climpred.bootstrap.bootstrap_perfect_model

climpred.bootstrap.bootstrap_perfect_model(init_pm, control, metric='pearson_r', comparison='m2e', dim=None, resample_dim='member', sig=95, iterations=500, pers_sig=None, reference_compute=<function compute_persistence>, **metric_kwargs)[source]
Bootstrap compute with replacement. Wrapper of
py:func:bootstrap_compute for perfect-model framework.
Parameters:
  • hind (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) – dimension to apply metric over. default: [‘init’, ‘member’].
  • resample_dim (str or list) –

    dimension to resample from. default: ‘member’.

    • ’member’: select a different set of members from hind
    • ’init’: select a different set of initializations from hind
  • 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.
  • reference_compute (func) – function to compute a reference forecast skill with. Default: climpred.prediction.compute_persistence().
  • metric_kwargs (**) – additional keywords to be passed to metric (see the arguments required for a given metric in Metrics).
Returns:

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

predictions:

  • init for the initialized hindcast hind and describes skill due to

initialization and external forcing

  • uninit for the uninitialized historical hist and approximates skill

from external forcing

  • pers for the reference forecast computed by reference_compute, which

defaults to compute_persistence

the different results:
  • skill: skill values
  • p: p value
  • low_ci and high_ci: high and low ends of confidence intervals based

on significance threshold sig
Return type:

results
Reference:
  • Goddard, L., A. Kumar, A. Solomon, D. Smith, G. Boer, P. Gonzalez, V. Kharin, et al. “A Verification Framework for Interannual-to-Decadal Predictions Experiments.” Climate Dynamics 40, no. 1–2 (January 1, 2013): 245–72. https://doi.org/10/f4jjvf.

See also

  • climpred.bootstrap.bootstrap_compute
  • climpred.prediction.compute_perfect_model

Example

>>> init = climpred.tutorial.load_dataset('MPI-PM-DP-1D')
>>> control = climpred.tutorial.load_dataset('MPI-control-1D')
>>> bootstrapped_s = climpred.bootstrap.bootstrap_perfect_model(init, control)
>>> bootstrapped_s.coords
Coordinates:
  * lead     (lead) int64 1 2 3 4 5 6 7 8 9 10
  * kind     (kind) object 'init' 'pers' 'uninit'
  * results  (results) <U7 'skill' 'p' 'low_ci' 'high_ci'