How to deal well with normal and cartopy projection on the same figure?

Question

I want to produce this kind of figure, taken from Sallee et al. (2021) directly from Python if it is possible :

There is a Cartopy projection cartopy.crs.Robinson(central_longitude=0, globe=None) in the main subplot and at the right of it something close to a density function (over the latitudes) of my value on the Cartopy projection. Managing the labels with Robinson projection is not convenient for me, whereas with cartopy.crs.PlateCarree(central_longitude=0.0, globe=None) I did not have any issues labelling axis.

This is the most related topics ( combination of normal and cartopy subplots within the same figure ) that I have founded on stack for now but that doesn't ring any bell since my goal plot is a bit more complicated (size of the colorbar above the Robinson projection, two dashed lines to link the subplots, labelling longitudes and latitudes).

Thank you !

Answer 1

Is there anything specific that you didn't manage to create? Most of what you ask for is readily available from Cartopy/Matplotlib.

Additional annotation, like the inset zoom lines are possible with Matplotlib, see for example:
https://matplotlib.org/stable/gallery/subplots_axes_and_figures/zoom_inset_axes.html

But I personally would avoid that and simply align the axes to make sure they share the same latitude. That's probably more intuitive for users trying to interpret the data.

A quick example with some random data:

lats = np.linspace(90-6, -90+6, 15)
data = np.random.randn(15, 32)

proj = ccrs.Robinson(central_longitude=0, globe=None)

fig = plt.figure(figsize=(11, 5), dpi=86, facecolor="w")
spec = fig.add_gridspec(9, 5)

ax1 = fig.add_subplot(spec[1:, :4], projection=proj)
im = ax1.imshow(
    data, cmap="Blues", vmin=-3, vmax=3,
    extent=[-180, 180,90,-90], transform=ccrs.PlateCarree())

cax = fig.add_subplot(spec[0, 1:-2])
cb1 = fig.colorbar(im, cax=cax, orientation="horizontal")
cax.set_title("Something [-]")
cax.xaxis.set_ticks_position('top')

grd = ax1.gridlines(
    draw_labels=True, 
    xlocs=range(-180, 181, 90), 
    ylocs=range(-60, 61, 30), 
    color='k',
)
grd.top_labels = False

ax1.add_feature(cfeature.LAND, facecolor="#eeeeee", zorder=99)


ax2 = fig.add_subplot(spec[1:, -1])
ax2.plot(data.mean(axis=1), lats)
ax2.axvline(0, color="k", lw=1)
ax2.set_xlim(-0.5, 0.5)
ax2.set_yticks(range(-60, 61, 30))
ax2.yaxis.set_label_position("right")
ax2.yaxis.tick_right()
ax2.grid(axis="y")
ax2.set_ylabel("Latitude [deg]")