def METHOD_NAME():
    # Load a sample air temperatures sequence.
    file_path = iris.sample_data_path("E1_north_america.nc")
    temperatures = iris.load_cube(file_path)
    # Create a year-number coordinate from the time information.
    iris.coord_categorisation.add_year(temperatures, "time")
    # Create a sample anomaly field for one chosen year, by extracting that
    # year and subtracting the time mean.
    sample_year = 1982
    year_temperature = temperatures.extract(iris.Constraint(year=sample_year))
    time_mean = temperatures.collapsed("time", iris.analysis.MEAN)
    anomaly = year_temperature - time_mean
    # Construct a plot title string explaining which years are involved.
    years = temperatures.coord("year").points
    plot_title = "Temperature anomaly"
    plot_title += "\n{} differences from {}-{} average.".format(
        sample_year, years[0], years[-1]
    )
    # Define scaling levels for the logarithmic colouring.
    minimum_log_level = 0.1
    maximum_scale_level = 3.0
    # Use a standard colour map which varies blue-white-red.
    # For suitable options, see the 'Diverging colormaps' section in:
    # http://matplotlib.org/stable/gallery/color/colormap_reference.html
    anom_cmap = "bwr"
    # Create a 'logarithmic' data normalization.
    anom_norm = mcols.SymLogNorm(
        linthresh=minimum_log_level,
        linscale=0.01,
        vmin=-maximum_scale_level,
        vmax=maximum_scale_level,
    )
    # Setting "linthresh=minimum_log_level" makes its non-logarithmic
    # data range equal to our 'zero band'.
    # Setting "linscale=0.01" maps the whole zero band to the middle colour value
    # (i.e., 0.5), which is the neutral point of a "diverging" style colormap.
    # Create an Axes, specifying the map projection.
    plt.axes(projection=ccrs.LambertConformal())
    # Make a pseudocolour plot using this colour scheme.
    mesh = iplt.pcolormesh(anomaly, cmap=anom_cmap, norm=anom_norm)
    # Add a colourbar, with extensions to show handling of out-of-range values.
    bar = plt.colorbar(mesh, orientation="horizontal", extend="both")
    # Set some suitable fixed "logarithmic" colourbar tick positions.
    tick_levels = [-3, -1, -0.3, 0.0, 0.3, 1, 3]
    bar.set_ticks(tick_levels)
    # Modify the tick labels so that the centre one shows "+/-<minumum-level>".
    tick_levels[3] = r"$\pm${:g}".format(minimum_log_level)
    bar.set_ticklabels(tick_levels)
    # Label the colourbar to show the units.
    bar.set_label("[{}, log scale]".format(anomaly.units))
    # Add coastlines and a title.
    plt.gca().coastlines()
    plt.title(plot_title)
    # Display the result.
    iplt.show()