easyclimate.field.equatorial_wave.mjo¶

Madden Julian Oscillation (MJO)

Functions¶

`draw_mjo_phase_space_basemap`([ax, add_phase_text, ...])	Create a base map for Madden-Julian Oscillation (MJO) phase space visualization.
`draw_mjo_phase_space`(mjo_data[, rmm1_dim, rmm2_dim, ...])	Visualize Madden-Julian Oscillation (MJO) phase space trajectory from RMM indices.

Module Contents¶

easyclimate.field.equatorial_wave.mjo.draw_mjo_phase_space_basemap(ax=None, add_phase_text: bool = True, add_location_text: bool = True)¶

Create a base map for Madden-Julian Oscillation (MJO) phase space visualization.

This function generates the standardized background diagram for MJO analysis, including phase division lines (1-8), amplitude circles, and optional geographical annotations. The diagram uses RMM1/RMM2 indices as axes in a Cartesian coordinate system (-4 to 4 range).

Parameters¶

axmatplotlib.axes.Axes, optional: Target axes object for plotting. If None, uses current axes (gca()).
add_phase_textbool, default: True: Whether to annotate MJO phase numbers (1-8) on the diagram.
add_location_textbool, default: True: Whether to annotate geographical regions corresponding to MJO phases: - Pacific Ocean (top) - Indian Ocean (bottom) - Western Hemisphere/Africa (left) - Maritime Continent (right)

Returns¶

matplotlib.axes.Axes: The configured axes object with MJO phase space basemap elements.

Notes¶

The diagram includes: 1. 8 radial lines dividing phases (45° intervals) 2. Unit circle indicating amplitude threshold 3. Axes labeled as RMM1 (x-axis) and RMM2 (y-axis)

Examples¶

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> ecl.field.equatorial_wave.draw_mjo_phase_space_basemap(ax=ax)
>>> plt.show()

Parameters¶

mjo_dataxarray.Dataset: Input dataset containing RMM indices. Must include: - RMM1 component (real-time multivariate MJO index 1) - RMM2 component (real-time multivariate MJO index 2) - Time coordinate
rmm1_dimstr, default: “RMM1”: Variable name for RMM1 component in the dataset
rmm2_dimstr, default: “RMM2”: Variable name for RMM2 component in the dataset
time_dimstr, default: “time”: Time coordinate dimension name
axmatplotlib.axes.Axes, optional: Target axes for plotting. Creates new axes if None.
colorstr or tuple, default: “blue”: Color specification for trajectory and points
start_textstr, default: “START”: Annotation text for trajectory starting point
add_start_textbool, default: True: Toggle for displaying start point annotation

Returns¶

matplotlib.axes.Axes: Configured axes object with MJO phase space trajectory

Returns¶

matplotlib.axes.Axes: Configured axes object with MJO phase space trajectory

Examples¶

>>> import xarray as xr
>>> import matplotlib.pyplot as plt
>>> # Load RMM indices dataset
>>> mjo_ds = xr.open_dataset('http://iridl.ldeo.columbia.edu/SOURCES/.BoM/.MJO/.RMM/dods', decode_times=False)
>>> T = mjo_ds.T.values
>>> mjo_ds['T'] = pd.date_range("1974-06-01", periods=len(T))
>>> mjo_ds = ecl.utility.get_compress_xarraydata(mjo_ds)
>>> fig, ax = plt.subplots(figsize=(8,8))
>>> ecl.field.equatorial_wave.draw_mjo_phase_space_basemap()
>>> ecl.field.equatorial_wave.draw_mjo_phase_space(
...:    mjo_data = mjo_data.sel(time = slice('2024-12-01', '2024-12-31')),
...:    rmm1_dim = "RMM1",
...:    rmm2_dim = "RMM2",
...:    time_dim = "time"rmm_data, ax=ax, color="red")
>>> plt.title("MJO Phase Space Trajectory")
>>> plt.show()

Note

Recommended to use with draw_mjo_phase_space_basemap() for basic map.
Temporal resolution affects trajectory smoothness (daily data recommended).