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mesmer.calibrate_mesmer.train_gv_AR

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mesmer.calibrate_mesmer.train_gv_AR#

mesmer.calibrate_mesmer.train_gv_AR(params_gv, gv, max_lag, sel_crit)#

Derive AR parameters of global variability under the assumption that gv does not depend on the scenario.

Parameters:

  • params_gv (dict) – parameter dictionary containing keys which do not depend on applied method

    • [“targ”] (variable, i.e., tas or tblend, str)

    • [“esm”] (Earth System Model, str)

    • [“method”] (applied method, i.e., AR, str)

    • [“scenarios”] (emission scenarios used for training, list of strs)

  • gv (dict) – nested global mean temperature variability (volcanic influence removed) dictionary with keys

    • [scen] (2d array (nr_runs, nr_ts) of globally-averaged temperature variability time series)

  • max_lag (int) – maximum number of lags considered during fitting

  • sel_crit (str) – selection criterion for the AR process order, e.g., ‘bic’ or ‘aic’

Returns:

params (dict) – parameter dictionary containing original keys plus

  • [“max_lag”] (maximum lag considered when finding suitable AR model, hardcoded to 15 here, int)

  • [“sel_crit”] (selection criterion applied to find suitable AR model, hardcoded to Bayesian Information Criterion bic here, str)

  • [“AR_int”] (intercept of the AR model, float)

  • [“AR_coefs”] (coefficients of the AR model for the lags which are contained in the selected AR model, list of floats)

  • [“AR_order_sel”] (selected AR order, int)

  • [“AR_std_innovs”] (standard deviation of the innovations of the selected AR model, float)

Notes

  • Assumptions

    • number of runs per scenario and the number of time steps in each scenario can vary

    • each scenario receives equal weight during training

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