I would like to generate surrogate spatiotemporal data that retains spatial and temporal autocorrelation of existing observational data. The observations are organized as univariate monthly timeseries on a semi-regular point cloud covering global non-ice land mass. At each individual location, the goal would be to create a phase-scrambled, random permutation with the same acf as the original monthly timeseries. There are over 11,000 locations and 20-30 years of data at each point.