Monitoring diagnostic to show multiple datasets in one plot (incl. biases)#

Monitoring diagnostic to show multiple datasets in one plot (incl. biases).

Description#

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

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

Please note that all benchmarking plot types (i.e. all plot types starting with benchmarking_) require exactly one dataset (the dataset to be benchmarked) to have the facet benchmark_dataset: true in the dataset entry of the recipe. For benchmarking line plots (i.e. benchmarking_annual_cycle, benchmarking_diurnal_cycle, benchmarking_timeseries), it is recommended to specify a particular line color and line style in the scripts section of the recipe for the dataset to be benchmarked (benchmark_dataset: true) so that this dataset is easy to identify in the plot.

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

  • Time series (plot type timeseries): for each variable separately, all datasets are plotted in one single figure. Input data needs to be 1D with single dimension time.

  • Annual cycle (plot type annual_cycle): for each variable separately, all datasets are plotted in one single figure. Input data needs to be 1D with single dimension month_number.

  • Diurnal cycle (plot type diurnal_cycle): for each variable separately, all datasets are plotted in one single figure. Input data needs to be 1D with single dimension hour.

  • Maps (plot type map): for each variable and dataset, an individual map is plotted. If a reference dataset is defined, also include this dataset and a bias plot into the figure. Note that if a reference dataset is defined, all input datasets need to be given on the same horizontal grid (you can use the preprocessor esmvalcore.preprocessor.regrid() for this). Input data needs to be 2D with dimensions latitude, longitude.

  • Zonal mean profiles (plot type zonal_mean_profile): for each variable and dataset, an individual profile is plotted. If a reference dataset is defined, also include this dataset and a bias plot into the figure. Note that if a reference dataset is defined, all input datasets need to be given on the same horizontal and vertical grid (you can use the preprocessors esmvalcore.preprocessor.regrid() and esmvalcore.preprocessor.extract_levels() for this). Input data needs to be 2D with dimensions latitude, altitude/air_pressure.

    Warning

    The plot_type profile for zonal mean profiles has been deprecated in ESMValTool version 2.9.0 and is scheduled for removal in version 2.11.0. Please use plot type zonal_mean_profile instead. This is an exact replacement.

  • 1D profiles (plot type 1d_profile): for each variable separately, all datasets are plotted in one single figure. Input data needs to be 1D with single dimension altitude / air_pressure

  • Variable vs. latitude plot (plot type variable_vs_lat): for each variable separately, all datasets are plotted in one single figure. Input data needs to be 1D with single dimension latitude.

  • Hovmoeller Z vs. time (plot type hovmoeller_z_vs_time): for each variable and dataset, an individual figure is plotted. If a reference dataset is defined, also include this dataset and a bias plot into the figure. Note that if a reference dataset is defined, all input datasets need to be given on the same temporal and vertical grid (you can use the preprocessors esmvalcore.preprocessor.regrid_time() and esmvalcore.preprocessor.extract_levels() for this). Input data needs to be 2D with dimensions time, altitude/air_pressure.

  • Hovmoeller time vs. latitude or longitude (plot type hovmoeller_time_vs_lat_or_lon): for each variable and dataset, an individual figure is plotted. If a reference dataset is defined, also include this dataset and a bias plot into the figure. Note that if a reference dataset is defined, all input datasets need to be given on the same temporal and horizontal grid (you can use the preprocessors esmvalcore.preprocessor.regrid_time() and esmvalcore.preprocessor.regrid() for this). Input data needs to be 2D with dimensions time, latitude/longitude.

  • Benchmarking plot annual cycles (benchmarking_annual_cycle): Same as plot type annual_cycle but including the range of metric results from an ensemble of models as shading.

  • Benchmarking box plots (benchmarking_boxplot): Box plots showing the metric results for given variables from a given model and the range from the first quartile to the third quartile, the median, and minimum and maximum values (excluding the outliers) from an ensemble of models for comparison.

  • Benchmarking plot diurnal cycles (benchmarking_diurnal_cycle): Same as plot type diurnal_cycle but including range of metric results from an ensemble of models as shading.

  • Benchmarking map plots (benchmarking_map): Same as plot type map but with stippled areas masking grid cells where the selected metric is smaller than the 90% percentile of corresponding values from an ensemble of models used for comparison.

  • Benchmarking plot time series (benchmarking_timeseries): Same as plot type timeseries but including the range of metric results from an ensemble of models as shading.

  • Benchmarking plot zonal mean profiles (plot type benchmarking_zonal): Same as plot type zonal_mean_profile but with stippled areas masking grid cells where the selected metric is smaller than the 90% percentile of corresponding values from an ensemble of models used for comparison.

Author#

Manuel Schlund (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.

group_variables_by: str, optional (default: ‘short_name’)

Facet which is used to create variable groups. For each variable group, an individual plot is created.

matplotlib_rc_params: dict, optional (default: {})

Optional matplotlib.RcParams used to customize matplotlib plots. Options given here will be passed to matplotlib.rc_context() and used for all plots produced with this diagnostic. Note: fontsizes specified here might be overwritten by the plot-type-specific option fontsize (see below).

plots: dict, optional

Plot types plotted by this diagnostic (see list above). Dictionary keys must be timeseries, annual_cycle, map, zonal_mean_profile, 1d_profile, variable_vs_lat, hovmoeller_z_vs_time, hovmoeller_time_vs_lat_or_lon. 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).

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.

Configuration options for plot type timeseries#

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.

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 argument(s) for these functions (if values are dictionaries, these are interpreted as keyword arguments; otherwise a single argument is assumed). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

time_format: str, optional (default: None)

strftime() format string that is used to format the time axis using matplotlib.dates.DateFormatter. If None, use the default formatting imposed by the iris plotting function.

Configuration options for plot type annual_cycle and diurnal_cycle#

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 argument(s) for these functions (if values are dictionaries, these are interpreted as keyword arguments; otherwise a single argument is assumed). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

Configuration options for plot type map#

cbar_label: str, optional (default: ‘{short_name} [{units}]’)

Colorbar label. Can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}.

cbar_label_bias: str, optional (default: ‘Δ{short_name} [{units}]’)

Colorbar label for plotting biases. Can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. This option has no effect if no reference dataset is given.

cbar_kwargs: dict, optional

Optional keyword arguments for matplotlib.pyplot.colorbar(). By default, uses orientation: horizontal, aspect: 30.

cbar_kwargs_bias: dict, optional

Optional keyword arguments for matplotlib.pyplot.colorbar() for plotting biases. These keyword arguments update (and potentially overwrite) the cbar_kwargs for the bias plot. This option has no effect if no reference dataset is given.

common_cbar: bool, optional (default: False)

Use a common colorbar for the top panels (i.e., plots of the dataset and the corresponding reference dataset) when using a reference dataset. If neither vmin and vmix nor levels is given in plot_kwargs, the colorbar bounds are inferred from the dataset in the top left panel, which might lead to an inappropriate colorbar for the reference dataset (top right panel). Thus, the use of the plot_kwargs vmin and vmax or levels is highly recommend when using this common_cbar: true. This option has no effect if no reference dataset is given.

fontsize: int, optional (default: None)

Fontsize used for ticks, labels and titles. For the latter, use the given fontsize plus 2. Does not affect suptitles. If not given, use default matplotlib values. For a more fine-grained definition of fontsizes, use the option matplotlib_rc_params (see above).

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.

plot_func: str, optional (default: ‘contourf’)

Plot function used to plot the maps. Must be a function of iris.plot that supports plotting of 2D cubes with coordinates latitude and longitude.

plot_kwargs: dict, optional

Optional keyword arguments for the plot function defined by plot_func. 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 the plot function defined by plot_func. String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: default: {levels: 2}, CMIP6: {vmin: 200, vmax: 250}. In addition to the normalization options supported by the plot function, the option norm: centered can be specified. In this case, the keywords vcenter and halfrange should be used instead of vmin or vmax (see CenteredNorm).

plot_kwargs_bias: dict, optional

Optional keyword arguments for the plot function defined by plot_func for plotting biases. These keyword arguments update (and potentially overwrite) the plot_kwargs for the bias plot. This option has no effect if no reference dataset is given. See option plot_kwargs for more details. By default, uses cmap: bwr and norm: centered.

projection: str, optional (default: ‘Robinson’)

Projection used for the map plot. Needs to be a valid projection class of cartopy.crs. Keyword arguments can be specified using the option projection_kwargs.

projection_kwargs: dict, optional

Optional keyword arguments for the projection given by projection. For the default projection Robinson, the default keyword arguments central_longitude: 10 are used.

pyplot_kwargs: dict, optional

Optional calls to functions of matplotlib.pyplot. Dictionary keys are functions of matplotlib.pyplot. Dictionary values are used as argument(s) for these functions (if values are dictionaries, these are interpreted as keyword arguments; otherwise a single argument is assumed). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

rasterize: bool, optional (default: True)

If True, use rasterization for map plots to produce smaller files. This is only relevant for vector graphics (e.g., output_file_type: pdf,svg,ps).

show_stats: bool, optional (default: True)

Show basic statistics on the plots.

x_pos_stats_avg: float, optional (default: 0.0)

Text x-position of average (shown on the left) in Axes coordinates. Can be adjusted to avoid overlap with the figure. Only relevant if show_stats: true.

x_pos_stats_bias: float, optional (default: 0.92)

Text x-position of bias statistics (shown on the right) in Axes coordinates. Can be adjusted to avoid overlap with the figure. Only relevant if show_stats: true.

Configuration options for plot type zonal_mean_profile#

cbar_label: str, optional (default: ‘{short_name} [{units}]’)

Colorbar label. Can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}.

cbar_label_bias: str, optional (default: ‘Δ{short_name} [{units}]’)

Colorbar label for plotting biases. Can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. This option has no effect if no reference dataset is given.

cbar_kwargs: dict, optional

Optional keyword arguments for matplotlib.pyplot.colorbar(). By default, uses orientation: vertical.

cbar_kwargs_bias: dict, optional

Optional keyword arguments for matplotlib.pyplot.colorbar() for plotting biases. These keyword arguments update (and potentially overwrite) the cbar_kwargs for the bias plot. This option has no effect if no reference dataset is given.

common_cbar: bool, optional (default: False)

Use a common colorbar for the top panels (i.e., plots of the dataset and the corresponding reference dataset) when using a reference dataset. If neither vmin and vmix nor levels is given in plot_kwargs, the colorbar bounds are inferred from the dataset in the top left panel, which might lead to an inappropriate colorbar for the reference dataset (top right panel). Thus, the use of the plot_kwargs vmin and vmax or levels is highly recommend when using this common_cbar: true. This option has no effect if no reference dataset is given.

fontsize: int, optional (default: None)

Fontsize used for ticks, labels and titles. For the latter, use the given fontsize plus 2. Does not affect suptitles. If not given, use default matplotlib values. For a more fine-grained definition of fontsizes, use the option matplotlib_rc_params (see above).

log_y: bool, optional (default: True)

Use logarithmic Y-axis.

plot_func: str, optional (default: ‘contourf’)

Plot function used to plot the profiles. Must be a function of iris.plot that supports plotting of 2D cubes with coordinates latitude and altitude/air_pressure.

plot_kwargs: dict, optional

Optional keyword arguments for the plot function defined by plot_func. 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 the plot function defined by plot_func. String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: default: {levels: 2}, CMIP6: {vmin: 200, vmax: 250}. In addition to the normalization options supported by the plot function, the option norm: centered can be specified. In this case, the keywords vcenter and halfrange should be used instead of vmin or vmax (see CenteredNorm).

plot_kwargs_bias: dict, optional

Optional keyword arguments for the plot function defined by plot_func for plotting biases. These keyword arguments update (and potentially overwrite) the plot_kwargs for the bias plot. This option has no effect if no reference dataset is given. See option plot_kwargs for more details. By default, uses cmap: bwr and norm: centered.

pyplot_kwargs: dict, optional

Optional calls to functions of matplotlib.pyplot. Dictionary keys are functions of matplotlib.pyplot. Dictionary values are used as argument(s) for these functions (if values are dictionaries, these are interpreted as keyword arguments; otherwise a single argument is assumed). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

rasterize: bool, optional (default: True)

If True, use rasterization for profile plots to produce smaller files. This is only relevant for vector graphics (e.g., output_file_type: pdf,svg,ps).

show_stats: bool, optional (default: True)

Show basic statistics on the plots.

show_y_minor_ticklabels: bool, optional (default: False)

Show tick labels for the minor ticks on the Y axis.

x_pos_stats_avg: float, optional (default: 0.01)

Text x-position of average (shown on the left) in Axes coordinates. Can be adjusted to avoid overlap with the figure. Only relevant if show_stats: true.

x_pos_stats_bias: float, optional (default: 0.7)

Text x-position of bias statistics (shown on the right) in Axes coordinates. Can be adjusted to avoid overlap with the figure. Only relevant if show_stats: true.

Configuration options for plot type 1d_profile#

aspect_ratio: float, optional (default: 1.5)

Aspect ratio of the plot. The default value results in a slender upright plot.

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.

log_x: bool, optional (default: False)

Use logarithmic X-axis. Note that for the logarithmic x axis tickmarks are set so that minor tickmarks show up. Setting of individual tickmarks by pyplot_kwargs is not recommended in this case.

log_y: bool, optional (default: True)

Use logarithmic Y-axis.

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 argument(s) for these functions (if values are dictionaries, these are interpreted as keyword arguments; otherwise a single argument is assumed). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

show_y_minor_ticklabels: bool, optional (default: False)

Show tick labels for the minor ticks on the Y axis.

Configuration options for plot type variable_vs_lat#

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 argument(s) for these functions (if values are dictionaries, these are interpreted as keyword arguments; otherwise a single argument is assumed). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

Configuration options for plot type hovmoeller_z_vs_time#

cbar_label: str, optional (default: ‘{short_name} [{units}]’)

Colorbar label. Can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}.

cbar_label_bias: str, optional (default: ‘Δ{short_name} [{units}]’)

Colorbar label for plotting biases. Can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. This option has no effect if no reference dataset is given.

cbar_kwargs: dict, optional

Optional keyword arguments for matplotlib.pyplot.colorbar(). By default, uses orientation: vertical.

cbar_kwargs_bias: dict, optional

Optional keyword arguments for matplotlib.pyplot.colorbar() for plotting biases. These keyword arguments update (and potentially overwrite) the cbar_kwargs for the bias plot. This option has no effect if no reference dataset is given.

common_cbar: bool, optional (default: False)

Use a common colorbar for the top panels (i.e., plots of the dataset and the corresponding reference dataset) when using a reference dataset. If neither vmin and vmix nor levels is given in plot_kwargs, the colorbar bounds are inferred from the dataset in the top left panel, which might lead to an inappropriate colorbar for the reference dataset (top right panel). Thus, the use of the plot_kwargs vmin and vmax or levels is highly recommend when using this common_cbar: true. This option has no effect if no reference dataset is given.

fontsize: int, optional (default: None)

Fontsize used for ticks, labels and titles. For the latter, use the given fontsize plus 2. Does not affect suptitles. If not given, use default matplotlib values. For a more fine-grained definition of fontsizes, use the option matplotlib_rc_params (see above).

log_y: bool, optional (default: True)

Use logarithmic Y-axis.

plot_func: str, optional (default: ‘contourf’)

Plot function used to plot the profiles. Must be a function of iris.plot that supports plotting of 2D cubes with coordinates latitude and altitude/air_pressure.

plot_kwargs: dict, optional

Optional keyword arguments for the plot function defined by plot_func. 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 the plot function defined by plot_func. String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: default: {levels: 2}, CMIP6: {vmin: 200, vmax: 250}. In addition to the normalization options supported by the plot function, the option norm: centered can be specified. In this case, the keywords vcenter and halfrange should be used instead of vmin or vmax (see CenteredNorm).

plot_kwargs_bias: dict, optional

Optional keyword arguments for the plot function defined by plot_func for plotting biases. These keyword arguments update (and potentially overwrite) the plot_kwargs for the bias plot. This option has no effect if no reference dataset is given. See option plot_kwargs for more details. By default, uses cmap: bwr and norm: centered.

pyplot_kwargs: dict, optional

Optional calls to functions of matplotlib.pyplot. Dictionary keys are functions of matplotlib.pyplot. Dictionary values are used as argument(s) for these functions (if values are dictionaries, these are interpreted as keyword arguments; otherwise a single argument is assumed). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

rasterize: bool, optional (default: True)

If True, use rasterization for profile plots to produce smaller files. This is only relevant for vector graphics (e.g., output_file_type: pdf,svg,ps).

show_stats: bool, optional (default: True)

Show basic statistics on the plots.

show_y_minor_ticklabels: bool, optional (default: False)

Show tick labels for the minor ticks on the Y axis.

x_pos_stats_avg: float, optional (default: 0.01)

Text x-position of average (shown on the left) in Axes coordinates. Can be adjusted to avoid overlap with the figure. Only relevant if show_stats: true.

x_pos_stats_bias: float, optional (default: 0.7)

Text x-position of bias statistics (shown on the right) in Axes coordinates. Can be adjusted to avoid overlap with the figure. Only relevant if show_stats: true.

time_format: str, optional (default: None)

strftime() format string that is used to format the time axis using matplotlib.dates.DateFormatter. If None, use the default formatting imposed by the iris plotting function.

Configuration options for plot type hovmoeller_time_vs_lat_or_lon#

cbar_label: str, optional (default: ‘{short_name} [{units}]’)

Colorbar label. Can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}.

cbar_label_bias: str, optional (default: ‘Δ{short_name} [{units}]’)

Colorbar label for plotting biases. Can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. This option has no effect if no reference dataset is given.

cbar_kwargs: dict, optional

Optional keyword arguments for matplotlib.pyplot.colorbar(). By default, uses orientation: vertical.

cbar_kwargs_bias: dict, optional

Optional keyword arguments for matplotlib.pyplot.colorbar() for plotting biases. These keyword arguments update (and potentially overwrite) the cbar_kwargs for the bias plot. This option has no effect if no reference dataset is given.

common_cbar: bool, optional (default: False)

Use a common colorbar for the top panels (i.e., plots of the dataset and the corresponding reference dataset) when using a reference dataset. If neither vmin and vmix nor levels is given in plot_kwargs, the colorbar bounds are inferred from the dataset in the top left panel, which might lead to an inappropriate colorbar for the reference dataset (top right panel). Thus, the use of the plot_kwargs vmin and vmax or levels is highly recommend when using this common_cbar: true. This option has no effect if no reference dataset is given.

fontsize: int, optional (default: None)

Fontsize used for ticks, labels and titles. For the latter, use the given fontsize plus 2. Does not affect suptitles. If not given, use default matplotlib values. For a more fine-grained definition of fontsizes, use the option matplotlib_rc_params (see above).

plot_func: str, optional (default: ‘contourf’)

Plot function used to plot the profiles. Must be a function of iris.plot that supports plotting of 2D cubes with coordinates latitude and height/air_pressure.

plot_kwargs: dict, optional

Optional keyword arguments for the plot function defined by plot_func. 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 the plot function defined by plot_func. String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: default: {levels: 2}, CMIP6: {vmin: 200, vmax: 250}. In addition to the normalization options supported by the plot function, the option norm: centered can be specified. In this case, the keywords vcenter and halfrange should be used instead of vmin or vmax (see CenteredNorm).

plot_kwargs_bias: dict, optional

Optional keyword arguments for the plot function defined by plot_func for plotting biases. These keyword arguments update (and potentially overwrite) the plot_kwargs for the bias plot. This option has no effect if no reference dataset is given. See option plot_kwargs for more details. By default, uses cmap: bwr and norm: centered.

pyplot_kwargs: dict, optional

Optional calls to functions of matplotlib.pyplot. Dictionary keys are functions of matplotlib.pyplot. Dictionary values are used as argument(s) for these functions (if values are dictionaries, these are interpreted as keyword arguments; otherwise a single argument is assumed). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

rasterize: bool, optional (default: True)

If True, use rasterization for profile plots to produce smaller files. This is only relevant for vector graphics (e.g., output_file_type: pdf,svg,ps).

show_y_minor_ticks: bool, optional (default: True)

Show minor ticks for time on the Y axis.

show_x_minor_ticks: bool, optional (default: True)

Show minor ticks for latitude or longitude on the X axis.

time_format: str, optional (default: None)

strftime() format string that is used to format the time axis using matplotlib.dates.DateFormatter. If None, use the default formatting imposed by the iris plotting function.

time_on: str, optional (default: y-axis)

Optional switch to change the orientation of the plot so that time is on the x-axis time_on: x-axis. Default orientation is time on y-axis and lat/lon on x-axis.

Configuration options for plot type benchmarking_annual_cycle#

Same as for plot type annual_cycle.

Configuration options for plot type benchmarking_boxplot#

fontsize: int, optional (default: None)

Fontsize used for ticks, labels and titles. For the latter, use the given fontsize plus 2. Does not affect suptitles. If not given, use default matplotlib values. For a more fine-grained definition of fontsizes, use the option matplotlib_rc_params (see above).

plot_kwargs: dict, optional

Optional keyword arguments for the plot function defined by plot_func. 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 the plot function defined by plot_func. String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: default: {levels: 2}, CMIP6: {vmin: 200, vmax: 250}.

pyplot_kwargs: dict, optional

Optional calls to functions of matplotlib.pyplot. Dictionary keys are functions of matplotlib.pyplot. Dictionary values are used as argument(s) for these functions (if values are dictionaries, these are interpreted as keyword arguments; otherwise a single argument is assumed). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

var_order: list of str, optional

Optional list of strings containing variable names to define the order of the variables plotted.

Configuration options for plot type benchmarking_diurnal_cycle#

Same as for plot type diurnal_cycle.

Configuration options for plot type benchmarking_map#

cbar_label: str, optional (default: ‘{short_name} [{units}]’)

Colorbar label. Can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}.

cbar_kwargs: dict, optional

Optional keyword arguments for matplotlib.pyplot.colorbar(). By default, uses orientation: horizontal, aspect: 30.

fontsize: int, optional (default: None)

Fontsize used for ticks, labels and titles. For the latter, use the given fontsize plus 2. Does not affect suptitles. If not given, use default matplotlib values. For a more fine-grained definition of fontsizes, use the option matplotlib_rc_params (see above).

plot_func: str, optional (default: ‘contourf’)

Plot function used to plot the maps. Must be a function of iris.plot that supports plotting of 2D cubes with coordinates latitude and longitude.

plot_kwargs: dict, optional

Optional keyword arguments for the plot function defined by plot_func. 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 the plot function defined by plot_func. String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: default: {levels: 2}, CMIP6: {vmin: 200, vmax: 250}. In addition to the normalization options supported by the plot function, the option norm: centered can be specified. In this case, the keywords vcenter and halfrange should be used instead of vmin or vmax (see CenteredNorm).

projection: str, optional (default: ‘Robinson’)

Projection used for the map plot. Needs to be a valid projection class of cartopy.crs. Keyword arguments can be specified using the option projection_kwargs.

projection_kwargs: dict, optional

Optional keyword arguments for the projection given by projection. For the default projection Robinson, the default keyword arguments central_longitude: 10 are used.

pyplot_kwargs: dict, optional

Optional calls to functions of matplotlib.pyplot. Dictionary keys are functions of matplotlib.pyplot. Dictionary values are used as argument(s) for these functions (if values are dictionaries, these are interpreted as keyword arguments; otherwise a single argument is assumed). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

rasterize: bool, optional (default: True)

If True, use rasterization for map plots to produce smaller files. This is only relevant for vector graphics (e.g., output_file_type: pdf,svg,ps).

Configuration options for plot type benchmarking_timeseries#

Same as for plot type timeseries.

Configuration options for plot type benchmarking_zonal#

cbar_label: str, optional (default: ‘{short_name} [{units}]’)

Colorbar label. Can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}.

cbar_kwargs: dict, optional

Optional keyword arguments for matplotlib.pyplot.colorbar(). By default, uses orientation: vertical.

fontsize: int, optional (default: None)

Fontsize used for ticks, labels and titles. For the latter, use the given fontsize plus 2. Does not affect suptitles. If not given, use default matplotlib values. For a more fine-grained definition of fontsizes, use the option matplotlib_rc_params (see above).

log_y: bool, optional (default: True)

Use logarithmic Y-axis.

plot_func: str, optional (default: ‘contourf’)

Plot function used to plot the profiles. Must be a function of iris.plot that supports plotting of 2D cubes with coordinates latitude and altitude/air_pressure.

plot_kwargs: dict, optional

Optional keyword arguments for the plot function defined by plot_func. 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 the plot function defined by plot_func. String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: default: {levels: 2}, CMIP6: {vmin: 200, vmax: 250}. In addition to the normalization options supported by the plot function, the option norm: centered can be specified. In this case, the keywords vcenter and halfrange should be used instead of vmin or vmax (see CenteredNorm).

pyplot_kwargs: dict, optional

Optional calls to functions of matplotlib.pyplot. Dictionary keys are functions of matplotlib.pyplot. Dictionary values are used as argument(s) for these functions (if values are dictionaries, these are interpreted as keyword arguments; otherwise a single argument is assumed). String arguments can include facets in curly brackets which will be derived from the corresponding dataset, e.g., {project}, {short_name}, {exp}. Examples: title: 'Awesome Plot of {long_name}', xlabel: '{short_name}', xlim: [0, 5].

rasterize: bool, optional (default: True)

If True, use rasterization for profile plots to produce smaller files. This is only relevant for vector graphics (e.g., output_file_type: pdf,svg,ps).

show_y_minor_ticklabels: bool, optional (default: False)

Show tick labels for the minor ticks on the Y axis.

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.