Figure 5.¶

Atmospheric circulation regimes of (crosses) Trappist-1e and (circles) Proxima b simulations with (blue) MassFlux, (orange) Adjust, and (green) NoCnvPm set-up, defined by the estimates of the non-dimensional Rossby deformation radius ( $L_d/R_p$ , x-axis) and the non-dimensional Rhines length ( $L_{R}/R_p$ , y-axis).

Skip code and jump to the figure

Import the necessary libraries.

In [1]:

import warnings

warnings.filterwarnings("ignore")

In [2]:

import matplotlib.pyplot as plt

In [3]:

from aeolus.calc import last_year_mean
from aeolus.core import Run
from aeolus.subset import l_range_constr

In [4]:

from commons import (
    GLM_MODEL_TIMESTEP,
    PLANET_ALIASES,
    RUN_ALIASES,
    OUTPUT_NAME_PREFIX,
)
from gl_diag import (
    calc_derived_cubes,
    nondim_rhines_number,
    nondim_rossby_deformation_radius,
)
import mypaths
from plot_func import MARKER_KW, add_custom_legend, use_style

Global stylesheet for figures.

In [5]:

use_style()

Load data¶

Create a dictionary of Run objects with preprocessed data.

In [6]:

runs = {}
for planet in PLANET_ALIASES.keys():
    for run_key in RUN_ALIASES.keys():
        label = f"{planet}_{run_key}"

        fname = mypaths.sadir / label / "_processed" / f"{label}.nc"

        runs[label] = Run(
            files=fname,
            name=label,
            planet=planet,
            timestep=GLM_MODEL_TIMESTEP,
            processed=True,
        )

        # Calculate additional diagnostics
        runs[label].add_data(calc_derived_cubes)

Calculate the non-dimensional numbers¶

Define a level height constraint to select data between 0 and 15 km.

In [7]:

HGT_0 = 0
HGT_1 = 15
hgt_cnstr = l_range_constr(HGT_0, HGT_1)

Loop over planets and runs again to populate dictionaries of Rhines and Rossby number estimates.

In [8]:

rhines = {}
rossby = {}
for planet in PLANET_ALIASES.keys():
    for run_key in RUN_ALIASES.keys():
        label = f"{planet}_{run_key}"
        rhines[label] = last_year_mean(
            nondim_rhines_number(runs[label].proc.extract(hgt_cnstr))
        )
        rossby[label] = last_year_mean(
            nondim_rossby_deformation_radius(runs[label].proc.extract(hgt_cnstr))
        )

Plot the results¶

In [9]:

common_plt_kw = dict(linestyle="", ms=15)

In [10]:

# Axes limits
xlim = [0.5, 1.5]
ylim = [0.5, 1.5]

fig, ax = plt.subplots()

for run_key in RUN_ALIASES.keys():
    for planet in PLANET_ALIASES.keys():
        label = f"{planet}_{run_key}"

        ax.plot(
            rossby[label].data,
            rhines[label].data,
            **MARKER_KW[run_key],
            **MARKER_KW[planet],
            **common_plt_kw,
        )

# Create two legends, for planets and runs, respectively
add_custom_legend(
    ax,
    {
        v: dict(color="k", **common_plt_kw, **MARKER_KW[k])
        for k, v in PLANET_ALIASES.items()
    },
    loc=3,
    title="Planets",
)

add_custom_legend(
    ax,
    {
        v: dict(marker=".", **common_plt_kw, **MARKER_KW[k])
        for k, v in RUN_ALIASES.items()
    },
    loc=2,
    title="Simulations",
)

# Vertical and horizontal guides
ax.hlines(1.0, *xlim, linestyle="--", linewidth=0.75)
ax.vlines(1.0, *ylim, linestyle="--", linewidth=0.75)
ax.set_xlim(xlim)
ax.set_ylim(ylim)

# Axes labels
ax.set_xlabel("Non-dimensional Rossby deformation radius")
ax.set_ylabel("Non-dimensional Rhines length")

# Annotations
ax.text(1.05, 0.55, "Rhines rotators", color="grey", fontsize=18)
ax.text(1.05, 1.45, "Slow rotators", color="grey", fontsize=18)
ax.text(0.75, 0.55, "Rapid rotators", color="grey", fontsize=18)

plt.close()  # Show the figure in a separate cell

Show the figure¶

In [11]:

fig

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And save it.

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imgname = mypaths.plotdir / f"{OUTPUT_NAME_PREFIX}__nondim_rossby_rhines__hgt{HGT_0}-{HGT_1}km.png"

In [13]:

fig.savefig(imgname, dpi=200)
print(f"Saved to ../{imgname.relative_to(mypaths.topdir)}")

Saved to ../plots/trap1e_proxb__grcs_llcs_all_rain_acoff__nondim_rossby_rhines__hgt0-15km.png