climpred.bootstrap.bootstrap_compute(initialized, verif, hist=None, alignment='same_verifs', metric='pearson_r', comparison='m2e', dim='init', reference=None, resample_dim='member', sig=95, iterations=500, pers_sig=None, compute=<function compute_hindcast>, resample_uninit=<function bootstrap_uninitialized_ensemble>, **metric_kwargs)[source]

Bootstrap compute with replacement.

  • initialized (xr.Dataset) – prediction ensemble.

  • verif (xr.Dataset) – Verification data.

  • hist (xr.Dataset) – historical/uninitialized simulation.

  • metric (str) – metric. Defaults to "pearson_r".

  • comparison (str) – comparison. Defaults to "m2e".

  • dim (str or list) – dimension(s) to apply metric over. Defaults to: “init”.

  • reference (str, list of str) – Type of reference forecasts with which to verify. One or more of [“persistence”, “uninitialized”]. If None or empty, returns no p value.

  • resample_dim (str) – dimension to resample from. Defaults to: “member”

    • “member”: select a different set of members from initialized

    • “init”: select a different set of initializations from initialized

  • sig (int) – Significance level for uninitialized and initialized skill. Defaults to 95.

  • pers_sig (int) – Significance level for persistence skill confidence levels. Defaults to sig.

  • iterations (int) – number of resampling iterations (bootstrap with replacement). Defaults to 500.

  • compute (Callable) – function to compute skill. Choose from [climpred.prediction.compute_perfect_model(), climpred.prediction.compute_hindcast()].

  • resample_uninit (Callable) – function to create an uninitialized ensemble from a control simulation or uninitialized large ensemble. Choose from: [bootstrap_uninitialized_ensemble(),


  • ** metric_kwargs (dict) – additional keywords to be passed to metric (see the arguments required for a given metric in Metrics).



(xr.Dataset): bootstrapped results for the three different skills:

  • initialized for the initialized hindcast initialized and

describes skill due to initialization and external forcing

  • uninitialized for the uninitialized/historical and approximates skill

from external forcing

  • persistence

  • climatology

the different results:
  • verify skill: skill values

  • p: p value

  • low_ci and high_ci: high and low ends of confidence intervals

based on significance threshold sig


Goddard et al. [2013]