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A methodology of adaptive time series analysis based on Empirical ModeDecomposition (EMD) has been employed to investigate $^{7}$Be activityconcentration variability, along with temperature. Analysed data were sampledat ground level by 28 different stations of the CTBTO-IMS network. The adaptivenature of the EMD algorithm allows it to deal with data that are both nonlinearand non-stationary, making no a priori assumptions on the expansion basis. Mainpurpose of the adopted methodology is to characterise the possible presence ofa trend, occurrence of AM-FM modulation of relevant oscillatory modes,residuals distributions and outlier occurrence. Trend component is firstestimated via simple EMD and removed. The recent time varying filter EMD(tvf-EMD) technique is then employed to extract local narrow band oscillatorymodes from the data. To establish their relevance, a denoising step is thencarried out, employing both the Hurst exponent as a thresholding parameter andfurther testing their statistical significance against white noise. The onesthat pass the denoising step are considered to be meaningful oscillatory modesof the data, and their AM-FM modulation is investigated. Possible applicationsof the adopted methodology regarding site characterisation and suggestions forfurther research are given in the conclusions.

tvf-EMD based time series analysis of $^{7}$Be of the CTBTO-IMS network