CST_ProxiesAttractor {CSTools}R Documentation

Computing two dinamical proxies of the attractor in s2dv_cube.


This function computes two dinamical proxies of the attractor: The local dimension (d) and the inverse of the persistence (theta) for an 's2dv_cube' object. These two parameters will be used as a condition for the computation of dynamical scores to measure predictability and to compute bias correction conditioned by the dynamics with the function DynBiasCorrection Funtion based on the matlab code (davide.faranda@lsce.ipsl.fr) used in


CST_ProxiesAttractor(data, quanti, ncores = NULL)



a s2dv_cube object with the data to create the attractor. Must be a matrix with the timesteps in nrow and the grids in ncol(dat(time,grids)


a number lower than 1 indicating the quantile to perform the computation of local dimension and theta


The number of cores to use in parallel computation


dim and theta


Carmen Alvarez-Castro, carmen.alvarez-castro@cmcc.it

Maria M. Chaves-Montero, mdm.chaves-montero@cmcc.it

Veronica Torralba, veronica.torralba@cmcc.it

Davide Faranda, davide.faranda@lsce.ipsl.fr


Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large scale atmospheric predictability. Nature Communications, 10(1), 1316. DOI = https://doi.org/10.1038/s41467-019-09305-8 "

Faranda, D., Gabriele Messori and Pascal Yiou. (2017). Dynamical proxies of North Atlantic predictability and extremes. Scientific Reports, 7-41278, 2017.


# Example 1: Computing the attractor using simple s2dv data
attractor <- CST_ProxiesAttractor(data = lonlat_data$obs, quanti = 0.6)

[Package CSTools version 4.0.1 Index]