Madden-Julian Oscillation precipitation Hovmöller diagnostic#

Overview#

This recipe computes lag-regression Hovmöller diagrams of the Madden-Julian Oscillation (MJO). For each dataset, daily tropical precipitation is averaged over a latitude band and its day-of-year climatology is removed. The resulting anomalies are Lanczos band-pass filtered to the 20-100 day MJO period range. A reference index is built by averaging the filtered field over a reference longitude sector, and the full filtered field is then regressed against this index at a range of lags.

The result is a longitude-lag diagram: an eastward-propagating diagonal band is the signature of MJO convection.

Available recipes and diagnostics#

Recipes are stored in recipes/

  • recipe_mjo_hovmoeller.yml

Diagnostics are stored in diag_scripts/mjo/

  • mjo_hovmoeller.py: compute the lag regression and plot the Hovmöller diagram.

User settings in recipe#

  1. Script mjo_hovmoeller.py

    Required settings for script

    • reference_longitudes: longitude sector [lon0, lon1] (in degrees East) used to build the MJO reference index that the filtered field is regressed against.

    • low_period: lower period cutoff (in days) of the Lanczos band-pass filter.

    • high_period: upper period cutoff (in days) of the Lanczos band-pass filter.

    • lanczos_weights: number of weights of the Lanczos band-pass filter. Must be an odd integer greater than 1.

    • max_lag: maximum lag (in days, in both directions) computed by the lag regression.

    Optional settings for script

    • longitude_limits: longitude axis limits of the Hovmöller plot (default: [0.0, 360.0]).

    • contour_levels: number of contour levels in the Hovmöller plot. Must be at least 3 (default: 21).

    • colormap: matplotlib colormap used for the Hovmöller contour plot (default: RdYlBu).

    • plot_title: title of the Hovmöller plot (default: MJO Hovmöller diagram).

    • colorbar_label: label for the figure’s colorbar (default: Precipitation regression coefficient).

    Required settings for variables

    • none beyond the standard short_name, mip, preprocessor and timerange.

    Optional settings for variables

    • none

    Required settings for preprocessor

    • extract_region: restrict the data to the tropical latitude band used for the diagnostic.

    • regrid: regrid all datasets onto a common regular grid.

    • meridional_statistics: average over the extracted latitude band (operator: mean).

    • daily_statistics: reduce the data to daily means (operator: mean).

    • anomalies: remove the day-of-year climatology (period: day).

    • convert_units: convert precipitation to kg m-2 day-1.

    Optional settings for preprocessor

    • none

    Color tables

    • none

Variables#

  • pr (atmos, daily mean, longitude latitude time)

Observations and reformat scripts#

Note: ERA5 is read directly through ESMValCore’s native6 support; no separate reformat script needs to be run beforehand.

  • ERA5 (native6 project, tier 3, frequency: 1hr)

References#

  • Hannah, W. M., Jones, C. R., Hillman, B. R., Norman, M. R., Bader, D. C., Taylor, M. A., et al. (2020). Initial results from the super-parameterized E3SM. Journal of Advances in Modeling Earth Systems. 12, e2019MS001863. https://doi.org/10.1029/2019MS001863

Example plots#

../_images/era5_mjo_hovmoeller.png

Fig. 187 Lag regression of 20-100 day filtered ERA5 precipitation against a precipitation index averaged over 80-100E, 1979-1983. Positive longitude-lag slope through the reference sector shows the eastward-propagating MJO precipitation signal.#