mesmer.calibrate_mesmer.train_gt

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mesmer.calibrate_mesmer.train_gt#

mesmer.calibrate_mesmer.train_gt(data, targ, esm, time, cfg, save_params=True)#

Derive global trend (emissions + volcanoes) parameters from specified ensemble type with specified method.

Parameters:

  • data (dict) – nested global mean variable dictionary with keys for each scenario employed for training

    • [scen] (2d array (run, time) of globally-averaged variable time series)

  • targ (str) – target variable (e.g., “tas”)

  • esm (str) – associated Earth System Model (e.g., “CanESM2” or “CanESM5”)

  • time (np.ndarray) – [scen] (1d array of years)

  • cfg (module) – config file containing metadata

  • save_params (bool, default True) – determines if parameters are saved or not, default = True

Returns:

params_gt (dict) – dictionary containing the trained parameters for the chosen method / ensemble type

  • [“targ”] (emulated variable, str)

  • [“esm”] (Earth System Model, str)

  • [“method”] (applied method, str)

  • [“preds”] (predictors, list of strs)

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

  • [xx] (additional params depend on method employed)

Notes

  • Assumptions:

    • All scens start at the same point in time

    • If historical data is present, historical data and future scenarios are transmitted as single time series

  • No perfect smoothness enforced at transition from historical to future scenario

  • No perfect overlap between future scenarios which share the same forcing in the beginning is enforced

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