xarray.Dataset.climepi.plot_uncertainty_interval_decomposition#

Dataset.climepi.plot_uncertainty_interval_decomposition(data_var=None, uncertainty_level=90, internal_variability_method=None, deg=3, lam=None, kwargs_baseline=None, **kwargs_area)[source]#

Plot contributions of climate uncertainty sources to uncertainty intervals.

Generates a plume plot showing contributions of internal variability, model uncertainty and scenario uncertainty (as applicable) to uncertainty intervals for a data variable over time.

Wraps hvplot.hvPlot.area().

Parameters:

  • data_var (str, optional) – Name of the data variable to plot. Should be provided unless there is a single non-bounds data variable.

  • uncertainty_level (float, optional) – Uncertainty level for the uncertainty intervals (percentage). Default is 90.

  • internal_variability_method (str, optional) – Whether to characterize internal variability by computing ensemble statistics directly at each time point (‘direct’) or by estimating them using a polynomial (‘polyfit’) or spline (‘splinefit’) fit to the time series, assuming the variance is constant in time. By default, the ‘direct’ method is used if multiple realizations are available (i.e., the dataset has a non-singleton ‘realization’ dimension), and the ‘polyfit’ method is used if only a single realization is available.

  • deg (int, optional) – Degree of the polynomial to fit to the time series if using the ‘polyfit’ method (ignored if using other methods). Default is 3.

  • lam (float, optional) – Smoothing parameter passed to scipy.interpolate.make_smoothing_spline() if using the ‘splinefit’ method (ignored if using other methods). Default is None.

  • kwargs_baseline (dict, optional) – Additional keyword arguments to pass to hvplot.hvPlot.line() for the baseline estimate.

  • **kwargs_area (dict, optional) – Additional keyword arguments to pass to hvplot.hvPlot.area() for the uncertainty interval plots.

Returns:

holoviews.Overlay – The resulting plot object.