Głogowski, ArkadiuszPerona, PaoloBrys, KrystynaBrys, Tadeusz2021-02-262022-02-262021-02-262021-02-2610.1007/s00484-021-02101-4https://infoscience.epfl.ch/handle/20.500.14299/175532Measured meteorological time series are frequently used to obtain information 8 about climate dynamics. We use time series analysis and nonlinear system identification 9 methods in order to assess outdoor-environment bioclimatic conditions starting from the 10 analysis of long historicalmeteorological data records.We investigate andmodel the stochas11 tic and deterministic properties of 117 years (1891-2007) of monthly measurements of air 12 temperature, precipitation and sunshine duration by separating their slow and fast compo13 nents of the dynamics. In particular, we reconstruct the trend behaviour at long terms by 14 modelling its dynamics via a phase space dynamical systems approach. The long-term re15 construction method reveals that an underlying dynamical system would drive the trend 16 behaviour of the meteorological variables and in turn of the calculated Universal Thermal 17 Climatic Index (UTCI), as representative of bioclimatic conditions. At longer terms, the 18 system would slowly be attracted to a limit cycle characterized by 50-60 years cycle fluctu19 ations that is reminiscent of the Atlantic Multidecadal Oscillation (AMO). Because of lack 20 of information about long historical wind speed data we performed a sensitivity analysis of 21 the UTCI to three constant wind speed scenarios (i.e., 0.5, 1 and 5 m/s). This methodology may be transferred to model bioclimatic conditions of nearby regions lacking of measured 23 data but experiencing similar climatic conditions.UTCIoutdoor environmenttime-seriesmachine learningAMOtext::journal::journal article::research article