Articles | Volume 25, issue 1
Nonlin. Processes Geophys., 25, 19–37, 2018
https://doi.org/10.5194/npg-25-19-2018
Nonlin. Processes Geophys., 25, 19–37, 2018
https://doi.org/10.5194/npg-25-19-2018
Research article
26 Jan 2018
Research article | 26 Jan 2018

On the intrinsic timescales of temporal variability in measurements of the surface solar radiation

Marc Bengulescu et al.

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Do modelled or satellite-based estimates of surface solar irradiance accurately describe its temporal variability?
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Cited articles

Alberti, T., Lepreti, F., Vecchio, A., Bevacqua, E., Capparelli, V., and Carbone, V.: Natural periodicities and Northern Hemisphere–Southern Hemisphere connection of fast temperature changes during the last glacial period: EPICA and NGRIP revisited, Clim. Past, 10, 1751–1762, https://doi.org/10.5194/cp-10-1751-2014, 2014.
Alberti, T., Piersanti, M., Vecchio, A., De Michelis, P., Lepreti, F., Carbone, V., and Primavera, L.: Identification of the different magnetic field contributions during a geomagnetic storm in magnetospheric and ground observations, Ann. Geophys., 34, 1069–1084, https://doi.org/10.5194/angeo-34-1069-2016, 2016.
Bazilevskaya, G., Broomhall, A.-M., Elsworth, Y., and Nakariakov, V.: A combined analysis of the observational aspects of the quasi-biennial oscillation in solar magnetic activity, in: The Solar Activity Cycle, Springer, 186, 359–386, https://doi.org/10.1007/978-1-4939-2584-1_12, 2015.
Beer, J., Vonmoos, M., and Muscheler, R.: Solar variability over the past several millennia, Space Sci. Rev., 125, 67–79, https://doi.org/10.1007/s11214-006-9047-4, 2006.
Bengulescu, M., Blanc, P., Boilley, A., and Wald, L.: Do modelled or satellite-based estimates of surface solar irradiance accurately describe its temporal variability?, Adv. Sci. Res., 14, 35–48, https://doi.org/10.5194/asr-14-35-2017, 2017.
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Short summary
We employ the Hilbert–Huang transform to study the temporal variability in time series of daily means of the surface solar irradiance (SSI) at different locations around the world. The data have a significant spectral peak corresponding to the yearly variability cycle and feature quasi-stochastic high-frequency "weather noise", irrespective of the geographical location or of the local climate. Our findings can improve models for estimating SSI from satellite images or forecasts of the SSI.