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Madden Julian Oscillation Climate Variability

Prior to NCL 6.2.0, the following four libraries needed to be loaded prior to invoking the diagnostic scripts:
   load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl"
   load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_csm.ncl"
   load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl"
   load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/diagnostics_cam.ncl"

From NCL 6.2.0 onward, only the following need be loaded:

   load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/diagnostics_cam.ncl"
The scripts below are intended to be 'guide' to usage. They may work directly but, generally, the user will have to make some changes to the scripts. For example, the changes may involve making the time variable similar to those used in the examples.
The US-CLIVAR MJO working group has developed diagnostics for objectively evaluating the MJO. The official US-CLIVAR MJO diagnostics website provides data, C-shell scripts, fortran code and GrADS scripts which implement and display all the suggested diagnostics. The NCL examples presented below perform many of the suggested diagnostics.


       MJO Simulation Diagnostics
       Waliser et al.
       2009, J. Clim.,  22: 3006-3030 
       DOI: 10.1175/2008JCLI2731.1
MJO Diagnostic Semantics

The diagnostics are categorized into two (really, three) levels:

  1. Level 1: Diagnostics meant to provide a basic indication of the spatial and temporal intraseasonal variability that can be easily understood and/or calculated by the non-MJO expert.
  2. Level 2: Diagnostics that provide a more comprehensive diagnosis of the MJO through multivariate EOF analysis and frequency wave-number decomposition.
  3. Other or Supplemental: Diagnostics that provide additional measures that have in some cases been found to play an important role in MJO simulation fidelity (e.g., mean state) or characterize the manner the MJO interacts with other important weather/climate processes (e.g., ENSO)

Observational Datasets used here [mainly netCDF]

       daily and monthly mean NCEP winds 
       daily and monthly NOAA  Interpolated OLR 
       monthly and pentad CMAP
       monthly GPCP
       daily GPCP
       pentad GPCP
       daily TRMM
       monthly OI SST V2
Anomalies and Prewhitening

Generally, it is suggested that the diagnostics should be performed on daily anomalies. Unfiltered anomalies are computed by subtracting the climatological daily (or pentad where appropriate) means calculated using all years of the data.

There are two approaches: (a) Compute the climatology and the anomalies 'on-the-fly', or, (b) Compute anomalies and save them to a file. For convenience, the latter approach is used in many of the examples.

Removing the climatological annual cycle is a form a "prewhitening". Prewhitening is the the removal of known signals prior to analysis so they will not confound the interpretation of the results. For example, a model may have a known bias. This should be removed prior to applying the methods shown below.

Active MJO periods and MJO Forecasts

When focusing on a specific period, several examples use the winter of 1996-1997. This was a particulary active period and is sometimes considered the "gold standrd" for MJO activitity.

Matt Wheeler (CAWCR: The Centre for Australian Weather and Climate Research) has several WWW links that perform real-time monitoring of the MJO. These show other periods of strong MJO activity. NOAA provides access to experimental MJO forecasts.

Carl Schreck ( CICS: Cooperative Institute for Climate and Satellites) has additional WWW site for Monitoring the MJO and Tropical Waves.

Lanczos Filter Weights

When needed, the weights for the suggested 20-100 day bandpass Lanczos filter are generated 'on-the-fly' using:

  ihp      = 2                             ; bpf=>band pass filter
  nWgt     = 201
  sigma    = 1.0                           ; Lanczos sigma
  fca      = 1./100.
  fcb      = 1./20.
  wgt      = filwgts_lanczos (nWgt, ihp, fca, fcb, sigma )
The band pass filter (BPF) weights are applied via a weighted running average. For example, an array x(time,lat,lon) would be filtered via
    xBPF = wgt_runave_Wrap (x(lat|:, lon|:, time|:), wgt, 0)
The resulting array would be xBPF(lat,lon,time). More filter examples
mjoclivar_1.ncl: This example illustrates plotting what is referred to as the Mean State which is a supplemental diagnostic. Specifically:

     (a) Read files containing year-month data, 
     (b) Create climatologies spanning user specified years 
     (c) Plot November-April and May-October climatologies
                over a user specified region

The MJO reference suggests using daily anomalies for the diagnostics. The following illustrates the process of creating anomalies.

Calculate the daily mean annual cycle and daily anomalies from the mean annual cycle. For illustration:

     (a) Read the raw daily data.
     (b) Compute raw and smoothed annual cycles
     (c) Create netCDF file containing the anomalies
     (d) Plots: (i) daily climatologies at different locations 
               (ii) sample anomalies using smooth and raw climatologies 
This example uses 26-years (1980-2005) of daily data. The variability of the raw climatological day-to-day values is a direct function of the number of years used to create the climatologies. Generally speaking, using more/fewer years will result in smaller/greater day-to-day variability. Note: the the scales are different for each plot on the leftmost figure.

Which is the proper climatological daily annual cycle to use: raw or smoothed? It depends on your usage. The smoothed annual cycle can be thought of as the values that would be obtained if there was an infinite ensemble of data under the same forcing conditions.

mjoclivar_3.ncl: This example uses 10-years (1990-1999) of daily anomaly data to compute seasonal variances and the ratio of the bandpass variance to the unfiltered variance.

mjoclivar_4.ncl: Use band_pass_area_time to:
     (a) Create a single time series of areal averaged data
     (b) Band pass filter the resulting area averaged time series
     (c) Calculate a running 91-day variance of (b)
     (d) plot using band_pass_area_time_plot
Note: The MJO-WG recommended band pass filter is used. However, the band_pass_area_time function can create and use other band pass weights.

mjoclivar_5.ncl: Use band_pass_hovmueller to create a band-pass filtered time series suitable for a time vs longitude (Hovmueller) plot. The returned values are plotted via band_pass_hovmueller_plot.

Note: The MJO-WG recommended band pass filter is used. However, the band_pass_area_time function can create and use other band pass weights.

mjoclivar_6.ncl: Create band-pass filtered time series at each lat/lon grid point via band_pass_latlon_time. Plot the total anomaly pattern and the corresponding band pass filtered pattern at a specific date. Note: A movie could readily be created.

mjoclivar_7.ncl: Unfiltered anomaly data are averaged over user specified domains described in Table 1 of the 'MJO Clivar' website. The spectra are calculated separately for each season and averaged across all years for the given season. The number of degrees of freddom is approximately 2*(number of seasons). The null, 5% and 95% red noise significance levels are included. Plot spectra with x-axis as frequency with log scaling and y-axis as power times frequency. The data are cosine area-weighted in latitude as specified by MJO Clivar.

The mjo_spectra procedure is a driver which computes and plots the seasonally averaged spectra. The seasonal spectra are calculated via the mjo_spectra_season function.

mjoclivar_8.ncl: For a specific season [here, winter 2000-2001], compute a lag correlation diagram using unfiltered and filtered daily data. The reference time series is the central Indian Ocean regional precipitation time series (See Table 1). This is correlated with precipitation and zonal wind anomalies in specified regions at different lags. Lag-longitude and lag-latitude plots of correlation values for different regions are shown. Color is for precipitation correlations while the lagged correlations for the zonal winds are the contours. These are analogous the Figures 5 and 6 in the reference article except they are for one season. The two leftmost are for unfiltered data while the two rightmost are for 20-100 day band passed filtered data.

The lag-cross-correlations for each season are calculated via the mjo_xcor_lag_season function. The results are plotted via mjo_xcor_lag_ovly procedure.

mjoclivar_9.ncl: Same as Example 8 except compute the climatological cross correlations over the period of record using 20-100 band pass filtered data.

The lag-cross-correlations for each season are calculated via the mjo_xcor_lag_season function. Each seasonal cross correlation is averaged by using the Fischer-z-transform. The results are plotted via mjo_xcor_lag_ovly_panel procedure.

mjoclivar_10.ncl: The wavenumber - frequency spectra for each season are calculated via the mjo_wavenum_freq_season function. The results are plotted via mjo_wavenum_freq_season_plot procedure. The vertical reference lines indicating "day" are drawn for 30 and 80 days (default). The option "dayLine" (introduced in v5.1.1) could be used to alter these. EG: opt@dayLines = (/40,90/) would draw these reference lines at 40 and 90 days, respectively. The two leftmost figures were generated when the OLR anomaly file was used. The two rightmost figures were generated when the NCEP zonal wind [U] anomaly file was used.

mjoclivar_11.ncl: Cross-spectra: coherence-squared and phase relationships in wavenumber-frequency space via mjo_cross function. This is a driver that calls the mjo_cross function. The results are plotted via mjo_cross_plot procedure. The leftmost figure shows the default plot with both coherence-squared and phase. The center plot uses the option to plot only where the probability is greater than 0.925 (not much difference here). The rightmost plot shows only the coherence-squared with no phases plotted.

mjoclivar_12.ncl: Conventional (covariance) univariate EOF analysis for 20-100 day band-pass filtered data: OLR (left), U850 (center), U200 (right). The time span used is 1995-1999 and the latitude span is 30S to 30N.
mjoclivar_13.ncl: Average the 20-100 day band-pass filtered data fields from 15S to 15N; normalize each of the averaged fields by the square-root of the zonal mean of their temporal variance; then perform a conventional univariate EOF analysis. The left figure shows the (spatial) longitude pattern for EOFs 1 and 2. The right figure show lag (+/- 25 day) correlations between EOF 1 and 2 time series for each variable.
mjoclivar_14.ncl: Multivariate (Combined) EOF; cross-correlations between EOF1 and EOF2; time-series of (PC1^2 + PC2^2) and 91-day running mean.

This script was updated 2 March 2015. Marcus N. Morgan (Florida Institute of Technology) submitted the code segment that calculates the percent variance for each component in the multivariate EOF.

This script was updated 29 July 2016. Eun-Pa Lim: Bureau of Meteorology, Australis

For comparison, the equivalent 3 plots from the Korean MJO-Diagnostics are (a) eof_all; (b) lag_all (c) time series .

mjoclivar_15.ncl: Read the simple netCDF file created in the previous example and plot a phase space diagram for a specified time period. Each month is indicated by a separate line color. The numbers correspond to the day of the month. The 'nice' plot is typical of very active MJO periods.
mjoclivar_16.ncl: Create composite life cycles. This example uses 20-100 day band passed filtered anomaly OLR, U850, V850 for the years 1995-1999. The netCF file created in Example 14, which contains the PC1 and PC2, is used to derive the appropriate MJO phase category. The size of the reference anomaly wind vector is in the upper right. The phase (eg P3, means "Phase 3") and the number of days used to create the composite are at the lower right.

This script was updated 29 July 2016. (Source: Eun-Pa Lim, Bureau of Meteorology, Australia)