English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Adaptive time series analysis has been applied to investigate variability ofCO2 concentration data, sampled weekly at Mauna Loa monitoring station. Due toits ability to mitigate mode mixing, the recent time varying filter EmpiricalMode Decomposition (tvf-EMD) methodology is employed to extract localnarrowband oscillatory modes. In order to perform data analysis, we developed aPython implementation of the tvf-EMD algorithm, referred to as pytvfemd. Thealgorithm allowed to extract the trend and both the six month and the one yearperiodicities, without mode mixing, even though the analysed data are noisy.Furthermore, subtracting such modes the residuals are obtained, which are foundto be described by a normal distribution. Outliers occurrence was alsoinvestigated and it is found that they occur in higher number toward the end ofthe dataset, corresponding to solar cycles characterised by smaller sunspotnumbers. A more pronounced oscillation of the residuals is also observed inthis regard, in relation to the solar cycles activity too.

Adaptive time series analysis of Mauna Loa CO2 data: tvf-EMD based detrend and extraction of seasonal variability