International audienceThis study investigates the characteristic timescales of variability found in long-term time-series of daily means of estimates of surface solar irradiance (SSI). The study is performed at various levels to better understand the causes of variability in the SSI. First, the variability of the solar irradiance at the top of the atmosphere is scrutinized. Then, estimates of the SSI in cloud-free conditions as provided by the McClear model are dealt with, in order to reveal the influence of the clear atmosphere (aerosols, water vapour, etc.). Lastly, the role of clouds on variability is inferred by the analysis of in-situ measurements. A description of how the atmosphere affects SSI variability is thus obtained on a timescale basis. The analysis is also performed with estimates of the SSI provided by the satellite-derived HelioClim-3 database and by two numerical weather re-analyses: ERA-Interim and MERRA2. It is found that HelioClim-3 estimates render an accurate picture of the variability found in ground measurements, not only globally, but also with respect to individual characteristic timescales. On the contrary, the variability found in re-analyses correlates poorly with all scales of ground measurements variability

International audienceThe intrinsic temporal scales of the variability of the surface solar radiation are addressed by means of the empirical mode decomposition. High quality measurements of the solar radiation impinging on a horizontal plane at ground level, from different BSRN ground stations, are analysed. By first extracting all the embedded oscillations that share a common local timescale , followed by Hilbert spectral analysis, the characteristic scales of variability, along with the fluctuations in the intensity of the pyranometric signal, are revealed. It is shown that data from stations with different local climates share some common features, most notably a high-frequency plateau of variability whose amplitude is found to be modulated by the seasonal cycle. The study has possible implications on the modelling and the forecast of the surface solar radiation, at different local timescales

Abstract. This work deals with the temporal variability of daily means of the global broadband surface solar irradiance (SSI) impinging on a horizontal plane by studying a decennial time-series of high-quality measurements recorded at a BSRN ground station. Since the data have a non-linear and non-stationary character, two time-frequency-energy representations of signal processing are compared in their ability to resolve the temporal variability of the pyranometric signal. First, the continuous wavelet transform is used to construct the wavelet power spectrum of the data. Second, the adaptive, noise-assisted empirical mode decomposition is employed to extract the intrinsic mode functions of the signal, followed by Hilbert spectral analysis. In both spectral representations, the temporal variability of the SSI is portrayed having clearly distinguishable features: a plateau between scales of two days and two-three months that has decreasing power with increasing scale, a large spectral peak corresponding to the annual variability cycle, and a low power regime in between the previous two. It is shown that the data-driven, noise-assisted method yields a somewhat more sparse representation and that it is a suitable tool for inspecting the temporal variability of SSI measurements.