Calculating and displaying multiple representations of the CH4 lifetime#

Lifetime diagnostic to show multiple datasets in one plot.

Description#

This diagnostic can be used to visualize lifetime of multiple datasets in one plot.

Input data needs to be 4D (time, air_pressure/atmosphere_hybrid_sigma_pressure_coordinate, latitude, longitude).

For some plot types, a reference dataset can be defined. For this, use the facet reference_for_lifetime_diags: true in the definition of the dataset in the recipe. Note that at most one reference dataset is supported.

Currently supported plot types (use the option plots to specify them):

  • Time series (plot type timeseries): all datasets are plotted in one single figure.

Author#

Franziska Winterstein (DLR, Germany)

Configuration options in recipe#

facet_used_for_labels: str, optional (default: ‘dataset’)

Facet used to label different datasets in plot titles and legends. For example, facet_used_for_labels: dataset will use dataset names in plot titles and legends; facet_used_for_labels: exp will use experiments in plot titles and legends. In addition, facet_used_for_labels is used to select the correct plot_kwargs for the different datasets (see configuration options for the different plot types below).

figure_kwargs: dict, optional

Optional keyword arguments for matplotlib.pyplot.figure(). By default, uses constrained_layout: true.

m_air: float, optional (default: 28.970)

Molar mass of air in g/mol.

molarmass: float

Molar mass of reactant in g/mol.

oxidant: dict

Oxidant reaction rates for chemical species, e.g., {“oh”: {“A”: 1.85e-20, “ER”: 987.0, “b”: 2.82}}.

plots: dict, optional

Plot types plotted by this diagnostic (see list above). Dictionary keys must be timeseries. Dictionary values are dictionaries used as options for the corresponding plot. The allowed options for the different plot types are given below.

plot_filename: str, optional

Filename pattern for the plots. Defaults to {plot_type}_{real_name}_{dataset}_{mip}_{exp}_{ensemble}. All tags (i.e., the entries in curly brackets, e.g., {dataset}, are replaced with the corresponding tags).

plot_folder: str, optional

Path to the folder to store figures. Defaults to {plot_dir}/../../{dataset}/{exp}/{modeling_realm}/{real_name}. All tags (i.e., the entries in curly brackets, e.g., {dataset}, are replaced with the corresponding tags). {plot_dir} is replaced with the default ESMValTool plot directory (i.e., output_dir/plots/diagnostic_name/script_name/, see Output).

reactant: str

Name of reactant, e.g., “CH4”.

regions: list of str, optional (default: [“TROP”])

Regions considered for line plots. Currently, only “TROP” (troposphere) and “STRA” (stratosphere) are supported.

savefig_kwargs: dict, optional

Optional keyword arguments for matplotlib.pyplot.savefig(). By default, uses bbox_inches: tight, dpi: 300, orientation: landscape.

seaborn_settings: dict, optional

Options for seaborn.set_theme() (affects all plots). By default, uses style: ticks.

units: str, optional (default: “years”)

Target units of the lifetime.

weight_type: str

Type of weighting. If “mass CH4”, use CH4 mass as weights. Otherwise, use equal weights.

Configuration options for plot type timeseries#

annual_mean: str, optional (default: False)

Optional switch to turn on annual means to be displayed ‘only’ or additional ‘both’ to the original timeseries. If not set or set to False only the original timeseries is shown.

annual_mean_kwargs: dict, optional

Optional keyword arguments for iris.plot.plot() for plotting annual means. These keyword arguments update (and potentially overwrite) the plot_kwargs for the annual mean plots. Use annual_mean_kwargs to not show annual means.

by_timestep: bool, optional (default: False)

Calculate lifetime for each time step individually. Slower, but less memory-intensive.

display_mean: bool, optional (default: False)

Show mean over entire period in plot label.

gridline_kwargs: dict, optional

Optional keyword arguments for grid lines. By default, color: lightgrey, alpha: 0.5 are used. Use gridline_kwargs: false to not show grid lines.

legend_kwargs: dict, optional

Optional keyword arguments for matplotlib.pyplot.legend(). Use legend_kwargs: false to not show legends.

plot_kwargs: dict, optional

Optional keyword arguments for iris.plot.plot(). Dictionary keys are elements identified by facet_used_for_labels or default, e.g., CMIP6 if facet_used_for_labels: project or historical if facet_used_for_labels: exp. Dictionary values are dictionaries used as keyword arguments for iris.plot.plot(). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: default: {linestyle: '-', label: '{project}'}, CMIP6: {color: red, linestyle: '--'}, OBS: {color: black}.

pyplot_kwargs: dict, optional

Optional calls to functions of matplotlib.pyplot. Dictionary keys are functions of matplotlib.pyplot. Dictionary values are used as single argument for these functions. String arguments can include facets in curly brackets which will be derived from the datasets plotted in the corresponding plot, e.g., {short_name}, {exp}. Facets like {project} that vary between the different datasets will be transformed to something like ambiguous_project. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

Hint

Extra arguments given to the recipe are ignored, so it is safe to use yaml anchors to share the configuration of common arguments with other monitor diagnostic script.