def METHOD_NAME(
    gdir,
    temp_bias_ts=None,
    prcp_fac_ts=None,
    ys=None,
    ye=None,
    y0=2014,
    halfsize=5,
    climate_filename="climate_historical",
    climate_input_filesuffix="",
    output_filesuffix="",
    init_model_fls=None,
    init_model_filesuffix=None,
    init_model_yr=None,
    bias=0,
    **kwargs
):
    """Runs a glacier with temperature and precipitation correction timeseries.
    Parameters
    ----------
    gdir : :py:class:`oggm.GlacierDirectory`
        the glacier directory to process
    temp_bias_ts : pandas DataFrame
        the temperature bias timeseries (in °C) (index: time as years)
    prcp_fac_ts : pandas DataFrame
        the precipitaion bias timeseries (in % change, positive or negative)
        (index: time as years)
    y0 : int
        central year of the constant climate period
    halfsize : int
        half-size of the constant climate period
    climate_filename : str
        name of the climate file, e.g. 'climate_historical' (default) or 'gcm_data'
    climate_input_filesuffix: str
        filesuffix for the input climate file
    output_filesuffix : str
        for the output file
    init_model_filesuffix : str
        if you want to start from a previous model run state. Can be
        combined with `init_model_yr`
    init_model_yr : int
        the year of the initial run you want to start from. The default
        is to take the last year available
    bias : float
        bias of the mb model. Default is to use the calibrated one, which
        is zero usually anyways.
    kwargs : dict
        kwargs to pass to the FluxBasedModel instance
    """
    if init_model_filesuffix is not None:
        fp = gdir.get_filepath("model_geometry", filesuffix=init_model_filesuffix)
        fmod = FileModel(fp)
        if init_model_yr is None:
            init_model_yr = fmod.last_yr
        # Avoid issues here
        if init_model_yr > fmod.y0:
            fmod.run_until(init_model_yr)
        else:
            fmod.run_until(fmod.y0)
        init_model_fls = fmod.fls
    # Final crop
    mb = BiasedConstantMassBalance(
        gdir,
        temp_bias_ts=temp_bias_ts,
        prcp_fac_ts=prcp_fac_ts,
        y0=y0,
        bias=bias,
        halfsize=halfsize,
        filename=climate_filename,
        input_filesuffix=climate_input_filesuffix,
    )
    # Decide from climate
    if ye is None:
        ye = mb.ye
    if ys is None:
        ys = mb.ys
    return flowline_model_run(
        gdir,
        output_filesuffix=output_filesuffix,
        mb_model=mb,
        ys=ys,
        ye=ye,
        init_model_fls=init_model_fls,
        **kwargs
    )