climpred.bootstrap.bootstrap_compute¶

climpred.bootstrap.bootstrap_compute(hind, reference, hist=None, metric='pearson_r', comparison='m2e', dim='init', sig=95, bootstrap=500, pers_sig=None, compute=<function compute_hindcast>, resample_uninit=<function bootstrap_uninitialized_ensemble>, **metric_kwargs)[source]¶

Bootstrap compute with replacement.

Parameters:

hind (xr.Dataset) – prediction ensemble.
reference (xr.Dataset) – reference simulation.
hist (xr.Dataset) – historical/uninitialized simulation.
metric (str) – metric. Defaults to ‘pearson_r’.
comparison (str) – comparison. Defaults to ‘m2e’.
dim (str or list) – dimension to apply metric over. default: ‘init’
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.
bootstrap (int) – number of resampling iterations (bootstrap with replacement). Defaults to 500.
compute (func) –
function to compute skill. Choose from [climpred.prediction.compute_perfect_model(),

climpred.prediction.compute_hindcast()].
resample_uninit (func) –
function to create an uninitialized ensemble from a control simulation or uninitialized large ensemble. Choose from: [bootstrap_uninitialized_ensemble(),

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

Returns:

(xr.Dataset): bootstrapped results

init_ci (xr.Dataset): confidence levels of init_skill
uninit_ci (xr.Dataset): confidence levels of uninit_skill
p_uninit_over_init (xr.Dataset): p-value of the hypothesis

that the difference of skill between the initialized and uninitialized simulations is smaller or equal to zero based on bootstrapping with replacement. Defaults to None.
pers_ci (xr.Dataset): confidence levels of pers_skill
p_pers_over_init (xr.Dataset): p-value of the hypothesis

that the difference of skill between the initialized and persistence simulations is smaller or equal to zero based on bootstrapping with replacement. Defaults to None.

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.