradiance and is expected to produce a global temperature response. The sensitivity of this response is, however, contentious. Empirical best estimates of global surface temperature sensitivity to solar forcing range from 0.08 to 0.18 K (W m−2)−1. In comparison, best estimates from general circulation models forced by solar variability range between 0.03 and 0.07 K (W m−2)−1, prompting speculation that physical mechanisms not included in general circulation models may amplify responses to solar variability. Using a lagged multiple linear regression method, we find a sensitivity of global-average surface temperature ranging between 0.02 and 0.09 K (W m−2)−1, depending on which predictor and temperature datasets are used. On the basis of likelihood maximization, we give a best estimate of the sensitivity to solar variability of 0.05 K (W m−2)−1 (0.03–0.09 K; 95% confidence interval). Furthermore, through updating a widely used compositing approach to incorporate recent observations, we revise prior global temperature sensitivity best estimates of 0.12–0.18 K (W m−2)−1 downward to 0.07–0.10 K (W m−2)−1. The finding of a most likely global temperature response of 0.05 K (W m−2)−1 supports a relatively modest role for solar cycle variability in driving global surface temperature variations over the twentieth century and removes the need to invoke processes that amplify the response relative to that exhibited in general circulation models.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: T. Amdur, amdur@g.harvard.edu

ABSTRACT

The 11-yr solar cycle is associated with a roughly 1 W m−2 trough-to-peak variation in total solar irradiance and is expected to produce a global temperature response. The sensitivity of this response is, however, contentious. Empirical best estimates of global surface temperature sensitivity to solar forcing range from 0.08 to 0.18 K (W m−2)−1. In comparison, best estimates from general circulation models forced by solar variability range between 0.03 and 0.07 K (W m−2)−1, prompting speculation that physical mechanisms not included in general circulation models may amplify responses to solar variability. Using a lagged multiple linear regression method, we find a sensitivity of global-average surface temperature ranging between 0.02 and 0.09 K (W m−2)−1, depending on which predictor and temperature datasets are used. On the basis of likelihood maximization, we give a best estimate of the sensitivity to solar variability of 0.05 K (W m−2)−1 (0.03–0.09 K; 95% confidence interval). Furthermore, through updating a widely used compositing approach to incorporate recent observations, we revise prior global temperature sensitivity best estimates of 0.12–0.18 K (W m−2)−1 downward to 0.07–0.10 K (W m−2)−1. The finding of a most likely global temperature response of 0.05 K (W m−2)−1 supports a relatively modest role for solar cycle variability in driving global surface temperature variations over the twentieth century and removes the need to invoke processes that amplify the response relative to that exhibited in general circulation models.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: T. Amdur, amdur@g.harvard.edu
Save