Data Fusion of Total Solar Irradiance Composite Time Series Using 41 Years of Satellite Measurements

Research output: Contribution to journalArticleResearchpeer review

Authors

  • J. P. Montillet
  • W. Finsterle
  • G. Kermarrec
  • R. Sikonja
  • M. Haberreiter
  • W. Schmutz
  • T. Dudok de Wit

External Research Organisations

  • Physikalisch-Meteorologisches Observatorium World Radiation Center (PMOD/WRC)
  • ETH Zurich
  • Universite d'Orleans
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Details

Original languageEnglish
Article numbere2021JD036146
JournalJournal of Geophysical Research: Atmospheres
Volume127
Issue number13
Early online date24 Jun 2022
Publication statusPublished - 2 Jul 2022

Abstract

Since the late 1970s, successive satellite missions have been monitoring the sun's activity and recording the total solar irradiance (TSI). Some of these measurements have lasted for more than a decade. In order to obtain a seamless record whose duration exceeds that of the individual instruments, the time series have to be merged. Climate models can be better validated using such long TSI time series which can also help to provide stronger constraints on past climate reconstructions (e.g., back to the Maunder minimum). We propose a 3-step method based on data fusion, including a stochastic noise model to take into account short and long-term correlations. Compared with previous products scaled at the nominal TSI value of ∼1361 W/m2, the difference is below 0.2 W/m2 in terms of solar minima. Next, we model the frequency spectrum of this 41-year TSI composite time series with a Generalized Gauss-Markov model to help describe an observed flattening at high frequencies. It allows us to fit a linear trend into these TSI time series by joint inversion with the stochastic noise model via a maximum-likelihood estimator. Our results show that the amplitude of such trend is ∼−0.004 ± 0.004 W/(m2yr) for the period 1980–2021. These results are compared with the difference of irradiance values estimated from two consecutive solar minima. We conclude that the trend in these composite time series is mostly an artifact due to the colored noise.

Keywords

    data fusion, solar physics, stochastic processes, time series analysis, total solar irradiance

ASJC Scopus subject areas

Sustainable Development Goals

Cite this

Data Fusion of Total Solar Irradiance Composite Time Series Using 41 Years of Satellite Measurements. / Montillet, J. P.; Finsterle, W.; Kermarrec, G. et al.
In: Journal of Geophysical Research: Atmospheres, Vol. 127, No. 13, e2021JD036146, 02.07.2022.

Research output: Contribution to journalArticleResearchpeer review

Montillet, JP, Finsterle, W, Kermarrec, G, Sikonja, R, Haberreiter, M, Schmutz, W & Dudok de Wit, T 2022, 'Data Fusion of Total Solar Irradiance Composite Time Series Using 41 Years of Satellite Measurements', Journal of Geophysical Research: Atmospheres, vol. 127, no. 13, e2021JD036146. https://doi.org/10.1029/2021JD036146
Montillet, J. P., Finsterle, W., Kermarrec, G., Sikonja, R., Haberreiter, M., Schmutz, W., & Dudok de Wit, T. (2022). Data Fusion of Total Solar Irradiance Composite Time Series Using 41 Years of Satellite Measurements. Journal of Geophysical Research: Atmospheres, 127(13), Article e2021JD036146. https://doi.org/10.1029/2021JD036146
Montillet JP, Finsterle W, Kermarrec G, Sikonja R, Haberreiter M, Schmutz W et al. Data Fusion of Total Solar Irradiance Composite Time Series Using 41 Years of Satellite Measurements. Journal of Geophysical Research: Atmospheres. 2022 Jul 2;127(13):e2021JD036146. Epub 2022 Jun 24. doi: 10.1029/2021JD036146
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