Abstract

Abstract. The Madden–Julian oscillation (MJO) is a major source of intraseasonal variability in the troposphere. Recently, studies have indicated that also the solar 27-day variability could cause variability in the troposphere. Furthermore, it has been indicated that both sources could be linked, and particularly that the occurrence of strong MJO events could be modulated by the solar 27-day cycle. In this paper, we analyze whether the temporal evolution of the MJO phases could also be linked to the solar 27-day cycle. We basically count the occurrences of particular MJO phases as a function of time lag after the solar 27-day extrema in about 38 years of MJO data. Furthermore, we develop a quantification approach to measure the strength of such a possible relationship and use this to compare the behavior for different atmospheric conditions and different datasets, among others. The significance of the results is estimated based on different variants of the Monte Carlo approach, which are also compared. We find indications for a synchronization between the MJO phase evolution and the solar 27-day cycle, which are most notable under certain conditions: MJO events with a strength greater than 0.5, during the easterly phase of the quasi-biennial oscillation, and during boreal winter. The MJO appears to cycle through its eight phases within two solar 27-day cycles. The phase relation between the MJO and the solar variation appears to be such that the MJO predominantly transitions from phase 8 to 1 or from phase 4 and 5 during the solar 27-day minimum. These results strongly depend on the MJO index used such that the synchronization is most clearly seen when using univariate indices like the OLR-based MJO index (OMI) in the analysis but can hardly be seen with multivariate indices like the real-time multivariate MJO index (RMM). One possible explanation could be that the synchronization pattern is encoded particularly in the underlying outgoing longwave radiation (OLR) data. A weaker dependence of the results on the underlying solar proxy is also observed but not further investigated. Although we think that these initial indications are already worth noting, we do not claim to unambiguously prove this relationship in the present study, neither in a statistical nor in a causal sense. Instead, we challenge these initial findings ourselves in detail by varying underlying datasets and methods and critically discuss resulting open questions to lay a solid foundation for further research.

Highlights

  • The solar electromagnetic radiation is the major energy source of the earth system

  • We develop a quantification approach to measure the strength of such a possible relationship and use this to compare the behavior for different atmospheric conditions and different datasets, among others

  • It has been suggested that the occurrence of strong Madden–Julian oscillation (MJO) events is modulated by the solar 27-day cycle

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Summary

Introduction

The solar electromagnetic radiation is the major energy source of the earth system. usually described with the solar constant (1361 W m−2), the total solar irradiance (TSI) is subject to variations on different timescales, with the most prominent one being the solar 11-year cycle. The modulations could result via a chain of effects in a change of upper tropospheric static stability and with that in a change of tropospheric deep convection, with implications for clouds and temperature Another mechanism, for a connection between clouds and the solar variability, has been proposed in a few variants (e.g., Svensmark, 1998; Marsh and Svensmark, 2000) but has been heavily criticized (e.g., Damon and Laut, 2011) and is mentioned here only for completeness. The study indicates that the MJO is influenced by solar 11-year variations during boreal winter This influence is roughly as important as the previously mentioned QBO modulation and might work with a similar mechanism: the modification of upper tropospheric stability.

Datasets and filtering
Brief description of the quantification approach
Influence of the numerical setup
Influence of atmospheric conditions
MJO strength threshold
Phase of the QBO
Seasons
Solar minimum or maximum as epoch trigger
Relaxed atmospheric filter criteria
Influence of the MJO index
Influence of the solar proxy
Significance estimation with different Monte Carlo variants
Discussion and conclusions
Common measure for the goodness of fit χ 2
Accounting for periodicity in the fitting process
Measuring the deviation including weights
Findings
Estimating the uncertainty of the days with maximum MJO phase occurrence

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